todisima

analisis/plots/20230906_CPTconmicromocion2/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py

@@ -231,9 +231,11 @@ def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
     que forman ambos láseres.
     """
     
-    kboltzmann = 1.38e-23 #J/K
+    kboltzmann = 1.380649e-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))
+    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.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(1*mcalcio))
 
     #print('tuk')
 
@@ -251,8 +253,8 @@ 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 = 0.5*np.sqrt(lwg**2 + db**2)
-    lwp = 0.5*np.sqrt(lwp**2 + db**2)    
+    lwg = np.sqrt(lwg**2 + db**2)
+    lwp = np.sqrt(lwp**2 + db**2)    
     
     CC = EffectiveL(gPS, gPD, lwg, lwp)
     
@@ -288,7 +290,7 @@ def FullL_MM(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, l
     L0 = np.array(np.matrix(Lfullpartial) + M)
     
     #ESTA PARTE ES CUANDO AGREGAS MICROMOCION
-    nmax = 3
+    nmax = 5
     
     #print(nmax)
     Ltemp, Omega = LtempCalculus(beta, drivefreq)
@@ -329,7 +331,7 @@ def CPTspectrum8levels_MM(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phid
     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
+    lwg, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwp*1e6
 
     rabG = sg*gPS
     rabP = sp*gPD

analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py

@@ -907,7 +907,7 @@ from scipy.optimize import curve_fit
 import time
 
 """
-MEDICION 3: SUPER AJUSTE (SA)
+SUPER AJUSTE (SA)
 """
 
 phidoppler, titadoppler = 0, 90
@@ -943,7 +943,7 @@ drivefreq = 2*np.pi*22.135*1e6
 
 
 SelectedCurveVec = [1,2,3,4,5,6,7,8,9]
-#SelectedCurveVec = [4]
+#SelectedCurveVec = [9]
 
 popt_SA_vec = []
 pcov_SA_vec = []
@@ -966,13 +966,25 @@ for selectedcurve in SelectedCurveVec:
 
     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
@@ -993,7 +1005,7 @@ 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, 1e-3], bounds=((0, -50, 0, 0, 0, 0, 0, 0), (1000, 0, 2, 20, 5e4, 5e4, 10, 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], 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)
@@ -1027,7 +1039,7 @@ for selectedcurve in SelectedCurveVec:
     
     print(f'listo med {selectedcurve}')
     print(popt_3_SA)
-    #print(f'Detdop:{popt_3_SA[1]},popt_3_SA:{popt[0]}')
+
     
 #%%
 """
@@ -1035,32 +1047,51 @@ 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
 
-paleta = sns.color_palette("rocket")
-
-voltages_dcA = Voltages[0][1:10]
+def hiperbola(x,a,b,c,x0):
+    return a*np.sqrt(((x-x0)**2+c**2))+b
 
-poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],Betas_vec[0:3])
-poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],Betas_vec[4:])
+hiperbola_or_linear = True
 
-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
+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()
 
-xini = np.linspace(-0.23,-0.13,100)
-xfin = np.linspace(-0.15,0.005,100)
+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()
 
-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])
@@ -1071,6 +1102,49 @@ 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()
+
+
+
+
+#%%
+def expo(x,tau,A,B):
+    return A*np.exp(x/tau)+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()
 
 
 #%%