para muri

analisis/plots/20220615_CPTvariandocompensacion/CPT_plotter_20220615.py

@@ -293,11 +293,11 @@ def FitEIT_MM_SA(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET, BETA
     #return ScaledFluo1
 
 
-popt_SA, pcov_SA = curve_fit(FitEIT_MM_SA, FreqsDR, CountsDR, p0=[425, -13, 0.9, 7.5, 4e3, 5e3, 4000, 3.8, 0.8, 0.2e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0,0, 0, 28e6), (1000, 0, 2, 20, 5e6, 5e6, 1e4, 10, 10,20e-3,40e6)))
+popt_SA, pcov_SA = curve_fit(FitEIT_MM_SA, FreqsDR, CountsDR, p0=[425, -13, 0.9, 7.5, 4e3, 5e3, 2500, 3.8, 0.8, 0.2e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 1500, 0,0, 0, 28e6), (1000, 0, 2, 20, 5e6, 5e6, 6000, 10, 10,20e-3,40e6)))
 
 
 FittedEITpi_short_SA, Detunings_short_SA = FitEIT_MM_SA(FreqsDR, *popt_SA, plot=True)
-freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+freqslong = np.arange(1*min(FreqsDR), 1*max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
 FittedEITpi_long_SA, Detunings_long_SA = FitEIT_MM_SA(freqslong, *popt_SA, plot=True)
 
 plt.figure()
@@ -306,6 +306,7 @@ plt.plot(Detunings_long_SA, FittedEITpi_long_SA, color='darkolivegreen', linewid
 plt.title('2 ion model')
 plt.xlabel('Detuning (MHz)')
 plt.ylabel('Counts')
+plt.xlim(-100,100)
 #plt.legend(loc='upper left', fontsize=20)
 plt.grid()
 

analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py

@@ -89,7 +89,7 @@ CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
 Ploteo la cpt de referencia / plotting the reference CPT
 """
 
-jvec = [4] # de la 1 a la 9 vale la pena, despues no
+jvec = [2] # de la 1 a la 9 vale la pena, despues no
 
 drs = [390.5, 399.5, 406, 413.5]
 
@@ -899,6 +899,9 @@ 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
 """
 
 
@@ -942,8 +945,8 @@ 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]
 
 popt_SA_vec = []
 pcov_SA_vec = []
@@ -985,7 +988,7 @@ for selectedcurve in SelectedCurveVec:
     alpha = 0
     
 
-    def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, plot=False):
+    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
@@ -994,7 +997,7 @@ for selectedcurve in SelectedCurveVec:
     
         freqs = [2*f*1e-6-offset for f in Freqs]
       
-        Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+        Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
     
         ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
         if plot:
@@ -1005,7 +1008,7 @@ for selectedcurve in SelectedCurveVec:
     
     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], bounds=((0, -50, 0, 0, 0, 0, 0, 0), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3)))
+        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)
@@ -1049,7 +1052,10 @@ Grafico distintas variables que salieron del SUper ajuste
 import seaborn as sns
 paleta = sns.color_palette("rocket")
 
-voltages_dcA = Voltages[0][1:10]
+medfin = 12
+
+
+voltages_dcA = Voltages[0][1:medfin]
 
 def lineal(x,a,b):
     return a*x+b
@@ -1060,12 +1066,12 @@ def hiperbola(x,a,b,c,x0):
 hiperbola_or_linear = True
 
 if hiperbola_or_linear:
-    popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
+    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,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
+    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')
@@ -1094,8 +1100,8 @@ else:
 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')
+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)')
@@ -1111,12 +1117,12 @@ 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))
+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,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
+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')
@@ -1126,26 +1132,66 @@ 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 aju8ste exponencial 
+Temperatura vs beta con un ajuste exponencial 
 """
 
-popt_exp, pcov_exp = curve_fit(expo,Betas_vec,[t*1e3 for t in Temp_vec])
-popt_quad, pcov_quad = curve_fit(cuadratica,Betas_vec,[t*1e3 for t in Temp_vec],p0=(1,10))
+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(0,2.7,0.01)
+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,[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),label='Ajuste exponencial')
-plt.plot(betaslong,cuadratica(betaslong,*popt_quad),label='Ajuste cuadratico')
+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')
@@ -1779,6 +1825,151 @@ plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
 plt.grid()
 
 
+#%%
+"""
+AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
+"""
+
+
+#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]
+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)