def compute(sample_positions, emitter_position, grid_size, step_size,
resolution, base_position):
env = Environment(emitter_position, 10.0, 0.5)
samples = []
for x, y in sample_positions:
t = (x, y, env.MeasuredPower(x, y))
samples.append(t)
return pyQxy(samples, resolution, grid_size)
def simulate_predictions(self, phenome, num_trials):
from nllsCuda import pyPredict, pyQxy, pyMultipleFitness
from Environment import Environment
env = Environment((self.emitter_x, self.emitter_y), 10.0,
self.noise_stddev)
samples = []
for trial in xrange(num_trials):
t = []
for position in phenome.get_position():
x, y = position
t.append((x, y, env.MeasuredPower(x, y)))
samples.append(t)
results = pyPredict(samples, [512, 512], (self.grid_x, self.grid_y))
return results
