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examples/flopping_f_simulation: fitting

This commit is contained in:
Sebastien Bourdeauducq 2015-01-21 14:35:37 +08:00
parent 0983862c03
commit ef32e7aa7a
2 changed files with 20 additions and 9 deletions

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@ -1,10 +1,18 @@
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=1500, A=80, B=40, t=0.02, tpi=0.03):
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)))
@ -16,6 +24,9 @@ class FloppingF(AutoDB):
min_freq = Argument(1000)
max_freq = Argument(2000)
F0 = Argument(1500)
noise_amplitude = Argument(0.1)
frequency = Result()
brightness = Result()
@ -30,16 +41,16 @@ class FloppingF(AutoDB):
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)
brightness = model(frequency, self.F0) + self.noise_amplitude*random.random()
self.frequency.append(frequency)
self.brightness.append(brightness)
time.sleep(0.1)
self.analyze()
def analyze(self):
min_f = self.frequency.read[0]
min_b = self.brightness.read[0]
for f, b in zip(self.frequency.read, self.brightness.read):
if b < min_b:
min_f, min_b = f, b
self.flopping_freq = min_f
popt, pcov = curve_fit(lambda xdata, F0: [model(x, F0) for x in xdata],
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)

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@ -1 +1 @@
{}
{"flopping_freq": 1500.0294421161527}