def fit_single_particle_psf(psf_scale, samples=100, imsize=64, sigma=0.05):
terrors = []
berrors = []
crbs = []
psf0 = np.array([2.0, 1.0, 4.0])
for scale in psf_scale:
print '=' * 79
print 'scale =', scale
s = init.create_single_particle_state(imsize,
radius=5.0,
sigma=0.05,
psfargs={'params': scale * psf0})
p = s.state[s.b_pos].reshape(-1, 3).copy()
bl = s.explode(s.b_pos)
crbs.append(
np.sqrt(np.diag(np.linalg.inv(
s.fisher_information(blocks=bl)))).reshape(-1, 3))
tmp_tp, tmp_bf = [], []
for i in xrange(samples):
print i
bench.jiggle_particles(s, pos=p)
t = bench.trackpy(s)
b = bench.bamfpy_positions(s, sweeps=30)
tmp_tp.append(bench.error(s, t))
tmp_bf.append(bench.error(s, b))
terrors.append(tmp_tp)
berrors.append(tmp_bf)
return np.array(crbs), np.array(terrors), np.array(berrors)
def fit_single_particle_rad(radii, samples=100, imsize=64, sigma=0.05):
terrors = []
berrors = []
crbs = []
for rad in radii:
print '=' * 79
print 'radius =', rad
s = init.create_single_particle_state(imsize, radius=rad, sigma=0.05)
p = s.state[s.b_pos].reshape(-1, 3).copy()
bl = s.explode(s.b_pos)
crbs.append(
np.sqrt(np.diag(np.linalg.inv(
s.fisher_information(blocks=bl)))).reshape(-1, 3))
tmp_tp, tmp_bf = [], []
for i in xrange(samples):
print i
bench.jiggle_particles(s, pos=p)
t = bench.trackpy(s)
b = bench.bamfpy_positions(s, sweeps=30)
tmp_tp.append(bench.error(s, t))
tmp_bf.append(bench.error(s, b))
terrors.append(tmp_tp)
berrors.append(tmp_bf)
return np.array(crbs), np.array(terrors), np.array(berrors)
def fit_two_particle_separation(separation,
radius=5.0,
samples=100,
imsize=64,
sigma=0.05):
terrors = []
berrors = []
crbs = []
for sep in separation:
print('=' * 79)
print('sep =', sep)
s = init.create_two_particle_state(imsize,
radius=radius,
delta=sep,
sigma=0.05,
axis='z')
p = s.state[s.b_pos].reshape(-1, 3).copy()
bl = s.explode(s.b_pos)
crbs.append(
np.sqrt(np.diag(np.linalg.inv(
s.fisher_information(blocks=bl)))).reshape(-1, 3))
tmp_tp, tmp_bf = [], []
for i in range(samples):
print(i)
bench.jiggle_particles(s, pos=p)
t = bench.trackpy(s)
b = bench.bamfpy_positions(s, sweeps=30)
tmp_tp.append(bench.error(s, t))
tmp_bf.append(bench.error(s, b))
terrors.append(tmp_tp)
berrors.append(tmp_bf)
return np.array(crbs), np.array(terrors), np.array(berrors)
def fit_edge(separation,
radius=5.0,
samples=100,
imsize=64,
sigma=0.05,
axis='z'):
"""
axis is 'z' or 'xy'
seps = np.linspace(0,2,20) 'z'
seps = np.linspace(-2,2,20) 'xy'
"""
terrors = []
berrors = []
crbs = []
for sep in separation:
print '=' * 79
print 'sep =', sep,
s = init.create_two_particle_state(imsize,
radius=radius,
delta=sep,
sigma=0.05,
axis='z',
psfargs={
'params': (2.0, 1.0, 4.0),
'error': 1e-8
},
stateargs={
'sigmapad': True,
'pad': const.PAD
})
# move off of a pixel edge (cheating for trackpy)
d = np.array([0, 0.5, 0.5])
s.obj.pos -= d
s.reset()
# move the particles to the edge
bl = s.blocks_particle(0)
s.update(bl[0], np.array([s.pad + radius]))
bl = s.blocks_particle(1)
s.update(bl[0], np.array([s.pad - radius]))
if axis == 'z':
bl = s.blocks_particle(1)
s.update(bl[0], s.state[bl[0]] - sep)
s.model_to_true_image()
if axis == 'xy':
bl = s.blocks_particle(1)
s.update(bl[2], s.state[bl[2]] + sep)
s.model_to_true_image()
# save where the particles were originally so we can jiggle
p = s.state[s.b_pos].reshape(-1, 3).copy()
print p[0], p[1]
# calculate the CRB for this configuration
bl = s.explode(s.b_pos)
crbs.append(
np.sqrt(np.diag(np.linalg.inv(
s.fisher_information(blocks=bl)))).reshape(-1, 3))
# calculate the featuring errors
tmp_tp, tmp_bf = [], []
for i in xrange(samples):
print i
bench.jiggle_particles(s, pos=p, sig=0.3, mask=np.array([1, 1, 1]))
t = bench.trackpy(s)
b = bench.bamfpy_positions(s, sweeps=15)
tmp_tp.append(bench.error(s, t))
tmp_bf.append(bench.error(s, b))
terrors.append(tmp_tp)
berrors.append(tmp_bf)
return np.array(crbs), np.array(terrors), np.array(berrors)