mirror of https://github.com/m-labs/artiq.git
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
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.experiment 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)*(
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1 - np.cos(np.pi*t/tpi*np.sqrt(4*tpi**2*(x - F0)**2 + 1))
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)
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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.setattr_argument("frequency_scan", Scannable(
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default=RangeScan(1000, 2000, 100)))
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self.setattr_argument("F0", NumberValue(1500, min=1000, max=2000))
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self.setattr_argument("noise_amplitude", NumberValue(
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0.1, min=0, max=100, step=0.01))
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self.setattr_device("scheduler")
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self.setattr_device("ccb")
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def run(self):
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l = len(self.frequency_scan)
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self.set_dataset("flopping_f_frequency",
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np.full(l, np.nan),
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broadcast=True, save=False)
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self.set_dataset("flopping_f_brightness",
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np.full(l, np.nan),
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broadcast=True)
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self.set_dataset("flopping_f_fit", np.full(l, np.nan),
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broadcast=True, save=False)
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self.ccb.issue("create_applet", "flopping_f",
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"${artiq_applet}plot_xy "
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"flopping_f_brightness --x flopping_f_frequency "
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"--fit flopping_f_fit")
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for i, f in enumerate(self.frequency_scan):
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m_brightness = model(f, self.F0) + self.noise_amplitude*random.random()
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self.mutate_dataset("flopping_f_frequency", i, f)
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self.mutate_dataset("flopping_f_brightness", i, m_brightness)
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time.sleep(0.1)
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self.scheduler.submit(due_date=time.time() + 20)
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def analyze(self):
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# Use get_dataset so that analyze can be run stand-alone.
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brightness = self.get_dataset("flopping_f_brightness")
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try:
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frequency = self.get_dataset("flopping_f_frequency", archive=False)
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except KeyError:
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# Since flopping_f_frequency is not saved, it is missing if
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# analyze() is run on HDF5 data. But assuming that the arguments
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# have been loaded from that same HDF5 file, we can reconstruct it.
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frequency = np.fromiter(self.frequency_scan, np.float)
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assert frequency.shape == brightness.shape
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self.set_dataset("flopping_f_frequency", frequency,
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broadcast=True, save=False)
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popt, pcov = curve_fit(model, frequency, brightness,
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p0=[self.get_dataset("flopping_freq", 1500.0,
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archive=False)])
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perr = np.sqrt(np.diag(pcov))
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if perr < 0.1:
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F0 = float(popt)
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self.set_dataset("flopping_freq", F0, persist=True, save=False)
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self.set_dataset("flopping_f_fit",
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np.array([model(x, F0) for x in frequency]),
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broadcast=True, save=False)
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