todo dd la compu

analisis/plots/20220526_CPTvariandoB_v2/CPT_plotter_20220520.py

@@ -469,6 +469,8 @@ ErrorDetuningfits = [np.sqrt(pcov_12)[3,3], np.sqrt(pcov_13)[3,3],np.sqrt(pcov_1
 ErrorPotIRfits = [np.sqrt(pcov_12)[2,2], np.sqrt(pcov_13)[2,2],np.sqrt(pcov_14)[2,2], np.sqrt(pcov_15)[2,2]]
 ErrorPotUVfits = [np.sqrt(pcov_12)[1,1], np.sqrt(pcov_13)[1,1],np.sqrt(pcov_14)[1,1], np.sqrt(pcov_15)[1,1]]
 
+ErrorLarmorsvecs = [np.sqrt(pcov_12)[0,0], np.sqrt(pcov_13)[0,0],np.sqrt(pcov_14)[0,0], np.sqrt(pcov_15)[0,0]]
+
 print(Detuningsfits)
 print(PotIRfits)
 print(PotUVfits)
@@ -522,6 +524,17 @@ plt.plot(freqslongpi_15, FittedEITpi_15)
 
 #%%
 #filtro y mejoro el plot
+"""
+Figura 4 del paper (apendice)
+"""
+
+Ufields = [popt_12[0], popt_13[0], popt_14[0], popt_15[0]]
+ErrorUfields = [np.sqrt(pcov_12)[0,0], np.sqrt(pcov_13)[0,0],np.sqrt(pcov_14)[0,0], np.sqrt(pcov_15)[0,0]]
+
+Bfields = [u/(2*np.pi)/1398190 for u in Ufields]
+ErrorBfields = [eu/(2*np.pi)/1398190 for eu in ErrorUfields]
+
+
 from scipy.signal import savgol_filter as sf
 import seaborn as sns
 
@@ -548,10 +561,9 @@ FiltCounts15 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_15, winl, po)]
 ErrorCounts15 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_15, winl, po)])
 
 
-
 #plot con escalas atomicas y lindo
 
-colors=sns.color_palette("rocket", 10)
+colors=sns.color_palette("mako", 10)
 
 color1=colors[1]
 color2=colors[2]
@@ -562,10 +574,10 @@ ms = 4
 
 plt.figure(figsize=(3.5, 3))
 
-plt.plot(FreqsDRpi_12, FiltCounts12, 'o', markersize=ms, color=color1, label=r'$S_{rep}=6.31$', alpha=0.3)
-plt.plot(FreqsDRpi_13, FiltCounts13, 'o', markersize=ms, color=color2, label=r'$S_{rep}=6.31$', alpha=0.3)
-plt.plot(FreqsDRpi_14, FiltCounts14, 'o', markersize=ms, color=color3, label=r'$S_{rep}=6.31$', alpha=0.3)
-plt.plot(FreqsDRpi_15, FiltCounts15, 'o', markersize=ms, color=color4, label=r'$S_{rep}=6.31$', alpha=0.3)
+plt.plot(FreqsDRpi_12, FiltCounts12, 'o', markersize=ms, color=color1, label=fr'$B=${round(1e3*Bfields[0])}({round(1e3*ErrorBfields[0])}) mG', alpha=0.3)
+plt.plot(FreqsDRpi_13, FiltCounts13, 'o', markersize=ms, color=color2, label=fr'$B=${round(1e3*Bfields[1])}({round(1e3*ErrorBfields[1])}) mG', alpha=0.3)
+plt.plot(FreqsDRpi_14, FiltCounts14, 'o', markersize=ms, color=color3, label=fr'$B=${round(1e3*Bfields[2])}({round(1e3*ErrorBfields[2])}) mG', alpha=0.3)
+plt.plot(FreqsDRpi_15, FiltCounts15, 'o', markersize=ms, color=color4, label=fr'$B=${round(1e3*Bfields[3])}({round(1e3*ErrorBfields[3])}) mG', alpha=0.3)
 
 plt.plot(freqslongpi_12, [(c-offs)*100/scal for c in FittedEITpi_12], color=color1, linewidth=3)
 plt.plot(freqslongpi_13, [(c-offs)*100/scal for c in FittedEITpi_13], color=color2, linewidth=3)
@@ -578,12 +590,22 @@ plt.fill_between(FreqsDRpi_14, FiltCounts14+ErrorCounts14, FiltCounts14-ErrorCou
 plt.fill_between(FreqsDRpi_15, FiltCounts15+ErrorCounts15, FiltCounts15-ErrorCounts15, color=color4, alpha=0.2)
 
 plt.xlim(-40,30)
-plt.xlabel('Repump detuning (MHz)', fontsize=11)
-plt.ylabel('Normalized fluorescence', fontsize=11)
-
+plt.ylim(-0.1,1.4)
+plt.xlabel('Repump detuning (MHz)', fontsize=12, fontname='STIXgeneral')
+plt.ylabel('Normalized fluorescence', fontsize=12, fontname='STIXgeneral')
+plt.xticks([-40, -20, 0, 20], fontsize=12, fontname='STIXgeneral')
+plt.yticks([0, 0.3, 0.6, 0.9, 1.2], fontsize=12, fontname='STIXgeneral')
+visible_ticks = {
+   "top": False,
+   "right": False
+}
+plt.tick_params(axis="x", which="both", **visible_ticks)
+plt.tick_params(axis="y", which="both", **visible_ticks)
+#plt.legend(loc='upper left', frameon=True, fontsize=7.6, handletextpad=0.1)
 plt.grid()
 plt.tight_layout()
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/CPT_exp.png',dpi=500)
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig3_final.pdf')
 
 #%%
 from scipy.optimize import curve_fit
@@ -611,8 +633,8 @@ IFit = [-1.2, -1.5, -1.75, -2]
 LarmorFit = [popt_12[0], popt_13[0], popt_14[0], popt_15[0]]
 #LarmorFit = [287282.374931893, 203998.09641511342, 158507.0255951109] por si fallan los ajustes
 
-MeanError = np.mean([np.sqrt(pcov_12[0,0]),np.sqrt(pcov_13[0,0]),np.sqrt(pcov_14[0,0]),np.sqrt(pcov_15[0,0])])
-#MeanError = 11139.353180216529 por si fallan los ajustes
+ErrorLarmorFit = [np.sqrt(pcov_12[0,0]),np.sqrt(pcov_13[0,0]),np.sqrt(pcov_14[0,0]),np.sqrt(pcov_15[0,0])]
+MeanError = 11139.353180216529 #por si fallan los ajustes
 
 Ilong = np.arange(2, -3, -0.01)
 popt_larmor, pcov_larmor = curve_fit(LinearLarmortoCurrent, IFit, LarmorFit)
@@ -625,14 +647,27 @@ plt.plot(IFit, LarmorFit, 'o', markersize=5)
 plt.plot(Ilong, LarmorLong)
 
 Bfitted = [ConvertLarmortoBfield(u) for u in LarmorFit]
+ErrorBfitted = [ConvertLarmortoBfield(u) for u in ErrorLarmorFit]
 BLong = [ConvertLarmortoBfield(u) for u in LarmorLong]
 
+
+#ESTE GRAFICO TIENE QUE IR EN LA TESIS CUANDO PONGA
+#COMO CALIBRE EL CAMPO MAGNETICO CON LA CORRIENTE
 plt.figure()
-plt.plot(Ilong, BLong)
-plt.plot(IFit, Bfitted, 'o', markersize=8)
+plt.plot(Ilong, [b*1e3 for b in BLong])
+plt.plot(IFit, [b*1e3 for b in Bfitted], 'o', markersize=8)
+plt.xlim(-2.2,-1)
+plt.ylim(0.012*1e3,0.045*1e3)
 plt.xlabel('Corriente (A)')
-plt.ylabel('Campo magnetico (G)')
+plt.ylabel('Campo magnetico (mG)')
 plt.grid()
 
-
-
+#%%
+plt.figure()
+plt.plot(Ilong, [b*1e-6 for b in LarmorLong])
+plt.errorbar(IFit, [b*1e-6 for b in LarmorFit], yerr=1e-6*MeanError, fmt='o', capsize=4, markersize=4)
+plt.xlim(-2.2,-1)
+plt.ylim(0.1,0.43)
+plt.xlabel('Coil current (A)')
+plt.ylabel('Larmor frequency (MHz)')
+plt.grid()

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analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/untitled0.py

@@ -8,11 +8,12 @@ Created on Thu Aug 31 16:18:18 2023
 from scipy.special import jv
 import numpy as np
 import matplotlib.pyplot as plt
+import seaborn as sns
 
 def CPTMicromotionSpectra(det, A, beta, x0, x1):
     ftrap=22.1
-    gamma=2
-    P = -A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+10 -A*(jv(0, beta)**2)/(((det-x1)**2)+(0.5*gamma)**2)+10-0.5*det
+    gamma=3
+    P = -A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+10 -A*(jv(0, beta)**2)/(((det-x1)**2)+(0.5*gamma)**2)+10-0.007*det
     i = 1
     #print(P)
     while i <= 10:
@@ -25,12 +26,15 @@ def CPTMicromotionSpectra(det, A, beta, x0, x1):
 detvec = np.arange(-50,50,0.1)
 
 A=1
-betavec=[0,0.5,1,1.5]
-x0=0
-x1 = 10
+betavec=[0,1.2]
+x0=-5
+x1 = 5
 
+ii=0
 plt.figure()
 for beta in betavec:
-    plt.plot(detvec,CPTMicromotionSpectra(detvec,A,beta,x0,x1))
+    plt.plot(detvec,CPTMicromotionSpectra(detvec,A,beta,x0,x1),label=f'beta: {beta}')
+    ii=ii+1
 plt.xlim(-50,50)
-plt.ylim(18.5,20.5)
+plt.ylim(19.25,20.5)
+plt.legend()

analisis/plots/20220615_CPTvariandocompensacion/CPT_plotter_20220615.py

@@ -8,7 +8,6 @@ from scipy.optimize import curve_fit
 import os
 from scipy import interpolate
 
-#Mediciones de CPT para campo magnetico bajo y terrestre
 
 #/home/nico/Documents/artiq_experiments/analisis/plots/20220615_CPTvariandocompensacion/Data
 
@@ -144,9 +143,9 @@ DR = [403.5,412.5,418,426.5]
 
 for dr in DR:
     plt.axvline(dr,color='blue',linestyle='--',alpha=0.3)
-    #plt.axvline(dr+22,color='crimson',linestyle='--',alpha=0.3)
+    plt.axvline(dr+22,color='crimson',linestyle='--',alpha=0.3)
     plt.axvline(dr-22,color='red',linestyle='--')    
-    #plt.axvline(dr+44,color='red',linestyle='--')
+    plt.axvline(dr+44,color='red',linestyle='--')
 
 #plt.legend()
 
@@ -188,15 +187,24 @@ CountsDR = Counts[10]
 
 freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
 
-def FitEIT_MM(freqs, SCALE, OFFSET, SG, SP, BETA, TEMP):
-    BETA = 1.8
-    Detunings, Fluorescence = PerformExperiment_8levels(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
-    ScaledFluo = [f*SCALE + OFFSET for f in Fluorescence]
-    return ScaledFluo
 
+def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2):
+    #BETA = 1.8
+    # SG = 0.6
+    # SP = 8.1
+    TEMP = 1e-3
+    Detunings, Fluorescence1 = PerformExperiment_8levels(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(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)
 
-popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[1e3, 1e4, 0.7, 5, 2, 5e-3], bounds=((0, 0, 0, 0, 0, 0), (1e5, 1e5, 1.5, 10, 3, 15e-3)))
+    ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
+    ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
+    return ScaledFluo1+ScaledFluo2
 
+
+popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 1e3, 1e3, 1e4, 1, 1], bounds=((0, 0, 0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 1e5, 10, 10)))
+
+#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
+       #1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
 FittedEITpi = FitEIT_MM(freqslong, *popt)
 
 plt.figure()

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analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py

@@ -88,8 +88,8 @@ def LtempCalculus(beta, drivefreq, forma=1):
     Hint = np.zeros((8, 8), dtype=np.complex_)
     
     ampg=beta*drivefreq
-    ampr=beta*drivefreq        
-    
+    ampr=beta*drivefreq*(397/866)  
+    #print('fixed')
     Hint[0,0] = ampg
     Hint[1,1] = ampg
     Hint[4,4] = ampr
@@ -282,7 +282,7 @@ def FullL(rabG, rabP, gPS = 0, gPD = 0, Detg = 0, Detp = 0, u = 0, lwg = 0, lwp
     L0 = np.array(np.matrix(Lfullpartial) + M)
     
     #ESTA PARTE ES CUANDO AGREGAS MICROMOCION
-    nmax = 7
+    nmax = 3
     #print(nmax)
     Ltemp, Omega = LtempCalculus(beta, drivefreq)
     #print(factor)

analisis/plots/20220615_CPTvariandocompensacion/Data/RabiVector.txt

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