a ver

analisis/plots/20230920_CPT_TemperatureSens_v2/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py

@@ -19,6 +19,16 @@ import random
 from scipy.signal import savgol_filter as sf
 from scipy.stats import norm
                              
+
+def prob_energia(E,T):
+    kboltz = 1.380649e-23
+    mcalcio = 6.655e-23*1e-3
+    prob =  np.exp(-E/(kboltz*T)) #*E**2 
+    return prob
+
+
+
+
 def PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, betag, betap, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, dephasing  = False, boltzmann = False):
     """
     solvemode=1: resuelve con np.linalg.solve
@@ -37,8 +47,9 @@ def PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinew
         
         velvec = np.linspace(-4*sigmaT,4*sigmaT,100)
         MBprobVec = norm.pdf(velvec,loc = 0, scale = sigmaT)
+        MBprobVec = MBprobVec/np.trapz(MBprobVec,velvec)
         for i in range(len(velvec)):
-            detVel = (detpvec - kp*velvec[i])/(2*np.pi*1e6)
+            detVel = detpvec - kp*velvec[i]/(2*np.pi*1e6)
             _, Fluorescence = CPTspectrum8levels( sg, sp, gPS, gPD, DetDoppler-kg*velvec[i]/(2*np.pi*1e6),u, DopplerLaserLinewidth, ProbeLaserLinewidth, 0,alpha, phidoppler, titadoppler, phiprobe, titaprobe,circularityprobe, betag, betap, drivefreq, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False,detpvec=detVel)
             
             FluorescencesVel.append(Fluorescence)    
@@ -54,21 +65,29 @@ def PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinew
         
 
     else:
-        drivefreq =2*np.pi* 1e3
+        drivefreq =2*np.pi* 0.6e6
         FluorescencesVel = []
         sigmaT = np.sqrt(T*kboltz/m)
         velvec = np.linspace(-4*sigmaT,4*sigmaT,100)
         MBprobVec = norm.pdf(velvec,loc = 0, scale = sigmaT)
+        
+        Evec = np.linspace(0,8*kboltz*T,100)
+        velvec = np.zeros(2*len(Evec)) 
+        velvec= np.sqrt(2 * Evec/m)
+        
+        MBprobVec = prob_energia(Evec, T)
+        MBprobVec = MBprobVec/np.trapz(MBprobVec,Evec)
+        
         betagVec = velvec*kg/drivefreq
         betapVec = velvec*kp/drivefreq
         for i in range(len(betagVec)): 
             betag,betap = betagVec[i],betapVec[i]
                                                          
-            Frequencies, Fluorescence = CPTspectrum8levels( sg, sp, gPS, gPD, DetDoppler,u, DopplerLaserLinewidth, ProbeLaserLinewidth, 0,alpha, phidoppler, titadoppler, phiprobe, titaprobe,circularityprobe, betag, betap, drivefreq, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False,detpvec=None)
+            Frequencies, Fluorescence = CPTspectrum8levels( sg, sp, gPS, gPD, DetDoppler,u, DopplerLaserLinewidth, ProbeLaserLinewidth, 0,alpha, phidoppler, titadoppler, phiprobe, titaprobe,circularityprobe, betag, betap, drivefreq, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False,detpvec=detpvec)
             FluorescencesVel.append(Fluorescence)
         FluorescencesVel = np.array(FluorescencesVel)
         MBprobMat = np.tile(MBprobVec,(FluorescencesVel.shape[1],1)).T
-        Fluorescence = np.trapz(FluorescencesVel*MBprobMat,velvec,axis = 0)
+        Fluorescence = np.trapz(FluorescencesVel*MBprobMat,Evec,axis = 0)
         ProbeDetuningVectorL = Frequencies
     
     #print('Done, Total time: ', round((tfinal-tinicial), 2), "s")   
@@ -106,15 +125,25 @@ def SmoothNoisyCPT(Fluo, window=11, poly=3):
 
 
 
-def fitCPT_8levels(Freq,Fluo,sg,sp,gPS,gPD,DetDoppler,u,DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False,dephasing = False, boltzmann = False ):
+def fitCPT_8levels(Freq,Fluo,sg,sp,gPS,gPD,DetDoppler,u,DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,betag,betar, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False,dephasing = False, boltzmann = False ):
     
-    def SpectrumForFit(Freq,sg,sp,T,DetDoppler):
-        freq,spectra = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=Freq)
-        return spectra
+    def SpectrumForFit(Freq,sg,sp,T,DetDoppler,A,bgnd,f0):
+        Freq = Freq - f0
+        freq,spectra = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, betag,betar, drivefreq, freqMin, freqMax, freqStep,circularityprobe=1, plot=False, detpvec=Freq, dephasing  = dephasing, boltzmann = boltzmann)
+        return spectra*A + bgnd
 
     
-    popt,pcov = curve_fit(SpectrumForFit,Freq,Fluo,p0 = [sg,sp,T,DetDoppler],bounds = ([0.01,0.01,0.00001,-50e6],[1,20,1,30e6]))
+    popt,pcov = curve_fit(SpectrumForFit,Freq,Fluo,p0 = [sg,sp,T,DetDoppler,20000,800,432],bounds = ([0.1,1,0.5e-3,-30,10000,0,420],[0.8,10,10e-3,0,100000,1000,500]))
     fitted_spectra = SpectrumForFit(Freq,*popt)
     return Freq, fitted_spectra,popt,pcov
 
 
+def try_fitCPT_8levels(A,bgnd,f0,Freq,Fluo,sg,sp,gPS,gPD,DetDoppler,u,DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,betag,betar, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False,dephasing = False, boltzmann = False ):
+    def SpectrumForFit(Freq,sg,sp,T,DetDoppler,A,bgnd,f0):
+        Freq = Freq - f0
+        freq,spectra = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, betag,betar, drivefreq, freqMin, freqMax, freqStep,circularityprobe=1, plot=False, detpvec=Freq, dephasing  = dephasing, boltzmann = boltzmann)
+        return spectra*A + bgnd
+    return SpectrumForFit(Freq, sg, sp, T, DetDoppler, A, bgnd, f0)
+    
+    
+    
\ No newline at end of file

analisis/plots/20230920_CPT_TemperatureSens_v2/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py

@@ -250,7 +250,7 @@ def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
     
     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))
+    gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(mcalcio))
 
     return gammaD
     
@@ -264,10 +264,16 @@ def FullL(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, lwp
     """
     
     
+    kg = 397e9
+    kp = 866e9
+    
+    fg = kg**2/(kg**2+kp**2)
+    fp = kp**2/(kg**2+kp**2)
+    
     db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
     
-    lwg = np.sqrt(lwg**2 + (db/2)**2)/2
-    lwp = np.sqrt(lwp**2 + (db/2)**2)/2    
+    lwg = np.sqrt(lwg**2 + (fg*db)**2)
+    lwp = np.sqrt(lwp**2 + (fp*db)**2)    
 
 
     
@@ -306,15 +312,18 @@ def FullL(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, lwp
     M = CalculateSingleMmatrix(gPS, gPD, lwg, lwp) 
     L0 = np.array(np.matrix(Lfullpartial) + M)
     
+    
+    if betag !=0 and betap !=0:
         #ESTA PARTE ES CUANDO AGREGAS MICROMOCION
-    nmax = 7
+        nmax = 2*int(betag)
         #print(nmax)
         Ltemp, Omega = LtempCalculus(betag,betap, drivefreq)
         #print(factor)
         L1 = GetL1(Ltemp, L0, Omega, nmax)
         Lfull = L0 + L1 #ESA CORRECCION ESTA EN L1
         #HASTA ACA
-    
+    else:
+        Lfull = L0
     #NORMALIZACION DE RHO
     i = 0 
     while i < 64:
@@ -423,6 +432,9 @@ def CPTspectrum8levels(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidopp
         DetProbeVector = detpvec*1e6 * 2*np.pi
     
     
+
+    
+   
     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
@@ -463,7 +475,7 @@ def CPTspectrum8levels(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidopp
         plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
         plt.legend()
         
-    return DetProbeVectorMHz, Fluovector
+    return DetProbeVectorMHz, np.array(Fluovector)
 
 
 def CPTspectrum8levels_vel(velvect,titavec,phivec,probvel,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):
@@ -613,7 +625,7 @@ def CPTspectrum8levels_vel(velvect,titavec,phivec,probvel,sg, sp, gPS, gPD, Detg
         plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
         plt.legend()
         
-    return DetProbeVectorMHz, Fluovector
+    return DetProbeVectorMHz, np.array(Fluovector)
 
 
 if __name__ == "__main__":

analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py

@@ -1008,9 +1008,14 @@ for selectedcurve in SelectedCurveVec:
 
     do_fit = True
     if do_fit:
+<<<<<<< HEAD
         print(1)
         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, 31e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 34e6)))
         print(2)
+=======
+        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)))
+
+>>>>>>> f197671e6d2f5bc2c74f8d1e8fb4a89fb518ddbe
         popt_SA_vec.append(popt_3_SA)
         pcov_SA_vec.append(pcov_3_SA)
 
@@ -1067,10 +1072,17 @@ def hiperbola(x,a,b,c,x0):
 hiperbola_or_linear = True
 
 if hiperbola_or_linear:
+<<<<<<< HEAD
     popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[0:9],Betas_vec[0:9],p0=(100,0.1,1,-0.15))
     
     xhip = np.linspace(-0.23,0.055,200)
     
+=======
+    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)
+
+>>>>>>> f197671e6d2f5bc2c74f8d1e8fb4a89fb518ddbe
     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))
@@ -1168,13 +1180,18 @@ def MicromotionSpectra(beta,det, gamma):
     P = (jv(0, beta)**2)/(((det)**2)+(0.5*gamma)**2)
     i = 1
     #print(P)
-    while i <= 2:
+    while i <= 5:
         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 P
 
 
+def polynomial(x,a,b,c,d,e):
+    b=0
+    d=0
+    return a+b*x+c*x*x+d*x*x*x+e*x*x*x*x
+
 def InverseDerivMicromotionSpectra(beta, det, gamma):
     ftrap=22.1
     #gamma=23
@@ -1182,7 +1199,7 @@ def InverseDerivMicromotionSpectra(beta, det, gamma):
     P = ((jv(0, beta)**2)/((((det)**2)+(0.5*gamma)**2)**2))*(-2*(det))
     i = 1
     #print(P)
-    while i <= 2:
+    while i <= 5:
         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)
@@ -1190,7 +1207,7 @@ def InverseDerivMicromotionSpectra(beta, det, gamma):
 
 
 def FinalTemp(beta,det, C,D):
-    gamma = 22
+    gamma = 21
     #det=-11
     #D=-0.8
     #C = 1.68656122e-03  
@@ -1201,6 +1218,9 @@ def FinalTemp(beta,det, C,D):
     return (C*MicromotionSpectra(beta,det,gamma)+D*beta**2)*InverseDerivMicromotionSpectra(beta, det, gamma)
     #return (C*MicromotionSpectra(beta,det,gamma))*InverseDerivMicromotionSpectra(beta, det, gamma)
 
+"""
+Temperatura vs beta con un ajuste exponencial
+"""
 
 
 popt_exp, pcov_exp = curve_fit(expo,Betas_vec[:9],[t*1e3 for t in Temp_vec[:9]])
@@ -1209,6 +1229,8 @@ popt_exp, pcov_exp = curve_fit(expo,Betas_vec[:9],[t*1e3 for t in Temp_vec[:9]])
 #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[:7], [t*1e3 for t in Temp_vec[:7]],p0=(-0.1, -10, 1)) #esto ajusta muy bien
 popt_rho22_balance, pcov_rho22_balance = curve_fit(FinalTemp,Betas_vec[:9], [t*1e3 for t in Temp_vec[:9]],p0=(-10, 10,1)) #esto ajusta muy bien
+popt_rho22_poly, pcov_rho22_poly = curve_fit(polynomial,Betas_vec[:9], [t*1e3 for t in Temp_vec[:9]],p0=(1,2,3,4,10)) #esto ajusta muy bien
+
 
 print(popt_rho22_balance)
 
@@ -1222,11 +1244,16 @@ print(f'betasquared coeff: {popt_rho22_balance[2]}')
 
 print(f'cociente de los coeff: {popt_rho22_balance[2]/popt_rho22_balance[1]}')
 
+
+print(f'params: {popt_rho22_balance}')
+print(f'errores: {np.sqrt(np.diag(pcov_rho22_balance))}')
+
 k_plot = 9
 
 plt.figure()
 plt.errorbar(Betas_vec[:k_plot],[t*1e3 for t in Temp_vec[:k_plot]],xerr=ErrorBetas_vec[:k_plot], yerr=[t*1e3 for t in ErrorTemp_vec[:k_plot]],fmt='o',capsize=5,markersize=5,color=paleta[3])
 #plt.plot(betaslong,expo(betaslong,*popt_exp),label='Ajuste exponencial')
+plt.plot(betaslong,polynomial(betaslong,*popt_rho22_poly),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,FinalTemp(betaslong,popt_rho22_balance[0],popt_rho22_balance[1],popt_rho22_balance[2]*1),label='Ajuste con espectro modulado')
@@ -2028,10 +2055,3 @@ for selectedcurve in SelectedCurveVec:
 
     print(f'listo med {selectedcurve}')
     print(popt_3_SA)
-
-
-
-
-
-
-

analisis/plots/20231123_CPTconmicromocion3/lolo_CPT_plotter_20231123.py

@@ -920,6 +920,9 @@ 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
 """
 
 
@@ -963,8 +966,8 @@ alpha = 0
 drivefreq = 2*np.pi*22.135*1e6
 
 
-SelectedCurveVec = [1,2,3,4,5,6,7,8,9]
-#SelectedCurveVec = [9]
+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:
 
@@ -1152,9 +1155,42 @@ 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
 """
@@ -1830,5 +1866,3 @@ if False:
 
 
     np.savez('PARAMETROS.npz', **GUARDAR )
-    
-

analisis/plots/20231218_CPT_muri/CPT_plotter_20231218.py

+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
+
+"""
+CPT con tres laseres pero lso dos IR son el mismo entonces las DD son mas finas
+"""
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211223_CPT_DosLaseres_v07_ChristmasSpecial\Data
+
+ALL_FILES = """000016420-IR_Scan_withcal_optimized
+""" 
+
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+print(SeeKeys(ALL_FILES))
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+Freqs = []
+
+AmpTisa = []
+UVCPTAmp = []
+No_measures = []
+
+for i, fname in enumerate(ALL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    #print(fname)
+    data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    # Aca hago algo repugnante para poder levantar los strings que dejamos
+    # que además tenian un error de tipeo al final. Esto no deberá ser necesario
+    # cuando se solucione el error este del guardado.
+    Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    Counts.append(np.array(data['datasets']['counts_spectrum']))
+    #AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
+    #UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
+    #No_measures.append(np.array(data['datasets']['no_measures']))
+
+
+
+#%%
+
+#Barriendo angulo del IR con tisa apagado
+
+jvec = [0]
+
+jselected = jvec
+
+plt.figure()
+i = 0
+for j in jvec:
+    if j in jselected:
+        plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
+    #plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb  arkersize=3)
+    i = i + 1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+plt.grid()
+plt.legend()
+
+#%%
+from scipy.optimize import curve_fit
+import time
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  90
+phiprobe = 0
+titaprobe = 0.1
+
+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
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 
+alpha = 0
+
+
+drivefreq = 2*np.pi*22.135*1e6
+noiseamplitude = 0
+
+selectedcurve=0
+
+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_1ion(Freqs, offset, DetDoppler, DetRepump, SG, SP, SR, SCALE1, OFFSET, TEMP, U, plot=False):
+#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
+    # BETA1 = 0
+    # SG = 0.6
+    # SP = 8.1
+    # TEMP = 0.2e-3
+    # U = 32.5e6
+    freqs = [2*f*1e-6-offset for f in Freqs]
+  
+    #Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    Detunings, Fluorescence1 = GenerateNoisyCPT_fit(SG, SR, SP, gPS, gPD, DetDoppler, DetRepump, U, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, TEMP,  alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+
+    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_1, pcov_1 = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[430, -25, 12, 0.9, 6.2, 3, 3e4, 2e3, 0.5e-3, 32e6], bounds=((0, -100, -20, 0, 0, 0, 0, 0, 0,20e6), (1000, 0, 50, 2, 20, 20, 5e6, 5e4, 15e-3,40e6)))
+
+    FittedEITpi_1_short, Detunings_1_short = FitEIT_MM_1ion(FreqsDR, *popt_1, plot=True)
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    FittedEITpi_1_long, Detunings_1_long = FitEIT_MM_1ion(freqslong, *popt_1, plot=True)
+
+#%%
+
+plt.figure()
+plt.errorbar(Detunings_1_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='red', alpha=0.5, capsize=2, markersize=2)
+plt.plot(Detunings_1_long, FittedEITpi_1_long, color='darkolivegreen', linewidth=3, label='med 1')
+#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+plt.xlabel('Detuning (MHz)')
+plt.ylabel('Counts')
+#plt.xlim(-20,0)
+plt.legend(loc='upper left', fontsize=20)
+plt.grid()
+
+#%%
+u = 32.5e6
+
+B = (u/(2*np.pi))/c
+
+correccion = 8 #con 8 fitea bien
+
+offsetxpi = 440+1+correccion
+DetDoppler = -5.0-correccion
+
+FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[5]]
+CountsDRpi_3 = Counts_B[5]
+
+freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
+
+#[1.71811842e+04 3.34325038e-17]
+
+def FitEITpi(freqs, SG, SP):
+    temp = 2e-3
+    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+    FinalFluo = [f*6.554e4 + 1.863e3 for f in MeasuredFluo]
+    return FinalFluo
+
+popt_tisaoff, pcov_tisaoff = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5], bounds=((0, 0), (2, 10)))
+
+print(popt_tisaoff)
+
+Sat_3 = popt_tisaoff[0]
+Det_3 = popt_tisaoff[1]
+
+FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt_tisaoff)
+
+plt.figure()
+plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
+plt.plot(freqslongpi_3, FittedEITpi_3)
+#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+
+FreqsCalibradas_B = FreqsDRpi_3
+
+
+
+
+
+
+
+
+
+

analisis/plots/20231218_CPT_muri/CPT_plotter_20231218_muri.py

@@ -14,7 +14,7 @@ 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('/home/nico/Documents/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/')
+os.chdir('/home/muri/nubeDF/Documents/codigos/artiq_experiments/analisis/plots/20231218_CPT_muri/Data')
 
 CPT_FILES = """000016262-IR_Scan_withcal_optimized
 000016239-IR_Scan_withcal_optimized
@@ -111,7 +111,84 @@ plt.grid()
 plt.legend()
 
 
+#%%
+from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels_MM
+
+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]
+CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
+CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
+
+freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+
+CircPr = 1
+alpha = 0
+
+
+def FitEIT_MM_single(freqs, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
+#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
+    #BETA = 1.8
+    # SG = 0.6
+    # SP = 8.1
+    # TEMP = 0.2e-3
+    
+    Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+
+    ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
+    return ScaledFluo1
+    #return ScaledFluo1
+
+do_fit = True
+if do_fit:
+    popt_1, pcov_1 = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2, 3e4, 1.34e3, 0, 1e-3], bounds=((0, 0, 0, 0, 0, 0), (2, 20, 7e4, 5e4, 0.000001, 15e-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()
 

analisis/plots/20231218_CPT_muri/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py

@@ -15,7 +15,7 @@ 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
+from EITfit.MM_eightLevel_2repumps_python_scripts import CPTspectrum8levels_MM
 import random
 from scipy.signal import savgol_filter as sf
 

analisis/plots/20231218_CPT_muri/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py

@@ -233,7 +233,7 @@ def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
     
     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))
+    gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(mcalcio))
 
     return gammaD
     
@@ -249,8 +249,14 @@ def FullL_MM(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, l
     
     db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
     
-    lwg = np.sqrt(lwg**2 + db**2)
-    lwp = np.sqrt(lwp**2 + db**2)    
+    kg = 1/397
+    kp = 1/866
+    
+    fg = kg**2/(kg**2+kp**2)
+    fp = kp**2/(kg**2+kp**2)
+    
+    lwg = np.sqrt(lwg**2 + (fg*db)**2)
+    lwp = np.sqrt(lwp**2 + (fp*db)**2)    
     
     CC = EffectiveL(gPS, gPD, lwg, lwp)