forked from M-Labs/artiq
57 lines
1.7 KiB
Python
57 lines
1.7 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(EnvExperiment):
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"""Flopping F simulation"""
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def build(self):
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self.attr_argument("frequency_scan", Scannable(
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default=LinearScan(1000, 2000, 100)))
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self.attr_argument("F0", NumberValue(1500, min=1000, max=2000))
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self.attr_argument("noise_amplitude", NumberValue(0.1, min=0, max=100))
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self.frequency = self.set_result("flopping_f_frequency", [], True)
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self.brightness = self.set_result("flopping_f_brightness", [], True)
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self.attr_device("scheduler")
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def run(self):
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for frequency in self.frequency_scan:
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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.submit(self.scheduler.pipeline_name, self.scheduler.expid,
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self.scheduler.priority, time.time() + 20, False)
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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.get_parameter("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.set_parameter("flopping_freq", float(popt))
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