def check_expectation_one(exp_class):
e = exp_class()
x = np.random.rand(2, 2)
dt = np.random.rand()
e.update(x, dt)
v = e.get_value()
assert_allclose(v, x)
def check_efficient_exp(exp_class, sequence):
""" Checks that the dt is working ok. """
exp1 = exp_class()
xs = []
T = 0
for x in sequence:
dt = np.random.rand()
xs.append(x * dt)
T += dt
exp1.update(x, dt=dt)
es1 = exp1.get_value()
expected = np.array(xs).sum(axis=0) / T
assert_allclose(es1, expected)
def check_my_doubt(exp_class):
values = [100.0, 200.0]
mean = 150.0
for nd in [1, 10, 100, 1000, 2000]:
samples = []
for v in values:
samples.extend([v] * nd)
samples = np.array(samples)
ex = exp_class()
for s in samples:
ex.update(np.array([s]))
# print('accum: %10.4f mass: %10.4f ' % (ex.accum, ex.accum_mass))
# ex.get_value()
result = ex.get_value()
# print('accum: %10.4f mass: %10.4f ' % (ex.accum, ex.accum_mass))
assert_allclose(result, mean)
def get_uniform_directions(fov_deg, num_sensels):
""" Returns a set of directions uniform in space """
if fov_deg == 360:
ray_dist = 2 * np.pi / (num_sensels)
directions = np.linspace(-np.pi + ray_dist / 2,
+ np.pi - ray_dist + ray_dist / 2,
num_sensels)
assert_allclose(directions[-1] - directions[0], 2 * np.pi - ray_dist)
t = np.rad2deg(directions)
a = t[1:] - t[:-1]
b = t[0] - t[-1] + 360
assert_allclose(a[0], b)
else:
fov_rad = np.radians(fov_deg)
directions = np.linspace(-fov_rad / 2, +fov_rad / 2, num_sensels)
assert_allclose(directions[-1] - directions[0], fov_rad)
assert len(directions) == num_sensels
return directions
