caro

analisis/plots/20250421_fluorescencorrelation/FluoCorrForCompensation.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Apr 23 14:52:32 2025
+
+@author: carolina
+"""
+
+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
+
+
+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)
+
+def sinusoidal(x, A, C, D):
+    return A * np.sin(2*np.pi*freqrf * x + C) + D
+
+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
+
+
+
+def dropnan(a):
+    return a[~np.isnan(a)]
+
+path = "/home/carolina/Documents/artiq_experiments/analisis/plots/20250421_fluorescencorrelation/"
+
+
+freqrf = 7262766.1
+taurf = 1/freqrf
+
+
+meds = [20889, 20890, 20891, 20892, 20893, 20894, 20895, 20896, 20897, 20898, 20899, 20900, 20901, 20903, 20904 , 20905 , 20906, 20907, 20908, 20909, 20910, 20911]
+Vdouble =  [0, 10, 20, 40, 50, 50, 55, 55, 55, 60, 65, 70, 75, 80, 85, 90,100, 110, 130, 150, 170, 190]
+
+
+
+y_mean_values = []
+y_max_of_norm_values = []
+y_max = []
+params_meds = []
+
+
+Vdouble_fitted = []
+amp_fitted = []
+amp_fitted_err = []
+offset_fitted = []
+
+meds = [meds[-1]]
+for i, med in enumerate(meds):
+    #print(i)
+
+    filename = f"0000{med}-FluorescenceCorrelation.h5"
+    data = h5py.File(path + filename, 'r')
+    
+    x_data = dropnan(np.array(data['datasets']['x_data']))
+    y_data = dropnan(np.array(data['datasets']['y_data']))
+    y_data_2 = dropnan(np.array(data['datasets']['y_data_2']))
+    
+    """
+    Grafico temporal replicado 3 veces a la frecuencia dada
+    """
+    plt.figure(1, figsize=(8, 4))
+
+    try:
+        params, pcov = curve_fit(sinusoidal, x_data, y_data)
+        perr = np.sqrt(np.diag(pcov))
+        plt.plot(x_data, sinusoidal(x_data, *params), linestyle='--', label="Ajuste sinusoidal", color='black',linewidth=3,zorder=2)
+        # params, _ = curve_fit(lorentzian, x_data, y_data)
+        # plt.plot(x_data, lorentzian(x_data, *params), linestyle='--', label="Ajuste completo", color='black',linewidth=3,zorder=2)
+        
+        Vdouble_fitted.append(Vdouble[i])
+        amp_fitted.append(params[0])
+        amp_fitted_err.append(perr[0])
+        offset_fitted.append(params[2])
+        
+    except:
+        pass
+    
+    plt.plot(x_data, y_data)
+    plt.xlabel("Tiempo bineado (repetido 3 veces)")
+
+    plt.show()
+
+    y_mean_values.append(np.mean(y_data_2))
+    y_max_of_norm_values.append(np.max(y_data))
+    y_max.append(np.max(y_data_2))
+    
+
+
+plt.figure(2), plt.clf()
+plt.errorbar(Vdouble_fitted, np.abs(amp_fitted),fmt = '.', yerr=amp_fitted_err)
+
+
+
+
+
+
+