1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
|
from __future__ import annotations
import random
from collections import namedtuple
import pytest
from dxtbx.serialize import load
from scitbx import matrix
from dials.algorithms.profile_model.gaussian_rs.transform import beam_vector_map
SequenceAndModel = namedtuple(
"SequenceAndModel", ("sequence", "beam", "detector", "gonio", "scan")
)
@pytest.fixture
def sequence_and_model(dials_data):
sequence = load.imageset(
dials_data("centroid_test_data", pathlib=True) / "sweep.json"
)
return SequenceAndModel(
sequence=sequence,
beam=sequence.get_beam(),
detector=sequence.get_detector(),
gonio=sequence.get_goniometer(),
scan=sequence.get_scan(),
)
def test_at_corners(sequence_and_model):
assert len(sequence_and_model.detector) == 1
# The detector beam vectors
ds1 = beam_vector_map(sequence_and_model.detector[0], sequence_and_model.beam, True)
expected_size = sequence_and_model.detector[0].get_image_size()[::-1]
expected_size = tuple(e + 1 for e in expected_size)
assert ds1.all() == expected_size
s0_length = matrix.col(sequence_and_model.beam.get_s0()).length()
# Ensure a few random points are correct
eps = 1e-7
for k in range(1000):
j = random.randint(0, ds1.all()[0] - 1)
i = random.randint(0, ds1.all()[1] - 1)
y = float(j)
x = float(i)
xyz = sequence_and_model.detector[0].get_pixel_lab_coord((x, y))
s11 = matrix.col(xyz).normalize() * s0_length
s12 = matrix.col(ds1[j, i])
assert (s11 - s12).length() <= eps
def test_sub_division_at_corners(sequence_and_model):
n_div = 2
# The detector beam vectors
ds1 = beam_vector_map(
sequence_and_model.detector[0],
sequence_and_model.beam,
n_div,
True,
)
expected_size = sequence_and_model.detector[0].get_image_size()[::-1]
expected_size = tuple(e * n_div + 1 for e in expected_size)
assert ds1.all() == expected_size
s0_length = matrix.col(sequence_and_model.beam.get_s0()).length()
# Ensure a few random points are correct
eps = 1e-7
for k in range(1000):
j = random.randint(0, ds1.all()[0] - 1)
i = random.randint(0, ds1.all()[1] - 1)
y = float(j) / n_div
x = float(i) / n_div
xyz = sequence_and_model.detector[0].get_pixel_lab_coord((x, y))
s11 = matrix.col(xyz).normalize() * s0_length
s12 = matrix.col(ds1[j, i])
assert (s11 - s12).length() <= eps
def test_sub_division_at_centres(sequence_and_model):
n_div = 2
# The detector beam vectors
ds1 = beam_vector_map(
sequence_and_model.detector[0],
sequence_and_model.beam,
n_div,
False,
)
expected_size = sequence_and_model.detector[0].get_image_size()[::-1]
expected_size = tuple(e * n_div for e in expected_size)
assert ds1.all() == expected_size
s0_length = matrix.col(sequence_and_model.beam.get_s0()).length()
# Ensure a few random points are correct
eps = 1e-7
for k in range(1000):
j = random.randint(0, ds1.all()[0] - 1)
i = random.randint(0, ds1.all()[1] - 1)
y = float(j + 0.5) / n_div
x = float(i + 0.5) / n_div
xyz = sequence_and_model.detector[0].get_pixel_lab_coord((x, y))
s11 = matrix.col(xyz).normalize() * s0_length
s12 = matrix.col(ds1[j, i])
assert (s11 - s12).length() <= eps
|
