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Commit f9b573e5 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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agrego codgos de analisis de cpt de blade trap

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analisis/plots/20250516_CPT_bladetrap/CPT_plotter_20231123.py 0 → 100644
View file @ f9b573e5
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
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/')
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20250516_CPT_bladetrap/Data')
CPT_FILES = """000021278-IR_Scan_simple
000021360-IR_Scan_simple
000021390-IR_Scan_simple
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
print(np.array(data['datasets']['no_measures']))
#%%
"""
Ploteo curvas para ver que tal son
"""
jvec = [1] # de la 1 a la 9 vale la pena, despues no
plt.figure()
i = 0
for j in jvec:
Frequencies = Freqs[j]
plt.errorbar([2*f*1e-6 for f in Frequencies], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
# plt.legend()
#%%
"""
HAGO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
ajusto una medicion con muy poca micromocion
"""
from EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
from scipy.optimize import curve_fit
import time
"""
SUPER AJUSTE (SA)
"""
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
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*7.262*1e6
SelectedCurveVec = [1]
popt_SA_vec = []
pcov_SA_vec = []
Detuningsshort_vec = []
Counts_vec = []
Detuningslong_vec = []
FittedCounts_vec = []
Betas_vec = []
ErrorBetas_vec = []
Temp_vec = []
ErrorTemp_vec = []
DetuningsUV_vec = []
ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
FreqsDR = Freqs[selectedcurve]
CountsDR = Counts[selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
freqs = [2*f*1e-6-offset for f in Freqs]
# BETA1=0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
t1 = time.time()
print(f'Beginning {selectedcurve}')
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR[1:], CountsDR, p0=[440, -15, 0.3, 10, 1e4, 500, 2, (np.pi**2)*1e-3, 49.5e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 48e6), (1000, 0, 2, 20, 5e4, 2e3, 3, (np.pi**2)*10e-3, 50e6)))
print(f'Ended {selectedcurve}, time elapsed: {round(time.time()-t1)} s')
#popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
popt_SA_vec.append(popt_3_SA)
pcov_SA_vec.append(pcov_3_SA)
#offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
# popt_3_SA[4]=1
# popt_3_SA[5]=0
# popt_3_SA[6]=0
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
corri = 0
DetuningsUV_vec.append(popt_3_SA[1])
ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
Betas_vec.append(popt_3_SA[6])
ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
Temp_vec.append(popt_3_SA[7])
ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
Detuningsshort_vec.append(Detunings_3_SA_short)
Counts_vec.append(CountsDR)
Detuningslong_vec.append(Detunings_3_SA_long)
FittedCounts_vec.append(FittedEITpi_3_SA_long)
plt.figure()
plt.errorbar(Detunings_3_SA_short[:], CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot([c-corri for c in Detunings_3_SA_long], FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
# plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA)
#%%
"""
HAGO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
Ajusto med 2, con un poco mas de micromocion supuestamente
"""
from EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
from scipy.optimize import curve_fit
import time
"""
SUPER AJUSTE (SA)
"""
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
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*7.262*1e6
SelectedCurveVec = [2]
popt_SA_vec = []
pcov_SA_vec = []
Detuningsshort_vec = []
Counts_vec = []
Detuningslong_vec = []
FittedCounts_vec = []
Betas_vec = []
ErrorBetas_vec = []
Temp_vec = []
ErrorTemp_vec = []
DetuningsUV_vec = []
ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
FreqsDR = Freqs[selectedcurve]
CountsDR = Counts[selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
freqs = [2*f*1e-6-offset for f in Freqs]
# BETA1=0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
t1 = time.time()
print(f'Beginning {selectedcurve}')
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR[1:], CountsDR, p0=[440, -15, 0.3, 10, 1e4, 500, 2, (np.pi**2)*1e-3, 49.5e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 48e6), (1000, 0, 2, 20, 5e4, 2e3, 3, (np.pi**2)*10e-3, 50e6)))
print(f'Ended {selectedcurve}, time elapsed: {round(time.time()-t1)} s')
#popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
popt_SA_vec.append(popt_3_SA)
pcov_SA_vec.append(pcov_3_SA)
#offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
# popt_3_SA[4]=1
# popt_3_SA[5]=0
# popt_3_SA[6]=0
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
corri = 0
DetuningsUV_vec.append(popt_3_SA[1])
ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
Betas_vec.append(popt_3_SA[6])
ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
Temp_vec.append(popt_3_SA[7])
ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
Detuningsshort_vec.append(Detunings_3_SA_short)
Counts_vec.append(CountsDR)
Detuningslong_vec.append(Detunings_3_SA_long)
FittedCounts_vec.append(FittedEITpi_3_SA_long)
plt.figure()
plt.errorbar(Detunings_3_SA_short[:], CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot([c-corri for c in Detunings_3_SA_long], FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
# plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA)
\ No newline at end of file
analisis/plots/20250516_CPT_bladetrap/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
"""
ESTE ES EL CODIGO QUE PLOTEA CPT CON MICROMOCION BIEN
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
#from EITfit.MM_eightLevel_2repumps_python_scripts import CPTspectrum8levels_MM
import random
from scipy.signal import savgol_filter as sf
def PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None):
"""
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
#tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe, beta, drivefreq, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
#tfinal = time.time()
#print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
return ProbeDetuningVectorL, Fluovector
def GenerateNoisyCPT_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_MM_fit(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs[0], freqs[-1], freqs[1]-freqs[0], circularityprobe, plot=False, solvemode=1, detpvec=None)
#NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, Fluovector
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
analisis/plots/20250516_CPT_bladetrap/Data/EITfit/MM_eightLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ f9b573e5
#!/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 = 2e6 #proportional to the magnetic field of around 5 G
B = (u/(2*np.pi))/c
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
lw = 0. #linewidth of the lasers, 0.1 MHz are the actual linewidths of both lasers
DopplerLaserLinewidth, ProbeLaserLinewidth = lw, lw #ancho de linea de los laseres
TempVec = [0e-3] #Temperature vector
alpha = 0 #angle between lasers, which is zero
#Polarization angles (we can keep it fixed in 90)
phidoppler, titadoppler = 0, 90
titaprobe = 90
phiprobe = 0
#este es el desfasaje exp(i.phi) de la componente de la polarizacion y respecto a la x. Con 1 la polarizacion es lineal
CircPr = 1 #this has to do with the circularity of the polarizations and since both are linear it is one
#Simulation parameters
center = -10
span = 200
freqMin = center-span*0.5
freqMax = center+span*0.5
freqStep = 2e-1
noiseamplitude = 0 #i dont know what it is
#parametros de saturacion de los laseres. g: doppler. p: probe (un rebombeo que scanea), r: repump (otro rebombeo fijo)
"""
Good case: sg=0.6, sp=9, DetDoppler=-15
"""
DetDoppler = -25 #nice range: -30 to 0
sgvec = [0.6] #nice range: 0.1 to 10 #g is for green but is the doppler
sp = 8 #nice range: 0.1 to 20 #p is for probe but is the repump
drivefreq=2*np.pi*22.135*1e6 #ignore it
#betavec = np.arange(0,1.1,0.1) #ignore it
betavec=[0] #ignore it
alphavec = [0] #ignore it
fig1, ax1 = plt.subplots()
FrequenciesVec = []
FluorescencesVec = []
for sg in sgvec:
for T in TempVec:
for alpha in alphavec:
for beta in betavec:
Frequencies, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
FrequenciesVec.append(Frequencies)
FluorescencesVec.append(Fluorescence)
ax1.plot(Frequencies, [100*f for f in Fluorescence], label=fr'$\alpha={int(alpha*180/np.pi)}°$')
ax1.set_xlabel('Detuning Rebombeo (MHz)')
ax1.set_ylabel('Fluorescencia (AU)')
ax1.set_title(f'Sdop: {sg}, Spr: {sp}, Temp: {int(T*1e3)} mK')
#ax1.legend()
ax1.grid()
#%%
import seaborn as sns
paleta=sns.color_palette('mako')
plt.figure()
plt.plot(Frequencies, [100*f for f in Fluorescence], color=paleta[1], linewidth=3)
plt.grid()
plt.axvline(-25,color=paleta[2], linestyle='dashed')
plt.xlabel(r'$\Delta_2$ (MHz)', fontsize=25, fontname='STIXgeneral')
plt.ylabel('Fluorescence', fontsize=18, fontname='STIXgeneral')
#%%
#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/20250516_CPT_bladetrap/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
"""
ESTE ES EL CODIGO QUE PLOTEA CPT CON MICROMOCION BIEN
"""
#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*(397/866)
#ampr=beta*drivefreq
Hint[0,0] = ampg
Hint[1,1] = ampg
Hint[4,4] = ampr
Hint[5,5] = ampr
Hint[6,6] = ampr
Hint[7,7] = ampr
if forma==1:
Ltemp = np.zeros((64, 64), dtype=np.complex_)
"""
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
Ltemp[r*8+q][k*8+j] = (-1j)*(Hint[r,k]*int(j==q) - Hint[j,q]*int(r==k))
"""
"""
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if r==k and j==q:
Ltemp[r*8+q][k*8+j] = (-1j)*(Hint[r,k] - Hint[j,q])
"""
for r in range(8):
for q in range(8):
if r!=q:
Ltemp[r*8+q][r*8+q] = (-1j)*(Hint[r,r] - Hint[q,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Ltemp = (-1j)*(np.kron(Hint, deltaKro) - np.kron(deltaKro, Hint))
Omega = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
Omega[i, i] = (1j)*drivefreq
return np.matrix(Ltemp), np.matrix(Omega)
def GetL1(Ltemp, L0, Omega, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*Omega))*0.5*np.matrix(Ltemp))
Sm = (-1)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*Omega))*0.5*np.matrix(Ltemp))
for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
Sp = (-1)*(np.matrix(np.linalg.inv(L0 - n*Omega + (0.5*Ltemp*np.matrix(Sp))))*0.5*np.matrix(Ltemp))
Sm = (-1)*(np.matrix(np.linalg.inv(L0 + n*Omega + (0.5*Ltemp*np.matrix(Sm))))*0.5*np.matrix(Ltemp))
L1 = 0.5*np.matrix(Ltemp)*(np.matrix(Sp) + np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwp):
"""
Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
"""
Leff = np.zeros((8, 8), dtype=np.complex_)
Leff[0, 0] = 2*lwg
Leff[1, 1] = 2*lwg
Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[4, 4] = 2*lwp
Leff[5, 5] = 2*lwp
Leff[6, 6] = 2*lwp
Leff[7, 7] = 2*lwp
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwp):
"""
Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
ya que los componentes de Cm son reales.
Esta M es la suma de las 8 matrices M.
"""
M = np.matrix(np.zeros((64, 64), dtype=np.complex_))
M[0,27] = (2/3)*gPS
M[9,18] = (2/3)*gPS
M[0,18] = (1/3)*gPS
M[1,19] = -(1/3)*gPS
M[8,26] = -(1/3)*gPS
M[9,27] = (1/3)*gPS
M[36,18] = (1/2)*gPD
M[37,19] = (1/np.sqrt(12))*gPD
M[44,26] = (1/np.sqrt(12))*gPD
M[45,27] = (1/6)*gPD
M[54,18] = (1/6)*gPD
M[55,19] = (1/np.sqrt(12))*gPD
M[62,26] = (1/np.sqrt(12))*gPD
M[63,27] = (1/2)*gPD
M[45,18] = (1/3)*gPD
M[46,19] = (1/3)*gPD
M[53,26] = (1/3)*gPD
M[54,27] = (1/3)*gPD
M[0,0] = 2*lwg
M[1,1] = 2*lwg
M[8,8] = 2*lwg
M[9,9] = 2*lwg
#M[36, 45] = lwp
for k in [36, 37, 38, 39, 44, 45, 46, 47, 52, 53, 54, 55, 60, 61, 62, 63]:
M[k,k]=2*lwp
return M
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
return gammaD
def FullL_MM(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, beta=0, drivefreq=2*np.pi*22.135*1e6, T = 0, alpha = 0, circularityprobe=1):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
lwg = np.sqrt(lwg**2 + db**2)
lwp = np.sqrt(lwp**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe) + CC
Heffdaga = np.matrix(Heff).getH()
Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j!=q and r!=k:
pass
elif j==q and r!=k:
if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2): #todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
elif j!=q and r==k:
if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
else:
if Heff[r,k] == Heffdaga[j,q]:
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
M = CalculateSingleMmatrix(gPS, gPD, lwg, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
#ESTA PARTE ES CUANDO AGREGAS MICROMOCION
nmax = 3
#print(nmax)
Ltemp, Omega = LtempCalculus(beta, drivefreq)
#print(factor)
L1 = GetL1(Ltemp, L0, Omega, nmax)
Lfull = L0 + L1 #ESA CORRECCION ESTA EN L1
#HASTA ACA
#NORMALIZACION DE RHO
i = 0
while i < 64:
if i%9 == 0:
Lfull[0, i] = 1
else:
Lfull[0, i] = 0
i = i + 1
return Lfull
"""
Scripts para correr un experimento y hacer el análisis de los datos
"""
def CPTspectrum8levels_MM(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, Circularityprobe, beta, drivefreq, freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
ESTA ES LA FUNCION QUE ESTAMOS USANDO
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6+0*freqStep*1e6, freqStep*1e6)
Detg = 2*np.pi*Detg*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwp = lwg*1e6, lwp*1e6
rabG = sg*gPS
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_MM(rabG, rabP, gPS, gPD, Detg, Detp, u, lwg, lwp, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, Temp, alpha, Circularityprobe)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27]))
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
#%%
if __name__ == "__main__":
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 25 #campo magnetico en gauss
u = c*B
sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
Detg = -25
Detr = 20 #detuning del doppler y repump
Temp = 0.0e-3 #temperatura en K
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe, titaprobe = 0, 90
plotCPT = False
freqMin = -50
freqMax = 50
freqStep = 5e-2
Frequencyvector, Fluovector = CPTspectrum8levels_MM(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
analisis/plots/20250516_CPT_bladetrap/Data/EITfit/lolo_modelo_full_3niveles.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
4 de ene 2024
@author: lolo
reingenieria del código que anda
Surge de fusionar
Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py
Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py
MAPA de FUNCIONES
GenerateNoisyCPT_fit
|--> PerformExperiment_8levels_fixedRabi
|--> CPTspectrum8levels_fixedRabi --> ndarray,ndarray
|--> FullL_efficient --> ndarray
|--> dopplerBroadening --> float
|--> EffectiveL --> ndarray
|--> H0matrix --> ndarray
|--> HImatrix --> ndarray
|--> CalculateSingleMmatrix --> ndarray
|--> Lplusminus --> ndarray,ndarray,ndarray
|--> GetL1 --> ndarray
"""
# pylint: disable=C0301,R0913,R0914,W0621
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
# from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
from numba import jit,njit
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@njit
def make_diag(vec):
"Construye matris diagonal desde una lista o vector"
return np.eye(len(vec))*np.array(vec).reshape(-1,1)
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
"""
Scripts para el calculo de la curva CPT
"""
@njit
def H0matrix(Detg, Detp, u):
"""
Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning
del repump y u es el campo magnético en Hz/Gauss.
Para esto se toma la energía del nivel P como 0
"""
eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5,
Detp-2*u/5, Detp+2*u/5, Detp+6*u/5)
#pagina 26 de Oberst. los lande del calcio son iguales a Bario.
# H0 = np.diag(eigenEnergies)
# H0 = np.eye(len(eigenEnergies))*np.array(eigenEnergies).reshape(-1,1)
H0 = make_diag(eigenEnergies)
return H0
@njit
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
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
@njit
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:
if True:
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)
return Lminus, Lplus, DeltaBar
@njit
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))
Sp = (-1)*(0.5*rabR)*(np.linalg.inv(L0 - (nmax+1)*DeltaBar)).dot(Lplus)
Sm = (-1)*(0.5*rabR)*(np.linalg.inv(L0 + (nmax+1)*DeltaBar)).dot(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))
Sp = (-1)*(rabR)*((np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus.dot(Sp)))).dot(Lplus))
Sm = (-1)*(rabR)*((np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus.dot(Sm)))).dot(Lminus))
L1 = 0.5*rabR*(Lminus.dot(Sp) + Lplus.dot(Sm))
return L1
@njit
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
@njit
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.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
@njit
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
return gammaD
@njit
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
lwr = np.sqrt(lwr**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
# Heffdaga = np.matrix(Heff).getH()
Heffdaga = Heff.transpose().conj()
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 = 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:
if True:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #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
@njit
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:
if True:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
#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)
"""
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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/20250516_CPT_bladetrap/Data/EITfit/lolo_modelo_full_8niveles.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
20 de dic 2023
@author: lolo
reingenieria del código que anda
Surge de fusionar
Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
Data/EITfit/MM_eightLevel_2repumps_python_scripts.py
MAPA de FUNCIONES
CPTspectrum8levels_MM
|--> FullL_MM => ndarray(64,64,np.complex_)
|--> dopplerBroadening => float
|--> EffectiveL => ndarray(8,8,np.complex_)
|--> H0matrix => ndarray(8,8,np.complex_)
|--> HImatrix => ndarray(8,8,np.complex_)
|--> CalculateSingleMmatrix => ndarray(64,64,np.complex_)
|--> LtempCalculus => ndarray,ndarray
|--> GetL1 => ndarray
"""
# pylint: disable=C0301,R0913,R0914,W0621
import time
import random
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import savgol_filter as sf
from numba import jit,njit
@njit
def PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None):
"""
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
rta = CPTspectrum8levels_MM(sg, sp, gPS, gPD, DetDoppler, u,
DopplerLaserLinewidth, ProbeLaserLinewidth,
T, alpha, phidoppler, titadoppler, phiprobe,
titaprobe, circularityprobe, beta, drivefreq,
freqMin=freqMin, freqMax=freqMax, freqStep=freqStep,
plot=False, solvemode=1)
ProbeDetuningVectorL, Fluovector = rta
return ProbeDetuningVectorL, Fluovector
def GenerateNoisyCPT_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
"""
Genera un resultado de PerformExperiment_8levels_MM con ruido normal agregado
"""
nFrequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return nFrequencyvector, NoisyFluovector
def GenerateNoisyCPT_MM_fit(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
"""
Este no se qué hace
"""
Frequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs[0], freqs[-1], freqs[1]-freqs[0], circularityprobe, plot=False, solvemode=1, detpvec=None)
#NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, Fluovector
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
#%% Estos son los auxiliares ###################################################
"""
Esta parte es la del modelo
"""
@njit
def make_diag(vec):
"Construye matris diagonal desde una lista o vector"
return np.eye(len(vec))*np.array(vec).reshape(-1,1)
# @njit
# def kron(a,b):
# "Hago el producto de Kronecker a mano"
# return np.vstack( [ np.hstack( [ a[k,j]*b for j in range(a.shape[1]) ] ) for k in range(a.shape[0])])
# @njit
# def kron(A, B):
# cola = A.shape[1]
# rowa = A.shape[0]
# colb = B.shape[1]
# rowb = B.shape[0]
#
# C = [[0] * (cola * colb) for _ in range(rowa * rowb) ]
#
# for i in range(rowa):
# for k in range(cola):
# for j in range(rowb):
# for l in range(colb):
# C[i * rowb + k][j * colb + l] = A[i][j] * B[k][l]
# return np.array(C)
import numba
@jit
def kron(A,B):
out=np.empty((A.shape[0],B.shape[0],A.shape[1],B.shape[1]),dtype=A.dtype)
for i in numba.prange(A.shape[0]):
for j in range(B.shape[0]):
for k in range(A.shape[1]):
for l in range(B.shape[1]):
out[i,j,k,l]=A[i,k]*B[j,l]
return out
@njit
def H0matrix(Detg, Detp, u):
"""
Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning
del repump y u es el campo magnético en Hz/Gauss.
Para esto se toma la energía del nivel P como 0
"""
eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5,
Detp-2*u/5, Detp+2*u/5, Detp+6*u/5)
#pagina 26 de Oberst. los lande del calcio son iguales a Bario.
# H0 = np.diag(eigenEnergies)
# H0 = np.eye(len(eigenEnergies))*np.array(eigenEnergies).reshape(-1,1)
H0 = make_diag(eigenEnergies)
return H0
@njit
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe=1):
"""
Calcula la matriz de interacción Hsp + Hpd, en donde rabR es la frecuencia de rabi de
la transición Doppler SP, rabP es la frecuencia de rabi de la transición repump DP,
y las componentes ei_r y ei_p son las componentes de la polarización
del campo eléctrico incidente de doppler y repump respectivamente. Deben
estar normalizadas a 1
"""
HI = np.zeros((8, 8), dtype=np.complex_)
i, j = 1, 3
HI[i-1, j-1] = (rabG/np.sqrt(3)) * np.cos(titadoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 1, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 3
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(-1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.cos(titadoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 5
HI[i-1, j-1] = -(rabP/2) * np.sin(titaprobe)*(np.cos(phiprobe)-1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 6
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 7
HI[i-1, j-1] = rabP/np.sqrt(12) * np.sin(titaprobe)*(np.cos(phiprobe)+1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 6
HI[i-1, j-1] = -(rabP/np.sqrt(12)) * np.sin(titaprobe)*(np.cos(phiprobe)-1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 7
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 8
HI[i-1, j-1] = (rabP/2) * np.sin(titaprobe)*(np.cos(phiprobe)+1j*np.sin(phiprobe)*circularityprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
# @jit
# def LtempCalculus(beta:float, drivefreq:float, forma=1):
# Hint = np.zeros((8, 8), dtype=np.complex_)
# ampg=beta*drivefreq
# ampr=beta*drivefreq*(397/866)
# Hint[0,0] = ampg
# Hint[1,1] = ampg
# Hint[4,4] = ampr
# Hint[5,5] = ampr
# Hint[6,6] = ampr
# Hint[7,7] = ampr
# if forma==1:
# Ltemp = np.zeros((64, 64), dtype=np.complex_)
# for r in range(8):
# for q in range(8):
# if r!=q:
# Ltemp[r*8+q][r*8+q] = (-1j)*(Hint[r,r] - Hint[q,q])
# if forma==2:
# # deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
# deltaKro = make_diag([1., 1., 1., 1., 1., 1., 1., 1.]).astype(np.complex_)
# # Ltemp = (-1j)*(np.kron(Hint, deltaKro) - np.kron(deltaKro, Hint))
# Ltemp = (-1j)*(kron(Hint, deltaKro) - kron(deltaKro, Hint))
# Omega = np.zeros((64, 64), dtype=np.complex_)
# for i in range(64):
# Omega[i, i] = (1j)*drivefreq
# return Ltemp, Omega
@njit
def LtempCalculus(beta:float, drivefreq:float, forma=1):
Hint = np.zeros((8, 8), dtype=np.complex_)
ampg=beta*drivefreq
ampr=beta*drivefreq*(397/866)
Hint[0,0] = ampg
Hint[1,1] = ampg
Hint[4,4] = ampr
Hint[5,5] = ampr
Hint[6,6] = ampr
Hint[7,7] = ampr
Ltemp = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
if r!=q:
Ltemp[r*8+q][r*8+q] = (-1j)*(Hint[r,r] - Hint[q,q])
Omega = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
Omega[i, i] = (1j)*drivefreq
return Ltemp, Omega
# LtempCalculus(0,1)
# raise ValueError('aaa')
@njit
def GetL1(Ltemp, L0, Omega, nmax):
"""
Devuelve Splus0 y Sminus0
"""
# Sp = (-1)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*Omega))*0.5*np.matrix(Ltemp))
# Sm = (-1)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*Omega))*0.5*np.matrix(Ltemp))
Sp = (-1)*np.linalg.inv(L0 - (nmax+1)*Omega).dot(0.5*Ltemp)
Sm = (-1)*np.linalg.inv(L0 + (nmax+1)*Omega).dot(0.5*Ltemp)
for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
# Sp = (-1)*(np.matrix(np.linalg.inv(L0 - n*Omega + (0.5*Ltemp*np.matrix(Sp))))*0.5*np.matrix(Ltemp))
# Sm = (-1)*(np.matrix(np.linalg.inv(L0 + n*Omega + (0.5*Ltemp*np.matrix(Sm))))*0.5*np.matrix(Ltemp))
Sp = (-1)*np.linalg.inv(L0 - n*Omega + (0.5*Ltemp.dot(Sp))).dot(0.5*Ltemp)
Sm = (-1)*np.linalg.inv(L0 + n*Omega + (0.5*Ltemp.dot(Sm))).dot(0.5*Ltemp)
# L1 = 0.5*np.matrix(Ltemp)*(np.matrix(Sp) + np.matrix(Sm))
L1 = 0.5*Ltemp.dot(Sp + Sm)
return L1
@njit
def EffectiveL(gPS, gPD, lwg, lwp):
"""
Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
"""
Leff = np.zeros((8, 8), dtype=np.complex_)
Leff[0, 0] = 2*lwg
Leff[1, 1] = 2*lwg
Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[4, 4] = 2*lwp
Leff[5, 5] = 2*lwp
Leff[6, 6] = 2*lwp
Leff[7, 7] = 2*lwp
return (-0.5j)*Leff
@njit
def CalculateSingleMmatrix(gPS, gPD, lwg, lwp):
"""
Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
ya que los componentes de Cm son reales.
Esta M es la suma de las 8 matrices M.
"""
M = np.zeros((64, 64), dtype=np.complex_)
M[0,27] = (2/3)*gPS
M[9,18] = (2/3)*gPS
M[0,18] = (1/3)*gPS
M[1,19] = -(1/3)*gPS
M[8,26] = -(1/3)*gPS
M[9,27] = (1/3)*gPS
M[36,18] = (1/2)*gPD
M[37,19] = (1/np.sqrt(12))*gPD
M[44,26] = (1/np.sqrt(12))*gPD
M[45,27] = (1/6)*gPD
M[54,18] = (1/6)*gPD
M[55,19] = (1/np.sqrt(12))*gPD
M[62,26] = (1/np.sqrt(12))*gPD
M[63,27] = (1/2)*gPD
M[45,18] = (1/3)*gPD
M[46,19] = (1/3)*gPD
M[53,26] = (1/3)*gPD
M[54,27] = (1/3)*gPD
M[0,0] = 2*lwg
M[1,1] = 2*lwg
M[8,8] = 2*lwg
M[9,9] = 2*lwg
for k in [36, 37, 38, 39, 44, 45, 46, 47, 52, 53, 54, 55, 60, 61, 62, 63]:
M[k,k]=2*lwp
return M
@njit
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(1*mcalcio))
return gammaD
@njit
def FullL_MM(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, beta=0,
drivefreq=2*np.pi*22.135*1e6, T = 0, alpha = 0, circularityprobe=1):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
lwg = np.sqrt(lwg**2 + (0.83*db)**2)
lwp = np.sqrt(lwp**2 + (0.17*db)**2)
CC = EffectiveL(gPS, gPD, lwg, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe) + CC
# Heffdaga = np.matrix(Heff).getH()
Heffdaga = np.conj(np.transpose(Heff))
Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j!=q and r!=k:
pass
elif j==q and r!=k:
if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2):
#todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
elif j!=q and r==k:
if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
else:
if Heff[r,k] == Heffdaga[j,q]:
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
M = CalculateSingleMmatrix(gPS, gPD, lwg, lwp)
# L0 = np.array(np.matrix(Lfullpartial) + M)
L0 = Lfullpartial + M
#ESTA PARTE ES CUANDO AGREGAS MICROMOCION
nmax = 3
#print(nmax)
Ltemp, Omega = LtempCalculus(beta, drivefreq)
#print(factor)
L1 = GetL1(Ltemp, L0, Omega, nmax)
Lfull = L0 + L1 #ESA CORRECCION ESTA EN L1
#HASTA ACA
#NORMALIZACION DE RHO
i = 0
while i < 64:
if i%9 == 0:
Lfull[0, i] = 1
else:
Lfull[0, i] = 0
i = i + 1
return Lfull
"""
Scripts para correr un experimento y hacer el análisis de los datos
"""
@njit
def CPTspectrum8levels_MM(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, Circularityprobe, beta, drivefreq, freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
ESTA ES LA FUNCION QUE ESTAMOS USANDO
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6+0*freqStep*1e6, freqStep*1e6)
Detg = 2*np.pi*Detg*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwp = lwg*1e6, lwp*1e6
rabG = sg*gPS
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
# tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_MM(rabG, rabP, gPS, gPD, Detg, Detp, u, lwg, lwp, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, Temp, alpha, Circularityprobe)
# if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27]))
Fluovector.append(Fluo)
# if solvemode == 2:
# Linv = np.linalg.inv(L)
# rhovectorized = [Linv[j][0] for j in range(len(Linv))]
# Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
# Fluovector.append(Fluo)
# tfinal = time.time()
# print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
# if plot:
# plt.xlabel('Probe detuning (MHz)')
# plt.ylabel('Fluorescence (A.U.)')
# plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
# plt.legend()
return DetProbeVectorMHz, Fluovector
# @njit
# def lolo():
# L = FullL_MM(100,200,12,123,14)
# return np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
# lolo()
# raise ValueError('áaa')
#%%
if __name__ == "__main__":
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 25 #campo magnetico en gauss
u = c*B
sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
Detg = -25
Detr = 20 #detuning del doppler y repump
Temp = 0.0e-3 #temperatura en K
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe, titaprobe = 0, 90
plotCPT = False
freqMin = -50
freqMax = 50
freqStep = 5e-2
# Frequencyvector, Fluovector = CPTspectrum8levels_MM(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
Frequencyvector, Fluovector = PerformExperiment_8levels_MM(0.9,6.2,135591138.92893547,8482300.164692441,-24.5,32500000.0,0.1,0.1,0.001,0,0,90,0,90,2.0,139078306.77442014,-54.39999999999998,26.26666666666671,0.6666666666666856,circularityprobe=1,plot=False,solvemode=1,detpvec=None)
plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
analisis/plots/20250516_CPT_bladetrap/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
else:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
def GetClosestIndex(Vector, value, tolerance=1e-3):
i = 0
while i<len(Vector):
if abs(Vector[i] - value) < tolerance:
return i
else:
i = i + 1
return GetClosestIndex(Vector, value, tolerance=2*tolerance)
def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
"""
Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
Si no encuentra, devuelve el valor teórico.
"""
IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
try:
FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
except:
FreqDR1 = TeoDR[0]
IndiceDR1 = IndiceDRteo1
try:
FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
except:
FreqDR2 = TeoDR[1]
IndiceDR2 = IndiceDRteo2
try:
FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
except:
FreqDR3 = TeoDR[2]
IndiceDR3 = IndiceDRteo3
try:
FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
except:
FreqDR4 = TeoDR[3]
IndiceDR4 = IndiceDRteo4
try:
FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
except:
FreqDR5 = TeoDR[4]
IndiceDR5 = IndiceDRteo5
try:
FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
except:
FreqDR6 = TeoDR[5]
IndiceDR6 = IndiceDRteo6
return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
"""
Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz
"""
IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
DistanciaFrecuenciaCociente = 25
if NormalizationCriterium==0:
print('che')
return FrequencyOfMinimums, FluorescenceOfMinimums
if NormalizationCriterium==1:
Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
indiceizquierda = k
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
indicederecha = l
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
#asintotico
FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
if NormalizationCriterium==4:
#este te tira la fluorescencia de detuning 0
FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
print('Esto: ', RelativeFluorescenceOfMinimums)
if NormalizationCriterium==2 and getindices==True:
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
"""
Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
La estructura es:
MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
"""
Mapa1 = MapasDR1[0]
Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
Fluo1 = RelMinMedido1[0]
Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
"""
Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
en blanco la zona donde el valor medido se puede hallar.
"""
CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
i = 0
while i<len(theoricalmap):
j = 0
while j<len(theoricalmap[0]):
if abs(theoricalmap[i][j]-target) < tolerance:
CoincidenceMatrix[i][j] = 1
j=j+1
i=i+1
return CoincidenceMatrix
def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
MatrixMapsVectors = []
for i in range(len(MapsVectors)):
MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
for i in range(len(MatrixMapsVectors)):
MatrixSum = MatrixSum + MatrixMapsVectors[i]
MaxNumberOfCoincidences = np.max(MatrixSum)
ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
for i in range(len(ListMatrixSum)):
for j in range(len(ListMatrixSum[0])):
if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
AbsoluteCoincidencesMatrix[i][j] = 1
return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
Xsum = 0
Xvec = []
Ysum = 0
Yvec = []
N = len(NonZeroIndices[0])
for i in range(N):
Xsum = Xsum + X[NonZeroIndices[1][i]]
Xvec.append(X[NonZeroIndices[1][i]])
Ysum = Ysum + Y[NonZeroIndices[0][i]]
Yvec.append(Y[NonZeroIndices[0][i]])
Xaverage = Xsum/N
Yaverage = Ysum/N
Xspread = np.std(Xvec)
Yspread = np.std(Yvec)
return Xaverage, Yaverage, N, Xspread, Yspread
def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
print('hola')
print(RelativeFluorescenceOfMinimums0)
return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def AddNoiseToCPT(Fluo, noisefactor):
return [f+noisefactor*(2*random.random()-1) for f in Fluo]
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
"""
FUNCION VIEJA
Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
Minimumcriterion:
1: Saca los minimos con funcion argelextrema
2: Directamente con las frecuencias teoricas busca las fluorescencias
Normalizationcriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
"""
FluorescenceOfMaximum = max(Fluo)
FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
#criterio para encontrar los minimos
#criterio usando minimos de la fluorescencia calculados con la curva
if MinimumCriterium == 1:
LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
#criterio con las DR teoricas
if MinimumCriterium == 2:
FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
FrequencyOfMinimums = []
FluorescenceOfMinimums =[]
print(FrecuenciasDRTeoricas)
k=0
ventanita = 0.001
while k < len(Freq):
if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
k = k + 1
print(FrequencyOfMinimums)
if len(FrequencyOfMinimums) != len(FrecuenciasDRTeoricas):
print('NO ANDA BIEN ESTO PAPI, revisalo')
#esto es para establecer un criterio para la fluorescencia relativa
DistanciaFrecuenciaCociente = 15
if NormalizationCriterium==1:
FluoNormDivisor = 1
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
FluoNormDivisor = Fluo[0]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
#primero buscamos las frecuencias referencia que se parezcan a las 6:
i = 0
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
while i < len(FrequencyOfMinimumsVector):
if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
print('Cool! Taking the DR identified with any curve')
break
else:
i = i + 1
if i==len(FrequencyOfMinimumsVector):
print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
if taketheoricalDR:
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
print('Ventana = ', Ventana)
DarkResonance1Frequency = []
DarkResonance1Fluorescence = []
DarkResonance2Frequency = []
DarkResonance2Fluorescence = []
DarkResonance3Frequency = []
DarkResonance3Fluorescence = []
DarkResonance4Frequency = []
DarkResonance4Fluorescence = []
DarkResonance5Frequency = []
DarkResonance5Fluorescence = []
DarkResonance6Frequency = []
DarkResonance6Fluorescence = []
i = 0
while i < len(FrequencyOfMinimumsVector):
j = 0
FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
while j < len(FrequencyOfMinimumsVector[i]):
if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[0] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[1] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[2] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[3] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[4] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[5] = 0
else:
#print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
pass
j = j + 1
if np.count_nonzero(FrecuenciasReferencia) > 0:
if FrecuenciasReferencia[0] != 0:
DarkResonance1Frequency.append(FrecuenciasReferencia[0])
DarkResonance1Fluorescence.append()
if FrecuenciasReferencia[1] != 0:
DarkResonance2Frequency.append(FrecuenciasReferencia[1])
DarkResonance2Fluorescence.append(0)
if FrecuenciasReferencia[2] != 0:
DarkResonance3Frequency.append(FrecuenciasReferencia[2])
DarkResonance3Fluorescence.append(0)
if FrecuenciasReferencia[3] != 0:
DarkResonance4Frequency.append(FrecuenciasReferencia[3])
DarkResonance4Fluorescence.append(0)
if FrecuenciasReferencia[4] != 0:
DarkResonance5Frequency.append(FrecuenciasReferencia[4])
DarkResonance5Fluorescence.append(0)
if FrecuenciasReferencia[5] != 0:
DarkResonance6Frequency.append(FrecuenciasReferencia[5])
DarkResonance6Fluorescence.append(0)
i = i + 1
return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
analisis/plots/20250516_CPT_bladetrap/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ f9b573e5
#!/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/20250516_CPT_bladetrap/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
def H0matrix(Detg, Detp, u):
"""
Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning del repump y u es el campo magnético en Hz/Gauss.
Para esto se toma la energía del nivel P como 0
"""
eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5, Detp-2*u/5, Detp+2*u/5, Detp+6*u/5) #pagina 26 de Oberst. los lande del calcio son iguales a Bario.
H0 = np.diag(eigenEnergies)
return H0
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
Calcula la matriz de interacción Hsp + Hpd, en donde rabR es la frecuencia de rabi de la transición Doppler SP,
rabP es la frecuencia de rabi de la transición repump DP, y las componentes ei_r y ei_p son las componentes de la polarización
del campo eléctrico incidente de doppler y repump respectivamente. Deben estar normalizadas a 1
"""
HI = np.zeros((8, 8), dtype=np.complex_)
i, j = 1, 3
HI[i-1, j-1] = (rabG/np.sqrt(3)) * np.cos(titadoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 1, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 3
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(-1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.cos(titadoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 5
HI[i-1, j-1] = -(rabP/2) * np.sin(titaprobe)*np.exp(-1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 6
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 7
HI[i-1, j-1] = rabP/np.sqrt(12) * np.sin(titaprobe)*np.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 6
HI[i-1, j-1] = -(rabP/np.sqrt(12)) * np.sin(titaprobe)*np.exp(-1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 7
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 8
HI[i-1, j-1] = (rabP/2) * np.sin(titaprobe)*np.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
Hintplus = np.zeros((8, 8), dtype=np.complex_)
Hintminus = np.zeros((8, 8), dtype=np.complex_)
Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
if forma==1:
Lplus = np.zeros((64, 64), dtype=np.complex_)
Lminus = np.zeros((64, 64), dtype=np.complex_)
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j==q:
if (k==2 or k==3) and r > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k])
if (r==2 or r==3) and k > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
elif r==k:
if (q==2 or q==3) and j > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q])
if (j==2 or j==3) and q > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
DeltaBar[i, i] = (1j)*(detr - detp)
return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
Sp = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwr, lwp):
"""
Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
"""
Leff = np.zeros((8, 8), dtype=np.complex_)
Leff[0, 0] = 2*lwg
Leff[1, 1] = 2*lwg
Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[4, 4] = 2*(lwr + lwp)
Leff[5, 5] = 2*(lwr + lwp)
Leff[6, 6] = 2*(lwr + lwp)
Leff[7, 7] = 2*(lwr + lwp)
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp):
"""
Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
ya que los componentes de Cm son reales.
Esta M es la suma de las 8 matrices M.
"""
M = np.matrix(np.zeros((64, 64), dtype=np.complex_))
M[0,27] = (2/3)*gPS
M[9,18] = (2/3)*gPS
M[0,18] = (1/3)*gPS
M[1,19] = -(1/3)*gPS
M[8,26] = -(1/3)*gPS
M[9,27] = (1/3)*gPS
M[36,18] = (1/2)*gPD
M[37,19] = (1/np.sqrt(12))*gPD
M[44,26] = (1/np.sqrt(12))*gPD
M[45,27] = (1/6)*gPD
M[54,18] = (1/6)*gPD
M[55,19] = (1/np.sqrt(12))*gPD
M[62,26] = (1/np.sqrt(12))*gPD
M[63,27] = (1/2)*gPD
M[45,18] = (1/3)*gPD
M[46,19] = (1/3)*gPD
M[53,26] = (1/3)*gPD
M[54,27] = (1/3)*gPD
M[0,0] = 2*lwg
M[1,1] = 2*lwg
M[8,8] = 2*lwg
M[9,9] = 2*lwg
factor1 = 1
factor2 = 1
factor3 = 1
factor4 = 1
#M[36, 45] = lwp
M[36,36] = 2*(lwr + factor1*lwp)
M[37,37] = 2*(lwr + factor1*lwp)
M[38,38] = 2*(lwr + factor1*lwp)
M[39,39] = 2*(lwr + factor1*lwp)
M[44,44] = 2*(lwr + factor2*lwp)
M[45,45] = 2*(lwr + factor2*lwp)
M[46,46] = 2*(lwr + factor2*lwp)
M[47,47] = 2*(lwr + factor2*lwp)
M[52,52] = 2*(lwr + factor3*lwp)
M[53,53] = 2*(lwr + factor3*lwp)
M[54,54] = 2*(lwr + factor3*lwp)
M[55,55] = 2*(lwr + factor3*lwp)
M[60,60] = 2*(lwr + factor4*lwp)
M[61,61] = 2*(lwr + factor4*lwp)
M[62,62] = 2*(lwr + factor4*lwp)
M[63,63] = 2*(lwr + factor4*lwp)
return M
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
return gammaD
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
lwr = np.sqrt(lwr**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
Heffdaga = np.matrix(Heff).getH()
Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j!=q and r!=k:
pass
elif j==q and r!=k:
if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2): #todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
elif j!=q and r==k:
if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
else:
if Heff[r,k] == Heffdaga[j,q]:
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
M = CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
nmax = 1
Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
factor1 = np.exp(1j*0.2*np.pi)
factor2 = np.exp(-1j*0.2*np.pi)
#print(factor)
L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
Lfull = L0 + L1
#NORMALIZACION DE RHO
i = 0
while i < 64:
if i%9 == 0:
Lfull[0, i] = 1
else:
Lfull[0, i] = 0
i = i + 1
return Lfull
"""
Scripts para correr un experimento y hacer el análisis de los datos
"""
def CalculoTeoricoDarkResonances(u, titadoppler):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return NegativeDR, PositiveDR
def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
rabG = sg*gPS
rabR = sr*gPD
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
#Fluo = np.abs(rhovectorized[coh])
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
#%%
if __name__ == "__main__":
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 25 #campo magnetico en gauss
u = c*B
sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
Detg = -25
Detr = 20 #detuning del doppler y repump
Temp = 0.0e-3 #temperatura en K
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe, titaprobe = 0, 90
plotCPT = False
freqMin = -50
freqMax = 50
freqStep = 5e-2
Frequencyvector, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
for PDR in PositiveDR:
plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
for NDR in NegativeDR:
plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
#parametros que andan piola:
"""
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 17 #campo magnetico en gauss
u = c*B
#u = 80e6
sr, sp = 0.53, 4.2
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
rabR, rabP = sr*gPS, sp*gPD
lw = 2*np.pi * 0.33e6
lwr, lwp = lw, lw #ancho de linea de los laseres
dr_spec = - 2*np.pi* 26e6
freqSteps = 500
freqMin = -100e6
freqMax = 100e6
dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
#dps = [-30e6]
alfar = 90*(np.pi/180)
ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
alfap = 90*(np.pi/180)
ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
"""
analisis/plots/20250516_CPT_bladetrap/analisis_superajuste.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Ploteo de datos y ajustes
@author: lolo
"""
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
#%% Importaciones extra
# /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
PARAMETROS = np.load('PARAMETROS.npz',allow_pickle=True)
for var_name in PARAMETROS.keys():
globals()[var_name] = PARAMETROS[var_name]
print(f'loaded: {var_name}')
#%%
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#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']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
def Split(array,n):
length=len(array)/n
splitlist = []
jj = 0
while jj<length:
partial = []
ii = 0
while ii < n:
partial.append(array[jj*n+ii])
ii = ii + 1
splitlist.append(partial)
jj = jj + 1
return splitlist
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
La 0 no ajusta bien incluso con todos los parametros libres
De la 1 a la 11 ajustan bien
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
SUPER AJUSTE (SA)
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [1,2,3,4,5,6,7,8,9,10,11]
#SelectedCurveVec = [10]
# if not 'popt_SA_vec' in globals().keys() or len(popt_SA_vec)==0:
popt_SA_vec = []
pcov_SA_vec = []
Detuningsshort_vec = []
Counts_vec = []
Detuningslong_vec = []
FittedCounts_vec = []
Betas_vec = []
ErrorBetas_vec = []
Temp_vec = []
ErrorTemp_vec = []
DetuningsUV_vec = []
ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
#selectedcurve = 2 #IMPORTANTE: SELECCIONA LA MEDICION
FreqsDR = Freqs[0]
CountsDR = CountsSplit[0][selectedcurve]
if selectedcurve==1:
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
if selectedcurve==2:
CountsDR[67]=0.5*(CountsDR[66]+CountsDR[68])
CountsDR[71]=0.5*(CountsDR[70]+CountsDR[72])
if selectedcurve==6:
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
if selectedcurve==7:
CountsDR[117]=0.5*(CountsDR[116]+CountsDR[118])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
popt_SA_vec.append(popt_3_SA)
pcov_SA_vec.append(pcov_3_SA)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA, plot=True)
DetuningsUV_vec.append(popt_3_SA[1])
ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
Betas_vec.append(popt_3_SA[6])
ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
Temp_vec.append(popt_3_SA[7])
ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
Detuningsshort_vec.append(Detunings_3_SA_short)
Counts_vec.append(CountsDR)
Detuningslong_vec.append(Detunings_3_SA_long)
FittedCounts_vec.append(FittedEITpi_3_SA_long)
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA)
#%%
"""
Grafico distintas variables que salieron del SUper ajuste
"""
import seaborn as sns
paleta = sns.color_palette("rocket")
medfin = 12
voltages_dcA = Voltages[0][1:medfin]
def lineal(x,a,b):
return a*x+b
def hiperbola(x,a,b,c,x0):
return a*np.sqrt(((x-x0)**2+c**2))+b
hiperbola_or_linear = True
if hiperbola_or_linear:
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec[:medfin-1],p0=(100,0.1,1,-0.15))
xhip = np.linspace(-0.23,0.005,200)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec[0:medfin-1],yerr=ErrorBetas_vec[:medfin-1],fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xhip,hiperbola(xhip,*popthip))
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
else:
poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],Betas_vec[0:3])
poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],Betas_vec[4:])
minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
xini = np.linspace(-0.23,-0.13,100)
xfin = np.linspace(-0.15,0.005,100)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xini,lineal(xini,*poptini))
plt.plot(xfin,lineal(xfin,*poptfin))
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
print([t*1e3 for t in Temp_vec])
plt.figure()
plt.errorbar(voltages_dcA,[t*1e3 for t in Temp_vec[:medfin-1]],yerr=[t*1e3 for t in ErrorTemp_vec[:medfin-1]],fmt='o',capsize=5,markersize=5,color=paleta[3])
#plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.axhline(0.538)
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
#plt.ylim(0,2)
#%%
"""
Ahora hago un ajuste con una hiperbola porque tiene mas sentido, por el hecho
de que en el punto optimo el ion no esta en el centro de la trampa
sino que esta a una distancia d
"""
def hiperbola(x,a,b,c,x0):
return a*np.sqrt(((x-x0)**2+c**2))+b
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec[:10],p0=(100,0.1,1,-0.15))
xhip = np.linspace(-0.23,0.005,200)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec[:10],yerr=ErrorBetas_vec[:10],fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xhip,hiperbola(xhip,*popthip))
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
#%%
from scipy.special import jv
def expo(x,tau,A,B):
return A*np.exp(x/tau)+B
def cuadratica(x,a,c):
return a*(x**2)+c
def InverseMicromotionSpectra(beta, A, det, x0, gamma, B):
ftrap=22.1
#gamma=30
P = ((jv(0, beta)**2)/((((det-x0)**2)+(0.5*gamma)**2)**2))*(-2*(det-x0))
i = 1
#print(P)
while i <= 5:
P = P + (-2*(det-x0))*((jv(i, beta))**2)/(((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det-x0))*(((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
#return 1/(A*P+B)
return 1/(A*P+B)
def InverseMicromotionSpectra_raw(beta, A, det, B):
ftrap=22.1
gamma=21
P = ((jv(0, beta)**2)/((((det)**2)+(0.5*gamma)**2)**2))*(-2*(det))
i = 1
#print(P)
while i <= 3:
P = P + (-2*(det))*((jv(i, beta))**2)/(((((det)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det))*(((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
return A/P+B
"""
Temperatura vs beta con un ajuste exponencial
"""
popt_exp, pcov_exp = curve_fit(expo,Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]])
popt_quad, pcov_quad = curve_fit(cuadratica,Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]],p0=(1,10))
#popt_rho22, pcov_rho22 = curve_fit(InverseMicromotionSpectra,Betas_vec,[t*1e3 for t in Temp_vec],p0=(10,10,-10,1,20)) #esto ajusta muy bien
#popt_rho22, pcov_rho22 = curve_fit(InverseMicromotionSpectra,Betas_vec, [t*1e3 for t in Temp_vec],p0=(-10,-10,10,1,20)) #esto ajusta muy bien
popt_rho22_raw, pcov_rho22_raw = curve_fit(InverseMicromotionSpectra_raw,Betas_vec[:10], [t*1e3 for t in Temp_vec[:10]],p0=(-10, -10, 1)) #esto ajusta muy bien
print(popt_rho22_raw)
betaslong = np.arange(0,2*2.7,0.01)
print(f'Min temp predicted: {InverseMicromotionSpectra_raw(betaslong,*popt_rho22_raw)[100]}')
plt.figure()
plt.errorbar(Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]],xerr=ErrorBetas_vec[:10], yerr=[t*1e3 for t in ErrorTemp_vec[:10]],fmt='o',capsize=5,markersize=5,color=paleta[3])
#plt.plot(betaslong,expo(betaslong,*popt_exp),label='Ajuste exponencial')
#plt.plot(betaslong,cuadratica(betaslong,*popt_quad),label='Ajuste cuadratico')
#plt.plot(betaslong,InverseMicromotionSpectra(betaslong,*popt_rho22),label='Ajuste cuadratico')
plt.plot(betaslong,InverseMicromotionSpectra_raw(betaslong,*popt_rho22_raw),label='Ajuste cuadratico')
#plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
#plt.axhline(0.538)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
#%%
"""
Esto no es del super ajuste sino de los ajustes anteriores en donde DetDoppler y offset son puestos a mano
Aca grafico los betas con su error en funcion de la tension variada.
Ademas, hago ajuste lineal para primeros y ultimos puntos, ya que espero que
si la tension hace que la posicion del ion varie linealmente, el beta varia proporcional a dicha posicion.
"""
import seaborn as sns
def lineal(x,a,b):
return a*x+b
paleta = sns.color_palette("rocket")
betavector = [beta1,beta2,beta3,beta4,beta5,beta6,beta7,beta8,beta9]
errorbetavector = [errorbeta1,errorbeta2,errorbeta3,errorbeta4,errorbeta5,errorbeta6,errorbeta7,errorbeta8,errorbeta9]
voltages_dcA = Voltages[0][1:10]
poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],betavector[0:3])
poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],betavector[4:])
minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
xini = np.linspace(-0.23,-0.13,100)
xfin = np.linspace(-0.15,0.005,100)
plt.figure()
plt.errorbar(voltages_dcA,betavector,yerr=errorbetavector,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xini,lineal(xini,*poptini))
plt.plot(xfin,lineal(xfin,*poptfin))
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
#%%
"""
Aca veo la temperatura del ion en funcion del voltaje del endcap, ya que
al cambiar la cantidad de micromocion, cambia la calidad del enfriado
"""
tempvector = np.array([temp1,temp2,temp3,temp4,temp5,temp6,temp7,temp8,temp9])*1e3
errortempvector = np.array([errortemp1,errortemp2,errortemp3,errortemp4,errortemp5,errortemp6,errortemp7,errortemp8,errortemp9])*1e3
voltages_dcA = Voltages[0][1:10]
plt.figure()
plt.errorbar(voltages_dcA,tempvector,yerr=errortempvector,fmt='o',capsize=5,markersize=5,color=paleta[3])
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
plt.ylim(0,2)
#%%
"""
Por las dudas, temperatura en funcion de beta
"""
plt.figure()
plt.errorbar(betavector,tempvector,yerr=errortempvector,xerr=errorbetavector,fmt='o',capsize=5,markersize=5)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
#%%
"""
Si quiero ver algun parametro del ajuste puntual. el orden es: 0:SG, 1:SP, 2:SCALE1, 3:OFFSET
"""
ki=2
plt.errorbar(np.arange(0,9,1),[popt_1[ki],popt_2[ki],popt_3[ki],popt_4[ki],popt_5[ki],popt_6[ki],popt_7[ki],popt_8[ki],popt_9[ki]],yerr=[np.sqrt(pcov_1[ki,ki]),np.sqrt(pcov_2[ki,ki]),np.sqrt(pcov_3[ki,ki]),np.sqrt(pcov_4[ki,ki]),np.sqrt(pcov_5[ki,ki]),np.sqrt(pcov_6[ki,ki]),np.sqrt(pcov_7[ki,ki]),np.sqrt(pcov_8[ki,ki]),np.sqrt(pcov_9[ki,ki])], fmt='o',capsize=3,markersize=3)
#%%
if False:
GUARDAR = {}
for var in [ kk for kk in globals().keys() if kk.startswith('pop') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('pcov') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('Fitted') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(var)
GUARDAR[var] = globals()[var]
np.savez('PARAMETROS.npz', **GUARDAR )
analisis/plots/20250516_CPT_bladetrap/lolo_CPT_plotter_20231123.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Ploteo de datos y ajustes
@author: lolo
"""
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
#%% Importaciones extra
# /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
PARAMETROS = np.load('PARAMETROS.npz',allow_pickle=True)
for var_name in PARAMETROS.keys():
globals()[var_name] = PARAMETROS[var_name]
print(f'loaded: {var_name}')
#%%
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
# os.chdir('../20231123_CPTconmicromocion3/Data/')
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#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']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
def Split(array,n):
length=len(array)/n
splitlist = []
jj = 0
while jj<length:
partial = []
ii = 0
while ii < n:
partial.append(array[jj*n+ii])
ii = ii + 1
splitlist.append(partial)
jj = jj + 1
return splitlist
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
#%%
"""
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [9] # de la 1 a la 9 vale la pena, despues no
drs = [390.5, 399.5, 406, 413.5]
drive=22.1
Frequencies = Freqs[0]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Frequencies], CountsSplit[0][j], yerr=np.sqrt(CountsSplit[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 1
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion+3*0.8
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 1
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
t0 = time.time()
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
# print(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], dict(circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None))
print('Done, Total time: ', round((time.time()-t0), 2), "s")
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_1' in globals().keys():
popt_1, pcov_1 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_1 = FitEIT_MM_single(freqslong, *popt_1)
beta1 = popt_1[4]
errorbeta1 = np.sqrt(pcov_1[4,4])
temp1 = popt_1[5]
errortemp1 = np.sqrt(pcov_1[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_1, color='darkolivegreen', linewidth=3, label='med 1')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 2
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion+1.6
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 2
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_2' in globals().keys():
popt_2, pcov_2 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_2 = FitEIT_MM_single(freqslong, *popt_2)
beta2 = popt_2[4]
errorbeta2 = np.sqrt(pcov_2[4,4])
temp2 = popt_2[5]
errortemp2 = np.sqrt(pcov_2[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2, color='darkolivegreen', linewidth=3, label='med 2')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 3
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion+0.8
DetDoppler = -11.5-correccion
print(offsetxpi,DetDoppler)
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 3
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_3' in globals().keys():
popt_3, pcov_3 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_3 = FitEIT_MM_single(freqslong, *popt_3)
beta3 = popt_3[4]
errorbeta3 = np.sqrt(pcov_3[4,4])
temp3 = popt_3[5]
errortemp3 = np.sqrt(pcov_3[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_3, color='darkolivegreen', linewidth=3, label='med 3')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 4
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 4
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_4' in globals().keys():
popt_4, pcov_4 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_4 = FitEIT_MM_single(freqslong, *popt_4)
beta4 = popt_4[4]
errorbeta4 = np.sqrt(pcov_4[4,4])
temp4 = popt_4[5]
errortemp4 = np.sqrt(pcov_4[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_4, color='darkolivegreen', linewidth=3, label='med 4')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 5
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-1
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 5
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
#TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_5' in globals().keys():
popt_5, pcov_5 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_5 = FitEIT_MM_single(freqslong, *popt_5)
beta5 = popt_5[4]
errorbeta5 = np.sqrt(pcov_5[4,4])
temp5 = popt_5[5]
errortemp5 = np.sqrt(pcov_5[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_5, color='darkolivegreen', linewidth=3, label='med 5')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 6
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-2.2
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_6' in globals().keys():
popt_6, pcov_6 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 5e4, 1e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_6 = FitEIT_MM_single(freqslong, *popt_6)
beta6 = popt_6[4]
errorbeta6 = np.sqrt(pcov_6[4,4])
temp6 = popt_6[5]
errortemp6 = np.sqrt(pcov_6[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_6, color='darkolivegreen', linewidth=3, label='med 6')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 7
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-3.7
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 7
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_7' in globals().keys():
popt_7, pcov_7 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_7 = FitEIT_MM_single(freqslong, *popt_7)
beta7 = popt_7[4]
errorbeta7 = np.sqrt(pcov_7[4,4])
temp7 = popt_7[5]
errortemp7 = np.sqrt(pcov_7[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_7, color='darkolivegreen', linewidth=3, label='med 7')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 8
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
offsetxpi = 419+correccion-4.9
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 8
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_8' in globals().keys():
popt_8, pcov_8 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10, 10e-3)))
FittedEITpi_8 = FitEIT_MM_single(freqslong, *popt_8)
beta8 = popt_8[4]
errorbeta8 = np.sqrt(pcov_8[4,4])
temp8 = popt_8[5]
errortemp8 = np.sqrt(pcov_8[5,5])
print()
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_8, color='darkolivegreen', linewidth=3, label='med 8')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
MEDICION 9
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 16
offsetxpi = 419+correccion-6
DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
selectedcurve = 9
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[0]]
CountsDR = CountsSplit[0][selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
#TEMP = 0.2e-3
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1
#return ScaledFluo1
if not 'popt_9' in globals().keys():
popt_9, pcov_9 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 2, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 5e4, 5e4, 10,10e-3)))
FittedEITpi_9 = FitEIT_MM_single(freqslong, *popt_9)
beta9 = popt_9[4]
errorbeta9 = np.sqrt(pcov_9[4,4])
temp9 = popt_9[5]
errortemp9 = np.sqrt(pcov_9[5,5])
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_9, color='darkolivegreen', linewidth=3, label='med 9')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
La 0 no ajusta bien incluso con todos los parametros libres
De la 1 a la 11 ajustan bien
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
SUPER AJUSTE (SA)
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [1,2,3,4,5,6,7,8,9,10,11]
#SelectedCurveVec = [10]
if not 'popt_SA_vec' in globals().keys() or len(popt_SA_vec)==0:
popt_SA_vec = []
pcov_SA_vec = []
Detuningsshort_vec = []
Counts_vec = []
Detuningslong_vec = []
FittedCounts_vec = []
Betas_vec = []
ErrorBetas_vec = []
Temp_vec = []
ErrorTemp_vec = []
DetuningsUV_vec = []
ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
#selectedcurve = 2 #IMPORTANTE: SELECCIONA LA MEDICION
FreqsDR = Freqs[0]
CountsDR = CountsSplit[0][selectedcurve]
if selectedcurve==1:
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
if selectedcurve==2:
CountsDR[67]=0.5*(CountsDR[66]+CountsDR[68])
CountsDR[71]=0.5*(CountsDR[70]+CountsDR[72])
if selectedcurve==6:
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
if selectedcurve==7:
CountsDR[117]=0.5*(CountsDR[116]+CountsDR[118])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
if True:
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3], bounds=((0, -50, 0, 0, 0, 0, 0, 0), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3)))
popt_SA_vec.append(popt_3_SA)
pcov_SA_vec.append(pcov_3_SA)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA, plot=True)
DetuningsUV_vec.append(popt_3_SA[1])
ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
Betas_vec.append(popt_3_SA[6])
ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
Temp_vec.append(popt_3_SA[7])
ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
Detuningsshort_vec.append(Detunings_3_SA_short)
Counts_vec.append(CountsDR)
Detuningslong_vec.append(Detunings_3_SA_long)
FittedCounts_vec.append(FittedEITpi_3_SA_long)
tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec)
for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve in zip(*tmp_datos):
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA)
#%%
"""
Grafico distintas variables que salieron del SUper ajuste
"""
import seaborn as sns
paleta = sns.color_palette("rocket")
voltages_dcA = Voltages[0][1:10]
def lineal(x,a,b):
return a*x+b
def hiperbola(x,a,b,c,x0):
return a*np.sqrt(((x-x0)**2+c**2))+b
hiperbola_or_linear = True
if hiperbola_or_linear:
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
xhip = np.linspace(-0.23,0.005,200)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xhip,hiperbola(xhip,*popthip))
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
else:
poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],Betas_vec[0:3])
poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],Betas_vec[4:])
minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
xini = np.linspace(-0.23,-0.13,100)
xfin = np.linspace(-0.15,0.005,100)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xini,lineal(xini,*poptini))
plt.plot(xfin,lineal(xfin,*poptfin))
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
print([t*1e3 for t in Temp_vec])
plt.figure()
plt.errorbar(voltages_dcA,[t*1e3 for t in Temp_vec],yerr=[t*1e3 for t in ErrorTemp_vec],fmt='o',capsize=5,markersize=5,color=paleta[3])
# plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
print(f'\n\nTE FALTA DEFINIR LA VARIABLE minimum_voltage\n\n')
plt.axhline(0.538)
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
#plt.ylim(0,2)
#%%
"""
Ahora hago un ajuste con una hiperbola porque tiene mas sentido, por el hecho
de que en el punto optimo el ion no esta en el centro de la trampa
sino que esta a una distancia d
"""
def hiperbola(x,a,b,c,x0):
return a*np.sqrt(((x-x0)**2+c**2))+b
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
xhip = np.linspace(-0.23,0.005,200)
plt.figure()
plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xhip,hiperbola(xhip,*popthip))
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
#%%
from scipy.special import jv
def expo(x,tau,A,B):
return A*np.exp(x/tau)+B
def cuadratica(x,a,c):
return a*(x**2)+c
def InverseMicromotionSpectra(beta, A, det, x0, gamma, B):
ftrap=22.1
#gamma=30
P = ((jv(0, beta)**2)/((((det-x0)**2)+(0.5*gamma)**2)**2))*(-2*(det-x0))
i = 1
#print(P)
while i <= 5:
P = P + (-2*(det-x0))*((jv(i, beta))**2)/(((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det-x0))*(((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
#return 1/(A*P+B)
return 1/(A*P+B)
def InverseMicromotionSpectra_raw(beta, A, det, B):
ftrap=22.1
gamma=21
P = ((jv(0, beta)**2)/((((det)**2)+(0.5*gamma)**2)**2))*(-2*(det))
i = 1
#print(P)
while i <= 3:
P = P + (-2*(det))*((jv(i, beta))**2)/(((((det)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det))*(((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
return A/P+B
"""
Temperatura vs
"""
popt_exp, pcov_exp = curve_fit(expo,Betas_vec,[t*1e3 for t in Temp_vec])
betaslong = np.arange(0,2.7,0.01)
plt.figure()
plt.errorbar(Betas_vec,[t*1e3 for t in Temp_vec],xerr=ErrorBetas_vec, yerr=[t*1e3 for t in ErrorTemp_vec],fmt='o',capsize=5,markersize=5,color=paleta[3])
plt.plot(betaslong,expo(betaslong,*popt_exp))
#plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
#plt.axhline(0.538)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
#%%
"""
Esto no es del super ajuste sino de los ajustes anteriores en donde DetDoppler y offset son puestos a mano
Aca grafico los betas con su error en funcion de la tension variada.
Ademas, hago ajuste lineal para primeros y ultimos puntos, ya que espero que
si la tension hace que la posicion del ion varie linealmente, el beta varia proporcional a dicha posicion.
"""
import seaborn as sns
def lineal(x,a,b):
return a*x+b
paleta = sns.color_palette("rocket")
betavector = [beta1,beta2,beta3,beta4,beta5,beta6,beta7,beta8,beta9]
errorbetavector = [errorbeta1,errorbeta2,errorbeta3,errorbeta4,errorbeta5,errorbeta6,errorbeta7,errorbeta8,errorbeta9]
voltages_dcA = Voltages[0][1:10]
poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],betavector[0:3])
poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],betavector[4:])
minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
xini = np.linspace(-0.23,-0.13,100)
xfin = np.linspace(-0.15,0.005,100)
plt.figure()
plt.errorbar(voltages_dcA,betavector,yerr=errorbetavector,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xini,lineal(xini,*poptini))
plt.plot(xfin,lineal(xfin,*poptfin))
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
#%%
"""
Aca veo la temperatura del ion en funcion del voltaje del endcap, ya que
al cambiar la cantidad de micromocion, cambia la calidad del enfriado
"""
tempvector = np.array([temp1,temp2,temp3,temp4,temp5,temp6,temp7,temp8,temp9])*1e3
errortempvector = np.array([errortemp1,errortemp2,errortemp3,errortemp4,errortemp5,errortemp6,errortemp7,errortemp8,errortemp9])*1e3
voltages_dcA = Voltages[0][1:10]
plt.figure()
plt.errorbar(voltages_dcA,tempvector,yerr=errortempvector,fmt='o',capsize=5,markersize=5,color=paleta[3])
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
plt.ylim(0,2)
#%%
"""
Por las dudas, temperatura en funcion de beta
"""
plt.figure()
plt.errorbar(betavector,tempvector,yerr=errortempvector,xerr=errorbetavector,fmt='o',capsize=5,markersize=5)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
#%%
"""
Si quiero ver algun parametro del ajuste puntual. el orden es: 0:SG, 1:SP, 2:SCALE1, 3:OFFSET
"""
ki=2
plt.errorbar(np.arange(0,9,1),[popt_1[ki],popt_2[ki],popt_3[ki],popt_4[ki],popt_5[ki],popt_6[ki],popt_7[ki],popt_8[ki],popt_9[ki]],yerr=[np.sqrt(pcov_1[ki,ki]),np.sqrt(pcov_2[ki,ki]),np.sqrt(pcov_3[ki,ki]),np.sqrt(pcov_4[ki,ki]),np.sqrt(pcov_5[ki,ki]),np.sqrt(pcov_6[ki,ki]),np.sqrt(pcov_7[ki,ki]),np.sqrt(pcov_8[ki,ki]),np.sqrt(pcov_9[ki,ki])], fmt='o',capsize=3,markersize=3)
#%%
"""
AHORA VAMOS A MEDICIONES CON MAS DE UN ION!!!
"""
"""
Ploteo la cpt de referencia / plotting the reference CPT
1: 2 iones, -100 mV dcA
2: 2 iones, -150 mV dcA
3: 2 iones, -50 mV dcA
4: 2 iones, 5 voltajes (el ion se va en la 4ta medicion y en la 5ta ni esta)
5, 6 y 7: 3 iones en donde el scaneo esta centrado en distintos puntos
"""
jvec = [3] # desde la 1, pero la 4 no porque es un merge de curvitas
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
"""
Mergeo la 5, 6 y 7
"""
Freqs5 = [2*f*1e-6 for f in Freqs[5]]
Freqs6 = [2*f*1e-6 for f in Freqs[6]]
Freqs7 = [2*f*1e-6 for f in Freqs[7]]
Counts5 = Counts[5]
Counts6 = Counts[6]
Counts7 = Counts[7]
i_1_ini = 0
i_1 = 36
i_2_ini = 0
i_2 = 24
f_1 = 18
f_2 = 30
scale_1 = 0.92
scale_2 = 0.98
#Merged_freqs_test = [f-f_2 for f in Freqs6[i_2_ini:i_2]]+[f-f_1 for f in Freqs5[i_1_ini:i_1]]+Freqs7
#plt.plot(Merged_freqs_test,'o')
Merged_freqs = [f-f_2 for f in Freqs6[0:i_2]]+[f-f_1 for f in Freqs5[0:i_1]]+Freqs7
Merged_counts = [scale_2*c for c in Counts6[0:i_2]]+[scale_1*c for c in Counts5[0:i_1]]+list(Counts7)
Merged_freqs_rescaled = np.linspace(np.min(Merged_freqs),np.max(Merged_freqs),len(Merged_freqs))
#drs = [391.5, 399.5, 405.5, 414]
drs = [370,379,385,391.5]
plt.figure()
i = 0
for j in jvec:
plt.plot([f-f_1 for f in Freqs5[0:i_1]], [scale_1*c for c in Counts5[0:i_1]],'o')
plt.plot([f-f_2 for f in Freqs6[0:i_2]], [scale_2*c for c in Counts6[0:i_2]],'o')
plt.plot(Freqs7, Counts7,'o')
plt.errorbar(Merged_freqs, Merged_counts, yerr=np.sqrt(Merged_counts), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
for dr in drs:
plt.axvline(dr)
plt.axvline(dr+drive, color='red', linestyle='dashed', alpha=0.3)
plt.axvline(dr-drive, color='red', linestyle='dashed', alpha=0.3)
plt.legend()
#%%
"""
ajusto la mergeada de 3 iones
"""
raise ValueError('STOP')
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 = -20
offsetxpi = 438+correccion
DetDoppler = -35-correccion-22
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [f-offsetxpi for f in Merged_freqs]
CountsDR = Merged_counts
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
import numba
@numba.jit
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence3 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA3, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
ScaledFluo3 = np.array([f*SCALE3 for f in Fluorescence3])
return ScaledFluo1+ScaledFluo2+ScaledFluo3+OFFSET
#return ScaledFluo1
if not 'popt_3ions' in globals().keys():
popt_3ions, pcov_3ions = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.6, 6.2, 3.5e5, 3.5e5, 3.5e5, 2e3, 1, 1, 1], bounds=((0, 0, 0, 0, 0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 5e8, 7e3, 10, 10, 10)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_3ions = FitEIT_MM(freqslong, *popt_3ions)
#FittedEITpi_3ions = FitEIT_MM(freqslong, popt_3ions[0],popt_3ions[1],popt_3ions[2],popt_3ions[3],popt_3ions[4],popt_3ions[5],4,2,0)
#FittedEITpi_3ions = FitEIT_MM(freqslong, *popt_3ions)
print(popt_3ions)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_3ions, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
Veo la medicion de varios voltajes uno atras de otro
Se va en medio de la medicion 4, y en la 5 ni esta
"""
jvec = [2] # desde la 1, pero la 4 no porque es un merge de curvitas
Freqs
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[4]], CountsSplit_2ions[0][j], yerr=np.sqrt(CountsSplit_2ions[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
AJUSTO LA CPT DE 2 IONES CON UN MODELO EN DONDE SUMO DOS ESPECTROS CON BETAS DISTINTOS
"""
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 = 27
offsetxpi = 421+correccion
DetDoppler = -16-correccion+5
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[1]]
CountsDR = Counts[1]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
BETA1, BETA2 = 3, 0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if not 'popt_2ions_1' in globals().keys():
popt_2ions_1, pcov_2ions_1 = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e3, 2.9e3, 3e3], bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 8e3)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_2sp = FitEIT_MM(freqslong, *popt_2ions_1)
#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
# beta1_2ions = popt_2ions_1[5]
# beta2_2ions = popt_2ions_1[6]
# errbeta1_2ions = np.sqrt(pcov_2ions_1[5,5])
# errbeta2_2ions = np.sqrt(pcov_2ions_1[6,6])
"""
Estos params dan bien poniendo beta2=0 y correccion=0 y son SG, SP, SCALE1, SCALE2, OFFSET, BETA1
#array([9.03123248e-01, 6.25865542e+00, 3.47684055e+04, 2.92076804e+04, 1.34556420e+03, 3.55045904e+00])
"""
"""
Ahora considerando ambos betas, con los parametros iniciales dados por los que se obtuvieron con beta2=0
y correccion=0 dan estos parametros que son los de antes pero con BETA2 incluido:
array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
"""
#arreglito = np.array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
FittedEITpi_2ions_1 = FitEIT_MM(freqslong, *popt_2ions_1)
print(popt_2ions_1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2ions_1, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
SUPER AJUSTE PARA MED DE 2 IONES
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [3]
if not 'popt_SA_vec_2ions' in globals().keys():
popt_SA_vec_2ions = []
pcov_SA_vec_2ions = []
for selectedcurve in SelectedCurveVec:
FreqsDR = Freqs[selectedcurve]
CountsDR = Counts[selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, TEMP, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
#SG = 0.6
#SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
if plot:
return ScaledFluo1+ScaledFluo2, Detunings
else:
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if True:
popt_3_SA_2ions, pcov_3_SA_2ions = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[448, -42, 0.6, 8.1, 4e4, 4e4, 6e3, 1, 1.2, 0.5e-3], bounds=((0, -100,0, 0, 0,0,0,0,0, 0), (1000, 0, 2, 20,5e6, 5e6,5e4, 10, 10,10e-3)))
#popt_3_SA_2ions = [448, -42, 8e4, 6e3, 2, 0.5e-3]
popt_SA_vec_2ions.append(popt_3_SA_2ions)
pcov_SA_vec_2ions.append(pcov_3_SA_2ions)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA_2ions, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA_2ions, plot=True)
raise ValueError('Acá tenes que levantar de nuevo los valores que van')
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA_2ions)
#print(f'Detdop:{popt_3_SA[1]},popt_3_SA:{popt[0]}')
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
AJUSTO LA CPT DE 2 IONES CON UN MODELO EN DONDE SUMO DOS ESPECTROS CON BETAS DISTINTOS
"""
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 = 27
offsetxpi = 421+correccion
DetDoppler = -16-correccion+5
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[1]]
CountsDR = Counts[1]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
BETA1, BETA2 = 3, 0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if not 'popt_2ions_1' in globals().keys():
popt_2ions_1, pcov_2ions_1 = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e3, 2.9e3, 3e3], bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 8e3)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_2sp = FitEIT_MM(freqslong, *popt_2ions_1)
#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
# beta1_2ions = popt_2ions_1[5]
# beta2_2ions = popt_2ions_1[6]
# errbeta1_2ions = np.sqrt(pcov_2ions_1[5,5])
# errbeta2_2ions = np.sqrt(pcov_2ions_1[6,6])
"""
Estos params dan bien poniendo beta2=0 y correccion=0 y son SG, SP, SCALE1, SCALE2, OFFSET, BETA1
#array([9.03123248e-01, 6.25865542e+00, 3.47684055e+04, 2.92076804e+04, 1.34556420e+03, 3.55045904e+00])
"""
"""
Ahora considerando ambos betas, con los parametros iniciales dados por los que se obtuvieron con beta2=0
y correccion=0 dan estos parametros que son los de antes pero con BETA2 incluido:
array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
"""
#arreglito = np.array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
FittedEITpi_2ions_1 = FitEIT_MM(freqslong, *popt_2ions_1)
print(popt_2ions_1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2ions_1, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
if False:
GUARDAR = {}
for var in [ kk for kk in globals().keys() if kk.startswith('pop') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('pcov') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('Fitted') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(var)
GUARDAR[var] = globals()[var]
np.savez('PARAMETROS.npz', **GUARDAR )
analisis/plots/20250516_CPT_bladetrap/lolo_CPT_plotter_20231123_multi.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Ploteo de datos y ajustes
@author: lolo
"""
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
#%% Funciones auxiliares
from scipy.stats.distributions import t,chi2
def estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05):
if nombres is None:
nombres = [ f'{j}' for j in range(len(parametros)) ]
# Cantidad de parámetros
P = len(parametros)
# Número de datos
N = len(datos_x)
# Grados de libertas (Degrees Of Freedom)
dof = N-P-1
# Cauculamos coordenadas del modelo
# modelo_x = datos_x if modelo_x_arr is None else modelo_x_arr
# modelo_y = modelo( modelo_x, *parametros )
# Predicción del modelo para los datos_x medidos
prediccion_modelo = modelo( datos_x, *parametros )
# Calculos de cantidades estadísticas relevantes
COV = pcov # Matriz de Covarianza
SE = np.sqrt(np.diag( COV )) # Standar Error / Error estandar de los parámetros
residuos = datos_y - prediccion_modelo # diferencia enrte el modelo y los datos
SSE = sum(( residuos )**2 ) # Resitual Sum of Squares
SST = sum(( datos_y - np.mean(datos_y))**2) # Total Sum of Squares
# http://en.wikipedia.org/wiki/Coefficient_of_determination
# Expresa el porcentaje de la varianza que logra explicar el modelos propuesto
Rsq = 1 - SSE/SST # Coeficiente de determinación
Rsq_adj = 1-(1-Rsq) * (N-1)/(N-P-1) # Coeficiente de determinación Ajustado
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#In_least_squares_regression_analysis
# Expresa la correlación que hay entre los datos y la predicción del modelo
r_pearson = np.corrcoef( datos_y , prediccion_modelo )[0,1]
# Reduced chi squared
# https://en.wikipedia.org/wiki/Reduced_chi-squared_statistic
chi2_ = sum( residuos**2 )/N
chi2_red = sum( residuos**2 )/(N-P)
# Chi squared test
chi2_test = sum( residuos**2 / abs(prediccion_modelo) )
# p-value del ajuste
p_val = chi2(dof).cdf( chi2_test )
sT = t.ppf(1.0 - alpha/2.0, N - P ) # student T multiplier
CI = sT * SE # Confidence Interval
print('R-squared ',Rsq)
print('R-sq_adjusted',Rsq_adj)
print('chi2 ',chi2_)
print('chi2_reduced ',chi2_red)
print('chi2_test ',chi2_test)
print('r-pearson ',r_pearson)
print('p-value ',p_val)
print('')
print('Error Estandard (SE):')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , SE[i])
print('')
print('Intervalo de confianza al '+str((1-alpha)*100)+'%:')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , CI[i])
return dict(R2=Rsq,R2_adj=Rsq_adj,chi2=chi2_,chi2_red=chi2_red,
chi2_test=chi2_test,r=r_pearson,pvalue=p_val,
SE=SE,CI=CI)
#%% Importaciones extra
# /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
PARAMETROS = np.load('PARAMETROS.npz',allow_pickle=True)
for var_name in PARAMETROS.keys():
globals()[var_name] = PARAMETROS[var_name]
print(f'loaded: {var_name}')
#%%
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
# os.chdir('../20231123_CPTconmicromocion3/Data/')
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#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']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
def Split(array,n):
length=len(array)/n
splitlist = []
jj = 0
while jj<length:
partial = []
ii = 0
while ii < n:
partial.append(array[jj*n+ii])
ii = ii + 1
splitlist.append(partial)
jj = jj + 1
return splitlist
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
#%%
"""
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [2] # de la 1 a la 9 vale la pena, despues no
drs = [390.5, 399.5, 406, 413.5]
drive=22.1
Frequencies = Freqs[0]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Frequencies], CountsSplit[0][j], yerr=np.sqrt(CountsSplit[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
#%%
"""
AHORA VAMOS A MEDICIONES CON MAS DE UN ION!!!
Las mediciones estan buenas, habria que ver de ajustarlas bien, yo no lo logre.
"""
"""
Ploteo la cpt de referencia / plotting the reference CPT
1: 2 iones, -100 mV dcA
2: 2 iones, -150 mV dcA
3: 2 iones, -50 mV dcA
4: 2 iones, 5 voltajes (el ion se va en la 4ta medicion y en la 5ta ni esta)
5, 6 y 7: 3 iones en donde el scaneo esta centrado en distintos puntos
"""
jvec = [3] # desde la 1, pero la 4 no porque es un merge de curvitas
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
"""
Mergeo la 5, 6 y 7
"""
Freqs5 = [2*f*1e-6 for f in Freqs[5]]
Freqs6 = [2*f*1e-6 for f in Freqs[6]]
Freqs7 = [2*f*1e-6 for f in Freqs[7]]
Counts5 = Counts[5]
Counts6 = Counts[6]
Counts7 = Counts[7]
i_1_ini = 0
i_1 = 36
i_2_ini = 0
i_2 = 24
f_1 = 18
f_2 = 30
scale_1 = 0.92
scale_2 = 0.98
#Merged_freqs_test = [f-f_2 for f in Freqs6[i_2_ini:i_2]]+[f-f_1 for f in Freqs5[i_1_ini:i_1]]+Freqs7
#plt.plot(Merged_freqs_test,'o')
Merged_freqs = [f-f_2 for f in Freqs6[0:i_2]]+[f-f_1 for f in Freqs5[0:i_1]]+Freqs7
Merged_counts = [scale_2*c for c in Counts6[0:i_2]]+[scale_1*c for c in Counts5[0:i_1]]+list(Counts7)
Merged_freqs_rescaled = np.linspace(np.min(Merged_freqs),np.max(Merged_freqs),len(Merged_freqs))
#drs = [391.5, 399.5, 405.5, 414]
drs = [370,379,385,391.5]
plt.figure()
i = 0
for j in jvec:
plt.plot([f-f_1 for f in Freqs5[0:i_1]], [scale_1*c for c in Counts5[0:i_1]],'o')
plt.plot([f-f_2 for f in Freqs6[0:i_2]], [scale_2*c for c in Counts6[0:i_2]],'o')
plt.plot(Freqs7, Counts7,'o')
plt.errorbar(Merged_freqs, Merged_counts, yerr=np.sqrt(Merged_counts), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
for dr in drs:
plt.axvline(dr)
plt.axvline(dr+drive, color='red', linestyle='dashed', alpha=0.3)
plt.axvline(dr-drive, color='red', linestyle='dashed', alpha=0.3)
plt.legend()
#%%
"""
ajusto la mergeada de 3 iones
"""
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 = -20
offsetxpi = 438+correccion
DetDoppler = -35-correccion-22
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [f-offsetxpi for f in Merged_freqs]
CountsDR = Merged_counts
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
import numba
@numba.jit
def FitEIT_MM1(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
return ScaledFluo1+OFFSET
#return ScaledFluo1
@numba.jit
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
freqs = np.array(freqs)
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence3 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA3, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
# ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
# ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
# ScaledFluo3 = np.array([f*SCALE3 for f in Fluorescence3])
# return ScaledFluo1+ScaledFluo2+ScaledFluo3+OFFSET
Fluorescence1 = np.array(Fluorescence1)
Fluorescence2 = np.array(Fluorescence2)
Fluorescence3 = np.array(Fluorescence3)
return SCALE1*Fluorescence1+SCALE2*Fluorescence2+SCALE3*Fluorescence3+OFFSET
if not 'popt_1ions' in globals().keys():
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
pp1 = estadistica(FreqsDR,CountsDR,FitEIT_MM1,pcov_1ions,popt_1ions,nombres=None,alpha=0.05)
print(f"time: {round(time.time()-t0,1)} seg")
if not 'popt_3ions' in globals().keys():
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
print(f"time: {round(time.time()-t0,1)} seg")
print("Arranamos FIT 3")
def fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3):
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
Fluorescence3 = FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
return Fluorescence3/sum(Fluorescence3)
par_ini = popt_1ions.tolist()[:2]+[1/3]*2 + [0.1] +popt_1ions.tolist()[-1:]*3
bounds = ((0, 0, 0, 0, 0, 0, 0, 0),
(2, 20, 1, 1, 1, 10, 10, 10))
popt_3ions, pcov_3ions = curve_fit(fun, FreqsDR, CountsDR, p0=par_ini,bounds=bounds )
print(f"time: {round(time.time()-t0,1)} seg")
freqs = np.array(FreqsDR)
SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = popt_3ions
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
# Fluorescence= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
Fluorescence= fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqs, Fluorescence*sum(CountsDR))
plt.plot(freqs, fun(freqs, *par_ini )*sum(CountsDR))
#%% Acá hay un ajuste del de 3 iones que da razonable
FreqsDR, CountsDR = np.array(FreqsDR) , np.array(CountsDR)
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
print(f"time: {round(time.time()-t0,1)} seg")
par_ini = popt_1ions.tolist()[:2]+[1/3]*2 + [0.1] +popt_1ions.tolist()[-1:]*3
bounds = ((0, 0, 0, 0, 0, 0, 0, 0),
(2, 20, 1, 1, 1, 10, 10, 10))
# bounds = (-np.inf, np.inf)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
l1, =plt.plot(freqs, fun(freqs, *par_ini )*sum(CountsDR))
plt.draw()
print("Arranamos FIT 3")
def fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3):
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
Fluorescence3 = FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
l1.set_ydata(Fluorescence3/sum(Fluorescence3)*sum(CountsDR))
print(SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
plt.pause(0.1)
return Fluorescence3/sum(Fluorescence3)
popt_3ions, pcov_3ions = curve_fit(fun, FreqsDR, CountsDR/CountsDR.sum(), p0=par_ini,bounds=bounds )
print(f"time: {round(time.time()-t0,1)} seg")
freqs = np.array(FreqsDR)
SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = popt_3ions
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
# Fluorescence= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
Fluorescence= fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
plt.plot(freqs, Fluorescence*sum(CountsDR))
if False:
popt_3ions = np.array([0.70790618, 7.41165226, 0.1707309 , 0.13974759, 0.02322333,
3.69298275, 3.68847693, 1.36216489])
#%%
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
if False:
# Ejemplo 3 iones
t0 = time.time()
freqs, SG, SP = np.array(FreqsDR), 0.65, 7.06
SCALE1, SCALE2, SCALE3, OFFSET = 1,1,1,1
BETA1, BETA2, BETA3 = 1.64, 2, 3
TEMP = 0.1e-3
Fluorescence3= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
print(f"time: {round(time.time()-t0,1)} seg")
Detunings, Fluorescence = np.array(freqs), np.array(Fluorescence3)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings, Fluorescence*sum(CountsDR)/sum(Fluorescence))
if False:
# Ejemplo 1 ion
t0 = time.time()
freqs, SG, SP, BETA1 = FreqsDR, 0.65, 7.06, 1.64
TEMP = 0.1e-3
Detunings, Fluorescence1 =PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
print(f"time: {round(time.time()-t0,1)} seg")
Detunings, Fluorescence1 = np.array(Detunings), np.array(Fluorescence1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings, Fluorescence1*sum(CountsDR)/sum(Fluorescence1))
FittedEITpi_3ions = FitEIT_MM(freqslong, *popt_3ions)
#FittedEITpi_3ions = FitEIT_MM(freqslong, popt_3ions[0],popt_3ions[1],popt_3ions[2],popt_3ions[3],popt_3ions[4],popt_3ions[5],4,2,0)
#FittedEITpi_3ions = FitEIT_MM(freqslong, *popt_3ions)
print(popt_3ions)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_3ions, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
Veo la medicion de varios voltajes uno atras de otro
Se va en medio de la medicion 4, y en la 5 ni esta
"""
jvec = [2] # desde la 1, pero la 4 no porque es un merge de curvitas
Freqs
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[4]], CountsSplit_2ions[0][j], yerr=np.sqrt(CountsSplit_2ions[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
AJUSTO LA CPT DE 2 IONES CON UN MODELO EN DONDE SUMO DOS ESPECTROS CON BETAS DISTINTOS
"""
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 = 27
offsetxpi = 421+correccion
DetDoppler = -16-correccion+5
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[1]]
CountsDR = Counts[1]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
BETA1, BETA2 = 3, 0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if not 'popt_2ions_1' in globals().keys():
popt_2ions_1, pcov_2ions_1 = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e3, 2.9e3, 3e3], bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 8e3)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_2sp = FitEIT_MM(freqslong, *popt_2ions_1)
#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
# beta1_2ions = popt_2ions_1[5]
# beta2_2ions = popt_2ions_1[6]
# errbeta1_2ions = np.sqrt(pcov_2ions_1[5,5])
# errbeta2_2ions = np.sqrt(pcov_2ions_1[6,6])
"""
Estos params dan bien poniendo beta2=0 y correccion=0 y son SG, SP, SCALE1, SCALE2, OFFSET, BETA1
#array([9.03123248e-01, 6.25865542e+00, 3.47684055e+04, 2.92076804e+04, 1.34556420e+03, 3.55045904e+00])
"""
"""
Ahora considerando ambos betas, con los parametros iniciales dados por los que se obtuvieron con beta2=0
y correccion=0 dan estos parametros que son los de antes pero con BETA2 incluido:
array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
"""
#arreglito = np.array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
FittedEITpi_2ions_1 = FitEIT_MM(freqslong, *popt_2ions_1)
print(popt_2ions_1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2ions_1, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
SUPER AJUSTE PARA MED DE 2 IONES
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [3]
if not 'popt_SA_vec_2ions' in globals().keys():
popt_SA_vec_2ions = []
pcov_SA_vec_2ions = []
for selectedcurve in SelectedCurveVec:
FreqsDR = Freqs[selectedcurve]
CountsDR = Counts[selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, TEMP, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
#SG = 0.6
#SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
if plot:
return ScaledFluo1+ScaledFluo2, Detunings
else:
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if True:
popt_3_SA_2ions, pcov_3_SA_2ions = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[448, -42, 0.6, 8.1, 4e4, 4e4, 6e3, 1, 1.2, 0.5e-3], bounds=((0, -100,0, 0, 0,0,0,0,0, 0), (1000, 0, 2, 20,5e6, 5e6,5e4, 10, 10,10e-3)))
#popt_3_SA_2ions = [448, -42, 8e4, 6e3, 2, 0.5e-3]
popt_SA_vec_2ions.append(popt_3_SA_2ions)
pcov_SA_vec_2ions.append(pcov_3_SA_2ions)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA_2ions, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA_2ions, plot=True)
raise ValueError('Acá tenes que levantar de nuevo los valores que van')
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA_2ions)
#print(f'Detdop:{popt_3_SA[1]},popt_3_SA:{popt[0]}')
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
AJUSTO LA CPT DE 2 IONES CON UN MODELO EN DONDE SUMO DOS ESPECTROS CON BETAS DISTINTOS
"""
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 = 27
offsetxpi = 421+correccion
DetDoppler = -16-correccion+5
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[1]]
CountsDR = Counts[1]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
BETA1, BETA2 = 3, 0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if not 'popt_2ions_1' in globals().keys():
popt_2ions_1, pcov_2ions_1 = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e3, 2.9e3, 3e3], bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 8e3)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_2sp = FitEIT_MM(freqslong, *popt_2ions_1)
#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
# beta1_2ions = popt_2ions_1[5]
# beta2_2ions = popt_2ions_1[6]
# errbeta1_2ions = np.sqrt(pcov_2ions_1[5,5])
# errbeta2_2ions = np.sqrt(pcov_2ions_1[6,6])
"""
Estos params dan bien poniendo beta2=0 y correccion=0 y son SG, SP, SCALE1, SCALE2, OFFSET, BETA1
#array([9.03123248e-01, 6.25865542e+00, 3.47684055e+04, 2.92076804e+04, 1.34556420e+03, 3.55045904e+00])
"""
"""
Ahora considerando ambos betas, con los parametros iniciales dados por los que se obtuvieron con beta2=0
y correccion=0 dan estos parametros que son los de antes pero con BETA2 incluido:
array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
"""
#arreglito = np.array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
FittedEITpi_2ions_1 = FitEIT_MM(freqslong, *popt_2ions_1)
print(popt_2ions_1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2ions_1, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
"""
#%%
"""
SUPER AJUSTE (SA)
"""
if False:
GUARDAR = {}
for var in [ kk for kk in globals().keys() if kk.startswith('pop') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('pcov') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('Fitted') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(var)
GUARDAR[var] = globals()[var]
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
#SelectedCurveVec = [1,2,3,4,5,6,7,8,9]
SelectedCurveVec = [0]
# popt_SA_vec = []
# pcov_SA_vec = []
# Detuningsshort_vec = []
# Counts_vec = []
# Detuningslong_vec = []
# FittedCounts_vec = []
# Betas_vec = []
# ErrorBetas_vec = []
# Temp_vec = []
# ErrorTemp_vec = []
# DetuningsUV_vec = []
# ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
#selectedcurve = 2 #IMPORTANTE: SELECCIONA LA MEDICION
FreqsDR = Freqs[0]
CountsDR = CountsSplit[0][selectedcurve]
if selectedcurve==1:
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
if selectedcurve==2:
CountsDR[67]=0.5*(CountsDR[66]+CountsDR[68])
CountsDR[71]=0.5*(CountsDR[70]+CountsDR[72])
if selectedcurve==6:
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
if selectedcurve==7:
CountsDR[117]=0.5*(CountsDR[116]+CountsDR[118])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
# popt_SA_vec.append(popt_3_SA)
# pcov_SA_vec.append(pcov_3_SA)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA, plot=True)
# DetuningsUV_vec.append(popt_3_SA[1])
# ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
# Betas_vec.append(popt_3_SA[6])
# ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
# Temp_vec.append(popt_3_SA[7])
# ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
# Detuningsshort_vec.append(Detunings_3_SA_short)
# Counts_vec.append(CountsDR)
# Detuningslong_vec.append(Detunings_3_SA_long)
# FittedCounts_vec.append(FittedEITpi_3_SA_long)
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA)
analisis/plots/20250516_CPT_bladetrap/lolo_ajuste_3iones_A.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Acá hacemos el ajuste de 3 iones del archivo
artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py
sacado de la parte que dice:
"ajusto la mergeada de 3 iones"
@author: lolo
"""
import h5py
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
import time
#%% Funciones auxiliares para información estadística
from scipy.stats.distributions import t,chi2
import inspect
def estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05):
if nombres is None:
nombres = inspect.getfullargspec(modelo).args[1:]
# Cantidad de parámetros
P = len(parametros)
# Número de datos
N = len(datos_x)
# Grados de libertas (Degrees Of Freedom)
dof = N-P-1
# Cauculamos coordenadas del modelo
# modelo_x = datos_x if modelo_x_arr is None else modelo_x_arr
# modelo_y = modelo( modelo_x, *parametros )
# Predicción del modelo para los datos_x medidos
prediccion_modelo = modelo( datos_x, *parametros )
# Calculos de cantidades estadísticas relevantes
COV = pcov # Matriz de Covarianza
SE = np.sqrt(np.diag( COV )) # Standar Error / Error estandar de los parámetros
residuos = datos_y - prediccion_modelo # diferencia enrte el modelo y los datos
SSE = sum(( residuos )**2 ) # Resitual Sum of Squares
SST = sum(( datos_y - np.mean(datos_y))**2) # Total Sum of Squares
# http://en.wikipedia.org/wiki/Coefficient_of_determination
# Expresa el porcentaje de la varianza que logra explicar el modelos propuesto
Rsq = 1 - SSE/SST # Coeficiente de determinación
Rsq_adj = 1-(1-Rsq) * (N-1)/(N-P-1) # Coeficiente de determinación Ajustado
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#In_least_squares_regression_analysis
# Expresa la correlación que hay entre los datos y la predicción del modelo
r_pearson = np.corrcoef( datos_y , prediccion_modelo )[0,1]
# Reduced chi squared
# https://en.wikipedia.org/wiki/Reduced_chi-squared_statistic
chi2_ = sum( residuos**2 )/N
chi2_red = sum( residuos**2 )/(N-P)
# Chi squared test
chi2_test = sum( residuos**2 / abs(prediccion_modelo) )
# p-value del ajuste
p_val = chi2(dof).cdf( chi2_test )
sT = t.ppf(1.0 - alpha/2.0, N - P ) # student T multiplier
CI = sT * SE # Confidence Interval
return dict(R2=Rsq,R2_adj=Rsq_adj,chi2=chi2_,chi2_red=chi2_red,
chi2_test=chi2_test,r=r_pearson,pvalue=p_val,
SE=SE,CI=CI,parametros=parametros,nombres=nombres,alpha=alpha,N=N)
def print_estadistica(dic):
pars = 'N,pvalue,R2,R2_adj,chi2,chi2_red,chi2_test,r,SE,CI,parametros,nombres,alpha'.split(',')
N,pvalue,R2,R2_adj,chi2,chi2_red,chi2_test,r,SE,CI,parametros,nombres,alpha = [ dic[k] for k in pars ]
P = len(parametros)
print('R-squared ',R2)
print('R-sq_adjusted',R2_adj)
print('chi2 ',chi2)
print('chi2_reduced ',chi2_red)
print('chi2_test ',chi2_test)
print('r-pearson ',r)
print('p-value ',pvalue)
print('')
print('Error Estandard (SE):')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , SE[i])
print('')
print('Intervalo de confianza al '+str((1-alpha)*100)+'%:')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , CI[i])
def fit(model,x_data, y_data, fixed=None, plot=False, **kwargs):
"""Usa los mismos argumentos que curve_fit pero permite fixear parámetros y
visualizar el ajuste
fixed: nombres de los parametros fijos, con espacios
plot: si plotea o no el ajuste
"""
# Extraemos los parámetros del modelo
param_names = inspect.getfullargspec(model).args[1:]
if not 'p0' in kwargs.keys():
kwargs['p0'] = np.ones(len(param_names))
params_initial = kwargs['p0']
# Nos fijamos si hay que fijar algo
if isinstance(fixed, str):
fixed = fixed.split()
if fixed is None:
fixed = []
# Armoa lista de True/False de los parametros fijos
params_fixed = [ True if p in fixed else False for p in param_names ]
if True in params_fixed and 'bounds' in kwargs.keys():
try:
b0 = [ bb for fi,bb in zip(params_fixed,kwargs['bounds'][0]) if fi==False ]
except:
b0 = kwargs['bounds'][0]
try:
b1 = [ bb for fi,bb in zip(params_fixed,kwargs['bounds'][1]) if fi==False ]
except:
b1 =kwargs['bounds'][0]
kwargs['bounds'] = (b0,b1)
print("Parámetros:", param_names)
# print(locals())
print('\n')
if plot:
plt.figure()
plt.plot(x_data, y_data,'.')
ylim=plt.gca().get_ylim()
l1, = plt.plot(x_data, model(x_data,*params_initial),'r-')
plt.ylim(ylim)
# Función auxiliar para el ajuste
def aux_func(x_data,*args):
all_params = np.ones(len(param_names))
jj=0
for ii in range(len(all_params)):
if params_fixed[ii]:
all_params[ii] = params_initial[ii]
else:
all_params[ii] = args[jj]
jj += 1
# print(params_fixed,all_params)
rta = model(x_data,*all_params)
if plot:
l1.set_ydata(rta)
plt.draw()
plt.pause(0.01)
return rta
# Hacemos el curve_fit
t0 = time.time()
print("Arancamos el fit")
kwargs['p0'] = [ p for p,ff in zip(params_initial,params_fixed) if ff is False ]
params, pcov = curve_fit(aux_func, x_data, y_data, **kwargs )
print(f"time: {round(time.time()-t0,1)} seg")
# Rearmamos los parámetros para incluir los fijos
params2 = np.ones(len(param_names))
jj=0
for ii in range(len(params2)):
if params_fixed[ii]:
params2[ii] = params_initial[ii]
else:
params2[ii] = params[jj]
jj += 1
# Rearmo la pcov
ii, a = 0, []
for fixed in params_fixed:
if fixed:
a.append( [0]*len(pcov[0]) )
else:
a.append( pcov[ii].tolist() )
ii+=1
a = np.array(a)
ii, b = 0, []
for fixed in params_fixed:
if fixed:
b.append( [0]*len(a.T[0]) )
else:
b.append( a.T[ii].tolist() )
ii+=1
pcov2 = np.array(b)
# Caluclo información estadística
info = estadistica(x_data,y_data,model,pcov2,params2,nombres=None,alpha=0.05)
print_estadistica(info)
return params2, pcov2 , info
# fit(modelo, FreqsDR, CountsDR/CountsDR.sum(), p0=(1,1,1),bounds=(-1e6,1e6) )
# fit(modelo, FreqsDR, CountsDR/CountsDR.sum(), fixed="", plot=True, p0=(1,2,3),bounds=(-1e6,1e6) )
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Levantamos los datos %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
# os.chdir('../20231123_CPTconmicromocion3/Data/')
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#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']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
def Split(array,n):
length=len(array)/n
splitlist = []
jj = 0
while jj<length:
partial = []
ii = 0
while ii < n:
partial.append(array[jj*n+ii])
ii = ii + 1
splitlist.append(partial)
jj = jj + 1
return splitlist
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
###############################################################################
"""
AHORA VAMOS A MEDICIONES CON MAS DE UN ION!!!
Las mediciones estan buenas, habria que ver de ajustarlas bien, yo no lo logre.
"""
"""
Ploteo la cpt de referencia / plotting the reference CPT
1: 2 iones, -100 mV dcA
2: 2 iones, -150 mV dcA
3: 2 iones, -50 mV dcA
4: 2 iones, 5 voltajes (el ion se va en la 4ta medicion y en la 5ta ni esta)
5, 6 y 7: 3 iones en donde el scaneo esta centrado en distintos puntos
"""
"""
Mergeo la 5, 6 y 7
"""
Freqs5 = [2*f*1e-6 for f in Freqs[5]]
Freqs6 = [2*f*1e-6 for f in Freqs[6]]
Freqs7 = [2*f*1e-6 for f in Freqs[7]]
Counts5 = Counts[5]
Counts6 = Counts[6]
Counts7 = Counts[7]
i_1_ini = 0
i_1 = 36
i_2_ini = 0
i_2 = 24
f_1 = 18
f_2 = 30
scale_1 = 0.92
scale_2 = 0.98
#Merged_freqs_test = [f-f_2 for f in Freqs6[i_2_ini:i_2]]+[f-f_1 for f in Freqs5[i_1_ini:i_1]]+Freqs7
#plt.plot(Merged_freqs_test,'o')
Merged_freqs = [f-f_2 for f in Freqs6[0:i_2]]+[f-f_1 for f in Freqs5[0:i_1]]+Freqs7
Merged_counts = [scale_2*c for c in Counts6[0:i_2]]+[scale_1*c for c in Counts5[0:i_1]]+list(Counts7)
Merged_freqs_rescaled = np.linspace(np.min(Merged_freqs),np.max(Merged_freqs),len(Merged_freqs))
#drs = [391.5, 399.5, 405.5, 414]
drs = [370,379,385,391.5]
if False:
plt.figure()
jvec = [2]
i = 0
drive=22.1
for j in jvec:
plt.plot([f-f_1 for f in Freqs5[0:i_1]], [scale_1*c for c in Counts5[0:i_1]],'o')
plt.plot([f-f_2 for f in Freqs6[0:i_2]], [scale_2*c for c in Counts6[0:i_2]],'o')
plt.plot(Freqs7, Counts7,'o')
plt.errorbar(Merged_freqs, Merged_counts, yerr=np.sqrt(Merged_counts), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
for dr in drs:
plt.axvline(dr)
plt.axvline(dr+drive, color='red', linestyle='dashed', alpha=0.3)
plt.axvline(dr-drive, color='red', linestyle='dashed', alpha=0.3)
plt.legend()
#%% Funciones para hacer ajustes
"""
ajusto la mergeada de 3 iones
"""
import numba
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 = -20
offsetxpi = 438+correccion
DetDoppler = -35-correccion-22
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [f-offsetxpi for f in Merged_freqs]
CountsDR = Merged_counts
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
# @numba.jit
def FitEIT_MM1(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
return ScaledFluo1+OFFSET
#return ScaledFluo1
# @numba.jit
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
freqs = np.array(freqs)
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence3 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA3, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
# ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
# ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
# ScaledFluo3 = np.array([f*SCALE3 for f in Fluorescence3])
# return ScaledFluo1+ScaledFluo2+ScaledFluo3+OFFSET
Fluorescence1 = np.array(Fluorescence1)
Fluorescence2 = np.array(Fluorescence2)
Fluorescence3 = np.array(Fluorescence3)
return SCALE1*Fluorescence1+SCALE2*Fluorescence2+SCALE3*Fluorescence3+OFFSET
#%% Acá hay un ajuste del de 3 iones que da razonable
# FreqsDR, CountsDR = np.array(FreqsDR) , np.array(CountsDR)
# freqs = np.array(FreqsDR)
# t0 = time.time()
# print("Arranamos FIT 1")
# par_ini = [0.65, 7.06, 86070, 3917, 1.64]
# popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
# print(f"time: {round(time.time()-t0,1)} seg")
# par_ini = popt_1ions.tolist()[:2]+[1/3]*2 + [0.1] +popt_1ions.tolist()[-1:]*3
# bounds = ((0, 0, 0, 0, 0, 0, 0, 0),
# (2, 20, 1, 1, 1, 10, 10, 10))
# # bounds = (-np.inf, np.inf)
# print("Arranamos FIT 3")
# def fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3):
# SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
# Fluorescence3 = FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
# try:
# l1.set_ydata(Fluorescence3/sum(Fluorescence3)*sum(CountsDR))
# except:
# print("Primer corrida")
# finally:
# print(SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
# plt.pause(0.1)
# return Fluorescence3/sum(Fluorescence3)
# plt.figure()
# plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
# l1, =plt.plot(freqs, fun(freqs, *par_ini )*sum(CountsDR))
# plt.draw()
# popt_3ions, pcov_3ions = curve_fit(fun, FreqsDR, CountsDR/CountsDR.sum(), p0=par_ini,bounds=bounds )
# print(f"time: {round(time.time()-t0,1)} seg")
# SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = popt_3ions
# SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
# # Fluorescence= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
# Fluorescence= fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
# plt.plot(freqs, Fluorescence*sum(CountsDR))
# if False:
# popt_3ions = np.array([0.70790618, 7.41165226, 0.1707309 , 0.13974759, 0.02322333,
# 3.69298275, 3.68847693, 1.36216489])
#%% Armo algo para meter parametros fijos
FreqsDR, CountsDR = np.array(FreqsDR) , np.array(CountsDR)
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions, info1 = fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
print(f"time: {round(time.time()-t0,1)} seg")
par_ini = popt_1ions.tolist()[:2]+[1/3]*2 + [0.1] +popt_1ions.tolist()[-1:]*3
bounds = ((0, 0, 0, 0, 0, 0, 0, 0),
(2, 20, 1, 1, 1, 10, 10, 10))
def fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3):
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
Fluorescence3 = FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
return Fluorescence3/sum(Fluorescence3)
par_ini = np.array([0.64872536, 7.06495239, 0.9985873 , 0.9985873 , 0.09090475,
1.63941009, 1.63941006, 1.80228481])
SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = par_ini
SCALE1, SCALE2 = 0.3, 0.3
par_ini = SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3
t0 = time.time()
print("Arranamos FIT 1")
popt_3ions, pcov_3ions, info2 = fit(fun, FreqsDR, CountsDR/sum(CountsDR), fixed="SG SP", plot=True, p0=par_ini, bounds=bounds)
print(f"time: {round(time.time()-t0,1)} seg")
par_ini = popt_3ions
t0 = time.time()
print("Arranamos FIT 1")
popt_3ions, pcov_3ions, info2 = fit(fun, FreqsDR, CountsDR/sum(CountsDR), fixed="", plot=True, p0=par_ini, bounds=bounds)
print(f"time: {round(time.time()-t0,1)} seg")
analisis/plots/20250516_CPT_bladetrap/lolo_analisis_superajuste.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Ploteo de datos y ajustes del barrido en voltaje del Endcap
equivalente a pasear el ion en algun entorno del punto de compensación ideal
y ver los efectos de micromoción (y tal vez temperatura)
@author: lolo
"""
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
from numba import jit,njit
from time import time
#%% Importaciones extra
# /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
# PARAMETROS = np.load('PARAMETROS.npz',allow_pickle=True)
# for var_name in PARAMETROS.keys():
# globals()[var_name] = PARAMETROS[var_name]
# print(f'loaded: {var_name}')
# Funciones auxiliares
from scipy.stats.distributions import t,chi2
def estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05):
if nombres is None:
nombres = [ f'{j}' for j in range(len(parametros)) ]
# Cantidad de parámetros
P = len(parametros)
# Número de datos
N = len(datos_x)
# Grados de libertas (Degrees Of Freedom)
dof = N-P-1
# Cauculamos coordenadas del modelo
# modelo_x = datos_x if modelo_x_arr is None else modelo_x_arr
# modelo_y = modelo( modelo_x, *parametros )
# Predicción del modelo para los datos_x medidos
prediccion_modelo = modelo( datos_x, *parametros )
# Calculos de cantidades estadísticas relevantes
COV = pcov # Matriz de Covarianza
SE = np.sqrt(np.diag( COV )) # Standar Error / Error estandar de los parámetros
residuos = datos_y - prediccion_modelo # diferencia enrte el modelo y los datos
SSE = sum(( residuos )**2 ) # Resitual Sum of Squares
SST = sum(( datos_y - np.mean(datos_y))**2) # Total Sum of Squares
# http://en.wikipedia.org/wiki/Coefficient_of_determination
# Expresa el porcentaje de la varianza que logra explicar el modelos propuesto
Rsq = 1 - SSE/SST # Coeficiente de determinación
Rsq_adj = 1-(1-Rsq) * (N-1)/(N-P-1) # Coeficiente de determinación Ajustado
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#In_least_squares_regression_analysis
# Expresa la correlación que hay entre los datos y la predicción del modelo
r_pearson = np.corrcoef( datos_y , prediccion_modelo )[0,1]
# Reduced chi squared
# https://en.wikipedia.org/wiki/Reduced_chi-squared_statistic
chi2_ = sum( residuos**2 )/N
chi2_red = sum( residuos**2 )/(N-P)
# Chi squared test
chi2_test = sum( residuos**2 / abs(prediccion_modelo) )
# p-value del ajuste
p_val = chi2(dof).cdf( chi2_test )
sT = t.ppf(1.0 - alpha/2.0, N - P ) # student T multiplier
CI = sT * SE # Confidence Interval
print('R-squared ',Rsq)
print('R-sq_adjusted',Rsq_adj)
print('chi2 ',chi2_)
print('chi2_reduced ',chi2_red)
print('chi2_test ',chi2_test)
print('r-pearson ',r_pearson)
print('p-value ',p_val)
print('')
print('Error Estandard (SE):')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , SE[i])
print('')
print('Intervalo de confianza al '+str((1-alpha)*100)+'%:')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , CI[i])
return dict(R2=Rsq,R2_adj=Rsq_adj,chi2=chi2_,chi2_red=chi2_red,
chi2_test=chi2_test,r=r_pearson,pvalue=p_val,
SE=SE,CI=CI)
#%%
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
# os.chdir('../20231123_CPTconmicromocion3/Data/')
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#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']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
def Split(array,n):
length=len(array)/n
splitlist = []
jj = 0
while jj<length:
partial = []
ii = 0
while ii < n:
partial.append(array[jj*n+ii])
ii = ii + 1
splitlist.append(partial)
jj = jj + 1
return splitlist
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
#%% Cargo parámetros fiteados de antes
PARAMETROS = np.load('analisis_superajuste_PARAMETROS.npz',allow_pickle=True)
for var_name in PARAMETROS.keys():
globals()[var_name] = PARAMETROS[var_name]
print(f'loaded: {var_name}')
if False:
# Esto es para correr en caso de necesidad de limpiar todos los vectores de parametros
print('Limpio los vectores de parámetros')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(f'del {var}')
del(globals()[var])
#%% Definiciones de Numba
@jit
def FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u,
DopplerLaserLinewidth, ProbeLaserLinewidth,
TEMP, alpha, phidoppler, titadoppler,
phiprobe, titaprobe, BETA1, drivefreq,
min(freqs), max(freqs)+(freqs[1]-freqs[0]),
freqs[1]-freqs[0], circularityprobe=CircPr,
plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1, Detunings
@jit
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
"Esta verison de la función devuelve sólo el eje y, para usar de modelo en un ajuste"
return FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP)[0]
param_names = 'offset DetDoppler SG SP SCALE1 OFFSET BETA1 TEMP'.split()
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
La 0 no ajusta bien incluso con todos los parametros libres
De la 1 a la 11 ajustan bien
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
"""
SUPER AJUSTE (SA)
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [1,2,3,4,5,6,7,8,9,10,11]
#SelectedCurveVec = [10]
CircPr = 1
alpha = 0
t0 = time()
if not 'popt_SA_vec' in globals().keys() or len(popt_SA_vec)==0:
popt_SA_vec = []
pcov_SA_vec = []
Detuningsshort_vec = []
Counts_vec = []
Detuningslong_vec = []
FittedCounts_vec = []
Betas_vec = []
ErrorBetas_vec = []
Temp_vec = []
ErrorTemp_vec = []
DetuningsUV_vec = []
ErrorDetuningsUV_vec = []
Estadistica_vec = []
for selectedcurve in SelectedCurveVec:
print(f"{round(time()-t0,1):6.1f}: Procesando la curva {selectedcurve}")
#selectedcurve = 2 #IMPORTANTE: SELECCIONA LA MEDICION
FreqsDR = Freqs[0]
CountsDR = CountsSplit[0][selectedcurve]
if selectedcurve==1:
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
if selectedcurve==2:
CountsDR[67]=0.5*(CountsDR[66]+CountsDR[68])
CountsDR[71]=0.5*(CountsDR[70]+CountsDR[72])
if selectedcurve==6:
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
if selectedcurve==7:
CountsDR[117]=0.5*(CountsDR[116]+CountsDR[118])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
if True:
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3], bounds=((0, -50, 0, 0, 0, 0, 0, 0), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3)))
popt_SA_vec.append(popt_3_SA)
pcov_SA_vec.append(pcov_3_SA)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single_plot(FreqsDR, *popt_3_SA)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single_plot(freqslong, *popt_3_SA)
# estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05)
est_tmp = estadistica(FreqsDR,CountsDR,FitEIT_MM_single,pcov_3_SA,popt_3_SA,
nombres=param_names,alpha=0.05)
Estadistica_vec.append(est_tmp)
DetuningsUV_vec.append(popt_3_SA[1])
ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
Betas_vec.append(popt_3_SA[6])
ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
Temp_vec.append(popt_3_SA[7])
ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
Detuningsshort_vec.append(Detunings_3_SA_short)
Counts_vec.append(CountsDR)
Detuningslong_vec.append(Detunings_3_SA_long)
FittedCounts_vec.append(FittedEITpi_3_SA_long)
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Graficamos todos los fiteos
# tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec)
# for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve in zip(*tmp_datos):
# plt.figure()
# plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
# plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
# #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
# plt.xlabel('Detuning (MHz)')
# plt.ylabel('Counts')
# plt.legend(loc='upper left', fontsize=20)
# plt.grid()
# print(f'listo med {selectedcurve}')
# print(popt_3_SA)
fig, axx = plt.subplots( 3,4, figsize=(13,8) , constrained_layout=True, sharex=True , sharey=True )
fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec,axx.flatten())
for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax in zip(*tmp_datos):
ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.3, capsize=2, markersize=2)
ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='black', linewidth=2, label=f'med {selectedcurve}', alpha=0.7)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
# ax.set_xlabel('Detuning (MHz)')
# ax.set_ylabel('Counts')
ax.legend(loc='upper left', fontsize=12)
ax.grid(True, ls=":")
print(f'listo med {selectedcurve}')
# print(popt_3_SA)
for ax in axx[:,0]:
ax.set_ylabel('Counts')
for ax in axx[-1,:]:
ax.set_xlabel('Detuning (MHz)')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Inspección de parámetros
param_names = 'offset DetDoppler SG SP SCALE1 OFFSET BETA1 TEMP'.split()
err_vecs = np.array([ np.sqrt(np.diag(el)) for el in pcov_SA_vec ])
num_med = np.arange(len(pcov_SA_vec)) +1
r2_values = np.array([ el['R2_adj'] for el in Estadistica_vec ])
fig, axx = plt.subplots( len(popt_SA_vec[0])+1,1, figsize=(13,8) , constrained_layout=True, sharex=True , sharey=False )
fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
for ax,param_vec,err_vec,par_name in zip(axx,popt_SA_vec.T,err_vecs.T,param_names) :
ax.plot(num_med, param_vec, '.-')
ax.errorbar( num_med, param_vec, yerr=err_vec,
fmt='s', mfc='none', elinewidth = 1, capsize=3, ms=1)
ax.grid(True, ls=":", color='lightgray')
ax.set_ylabel(par_name)
ax=axx[-1]
ax.plot( num_med , r2_values, '.-')
ax.set_ylabel(r'$R^2$')
ax.grid(True, ls=":", color='lightgray')
fig.align_ylabels()
ax.set_xticks(num_med)
ax.set_xlabel('Num. de medición')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Endcap hiperbola (con residuos)
"""
Veamos cómo varía el Beta con el voltaje del endcap
"""
import seaborn as sns
paleta = sns.color_palette("rocket")
I = slice(None,9)
voltages_dcA = Voltages[0][SelectedCurveVec]
def lineal(x,a,b):
return a*x+b
def hiperbola(x,a,y0,b,x0):
"""
Hiperbola de ecuación:
1 =(y-y0)²/a² - (x-x0)²/b²
"""
return a*np.sqrt(((x-x0)**2+b**2))+y0
es_hiperbola = False
# par_inicial = (100,0.1,1,-0.15)
# a y0 b x0
par_inicial = (12,0.1,1,-0.13)
popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
xhip = np.linspace(-0.23,0.005,200)
# plt.figure()
# plt.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
# plt.plot(xhip,hiperbola(xhip,*popthip))
# # plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
# plt.xlabel('Endcap voltage (V)')
# plt.ylabel('Modulation factor')
# plt.grid()
fig, axx = plt.subplots( 2, figsize=(10,7) ,
constrained_layout=True, sharex=True,
gridspec_kw=dict(height_ratios=[10,2]))
fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
ax = axx[0]
ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
ax.plot(xhip,hiperbola(xhip,*popthip))
# plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
ax.set_ylabel('Modulation factor')
ax = axx[1]
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
ax.set_ylabel('Res.')
ax.set_xlabel('Endcap voltage (V)')
for ax in axx:
ax.grid(True, ls=":", color='lightgray')
print([t*1e3 for t in Temp_vec])
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Este hay que armarlo aún
plt.figure()
plt.errorbar(voltages_dcA,[t*1e3 for t in Temp_vec],yerr=[t*1e3 for t in ErrorTemp_vec],fmt='o',capsize=5,markersize=5,color=paleta[3])
# plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
print(f'\n\nTE FALTA DEFINIR LA VARIABLE minimum_voltage\n\n')
plt.axhline(0.538)
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
#plt.ylim(0,2)
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Ajuste de los betas y la temperatura
from scipy.special import jv
def expo(x,tau,A,B):
return A*np.exp(x/tau)+B
def cuadratica(x,a,c):
return a*(x**2)+c
def InverseMicromotionSpectra(beta, A, det, x0, gamma, B):
ftrap=22.1
#gamma=30
P = ((jv(0, beta)**2)/((((det-x0)**2)+(0.5*gamma)**2)**2))*(-2*(det-x0))
i = 1
#print(P)
while i <= 5:
P = P + (-2*(det-x0))*((jv(i, beta))**2)/(((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det-x0))*(((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
#return 1/(A*P+B)
return 1/(A*P+B)
def InverseMicromotionSpectra_raw(beta, A, det, B):
ftrap=22.1
gamma=21
P = ((jv(0, beta)**2)/((((det)**2)+(0.5*gamma)**2)**2))*(-2*(det))
i = 1
#print(P)
while i <= 3:
P = P + (-2*(det))*((jv(i, beta))**2)/(((((det)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det))*(((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
return A/P+B
"""
Temperatura vs beta con un ajuste exponencial
"""
popt_exp, pcov_exp = curve_fit(expo,Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]])
popt_quad, pcov_quad = curve_fit(cuadratica,Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]],p0=(1,10))
#popt_rho22, pcov_rho22 = curve_fit(InverseMicromotionSpectra,Betas_vec,[t*1e3 for t in Temp_vec],p0=(10,10,-10,1,20)) #esto ajusta muy bien
#popt_rho22, pcov_rho22 = curve_fit(InverseMicromotionSpectra,Betas_vec, [t*1e3 for t in Temp_vec],p0=(-10,-10,10,1,20)) #esto ajusta muy bien
popt_rho22_raw, pcov_rho22_raw = curve_fit(InverseMicromotionSpectra_raw,Betas_vec[:10], [t*1e3 for t in Temp_vec[:10]],p0=(-10, -10, 1)) #esto ajusta muy bien
print(popt_rho22_raw)
betaslong = np.arange(0,2*2.7,0.01)
print(f'Min temp predicted: {InverseMicromotionSpectra_raw(betaslong,*popt_rho22_raw)[100]}')
plt.figure()
plt.errorbar(Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]],xerr=ErrorBetas_vec[:10], yerr=[t*1e3 for t in ErrorTemp_vec[:10]],fmt='o',capsize=5,markersize=5,color=paleta[3])
#plt.plot(betaslong,expo(betaslong,*popt_exp),label='Ajuste exponencial')
#plt.plot(betaslong,cuadratica(betaslong,*popt_quad),label='Ajuste cuadratico')
#plt.plot(betaslong,InverseMicromotionSpectra(betaslong,*popt_rho22),label='Ajuste cuadratico')
plt.plot(betaslong,InverseMicromotionSpectra_raw(betaslong,*popt_rho22_raw),label='Ajuste cuadratico')
#plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
#plt.axhline(0.538)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
#%%
"""
Esto no es del super ajuste sino de los ajustes anteriores en donde DetDoppler y offset son puestos a mano
Aca grafico los betas con su error en funcion de la tension variada.
Ademas, hago ajuste lineal para primeros y ultimos puntos, ya que espero que
si la tension hace que la posicion del ion varie linealmente, el beta varia proporcional a dicha posicion.
"""
import seaborn as sns
def lineal(x,a,b):
return a*x+b
paleta = sns.color_palette("rocket")
betavector = [beta1,beta2,beta3,beta4,beta5,beta6,beta7,beta8,beta9]
errorbetavector = [errorbeta1,errorbeta2,errorbeta3,errorbeta4,errorbeta5,errorbeta6,errorbeta7,errorbeta8,errorbeta9]
voltages_dcA = Voltages[0][1:10]
poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],betavector[0:3])
poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],betavector[4:])
minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
xini = np.linspace(-0.23,-0.13,100)
xfin = np.linspace(-0.15,0.005,100)
plt.figure()
plt.errorbar(voltages_dcA,betavector,yerr=errorbetavector,fmt='o',capsize=5,markersize=5,color=paleta[1])
plt.plot(xini,lineal(xini,*poptini))
plt.plot(xfin,lineal(xfin,*poptfin))
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Modulation factor')
plt.grid()
#%%
"""
Aca veo la temperatura del ion en funcion del voltaje del endcap, ya que
al cambiar la cantidad de micromocion, cambia la calidad del enfriado
"""
tempvector = np.array([temp1,temp2,temp3,temp4,temp5,temp6,temp7,temp8,temp9])*1e3
errortempvector = np.array([errortemp1,errortemp2,errortemp3,errortemp4,errortemp5,errortemp6,errortemp7,errortemp8,errortemp9])*1e3
voltages_dcA = Voltages[0][1:10]
plt.figure()
plt.errorbar(voltages_dcA,tempvector,yerr=errortempvector,fmt='o',capsize=5,markersize=5,color=paleta[3])
plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
plt.xlabel('Endcap voltage (V)')
plt.ylabel('Temperature (mK)')
plt.grid()
plt.ylim(0,2)
#%%
"""
Por las dudas, temperatura en funcion de beta
"""
plt.figure()
plt.errorbar(betavector,tempvector,yerr=errortempvector,xerr=errorbetavector,fmt='o',capsize=5,markersize=5)
plt.xlabel('Modulation factor')
plt.ylabel('Temperature (mK)')
plt.grid()
#%%
"""
Si quiero ver algun parametro del ajuste puntual. el orden es: 0:SG, 1:SP, 2:SCALE1, 3:OFFSET
"""
ki=2
plt.errorbar(np.arange(0,9,1),[popt_1[ki],popt_2[ki],popt_3[ki],popt_4[ki],popt_5[ki],popt_6[ki],popt_7[ki],popt_8[ki],popt_9[ki]],yerr=[np.sqrt(pcov_1[ki,ki]),np.sqrt(pcov_2[ki,ki]),np.sqrt(pcov_3[ki,ki]),np.sqrt(pcov_4[ki,ki]),np.sqrt(pcov_5[ki,ki]),np.sqrt(pcov_6[ki,ki]),np.sqrt(pcov_7[ki,ki]),np.sqrt(pcov_8[ki,ki]),np.sqrt(pcov_9[ki,ki])], fmt='o',capsize=3,markersize=3)
#%%
if False:
GUARDAR = {}
# for var in [ kk for kk in globals().keys() if kk.startswith('pop') ]:
# print(var)
# GUARDAR[var] = globals()[var]
# print('')
# for var in [ kk for kk in globals().keys() if kk.startswith('pcov') ]:
# print(var)
# GUARDAR[var] = globals()[var]
# print('')
# for var in [ kk for kk in globals().keys() if kk.startswith('Fitted') ]:
# print(var)
# GUARDAR[var] = globals()[var]
# print('')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(var)
GUARDAR[var] = globals()[var]
np.savez('analisis_superajuste_PARAMETROS.npz', **GUARDAR )
analisis/plots/20250516_CPT_bladetrap/lolo_graficos_pub.py 0 → 100644
View file @ f9b573e5
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Ploteo de datos y ajustes del barrido en voltaje del Endcap
equivalente a pasear el ion en algun entorno del punto de compensación ideal
y ver los efectos de micromoción (y tal vez temperatura)
@author: lolo
"""
import h5py
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from time import time
from numba import jit,njit
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Carga de datos y librerías auxiliares
## Librerias ##################################################################
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
# PARAMETROS = np.load('PARAMETROS.npz',allow_pickle=True)
# for var_name in PARAMETROS.keys():
# globals()[var_name] = PARAMETROS[var_name]
# print(f'loaded: {var_name}')
# Funciones auxiliares
from scipy.stats.distributions import t,chi2
import inspect
def estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05):
if nombres is None:
nombres = inspect.getfullargspec(modelo).args[1:]
# Cantidad de parámetros
P = len(parametros)
# Número de datos
N = len(datos_x)
# Grados de libertas (Degrees Of Freedom)
dof = N-P-1
# Cauculamos coordenadas del modelo
# modelo_x = datos_x if modelo_x_arr is None else modelo_x_arr
# modelo_y = modelo( modelo_x, *parametros )
# Predicción del modelo para los datos_x medidos
prediccion_modelo = modelo( datos_x, *parametros )
# Calculos de cantidades estadísticas relevantes
COV = pcov # Matriz de Covarianza
SE = np.sqrt(np.diag( COV )) # Standar Error / Error estandar de los parámetros
residuos = datos_y - prediccion_modelo # diferencia enrte el modelo y los datos
SSE = sum(( residuos )**2 ) # Resitual Sum of Squares
SST = sum(( datos_y - np.mean(datos_y))**2) # Total Sum of Squares
# http://en.wikipedia.org/wiki/Coefficient_of_determination
# Expresa el porcentaje de la varianza que logra explicar el modelos propuesto
Rsq = 1 - SSE/SST # Coeficiente de determinación
Rsq_adj = 1-(1-Rsq) * (N-1)/(N-P-1) # Coeficiente de determinación Ajustado
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#In_least_squares_regression_analysis
# Expresa la correlación que hay entre los datos y la predicción del modelo
r_pearson = np.corrcoef( datos_y , prediccion_modelo )[0,1]
# Reduced chi squared
# https://en.wikipedia.org/wiki/Reduced_chi-squared_statistic
chi2_ = sum( residuos**2 )/N
chi2_red = sum( residuos**2 )/(N-P)
# Chi squared test
chi2_test = sum( residuos**2 / abs(prediccion_modelo) )
# p-value del ajuste
p_val = chi2(dof).cdf( chi2_test )
sT = t.ppf(1.0 - alpha/2.0, N - P ) # student T multiplier
CI = sT * SE # Confidence Interval
return dict(R2=Rsq,R2_adj=Rsq_adj,chi2=chi2_,chi2_red=chi2_red,
chi2_test=chi2_test,r=r_pearson,pvalue=p_val,
SE=SE,CI=CI,parametros=parametros,nombres=nombres,alpha=alpha,N=N)
def print_estadistica(dic):
pars = 'N,pvalue,R2,R2_adj,chi2,chi2_red,chi2_test,r,SE,CI,parametros,nombres,alpha'.split(',')
N,pvalue,R2,R2_adj,chi2,chi2_red,chi2_test,r,SE,CI,parametros,nombres,alpha = [ dic[k] for k in pars ]
P = len(parametros)
print('R-squared ',R2)
print('R-sq_adjusted',R2_adj)
print('chi2 ',chi2)
print('chi2_reduced ',chi2_red)
print('chi2_test ',chi2_test)
print('r-pearson ',r)
print('p-value ',pvalue)
print('')
print('Error Estandard (SE):')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , SE[i])
print('')
print('Intervalo de confianza al '+str((1-alpha)*100)+'%:')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , CI[i])
## Datos ######################################################################
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
# os.chdir('../20231123_CPTconmicromocion3/Data/')
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-IR_Scan_withcal_optimized
"""
def SeeKeys(files):
for i, fname in enumerate(files.split()):
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
print(fname)
print(list(data['datasets'].keys()))
print(SeeKeys(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#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']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
def Split(array,n):
length=len(array)/n
splitlist = []
jj = 0
while jj<length:
partial = []
ii = 0
while ii < n:
partial.append(array[jj*n+ii])
ii = ii + 1
splitlist.append(partial)
jj = jj + 1
return splitlist
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
## Cargo parámetros fiteados de antes #########################################
PARAMETROS = np.load('analisis_superajuste_PARAMETROS.npz',allow_pickle=True)
for var_name in PARAMETROS.keys():
globals()[var_name] = PARAMETROS[var_name]
print(f'loaded: {var_name}')
if False:
# Esto es para correr en caso de necesidad de limpiar todos los vectores de parametros
print('Limpio los vectores de parámetros')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(f'del {var}')
del(globals()[var])
## Definiciones de Numba ######################################################
@jit
def FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u,
DopplerLaserLinewidth, ProbeLaserLinewidth,
TEMP, alpha, phidoppler, titadoppler,
phiprobe, titaprobe, BETA1, drivefreq,
min(freqs), max(freqs)+(freqs[1]-freqs[0]),
freqs[1]-freqs[0], circularityprobe=CircPr,
plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
return ScaledFluo1, Detunings
@jit
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
"Esta verison de la función devuelve sólo el eje y, para usar de modelo en un ajuste"
return FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP)[0]
param_names = 'offset DetDoppler SG SP SCALE1 OFFSET BETA1 TEMP'.split()
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
La 0 no ajusta bien incluso con todos los parametros libres
De la 1 a la 11 ajustan bien
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
"""
SUPER AJUSTE (SA)
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [1,2,3,4,5,6,7,8,9,10,11]
#SelectedCurveVec = [10]
CircPr = 1
alpha = 0
voltages_dcA = Voltages[0][SelectedCurveVec]
def hiperbola(x,a,y0,b,x0):
"""
Hiperbola de ecuación:
1 =(y-y0)²/a² - (x-x0)²/b²
"""
return a*np.sqrt(((x-x0)**2+b**2))+y0
def hiperbola2(x,a,b,x0):
"""
Hiperbola de ecuación:
1 =(y)²/a² - (x-x0)²/b²
"""
return a*np.sqrt(((x-x0)**2+b**2))
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Graficamos todos los fiteos
# fig, axx = plt.subplots( 3,4, figsize=(13,8) , constrained_layout=True, sharex=True , sharey=True )
# fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
# tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec,axx.flatten())
# for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax in zip(*tmp_datos):
# ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.3, capsize=2, markersize=2)
# ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='black', linewidth=2, label=f'med {selectedcurve}', alpha=0.7)
# #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
# # ax.set_xlabel('Detuning (MHz)')
# # ax.set_ylabel('Counts')
# ax.legend(loc='upper left', fontsize=12)
# ax.grid(True, ls=":")
# print(f'listo med {selectedcurve}')
# # print(popt_3_SA)
# for ax in axx[:,0]:
# ax.set_ylabel('Counts')
# for ax in axx[-1,:]:
# ax.set_xlabel('Detuning (MHz)')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Gráfico central OPCION A
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 8})
# Para esto hace falta:
# sudo apt install dvipng
plt.rcParams['text.usetex']=True
# plt.rcParams['text.latex.unicode']=True
# params= {'text.latex.preamble' : [r'\usepackage{amsmath}']}
# plt.rcParams.update(params)
# fig, axx = plt.subplots( 3,4, figsize=(8.6/2.54*2,4) , constrained_layout=True, sharex=True , sharey=True )
# fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
## Layout ###########################
from matplotlib import gridspec
figsize=(8.6/2.54*2,4)
width_ratios=[1,2,1]
fig, axx = plt.subplots(ncols=3, nrows=3, sharex=True, sharey=False, constrained_layout=True,
figsize=figsize, gridspec_kw=dict(width_ratios=width_ratios))
fig.set_constrained_layout_pads(w_pad=1/72, h_pad=1/72, hspace=0, wspace=0)
fig.set_constrained_layout_pads(w_pad=0, h_pad=0, hspace=0, wspace=0)
gs = axx[0, 0].get_gridspec()
# remove the underlying axes
for ax in axx[:,1]:
ax.remove()
gs2 = gridspec.GridSpec(4,3, width_ratios=width_ratios,left=0.16,right=0.9,bottom=0.12)
ax_central = fig.add_subplot(gs2[:-1, 1])
ax_res = fig.add_subplot(gs2[-1, 1], sharex=ax_central,zorder=-5)
plt.setp(ax_central.get_xticklabels(), visible=False)
for ax in axx[:,2]:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
## CPTs ########################################
selection= (0,1,3,4,7,8)
axes_vec = (axx[0,0],axx[1,0],axx[2,0],axx[2,2],axx[1,2],axx[0,2])
tmp_datos=(Detuningsshort_vec[selection,:],
Counts_vec[selection,:],
Detuningslong_vec[selection,:],
FittedCounts_vec[selection,:],
np.array(selection)+1,
axes_vec,'abcfed')
for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax,le in zip(*tmp_datos):
ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR),
fmt='o', color='darkgreen', alpha=0.2, capsize=2,
markersize=2, label='measured data')
ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long,
color='black', linewidth=2, label=f'micromotion model', alpha=0.7)
ax.grid(True, ls=":", color='lightgray')
ax.set_yticklabels([ f"{int(y/1000)}k" for y in ax.get_yticks() ])
ax.set_title(f'({le})', x=0.1, y=0.78, color='gray')
# ax.legend()
# ax.legend(ax.get_legend_handles_labels()[0], f'{selectedcurve}')
print(f'listo med {selectedcurve}')
axx[2,0].set_xlabel('Detuning [MHz]')
axx[2,2].set_xlabel('Detuning [MHz]')
axx[1,0].set_ylabel('Counts')
axx[1,2].set_ylabel('Counts')
## Grafico central ###############################
I = slice(None,9)
par_inicial = (12,0.1,1,-0.13)
param,pcov = curve_fit(hiperbola,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
x_hip = np.linspace(-0.23,0.005,200)
EST=estadistica(voltages_dcA[I],Betas_vec[I],hiperbola,pcov,param,nombres=None,alpha=0.05)
print_estadistica(EST)
I = slice(None,9)
par_inicial = (12,1,-0.13)
param2,pcov2 = curve_fit(hiperbola2,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
x_hip = np.linspace(-0.23,0.005,200)
EST2=estadistica(voltages_dcA[I],Betas_vec[I],hiperbola2,pcov2,param2,nombres=None,alpha=0.05)
print_estadistica(EST2)
ax = ax_central
ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',
capsize=4,markersize=3,color='C3', label=r'fitted $\beta$')
ax.plot(x_hip,hiperbola(x_hip,*param),color='C0', label=r'hyperbola model')
ax.plot(x_hip,hiperbola2(x_hip,*param2),'--',color='C4', label=r'hyperbola model')
ax.set_ylabel(r'Modulation factor $\beta$', labelpad=-5)
ax.set_ylim(-0.05,3)
ax.set_xlim(-0.22,0)
ax.set_title(f'(g)', x=0.95, y=0.006, color='gray')
ax = ax_res
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*param),
yerr=ErrorBetas_vec[I],fmt='o',capsize=4,markersize=3,color='C3')
ax.errorbar(voltages_dcA[I]+0.005,Betas_vec[I]-hiperbola2(voltages_dcA[I],*param2),
yerr=ErrorBetas_vec[I],fmt='o',capsize=4,markersize=3,color='C4')
ax.axhline( 0 , color='C0')
ax.set_ylabel('Res.', labelpad=-5)
ax.set_xlabel('Endcap voltage [V]')
ax.set_title(f'(h)', x=0.95, y=0.72, color='gray')
ax_res.get_xticklabels()[-1].set_visible(False)
for ax in [ax_central,ax_res]:
ax.grid(True, ls=":", color='lightgray')
# print([t*1e3 for t in Temp_vec])
# Anotaciones ##########################3
for jj,ax in zip(selection,axes_vec):
Axes_x = 1 if axx.flatten().tolist().index(ax)%3==0 else 0
ax_central.annotate("",
xy=(ax_central.get_lines()[0].get_xdata()[jj], ax_central.get_lines()[0].get_ydata()[jj]),
xycoords=ax_central.transData,
xytext=(Axes_x, 0.5), textcoords=ax.transAxes,
arrowprops=dict(arrowstyle="<-",connectionstyle="arc3,rad=-0.2", color='gray', alpha=0.5),
zorder=-2)
# Leyenda ##############################
# h1, l1 = ax_central.get_legend_handles_labels()
# h2, l2 = axx[0,0].get_legend_handles_labels()
# ax_central.legend(h1+h2, l1+l2, loc='upper left')
fig.align_ylabels([ax_central,ax_res])
fig.tight_layout()
# fig.savefig('grafico_central_opcion_A.png', dpi=300)
# fig.savefig('grafico_central_opcion_A.pdf')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Gráfico central OPCION B
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 8})
# Para esto hace falta:
# sudo apt install dvipng
plt.rcParams['text.usetex']=True
# plt.rcParams['text.latex.unicode']=True
# params= {'text.latex.preamble' : [r'\usepackage{amsmath}']}
# plt.rcParams.update(params)
# fig, axx = plt.subplots( 3,4, figsize=(8.6/2.54*2,4) , constrained_layout=True, sharex=True , sharey=True )
# fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
voltages_dcA = Voltages[0][SelectedCurveVec]*4
## Layout ###########################
from matplotlib import gridspec
figsize=(8.6/2.54*2,4)
width_ratios=[1,2,1]
fig, axx = plt.subplots(ncols=3, nrows=3, sharex=True, sharey=False, constrained_layout=True,
figsize=figsize, gridspec_kw=dict(width_ratios=width_ratios))
fig.set_constrained_layout_pads(w_pad=1/72, h_pad=1/72, hspace=0, wspace=0)
fig.set_constrained_layout_pads(w_pad=0, h_pad=0, hspace=0, wspace=0)
gs = axx[0, 0].get_gridspec()
# remove the underlying axes
for ax in axx[:,1]:
ax.remove()
gs2 = gridspec.GridSpec(4,3, width_ratios=width_ratios,left=0.16,right=0.9,bottom=0.12)
ax_central = fig.add_subplot(gs2[1:-1, 1])
ax_dib = fig.add_subplot(gs2[0, 1], sharex=ax_res, zorder=-20)
ax_dib.spines[:].set_visible(False)
ax_dib.xaxis.set_tick_params(labelbottom=False)
ax_dib.yaxis.set_tick_params(labelleft=False)
ax_dib.set_xticks([])
ax_dib.set_yticks([])
ax_res = fig.add_subplot(gs2[-1, 1], sharex=ax_central,zorder=-5)
plt.setp(ax_central.get_xticklabels(), visible=False)
for ax in axx[:,2]:
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
## CPTs ########################################
selection= (0,1,3,4,7,8)
axes_vec = (axx[0,0],axx[1,0],axx[2,0],axx[2,2],axx[1,2],axx[0,2])
tmp_datos=(Detuningsshort_vec[selection,:],
Counts_vec[selection,:],
Detuningslong_vec[selection,:],
FittedCounts_vec[selection,:],
np.array(selection)+1,
axes_vec,'abcfed')
from scipy.signal import savgol_filter as savgol
for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax,le in zip(*tmp_datos):
# ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR),
# fmt='o', color='darkgreen', alpha=0.2, capsize=2,
# markersize=2, label='measured data')
yerr_max = CountsDR+2*np.sqrt(CountsDR)
yerr_min = CountsDR-2*np.sqrt(CountsDR)
yerr_max = savgol(yerr_max,3,2)
yerr_min = savgol(yerr_min,3,2)
ax.fill_between(Detunings_3_SA_short, yerr_max, yerr_min,
color='darkgreen', alpha=0.2)
ax.plot(Detunings_3_SA_short, CountsDR, '.', ms=2,
color='darkgreen', alpha=0.9)
ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long,
color='black', linewidth=1, label=f'micromotion model', alpha=0.7)
ax.grid(True, ls=":", color='lightgray')
ax.set_yticklabels([ f"{int(y/1000)}k" for y in ax.get_yticks() ])
ax.set_title(f'({le})', x=0.1, y=0.78, color='gray')
# ax.legend()
# ax.legend(ax.get_legend_handles_labels()[0], f'{selectedcurve}')
print(f'listo med {selectedcurve}')
axx[2,0].set_xlabel('Detuning [MHz]')
axx[2,2].set_xlabel('Detuning [MHz]')
axx[1,0].set_ylabel('Counts')
axx[1,2].set_ylabel('Counts')
## Grafico central ###############################
I = slice(None,9)
par_inicial = (12,1,-0.13)
param,pcov = curve_fit(hiperbola2,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
x_hip = np.linspace(-0.23*4,0.005,200)
EST=estadistica(voltages_dcA[I],Betas_vec[I],hiperbola2,pcov,param,nombres=None,alpha=0.05)
print_estadistica(EST)
ax = ax_central
ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',
capsize=4,markersize=3,color='C3', label=r'fitted $\beta$')
ax.plot(x_hip,hiperbola2(x_hip,*param),color='C0', label=r'hyperbola model')
ax.set_ylabel(r'Modulation factor $\beta$', labelpad=-5)
ax.set_ylim(-0.05,3)
ax.set_xlim(-0.22*4,0)
ax.set_title(f'(h)', x=0.95, y=0.006, color='gray')
ax = ax_res
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola2(voltages_dcA[I],*param),
yerr=ErrorBetas_vec[I],fmt='o',capsize=4,markersize=3,color='C3')
ax.axhline( 0 , color='C0')
ax.set_ylabel('Res.', labelpad=-5)
ax.set_xlabel('Endcap voltage [V]')
ax.set_title(f'(i)', x=0.95, y=0.72, color='gray')
ax_res.get_xticklabels()[-1].set_visible(False)
for ax in [ax_central,ax_res]:
ax.grid(True, ls=":", color='lightgray')
# print([t*1e3 for t in Temp_vec])
# Anotaciones ##########################
for jj,ax in zip(selection,axes_vec):
rad = 0.2 if jj==8 or jj==3 else -0.2
Axes_x = 1 if axx.flatten().tolist().index(ax)%3==0 else 0
ax_central.annotate("",
xy=(ax_central.get_lines()[0].get_xdata()[jj], ax_central.get_lines()[0].get_ydata()[jj]),
xycoords=ax_central.transData,
xytext=(Axes_x, 0.5), textcoords=ax.transAxes,
arrowprops=dict(arrowstyle="<-",connectionstyle=f"arc3,rad={rad}", color='gray', alpha=0.5),
zorder=-2)
# dibujos ##########################
# a, y0, b, x0 = param
a, b, x0 = param
y0 = -0.212663964284234
ax_dib.plot( x_hip , x_hip*0+y0 , color='gray')
ax_dib.set_ylim(-y0*0.1,y0*1.2)
ax_dib.plot( [x0], [0], 'x' )
ax_dib.text(x0+0.01, 0, 'trap center', fontsize = 8, ha='left',va='center', color='C0')
ax_dib.plot( voltages_dcA[I], voltages_dcA[I]*0+y0, '.' )
for Vx in voltages_dcA[I]:
ax_dib.plot( [Vx,x0], [y0,0], ':', color='lightgray', lw=1)
import matplotlib.patches as mpatches
arr = mpatches.FancyArrowPatch((x0, y0*1.1), (x0/2, y0*1.1),
arrowstyle='->,head_width=.15', mutation_scale=10, color='C1')
ax_dib.add_patch(arr)
ax_dib.annotate("ion position", (.5, .5), xycoords=arr, ha='center', va='bottom', color='C1')
# r
arr = mpatches.FancyArrowPatch((x0,0), ( voltages_dcA[5], y0),
arrowstyle='->,head_width=.25', mutation_scale=10, color='black')
ax_dib.add_patch(arr)
ax_dib.annotate(r"$\vec r$", (.5, .5), xycoords=arr, ha='center', va='bottom', color='black')
bbox = ax_dib.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
aspect_ratio = bbox.width / bbox.height
ax_dib.plot(0.1, 0, ">k", transform=ax_dib.transAxes, clip_on=False, ms=3, lw=1)
ax_dib.plot([0,0.1], [0,0], "-k", transform=ax_dib.transAxes, clip_on=False, lw=1)
ax_dib.plot(0, 0.1*aspect_ratio, "^k", transform=ax_dib.transAxes, clip_on=False, ms=3, lw=1)
ax_dib.plot([0,0], [0,0.1*aspect_ratio], "-k", transform=ax_dib.transAxes, clip_on=False, lw=1)
ax_dib.text(0.1, 0.13, "x", transform=ax_dib.transAxes,
fontsize=8, va='top')
ax_dib.text(0.03, 0.13*aspect_ratio, "y", transform=ax_dib.transAxes,
fontsize=8, va='top')
ax_dib.set_title(f'(g)', x=0.95, y=0.5, color='gray')
# Leyenda ##############################
# h1, l1 = ax_central.get_legend_handles_labels()
# h2, l2 = axx[0,0].get_legend_handles_labels()
# ax_central.legend(h1+h2, l1+l2, loc='upper left')
fig.align_ylabels([ax_central,ax_res])
fig.tight_layout()
fig.savefig('grafico_central_opcion_B.png', dpi=300)
fig.savefig('grafico_central_opcion_B.pdf')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Grafico de RF Heating y temp
from scipy.special import jv
import seaborn as sns
paleta = sns.color_palette("rocket")
"""
Por si no quiero correr todo de nuevo,
los vectores del grafico son estos
Betas_vec = [1.5063238290734708, 0.901629236713104, 0.5280696724084589, 0.32347492275852474, 0.581352716043054, 0.9655636982224044, 1.6186284658310388, 2.1055294502704047, 2.548008896618126, 3.130666254406969, 3.1665902866111795]
ErrorBetas_vec = [0.034184989382424424, 0.04827624922956116, 0.054704762232730145, 0.06708926613955447, 0.047526038227881616, 0.05210296764897591, 0.03630379898730492, 0.034064021790261405, 0.050405155463964166, 0.10838273932613045, 0.2828605486723366]
Temp_vec = [0.0017384071359855713, 0.0006380145223733723, 0.0007457923288975645, 0.0006442238745101592, 0.0006270680749881928, 0.0008995355804703286, 0.0017799223158665226, 0.002941224610139307, 0.008378628768005558, 0.026250695067608725, 0.09869604401089357]
ErrorTemp_vec = [0.0004229476096548473, 0.00014439375508413987, 0.00013204015146487435, 9.307939678673377e-05, 0.000100129717662808, 0.0001841318633900307, 0.0003595040837509976, 0.0005950353892849986, 0.001866844309182069, 0.012656306714647434, 0.13143081065882864]
"""
Betas_vec = [1.5063238290734708, 0.901629236713104, 0.5280696724084589, 0.32347492275852474, 0.581352716043054, 0.9655636982224044, 1.6186284658310388, 2.1055294502704047, 2.548008896618126, 3.130666254406969, 3.1665902866111795]
ErrorBetas_vec = [0.034184989382424424, 0.04827624922956116, 0.054704762232730145, 0.06708926613955447, 0.047526038227881616, 0.05210296764897591, 0.03630379898730492, 0.034064021790261405, 0.050405155463964166, 0.10838273932613045, 0.2828605486723366]
Temp_vec = [0.0017384071359855713, 0.0006380145223733723, 0.0007457923288975645, 0.0006442238745101592, 0.0006270680749881928, 0.0008995355804703286, 0.0017799223158665226, 0.002941224610139307, 0.008378628768005558, 0.026250695067608725, 0.09869604401089357]
ErrorTemp_vec = [0.0004229476096548473, 0.00014439375508413987, 0.00013204015146487435, 9.307939678673377e-05, 0.000100129717662808, 0.0001841318633900307, 0.0003595040837509976, 0.0005950353892849986, 0.001866844309182069, 0.012656306714647434, 0.13143081065882864]
def MicromotionSpectra(beta,det, gamma):
"""
Espectro de transicion doppler considerando micromocion
"""
ftrap=2*np.pi*22.1e6
#gamma=23
P = (jv(0, beta)**2)/(((det)**2)+(0.5*gamma)**2)
i = 1
#print(P)
while i <= 2: #numero de bandas de micromocion a considerar
P = P + ((jv(i, beta))**2)/((((det)+i*ftrap)**2)+(0.5*gamma)**2) + ((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)
i = i + 1
#print(P)
return (gamma/2*np.pi)*P
#return P
def InverseDerivMicromotionSpectra(beta, det, gamma):
"""
La inversa de la derivada del espectro de micromocion
"""
ftrap=2*np.pi*22.1e6
#gamma=23
#det = -gamma/2
P = ((jv(0, beta)**2)/((((det)**2)+(0.5*gamma)**2)**2))*(-2*(det))
i = 1
#print(P)
while i <= 2:
P = P + (-2*(det))*((jv(i, beta))**2)/(((((det)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det))*(((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)**2)
i = i + 1
#print(P)
return 1/((gamma/2*np.pi)*P)
#return 1/P
def FinalTemp(beta,det, C,D):
"""
Funcion para ajustar la tmeperatura final en funcion de beta.
Esta funcion tiene un parametro de ajuste C para estudiar posibles desviacioens de la temperatura
doppler teorica para un sistema de dos niveles.
"""
gamma = 23e6
hbar = 1.05e-34
kb = 1.38e-23
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
def FinalTemp_withoutC(beta,det, D, forgetmicromotion=False):
"""
Funcion para ajustar la temperatura final en funcion de beta.
Esta funcion no tiene ese parametro de ajuste ya que vi que era muy cercano a 1.
"""
gamma = 23e6
hbar = 1.05e-34
kb = 1.38e-23
C = 1 #justamente aca dejo fijo ese parametro
if forgetmicromotion:
D=0
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
def FinalTemp_withoutmicromotion(beta,det):
"""
Funcion para ajustar la temperatura final en funcion de beta.
Esta funcion no tiene ese parametro de ajuste ya que vi que era muy cercano a 1.
"""
gamma = 23e6
hbar = 1.05e-34
kb = 1.38e-23
C = 1 #justamente aca dejo fijo ese parametro
D=0
return ((hbar/(kb))*C*MicromotionSpectra(beta,2*np.pi*det,2*np.pi*gamma)+D*(beta**2))*InverseDerivMicromotionSpectra(beta, 2*np.pi*det, 2*np.pi*gamma)
#return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
def CoolingLimit_noMM(det):
gamma = 2*np.pi*23e6
hbar = 1.05e-34
kb = 1.38e-23
beta = 0
rta = ((2*hbar/(2*kb))*MicromotionSpectra(beta,det,gamma))
rta *= InverseDerivMicromotionSpectra(beta, det, gamma)
return rta
DetVec = np.arange(-2*np.pi*30e6,-2*np.pi*1e6,2*np.pi*0.1e6)
Temps_limit = []
for detuning in DetVec:
Temps_limit.append(CoolingLimit_noMM(detuning))
print(f'best: {1e3*np.min(Temps_limit)}')
if False:
plt.figure()
plt.plot(DetVec*1e-6/(2*np.pi), np.array(Temps_limit)*1e3)
plt.grid()
plt.xlabel('detuning (MHz)')
plt.ylabel('final temperature')
#Dos pruebas... ignorar
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:9]), [t for t in Temp_vec[:9]],p0=(-11e6,0.8,4e-28)) #esto ajusta muy bien
#popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,0.8,5e-20)) #esto ajusta muy bien
#Buen ajuste final:
popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp_withoutC,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,5e-20)) #esto ajusta muy bien
popt_rho22_balance_noMM, pcov_rho22_balance_noMM = curve_fit(FinalTemp_withoutmicromotion,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6)) #esto ajusta muy bien
betaslong = np.arange(0,2.8,0.01)
print(f'Min temp predicted: {FinalTemp_withoutC(betaslong,*popt_rho22_balance)[0]}')
#print(f'Min temp predicted: {1e3*FinalTemp(betaslong,*popt_rho22_balance)[0]} mK')
print(f'Detuning: {1e-6*popt_rho22_balance[0]} MHz')
print(f'params: {popt_rho22_balance}')
print(f'errores: {np.sqrt(np.diag(pcov_rho22_balance))}')
k_plot = 9
# #plt.plot(betaslong,FinalTemp_fixedall(betaslong,*popt_rho22_balance),label='Ajuste con espectro modulado')
# # plt.xlim(-0.1,1.1)
# #plt.ylim(0,1)
# #plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
# #plt.axhline(0.538)
# plt.xlabel('Modulation factor')
# plt.ylabel('Temperature (mK)')
# plt.legend()
# plt.grid()
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 8})
# Para esto hace falta:
# sudo apt install dvipng
plt.rcParams['text.usetex']=True
# Acá estimo un detuning de (10 +- 4) MHz
Detuning_range = ( 10 + 4*np.array([-1,1]) )*1e6
Doppler_limit_area = CoolingLimit_noMM(-2*np.pi*Detuning_range)*1e3
figsize=(8.6/2.54,3.5)
fig, ax = plt.subplots(1,1, constrained_layout=True,
figsize=figsize)
# fig.set_constrained_layout_pads(w_pad=1/72, h_pad=1/72, hspace=0, wspace=0)
# fig.set_constrained_layout_pads(w_pad=0, h_pad=0, hspace=0, wspace=0)
ax.errorbar(Betas_vec[:k_plot],[t*1e3 for t in Temp_vec[:k_plot]],xerr=ErrorBetas_vec[:k_plot],
yerr=[t*1e3 for t in ErrorTemp_vec[:k_plot]],fmt='o',
capsize=3,markersize=3,color='C3',zorder=10,alpha=0.9, label='inferred data')
#plt.plot(betaslong,[t*1e3 for t in FinalTemp(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
ax.plot(betaslong,[t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_balance)],
'-', color='C0', label='model w/ RF heating')
ax.plot(betaslong,[t*1e3 for t in FinalTemp_withoutmicromotion(betaslong,*popt_rho22_balance_noMM)],
'--', color='C1',label='model w/o RF heating')
ax.fill_between( [0, 2.8] , np.ones(2)*Doppler_limit_area[0], np.ones(2)*Doppler_limit_area[1],
color='gray', alpha=0.5, label='Doppler limit w/o micromotion')
ax.grid(True, ls=":", color='lightgray')
# ax.set_yticklabels([ f"{int(y/1000)}k" for y in ax.get_yticks() ])
# ax.set_title(f'({le})', x=0.1, y=0.78, color='gray')
ax.set_xlabel(r'Modulation factor $\beta$')
ax.set_ylabel('Temperature [mK]')
ax.set_xlim(0,2.8)
# ax.legend(fontsize=8)
# Leyenda ##############################
h1, l1 = ax.get_legend_handles_labels()
ax.legend(h1[-1:]+h1[:-1], l1[-1:]+l1[:-1], loc='upper left', fontsize=8)
fig.savefig('grafico_temp_A.png', dpi=300)
fig.savefig('grafico_temp_A.pdf')
#%% Versión B
#Buen ajuste final:
popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp_withoutC,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6,5e-20)) #esto ajusta muy bien
popt_rho22_balance_noMM, pcov_rho22_balance_noMM = curve_fit(FinalTemp_withoutmicromotion,list(Betas_vec[:10]), [t for t in Temp_vec[:10]],sigma=ErrorTemp_vec[:10],absolute_sigma=False,p0=(-10e6)) #esto ajusta muy bien
betaslong = np.arange(0,2.8,0.01)
print(f'Min temp predicted: {FinalTemp_withoutC(betaslong,*popt_rho22_balance)[0]}')
#print(f'Min temp predicted: {1e3*FinalTemp(betaslong,*popt_rho22_balance)[0]} mK')
print(f'Detuning: {1e-6*popt_rho22_balance[0]} MHz')
print(f'params: {popt_rho22_balance}')
print(f'errores: {np.sqrt(np.diag(pcov_rho22_balance))}')
k_plot = 9
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 8})
plt.rcParams['text.usetex']=True
# Acá estimo un detuning de (10 +- 4) MHz
Detuning_range = ( 10 + 4*np.array([-1,1]) )*1e6
Doppler_limit_area = CoolingLimit_noMM(-2*np.pi*Detuning_range)*1e3
figsize=(8.6/2.54,4)
height_ratios=[10,3]
fig, axx = plt.subplots(2,1, sharex=True, constrained_layout=True,
figsize=figsize, gridspec_kw=dict(height_ratios=height_ratios))
fig.set_constrained_layout_pads(w_pad=1/72, h_pad=0, hspace=0, wspace=0)
ax=axx[0]
ax.errorbar(Betas_vec[:k_plot],[t*1e3 for t in Temp_vec[:k_plot]],xerr=ErrorBetas_vec[:k_plot],
yerr=[t*1e3 for t in ErrorTemp_vec[:k_plot]],fmt='o',
capsize=3,markersize=3,color='C3',zorder=10,alpha=0.9, label='inferred data')
#plt.plot(betaslong,[t*1e3 for t in FinalTemp(betaslong,*popt_rho22_balance)],label='Ajuste con espectro modulado')
ax.plot(betaslong,[t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_balance)],
'-', color='C0', label='model w/ RF heating')
ax.plot(betaslong,[t*1e3 for t in FinalTemp_withoutmicromotion(betaslong,*popt_rho22_balance_noMM)],
'--', color='C1',label='model w/ RF heating')
ax.fill_between( [0, 2.8] , np.ones(2)*Doppler_limit_area[0], np.ones(2)*Doppler_limit_area[1],
color='gray', alpha=0.5, label='Doppler limit w/o micromotion')
ax.grid(True, ls=":", color='lightgray')
# ax.set_yticklabels([ f"{int(y/1000)}k" for y in ax.get_yticks() ])
# ax.set_title(f'({le})', x=0.1, y=0.78, color='gray')
ax.set_ylabel('Temperature [mK]')
# Leyenda ##############################
h1, l1 = ax.get_legend_handles_labels()
ax.legend(h1[-1:]+h1[:-1], l1[-1:]+l1[:-1], loc='upper left', fontsize=8)
ax=axx[1]
residuos = np.array([t*1e3 for t in Temp_vec[:k_plot]]) - np.array([t*1e3 for t in FinalTemp_withoutC(np.array(Betas_vec[:k_plot]),*popt_rho22_balance)])
ax.errorbar(Betas_vec[:k_plot],residuos,xerr=ErrorBetas_vec[:k_plot],
yerr=[t*1e3 for t in ErrorTemp_vec[:k_plot]],fmt='o',
capsize=3,markersize=3,color='C3',zorder=10,alpha=0.9, label='inferred data')
modelA = np.array([t*1e3 for t in FinalTemp_withoutC(betaslong,*popt_rho22_balance)])
modelB = np.array([t*1e3 for t in FinalTemp_withoutmicromotion(betaslong,*popt_rho22_balance_noMM)])
ax.plot(betaslong,modelB-modelA,
'--', color='C1',label='model w/ RF heating')
ax.axhline( 0 , color='C0')
ax.set_xlim(0,2.8)
ax.set_xlabel(r'Modulation factor $\beta$')
ax.set_ylabel('Res. [mK]')
ax.grid(True, ls=":", color='lightgray')
ax.set_ylim(-5,5)
# ax.legend(fontsize=8)
fig.align_ylabels(axx)
fig.savefig('grafico_temp_B.png', dpi=300)
fig.savefig('grafico_temp_B.pdf')
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