def test_preference(self):
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.5)
self.assertAlmostEqual(self.fm2.weighted_average()[i,j], 0.5)
# To test the update function
self.fm1.update(self.a1,0.7)
self.fm2.update(self.a1,0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.6)
vect_sum = wrap(0,1,arg(exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,j],vect_sum)
# To test the keep_peak=True
self.fm1.update(self.a1,0.7)
self.fm2.update(self.a1,0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.6)
vect_sum =wrap(0,1,arg(exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,j],vect_sum)
self.fm1.update(self.a2,0.7)
self.fm2.update(self.a2,0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(self.fm1.weighted_average()[i,j], 0.65)
vect_sum =wrap(0,1,arg(3*exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,j],vect_sum)
# to even test more....
self.fm1.update(self.a3,0.9)
self.fm2.update(self.a3,0.9)
for i in range(3):
self.assertAlmostEqual(self.fm1.weighted_average()[i,0], 0.65)
self.assertAlmostEqual(self.fm1.weighted_average()[i,1], 0.7)
vect_sum = wrap(0,1,arg(3*exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,0],vect_sum)
vect_sum = wrap(0,1,arg(3*exp(0.7*2*pi*1j)+exp(0.5*2*pi*1j)+exp(0.9*2*pi*1j))/(2*pi))
self.assertAlmostEqual(self.fm2.weighted_average()[i,1],vect_sum)
def vector_sum(self, d):
"""
Return the vector sum of the distribution as a tuple (magnitude, avgbinnum).
Each bin contributes a vector of length equal to its value, at
a direction corresponding to the bin number. Specifically,
the total bin number range is mapped into a direction range
[0,2pi].
For a cyclic distribution, the avgbinnum will be a continuous
measure analogous to the max_value_bin() of the distribution.
But this quantity has more precision than max_value_bin()
because it is computed from the entire distribution instead of
just the peak bin. However, it is likely to be useful only
for uniform or very dense sampling; with sparse, non-uniform
sampling the estimates will be biased significantly by the
particular samples chosen.
The avgbinnum is not meaningful when the magnitude is 0,
because a zero-length vector has no direction. To find out
whether such cases occurred, you can compare the value of
undefined_vals before and after a series of calls to this
function.
"""
# vectors are represented in polar form as complex numbers
h = d._data
r = h.values()
theta = d._bins_to_radians(array(h.keys()))
v_sum = innerproduct(r, exp(theta * 1j))
magnitude = abs(v_sum)
direction = arg(v_sum)
if v_sum == 0:
d.undefined_vals += 1
direction_radians = d._radians_to_bins(direction)
# wrap the direction because arctan2 returns principal values
wrapped_direction = wrap(d.axis_bounds[0], d.axis_bounds[1],
direction_radians)
return (magnitude, wrapped_direction)
def vector_sum(self, d ):
"""
Return the vector sum of the distribution as a tuple (magnitude, avgbinnum).
Each bin contributes a vector of length equal to its value, at
a direction corresponding to the bin number. Specifically,
the total bin number range is mapped into a direction range
[0,2pi].
For a cyclic distribution, the avgbinnum will be a continuous
measure analogous to the max_value_bin() of the distribution.
But this quantity has more precision than max_value_bin()
because it is computed from the entire distribution instead of
just the peak bin. However, it is likely to be useful only
for uniform or very dense sampling; with sparse, non-uniform
sampling the estimates will be biased significantly by the
particular samples chosen.
The avgbinnum is not meaningful when the magnitude is 0,
because a zero-length vector has no direction. To find out
whether such cases occurred, you can compare the value of
undefined_vals before and after a series of calls to this
function.
"""
# vectors are represented in polar form as complex numbers
h = d._data
r = h.values()
theta = d._bins_to_radians(array( h.keys() ))
v_sum = innerproduct(r, exp(theta*1j))
magnitude = abs(v_sum)
direction = arg(v_sum)
if v_sum == 0:
d.undefined_vals += 1
direction_radians = d._radians_to_bins(direction)
# wrap the direction because arctan2 returns principal values
wrapped_direction = wrap(d.axis_bounds[0], d.axis_bounds[1], direction_radians)
return (magnitude, wrapped_direction)
def test_preference(self):
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.5)
self.assertAlmostEqual(weighted_average(self.fm2)[i, j], 0.5)
# To test the update function
self.fm1.update(self.a1, 0.7)
self.fm2.update(self.a1, 0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.6)
vect_sum = wrap(
0, 1,
arg(exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(
weighted_average(self.fm2)[i, j], vect_sum)
# To test the keep_peak=True
self.fm1.update(self.a1, 0.7)
self.fm2.update(self.a1, 0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.6)
vect_sum = wrap(
0, 1,
arg(exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(
weighted_average(self.fm2)[i, j], vect_sum)
self.fm1.update(self.a2, 0.7)
self.fm2.update(self.a2, 0.7)
for i in range(3):
for j in range(2):
self.assertAlmostEqual(weighted_average(self.fm1)[i, j], 0.65)
vect_sum = wrap(
0, 1,
arg(3 * exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(
weighted_average(self.fm2)[i, j], vect_sum)
# to even test more....
self.fm1.update(self.a3, 0.9)
self.fm2.update(self.a3, 0.9)
for i in range(3):
self.assertAlmostEqual(weighted_average(self.fm1)[i, 0], 0.65)
self.assertAlmostEqual(weighted_average(self.fm1)[i, 1], 0.7)
vect_sum = wrap(
0, 1,
arg(3 * exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j)) /
(2 * pi))
self.assertAlmostEqual(weighted_average(self.fm2)[i, 0], vect_sum)
vect_sum = wrap(
0, 1,
arg(3 * exp(0.7 * 2 * pi * 1j) + exp(0.5 * 2 * pi * 1j) +
exp(0.9 * 2 * pi * 1j)) / (2 * pi))
self.assertAlmostEqual(weighted_average(self.fm2)[i, 1], vect_sum)
def get_input_params(old_compat=False):
"""
Return iterators over list of float values for C++ LISSOM's cx,
cy, and theta for multiple eyes, as well as sign.
Expects log file with values held in lines like this::
'Iteration: 000000 [Eye0 [Obj0 cx:02.1 cy:11.6 theta:074.0]]\n'
or this:
'Iteration: 000000 [Eye0 [Obj0 cx:23.4 cy:10.0 theta:059.6]] [Eye1 [Obj0 cx:22.6 cy:10.5 theta:059.6]] [Eye2 [Obj0 cx:21.7 cy:11.1 theta:059.6]] [Eye3 [Obj0 cx:20.8 cy:11.6 theta:059.6]]\n'
"""
logfile = filename_base + "log"
print "Reading input params from %s" % logfile
f = open(logfile, "r")
# first iter is test in c++ lissom; use to get num eyes
n_eyes = len(f.readline().split("Eye")[1::])
input_params = dict([(i, dict(cx=list(), cy=list(), theta=list(), sign=list())) for i in range(n_eyes)])
lines = f.readlines()
for line, lineno in zip(lines, range(len(lines))):
eyes = line.split("Eye")[1::]
for eye, i in zip(eyes, range(n_eyes)):
# e.g. eye='1 [Obj0 cx:-0.2 cy:13.8 theta:011.7]] ['
cx, cy, theta = [W.split(":")[1].split("]")[0] for W in eye.split(" ")[2:5]]
if not old_compat:
for X in ["cx", "cy", "theta"]:
input_params[i][X].append(round(float(locals()[X]), 1))
else: # old compat: log file with degrees etc
input_params[i]["cx"].append(float(cx))
input_params[i]["cy"].append(float(cy))
input_params[i]["theta"].append(float(theta))
del cx
del cy
del theta
if len(eyes) > 1: # CeBALERLT: not general (assuming motion just because more than 1 eye)
##### get sign
X1 = complex(input_params[0]["cx"][lineno], input_params[0]["cy"][lineno])
X2 = complex(input_params[len(eyes) - 1]["cx"][lineno], input_params[len(eyes) - 1]["cy"][lineno])
realdir = arg(X2 - X1)
# for 0<=realdir<2pi (instead of -pi<=realdir<pi)
if realdir < 0:
realdir += 2 * pi
topo_dir = input_params[0]["theta"][lineno] + pi / 2 # dir that will be given to topographica
# if realdir & topo_dir aren't the same, it's because
# c lissom used sign=-1 (& the dirs will be pi apart)
E = 0.05 # (imprecision from getting dir from 1-dp positions)
if abs(realdir - topo_dir) < E:
s = 1
elif pi - E <= abs(realdir - topo_dir) < pi + E:
s = -1
else:
assert False # if using speed=0, just replace this line with s=0
# CEBALERT: should actually handle that case!
#####
for i in range(n_eyes):
input_params[i]["sign"].append(s)
for i in input_params:
for val in input_params[i]:
input_params[i][val] = iter(input_params[i][val])
return len(lines), input_params
def get_input_params(old_compat=False):
"""
Return iterators over list of float values for C++ LISSOM's cx,
cy, and theta for multiple eyes, as well as sign.
Expects log file with values held in lines like this::
'Iteration: 000000 [Eye0 [Obj0 cx:02.1 cy:11.6 theta:074.0]]\n'
or this:
'Iteration: 000000 [Eye0 [Obj0 cx:23.4 cy:10.0 theta:059.6]] [Eye1 [Obj0 cx:22.6 cy:10.5 theta:059.6]] [Eye2 [Obj0 cx:21.7 cy:11.1 theta:059.6]] [Eye3 [Obj0 cx:20.8 cy:11.6 theta:059.6]]\n'
"""
logfile = filename_base + 'log'
print "Reading input params from %s" % logfile
f = open(logfile, 'r')
# first iter is test in c++ lissom; use to get num eyes
n_eyes = len(f.readline().split('Eye')[1::])
input_params = dict([(i,
dict(cx=list(), cy=list(), theta=list(),
sign=list())) for i in range(n_eyes)])
lines = f.readlines()
for line, lineno in zip(lines, range(len(lines))):
eyes = line.split('Eye')[1::]
for eye, i in zip(eyes, range(n_eyes)):
# e.g. eye='1 [Obj0 cx:-0.2 cy:13.8 theta:011.7]] ['
cx, cy, theta = [
W.split(":")[1].split("]")[0] for W in eye.split(" ")[2:5]
]
if not old_compat:
for X in ['cx', 'cy', 'theta']:
input_params[i][X].append(round(float(locals()[X]), 1))
else: # old compat: log file with degrees etc
input_params[i]['cx'].append(float(cx))
input_params[i]['cy'].append(float(cy))
input_params[i]['theta'].append(float(theta))
del cx
del cy
del theta
if len(
eyes
) > 1: # CeBALERLT: not general (assuming motion just because more than 1 eye)
##### get sign
X1 = complex(input_params[0]['cx'][lineno],
input_params[0]['cy'][lineno])
X2 = complex(input_params[len(eyes) - 1]['cx'][lineno],
input_params[len(eyes) - 1]['cy'][lineno])
realdir = arg(X2 - X1)
# for 0<=realdir<2pi (instead of -pi<=realdir<pi)
if realdir < 0: realdir += 2 * pi
topo_dir = input_params[0]['theta'][
lineno] + pi / 2 # dir that will be given to topographica
# if realdir & topo_dir aren't the same, it's because
# c lissom used sign=-1 (& the dirs will be pi apart)
E = 0.05 # (imprecision from getting dir from 1-dp positions)
if abs(realdir - topo_dir) < E:
s = 1
elif pi - E <= abs(realdir - topo_dir) < pi + E:
s = -1
else:
assert False # if using speed=0, just replace this line with s=0
# CEBALERT: should actually handle that case!
#####
for i in range(n_eyes):
input_params[i]['sign'].append(s)
for i in input_params:
for val in input_params[i]:
input_params[i][val] = iter(input_params[i][val])
return len(lines), input_params