from math import sqrt, cos, pi import time import random import numpy as np from scipy.optimize import curve_fit from artiq import * def model(x, F0): t = 0.02 tpi = 0.03 A = 80 B = 40 return A+(B-A)/2/(4*tpi**2*(x-F0)**2+1)*(1-cos(pi*t/tpi*sqrt(4*tpi**2*(x-F0)**2+1))) def model_numpy(xdata, F0): r = np.zeros(len(xdata)) for i, x in enumerate(xdata): r[i] = model(x, F0) return r class FloppingF(Experiment, AutoDB): """Flopping F simulation""" class DBKeys: npoints = Argument(100) min_freq = Argument(1000) max_freq = Argument(2000) F0 = Argument(1500) noise_amplitude = Argument(0.1) frequency = Result() brightness = Result() flopping_freq = Parameter() realtime_results = { ("frequency", "brightness"): "xy" } def run(self): for i in range(self.npoints): frequency = (self.max_freq-self.min_freq)*i/(self.npoints - 1) + self.min_freq brightness = model(frequency, self.F0) + self.noise_amplitude*random.random() self.frequency.append(frequency) self.brightness.append(brightness) time.sleep(0.1) self.scheduler.submit(self.scheduler.pipeline_name, self.scheduler.expid, self.scheduler.priority, time.time() + 20, False) def analyze(self): popt, pcov = curve_fit(model_numpy, self.frequency.read, self.brightness.read, p0=[self.flopping_freq]) perr = np.sqrt(np.diag(pcov)) if perr < 0.1: self.flopping_freq = float(popt)