forked from M-Labs/artiq
examples/flopping_f_simulation: fitting
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@ -1,10 +1,18 @@
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from math import sqrt, cos, pi
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import time
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import random
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import numpy as np
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from scipy.optimize import curve_fit
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from artiq import *
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def model(x, F0=1500, A=80, B=40, t=0.02, tpi=0.03):
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def model(x, F0):
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t = 0.02
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tpi = 0.03
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A = 80
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B = 40
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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)))
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@ -16,6 +24,9 @@ class FloppingF(AutoDB):
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min_freq = Argument(1000)
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max_freq = Argument(2000)
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F0 = Argument(1500)
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noise_amplitude = Argument(0.1)
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frequency = Result()
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brightness = Result()
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@ -30,16 +41,16 @@ class FloppingF(AutoDB):
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def run(self):
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for i in range(self.npoints):
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frequency = (self.max_freq-self.min_freq)*i/(self.npoints - 1) + self.min_freq
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brightness = model(frequency)
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brightness = model(frequency, self.F0) + self.noise_amplitude*random.random()
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self.frequency.append(frequency)
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self.brightness.append(brightness)
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time.sleep(0.1)
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self.analyze()
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def analyze(self):
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min_f = self.frequency.read[0]
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min_b = self.brightness.read[0]
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for f, b in zip(self.frequency.read, self.brightness.read):
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if b < min_b:
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min_f, min_b = f, b
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self.flopping_freq = min_f
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popt, pcov = curve_fit(lambda xdata, F0: [model(x, F0) for x in xdata],
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self.frequency.read, self.brightness.read,
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p0=[self.flopping_freq])
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perr = np.sqrt(np.diag(pcov))
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if perr < 0.1:
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self.flopping_freq = float(popt)
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@ -1 +1 @@
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{}
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{"flopping_freq": 1500.0294421161527}
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