dif en bvsk

analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.py

@@ -25,7 +25,7 @@ Calib_Files_IR = """000007808-IR_Saturation
 000007830-IR_Saturation
 000007831-IR_Saturation""" 
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data')
 
 #carpeta pc nico labo escritorio:
 #/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data
@@ -265,12 +265,21 @@ if plotFreqFreq == True:
 
 else:
     #plt.errorbar([b*1e3 for b in Bvec], MaxsPotsExp/propor, xerr=1e3*MeanError/(2*np.pi)/c, yerr=yerr0/propor, color=colores[0], fmt="o", markersize=4, zorder=3,  elinewidth=1)
-    plt.errorbar([f*1e-6 for f in ConvertBfieldtoLarmor(np.array([b*1e0 for b in Bvec]))], [c*1e-12/((2*np.pi)**2) for c in ConvertToRabiSq(np.array(MaxsPotsExp)/propor, 2*np.pi*1.35e6)], xerr=1e-6*MeanError, yerr=(1e-12/((2*np.pi)**2))*ConvertToRabiSq(yerr0, 2*np.pi*1.35e6)/propor, color=colores[0], fmt="o", markersize=4, zorder=3,  elinewidth=1)
-    plt.plot([f*1e-9 for f in ConvertBfieldtoLarmor(CamposVector2)], [r*1e-12/((2*np.pi)**2) for r in ConvertToRabiSq(RabiVector2,2*np.pi*1.35e6)], linestyle='dashed', linewidth=1., color='grey') #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica
+    plt.errorbar([(4/5)*f*1e-6 for f in ConvertBfieldtoLarmor(np.array([b*1e0 for b in Bvec]))], [c*1e-12/((2*np.pi)**2) for c in ConvertToRabiSq(np.array(MaxsPotsExp)/propor, 2*np.pi*1.35e6)], xerr=(4/5)*1e-6*MeanError, yerr=(1e-12/((2*np.pi)**2))*ConvertToRabiSq(yerr0, 2*np.pi*1.35e6)/propor, color=colores[0], fmt="o", markersize=4, zorder=3,  elinewidth=1)
+    plt.plot([(4/5)*f*1e-9 for f in ConvertBfieldtoLarmor(CamposVector2)], [r*1e-12/((2*np.pi)**2) for r in ConvertToRabiSq(RabiVector2,2*np.pi*1.35e6)], linestyle='dashed', linewidth=1., color='grey') #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica
     #plt.ylabel(r'Rabi Frequency Squared (MHz$^2$)', fontsize=12,  fontname='STIXGeneral')
+    #plt.plot([f*1e-9 for f in ConvertBfieldtoLarmor(CamposVector2)], [15*(r*1e-12/((2*np.pi)**2))/popt for r in ConvertToRabiSq(RabiVector2,2*np.pi*1.35e6)], linestyle='dashed', linewidth=1., color='grey') #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica #esto seria con pendiente 15 que seria lo que da la teoria con el factor de lande correspondiente
+
     plt.ylabel(r'$\Omega_{\mathrm{DP}}^2$ (MHz$^2$)', fontsize=12,  fontname='STIXGeneral')
 
 
+popt, pcov = curve_fit(LinearFitPotvsB, [(4/5)*f*1e-9 for f in ConvertBfieldtoLarmor(CamposVector2)], [r*1e-12/((2*np.pi)**2) for r in ConvertToRabiSq(RabiVector2,2*np.pi*1.35e6)])
+
+poptexp,pcovexp = curve_fit(LinearFitPotvsB, [(4/5)*f*1e-6 for f in ConvertBfieldtoLarmor(np.array([b*1e0 for b in Bvec]))], [c*1e-12/((2*np.pi)**2) for c in ConvertToRabiSq(np.array(MaxsPotsExp)/propor, 2*np.pi*1.35e6)])
+
+
+print(popt)
+print(poptexp)
 # plt.plot([f*1e-6 for f in ConvertBfieldtoLarmor(np.array([b*1e3 for b in Bvec]))], MaxsPotsExp,"o", color=colores[0], markersize=4, zorder=3)
 # plt.ylim(0,80)
 # plt.xlim(0,270)
@@ -278,17 +287,17 @@ else:
 
 
 
-plt.xlabel('Larmor Frequency (MHz)', fontsize=12, fontname='STIXGeneral')
+plt.xlabel(r'$\delta/2\pi $ (MHz)', fontsize=12, fontname='STIXGeneral')
 
-plt.xlim(0,0.27)
-plt.xticks([0, 0.05, 0.1, 0.15, 0.2, 0.25], fontsize=12,fontname='STIXGeneral')
+plt.xlim(0,(4/5)*0.27)
+plt.xticks([0, 0.05, 0.1, 0.15, 0.2], fontsize=12,fontname='STIXGeneral')
 plt.yticks([0, 1, 2, 3, 4], fontsize=12,fontname='STIXGeneral')
 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('C:/Users/nicon/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2b_v3.pdf')
 
 
 
@@ -297,6 +306,9 @@ plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenb
 Figura 2a) del paper. Curvas de fluorescencia vs potencia del IR
 '''
 
+'''
+Este es el modelo viejo que ajusta super lindo, lo dejo por las dudas
+'''
 
 from scipy.signal import savgol_filter as sf
 
@@ -384,9 +396,110 @@ 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.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2a_v2.pdf')
+
+#%%
+'''
+Figura 2a) del paper. Curvas de fluorescencia vs potencia del IR
+'''
+
+'''
+Este es el modelo NUEVO de ceci, veremos que onda
+'''
+
+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 = [8, 2, 1, 0]
+
+Bcampos = [b*1e3 for b in Bvec] 
+Bfields = [Bcampos[6], 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[6],PotenciasMaximos_parcial[2],PotenciasMaximos_parcial[1],PotenciasMaximos_parcial[0]]
+
+def FuncTest(rsq, A, det, delta, gs, gd):
+    c=1
+    gt = gs + gd
+    return A*((gd/gt)*((gt*gt+4*det*det+(8/3)*delta*delta)/(c*rsq))+1.5*c*rsq/(delta*delta) + 4*gs/gt)**(-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]]
+    #cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],7,3))) #este va bien
+    cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],13,smoothen_order[j]))) #este va bien
+    #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-2,2*np.pi*21,2*np.pi*1), bounds=((2e2,0,0,2*np.pi*20.5,2*np.pi*1),(2e7,10,1e8,2*np.pi*21.5,2*np.pi*1.1)))
+    #print(popt)
+    print(popt[1])
+    print(popt[2]/(2*np.pi))
+    
+    #[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.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(0,3.7)
+#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_v3.pdf')
 
 
+plt.savefig('C:/Users/nicon/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2a_v3.pdf')
 
     

analisis/plots/20221004_transitorios/Transitorios_01.py

@@ -105,7 +105,7 @@ plt.xlim(-0.1, 5)
 
 #%%  
 
-
+laser_UV_amp=0.1
 allamps = np.array([])
 allpows = np.array([])
 alltaus = np.array([])

analisis/plots/20221006_transitoriosv2/Transitorios_02.py

@@ -7,7 +7,7 @@ import ast
 from scipy.optimize import curve_fit
 import os
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221006_transitoriosv2')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221006_transitoriosv2')
 # Solo levanto algunos experimentos
 SP_files = [8445, 8457, 8458, 8459, 8460, 8461, 8462, 8463, 8464, 8465, 8466, 8467, 8468, 8469, 84840, 88040]
 SP_files_v2 = [8763, 8764, 8765, 8766, 8767, 8768, 8769, 8770, 8771, 8772, 8773, 8774, 8775, 8776]
@@ -19,6 +19,7 @@ Calib_files = ['Cal_DP_5M', 'Cal_SP_5M', 'Cal_DP_25M', 'Cal_SP_25M']
 Random_files = [8749]
 
 def expo(T, tau, N0, C):
+    
     global T0
     return N0*np.exp(-(T-T0)/tau) + C
 
@@ -184,7 +185,8 @@ from scipy.optimize import curve_fit
 RefBins = [t*1e6 for t in SP_Bins[0][:-1]]
 
 plt.figure()
-for Height in SP_Heigths:
+for Height in SP_Heigths[:-1]:
+    print(len(Height))
     plt.plot(RefBins, Height)
     
 plt.xlim(-0.2, 3)
@@ -234,6 +236,10 @@ for j in range(len(SP_Heigths)-1):
     ErrorTaus.append(np.sqrt(pcov)[0][0])
     
 #%%
+"""
+TESIS
+"""
+
 plt.figure()
 plt.plot(UVpotVec[:-5], Taus[:-6],'o', markersize=10, color='purple')
 #plt.errorbar(UVpotVec[:-5], Taus[:-6], yerr=1e1*np.array(ErrorTaus[:-6]), fmt='.', capsize=2, markersize=2)
@@ -245,12 +251,12 @@ plt.grid()
 
 #%%
 plt.figure()
-plt.plot(UVpotVec, Amps,'o')
+plt.plot(UVpotVec, Amps[:-1],'o')
 plt.xlabel('UV power (mW)')
 plt.ylabel('Characteristic time (us)')
 
 plt.figure()
-plt.plot(UVpotVec, Offsets,'o')
+plt.plot(UVpotVec, Offsets[:-1],'o')
 
 
 #%%
@@ -361,6 +367,10 @@ plt.figure()
 plt.plot(UVpotVec, Offsets_v2,'o')
 
 #%%
+"""
+TESIS
+"""
+
 """
 FIGURA PAPER
 """
@@ -409,7 +419,7 @@ plt.savefig('fig3_01.pdf')
 plt.savefig('fig3_01.svg')
 
 
-        #%%
+#%%
 
 """
 VEMOS UNA DP CON POTENCIA ALTA A VER SI DA LO QUE TIENE QUE DAR EL ANCHO DE LINEA

analisis/plots/20221017_IonStatistics/IonStatistics.py

@@ -10,7 +10,7 @@ import scipy.stats as sts
 import seaborn as sns
 
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221017_IonStatistics')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221017_IonStatistics')
 
 plt.rcParams.update({
     "font.family": "STIXGeneral"
@@ -79,6 +79,10 @@ for i, fname in enumerate(Stat_files[3:6]):
 
     
 #%%
+"""
+TESIS
+"""
+
 import matplotlib
 import seaborn as sns
 
@@ -113,26 +117,32 @@ bins3 = np.arange(30,100,1)
 FIGURA ESTADISTICA POISSON
 """
 
-plt.figure(figsize = (3.8,2.8))
-plt.hist(Stat_Heigths[0], bins=bins1, histtype='step',density = True,color = color1, alpha = 0.6)#,label = 'BG')
-plt.hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True,color = color2, alpha = 0.6)#,label = 'UV laser')
-plt.hist(Stat_Heigths[2], bins=bins3, histtype='step',density = True,color = color3, alpha = 0.6)#,label = 'Ion')
+fig,ax=plt.subplots(1,2,gridspec_kw={'width_ratios': [1, 1]},figsize=(6.5,3.))
+
+#plt.figure(figsize = (9,3.5))
+ax[1].hist(Stat_Heigths[0], bins=bins1, histtype='step',density = True,color = color1,linewidth=2, alpha = 0.6)#,label = 'BG')
+ax[1].hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True,color = color2,linewidth=2, alpha = 0.6)#,label = 'UV laser')
+ax[1].hist(Stat_Heigths[2], bins=bins3, histtype='step',density = True,color = color3,linewidth=2, alpha = 0.6)#,label = 'Ion')
 
 
 poisson1= sts.poisson.pmf(bins1,np.mean(Stat_Heigths[0]))
 poisson2= sts.poisson.pmf(bins2,np.mean(Stat_Heigths[1]))
 poisson3= sts.poisson.pmf(bins3,np.mean(Stat_Heigths[2]))
 
-plt.plot(bins2+0.5,poisson2,color = color2,label = 'Background')
-plt.plot(bins3+0.5,poisson3,color = color3,label = 'UV laser')
-plt.plot(bins1+0.5,poisson1,color = color1,label = 'Ion')
-plt.legend(loc=(0.15,0.65), prop={'size': 11})
-plt.grid()
-plt.tight_layout()
-plt.xticks([0, 100, 200, 300], fontname='STIXGeneral')
-plt.yticks([0,0.02, 0.04, 0.06, 0.08], fontname='STIXGeneral')
-plt.xlabel('Counts', fontname='STIXGeneral')
-plt.ylabel('Event frequency', fontname='STIXGeneral')
+ax[1].plot(bins2+0.5,poisson2,color = color2,label = 'Señal de fondo',linewidth=1.5)
+ax[1].plot(bins3+0.5,poisson3,color = color3,label = 'Láser UV',linewidth=1.5)
+ax[1].plot(bins1+0.5,poisson1,color = color1,label = 'Ion',linewidth=1.5)
+ax[1].legend(loc='upper right', prop={'size': 10})
+ax[1].grid()
+ax[1].set_xticks([0, 100, 200, 300])
+ax[1].set_xticklabels([0, 100, 200, 300], fontsize=10,fontname='STIXGeneral')
+ax[1].set_yticks([0,0.02, 0.04, 0.06, 0.08])
+ax[1].set_yticklabels([0,0.02, 0.04, 0.06, 0.08], fontsize=10,fontname='STIXGeneral')
+ax[1].set_xlabel('Cuentas', fontsize=10, fontname='STIXGeneral')
+ax[1].set_ylabel('Frecuencia de eventos', fontsize=10, fontname='STIXGeneral')
+
+#plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_poisson.pdf')
+
 
 #poissoneidad =  np.var(Stat_Heigths)/np.mean(Stat_Heigths)
 #plt.title('Varianza/media = {:.4f}'.format(poissoneidad))
@@ -140,39 +150,29 @@ plt.ylabel('Event frequency', fontname='STIXGeneral')
 
 name='fig01a'
 
-#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.pdf')
-#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg')
-
-
-
-#%%
-import matplotlib
-
-matplotlib.rcParams['mathtext.fontset'] = 'stix'
-matplotlib.rcParams['font.family'] = 'STIXGeneral'
-plt.style.use('seaborn-bright')
-plt.rcParams.update({
-    "text.usetex": False,
-})
-
 """
 FIGURA BACKGROUND, ENCENDIDO AOM Y ENCENDIDO ION
 """
 
-plt.figure(figsize=(4.5,3))
-plt.plot([s*1e6 for s in OnOff_Bins[1][:-1]],OnOff_Heights[1], color=color2)
-plt.plot([s*1e6 for s in OnOff_Bins[0][:-1]],[(OnOff_Heights[0][j]-OnOff_Heights[1][j])*3.13+OnOff_Heights[1][j] for j in range(len(OnOff_Heights[0]))], color=color3)
-plt.plot([s*1e6 for s in OnOff_Bins[2][:-1]],OnOff_Heights[2], color=color1)
-plt.xlim(0,1)
-plt.grid()
-plt.xlabel(r'Time ($\mu$s)', fontname='STIXGeneral')
-plt.ylabel(r'Counts /10$~\mu$s', fontname='STIXGeneral')
-plt.xticks([0,0.2, 0.4, 0.6, 0.8, 1], fontname='STIXGeneral')
-plt.yticks([0,5000, 10000], fontname='STIXGeneral')
+#plt.figure(figsize=(4.5,3))
+ax[0].plot([s*1e6 for s in OnOff_Bins[1][:-1]],OnOff_Heights[1], color=color2,linewidth=3)
+ax[0].plot([s*1e6 for s in OnOff_Bins[0][:-1]],[(OnOff_Heights[0][j]-OnOff_Heights[1][j])*3.13+OnOff_Heights[1][j] for j in range(len(OnOff_Heights[0]))], color=color3,linewidth=3)
+ax[0].plot([s*1e6 for s in OnOff_Bins[2][:-1]],OnOff_Heights[2], color=color1,linewidth=3)
+ax[0].set_xlim(0,1)
+ax[0].grid()
+ax[0].set_xlabel(r'Tiempo ($\mu$s)', fontname='STIXGeneral', fontsize=10)
+ax[0].set_ylabel(r'Cuentas /10$~\mu$s', fontname='STIXGeneral', fontsize=10)
+ax[0].set_xticks([0,0.2, 0.4, 0.6, 0.8, 1])
+ax[0].set_yticks([0,5000, 10000])
+ax[0].set_xticklabels([0,0.2, 0.4, 0.6, 0.8, 1], fontname='STIXGeneral', fontsize=10)
+ax[0].set_yticklabels([0,5000, 10000], fontname='STIXGeneral', fontsize=10)
+
 plt.tight_layout()
 
 name='fig01b'
 
+plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_statistics.pdf')
+
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.pdf')
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'_dump.svg')
 

analisis/plots/20221017_IonStatistics/Simulaciones/plot_decaytimes_vs_intensity_detunings.py

@@ -23,15 +23,15 @@ for det in detuning_lines:
 for cambio in [0]: # esto es para shiftear la potencia medida
     potencias = dataREAL.Pow - cambio
     for rad in [85.7]: # esto evalua con distintos radios
-        ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus, yerr=np.mean(1e6*errsSIM.stdval.values),
+        ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus[:-1], yerr=np.mean(1e6*errsSIM.stdval.values),
                     fmt='.--', ms=6, lw=.5, \
                     color="#3949ab",
                     capsize=3)
 
-        ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus_v2, yerr=np.mean(1e6*errsSIM.stdval.values),
-                     fmt='.--', ms=6, lw=.5, \
-                     color="#3949ab",
-                     capsize=3)
+        # ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus_v2[:-1], yerr=np.mean(1e6*errsSIM.stdval.values),
+        #              fmt='.--', ms=6, lw=.5, \
+        #              color="#3949ab",
+        #              capsize=3)
     
     
         # ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \

analisis/plots/20221024_BranchingFractionMeasurement/Transitorios_BF.py

@@ -7,7 +7,7 @@ import ast
 from scipy.optimize import curve_fit
 import os
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221024_BranchingFractionMeasurement')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221024_BranchingFractionMeasurement')
 
 Long_files = ['DP_long', 'SP_long']
 Calib_files = ['Fondo_IR_50M', 'Fondo_UV_50M']
@@ -183,6 +183,10 @@ plt.plot(Long_Bins[1][:-1], SP_corrected)
 plt.plot(Long_Bins[0][:-1], DP_corrected)
 
 #%%
+"""
+TESIS
+"""
+
 """
 Figura branching
 """
@@ -212,24 +216,24 @@ bins2 = np.arange(0,50,1)
 bins3 = np.arange(30,100,1)
 
 
-plt.figure(figsize = (3.8,3))
+plt.figure(figsize = (4.5,3.5))
 
-plt.plot([1e6*b-0.18 for b in Long_Bins[1][:-1]], SP_corrected, color=(0,0,128/255, 1),linewidth=2.5,label='SP transition')
-plt.plot([1e6*b+0.09 for b in Long_Bins[0][:-1]], DP_corrected, color=(212/255,0,0, 1),linewidth=2.5,label='DP transition')
+plt.plot([1e6*b-0.18 for b in Long_Bins[1][:-1]], SP_corrected, color=(0,0,128/255, 1),linewidth=2.5,label='Transición SP')
+plt.plot([1e6*b+0.09 for b in Long_Bins[0][:-1]], DP_corrected, color=(212/255,0,0, 1),linewidth=2.5,label='Transición DP')
 
 #plt.hist(Stat_Heigths[0], bins=bins1, histtype='step',density = True,color = color1, alpha = 0.6)#,label = 'BG')
 #plt.hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True,color = color2, alpha = 0.6)#,label = 'UV laser')
 #plt.hist(Stat_Heigths[2], bins=bins3, histtype='step',density = True,color = color3, alpha = 0.6)#,label = 'Ion')
 
-#plt.legend(loc=(0.15,0.65), prop={'size': 11})
-plt.legend()
+plt.legend(loc='upper right', prop={'family':'STIXgeneral','size': 10})
+#plt.legend()
 plt.grid()
 plt.tight_layout()
 plt.xlim(-0.3, 2)
-plt.xticks([0, 1, 2], fontname='STIXGeneral')
-plt.yticks([0,5000, 10000, 15000], fontname='STIXGeneral')
-plt.xlabel('Time (us)', fontname='STIXGeneral')
-plt.ylabel('Counts', fontname='STIXGeneral')
+plt.xticks([0, 0.5, 1, 1.5, 2],fontsize=10, fontname='STIXGeneral')
+plt.yticks([0,5000, 10000, 15000],fontsize=10, fontname='STIXGeneral')
+plt.xlabel(r'Tiempo ($\mu$s)', fontsize=10,fontname='STIXGeneral')
+plt.ylabel('Cuentas', fontsize=10, fontname='STIXGeneral')
 
 #poissoneidad =  np.var(Stat_Heigths)/np.mean(Stat_Heigths)
 #plt.title('Varianza/media = {:.4f}'.format(poissoneidad))
@@ -237,8 +241,10 @@ plt.ylabel('Counts', fontname='STIXGeneral')
 
 name='fig02a'
 
-plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.pdf')
-plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg')
+plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_SP_DP.pdf')
+
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.pdf')
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg')
 
 
 #%%

analisis/plots/20230523_transitorioshighpower/Transitorios_03.py

@@ -7,7 +7,7 @@ import ast
 from scipy.optimize import curve_fit
 import os
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230523_transitorioshighpower/')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230523_transitorioshighpower/')
 # Solo levanto algunos experimentos
 SP_files = [12221, 12222, 12223, 12224]
 

analisis/plots/20230912_RotationalDopplerShift_v7/teoryanalysis.py

+# -*- coding: utf-8 -*-
+"""
+Created on Wed Oct  4 12:24:54 2023
+
+@author: nicon
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def RDE(r,l,v):
+    return 2*l*v/r
+
+def OAM(r,w,amp):
+    width=2/(amp*np.sqrt(2*np.pi))
+    return amp*np.exp(-((r-w)**2)/(2*width**2))
+
+
+r=np.arange(0,3,0.001)
+
+w1 = 1
+amp1 = 2*np.pi/w1
+w2 = 1.5
+amp2=2*np.pi/w2
+
+plt.figure()
+plt.plot(r,1*OAM(r,w1,amp1))
+plt.plot(r,1*OAM(r,w2,amp2))
+#%%
+plt.figure()
+plt.plot(r,OAM(r,w1,amp1)/RDE(r,2,2))
+plt.plot(r,OAM(r,w2,amp2)/RDE(r,2,2))
+
+
+#%%
+plt.figure()
+plt.plot(r/w1,(OAM(r,w1,amp1)/RDE(r,2,2)))
+plt.plot(r/w2,(OAM(r,w2,amp2)/RDE(r,2,2)))
+
+
+#%%
+from math import factorial as fac
+
+def LGbeam(r,l,w,P):
+    E = np.sqrt(4*P/(w**2))
+    return E*np.sqrt(2/(np.pi*(fac(np.abs(l)))))*(1/1)*(((np.sqrt(2)*r)/(w))**np.abs(l))*np.exp(-((r**2)/(w**2)))
+
+def RDE(r,l,v,w):
+    #return 2*l*v*(1/r)
+    return 2*l*v*(r**2.21)    #con una exponencial se chotea
+    #return 2*l*v
+
+    
+r = np.arange(0.01,200,0.01)
+
+w1 = 40
+w2 = 80
+
+P = 1e50
+
+l=2
+
+vel = 1e1
+
+# plt.figure()
+# plt.plot(r,(LGbeam(r,l,w1,P))**2)
+# plt.plot(r,(LGbeam(r,l,w2,P))**2)
+# plt.title('Intensity of two LG beams with different waists')
+
+
+# plt.figure()
+# plt.plot(r,((LGbeam(r,l,w1,P))**2)/RDE(r,l,vel,w1))
+# plt.plot(r,((LGbeam(r,l,w2,P))**2)/RDE(r,l,vel,w2))
+# plt.title('Ratio between the LG intensity and the RDE term')
+
+
+LG1 = (((LGbeam(r,l,w1,P))**2)/RDE(r,l,vel,w1))
+LG2 = (((LGbeam(r,l,w2,P))**2)/RDE(r,l,vel,w2))
+
+fact = (np.max(LG1)/np.max(LG2))
+
+
+plt.figure()
+plt.plot(r/w1,LG1)
+plt.plot(r/w2,LG2*fact)
+plt.title('Scaled ratio between LG and RDE, x axis scaled with LG waist')
+
+
+#%%
+def ShiftFull(r,l,vz,vr,vphi,w,wl,z):
+    k = 2*np.pi/wl
+    zr = 0.5*k*w*w
+    deltaz = (k + (k*r*r/(2*(z*z +zr*zr))*((2*z*z)/(z*z + zr*zr) - 1) - (l+1)*zr/(z*z+zr*zr)))*vz
+    deltar = (k*r*z/(z*z+zr*zr))*vr
+    deltaphi = (l/r)*vphi
+    return deltaz+deltar+deltaphi
+
+
+r = np.arange(0.0001,200,0.01)
+
+w1 = 40
+w2 = 60
+
+wl = 0.866
+z=1
+
+P = 1e15
+
+l=2
+
+velz = 1e10*0
+velr = 1e10*0
+velphi = 1e10
+
+
+# plt.figure()
+# plt.plot(r,(LGbeam(r,2,w1,1))**2)
+# plt.plot(r,(LGbeam(r,2,w2,1))**2)
+# plt.title('Intensity of two LG beams with different waists')
+
+
+# plt.figure()
+# plt.plot(r,((LGbeam(r,2,w1,1))**2)/RDE(r,2,2))
+# plt.plot(r,((LGbeam(r,2,w2,1))**2)/RDE(r,2,2))
+# plt.title('Ratio between the LG intensity and the RDE term')
+
+
+LG1 = (((LGbeam(r,l,w1,P))**2)/ShiftFull(r,2,velz,velr,velphi,w1,wl,z))
+LG2 = (((LGbeam(r,l,w2,P))**2)/ShiftFull(r,2,velz,velr,velphi,w2,wl,z))
+
+fact = (np.max(LG1)/np.max(LG2))
+
+plt.figure()
+plt.plot(r/w1,LG1)
+plt.plot(r/w2,LG2*fact)
+plt.title('Scaled ratio between LG and RDE, x axis scaled with LG waist')
+
+
+
+
+
+
+
+