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Commit 55502aaf authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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Merge branch 'master' of https://code.df.uba.ar/nnunez/artiq_experiments

parents 462672c7 1413067c
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analisis/plots/20221006_transitoriosv2/Transitorios_02.py
View file @ 55502aaf
...@@ -355,14 +355,22 @@ FIGURA PAPER ...@@ -355,14 +355,22 @@ FIGURA PAPER
jselected = [1, 5, 12] jselected = [1, 5, 12]
t0=0.18
plt.figure() plt.figure()
plt.plot([f*1e6 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[0]]) plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[0]])
plt.plot(RefBins[90:], [expo(r, *popt_vec[jselected[0]]) for r in RefBins[90:]]) plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[0]]) for r in RefBins[90:]])
plt.plot([f*1e6 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[1]]) plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[1]])
plt.plot(RefBins[90:], [expo(r, *popt_vec[jselected[1]]) for r in RefBins[90:]]) plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[1]]) for r in RefBins[90:]])
plt.plot([f*1e6 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[2]]) plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[2]])
plt.plot(RefBins[90:], [expo(r, *popt_vec[jselected[2]]) for r in RefBins[90:]]) plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[2]]) for r in RefBins[90:]])
plt.xlim(-0.5,4) plt.xlim(-0.5,3)
plt.xlabel('Time (us)')
plt.ylabel('Counts')
plt.savefig('fig3_01.pdf')
plt.savefig('fig3_01.svg')
#%% #%%
""" """
......
analisis/plots/20221006_transitoriosv2/fig3_01.svg 0 → 100644
View file @ 55502aaf
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analisis/plots/20221017_IonStatistics/Simulaciones/plot_decaytimes_vs_intensity_detunings.py
View file @ 55502aaf
...@@ -29,12 +29,12 @@ for cambio in [0]: # esto es para shiftear la potencia medida ...@@ -29,12 +29,12 @@ for cambio in [0]: # esto es para shiftear la potencia medida
capsize=3) capsize=3)
#ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \ ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \
# yerr = 1e6*errsSIM.stdval.values, \ yerr = 1e6*errsSIM.stdval.values, \
# fmt='.--', ms=6, lw=.5, \ fmt='.--', ms=6, lw=.5, \
# color="#3949ab", color="#3949ab",
# capsize=3, capsize=3,
# label=f'r={rad}; cambio={cambio}') label=f'r={rad}; cambio={cambio}')
ax2.set_xlim([1.2e-4, 0.2e-1]) ax2.set_xlim([1.2e-4, 0.2e-1])
ax2.set_ylim([2e-1, 0.8e1]) ax2.set_ylim([2e-1, 0.8e1])
...@@ -47,4 +47,13 @@ ax2.grid(which='minor', alpha=0.2) ...@@ -47,4 +47,13 @@ ax2.grid(which='minor', alpha=0.2)
ax2.set_xscale("log") ax2.set_xscale("log")
ax2.set_yscale("log") ax2.set_yscale("log")
plt.savefig('fig3_02.pdf')
plt.savefig('fig3_02.svg')
analisis/plots/20221024_BranchingFractionMeasurement/OBE3levels_paper.py 0 → 100644
View file @ 55502aaf
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 15 18:11:00 2020
@author: oem
"""
import numpy as np
import matplotlib.pyplot as plt
from odeintw import odeintw
import time
#matplotlib.use('Qt5Agg')
"""
odeintw es un wrapper de la funcion odeint de scipy.integrate que sirve para integrar
sistemas de ecuaciones con parámetros complejos.
En este script se resuelven las ecuaciones
de Bloch de un sistema de 3 niveles en configuración lambda (1 y 3 son ground, 2 es excitado)
para ion de Bario o de Calcio.
Este script considera que hay dos láseres repump prendidos con sus respectivas frecuencias de
rabi y detuning.
Además, se usan dos distintos sistemas de referencia rotantes, condensados en las funciones
RepumpReferenceSystemModel 1 y 2. La 2 es más útil para prender de a poco el láser repump2, y
cuando su frec. de rabi es cero, no aparece en las ecuaciones.
https://github.com/WarrenWeckesser/odeintw
"""
def EquationSystemModel(z, t, rabi12, rabi23func, g21, g23, glg, glr, detrfunc, detg):
"""
*La parte unitaria esta bien (26/10/22)
*La parte de decoherencia de emision espontanea esta bien (26/10/22)
"""
rho33, rho22, rho11, rho12, rho13, rho23 = z
rho21 = np.conjugate(rho12)
rho31 = np.conjugate(rho13)
rho32 = np.conjugate(rho23)
f=1
drho33dt = (-1j)*0.5*(rabi23func*rho23 - np.conjugate(rabi23func)*rho32) + g23*rho22
drho22dt = (-1j)*0.5*rabi12*(rho12 - rho21) - 1j*0.5*(np.conjugate(rabi23func)*rho32 - rabi23func*rho23) - rho22*(g21 + g23)
drho11dt = (-1j)*0.5*rabi12*(rho21 - rho12) + rho22*g21
drho12dt = (-1j)*0.5*rabi12*(rho22 - rho11) - (1j)*(detg*rho12 - 0.5*rabi23func*rho13) - rho12*(0.5*g21 + 0.5*g23 + f*glg)
#drho13dt = (-1j)*(detg*rho13 + 0.5*rabi12*rho23 - 0.5*np.conjugate(rabi23func)*rho12 - detrfunc*rho13) - rho13*(f*glg + f*glr + f*glr + 0.5*g21 + 0.5*g23) #uno de los glr es glp pero asumo que son el mismo laser
drho13dt = (-1j)*(detg*rho13 + 0.5*rabi12*rho23 - 0.5*np.conjugate(rabi23func)*rho12 - detrfunc*rho13) - rho13*(f*glg + f*glr + f*glr) #uno de los glr es glp pero asumo que son el mismo laser
drho23dt = (-1j)*0.5*rabi12*rho13 - (1j)*0.5*np.conjugate(rabi23func)*(rho33-rho22) + (1j)*detrfunc*rho23 - rho23*(0.5*g21 + 0.5*g23 + f*glr + f*glr)
dzdt = [drho33dt, drho22dt, drho11dt, drho12dt, drho13dt, drho23dt]
return dzdt
def RepumpReferenceSystemModel(rabi23repump1, rabi23repump2, Detuningrepump1, Detuningrepump2, t):
rabi23 = [rabi23repump1 + 0*rabi23repump2*np.exp(-1*(Detuningrepump1-Detuningrepump2)*1j*tiemp) for tiemp in t]
DetuningMedioRepump = Detuningrepump1
return rabi23, DetuningMedioRepump
def GetTransitionLinewidths(species):
if species == 'Calcium':
g21, g23, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
elif species == 'Barium':
g21, g23, = 2*np.pi*15.1e6, 2*np.pi*5.3e6 #anchos de linea de las transicionesrabi12, rabi23 = sr*g21, sp*g23 #frecuencias de rabi
else:
raise
return g21, g23
def Solve3LevelBloch(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep1, DetRep2,
glg, glr, initcond=1, tfin=10, paso=1e-3, plotfull=False, plotzoom=False, sweepingrepump=False, printprogress=False):
"""
Resuelve las ecuaciones de bloch del modelo dado. Los inputs son:
glg, glr: anchos de línea de doppler y repump en MHz
SatPar: parámetro de saturación de cada láser
Det: detunings en MHz
"""
t = np.arange(0, tfin*1e-6, paso*1e-6)
g21, g23 = GetTransitionLinewidths(species)
DopplerLaserLinewidth = glg*2*np.pi*1e6
RepumpLaserLinewidth = glr*2*np.pi*1e6
DetuningDoppler = DetDoppler*2*np.pi*1e6
Detuningrepump1 = DetRep1*2*np.pi*1e6
Detuningrepump2 = DetRep2*2*np.pi*1e6
rabi12 = SatParDoppler*g21
rabi23repump1 = SatParRep1*g23
rabi23repump2 = SatParRep2*g23
rabi23vst, DetuningMedioRepump = RepumpReferenceSystemModel(rabi23repump1, rabi23repump2, Detuningrepump1, Detuningrepump2, t)
rho33sol = []
rho22sol = []
rho11sol = []
rho12sol = []
rho13sol = []
rho23sol = []
#condicion inicial: poblacion en el nivel 1
if initcond==1:
z0 = [0+0j, 0+0j, 1+0j, 0+0j, 0+0j, 0+0j] #formato: [rho33, rho22, rho11, rho12, rho13, rho23]
elif initcond==2:
z0 = [0+0j, 1+0j, 0+0j, 0+0j, 0+0j, 0+0j]
elif initcond==3:
z0 = [1+0j, 0+0j, 0+0j, 0+0j, 0+0j, 0+0j]
else:
print('mala condicion inicial')
raise
for i in range(1,len(t)):
# span for next time step
tspan = [t[i-1],t[i]]
# solve for next step
#z = odeintw(EquationSystemModel, z0, tspan, args=(rabi12, rabi23vst[i], g21, g23, DopplerLaserLinewidth, RepumpLaserLinewidth, DetuningMedioRepump, DetuningDoppler), full_output=True)
z = odeintw(EquationSystemModel, z0, tspan, args=(rabi12, rabi23repump1, g21, g23, DopplerLaserLinewidth, RepumpLaserLinewidth, Detuningrepump1, DetuningDoppler), full_output=True)
# store solution for plotting
rho22sol.append(z[0][1][1])
rho33sol.append(z[0][1][0])
rho11sol.append(z[0][1][2])
rho12sol.append(z[0][1][3])
rho13sol.append(z[0][1][4])
rho23sol.append(z[0][1][5])
# next initial condition
z0 = z[0][1]
if printprogress:
print(i, '/', len(t))
if plotfull:
plt.figure()
plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r33.real for r33 in rho33sol], 'o', label='Rho33', linewidth=0.5, markersize=1)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r22.real for r22 in rho22sol], 'o', label='Rho22', linewidth=0.5, markersize=1)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r11.real for r11 in rho11sol], 'o', label='Rho11', linewidth=0.5, markersize=1)
#plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r12.real for r12 in rho12sol], 'o', label='Rho12', linewidth=0.5)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [np.abs(r13) for r13 in rho13sol], 'o', label='Rho13', linewidth=0.5)
#plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r23.real for r23 in rho23sol], 'o', label='Rho23', linewidth=0.5)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [rho11sol[i].real + rho22sol[i].real + rho33sol[i].real for i in range(len(rho33sol))], '--', label='Normalization')
plt.xlabel('Time (us)')
plt.ylabel('Rho component')
plt.ylim(-0.2, 1.1)
plt.legend()
if plotzoom:
plt.figure()
tinicialplot = int(len(rho33sol)*0.80)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]][tinicialplot:], [r33.real for r33 in rho33sol][tinicialplot:], 'o', label='Rho33', linewidth=0.5, markersize=3)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]][tinicialplot:], [r22.real for r22 in rho22sol][tinicialplot:], 'o', label='Rho22', linewidth=0.5, markersize=3)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]][tinicialplot:], [r11.real for r11 in rho11sol][tinicialplot:], 'o', label='Rho11', linewidth=0.5, markersize=3)
#plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r12.real for r12 in rho12sol], 'o', label='Rho12', linewidth=0.5)
plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [np.abs(r13) for r13 in rho13sol], 'o', label='Rho13', linewidth=0.5)
#plt.plot([j*1e6 for j in t[0:len(rho33sol)]], [r23.real for r23 in rho23sol], 'o', label='Rho23', linewidth=0.5)
plt.xlabel('Time (us)')
plt.ylabel('Rho component')
plt.title('Doppler lw: ' + str(int((1/(2*np.pi))*DopplerLaserLinewidth*1e-6)) + ' MHz, ' + 'Repump lw: ' + str(int((1/(2*np.pi))*RepumpLaserLinewidth*1e-6)) + ' MHz')
plt.ylim(-0.2, 1.1)
plt.legend()
RealRho22 = [r.real for r in rho22sol]
if not sweepingrepump:
RealRho11 = [r.real for r in rho11sol]
RealRho33 = [r.real for r in rho33sol]
AbsRho12 = [np.abs(r) for r in rho12sol]
AbsRho13 = [np.abs(r) for r in rho13sol]
AbsRho23 = [np.abs(r) for r in rho23sol]
return t, RealRho11, RealRho22, RealRho33, AbsRho12, AbsRho13, AbsRho23
def RepumpSpectrum(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep2,
glg, glr, tfinOBE=10, pasoOBE=1e-3, frecini=-100, frecfin=100, pasofrec=1, consideroscillations=True, plotSpectrum=True, sweepingrepump=True, initcond=1):
"""
Hace un barrido en la frecuencia del repump.
Considerando que puede haber oscilaciones de la población por haber dos repumps,
si consideroscillations=True el punto que plotea es un promedio del último 20% del plot.
"""
g21, g23 = GetTransitionLinewidths(species)
FluorescenceVector = []
t = np.arange(0, tfinOBE*1e-6, pasoOBE*1e-6)
DopplerLaserLinewidth = glg*2*np.pi*1e6
RepumpLaserLinewidth = glr*2*np.pi*1e6
DetuningDoppler = DetDoppler*2*np.pi*1e6
Detuningrepump2 = DetRep2*2*np.pi*1e6
rabi12 = SatParDoppler*g21
rabi23repump1 = SatParRep1*g23
rabi23repump2 = SatParRep2*g23
Repump1DetuningVector = np.arange(frecini*2*np.pi*1e6, frecfin*2*np.pi*1e6, pasofrec*2*np.pi*1e6)
counter = 1
FluorescenceVector = []
for DetRep1 in Repump1DetuningVector:
tini = time.time()
rho33sol = []
rho22sol = []
rho11sol = []
rho12sol = []
rho13sol = []
rho23sol = []
rabi23vst, DetuningMedioRepump = RepumpReferenceSystemModel(rabi23repump1, rabi23repump2, DetRep1, Detuningrepump2, t)
z0 = [0+0j, 0+0j, 1+0j, 0+0j, 0+0j, 0+0j] #formato: [rho33, rho22, rho11, rho12, rho13, rho23]
for i in range(1,len(t)):
# span for next time step
tspan = [t[i-1],t[i]]
# solve for next step
z = odeintw(EquationSystemModel,z0,tspan, args=(rabi12, rabi23vst[i], g21, g23, DopplerLaserLinewidth, RepumpLaserLinewidth, DetuningMedioRepump, DetuningDoppler), full_output=True)
# store solution for plotting
rho22sol.append(z[0][1][1])
rho33sol.append(z[0][1][0])
rho11sol.append(z[0][1][2])
rho12sol.append(z[0][1][3])
rho13sol.append(z[0][1][4])
rho23sol.append(z[0][1][5])
# next initial condition
z0 = z[0][1]
# print(i, '/', len(t))
print
#FluorescenceVector.append(np.mean([r.real for r in rho22sol[int(len(rho22sol)*0.8):]]))
#FluorescenceVector.append(np.mean([r.imag for r in rho12sol[int(len(rho22sol)*0.8):]]))
FluorescenceVector.append(np.real(rho12sol[-1]))
tfin = time.time()
print(counter, '/', len(Repump1DetuningVector), ', elapsed time: ', round(tfin-tini, 2), ' s')
counter = counter + 1
if plotSpectrum:
plt.plot([1e-6*d/(2*np.pi) for d in Repump1DetuningVector], [100*f for f in FluorescenceVector], 'o')
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.title('Doppler detuning: ' + str(DetDoppler) + ' MHz, Repump 2 detuning: ' + str(DetRep2) + ' MHz')
return Repump1DetuningVector, FluorescenceVector
def FindFluoAtTime(fluo, time, ti, pasoti):
ti = ti*1e-6
pasoti = pasoti*1e-6
i = 0
fluoattivec = []
while i < len(fluo):
if abs(time[i]-ti) < 2*pasoti:
fluoattivec.append(fluo[i])
i = i + 1
return np.mean(fluoattivec)
def IntegrateWindow(fluo, time, ti, window):
ti = ti*1e-6
window = window*1e-6
i = 0
fluowindow = []
while i<len(fluo):
if time[i]>ti and time[i]<=ti+window:
fluowindow.append(fluo[i])
i = i + 1
if time[i]>ti+window:
return np.mean(fluowindow)
def Lorentz(x, x0, gamma, ymax):
scale = ymax*np.pi*gamma*0.5
return (scale/np.pi)*(0.5*gamma)/((x-x0)**2 + (0.5*gamma)**2)
#%%
"""
PLOT DE TRANSICION SP
"""
species = 'Calcium'
SatParDoppler, SatParRep1, SatParRep2 = 0.8, 0, 0
DetDoppler = 0
#DetDopplerVec = np.arange(-40, 40, 0.5)
DetRep2 = 0
#DetRep1Vec = np.arange(-40, 40, 0.5)
DetRep1 = 0
DopplerLaserLinewidth, RepumpLaserLinewidth = 0, 0
plotfull = False
plotzoom = False
tfinOBE = 2
pasoOBE = 5e-4
PulsedFluoMax = []
PulsedFluoAdiab = []
PulsedFluo1us = []
PulsedFluo3us = []
PulsedFluo5us = []
# fig2, ax2 = plt.subplots()
t, RealRho11, RealRho22, RealRho33, AbsRho12, AbsRho13, AbsRho23 = Solve3LevelBloch(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep1, DetRep2, DopplerLaserLinewidth, RepumpLaserLinewidth, tfin=tfinOBE, paso=pasoOBE, plotfull=plotfull, plotzoom=plotzoom, initcond=1)
PulsedFluoMax.append(max(RealRho22))
PulsedFluoAdiab.append(RealRho22[-1])
PulsedFluo1us.append(FindFluoAtTime(RealRho22, t, 1, 1e-3))
print('Done ', DetDoppler)
import matplotlib
import seaborn as sns
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
plt.style.use('seaborn-bright')
plt.rcParams.update({
"text.usetex": False,
})
#plt.figure()
#plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
#plot figuras papers
colors1=sns.color_palette("rocket", 10)
colors2=sns.color_palette("mako", 10)
color1 = (0,0,128/255, 1)
color2 = 'dodgerblue'
color3 = 'darkmagenta'
plt.figure(figsize=(2.8, 1.7))
plt.plot([j*1e6 for j in t[0:len(RealRho11[0:int(1*len(RealRho22))])]], [1*r22 for r22 in RealRho11[0:int(1*len(RealRho22))]], markersize=1, color=color2, linestyle='--')
plt.plot([j*1e6 for j in t[0:len(RealRho33[0:int(1*len(RealRho22))])]], [1*r22 for r22 in RealRho33[0:int(1*len(RealRho22))]], markersize=1, color=color3, linestyle='--')
plt.plot([j*1e6 for j in t[0:len(RealRho22[0:int(1*len(RealRho22))])]], [1*r22 for r22 in RealRho22[0:int(1*len(RealRho22))]], markersize=1, color=color1)
plt.yticks([0, 0.5, 1], fontname='STIXGeneral', fontsize=9)
plt.xticks([0, 0.5, 1, 1.5, 2], fontname='STIXGeneral', fontsize=9)
plt.grid()
plt.xlabel('Time (us)', fontname='STIXGeneral', fontsize=9)
plt.ylabel('Atomic population', fontname='STIXGeneral', fontsize=9)
plt.ylim(-0.1, 1.1)
plt.tight_layout()
name='fig02b'
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')
#%%
"""
PLOT DE TRANSICION DP
"""
species = 'Calcium'
SatParDoppler, SatParRep1, SatParRep2 = 0, 5, 0
DetDoppler = 0
#DetDopplerVec = np.arange(-40, 40, 0.5)
DetRep2 = 0
#DetRep1Vec = np.arange(-40, 40, 0.5)
DetRep1 = 0
DopplerLaserLinewidth, RepumpLaserLinewidth = 0, 0
plotfull = False
plotzoom = False
tfinOBE = 2
pasoOBE = 5e-4
PulsedFluoMax = []
PulsedFluoAdiab = []
PulsedFluo1us = []
PulsedFluo3us = []
PulsedFluo5us = []
# fig2, ax2 = plt.subplots()
t, RealRho11, RealRho22, RealRho33, AbsRho12, AbsRho13, AbsRho23 = Solve3LevelBloch(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep1, DetRep2, DopplerLaserLinewidth, RepumpLaserLinewidth, tfin=tfinOBE, paso=pasoOBE, plotfull=plotfull, plotzoom=plotzoom, initcond=3)
PulsedFluoMax.append(max(RealRho22))
PulsedFluoAdiab.append(RealRho22[-1])
PulsedFluo1us.append(FindFluoAtTime(RealRho22, t, 1, 1e-3))
print('Done ', DetDoppler)
import matplotlib
import seaborn as sns
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
plt.style.use('seaborn-bright')
plt.rcParams.update({
"text.usetex": False,
})
#plt.figure()
#plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
#plot figuras papers
colors1=sns.color_palette("rocket", 10)
colors2=sns.color_palette("mako", 10)
color1 = (212/255,0,0, 1)
color2 = 'lightcoral'
color3 = 'maroon'
plt.figure(figsize=(2.8, 1.7))
plt.plot([j*1e6 for j in t[0:len(RealRho11[0:int(1*len(RealRho22))])]], [1*r22 for r22 in RealRho11[0:int(1*len(RealRho22))]], markersize=1, color=color2, linestyle='--')
plt.plot([j*1e6 for j in t[0:len(RealRho33[0:int(1*len(RealRho22))])]], [1*r22 for r22 in RealRho33[0:int(1*len(RealRho22))]], markersize=1, color=color3, linestyle='--')
plt.plot([j*1e6 for j in t[0:len(RealRho22[0:int(1*len(RealRho22))])]], [1*r22 for r22 in RealRho22[0:int(1*len(RealRho22))]], markersize=1, color=color1)
plt.yticks([0, 0.5, 1], fontname='STIXGeneral', fontsize=9)
plt.xticks([0, 0.5, 1, 1.5, 2], fontname='STIXGeneral', fontsize=9)
plt.grid()
plt.ylim(-0.1, 1.1)
plt.xlabel('Time (us)', fontname='STIXGeneral', fontsize=9)
plt.ylabel('Atomic population', fontname='STIXGeneral', fontsize=9)
plt.tight_layout()
name='fig02c'
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')
\ No newline at end of file
analisis/plots/20221024_BranchingFractionMeasurement/Transitorios_BF.py
View file @ 55502aaf
...@@ -11,6 +11,8 @@ os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221024_Branchi ...@@ -11,6 +11,8 @@ os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221024_Branchi
Long_files = ['DP_long', 'SP_long'] Long_files = ['DP_long', 'SP_long']
Calib_files = ['Fondo_IR_50M', 'Fondo_UV_50M'] Calib_files = ['Fondo_IR_50M', 'Fondo_UV_50M']
Short_files = [9132, 9134, 9136, 9138, 9140, 9142]
Fondo_files = [9133, 9135, 9137, 9139, 9141, 9143]
def expo(T, tau, N0, C): def expo(T, tau, N0, C):
global T0 global T0
...@@ -91,6 +93,44 @@ for i, fname in enumerate(Calib_files): ...@@ -91,6 +93,44 @@ for i, fname in enumerate(Calib_files):
Calib_Bins.append(binsf) Calib_Bins.append(binsf)
BINW_short = 5e-9
T0_short = -0.4e-6
Short_Heigths = []
Short_Bins = []
for i, fname in enumerate(Short_files):
#print(i)
#print(fname)
data = h5py.File('Data/Cortas/'+'00000'+str(fname)+'-SingleLine.h5', 'r')
counts = np.array(data['datasets']['counts'])
bines = np.arange(counts.min(), counts.max()+BINW_short, BINW_short)
heigs, binsf = np.histogram(counts, bines[bines>T0_short])
Short_Heigths.append(heigs)
Short_Bins.append(binsf)
BINW_fondo = 5e-9
T0_fondo = -0.4e-6
Fondo_Heigths = []
Fondo_Bins = []
for i, fname in enumerate(Fondo_files):
#print(i)
#print(fname)
data = h5py.File('Data/Fondos/00000'+str(fname)+'-SingleLine.h5', 'r')
counts = np.array(data['datasets']['counts'])
bines = np.arange(counts.min(), counts.max()+BINW_fondo, BINW_fondo)
heigs, binsf = np.histogram(counts, bines[bines>T0_fondo])
Fondo_Heigths.append(heigs)
Fondo_Bins.append(binsf)
#%% #%%
""" """
...@@ -109,8 +149,8 @@ RefBins = [t*1e6 for t in Long_Bins[0][:-1]] ...@@ -109,8 +149,8 @@ RefBins = [t*1e6 for t in Long_Bins[0][:-1]]
plt.figure() plt.figure()
#for i in range(len(Long_Heigths)): for i in range(len(Long_Heigths)):
# plt.plot(Long_Bins[i][:-1], Long_Heigths[i]) plt.plot(Long_Bins[i][:-1], Long_Heigths[i])
for i in range(len(Calib_Heigths)): for i in range(len(Calib_Heigths)):
plt.plot(Calib_Bins[i][:-1], Calib_Heigths[i]) plt.plot(Calib_Bins[i][:-1], Calib_Heigths[i])
...@@ -131,6 +171,109 @@ branch = TotalSPfinal/(TotalSPfinal+TotalDPfinal) ...@@ -131,6 +171,109 @@ branch = TotalSPfinal/(TotalSPfinal+TotalDPfinal)
print(branch) print(branch)
#%%
"""
Acondicionamiento señales
"""
SP_corrected = [Long_Heigths[1][k]-Calib_Heigths[1][k] for k in range(len(Long_Heigths[1]))]
DP_corrected = [Long_Heigths[0][k]-Calib_Heigths[0][k] for k in range(len(Long_Heigths[0]))]
plt.plot(Long_Bins[1][:-1], SP_corrected)
plt.plot(Long_Bins[0][:-1], DP_corrected)
#%%
"""
Figura branching
"""
import matplotlib
import seaborn as sns
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'
plt.style.use('seaborn-bright')
plt.rcParams.update({
"text.usetex": False,
})
#plt.figure()
#plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
#plot figuras papers
colors1=sns.color_palette("rocket", 10)
colors2=sns.color_palette("mako", 10)
color2 = colors2[1]
color3 = colors2[4]
color1 = colors2[8]
bins1 = np.arange(150,300, 1)
bins2 = np.arange(0,50,1)
bins3 = np.arange(30,100,1)
plt.figure(figsize = (3.8,3))
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.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.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')
#poissoneidad = np.var(Stat_Heigths)/np.mean(Stat_Heigths)
#plt.title('Varianza/media = {:.4f}'.format(poissoneidad))
#plt.savefig('bg_laser_ion_stats.pdf',dpi = 600 )
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')
#%%
"""
Ahora mido de nuevo pero alternando medicion y fondo para que de mejor si hay derivas de potencias.
Estas mediciones DAN MUY BIEN. Tanto branching fraction como detection efficiency.
"""
med = 3
plt.plot(Short_Bins[med][:-1],Short_Heigths[med])
plt.plot(Fondo_Bins[med][:-1],Fondo_Heigths[med])
TotalSP = np.sum(Short_Heigths[2])-np.sum(Fondo_Heigths[2])+np.sum(Short_Heigths[4])-np.sum(Fondo_Heigths[4])
TotalDP = np.sum(Short_Heigths[3])-np.sum(Fondo_Heigths[3])+np.sum(Short_Heigths[5])-np.sum(Fondo_Heigths[5])
branch = TotalSP/(TotalSP+TotalDP)
print(branch)
ErrorSP = np.sqrt(np.sum(Short_Heigths[2])+np.sum(Fondo_Heigths[2])+np.sum(Short_Heigths[4])+np.sum(Fondo_Heigths[4]))
ErrorDP = np.sqrt(np.sum(Short_Heigths[3])+np.sum(Fondo_Heigths[3])+np.sum(Short_Heigths[5])+np.sum(Fondo_Heigths[5]))
Errorbranch =np.sqrt((1/(TotalSP + TotalDP) - TotalSP/(TotalSP+TotalDP)**2)**2 * ErrorSP**2 + (TotalSP/(TotalSP+TotalDP)**2)**2*ErrorDP**2)
DetectionEfficiency = 100*TotalDP/10e6
ErrorDetectionEfficiency = 100*np.sqrt(TotalDP)/10e6
Ratio = TotalSP/TotalDP
ErrorRatio = np.sqrt((1/(TotalDP**2))*(ErrorSP**2)+((TotalSP/(TotalDP**2))**2)*(ErrorDP**2))
print(ErrorRatio)
......
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