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artiq/examples/master/repository/flopping_f_simulation.py

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Python

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)
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)