def run(self):
"""
Run stuff and save stuff.
:return:
"""
out_dir = "out_directories"
name = "test1.avi"
out_path = os.path.abspath(os.path.join(out_dir, name))
self.make_temp_directory(out_dir)
self.ani.run(out_path, 10, 10)
self.estmd.open_movie(out_path)
self.estmd.run(by_frame=True)
self.estmd.run(by_frame=True)
frame_array = self.estmd.create_list_of_arrays()
n = len(frame_array)
for i in range(n):
frame_array[i] = {'frame': frame_array[i].flatten()}
self.cstmd.input = frame_array
self.cstmd.spike_trains = cstmd.run()[1]
# Adding simulation
sim = Simulation("Test", True)
sim.spike_trains = self.cstmd.spike_trains
sim.start_positions = 0
sim.pattern_duration = self.cstmd.duration
sim.num_afferents = self.cstmd.spike_trains.shape[0]
sim.duration = self.cstmd.spike_trains.shape[1]
sim.sampling_interval = math.ceil(self.cstmd.duration / 5)
n1 = sim.add_neuron(0.03125, .95, 300)
n2 = sim.add_neuron(0.03125, 0.91, 125)
n3 = sim.add_neuron(0.03125, 0.91, 125)
n1.connect(n2)
n1.connect(n3)
n2.connect(n3)
sim.run()
# Adding ActionSelection
pattern_input = []
for neuron in sim.neurons:
pattern_input.append(neuron.spike_times)
print "================================"
print pattern_input
print "================================"
a_s = ActionSelection(pattern_input=pattern_input,
pattern_duration=self.ani.total_frames,
animation=self.ani)
a_s.run(show_plots=False)
def run_simulation(self,
N=4,
taum=10*ms,
taupre=20*ms,
taupost=20*ms,
tauc=20*ms,
tauDop=20*ms,
Ee=0*mV,
vt=-54*mV,
vr=-60*mV,
El=-74*mV,
taue=5*ms,
F=15*Hz,
gmax=1,
dApre=1,
sim_time=100.0*ms,
frame_length=10.0*ms,
dopBoost=0.5,
reward_distance=40,
SPEED_FACTOR=2*second,
dragonfly_start=[300, 300, 0.0],
description="",
pattern_duration=None,
pattern_input=None,
animation=None,
animation_id=None,
pattern_recognition_id=None,
saved_weights=None,
training=True):
"""
Generates and saves a simulation.
"""
# Get the input.
# Instantiate an ActionSelection object.
a_s = ActionSelection(N=N,
taum=taum,
taupre=taupre,
taupost=taupost,
tauc=tauc,
tauDop=tauDop,
Ee=Ee,
vt=vt,
vr=vr,
El=El,
taue=taue,
F=F,
gmax=gmax,
dApre=dApre,
sim_time=sim_time,
frame_length=frame_length,
dopBoost=dopBoost,
reward_distance=reward_distance,
SPEED_FACTOR=SPEED_FACTOR,
dragonfly_start=dragonfly_start,
description=description,
animation=animation,
pattern_duration=pattern_duration,
pattern_input=pattern_input,
animation_id=animation_id,
pattern_recognition_id=pattern_recognition_id,
saved_weights=saved_weights,
training=training)
a_s.run()
# Save action selection simulation.
_id = self.save(a_s)
# Save video to filesystem.
self.save_video(a_s, str(_id))
# Save graphs.
self.save_graphs(a_s, str(_id))
return _id