def add_noise_helper(self, node, noise_std):
if (not (node.depth == 0 or node.depth == 1)):
node.noised_position = [
norm(node.position[0], noise_std),
norm(node.position[1], noise_std),
norm(node.position[2], noise_std)
]
else:
node.noised_position = node.position[:3]
for child in node.children:
self.add_noise_helper(child, noise_std)
def source_wander(self, scale = BODY_LEN):
"""Where is that smell coming from?"""
if SOURCE_WANDERS is True:
self.source = [s+norm(scale=scale) for s in self.source]
if any((self.source[0]<0, self.source[0]>BOX_SIZE[0],
self.source[0]<0, self.source[0]>BOX_SIZE[0])):
self.source_wander(scale)
def test_multiple_update(self):
measurement_count = 60
initial_truth = np.array([24, 45])
error_variance = 5.0
error_covariance = np.multiply(error_variance, np.identity(2))
measurements = norm(mean=initial_truth,
cov=error_covariance,
size=measurement_count)
# pass in first point: without previous state continuity
priorEvent = None
events = []
results = np.empty_like(measurements)
resultIndex = 0
for location in measurements:
currentEvent = createEvent(location, prior=priorEvent)
result = lambda_handler(currentEvent, None)
results[resultIndex] = [result['latitude'], result['longitude']]
resultIndex += 1
# if 'prior' in currentEvent:
# print("....[{:>8.6}, {:>8.6}] => [{:>8.6}, {:>8.6}]( {:>6.6})".format(
# location[0], location[1],
# currentEvent['prior']['latitude'], currentEvent['prior']['longitude'], currentEvent['prior']['variance']))
# print("........................... [{:>8.6}, {:>8.6}]( {:>6.6})".format(
# result['latitude'], result['longitude'], result['variance']))
priorEvent = result
# print( results )
self.assertAlmostEqual(result['latitude'], initial_truth[0], delta=5)
self.assertAlmostEqual(result['longitude'], initial_truth[1], delta=5)
self.assertAlmostEqual(result['variance'], 1, delta=2)
def add_noise_vary_helper(self, node, noise_std):
if (not (node.depth == 0 or node.depth == 1)):
if ("Hand" in node.name or "Foot" in node.name):
node.noised_position = [
norm(node.position[0], noise_std),
norm(node.position[1], noise_std),
norm(node.position[2], noise_std)
]
else:
node.noised_position = [
norm(node.position[0], 0.1),
norm(node.position[1], 0.1),
norm(node.position[2], 0.1)
]
else:
node.noised_position = node.position[:3]
for child in node.children:
self.add_noise_vary_helper(child, noise_std)
from numpy.random import normal as norm
import matplotlib.pyplot as plt
from general_class_balancer import *
# Sample script showing how this balances on simulated, random data.
show_balanced = True
N = 12000 # Number of datapoints
confounds = np.random.rand(2, N)
# Unfortunately, numpy arrays don't support mixed types, so in order to mix
# floats (i.e., continuous covariates) and strings (i.e., discrete, or labeled,
# covariates), it is necessary to cast them all as objects. Slows things down,
# but if covariates are all of one type, they can be cast as one or the other.
c1 = np.array([norm(1, 2, N / 3),
norm(0.5, 2, N / 3),
norm(2, 1, N / 3)],
dtype=object)
c2 = np.array([norm(3, 2, N / 3),
norm(0.5, 2, N / 3),
norm(3.1, 0.25, N / 3)],
dtype=object)
c3 = np.array([norm(2, 1, N / 3),
norm(0.5, 2, N / 3),
norm(2.9, 1, N / 3)],
dtype=object)
confounds = np.concatenate((c1, c2, c3), axis=1)
c4 = np.array([[np.random.choice(["a", "b", "c"]) for x in range(N)]],
dtype=object)
confounds = np.concatenate((confounds, c4), axis=0)
for n in range(numtrials):
r = N / (4 * numharm) + uniform(0.0, 1.0,
1)[0] # average freq over "observation"
z = uniform(-100, 100, 1)[0] # average fourier f-dot
w = uniform(-600, 600, 1)[0] # fourier freq double deriv
# w = 0.0 # fourier freq double deriv
data = np.zeros_like(us)
for ii in range(numharm):
rh = r * (ii + 1)
zh = z * (ii + 1)
wh = w * (ii + 1)
r0 = rh - 0.5 * zh + wh / 12.0 # Make symmetric for all z and w
z0 = zh - 0.5 * wh
phss = 2.0 * np.pi * (us * (us * (us * wh / 6.0 + z0 / 2.0) + r0))
data += np.cos(phss)
data += noiseamp * norm(N)
ft = presto.rfft(data)
offset = uniform(-1.0, 1.0, 3) * np.array([0.5, 2.0, 20.0]) / numharm
a = time.clock()
if (numharm > 1):
[maxpow, rmax, zmax, rds] = presto.maximize_rz_harmonics(ft,
r + offset[0],
z + offset[1],
numharm,
norm=1.0)
else:
[maxpow, rmax, zmax, rd] = presto.maximize_rz(ft,
r + offset[0],
z + offset[1],
import ppgplot
from Pgplot import pgpalette
from numpy.random import standard_normal as norm
import time
N = 2**14
r = N/4.0 # average freq over "observation"
#r = N/4.0 + 0.5 # average freq over "observation"
rint = num.floor(r)
dr = 1.0/32.0
dz = 0.18
np = 512 # number of pixels across for f-fdot image
z = 10.0 # average fourier f-dot
w = -40.0 # fourier freq double deriv
noise = 0.0
noise = 1.0*norm(N)
us = num.arange(N, dtype=num.float64) / N # normalized time coordinate
r0 = r - 0.5 * z + w / 12.0 # Make symmetric for all z and w
z0 = z - 0.5 * w
phss = 2.0 * num.pi * (us * (us * (us * w/6.0 + z0/2.0) + r0))
ft = presto.rfft(num.cos(phss)+noise)
ffdot = presto.ffdot_plane(ft, rint-np/2*dr, dr, np, 0.0-np/2*dz, dz, np)
pffdot = presto.spectralpower(ffdot.flat)
theo_max_pow = N**2.0/4.0
frp = max(pffdot) / theo_max_pow # Fraction of recovered power
print "Fraction of recovered signal power = %f" % frp
a = time.clock()
[maxpow, rmax, zmax, rd] = presto.maximize_rz(ft, r+norm(1)[0]/5.0,
z+norm(1)[0], norm=1.0)
print "Time for rz:", time.clock()-a
for n in range(numtrials):
r = N/(4*numharm) + uniform(0.0, 1.0, 1)[0] # average freq over "observation"
z = uniform(-100, 100, 1)[0] # average fourier f-dot
w = uniform(-600, 600, 1)[0] # fourier freq double deriv
w = 0.0 # fourier freq double deriv
data = np.zeros_like(us)
for ii in range(numharm):
rh = r * (ii + 1)
zh = z * (ii + 1)
wh = w * (ii + 1)
r0 = rh - 0.5 * zh + wh / 12.0 # Make symmetric for all z and w
z0 = zh - 0.5 * wh
phss = 2.0 * np.pi * (us * (us * (us * wh/6.0 + z0/2.0) + r0))
data += np.cos(phss)
data += noiseamp * norm(N)
ft = presto.rfft(data)
offset = uniform(-1.0, 1.0, 3) * np.array([0.5, 2.0, 20.0]) / (0.5 * numharm)
a = time.clock()
if (numharm > 1):
[maxpow, rmax, zmax, rds] = presto.maximize_rz_harmonics(ft, r+offset[0],
z+offset[1], numharm,
norm=1.0)
else:
[maxpow, rmax, zmax, rd] = presto.maximize_rz(ft, r+offset[0],
z+offset[1],
norm=1.0)
rztime += time.clock() - a
rzerrs[n] = (maxpow/numharm - theo_max_pow) / theo_max_pow, rmax - r, zmax - z
def gaussianSample(lower, upper):
dis = upper - lower
ans = int(norm((lower + upper) / 2, dis / 6))
if ans >= upper: ans = upper - 1
if ans < lower: ans = lower
return ans
