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examples/flopping_f_simulation: numpy outputs

This commit is contained in:
Sebastien Bourdeauducq 2015-10-26 00:33:11 +08:00
parent a6d38b8a97
commit 24e728f075
1 changed files with 12 additions and 8 deletions
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20
examples/master/repository/flopping_f_simulation.py
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@ -37,16 +37,20 @@ class FloppingF(EnvExperiment):
self.setattr_device("scheduler") self.setattr_device("scheduler")
def run(self): def run(self):
frequency = self.set_dataset("flopping_f_frequency", [], l = len(self.frequency_scan)
frequency = self.set_dataset("flopping_f_frequency",
np.full(l, np.nan),
broadcast=True, save=False) broadcast=True, save=False)
brightness = self.set_dataset("flopping_f_brightness", [], brightness = self.set_dataset("flopping_f_brightness",
np.full(l, np.nan),
broadcast=True) broadcast=True)
self.set_dataset("flopping_f_fit", [], broadcast=True, save=False) self.set_dataset("flopping_f_fit", np.full(l, np.nan),
broadcast=True, save=False)
for f in self.frequency_scan: for i, f in enumerate(self.frequency_scan):
m_brightness = model(f, self.F0) + self.noise_amplitude*random.random() m_brightness = model(f, self.F0) + self.noise_amplitude*random.random()
frequency.append(f) frequency[i] = f
brightness.append(m_brightness) brightness[i] = m_brightness
time.sleep(0.1) time.sleep(0.1)
self.scheduler.submit(self.scheduler.pipeline_name, self.scheduler.expid, self.scheduler.submit(self.scheduler.pipeline_name, self.scheduler.expid,
self.scheduler.priority, time.time() + 20, False) self.scheduler.priority, time.time() + 20, False)
@ -57,11 +61,11 @@ class FloppingF(EnvExperiment):
brightness = self.get_dataset("flopping_f_brightness") brightness = self.get_dataset("flopping_f_brightness")
popt, pcov = curve_fit(model_numpy, popt, pcov = curve_fit(model_numpy,
frequency, brightness, frequency, brightness,
p0=[self.get_dataset("flopping_freq")]) p0=[self.get_dataset("flopping_freq", 1500.0)])
perr = np.sqrt(np.diag(pcov)) perr = np.sqrt(np.diag(pcov))
if perr < 0.1: if perr < 0.1:
F0 = float(popt) F0 = float(popt)
self.set_dataset("flopping_freq", F0, persist=True, save=False) self.set_dataset("flopping_freq", F0, persist=True, save=False)
self.set_dataset("flopping_f_fit", self.set_dataset("flopping_f_fit",
[model(x, F0) for x in frequency], np.array([model(x, F0) for x in frequency]),
broadcast=True, save=False) broadcast=True, save=False)