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artiq_experiments
Commit 92677253 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

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analisis/plots/20250716_fluorescence_correlation/check_rffrequency.py deleted 100644 → 0
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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/20250408_FluorescenceCorrelationsTests/Data1')
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 = [22838]
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('./Data/0000'+str(fname)+'-FluorescenceCorrelation.h5', 'r')
# counts = np.array(data['datasets']['countsrf'])
counts = np.array(data['datasets']['time_stamps'])
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]))
#%%
"""
Esto deberia hacerse asi para ver la oscilacion a la RF (micromocion)
"""
BINW = 1e-3
T0 = -0.4e-6
files = [22845]
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)
SP_Heigths = []
SP_Bins = []
plt.figure()
for i, fname in enumerate(files):
#print(i)
#print(fname)
data = h5py.File('./Data/0000'+str(fname)+'-FluorescenceCorrelation.h5', 'r')
counts = np.array(data['datasets']['time_stamps'])
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)
"""
Amplitud en funcion de la frecuencia de bineo de la rf
"""
freqrf = 7186226.67
freqrfvec = freqrf + np.arange(-5,5,0.01)
amps = []
for freqrf in freqrfvec:
taurf = 1/freqrf
tiemposarreglados = bin_time_arrivals(counts, taurf)
b = taurf/500 # 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([f*1e-6 for f in freqrfvec],amps,'o')
print(freqrfvec[np.argmax(amps)])
plt.xlabel('Frecuencia RF (MHz)')
plt.ylabel('Amplitud oscilacion')
#plt.axvline(7262766.13*1e-6,linewidth=4,color='red',linestyle='dashed')
#%%
"""
Grafico temporal replicado 3 veces a la frecuencia dada
"""
#freqrf = 7262766.13
freqrf = 7186226.62
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
def lorentzian(x,A,b,C,det,fase,cte):
det = -10e6*2*np.pi
return A*((C**2)/(C**2 + (det-b*2*np.pi*freqrf*np.sin(2*np.pi*freqrf*x+fase))**2))+cte
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)])
#params, _ = curve_fit(lorentzian, x_extended, y_extended, p0=[1,1e-3,1e7,1e4,1,1])
amps.append(np.abs(params[0]))
# Graficar los datos y el ajuste
plt.figure(figsize=(8, 4))
#plt.plot(x_extended_dense, sinusoidal(x_extended_dense, *params), linestyle='--', label="Ajuste sinusoidal", color='black',linewidth=3,zorder=2)
#plt.plot(x_extended_dense, lorentzian(x_extended_dense, *params), linestyle='--', label="Ajuste completo", 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 completo")
#plt.ylim(0,np.max(hist)*1.2)
plt.ylim(0,400)
plt.legend()
plt.show()
print(f'Amplitud normalizada: {np.abs(round(100*params[0]/params[2],3))}%')
print(f'Beta: {np.abs(params[1])}')
print(f'Amplitud: {params[0]}')
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