def tst_getters(self):
from math import sqrt
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
image_size = sampler.image_size()
scan_range = sampler.scan_range()
image_centre = sampler.image_centre()
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
size = len(sampler)
assert(width == image_size[0])
assert(height == image_size[1])
assert(width // 2 == image_centre[0])
assert(height // 2 == image_centre[1])
assert(r0 == min([width // 2, height // 2]))
assert(r1 == r0 / 3.0)
assert(r2 == r1 * sqrt(5.0))
assert(9 * nz == size)
print 'OK'
def test_nearest():
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(scan_range[0], scan_range[1])
r = math.sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = math.atan2(y - yc, x - xc)
ai = int(math.floor(t * 8 / (2 * math.pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z - scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest(0, (x, y, z))
assert index0 == index1
def tst_getters(self):
from math import sqrt
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
image_size = sampler.image_size()
scan_range = sampler.scan_range()
image_centre = sampler.image_centre()
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
size = len(sampler)
assert (width == image_size[0])
assert (height == image_size[1])
assert (width // 2 == image_centre[0])
assert (height // 2 == image_centre[1])
assert (r0 == min([width // 2, height // 2]))
assert (r1 == r0 / 3.0)
assert (r2 == r1 * sqrt(5.0))
assert (9 * nz == size)
print 'OK'
def tst_nearest_n(self):
from math import sqrt, atan2, pi, floor
from random import uniform
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(scan_range[0], scan_range[1])
r = sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = atan2(y - yc, x - xc)
ai = int(floor(t * 8 / (2 * pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z-scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest_n(0, (x, y, z))
assert(index0 == index1[0])
if index0 % 9 == 0:
assert(len(index1) == 9)
assert(all(idx == index0 + i for i, idx in enumerate(index1)))
else:
assert(len(index1) == 4)
assert(index1[1] == (index0 // 9) * 9)
if (index0 % 9) == 1:
assert(index1[2] == index0 + 1)
assert(index1[3] == index0 + 7)
elif (index0 % 9) == 8:
assert(index1[2] == index0 - 7)
assert(index1[3] == index0 - 1)
else:
assert(index1[2] == index0 + 1)
assert(index1[3] == index0 - 1)
print 'OK'
def tst_nearest_n(self):
from math import sqrt, atan2, pi, floor
from random import uniform
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(scan_range[0], scan_range[1])
r = sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = atan2(y - yc, x - xc)
ai = int(floor(t * 8 / (2 * pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z - scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest_n((x, y, z))
assert (index0 == index1[0])
if index0 % 9 == 0:
assert (len(index1) == 9)
assert (all(idx == index0 + i for i, idx in enumerate(index1)))
else:
assert (len(index1) == 4)
assert (index1[1] == (index0 // 9) * 9)
if (index0 % 9) == 1:
assert (index1[2] == index0 + 1)
assert (index1[3] == index0 + 7)
elif (index0 % 9) == 8:
assert (index1[2] == index0 - 7)
assert (index1[3] == index0 - 1)
else:
assert (index1[2] == index0 + 1)
assert (index1[3] == index0 - 1)
print 'OK'
def test_nearest_n():
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(scan_range[0], scan_range[1])
r = math.sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = math.atan2(y - yc, x - xc)
ai = int(math.floor(t * 8 / (2 * math.pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z - scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest_n(0, (x, y, z))
assert index0 == index1[0]
if index0 % 9 == 0:
assert len(index1) == 9
assert all(idx == index0 + i for i, idx in enumerate(index1))
else:
assert len(index1) == 4
assert index1[1] == (index0 // 9) * 9
if (index0 % 9) == 1:
assert index1[2] == index0 + 1
assert index1[3] == index0 + 7
elif (index0 % 9) == 8:
assert index1[2] == index0 - 7
assert index1[3] == index0 - 1
else:
assert index1[2] == index0 + 1
assert index1[3] == index0 - 1
def tst_weights(self):
from dials.algorithms.profile_model.modeller import CircleSampler
from math import exp, log
from scitbx import matrix
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 1
sampler = CircleSampler((width, height), scan_range, nz)
# Check the weight at the coord in 1.0
eps = 1e-7
for i in range(len(sampler)):
coord = sampler.coord(i)
weight = sampler.weight(i, 0, coord)
assert(abs(weight - 1.0) < eps)
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
r = r2 / (2.0*r1)
expected = exp(-4.0*r*r*log(2.0))
for i in range(1, 9):
coord = sampler.coord(i)
weight = sampler.weight(0, 0, coord)
assert(abs(weight - expected) < eps)
r = r2 / (2.0*(r2 - r1))
expected = exp(-4.0*r*r*log(2.0))
for i in range(1, 9):
coord = sampler.coord(0)
weight = sampler.weight(i, 0, coord)
assert(abs(weight - expected) < eps)
for i in range(1, 9):
coord1 = matrix.col(sampler.coord(0))
coord2 = matrix.col(sampler.coord(i))
coord = coord1 + r1 * (coord2 - coord1) / r2
weight = sampler.weight(0, 0, coord)
assert(abs(weight - 0.5) < eps)
weight = sampler.weight(i, 0, coord)
assert(abs(weight - 0.5) < eps)
print 'OK'
def tst_weights(self):
from dials.algorithms.profile_model.modeller import CircleSampler
from math import exp, log
from scitbx import matrix
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 1
sampler = CircleSampler((width, height), scan_range, nz)
# Check the weight at the coord in 1.0
eps = 1e-7
for i in range(len(sampler)):
coord = sampler.coord(i)
weight = sampler.weight(i, coord)
assert (abs(weight - 1.0) < eps)
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
r = r2 / (2.0 * r1)
expected = exp(-4.0 * r * r * log(2.0))
for i in range(1, 9):
coord = sampler.coord(i)
weight = sampler.weight(0, coord)
assert (abs(weight - expected) < eps)
r = r2 / (2.0 * (r2 - r1))
expected = exp(-4.0 * r * r * log(2.0))
for i in range(1, 9):
coord = sampler.coord(0)
weight = sampler.weight(i, coord)
assert (abs(weight - expected) < eps)
for i in range(1, 9):
coord1 = matrix.col(sampler.coord(0))
coord2 = matrix.col(sampler.coord(i))
coord = coord1 + r1 * (coord2 - coord1) / r2
weight = sampler.weight(0, coord)
assert (abs(weight - 0.5) < eps)
weight = sampler.weight(i, coord)
assert (abs(weight - 0.5) < eps)
print 'OK'
def test_weights():
from scitbx import matrix
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
nz = 1
sampler = CircleSampler((width, height), scan_range, nz)
# Check the weight at the coord in 1.0
eps = 1e-7
for i in range(len(sampler)):
coord = sampler.coord(i)
weight = sampler.weight(i, 0, coord)
assert abs(weight - 1.0) < eps
r1 = sampler.r1()
r2 = sampler.r2()
r = r2 / (2.0 * r1)
expected = math.exp(-4.0 * r * r * math.log(2.0))
for i in range(1, 9):
coord = sampler.coord(i)
weight = sampler.weight(0, 0, coord)
assert abs(weight - expected) < eps
r = r2 / (2.0 * (r2 - r1))
expected = math.exp(-4.0 * r * r * math.log(2.0))
for i in range(1, 9):
coord = sampler.coord(0)
weight = sampler.weight(i, 0, coord)
assert abs(weight - expected) < eps
for i in range(1, 9):
coord1 = matrix.col(sampler.coord(0))
coord2 = matrix.col(sampler.coord(i))
coord = coord1 + r1 * (coord2 - coord1) / r2
weight = sampler.weight(0, 0, coord)
assert abs(weight - 0.5) < eps
weight = sampler.weight(i, 0, coord)
assert abs(weight - 0.5) < eps
def test_getters():
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
image_size = sampler.image_size()
image_centre = sampler.image_centre()
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
size = len(sampler)
assert width == image_size[0]
assert height == image_size[1]
assert width // 2 == image_centre[0]
assert height // 2 == image_centre[1]
assert r0 == min([width // 2, height // 2])
assert r1 == r0 / 3.0
assert r2 == r1 * math.sqrt(5.0)
assert 9 * nz == size
def tst_nearest(self):
from math import sqrt, atan2, pi, floor
from random import uniform
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(scan_range[0], scan_range[1])
r = sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = atan2(y - yc, x - xc)
ai = int(floor(t * 8 / (2 * pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z-scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest(0, (x, y, z))
assert(index0 == index1)
print 'OK'
