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Commit d80cf312 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.py
View file @ d80cf312
......@@ -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
View file @ d80cf312
......@@ -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
View file @ d80cf312
......@@ -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/20221006_transitoriosv2/fig3_01.svg
View file @ d80cf312
......@@ -6,11 +6,11 @@
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<cc:Work>
<dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
<dc:date>2023-04-27T16:04:44.519667</dc:date>
<dc:date>2023-10-03T15:31:08.257400</dc:date>
<dc:format>image/svg+xml</dc:format>
<dc:creator>
<cc:Agent>
<dc:title>Matplotlib v3.5.1, https://matplotlib.org/</dc:title>
<dc:title>Matplotlib v3.7.0, https://matplotlib.org/</dc:title>
</cc:Agent>
</dc:creator>
</cc:Work>
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......
analisis/plots/20221017_IonStatistics/IonStatistics.py
View file @ d80cf312
......@@ -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
View file @ d80cf312
......@@ -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
View file @ d80cf312
......@@ -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
View file @ d80cf312
......@@ -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 0 → 100644
View file @ d80cf312
# -*- 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')
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