def test(dials_regression, tmpdir):
tmpdir.chdir()
from dxtbx.serialize import load
from dials.algorithms import shoebox
from dials.array_family import flex
# Load the sweep and crystal
sweep_filename = os.path.join(dials_regression, 'centroid_test_data', 'sweep.json')
crystal_filename = os.path.join(dials_regression, 'centroid_test_data', 'crystal.json')
sweep = load.imageset(sweep_filename)
crystal = load.crystal(crystal_filename)
# Get models from the sweep
beam = sweep.get_beam()
detector = sweep.get_detector()
gonio = sweep.get_goniometer()
scan = sweep.get_scan()
# Get the reflections and overlaps
reflections, adjacency_list = predict_reflections(sweep, crystal)
reflections['shoebox'] = flex.shoebox(
reflections['panel'],
reflections['bbox'])
reflections['shoebox'].allocate_with_value(shoebox.MaskCode.Valid)
# If the adjacency list is given, then create the reflection mask
assert(len(detector) == 1)
image_size = detector[0].get_image_size()
shoeboxes = reflections['shoebox']
coords = reflections['xyzcal.px']
shoebox_masker = shoebox.MaskOverlapping()
shoebox_masker(shoeboxes, coords, adjacency_list)
# Loop through all edges
overlapping = []
for e in adjacency_list.edges():
v1, v2 = adjacency_list.source(e), adjacency_list.target(e)
overlapping.append(v1)
overlapping.append(v2)
# Ensure elements are unique
overlapping = set(overlapping)
# Ensure we have some overlaps
assert len(overlapping) > 0
# Get all non-overlapping reflections
all_r = set(range(len(reflections)))
non_overlapping = all_r.difference(overlapping)
# Run the tests
tst_non_overlapping(reflections, non_overlapping,
detector[0].get_image_size())
tst_overlapping(reflections, overlapping, adjacency_list,
image_size)
def __init__(self):
import os
import libtbx.load_env
from dxtbx.serialize import load
from dials.algorithms.profile_model.gaussian_rs import Model
from dials.algorithms.profile_model.gaussian_rs import MaskCalculator3D
from dxtbx.model.experiment_list import Experiment, ExperimentList
try:
dials_regression = libtbx.env.dist_path('dials_regression')
except KeyError:
print 'FAIL: dials_regression not configured'
exit(0)
# Set the sweep filename and load the sweep
sweep_filename = os.path.join(dials_regression, 'centroid_test_data',
'sweep.json')
crystal_filename = os.path.join(dials_regression, 'centroid_test_data',
'crystal.json')
# Load the sweep
self.sweep = load.imageset(sweep_filename)
self.crystal = load.crystal(crystal_filename)
self.beam = self.sweep.get_beam()
self.detector = self.sweep.get_detector()
self.goniometer = self.sweep.get_goniometer()
self.scan = self.sweep.get_scan()
self.delta_d = 3 * self.beam.get_sigma_divergence(deg=False)
try:
mosaicity = self.crystal.get_mosaicity(deg=False)
except AttributeError:
mosaicity = 0
self.delta_m = 3 * mosaicity
self.nsigma = 3
self.profile_model = Model(None, self.nsigma,
self.beam.get_sigma_divergence(deg=False),
mosaicity)
self.experiment = ExperimentList()
self.experiment.append(
Experiment(imageset=self.sweep,
beam=self.beam,
detector=self.detector,
goniometer=self.goniometer,
scan=self.scan,
crystal=self.crystal,
profile=self.profile_model))
assert (len(self.detector) == 1)
# Get the function object to mask the foreground
self.mask_foreground = MaskCalculator3D(self.beam, self.detector,
self.goniometer, self.scan,
self.delta_d, self.delta_m)
def test(dials_data):
from dxtbx.serialize import load
from dials.algorithms import shoebox
from dials.array_family import flex
# Load the sequence and crystal
sequence = load.imageset(
dials_data("centroid_test_data").join("sweep.json").strpath
)
crystal = load.crystal(
dials_data("centroid_test_data").join("crystal.json").strpath
)
# Get models from the sequence
detector = sequence.get_detector()
# Get the reflections and overlaps
reflections, adjacency_list = predict_reflections(sequence, crystal)
reflections["shoebox"] = flex.shoebox(reflections["panel"], reflections["bbox"])
reflections["shoebox"].allocate_with_value(shoebox.MaskCode.Valid)
# If the adjacency list is given, then create the reflection mask
assert len(detector) == 1
image_size = detector[0].get_image_size()
shoeboxes = reflections["shoebox"]
coords = reflections["xyzcal.px"]
shoebox_masker = shoebox.MaskOverlapping()
shoebox_masker(shoeboxes, coords, adjacency_list)
# Loop through all edges
overlapping = []
for e in adjacency_list.edges():
v1, v2 = adjacency_list.source(e), adjacency_list.target(e)
overlapping.append(v1)
overlapping.append(v2)
# Ensure elements are unique
overlapping = set(overlapping)
# Ensure we have some overlaps
assert len(overlapping) > 0
# Get all non-overlapping reflections
all_r = set(range(len(reflections)))
non_overlapping = all_r.difference(overlapping)
# Run the tests
tst_non_overlapping(reflections, non_overlapping, detector[0].get_image_size())
tst_overlapping(reflections, overlapping, adjacency_list, image_size)
def run(self):
from dxtbx.serialize import load
from dials.algorithms import shoebox
from dials.array_family import flex
# Load the sweep and crystal
self.sweep = load.imageset(self.sweep_filename)
self.crystal = load.crystal(self.crystal_filename)
# Get the reflections and overlaps
reflections, adjacency_list = self.predict_reflections()
reflections['shoebox'] = flex.shoebox(reflections['panel'],
reflections['bbox'])
reflections['shoebox'].allocate_with_value(shoebox.MaskCode.Valid)
# If the adjacency list is given, then create the reflection mask
assert (len(self.detector) == 1)
image_size = self.detector[0].get_image_size()
shoeboxes = reflections['shoebox']
coords = reflections['xyzcal.px']
shoebox_masker = shoebox.MaskOverlapping()
shoebox_masker(shoeboxes, coords, adjacency_list)
# Loop through all edges
overlapping = []
for e in adjacency_list.edges():
v1, v2 = adjacency_list.source(e), adjacency_list.target(e)
overlapping.append(v1)
overlapping.append(v2)
# Ensure elements are unique
overlapping = set(overlapping)
# Ensure we have some overlaps
assert (len(overlapping) > 0)
# Get all non-overlapping reflections
all_r = set(range(len(reflections)))
non_overlapping = all_r.difference(overlapping)
# Run the tests
self.tst_non_overlapping(reflections, non_overlapping,
self.detector[0].get_image_size())
self.tst_overlapping(reflections, overlapping, adjacency_list,
image_size)
def test(dials_data):
from dxtbx.model.experiment_list import Experiment, ExperimentList
from dxtbx.serialize import load
from dials.algorithms.profile_model.gaussian_rs import MaskCalculator3D, Model
sequence = load.imageset(
dials_data("centroid_test_data").join("sweep.json").strpath)
crystal = load.crystal(
dials_data("centroid_test_data").join("crystal.json").strpath)
beam = sequence.get_beam()
detector = sequence.get_detector()
goniometer = sequence.get_goniometer()
scan = sequence.get_scan()
delta_d = 3 * beam.get_sigma_divergence(deg=False)
try:
mosaicity = crystal.get_mosaicity(deg=False)
except AttributeError:
mosaicity = 0
delta_m = 3 * mosaicity
nsigma = 3
profile_model = Model(None, nsigma, beam.get_sigma_divergence(deg=False),
mosaicity)
experiment = ExperimentList()
experiment.append(
Experiment(
imageset=sequence,
beam=beam,
detector=detector,
goniometer=goniometer,
scan=scan,
crystal=crystal,
profile=profile_model,
))
assert len(detector) == 1
# Get the function object to mask the foreground
mask_foreground = MaskCalculator3D(beam, detector, goniometer, scan,
delta_d, delta_m)
from scitbx.array_family import flex
from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem
from dials.algorithms.shoebox import MaskCode
s0 = beam.get_s0()
m2 = goniometer.get_rotation_axis()
s0_length = matrix.col(beam.get_s0()).length()
width, height = detector[0].get_image_size()
zrange = scan.get_array_range()
phi0, dphi = scan.get_oscillation(deg=False)
# Generate some reflections
reflections = generate_reflections(detector, beam, scan, experiment, 10)
# Mask the foreground in each
mask_foreground(
reflections["shoebox"],
reflections["s1"],
reflections["xyzcal.px"].parts()[2],
reflections["panel"],
)
# Loop through all the reflections and check the mask values
shoebox = reflections["shoebox"]
beam_vector = reflections["s1"]
rotation_angle = reflections["xyzcal.mm"].parts()[2]
for l in range(len(reflections)):
mask = shoebox[l].mask
x0, x1, y0, y1, z0, z1 = shoebox[l].bbox
s1 = beam_vector[l]
phi = rotation_angle[l]
cs = CoordinateSystem(m2, s0, s1, phi)
def rs_coord(i, j, k):
s1d = detector[0].get_pixel_lab_coord((i, j))
s1d = matrix.col(s1d).normalize() * s0_length
e1, e2 = cs.from_beam_vector(s1d)
e3 = cs.from_rotation_angle_fast(phi0 + (k - zrange[0]) * dphi)
return e1, e2, e3
new_mask = flex.int(mask.accessor(), 0)
for k in range(z1 - z0):
for j in range(y1 - y0):
for i in range(x1 - x0):
# value1 = mask[k, j, i]
e11, e12, e13 = rs_coord(x0 + i, y0 + j, z0 + k)
e21, e22, e23 = rs_coord(x0 + i + 1, y0 + j, z0 + k)
e31, e32, e33 = rs_coord(x0 + i, y0 + j + 1, z0 + k)
e41, e42, e43 = rs_coord(x0 + i, y0 + j, z0 + k + 1)
e51, e52, e53 = rs_coord(x0 + i + 1, y0 + j + 1, z0 + k)
e61, e62, e63 = rs_coord(x0 + i + 1, y0 + j, z0 + k + 1)
e71, e72, e73 = rs_coord(x0 + i, y0 + j + 1, z0 + k + 1)
e81, e82, e83 = rs_coord(x0 + i + 1, y0 + j + 1,
z0 + k + 1)
de1 = (e11 / delta_d)**2 + (
e12 / delta_d)**2 # +(e13/delta_m)**2
de2 = (e21 / delta_d)**2 + (
e22 / delta_d)**2 # +(e23/delta_m)**2
de3 = (e31 / delta_d)**2 + (
e32 / delta_d)**2 # +(e33/delta_m)**2
de4 = (e41 / delta_d)**2 + (
e42 / delta_d)**2 # +(e43/delta_m)**2
de5 = (e51 / delta_d)**2 + (
e52 / delta_d)**2 # +(e53/delta_m)**2
de6 = (e61 / delta_d)**2 + (
e62 / delta_d)**2 # +(e63/delta_m)**2
de7 = (e71 / delta_d)**2 + (
e72 / delta_d)**2 # +(e73/delta_m)**2
de8 = (e81 / delta_d)**2 + (
e82 / delta_d)**2 # +(e83/delta_m)**2
de = math.sqrt(
min([de1, de2, de3, de4, de5, de6, de7, de8]))
if (x0 + i < 0 or y0 + j < 0 or x0 + i >= width
or y0 + j >= height or z0 + k < zrange[0]
or z0 + k >= zrange[1]):
value2 = MaskCode.Valid
else:
if de <= 1.0:
value2 = MaskCode.Valid | MaskCode.Foreground
else:
value2 = MaskCode.Valid | MaskCode.Background
new_mask[k, j, i] = value2
if not all(m1 == m2 for m1, m2 in zip(mask, new_mask)):
np.set_printoptions(threshold=10000)
diff = (mask == new_mask).as_numpy_array()
print(diff.astype(np.int))
# print mask.as_numpy_array()
# print new_mask.as_numpy_array()
# print (new_mask.as_numpy_array()[:,:,:] %2) * (new_mask.as_numpy_array() == 5)
assert False