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from __future__ import annotations
import math
import pickle
import random
def test_getters():
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, nz))
image_size = sampler.image_size()
scan_range = sampler.scan_range()
grid_size = sampler.grid_size()
step_size = sampler.step_size()
size = len(sampler)
assert width == image_size[0]
assert height == image_size[1]
assert scan_range[0] == scan_range[0]
assert scan_range[1] == scan_range[1]
assert nx == grid_size[0]
assert ny == grid_size[1]
assert nz == grid_size[2]
assert step_size[0] == width / nx
assert step_size[1] == height / ny
assert step_size[2] == depth / nz
assert nx * ny * nz == size
def test_indexing():
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, nz))
xstep, ystep, zstep = sampler.step_size()
xind = [list(range(nx))] * ny * nz
yind = [[j] * nx for j in range(ny)] * nz
zind = [[k] * nx * ny for k in range(nz)]
xind = [i for j in xind for i in j]
yind = [i for j in yind for i in j]
zind = [i for j in zind for i in j]
xp = [(x + 0.5) * xstep for x in xind]
yp = [(y + 0.5) * ystep for y in yind]
zp = [(z + 0.5) * zstep + scan_range[0] for z in zind]
eps = 1e-10
for x0, y0, z0, i in zip(xp, yp, zp, range(len(sampler))):
x1, y1, z1 = sampler.coord(i)
assert abs(x0 - x1) <= eps
assert abs(y0 - y1) <= eps
assert abs(z0 - z1) <= eps
def test_nearest():
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, nz))
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(*scan_range)
i = int(math.floor(x / (width / nx)))
j = int(math.floor(y / (height / ny)))
k = int(math.floor((z - scan_range[0]) / (depth / nz)))
if i >= nx:
i = nx - 1
if j >= ny:
j = ny - 1
if k >= nz:
k = nz - 1
index0 = i + j * nx + k * nx * ny
index1 = sampler.nearest(0, (x, y, z))
assert index0 == index1
def test_nearest_n():
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, nz))
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(*scan_range)
i = int(math.floor(x * nx / width))
j = int(math.floor(y * ny / height))
k = int(math.floor((z - scan_range[0]) * nz / depth))
if i >= nx:
i = nx - 1
if j >= ny:
j = ny - 1
if k >= nz:
k = nz - 1
index0 = i + j * nx + k * nx * ny
index1 = sampler.nearest_n(0, (x, y, z))
assert len(set(index1)) == len(index1)
assert index0 == index1[-1]
for ind in index1:
ii = ind % nx
jk = ind // nx
jj = jk % ny
kk = jk // ny
assert abs(ii - i) <= 1
assert abs(jj - j) <= 1
assert abs(kk - k) <= 1
def test_weights():
from scitbx import matrix
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, 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
# Ensure we get the expected weight at the next grid point at half way
# between grid points
expected = math.exp(-4.0 * math.log(2.0))
for k in range(nz):
for j in range(ny):
for i in range(nx):
l1 = (i + 0) + ((j + 0) + (k + 0) * ny) * nx
l2 = (i + 1) + ((j + 0) + (k + 0) * ny) * nx
l3 = (i - 1) + ((j + 0) + (k + 0) * ny) * nx
l4 = (i + 0) + ((j + 1) + (k + 0) * ny) * nx
l5 = (i + 0) + ((j - 1) + (k + 0) * ny) * nx
l6 = (i + 0) + ((j + 0) + (k + 1) * ny) * nx
l7 = (i + 0) + ((j + 0) + (k - 1) * ny) * nx
coord1 = matrix.col(sampler.coord(l1))
if i < nx - 1:
coord = matrix.col(sampler.coord(l2))
weight = sampler.weight(l1, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(l1, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
if i > 0:
coord = matrix.col(sampler.coord(l3))
weight = sampler.weight(l1, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(l1, 0, (coord1 + coord) / 2.0)
assert abs(weight - 0.5) < eps
if j < ny - 1:
coord = matrix.col(sampler.coord(l4))
weight = sampler.weight(l1, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(l1, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
if j > 0:
coord = matrix.col(sampler.coord(l5))
weight = sampler.weight(l1, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(l1, 0, (coord1 + coord) / 2.0)
assert abs(weight - 0.5) < eps
if k < nz - 1:
coord = matrix.col(sampler.coord(l6))
weight = sampler.weight(l1, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(l1, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
if k > 0:
coord = matrix.col(sampler.coord(l7))
weight = sampler.weight(l1, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(l1, 0, (coord1 + coord) / 2.0)
assert abs(weight - 0.5) < eps
def test_self_consistent():
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, nz))
for i in range(len(sampler)):
coord = sampler.coord(i)
index = sampler.nearest(0, coord)
assert index == i
def test_pickle():
from dials.algorithms.profile_model.modeller import GridSampler
width = 1000
height = 1000
scan_range = (2, 12)
nx = 10
ny = 10
nz = 2
sampler = GridSampler((width, height), scan_range, (nx, ny, nz))
sampler2 = pickle.loads(pickle.dumps(sampler))
assert sampler.image_size() == sampler2.image_size()
assert sampler.grid_size() == sampler2.grid_size()
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