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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

parent 03827e15
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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
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()
analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.py
View file @ 19fbd62a
......@@ -163,6 +163,16 @@ def ConvertLarmortoBfield(u):
c = 1398190.0452488689
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
FluovsB = []
......@@ -173,14 +183,14 @@ Bvec = [ConvertLarmortoBfield(u) for u in LinearLarmortoCurrentCalibrated(np.arr
for j in jselected:
FluovsB.append(IR_fluorescence_vec[j])
"""
plt.figure()
for j in range(len(jselected)):
plt.plot(PotenciasIR, sf([f-bkgr for f in FluovsB[j]], 13, 3), 'o')
plt.xlim(0, 90)
plt.xlabel('Potencia IR (uW)')
plt.ylabel('Cuentas')
"""
# plt.figure()
# for j in range(len(jselected)):
# plt.plot(PotenciasIR, sf([f-bkgr for f in FluovsB[j]], 13, 3), 'o')
# plt.xlim(0, 90)
# plt.xlabel('Potencia IR (uW)')
# plt.ylabel('Cuentas')
MaxsFluosExp = []
MaxsPotsExp = []
......@@ -201,13 +211,13 @@ for k in range(len(jselected)):
MaxsFluosExp.append(0)
MaxsPotsExp.append(0)
"""
plt.figure()
plt.plot(Ivec, MaxsPotsExp,'rx')
plt.xlabel('Corriente bobina (A)')
plt.ylabel('Potencia umbral (mW)')
plt.grid()
"""
# plt.figure()
# plt.plot(Ivec, MaxsPotsExp,'rx')
# plt.xlabel('Corriente bobina (A)')
# plt.ylabel('Potencia umbral (mW)')
# plt.grid()
#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])
......@@ -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
#colores=sns.color_palette("mako")
#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("rocket")
MeanError = 11139.353180216529
plotFreqFreq = False
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)
#plt.plot([b*1e3 for b in longBvec], LinearFitPotvsB(longBvec, *popt_expvspot), linewidth=1., color=colores[3])
plt.plot(CamposVector[0], RabiVector[0], 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('Threshold power (mW)', fontsize=11, fontname='STIXGeneral')
plt.ylabel(r'$\left(\Omega_{DP}/\Gamma_{\mathrm{DP}}\right)^2$', fontsize=12, fontname='STIXGeneral')
plt.xticks([0,5,10,15,20,25,30], fontsize=12, fontname='STIXGeneral')
plt.yticks([0.,0.5,1.,1.5,2.], fontsize=12, fontname='STIXGeneral')
plt.xlim(0,32)
#plt.ylim(0,81)
if plotFreqFreq == True:
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([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.ylabel(r'Rabi Frequency (MHz)', fontsize=12, fontname='STIXGeneral')
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.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.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/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):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
......@@ -291,6 +338,11 @@ PotenciasMaximos_parcial = MaxsPotsExp/propor
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]
err = [2,1,1,1]
orden=5
......@@ -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
#maximumfluo_index = np.array(cuentas[1:-1]).argmax()
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)
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)
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.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)):
# plt.axvline(pot)
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.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.ylim(0,3.7)
#plt.xlim(0,2.3)
plt.tight_layout()
plt.legend(loc='upper left', frameon=True, fontsize=7, handletextpad=0.1)
#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Measurement_thresholds_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')
......
analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/CamposVector.txt 0 → 100644
View file @ 19fbd62a
0.000000000000000000e+00
1.138292635058539559e-01
2.276585270117079118e-01
3.414877905175618955e-01
4.553170540234158237e-01
5.691463175292698073e-01
6.829755810351237910e-01
7.968048445409777747e-01
9.106341080468316473e-01
1.024463371552685631e+00
1.138292635058539615e+00
1.252121898564393598e+00
1.365951162070247582e+00
1.479780425576101566e+00
1.593609689081955549e+00
1.707438952587809311e+00
1.821268216093663295e+00
1.935097479599517500e+00
2.048926743105371262e+00
2.162756006611225246e+00
2.276585270117079229e+00
2.390414533622933213e+00
2.504243797128787197e+00
2.618073060634641180e+00
2.731902324140495164e+00
2.845731587646348704e+00
2.959560851152203131e+00
3.073390114658057115e+00
3.187219378163911099e+00
3.301048641669765082e+00
3.414877905175618622e+00
3.528707168681472606e+00
3.642536432187326589e+00
3.756365695693181017e+00
3.870194959199035001e+00
3.984024222704888540e+00
4.097853486210742524e+00
4.211682749716596952e+00
4.325512013222450491e+00
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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
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
View file @ 19fbd62a
......@@ -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()
......
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):
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 0 → 100644
View file @ 19fbd62a
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