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artiq_experiments
Commit 19fbd62a authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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todo dd la compu

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analisis/plots/20220526_CPTvariandoB_v2/CPT_plotter_20220520.py
View file @ 19fbd62a
...@@ -469,6 +469,8 @@ ErrorDetuningfits = [np.sqrt(pcov_12)[3,3], np.sqrt(pcov_13)[3,3],np.sqrt(pcov_1 ...@@ -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]] 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]] 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(Detuningsfits)
print(PotIRfits) print(PotIRfits)
print(PotUVfits) print(PotUVfits)
...@@ -522,6 +524,17 @@ plt.plot(freqslongpi_15, FittedEITpi_15) ...@@ -522,6 +524,17 @@ plt.plot(freqslongpi_15, FittedEITpi_15)
#%% #%%
#filtro y mejoro el plot #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 from scipy.signal import savgol_filter as sf
import seaborn as sns import seaborn as sns
...@@ -548,10 +561,9 @@ FiltCounts15 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_15, winl, po)] ...@@ -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)]) ErrorCounts15 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_15, winl, po)])
#plot con escalas atomicas y lindo #plot con escalas atomicas y lindo
colors=sns.color_palette("rocket", 10) colors=sns.color_palette("mako", 10)
color1=colors[1] color1=colors[1]
color2=colors[2] color2=colors[2]
...@@ -562,10 +574,10 @@ ms = 4 ...@@ -562,10 +574,10 @@ ms = 4
plt.figure(figsize=(3.5, 3)) 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_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=r'$S_{rep}=6.31$', 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=r'$S_{rep}=6.31$', 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=r'$S_{rep}=6.31$', 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_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) 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 ...@@ -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.fill_between(FreqsDRpi_15, FiltCounts15+ErrorCounts15, FiltCounts15-ErrorCounts15, color=color4, alpha=0.2)
plt.xlim(-40,30) plt.xlim(-40,30)
plt.xlabel('Repump detuning (MHz)', fontsize=11) plt.ylim(-0.1,1.4)
plt.ylabel('Normalized fluorescence', fontsize=11) 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.grid()
plt.tight_layout() 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/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 from scipy.optimize import curve_fit
...@@ -611,8 +633,8 @@ IFit = [-1.2, -1.5, -1.75, -2] ...@@ -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 = [popt_12[0], popt_13[0], popt_14[0], popt_15[0]]
#LarmorFit = [287282.374931893, 203998.09641511342, 158507.0255951109] por si fallan los ajustes #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])]) 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 MeanError = 11139.353180216529 #por si fallan los ajustes
Ilong = np.arange(2, -3, -0.01) Ilong = np.arange(2, -3, -0.01)
popt_larmor, pcov_larmor = curve_fit(LinearLarmortoCurrent, IFit, LarmorFit) popt_larmor, pcov_larmor = curve_fit(LinearLarmortoCurrent, IFit, LarmorFit)
...@@ -625,14 +647,27 @@ plt.plot(IFit, LarmorFit, 'o', markersize=5) ...@@ -625,14 +647,27 @@ plt.plot(IFit, LarmorFit, 'o', markersize=5)
plt.plot(Ilong, LarmorLong) plt.plot(Ilong, LarmorLong)
Bfitted = [ConvertLarmortoBfield(u) for u in LarmorFit] Bfitted = [ConvertLarmortoBfield(u) for u in LarmorFit]
ErrorBfitted = [ConvertLarmortoBfield(u) for u in ErrorLarmorFit]
BLong = [ConvertLarmortoBfield(u) for u in LarmorLong] 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.figure()
plt.plot(Ilong, BLong) plt.plot(Ilong, [b*1e3 for b in BLong])
plt.plot(IFit, Bfitted, 'o', markersize=8) 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.xlabel('Corriente (A)')
plt.ylabel('Campo magnetico (G)') plt.ylabel('Campo magnetico (mG)')
plt.grid() 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()
analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.py
View file @ 19fbd62a
...@@ -163,6 +163,16 @@ def ConvertLarmortoBfield(u): ...@@ -163,6 +163,16 @@ def ConvertLarmortoBfield(u):
c = 1398190.0452488689 c = 1398190.0452488689
return u/(2*np.pi)/c return u/(2*np.pi)/c
def ConvertBfieldtoLarmor(B):
c = 1398190.0452488689
return B*(2*np.pi)*c
def ConvertToRabi(kk,gamma):
return np.sqrt(kk)*gamma
def ConvertToRabiSq(kk,gamma):
return kk*(gamma**2)
c = 1398190 c = 1398190
FluovsB = [] FluovsB = []
...@@ -173,14 +183,14 @@ Bvec = [ConvertLarmortoBfield(u) for u in LinearLarmortoCurrentCalibrated(np.arr ...@@ -173,14 +183,14 @@ Bvec = [ConvertLarmortoBfield(u) for u in LinearLarmortoCurrentCalibrated(np.arr
for j in jselected: for j in jselected:
FluovsB.append(IR_fluorescence_vec[j]) FluovsB.append(IR_fluorescence_vec[j])
"""
plt.figure() # plt.figure()
for j in range(len(jselected)): # for j in range(len(jselected)):
plt.plot(PotenciasIR, sf([f-bkgr for f in FluovsB[j]], 13, 3), 'o') # plt.plot(PotenciasIR, sf([f-bkgr for f in FluovsB[j]], 13, 3), 'o')
plt.xlim(0, 90) # plt.xlim(0, 90)
plt.xlabel('Potencia IR (uW)') # plt.xlabel('Potencia IR (uW)')
plt.ylabel('Cuentas') # plt.ylabel('Cuentas')
"""
MaxsFluosExp = [] MaxsFluosExp = []
MaxsPotsExp = [] MaxsPotsExp = []
...@@ -201,13 +211,13 @@ for k in range(len(jselected)): ...@@ -201,13 +211,13 @@ for k in range(len(jselected)):
MaxsFluosExp.append(0) MaxsFluosExp.append(0)
MaxsPotsExp.append(0) MaxsPotsExp.append(0)
"""
plt.figure() # plt.figure()
plt.plot(Ivec, MaxsPotsExp,'rx') # plt.plot(Ivec, MaxsPotsExp,'rx')
plt.xlabel('Corriente bobina (A)') # plt.xlabel('Corriente bobina (A)')
plt.ylabel('Potencia umbral (mW)') # plt.ylabel('Potencia umbral (mW)')
plt.grid() # plt.grid()
"""
#longBvec = np.arange(np.min(Bvec), np.max(Bvec), 0.1*Bvec[1]-0.1*Bvec[0]) #longBvec = np.arange(np.min(Bvec), np.max(Bvec), 0.1*Bvec[1]-0.1*Bvec[0])
longBvec = np.arange(-0.005, 0.033, 0.01*Bvec[1]-0.01*Bvec[0]) longBvec = np.arange(-0.005, 0.033, 0.01*Bvec[1]-0.01*Bvec[0])
...@@ -226,38 +236,75 @@ print(popt_expvspot) ...@@ -226,38 +236,75 @@ print(popt_expvspot)
#%% #%%
''' '''
Figura paper. Umbral vs campo magnetico con la calibracion y la teoria superpuesta (la teoria sale de threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica) Figura 2b) del paper. Umbral vs campo magnetico con la calibracion y la teoria superpuesta (la teoria sale de threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica)
''' '''
propor = LinearFitPotvsB(longBvec, *popt_expvspot)[-1]/RabiVector[0][-1] #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica de la carpeta Work (no de Papers) def LinearFitPotvsB(b, pendiente):
#ordenada=0
return pendiente*b
#CamposVector2 = np.loadtxt('CamposVector.txt')
#RabiVector2 = np.loadtxt('RabiVector.txt')
propor = LinearFitPotvsB(longBvec, *popt_expvspot)[-1]/RabiVector2[-1] #esto lo cargo con las lineas de antes
#propor = LinearFitPotvsB(longBvec, *popt_expvspot)[-1]/RabiVector[0][-1] #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica de la carpeta Work (no de Papers)
#colores=sns.color_palette("mako") #
colores=sns.color_palette("rocket") colores=sns.color_palette("rocket")
MeanError = 11139.353180216529
plotFreqFreq = False
plt.figure(figsize=(3.5, 3)) plt.figure(figsize=(3.5, 3))
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.plot([0],[0],'o', markersize=5) if plotFreqFreq == True:
#plt.plot([b*1e3 for b in longBvec], LinearFitPotvsB(longBvec, *popt_expvspot), linewidth=1., color=colores[3]) plt.errorbar([f*1e-6 for f in ConvertBfieldtoLarmor(np.array([b*1e3 for b in Bvec]))], [c*1e-6 for c in ConvertToRabi(MaxsPotsExp/propor, 2*np.pi*1.35e6)], xerr=1e-3*MeanError, yerr=1e-6*ConvertToRabi(yerr0, 2*np.pi*1.35e6)/propor, color=colores[0], fmt="o", markersize=4, zorder=3, elinewidth=1)
plt.plot(CamposVector[0], RabiVector[0], linewidth=1., color=colores[3]) #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica plt.plot([f*1e-6 for f in ConvertBfieldtoLarmor(CamposVector2)], [c*1e-6 for c in ConvertToRabi(RabiVector2,2*np.pi*1.35e6)], linewidth=1., color=colores[3]) #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica
plt.xlabel('Magnetic field (mG)', fontsize=12, fontname='STIXGeneral') plt.ylabel(r'Rabi Frequency (MHz)', fontsize=12, fontname='STIXGeneral')
#plt.ylabel('Threshold power (mW)', fontsize=11, fontname='STIXGeneral')
plt.ylabel(r'$\left(\Omega_{DP}/\Gamma_{\mathrm{DP}}\right)^2$', fontsize=12, fontname='STIXGeneral') else:
plt.xticks([0,5,10,15,20,25,30], fontsize=12, fontname='STIXGeneral') #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.yticks([0.,0.5,1.,1.5,2.], fontsize=12, fontname='STIXGeneral') 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.xlim(0,32) 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.ylim(0,81) #plt.ylabel(r'Rabi Frequency Squared (MHz$^2$)', fontsize=12, fontname='STIXGeneral')
plt.ylabel(r'$\Omega_{\mathrm{DP}}^2$ (MHz$^2$)', fontsize=12, fontname='STIXGeneral')
# 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)
#plt.plot([f*1e-6 for f in ConvertBfieldtoLarmor(CamposVector2)], [c*1e-6 for c in ConvertToRabi(RabiVector2,2*np.pi*1.35e6)], linewidth=1., color=colores[3]) #esto viene del threeLevel_2repumps_CPTPlotter.py de Figura CPT Teorica
plt.xlabel('Larmor Frequency (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.yticks([0, 1, 2, 3, 4], fontsize=12,fontname='STIXGeneral')
plt.ylim(0,4.1)
plt.grid() plt.grid()
plt.tight_layout() 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/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/thresholdvsB_exp.pdf') plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2b_v2.pdf')
#%% #%%
''' '''
Figura paper. Curvas de fluorescencia vs potencia del IR 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): def find_nearest(array, value):
array = np.asarray(array) array = np.asarray(array)
idx = (np.abs(array - value)).argmin() idx = (np.abs(array - value)).argmin()
...@@ -291,6 +338,11 @@ PotenciasMaximos_parcial = MaxsPotsExp/propor ...@@ -291,6 +338,11 @@ PotenciasMaximos_parcial = MaxsPotsExp/propor
PotenciasMaximos = [PotenciasMaximos_parcial[6],PotenciasMaximos_parcial[2],PotenciasMaximos_parcial[1],PotenciasMaximos_parcial[0]] PotenciasMaximos = [PotenciasMaximos_parcial[6],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] smoothen_order = [3,3,3,1]
err = [2,1,1,1] err = [2,1,1,1]
orden=5 orden=5
...@@ -302,9 +354,17 @@ for j in range(len(jselected)): ...@@ -302,9 +354,17 @@ for j in range(len(jselected)):
cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],13,smoothen_order[j]))) #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 = np.array(cuentas[1:-1]).argmax()
maximumfluo_index = find_nearest(PotenciasIR[1:-1]/propor, PotenciasMaximos[j]) maximumfluo_index = find_nearest(PotenciasIR[1:-1]/propor, PotenciasMaximos[j])
plt.plot(PotenciasIR[1:-1]/propor, cuentas[1:-1]/1000, 'o', color=colorsselected[j], markersize=3.5, label=f"$B={int(round(Bfields[j],0))}({err[j]})$ mG",zorder=orden)
plt.vlines(x=(1/propor)*PotenciasIR[1:-1][maximumfluo_index],ymin=0,ymax=cuentas[1:-1][maximumfluo_index]/1000,color=colorsselected[j],zorder=1,linewidth=2) 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)))
plt.fill_between(PotenciasIR[1:-1]/propor, (cuentas[1:-1]-1*np.sqrt(cuentas)[1:-1])/1000, (cuentas[1:-1]+1*np.sqrt(cuentas)[1:-1])/1000, color=colorsselected[j], alpha=0.4, zorder=orden) 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 orden=orden-1
#plt.errorbar(PotenciasIR, cuentas, yerr=1*np.sqrt(cuentas), color='r', fmt='o', capsize=2, markersize=4) #plt.errorbar(PotenciasIR, cuentas, yerr=1*np.sqrt(cuentas), color='r', fmt='o', capsize=2, markersize=4)
...@@ -315,15 +375,16 @@ for j in range(len(jselected)): ...@@ -315,15 +375,16 @@ for j in range(len(jselected)):
# plt.axvline(pot) # plt.axvline(pot)
plt.grid() plt.grid()
plt.xlabel(r'Laser intensity $\left(\Omega_{\mathrm{DP}}/\Gamma_{\mathrm{DP}}\right)^2$', fontsize=12, fontname='STIXGeneral') plt.xlabel(r'$\Omega_{\mathrm{DP}}^2$ (MHz$^2$)', fontsize=12, fontname='STIXGeneral')
#plt.xlabel(r'$\Gamma$', fontsize=12, fontname='STIXGeneral') #plt.xlabel(r'$\Gamma$', fontsize=12, fontname='STIXGeneral')
plt.ylabel('Fluorescence (kcounts/s)', fontsize=12, fontname='STIXGeneral') plt.ylabel('Fluorescence (kcounts/s)', fontsize=12, fontname='STIXGeneral')
plt.xticks([0.,0.5,1.,1.5,2.], 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.yticks([0,1,2,3], fontsize=12, fontname='STIXGeneral')
plt.ylim(0,3.7) plt.ylim(0,3.7)
#plt.xlim(0,2.3)
plt.tight_layout() plt.tight_layout()
plt.legend(loc='upper left', frameon=True, fontsize=7, handletextpad=0.1) #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/Measurement_thresholds_v2.pdf') plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig2a_v2.pdf')
......
analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/CamposVector.txt 0 → 100644
View file @ 19fbd62a
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analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/RabiVector.txt 0 → 100644
View file @ 19fbd62a
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analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/untitled0.py
View file @ 19fbd62a
...@@ -8,11 +8,12 @@ Created on Thu Aug 31 16:18:18 2023 ...@@ -8,11 +8,12 @@ Created on Thu Aug 31 16:18:18 2023
from scipy.special import jv from scipy.special import jv
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import seaborn as sns
def CPTMicromotionSpectra(det, A, beta, x0, x1): def CPTMicromotionSpectra(det, A, beta, x0, x1):
ftrap=22.1 ftrap=22.1
gamma=2 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)+10-0.5*det 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 i = 1
#print(P) #print(P)
while i <= 10: while i <= 10:
...@@ -25,12 +26,15 @@ def CPTMicromotionSpectra(det, A, beta, x0, x1): ...@@ -25,12 +26,15 @@ def CPTMicromotionSpectra(det, A, beta, x0, x1):
detvec = np.arange(-50,50,0.1) detvec = np.arange(-50,50,0.1)
A=1 A=1
betavec=[0,0.5,1,1.5] betavec=[0,1.2]
x0=0 x0=-5
x1 = 10 x1 = 5
ii=0
plt.figure() plt.figure()
for beta in betavec: 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.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
View file @ 19fbd62a
...@@ -8,7 +8,6 @@ from scipy.optimize import curve_fit ...@@ -8,7 +8,6 @@ from scipy.optimize import curve_fit
import os import os
from scipy import interpolate from scipy import interpolate
#Mediciones de CPT para campo magnetico bajo y terrestre
#/home/nico/Documents/artiq_experiments/analisis/plots/20220615_CPTvariandocompensacion/Data #/home/nico/Documents/artiq_experiments/analisis/plots/20220615_CPTvariandocompensacion/Data
...@@ -144,9 +143,9 @@ DR = [403.5,412.5,418,426.5] ...@@ -144,9 +143,9 @@ DR = [403.5,412.5,418,426.5]
for dr in DR: for dr in DR:
plt.axvline(dr,color='blue',linestyle='--',alpha=0.3) 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-22,color='red',linestyle='--')
#plt.axvline(dr+44,color='red',linestyle='--') plt.axvline(dr+44,color='red',linestyle='--')
#plt.legend() #plt.legend()
...@@ -188,15 +187,24 @@ CountsDR = Counts[10] ...@@ -188,15 +187,24 @@ CountsDR = Counts[10]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0])) 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) FittedEITpi = FitEIT_MM(freqslong, *popt)
plt.figure() plt.figure()
......
analisis/plots/20220615_CPTvariandocompensacion/Data/CamposVector.txt 0 → 100644
View file @ 19fbd62a
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analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py
View file @ 19fbd62a
...@@ -88,8 +88,8 @@ def LtempCalculus(beta, drivefreq, forma=1): ...@@ -88,8 +88,8 @@ def LtempCalculus(beta, drivefreq, forma=1):
Hint = np.zeros((8, 8), dtype=np.complex_) Hint = np.zeros((8, 8), dtype=np.complex_)
ampg=beta*drivefreq ampg=beta*drivefreq
ampr=beta*drivefreq ampr=beta*drivefreq*(397/866)
#print('fixed')
Hint[0,0] = ampg Hint[0,0] = ampg
Hint[1,1] = ampg Hint[1,1] = ampg
Hint[4,4] = ampr 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 ...@@ -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) L0 = np.array(np.matrix(Lfullpartial) + M)
#ESTA PARTE ES CUANDO AGREGAS MICROMOCION #ESTA PARTE ES CUANDO AGREGAS MICROMOCION
nmax = 7 nmax = 3
#print(nmax) #print(nmax)
Ltemp, Omega = LtempCalculus(beta, drivefreq) Ltemp, Omega = LtempCalculus(beta, drivefreq)
#print(factor) #print(factor)
......
analisis/plots/20220615_CPTvariandocompensacion/Data/RabiVector.txt 0 → 100644
View file @ 19fbd62a
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