agrego script para ver el prendido del aom uv

analisis/plots/20210705_EncendidoAOM_UV/Encendido_AOM_UV.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  7 16:12:04 2021
+
+@author: oem
+"""
+
+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
+from scipy.optimize import curve_fit
+from scipy.special import factorial
+from scipy.stats import poisson
+
+# Solo levanto algunos experimentos
+ALL_FILES_SP = """000001556-SingleLine.h5
+000001558-SingleLine.h5
+000001559-SingleLine.h5
+000001560-SingleLine.h5"""
+
+#000001514-SingleLine.h5 #este tiene amplitud 0.08 que es muy poquito
+
+BINW = 1e-9
+T0 = 0.0e-6
+
+os.chdir('/home/oem/Documentos/Doctorado/Artiq/Repositorio/artiq_experiments/artiq_master/results/2021-07-05/16')
+
+fig0, ax0 = plt.subplots()
+#ax1_b = ax1_a.twinx()
+
+for i, fname in enumerate(ALL_FILES_SP.split()):
+    if i == 3:
+        print(i)
+        print(fname)
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+    
+        # Aca hago algo repugnante para poder levantar los strings que dejamos
+        # que además tenian un error de tipeo al final. Esto no deberá ser necesario
+        # cuando se solucione el error este del guardado.
+        laser_UV_amp = data['datasets']['laser_UV_amp']
+        laser_UV_freq = data['datasets']['laser_UV_freq']
+        counts = np.array(data['datasets']['counts'])
+        bines = np.arange(counts.min(), counts.max()+BINW, BINW)
+        
+        heigs, binsf  = np.histogram(counts, bines[bines>T0])
+        
+        times = [t*1e6 for t in binsf[:-1]]
+        
+        #ax0.step([t*1e6 for t in binsf[:-1]], heigs, label=f"AMP: {laser_UV_amp}", where='mid', color='blue', lw=1, alpha=0.4)
+        ax0.plot(times, heigs, 'o-', color='blue', lw=1)
+        #ax0.set_xlim(0.95, 1.2)
+        ti = 1.045
+        tf = 1.1
+        #ax0.axvline(ti, color='red', linestyle='dashed', linewidth=2)
+        #ax0.axvline(tf, color='red', linestyle='dashed', linewidth=2)
+        ax0.set_title(f'UV AOM on, 5 M meds, bin {int(BINW*1e9)} ns, turning on: {round((tf-ti)*1000)} ns')
+        ax0.set_xlabel('Time (us)')
+        ax0.set_ylabel('Counts')
+        ax0.grid()
+
+#%%
+
+"""
+Ahora veo el comportamiento estadistico de oscuridad y del laser prendido a ver si es poissoniano
+"""
+
+def fit_function(k, lamb):
+    '''poisson function, parameter lamb is the fit parameter'''
+    return poisson.pmf(k, lamb)
+
+
+binw = 1
+
+counts_UVoff = heigs[0:int(0.45*len(heigs))]
+counts_UVon = heigs[int(0.55*len(heigs)):]
+
+N = len(counts_UVoff)
+
+mediaoff = np.mean(counts_UVoff)
+mediaon = np.mean(counts_UVon)
+
+binesoff = np.arange(counts_UVoff.min(), counts_UVoff.max()+binw, binw)
+bineson = np.arange(counts_UVon.min(), counts_UVon.max()+binw, binw)
+
+heigsoff, binsoff = np.histogram(counts_UVoff, binesoff)
+heigson, binson = np.histogram(counts_UVon, bineson)
+
+
+#plt.figure()
+#plt.plot(binsoff[:-1], heigsoff, 'o-', bineson[:-1], heigson, 'o-')
+
+
+"""
+#Puedo ajustar con poissoniana o directamente plotearle una
+
+parametersoff, cov_matrixoff = curve_fit(fit_function, binsoff[:-1], heigsoff/sum(heigsoff))
+parameterson, cov_matrixon = curve_fit(fit_function, binson[:-1], heigson/sum(heigson))
+"""
+
+xoff = binsoff[:-1]
+xon = binson[:-1]
+
+plt.figure()
+plt.plot(binsoff[:-1], heigsoff/sum(heigsoff), 'ko', label='Laser UV off')
+plt.plot(xoff, [fit_function(k, np.mean(counts_UVoff)) for k in xoff], 'r', lw=3)
+plt.plot(binson[:-1], heigson/sum(heigson), 'bo', label='Laser UV on')
+plt.plot(xon,[fit_function(k, np.mean(counts_UVon)) for k in xon], 'r', lw=3)
+plt.xlabel('Counts')
+plt.ylabel('Occurrences')
+plt.title(f'Bin: {binw} ns, {N} measurements, no ions')
+plt.legend()
+
+
+
+
+
+