forked from M-Labs/artiq
66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
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):
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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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def model_numpy(xdata, F0):
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r = np.zeros(len(xdata))
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for i, x in enumerate(xdata):
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r[i] = model(x, F0)
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return r
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class FloppingF(AutoDB):
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__artiq_unit__ = "Flopping F simulation"
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class DBKeys:
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implicit_core = False
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npoints = Argument(100)
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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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flopping_freq = Parameter()
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@staticmethod
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def realtime_results():
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return {
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("frequency", "brightness"): "xy"
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}
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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, 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.scheduler.run_timed(self.run_params, time.time() + 20)
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def analyze(self):
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popt, pcov = curve_fit(model_numpy,
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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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