agrego cosas pendientes

analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.py

@@ -89,7 +89,7 @@ colors=sns.color_palette("rocket", 10)
 colorsselected=[colors[8],colors[5],colors[3],colors[0]]
 
 FluovsBshort = []
-jselected = [0, 1, 4, 8]
+jselected = [0, 1, 4, 6]
 
 for j in jselected:
     FluovsBshort.append(IR_fluorescence_vec[j])
@@ -288,7 +288,7 @@ plt.ylim(0,4.1)
 plt.grid()
 plt.tight_layout()
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/umbralvsB_exp.png',dpi=500)
-plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2b_v2.pdf')
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2b_v2.pdf')
 
 
 
@@ -383,10 +383,237 @@ plt.yticks([0,1,2,3], fontsize=12, fontname='STIXGeneral')
 plt.ylim(0,3.7)
 #plt.xlim(0,2.3)
 plt.tight_layout()
-#plt.legend(loc='upper left', frameon=True, fontsize=7.6, handletextpad=0.1)
-plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2a_v2.pdf')
+plt.legend(loc='upper left', frameon=True, fontsize=7.6, handletextpad=0.1)
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2a_v2.pdf')
 
 
+#%%
+'''
+INTENTOS DE VER SI QUEDA MAS LINDA, PERO LA DEL ARXIV ES LA ANTERIOR
+Figura 2a) del paper. Curvas de fluorescencia vs potencia del IR
+'''
+
+
+from scipy.signal import savgol_filter as sf
+
+# FluoSel = IR_fluorescence_vec[0][1:]
+# PotsSel = PotenciasIR[1:]
+
+# popt, pcov = curve_fit(FuncTest, PotsSel, FluoSel,p0=(1000,500,0,100,0))
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+from scipy.signal import savgol_filter as sf
+import seaborn as sns
+
+plt.style.use('seaborn-ticks')
+
+colors=sns.color_palette("rocket", 10)
+
+colorsselected=[colors[0],colors[3],colors[5],colors[8]]
+
+FluovsBshort = []
+#jselected = [0, 1, 4, 8]
+
+jselected = [6, 2, 1, 0]
+
+Bcampos = [b*1e3 for b in Bvec] 
+Bfields = [Bcampos[4], Bcampos[2], Bcampos[1], Bcampos[0]]
+
+for j in jselected:
+    FluovsBshort.append(IR_fluorescence_vec[j])
+
+#bkgr2 = np.min([FluovsBshort[0][1],FluovsBshort[1][1],FluovsBshort[2][1],FluovsBshort[3][1]])
+
+#bkgrposta = np.min()
+
+PotenciasMaximos_parcial = MaxsPotsExp/propor
+
+PotenciasMaximos = [PotenciasMaximos_parcial[4],PotenciasMaximos_parcial[2],PotenciasMaximos_parcial[1],PotenciasMaximos_parcial[0]]
+
+def FuncTest(rsq, A, det, delta, g):
+    c=1
+    return A*(((0.25*g*g+det*det+(2/3)*delta*delta)/(c*rsq))+6*c*rsq/(delta*delta))**(-1)
 
 
+smoothen_order = [3,3,3,1]
+err = [2,1,1,1]
+orden=5
+plt.figure(figsize=(3.5, 3))
+for j in range(len(jselected)):
+    print(j)
+    rawcuentas = [f-bkgr for f in FluovsBshort[j]]
+    
+    if j==0:
+        print('tukson')
+        print(rawcuentas)
+        rawcuentas[34]=0.5*(rawcuentas[33]+rawcuentas[35])
+        rawcuentas[37]=0.5*(rawcuentas[36]+rawcuentas[38])
+    
+    cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],13,smoothen_order[j]))) #este va bien
+    #cuentas = np.array(rawcuentas[0:]) #pruebo sin smooth
+
+
+    #maximumfluo_index = np.array(cuentas[1:-1]).argmax()
+    maximumfluo_index = find_nearest(PotenciasIR[1:-1]/propor, PotenciasMaximos[j])
+    
+    popt, pcov = curve_fit(FuncTest, PotenciasIR[1:-1]/propor, cuentas[1:-1]/1000, p0=(1e5,1.5,1e-2,3.5e2), bounds=((7e4,1.49,0,3.5e2),(2e5,1.51,1e6,3.501e2)))
+    print(popt)
+    #[r*1e-12 for r in ConvertToRabiSq(     ,2*np.pi*1.35e6)]
+    #PotsLong = np.arange(0*np.min(PotenciasIR[1:-1]/propor), np.max(PotenciasIR[1:-1]/propor),0.01)
+    PotsLong = np.arange(0*np.min(PotenciasIR[1:-1]/propor), np.max(PotenciasIR[1:-1]/propor),0.01)
+    #plt.plot([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotsLong,2*np.pi*1.35e6)],FuncTest(np.array(PotsLong),*popt),color=colorsselected[j], linewidth=0.9, zorder=5)
+    plt.plot([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotsLong,2*np.pi*1.35e6)],FuncTest(np.array(PotsLong),*popt),color=colorsselected[j], linestyle=(0,(5,1)), linewidth=0.9, zorder=5)
+    plt.plot([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotenciasIR[1:-1]/propor,2*np.pi*1.35e6)], cuentas[1:-1]/1000, 'o', color=colorsselected[j], markersize=1.5, alpha=0.9, label=fr"$B={int(round(Bfields[j],0))}({err[j]})$"+r" $\mathrm{mG}$",zorder=orden)
+    plt.vlines(x=((2*np.pi*1.35e6)**2)*(1e-12/(propor*(((2*np.pi)**2))))*PotenciasIR[1:-1][maximumfluo_index],ymin=0.2,ymax=cuentas[1:-1][maximumfluo_index]/1000,color=colorsselected[j],zorder=1,linewidth=1, linestyle='dotted')
+    plt.fill_between([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotenciasIR[1:-1]/propor,2*np.pi*1.35e6)], (cuentas[1:-1]-1*np.sqrt(cuentas)[1:-1])/1000, (cuentas[1:-1]+1*np.sqrt(cuentas)[1:-1])/1000, color=colorsselected[j], alpha=0.3, zorder=orden)
+    orden=orden-1
+    #plt.xlim(0,2)
+    #plt.errorbar(PotenciasIR, cuentas, yerr=1*np.sqrt(cuentas), color='r', fmt='o', capsize=2, markersize=4)
+    #plt.xlim(0, 90)    
+    #plt.ylim(-100,3500)
+
+#for pot in MaxsPotsExp/propor:
+#    plt.axvline(pot)
+
+plt.grid()
+plt.xlabel(r'$\Omega_{\mathrm{DP}}^2$ (MHz$^2$)', fontsize=12, fontname='STIXGeneral')
+#plt.xlabel(r'$\Gamma$', fontsize=12, fontname='STIXGeneral')
+plt.ylabel('Fluorescence (kcounts/s)', fontsize=12, fontname='STIXGeneral')
+plt.xticks([0, 1, 2, 3, 4], fontsize=12, fontname='STIXGeneral')
+plt.yticks([0,1,2,3], fontsize=12, fontname='STIXGeneral')
+plt.ylim(0,3.2)
+#plt.xlim(0,2.3)
+plt.tight_layout()
+plt.legend(loc='upper left', frameon=True, fontsize=7.6, handletextpad=0.1)
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2a_paralegend.pdf')
+
+
+#%%
+'''
+INTENTOS DE VER SI QUEDA MAS LINDA, PERO LA DEL ARXIV ES LA ANTERIOR
+Figura 2a) del paper. Curvas de fluorescencia vs potencia del IR
+    
+INTENTO GRAFICAR TOADS LAS MEDS PARA PASARLE AL PIBITO
+    
+'''
+
+
+from scipy.signal import savgol_filter as sf
+
+# FluoSel = IR_fluorescence_vec[0][1:]
+# PotsSel = PotenciasIR[1:]
+
+# popt, pcov = curve_fit(FuncTest, PotsSel, FluoSel,p0=(1000,500,0,100,0))
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+from scipy.signal import savgol_filter as sf
+import seaborn as sns
+
+plt.style.use('seaborn-ticks')
+
+colors=sns.color_palette("rocket", 10)
+
+colorsselected=[colors[0],colors[3],colors[5],colors[8]]
+
+colors2 = sns.color_palette("mako",10)
+
+FluovsBshort = []
+#jselected = [0, 1, 4, 8]
+
+#jselected = [3, 2, 1, 0]
+
+#jselected = [8,7,5,4,3,2,1,0]
+
+jselected = [8,7,5,4,2,1,0]
+
+Bcampos = [b*1e3 for b in Bvec] 
+Bfields = [Bcampos[6], Bcampos[2], Bcampos[1], Bcampos[0]]
+print('campos')
+print(Bcampos)
+for j in jselected:
+    FluovsBshort.append(IR_fluorescence_vec[j])
+
+#bkgr2 = np.min([FluovsBshort[0][1],FluovsBshort[1][1],FluovsBshort[2][1],FluovsBshort[3][1]])
+
+#bkgrposta = np.min()
+
+PotenciasMaximos_parcial = MaxsPotsExp/propor
+
+PotenciasMaximos = [PotenciasMaximos_parcial[4],PotenciasMaximos_parcial[2],PotenciasMaximos_parcial[1],PotenciasMaximos_parcial[0]]
+
+def FuncTest(rsq, A, det, delta, g):
+    c=1
+    return A*(((0.25*g*g+det*det+(2/3)*delta*delta)/(c*rsq))+6*c*rsq/(delta*delta))**(-1)
+
+
+smoothen_order = [3,3,3,1]
+err = [2,1,1,1]
+orden=5
+plt.figure(figsize=(3.5, 3))
+k=0
+for j in range(len(jselected)):
+    print(j)
+    rawcuentas = [f-bkgr for f in FluovsBshort[j]]
+    
+    if j==0:
+        print('tukson')
+        print(rawcuentas)
+        rawcuentas[34]=0.5*(rawcuentas[33]+rawcuentas[35])
+        rawcuentas[37]=0.5*(rawcuentas[36]+rawcuentas[38])
+    
+    try:
+        cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],13,smoothen_order[j]))) #este va bien
+    except:
+        j=0
+        cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],13,smoothen_order[j]))) #este va bien
+    #cuentas = np.array(rawcuentas[0:]) #pruebo sin smooth
+
+
+    #maximumfluo_index = np.array(cuentas[1:-1]).argmax()
+    maximumfluo_index = find_nearest(PotenciasIR[1:-1]/propor, PotenciasMaximos[j])
+    
+    popt, pcov = curve_fit(FuncTest, PotenciasIR[1:-1]/propor, cuentas[1:-1]/1000, p0=(1e5,1.5,1e-2,3.5e2), bounds=((7e4,1.49,0,3.5e2),(2e5,1.51,1e6,3.501e2)))
+    print(popt)
+    #[r*1e-12 for r in ConvertToRabiSq(     ,2*np.pi*1.35e6)]
+    #PotsLong = np.arange(0*np.min(PotenciasIR[1:-1]/propor), np.max(PotenciasIR[1:-1]/propor),0.01)
+    PotsLong = np.arange(0*np.min(PotenciasIR[1:-1]/propor), np.max(PotenciasIR[1:-1]/propor),0.01)
+    #plt.plot([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotsLong,2*np.pi*1.35e6)],FuncTest(np.array(PotsLong),*popt),color=colorsselected[j], linewidth=0.9, zorder=5)
+    plt.plot([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotsLong,2*np.pi*1.35e6)],FuncTest(np.array(PotsLong),*popt),color=colors2[k], linestyle=(0,(5,1)), linewidth=0.9, zorder=5)
+    plt.plot([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotenciasIR[1:-1]/propor,2*np.pi*1.35e6)], cuentas[1:-1]/1000, 'o', color=colors2[k], markersize=1.5, alpha=0.9, label=fr"$B={int(round(Bfields[j],0))}({err[j]})$"+r" $\mathrm{mG}$",zorder=orden)
+    #plt.vlines(x=((2*np.pi*1.35e6)**2)*(1e-12/(propor*(((2*np.pi)**2))))*PotenciasIR[1:-1][maximumfluo_index],ymin=0.2,ymax=cuentas[1:-1][maximumfluo_index]/1000,color=colorsselected[j],zorder=1,linewidth=1, linestyle='dotted')
+    plt.fill_between([(r/((2*np.pi)**2))*1e-12 for r in ConvertToRabiSq(PotenciasIR[1:-1]/propor,2*np.pi*1.35e6)], (cuentas[1:-1]-1*np.sqrt(cuentas)[1:-1])/1000, (cuentas[1:-1]+1*np.sqrt(cuentas)[1:-1])/1000, color=colors2[k], alpha=0.3, zorder=orden)
+    orden=orden-1
+    k=k+1
+    #plt.xlim(0,2)
+    #plt.errorbar(PotenciasIR, cuentas, yerr=1*np.sqrt(cuentas), color='r', fmt='o', capsize=2, markersize=4)
+    #plt.xlim(0, 90)    
+    #plt.ylim(-100,3500)
+
+#for pot in MaxsPotsExp/propor:
+#    plt.axvline(pot)
+
+plt.grid()
+plt.xlabel(r'$\Omega_{\mathrm{DP}}^2$ (MHz$^2$)', fontsize=12, fontname='STIXGeneral')
+#plt.xlabel(r'$\Gamma$', fontsize=12, fontname='STIXGeneral')
+plt.ylabel('Fluorescence (kcounts/s)', fontsize=12, fontname='STIXGeneral')
+plt.xticks([0, 1, 2, 3, 4], fontsize=12, fontname='STIXGeneral')
+plt.yticks([0,1,2,3], fontsize=12, fontname='STIXGeneral')
+#plt.ylim(0,3.2)
+#plt.xlim(0,2.3)
+plt.tight_layout()
+plt.legend(loc='upper left', frameon=True, fontsize=7.6, handletextpad=0.1)
+
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/allcurves.pdf')
+
+
+    
+
     

analisis/plots/20220615_CPTvariandocompensacion/CPT_plotter_20220615.py

@@ -204,12 +204,12 @@ CircPr = 1
 alpha = 0
 
 
-def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, TEMP):
+def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2):
 #def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
     #BETA = 1.8
     # SG = 0.6
     # SP = 8.1
-    # TEMP = 0.2e-3
+    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)
     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)
@@ -220,7 +220,7 @@ def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, TEMP):
     #return ScaledFluo1
 
 
-popt_2sp, pcov_2sp = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e4, 2.9e4, 1.34e3, 3.5, 0.1, 1e-3], bounds=((0, 0, 0, 0, 0, 0, 0, 0), (2, 10, 5e5, 5e5, 4e3, 10, 2, 15e-3)))
+popt_2sp, pcov_2sp = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e4, 2.9e4, 1.34e3, 3.5, 0.1], bounds=((0, 0, 0, 0, 0, 0, 0), (2, 10, 5e5, 5e5, 4e3, 10, 2)))
 #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,
@@ -246,6 +246,11 @@ 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_2sp = FitEIT_MM(freqslong, *popt_2sp)
+
+
 
 plt.figure()
 plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
@@ -361,6 +366,230 @@ popt_1sp, pcov_1sp = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[0.9, 6.2
        #1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
 
 
+#%%
+#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
+from scipy.optimize import curve_fit
+import time
+
+"""
+Y AHORA LO AJUSTO CON 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 = -1
+
+offsetxpi = 427+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
+
+FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[10]]
+CountsDR = Counts[10]
+
+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, SCALE3, SCALE4, OFFSET, BETA1, BETA2, BETA3, BETA4):
+#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)
+    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)
+    Detunings, Fluorescence4 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA4, 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])
+    ScaledFluo3 = np.array([f*SCALE3 + OFFSET for f in Fluorescence3])
+    ScaledFluo4 = np.array([f*SCALE4 + OFFSET for f in Fluorescence4])
+
+
+    return ScaledFluo1+ScaledFluo2+ScaledFluo3+ScaledFluo4
+    #return ScaledFluo1
+
+
+popt_2sp, pcov_2sp = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e4, 2.9e4, 1e3, 1e3, 1.34e3, 3.5, 1, 1, 2], bounds=((0, 0, 0, 0, 0, 0, 0,0,0,0,0), (2, 10, 5e5, 5e5,5e5,5e5, 4e3, 10, 5,5,5)))
+#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_2sp)
+#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
+
+beta1 = popt_2sp[5]
+beta2 = popt_2sp[6]
+
+errbeta1 = np.sqrt(pcov_2sp[5,5])
+errbeta2 = np.sqrt(pcov_2sp[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_2sp = FitEIT_MM(freqslong, *popt_2sp)
+
+
+
+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_2sp, 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.grid()
+
+
+#%%
+#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
+from scipy.optimize import curve_fit
+import time
+
+"""
+AJUSTO LA CPT CON N IONES A VER SI DA QUE N = 2
+
+WORK IN PROGRESS
+
+"""
+
+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 = -1
+
+offsetxpi = 427+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
+
+FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[10]]
+CountsDR = Counts[10]
+
+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, N, SCALEvec, BETAvec, OFFSET):
+#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
+    #BETA = 1.8
+    # SG = 0.6
+    # SP = 8.1
+    TEMP = 0.2e-3
+
+    FluoTotal = np.zeros(len(freqs))
+    for n in range(N):
+        Detunings, Fluorescence = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETAvec[n], drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+        FluoTotal = FluoTotal + SCALEvec[n]*np.array(Fluorescence)
+    return FluoTotal
+    #return ScaledFluo1
+
+
+popt_2sp, pcov_2sp = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3,], bounds=((0, 0, 0, 0, 0, 0, 0), (2, 10, 5e5, 5e5, 4e3, 10, 2)))
+#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_2sp)
+#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
+
+beta1 = popt_2sp[5]
+beta2 = popt_2sp[6]
+
+errbeta1 = np.sqrt(pcov_2sp[5,5])
+errbeta2 = np.sqrt(pcov_2sp[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_2sp = FitEIT_MM(freqslong, *popt_2sp)
+
+
+
+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_2sp, 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.grid()
+
+
+
 #%%
 FittedEITpi_1sp = FitEIT_MM_single(freqslong, *popt_1sp)
 #FittedEITpi_1sp = FitEIT_MM(freqslong, 0.9, 6.2, 4e4, 2.9e3, 2)

analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py

@@ -12,7 +12,7 @@ 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
+#from EITfit.MM_eightLevel_2repumps_python_scripts import CPTspectrum8levels
 import random
 from scipy.signal import savgol_filter as sf
 

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

@@ -89,7 +89,7 @@ def LtempCalculus(beta, drivefreq, forma=1):
     
     ampg=beta*drivefreq
     ampr=beta*drivefreq*(397/866)  
-    #print('fixed')
+    print('fixed')
     Hint[0,0] = ampg
     Hint[1,1] = ampg
     Hint[4,4] = ampr

analisis/plots/20230421_CPTconmicromocion/CPT_plotter_20230421.py

@@ -110,7 +110,7 @@ plt.errorbar([2*f*1e-6 for f in Freqs_merged], Counts_merged, yerr=np.sqrt(np.ar
 
 
 #%%
-#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels_MM, GenerateNoisyCPT_MM_fit
+from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels_MM, GenerateNoisyCPT_MM_fit
 from scipy.optimize import curve_fit
 
 """

analisis/plots/20230906_CPTconmicromocion2/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py

@@ -6,6 +6,9 @@ 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

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

@@ -6,6 +6,10 @@ 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
@@ -90,7 +94,6 @@ def LtempCalculus(beta, drivefreq, forma=1):
     ampg=beta*drivefreq
     ampr=beta*drivefreq*(397/866)        
     #ampr=beta*drivefreq
-    
     Hint[0,0] = ampg
     Hint[1,1] = ampg
     Hint[4,4] = ampr