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examples/flopping_f_simulation: demonstrate previous functionality

This commit is contained in:
Sebastien Bourdeauducq 2015-08-06 18:43:54 +08:00
parent 9261254653
commit 1f5a49d263
1 changed files with 17 additions and 6 deletions
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23
examples/master/repository/flopping_f_simulation.py
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@ -31,14 +31,18 @@ class FloppingF(EnvExperiment):
default=LinearScan(1000, 2000, 100))) default=LinearScan(1000, 2000, 100)))
self.attr_argument("F0", NumberValue(1500, min=1000, max=2000)) self.attr_argument("F0", NumberValue(1500, min=1000, max=2000))
self.attr_argument("noise_amplitude", NumberValue(0.1, min=0, max=100)) self.attr_argument("noise_amplitude", NumberValue(0.1, min=0, max=100,
step=0.01))
self.frequency = self.set_result("flopping_f_frequency", [], True)
self.brightness = self.set_result("flopping_f_brightness", [], True)
self.attr_device("scheduler") self.attr_device("scheduler")
def run(self): def run(self):
self.frequency = self.set_result("flopping_f_frequency", [],
realtime=True, store=False)
self.brightness = self.set_result("flopping_f_brightness", [],
realtime=True)
self.set_result("flopping_f_fit", [], realtime=True, store=False)
for frequency in self.frequency_scan: for frequency in self.frequency_scan:
brightness = model(frequency, self.F0) + self.noise_amplitude*random.random() brightness = model(frequency, self.F0) + self.noise_amplitude*random.random()
self.frequency.append(frequency) self.frequency.append(frequency)
@ -48,9 +52,16 @@ class FloppingF(EnvExperiment):
self.scheduler.priority, time.time() + 20, False) self.scheduler.priority, time.time() + 20, False)
def analyze(self): def analyze(self):
# Use get_result so that analyze can be run stand-alone.
frequency = self.get_result("flopping_f_frequency")
brightness = self.get_result("flopping_f_brightness")
popt, pcov = curve_fit(model_numpy, popt, pcov = curve_fit(model_numpy,
self.frequency.read, self.brightness.read, frequency, brightness,
p0=[self.get_parameter("flopping_freq")]) p0=[self.get_parameter("flopping_freq")])
perr = np.sqrt(np.diag(pcov)) perr = np.sqrt(np.diag(pcov))
if perr < 0.1: if perr < 0.1:
self.set_parameter("flopping_freq", float(popt)) F0 = float(popt)
self.set_parameter("flopping_freq", F0)
self.set_result("flopping_f_fit",
[model(x, F0) for x in frequency],
realtime=True, store=False)