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artiq_experiments
Commit 50ae040c authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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cosas de la compu de nico

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analisis/plots/20241210_posibleosc/Transitorios_osci.py
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...@@ -181,7 +181,7 @@ Esto deberia hacerse asi para ver la oscilacion a la RF (micromocion) ...@@ -181,7 +181,7 @@ Esto deberia hacerse asi para ver la oscilacion a la RF (micromocion)
BINW = 1e-2 BINW = 1e-2
T0 = -0.4e-6 T0 = -0.4e-6
files = [19607] #el 19613 es el que muestra el pico en 48.5 Hz files = [19614] #el 19613 es el que muestra el pico en 48.5 Hz
SP_Heigths = [] SP_Heigths = []
SP_Bins = [] SP_Bins = []
...@@ -219,7 +219,7 @@ taurf = 1/freqrf ...@@ -219,7 +219,7 @@ taurf = 1/freqrf
tiemposarreglados = bin_time_arrivals(counts, taurf) tiemposarreglados = bin_time_arrivals(counts, taurf)
b = taurf/50 # Ancho de bineo b = taurf/100 # Ancho de bineo
bins = np.arange(0, taurf, b) bins = np.arange(0, taurf, b)
hist, bin_edges = np.histogram(tiemposarreglados, bins=bins) hist, bin_edges = np.histogram(tiemposarreglados, bins=bins)
......
analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/Fig03_varyingUVpower.svg 0 → 100644
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analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/OBE3levels_chtime_fitting.py 0 → 100644
View file @ 50ae040c
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from OBE3levels_functions import *
from scipy.optimize import curve_fit
from scipy.stats import poisson, norm
import os
import sys
plt.ion()
"""
Este código simula la dinámica temporal de un ion de calcio sometido a un láser UV y dos láseres IR.
"""
def expo(T, tau, N0, C):
#global T0
return N0*np.exp(-(T-0e-6)/tau) + C
ADD_NOISE = True
NOISE_VAR = 30 # Varianza del ruido
NOISE_ADJ = 78 # Escaleo del ruido versus el maximo
NOISE_MEAN = 0 # Media del ruido
NRO_REPS = 100 # Veces a promediar
species = 'Calcium'
#Parámetros de saturacion de los láseres rebombeo (IR), que son proporcionales a la intensidad de los mismos
#Para reproducir SP, hay que poner 0 a los dos
SatParRep1, SatParRep2 = 0, 0
sat_intensity = 43.3e-5 # uW um-2
g21 = 135591138.92893547
#El número de los láseres IR aplicados en el experimento. Para el experimento de la transicion SP, es 0.
Number_IR_lasers = 0
#Detunings de los láseres en MHz. Si es 0, están en resonancia. En principio
#podría ser un parámetro libre a ajustar porque influye mucho
DetDoppler = -int(sys.argv[1])
DetRep2 = 0
DetRep1 = 0
#Anchos de línea de los láseres, en MHz. No son tan cruciales, están entre 0.1 y 0.5 MHz
DopplerLaserLinewidth, RepumpLaserLinewidth = 1e3, 1
# Para trabajar con las curvas teóricas:
# range_inicio = np.arange(sat_intensity/10, sat_intensity, sat_intensity/50)
# range_fin = np.arange(sat_intensity, 30*sat_intensity, sat_intensity/5)
# laser_intensities = np.append(range_inicio, range_fin)
# Para trabajar con nuestros datos, saco las intensidades, a un radio de 100um:
laser_intensities = np.array([0.00662085, 0.00582507, 0.0049338, 0.00397887, 0.00305577, 0.00226, 0.00155972, 0.00101859, 0.00060479, 0.00031831])
OmegaRabi2 = (g21**2) * (laser_intensities / sat_intensity)
SatParDopplerVec = OmegaRabi2 / ((DetDoppler*2*np.pi*1e6)**2 + g21**2)
#Parámetros de la simulación, en us
tfinOBE = 1
pasoOBE = 1e-5
#Estado electrónico inicial de la simulacion. Posibilidades: |S> = 1, |P> = 2, |D> = 3
initial_state = 1
#Corre y simula la dinámica. Devuelve elementos de matriz densidad
# fig1, ax1 = plt.subplots()
alltaus = list()
allN0 = list()
allmeans = list()
allstds = list()
temporal_taus = list()
temporal_N0 = list()
for i, SatParDoppler in enumerate(SatParDopplerVec):
t, RealRho11, RealRho22, RealRho33, AbsRho12, AbsRho13, AbsRho23 = Solve3LevelBloch(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep1, DetRep2, DopplerLaserLinewidth, RepumpLaserLinewidth, no_IR_lasers=Number_IR_lasers, initcond=initial_state, tfin=tfinOBE, paso=pasoOBE, printprogress=False)
k = int(0.1*len(t)) #parametro para que fitee solo la parte final
k = np.argmin(np.abs(t - 0.2e-6))
temporal_taus = list()
incert_estad = 0
for nro_rep in range(NRO_REPS):
poblacion_fit = RealRho22[k:].copy()
if ADD_NOISE:
# Genero ruido similar al medido
ruido_poisson = poisson.rvs(NOISE_VAR, size=len(poblacion_fit))
# Reescaleo el ruido para que sea compatible con las simulaciones
ruido_poisson *= np.max(poblacion_fit)/NOISE_ADJ
poblacion_fit += ruido_poisson
# poblacion_fit += norm.rvs(0, NOISE_VAR, size=len(poblacion_fit))*np.max(poblacion_fit)/NOISE_ADJ
poblacion_fit[np.where(poblacion_fit < 0 )] = 0 # Limpio posibles valores negativos
t_fit = t[k+1:]
popt, pcov = curve_fit(expo, t_fit, poblacion_fit, p0=(1e-6, 1e-2, 1e-2))
temporal_taus.append(popt[0])
incert_estad += np.sqrt(pcov[0,0])
# temporal_N0.append(popt[1])
if not ADD_NOISE: # si no quiere que agregue ruido, entonces solo hace el for una vez
break
print(f" {nro_rep} ", end='\r')
if ADD_NOISE:
allmeans.append(np.mean(temporal_taus))
allstds.append(np.std(temporal_taus) + incert_estad/NRO_REPS)
else:
allmeans.append(np.mean(temporal_taus))
#ploteo en funcion de 100*Rho22 para que me devuelva en porcentaje la población del excitado que es proporcional a la fluorescencia detectada
# lbl='Detuning Rep1:' + str(DetRep1) + ' MHz'
# ax1.plot(t[:-1]*1e6, RealRho22, '.', label=lbl, markersize=1)
# ax1.plot(t[:-1]*1e6, 100*RealRho22, '.', label=lbl, markersize=1)
# ax1.plot(t_fit*1e6, 100*expo(t_fit, *popt))
print(f'({i:02d}/{len(SatParDopplerVec)}) Done {SatParDoppler}'.ljust(50, ' '))
# ax1.legend(SatParDopplerVec)
# ax1.set_xlabel('Time (us)')
# ax1.set_ylabel('100*Rho22')
#%% #####################################################################################
### Guardo valores al CSV para analizar luego
# nDet=f"Det{np.abs(DetDoppler)}"
# CSV_FNAME = f"error_teorico_tau_{nDet}.csv"
# try:
# import pandas as pd
# data = pd.read_csv(CSV_FNAME)
# data["meanval"] = allmeans
# data["stdval"] = allstds
# data.to_csv(CSV_FNAME, index=False)
# print(f"SAVED {nDet}".ljust(50, ' '))
# except FileNotFoundError:
# data = pd.DataFrame({'I': laser_intensities, "meanval": allmeans, "stdval": allstds})
# data.to_csv(CSV_FNAME, index=False)
# print(f"CREATED CSV FILE".ljust(50, ' '))
# print(f"SAVED DETUNING {-DetDoppler}".ljust(50, ' '))
#%% ####################################################################################
### Plots de los valores de intensidades medidas para distintos parametros
### Cada axes arma un eje horizontal distinto, es para comparar las posibilidades
# fig4, ax4 = plt.subplots()
# allmeans = [t*1e6 for t in allmeans]
# ax4b = ax4.twinx()
# ax4.plot(SatParDopplerVec, allmeans, '-o', lw=0.4)
# ax4b.plot(SatParDopplerVec, allN0, 'k-^', lw=0.4)
# ax4.set_xlabel("Parametro de saturación")
# ax4.set_ylabel("Tau (circulo)")
# ax4b.set_ylabel("Alturas (triang)")
# ax4.set_title(f"SP; IR_Lasers=0; DetuningDoppler={DetDoppler}; LineWidth={DopplerLaserLinewidth}")
# ax4by = ax4.twiny()
# ax4by.plot(laser_intensities, allmeans, '-', lw=0)
# ax4by.xaxis.set_label_position('bottom')
# ax4by.xaxis.tick_bottom()
# ax4by.spines['bottom'].set_position(("axes", -0.27))
# ax4by.set_xlabel(r"Intensidad [ $\mu$W/$\mu$m$^2$ ]")
# ax4cy = ax4.twiny()
# ax4cy.plot(laser_intensities*np.pi*(35**2), allmeans, '-', lw=0)
# ax4cy.xaxis.set_label_position('bottom')
# ax4cy.xaxis.tick_bottom()
# ax4cy.spines['bottom'].set_position(("axes", -0.58))
# ax4cy.set_xlabel(r"Potencia (r=35$\mu$m) [ $\mu$W ]")
# ax4dy = ax4.twiny()
# ax4dy.plot(laser_intensities*np.pi*(50**2), allmeans, '-', lw=0)
# ax4dy.xaxis.set_label_position('bottom')
# ax4dy.xaxis.tick_bottom()
# ax4dy.spines['bottom'].set_position(("axes", -0.9))
# ax4dy.set_xlabel(r"Potencia (r=50$\mu$m) [ $\mu$W ]")
analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/OBE3levels_functions.py 0 → 100644
View file @ 50ae040c
#!/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
"""
#CHEQUEADOS LOS DAMPINGS
def EquationSystemModel(z, t, rabi12, rabi23, g21, g23, glg, glr, detr, detg, no_IR_lasers):
"""
Se plantean acá las ecuaciones dinámicas de la matriz densidad (OBE: Optical Bloch Equations)
para la interacción entre un átomo y un campo electromagnético.
-rabi12, rabi23: frecuencias de rabi, proporcionales a la intensidad de los láseres
-g21, g23: anchos de línea naturales de las transiciones
-glg, glr: anchos de línea de los láseres doppler (UV) y repump (IR) respectivamente
-detg, detr: detunings de transición doppler (UV) y repump (IR) respectivamente
-no_IR_lasers: número de láseres IR aplicados
"""
rho33, rho22, rho11, rho12, rho13, rho23 = z
rho21 = np.conjugate(rho12)
rho31 = np.conjugate(rho13)
rho32 = np.conjugate(rho23)
drho11dt = (1j)*0.5*rabi12*(rho12 - rho21) + rho22*g21
drho22dt = (-1j)*0.5*rabi12*(rho12 - rho21) - 1j*0.5*(np.conjugate(rabi23)*rho32 - rabi23*rho23) - rho22*(g21 + g23)
drho33dt = (-1j)*0.5*(rabi23*rho23 - np.conjugate(rabi23)*rho32) + g23*rho22
drho12dt = (-1j)*0.5*rabi12*(rho22 - rho11) - (1j)*(detg*rho12 - 0.5*rabi23*rho13) - rho12*(0.5*g21 + 0.5*g23 + glg)
drho13dt = (-1j)*(detg*rho13 + 0.5*rabi12*rho23 - 0.5*np.conjugate(rabi23)*rho12 - detr*rho13) - rho13*(glg + no_IR_lasers*glr)
drho23dt = (-1j)*0.5*rabi12*rho13 - (1j)*0.5*np.conjugate(rabi23)*(rho33-rho22) + (1j)*detr*rho23 - rho23*(0.5*g21 + 0.5*g23 + no_IR_lasers*glr)
dzdt = [drho33dt, drho22dt, drho11dt, drho12dt, drho13dt, drho23dt]
return dzdt
def RepumpReferenceSystemModel(rabi23repump1, rabi23repump2, Detuningrepump1, Detuningrepump2, t):
"""
Cuando hay más de un láser en una transición se producen batidos entre los mismos, haciendo que
la frecuencia de rabi sea un parámetro que varía en el tiempo. Para resolver eso numéricamente se
implementa de esta manera, calculando la frecuencia de rabi efectiva antes de resolver las OBE.
Al resolver el sistema, se pasa a un marco de referencia rotante respecto a los láseres. Como los dos láseres
IR excitan la misma transición, hay que elegir una de ambas frecuencias para rotar. Esto elige al repump1
como tal referencia, pero es posible elegir a repump2, o incluso a un promedio entre ambas frecuencias (por eso se llama detuningmedio).
Es decir, esto considera a la matriz de rotación como:
U = exp(-i.wg.t)|1><1| + |2><2| + exp(-i.wr1.t)|3><3|
"""
rabi23 = [rabi23repump1 + rabi23repump2*np.exp(-1*(Detuningrepump1-Detuningrepump2)*1j*tiemp) for tiemp in t]
DetuningMedioRepump = Detuningrepump1
return rabi23, DetuningMedioRepump
def GetTransitionLinewidths(species):
"""
Devuelve los anchos de línea naturales según la especie iónica
"""
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 transiciones
else:
print('Species not implemented')
raise
return g21, g23
def Solve3LevelBloch(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep1, DetRep2,
glg, glr, no_IR_lasers, initcond=1, tfin=10, paso=1e-3, 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
rabi12, rabi23 = sr*g21, sp*g23 #frecuencias de rabi
"""
t = np.arange(0, tfin*1e-6, paso*1e-6)
g21, g23 = GetTransitionLinewidths(species)
#convierto todo a MHz
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 = np.sqrt(SatParDoppler*(4*(DetuningDoppler**2) + g21**2)/2)
rabi23repump1 = np.sqrt(SatParRep1*(4*(Detuningrepump1**2) + g23**2)/2)
rabi23repump2 = np.sqrt(SatParRep2*(4*(Detuningrepump2**2) + g23**2)/2)
rabi23vst, _ = 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
#z0 = [0.5+0j, 0+0j, 0.5+0j, -0.5+0j, 0+0j, 0+0j] DARK STATE
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, no_IR_lasers), 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), end="\r", flush=True)
#Tomo la parte real de los diagonales y el módulo de las coherencias
RealRho22 = [r.real for r in rho22sol]
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, plotSpectrum=True, sweepingrepump=True):
"""
Hace un barrido en la frecuencia del repump.
Considerando que puede haber oscilaciones de la población por haber dos repumps,
"""
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 = np.sqrt(SatParDoppler*(4*(DetuningDoppler**2) + g21**2)/2)
rabi23repump1 = np.sqrt(SatParRep1*(4*(Detuningrepump1**2) + g23**2)/2)
rabi23repump2 = np.sqrt(SatParRep2*(4*(Detuningrepump2**2) + g23**2)/2)
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.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
#Funciones auxiliares para el análisis
def FindFluoAtTime(fluo, time, timetarget, pasoti):
"""
Encuentra la fluorescencia para determinado tiempo (en us).
El pasoti es la tolerancia temporal para el cual busca esa fluorescencia
"""
ti = timetarget*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 FindTimeForFluo(fluo, time, fluotarget, tolerancefluo=1e-3):
i = 0
timesforfluo = []
while i < len(time)-1:
if np.abs(fluo[i]-fluotarget) < 2*tolerancefluo:
timesforfluo.append(time[i])
i = i + 1
if len(timesforfluo) == 0:
print('La fluorescencia no toma ese valor')
return 0
elif len(timesforfluo) == 1:
return timesforfluo[0]
else:
# return np.mean(timesforfluo[1:])
return timesforfluo[-1]
def FindCharacteristicDecayTime(fluo, time):
pass
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)
def rho23model(rabip, gamma, gammas, detuningp, rho0, t):
rho23 = ((1j)*rho0*rabip/(2*gamma))*(np.exp(-gamma*t) - np.exp(-(gammas+(rabip**2)/(4*gamma))*t -(1j)*detuningp*t))
return [np.real(rho23), np.abs(np.imag(rho23))]
def dopplerBroadening(T, wldopp=0.397e-6, wlrep=0.866e-6, alpha=0, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlr es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wldopp*wldopp)) + (1/(wlrep*wlrep)) - 2*(1/(wldopp*wlrep))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
return gammaD
analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/error_teorico_tau_det20.csv 0 → 100644
View file @ 50ae040c
I,meanval,stdval
0.00662085,2.63563456542463e-07,3.62795752e-08
0.00582507,2.674966392578803e-07,3.99478821e-08
0.0049338,2.7426723419290064e-07,3.66249293e-08
0.00397887,2.8263714050526124e-07,3.83554920e-08
0.00305577,2.9770905441813384e-07,3.90291265e-08
0.00226,3.1786383950851434e-07,4.27893803e-08
0.00155972,3.562791436115684e-07,4.67339215e-08
0.00101859,4.198979464288562e-07,5.28900825e-08
0.00060479,5.457916989936301e-07,6.95843326e-08
0.00031831,8.275611779916097e-07,1.30369576e-07
analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/medidos_powers_taus.csv 0 → 100644
View file @ 50ae040c
Pow,Tau,N0
208.0,4.107136373675053e-07,242.3949275128121
183.0,3.994370315889412e-07,255.700355179914
155.0,4.4882562779198994e-07,226.84461233128172
125.0,4.727871415748306e-07,217.84774878119222
96.0,5.254285404397582e-07,222.57732196795308
71.0,6.079817946589069e-07,189.5426628192835
49.0,7.274218641328625e-07,168.69185328968453
32.0,8.823248220314772e-07,140.40570782757854
19.0,1.280491601444429e-06,90.5188484395718
10.0,1.96934320559994e-06,60.092743064494314
analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/plot_decaytimes_vs_intensity_detunings.py 0 → 100644
View file @ 50ae040c
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#plt.ion()
#dataREAL = pd.read_csv("medidos_powers_taus.csv")
dataSIM = pd.read_csv("sim_intensidad_taus_full.csv")
errsSIM = pd.read_csv("error_teorico_tau_det20.csv")
I_sat = 43.3*1e-5 #uW/um2
#%% #####################################################################################
### Plot de lineas teoricas tau-intensidad variando detuning + datos medidos
UVpotVec=[4,6,12,17,26,36, 48, 77, 112, 151, 190, 226, 255, 279]
Taus = [2.6474524026975392, 1.651982852985559, 1.0793431020294597, 0.8171909874107741, 0.6329192159654833, 0.5246427838474966, 0.468752585310023, 0.3943320198588099, 0.34233658233849174, 0.314035400291993, 0.30338246040910105,0.2891087574893047, 0.2861944000648665,0.2804274973493638]
Taus_v2 = [1.6655917442406167, 1.1754160164890701, 1.0129299348502132, 0.6747311668331314, 0.5395244011650271, 0.4699144306437099, 0.41805942894076137, 0.3948601586682529, 0.3849453824721735, 0.3752507710597908]
fig2, ax2 = plt.subplots(figsize=(4,3))
detuning_lines = ['Det10', 'Det20', 'Det30', 'Det40', 'Det50', 'Det60', 'Det70', 'Det80', 'Det90']
# Curvas de las simulaciones con tau en unidades de microseg
for det in detuning_lines:
ax2.plot(dataSIM.I**1, [d for d in dataSIM[det]*1e6], 'k-', ms=1, lw=0.5)
# Curvas de las mediciones cambiando el radio posible con tau en microseg
for cambio in [0]: # esto es para shiftear la potencia medida
potencias = dataREAL.Pow - cambio
for rad in [128]: # esto evalua con distintos radios
ax2.errorbar([(p/(np.pi*(rad**2)))**1 for p in UVpotVec], [t for t in Taus], yerr=np.mean(1e6*errsSIM.stdval.values),
fmt='.--', ms=6, lw=.5, \
color="#3949ab",
capsize=2)
ax2.errorbar([(p/(np.pi*(rad**2)))**1 for p in UVpotVec][4:], [t for t in Taus_v2], yerr=np.mean(1e6*errsSIM.stdval.values),
fmt='.--', ms=6, lw=.5, \
color="#3949ab",
capsize=2)
fs = 9
ax2.set_xlim([0.47e-4, 0.8e-2])
#ax2.set_xlim([0., 0.000005])
#ax2.set_ylim([2e-1, 4e1])
ax2.set_ylabel(r"Characteristic time ($\mu$s)", fontname='STIXGeneral')
#ax2.set_xlabel(r"$I = P / (\pi\,r^2)$ [$\mu$W/$\mu$m$^2$]")
ax2.set_xlabel(r'UV intensity ($\mu$W/$\mu$m$^2$)', fontname='STIXGeneral')
# ax2.set_title(r"datos(punteadas) $r \in [30; 190]\ \mu m$ | simulacion(llenas) $\Delta \in -[10; 90]$ MHz")
#ax2.legend(markerscale=2)
#ax2.set_xticks([1e-4, 1e-3])
ax2.set_xticklabels([1e-4, 1e-3], fontsize=fs, fontname='STIXGeneral')
#ax2.set_yticks([1e0, 1e1])
ax2.set_yticklabels([1e0, 1e1], fontsize=fs, fontname='STIXGeneral')
ax2.grid(which='minor', alpha=0.2)
ax2.set_xscale("log")
ax2.set_yscale("log")
plt.savefig('Fig03_varyingUVpower.pdf')
plt.savefig('Fig03_varyingUVpower.svg')
analisis/plots/20250313_fluorescencecorrelationtests/Simulaciones/sim_intensidad_taus_full.csv 0 → 100644
View file @ 50ae040c
This source diff could not be displayed because it is too large. You can view the blob instead.
analisis/plots/20250313_fluorescencecorrelationtests/Transitorios_osci.py 0 → 100644
View file @ 50ae040c
import h5py
import matplotlib.pyplot as plt
import numpy as np
import sys
import re
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/20250313_fluorescencecorrelationtests/Data')
def expo(T, tau, N0, C):
global T0
return N0*np.exp(-(T-T0)/tau) + C
def pow_from_amp(amp):
"""Paso de amplitud urukul a potencia medida por Nico"""
# Forma altamente ineficiente de hacer esto, pero me salio asi
amplitudes_UV = np.flip(np.array([0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30]))
assert amp in amplitudes_UV
potencias_UV = np.flip(np.array([4, 10, 19, 32, 49, 71, 96, 125, 155, 183, 208, 229]))
return potencias_UV[np.where(amplitudes_UV == amp)][0]
def SP_Bkgr_builder(amp_in, amp_fin, derivadainicio, derivadafin, longbins):
CalibCurve = []
j=0
while j<longbins:
if j<=derivadainicio:
CalibCurve.append(amp_in)
elif j>=derivadainicio and j<=derivadafin:
pendiente=(amp_fin-amp_in)/(derivadafin-derivadainicio)
CalibCurve.append(amp_in+pendiente*(j-derivadainicio))
else:
CalibCurve.append(amp_fin)
j=j+1
return CalibCurve
"""
plt.plot(amplitudes_UV, potencias_UV, 'ko-', lw=0.2)
plt.xlabel("Amplitud Urukul")
plt.ylabel("Potencia /uW")
plt.grid()
"""
#%%
import scipy.fftpack
BINW = 1e-2
T0 = -0.4e-6
files = [20322]
SP_Heigths = []
SP_Bins = []
for i, fname in enumerate(files):
#print(i)
#print(fname)
#data = h5py.File('0000'+str(fname)+'-MicromotionCompensation.h5', 'r')
data = h5py.File('0000'+str(fname)+'-CRB.h5', 'r')
# counts = np.array(data['datasets']['countsrf'])
counts = np.array(data['datasets']['counts'])
bines = np.arange(counts.min(), counts.max()+BINW, BINW)
heigs, binsf = np.histogram(counts, bines[bines>T0])
SP_Heigths.append(heigs)
SP_Bins.append(binsf)
#freq: 7.262 mhZ
for k in range(len(counts)):
print(counts[k]-counts[k-1])
#print(1e-6/(counts[k]-counts[k-1]))
#%%
from scipy.optimize import curve_fit
refe = len(SP_Bins[0])
prop = 1
RefBins = [t for t in SP_Bins[0][:int(prop*refe)]]
# plt.figure()
# plt.plot(RefBins[:-1], SP_Heigths[0][:int(prop*refe)],'-o')
x = RefBins[:-1]
y = SP_Heigths[0][:int(prop*refe)]
cc = np.ones(len(counts))
fluo = np.cumsum(cc) - np.polyval(np.polyfit(counts,np.cumsum(cc),1),counts)
plt.figure()
plt.plot(counts,fluo)
plt.xlabel('Tiempo (s)')
plt.ylabel('Cumsum photons')
y = np.array([f for f in fluo])
dt = np.mean(np.diff(np.array(counts)))
print(dt)
plt.figure()
# plt.plot(x, y, label='Señal original')
plt.magnitude_spectrum(y*np.hanning(len(y)),Fs=1/dt)
plt.xlabel('Frecuencia (Hz)')
plt.ylabel('Amplitud')
# plt.xlim(-10,200)
plt.grid(True)
# plt.legend()
#%%
"""
Esto binea fotones asumienod que la cantidad de fotones que llegan en un intervalo tau
esta modulada por una funcion periodica. Entonces a los tiempos de llegada entre
0 y tau no les hace nada, a los tiempos entre tau y 2tau les resta tau,
a los tiempos entre 2tau y 3tau les resta tau, y asi. Y despues
binea y ajusta con una sinusoidal y devuelve la frecuencia del ajuste.
Asi anda barbaro para ver la oscilacion a casi 50 Hz
"""
def bin_time_arrivals(arrival_times, tau):
"""
Binea los tiempos de llegada de los fotones según el periodo tau.
Parameters:
arrival_times (numpy array): Vector con los tiempos de llegada de los fotones.
tau (float): Periodo de bineado.
Returns:
numpy array: Vector con los tiempos bienados.
"""
return arrival_times - tau * (arrival_times // tau)
#taurf = 1/(22.135e6)
taurf = 1/(22.135e6)
tiemposarreglados = bin_time_arrivals(counts, taurf)
b = taurf/100 # Ancho de bineo
bins = np.arange(0, taurf, b)
hist, bin_edges = np.histogram(tiemposarreglados, bins=bins)
colors = ['b', 'g', 'r'] # Colores para cada repetición
x_extended = np.concatenate([bin_edges[:-1] + i * taurf for i in range(3)])
y_extended = np.tile(hist, 3)
def sinusoidal(x, A, B, C, D):
return A * np.sin(B * x + C) + D
x_extended_dense = np.arange(np.min(x_extended),np.max(x_extended),0.1*(x_extended[1]-x_extended[0]))
params, _ = curve_fit(sinusoidal, x_extended, y_extended, p0=[max(y_extended), 2*np.pi/taurf, 0, np.mean(y_extended)])
# Graficar los datos y el ajuste
plt.figure(figsize=(8, 4))
plt.plot(x_extended, y_extended, marker='o', linestyle='-', label="Datos", color='gray')
plt.plot(x_extended_dense, sinusoidal(x_extended_dense, *params), linestyle='--', label="Ajuste sinusoidal", color='red')
for i in range(3):
plt.plot(bin_edges[:-1] + i * taurf, hist, marker='o', linestyle='-', color=colors[i])
plt.xlabel("Tiempo bineado (repetido 3 veces)")
plt.ylabel("Frecuencia")
plt.title("Distribución de tiempos bineados con ajuste sinusoidal")
plt.legend()
plt.show()
print(f'Frecuencia del ajuste: {round(params[1]/(2*np.pi),2)} Hz')
#%%
"""
Esto deberia hacerse asi para ver la oscilacion a la RF (micromocion)
"""
BINW = 1e-2
T0 = -0.4e-6
files = [20476] #el 19613 es el que muestra el pico en 48.5 Hz
SP_Heigths = []
SP_Bins = []
for i, fname in enumerate(files):
#print(i)
#print(fname)
data = h5py.File('0000'+str(fname)+'-CRB.h5', 'r')
counts = np.array(data['datasets']['counts'])
bines = np.arange(counts.min(), counts.max()+BINW, BINW)
heigs, binsf = np.histogram(counts, bines[bines>T0])
SP_Heigths.append(heigs)
SP_Bins.append(binsf)
def bin_time_arrivals(arrival_times, tau):
"""
Binea los tiempos de llegada de los fotones según el periodo tau.
Parameters:
arrival_times (numpy array): Vector con los tiempos de llegada de los fotones.
tau (float): Periodo de bineado.
Returns:
numpy array: Vector con los tiempos bienados.
"""
return arrival_times - tau * (arrival_times // tau)
"""
Amplitud en funcion de la frecuencia de bineo de la rf
"""
freqrf = 7.2618e6
freqrfvec = np.arange(7262561,7262972,0.1)
amps = []
plt.figure()
for freqrf in freqrfvec:
taurf = 1/freqrf
tiemposarreglados = bin_time_arrivals(counts, taurf)
b = taurf/50 # Ancho de bineo
bins = np.arange(0, taurf, b)
hist, bin_edges = np.histogram(tiemposarreglados, bins=bins)
colors = ['b', 'g', 'r'] # Colores para cada repetición
x_extended = np.concatenate([bin_edges[:-1] + i * taurf for i in range(3)])
y_extended = np.tile(hist, 3)
def sinusoidal(x, A, C, D):
return A * np.sin(2*np.pi*freqrf * x + C) + D
x_extended_dense = np.arange(np.min(x_extended),np.max(x_extended),0.1*(x_extended[1]-x_extended[0]))
params, _ = curve_fit(sinusoidal, x_extended, y_extended, p0=[max(y_extended), 0, np.mean(y_extended)])
amps.append(np.abs(params[0]))
plt.plot(freqrfvec,amps,'o')
#%%
"""
Grafico temporal replicado 3 veces a la frecuencia dada
"""
freqrf = 7262766
taurf = 1/freqrf
tiemposarreglados = bin_time_arrivals(counts, taurf)
b = taurf/50 # Ancho de bineo
bins = np.arange(0, taurf, b)
hist, bin_edges = np.histogram(tiemposarreglados, bins=bins)
colors = ['b', 'g', 'r'] # Colores para cada repetición
x_extended = np.concatenate([bin_edges[:-1] + i * taurf for i in range(3)])
y_extended = np.tile(hist, 3)
def sinusoidal(x, A, C, D):
return A * np.sin(2*np.pi*freqrf * x + C) + D
x_extended_dense = np.arange(np.min(x_extended),np.max(x_extended),0.1*(x_extended[1]-x_extended[0]))
params, _ = curve_fit(sinusoidal, x_extended, y_extended, p0=[max(y_extended), 0, np.mean(y_extended)])
amps.append(np.abs(params[0]))
# Graficar los datos y el ajuste
plt.figure(figsize=(8, 4))
# plt.plot(x_extended, y_extended, marker='o', linestyle='-', label="Datos", color='gray')
plt.plot(x_extended_dense, sinusoidal(x_extended_dense, *params), linestyle='--', label="Ajuste sinusoidal", color='black',linewidth=3,zorder=2)
for i in range(3):
plt.plot(bin_edges[:-1] + i * taurf, hist, marker='o', linestyle='-', color=colors[i],markersize=3,zorder=1)
plt.xlabel("Tiempo bineado (repetido 3 veces)")
plt.ylabel("Frecuencia")
plt.title("Distribución de tiempos bineados con ajuste sinusoidal")
plt.ylim(0,np.max(hist)*1.2)
plt.legend()
plt.show()
print(f'Amplitud normalizada: {np.abs(round(100*params[0]/params[2],3))}%')
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