def overall_report(data):
# Start by adding some overall numbers
report = collections.OrderedDict()
report["n"] = len(reflections)
report["n_full"] = data["full"].count(True)
report["n_partial"] = data["full"].count(False)
report["n_overload"] = data["over"].count(True)
report["n_ice"] = data["ice"].count(True)
report["n_summed"] = data["sum"].count(True)
report["n_fitted"] = data["prf"].count(True)
report["n_integated"] = data["int"].count(True)
report["n_invalid_bg"] = data["ninvbg"].count(True)
report["n_invalid_fg"] = data["ninvfg"].count(True)
report["n_failed_background"] = data["fbgd"].count(True)
report["n_failed_summation"] = data["fsum"].count(True)
report["n_failed_fitting"] = data["fprf"].count(True)
# Compute mean background
try:
report["mean_background"] = flex.mean(
data["background.mean"].select(data["int"])
)
except Exception:
report["mean_background"] = 0.0
# Compute mean I/Sigma summation
try:
report["ios_sum"] = flex.mean(data["intensity.sum.ios"].select(data["sum"]))
except Exception:
report["ios_sum"] = 0.0
# Compute mean I/Sigma profile fitting
try:
report["ios_prf"] = flex.mean(data["intensity.prf.ios"].select(data["prf"]))
except Exception:
report["ios_prf"] = 0.0
# Compute the mean profile correlation
try:
report["cc_prf"] = flex.mean(
data["profile.correlation"].select(data["prf"])
)
except Exception:
report["cc_prf"] = 0.0
# Compute the correlations between summation and profile fitting
try:
mask = data["sum"] & data["prf"]
Isum = data["intensity.sum.value"].select(mask)
Iprf = data["intensity.prf.value"].select(mask)
report["cc_pearson_sum_prf"] = pearson_correlation_coefficient(Isum, Iprf)
report["cc_spearman_sum_prf"] = spearman_correlation_coefficient(Isum, Iprf)
except Exception:
report["cc_pearson_sum_prf"] = 0.0
report["cc_spearman_sum_prf"] = 0.0
# Return the overall report
return report
def overall_report(data):
# Start by adding some overall numbers
report = OrderedDict()
report['n'] = len(reflections)
report['n_full'] = data['full'].count(True)
report['n_partial'] = data['full'].count(False)
report['n_overload'] = data['over'].count(True)
report['n_ice'] = data['ice'].count(True)
report['n_summed'] = data['sum'].count(True)
report['n_fitted'] = data['prf'].count(True)
report['n_integated'] = data['int'].count(True)
report['n_invalid_bg'] = data['ninvbg'].count(True)
report['n_invalid_fg'] = data['ninvfg'].count(True)
report['n_failed_background'] = data['fbgd'].count(True)
report['n_failed_summation'] = data['fsum'].count(True)
report['n_failed_fitting'] = data['fprf'].count(True)
# Compute mean background
try:
report['mean_background'] = flex.mean(
data['background.mean'].select(data['int']))
except Exception:
report['mean_background'] = 0.0
# Compute mean I/Sigma summation
try:
report['ios_sum'] = flex.mean(
data['intensity.sum.ios'].select(data['sum']))
except Exception:
report['ios_sum'] = 0.0
# Compute mean I/Sigma profile fitting
try:
report['ios_prf'] = flex.mean(
data['intensity.prf.ios'].select(data['prf']))
except Exception:
report['ios_prf'] = 0.0
# Compute the mean profile correlation
try:
report['cc_prf'] = flex.mean(
data['profile.correlation'].select(data['prf']))
except Exception:
report['cc_prf'] = 0.0
# Compute the correlations between summation and profile fitting
try:
mask = data['sum'] & data['prf']
Isum = data['intensity.sum.value'].select(mask)
Iprf = data['intensity.prf.value'].select(mask)
report['cc_pearson_sum_prf'] = pearson_correlation_coefficient(Isum, Iprf)
report['cc_spearman_sum_prf'] = spearman_correlation_coefficient(Isum, Iprf)
except Exception:
report['cc_pearson_sum_prf'] = 0.0
report['cc_spearman_sum_prf'] = 0.0
# Return the overall report
return report
def tst_conservation_of_counts(self):
from scitbx import matrix
from random import uniform, seed
from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem
from dials.algorithms.profile_model.gaussian_rs import transform
from scitbx.array_family import flex
seed(0)
assert (len(self.detector) == 1)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
s0_length = matrix.col(self.beam.get_s0()).length()
# Create an s1 map
s1_map = transform.beam_vector_map(self.detector[0], self.beam, True)
for i in range(100):
# Get random x, y, z
x = uniform(300, 1800)
y = uniform(300, 1800)
z = uniform(500, 600)
# Get random s1, phi, panel
s1 = matrix.col(self.detector[0].get_pixel_lab_coord(
(x, y))).normalize() * s0_length
phi = self.scan.get_angle_from_array_index(z, deg=False)
panel = 0
# Calculate the bounding box
bbox = self.calculate_bbox(s1, z, panel)
x0, x1, y0, y1, z0, z1 = bbox
# Create the coordinate system
cs = CoordinateSystem(m2, s0, s1, phi)
if abs(cs.zeta()) < 0.1:
continue
# The grid index generator
step_size = self.delta_divergence / self.grid_size
grid_index = transform.GridIndexGenerator(cs, x0, y0,
(step_size, step_size),
self.grid_size, s1_map)
# Create the image
#image = flex.double(flex.grid(z1 - z0, y1 - y0, x1 - x0), 1)
image = gaussian((z1 - z0, y1 - y0, x1 - x0), 10.0,
(z - z0, y - y0, x - x0), (2.0, 2.0, 2.0))
mask = flex.bool(flex.grid(image.all()), False)
for j in range(y1 - y0):
for i in range(x1 - x0):
inside = False
gx00, gy00 = grid_index(j, i)
gx01, gy01 = grid_index(j, i + 1)
gx10, gy10 = grid_index(j + 1, i)
gx11, gy11 = grid_index(j + 1, i + 1)
mingx = min([gx00, gx01, gx10, gx11])
maxgx = max([gx00, gx01, gx10, gx11])
mingy = min([gy00, gy01, gy10, gy11])
maxgy = max([gy00, gy01, gy10, gy11])
if (mingx >= 0 and maxgx < 2 * self.grid_size + 1
and mingy >= 0 and maxgy < 2 * self.grid_size + 1):
inside = True
for k in range(1, z1 - z0 - 1):
mask[k, j, i] = inside
# Transform the image to the grid
transformed = transform.TransformForwardNoModel(
self.spec, cs, bbox, 0, image.as_double(), mask)
grid = transformed.profile()
# Get the sums and ensure they're the same
eps = 1e-7
sum_grid = flex.sum(grid)
sum_image = flex.sum(flex.double(flex.select(image, flags=mask)))
assert (abs(sum_grid - sum_image) <= eps)
mask = flex.bool(flex.grid(image.all()), True)
transformed = transform.TransformForwardNoModel(
self.spec, cs, bbox, 0, image.as_double(), mask)
grid = transformed.profile()
# Boost the bbox to make sure all intensity is included
x0, x1, y0, y1, z0, z1 = bbox
bbox2 = (x0 - 10, x1 + 10, y0 - 10, y1 + 10, z0 - 10, z1 + 10)
# Do the reverse transform
transformed = transform.TransformReverseNoModel(
self.spec, cs, bbox2, 0, grid)
image2 = transformed.profile()
# Check the sum of pixels are the same
sum_grid = flex.sum(grid)
sum_image = flex.sum(image2)
assert (abs(sum_grid - sum_image) <= eps)
# Do the reverse transform
transformed = transform.TransformReverseNoModel(
self.spec, cs, bbox, 0, grid)
image2 = transformed.profile()
from dials.algorithms.statistics import pearson_correlation_coefficient
cc = pearson_correlation_coefficient(image.as_1d().as_double(),
image2.as_1d())
assert (cc >= 0.99)
# if cc < 0.99:
# print cc, bbox
# from matplotlib import pylab
# pylab.plot(image.as_numpy_array()[(z1-z0)/2,(y1-y0)/2,:])
# pylab.show()
# pylab.plot(image2.as_numpy_array()[(z1-z0)/2,(y1-y0)/2,:])
# pylab.show()
# pylab.plot((image.as_double()-image2).as_numpy_array()[(z1-z0)/2,(y1-y0)/2,:])
# pylab.show()
# Test passed
print 'OK'
def test_forward_no_model(dials_data):
sequence = load.imageset(
dials_data("centroid_test_data").join("sweep.json").strpath)
# Get the models
beam = sequence.get_beam()
detector = sequence.get_detector()
gonio = sequence.get_goniometer()
scan = sequence.get_scan()
scan.set_image_range((0, 1000))
# Set some parameters
sigma_divergence = beam.get_sigma_divergence(deg=False)
mosaicity = 0.157 * math.pi / 180
n_sigma = 3
grid_size = 20
delta_divergence = n_sigma * sigma_divergence
step_size = delta_divergence / grid_size
delta_divergence2 = delta_divergence + step_size * 0.5
delta_mosaicity = n_sigma * mosaicity
# Create the bounding box calculator
calculate_bbox = BBoxCalculator3D(beam, detector, gonio, scan,
delta_divergence2, delta_mosaicity)
# Initialise the transform
spec = transform.TransformSpec(beam, detector, gonio, scan,
sigma_divergence, mosaicity, n_sigma + 1,
grid_size)
# tst_conservation_of_counts(self):
random.seed(0)
assert len(detector) == 1
s0 = beam.get_s0()
m2 = gonio.get_rotation_axis()
s0_length = matrix.col(beam.get_s0()).length()
# Create an s1 map
s1_map = transform.beam_vector_map(detector[0], beam, True)
for i in range(100):
# Get random x, y, z
x = random.uniform(300, 1800)
y = random.uniform(300, 1800)
z = random.uniform(500, 600)
# Get random s1, phi, panel
s1 = matrix.col(detector[0].get_pixel_lab_coord(
(x, y))).normalize() * s0_length
phi = scan.get_angle_from_array_index(z, deg=False)
panel = 0
# Calculate the bounding box
bbox = calculate_bbox(s1, z, panel)
x0, x1, y0, y1, z0, z1 = bbox
# Create the coordinate system
cs = CoordinateSystem(m2, s0, s1, phi)
if abs(cs.zeta()) < 0.1:
continue
# The grid index generator
step_size = delta_divergence / grid_size
grid_index = transform.GridIndexGenerator(cs, x0, y0,
(step_size, step_size),
grid_size, s1_map)
# Create the image
# image = flex.double(flex.grid(z1 - z0, y1 - y0, x1 - x0), 1)
image = gaussian((z1 - z0, y1 - y0, x1 - x0), 10.0,
(z - z0, y - y0, x - x0), (2.0, 2.0, 2.0))
mask = flex.bool(flex.grid(image.all()), False)
for j in range(y1 - y0):
for i in range(x1 - x0):
inside = False
gx00, gy00 = grid_index(j, i)
gx01, gy01 = grid_index(j, i + 1)
gx10, gy10 = grid_index(j + 1, i)
gx11, gy11 = grid_index(j + 1, i + 1)
mingx = min([gx00, gx01, gx10, gx11])
maxgx = max([gx00, gx01, gx10, gx11])
mingy = min([gy00, gy01, gy10, gy11])
maxgy = max([gy00, gy01, gy10, gy11])
if (mingx >= 0 and maxgx < 2 * grid_size + 1 and mingy >= 0
and maxgy < 2 * grid_size + 1):
inside = True
for k in range(1, z1 - z0 - 1):
mask[k, j, i] = inside
# Transform the image to the grid
transformed = transform.TransformForwardNoModel(
spec, cs, bbox, 0, image.as_double(), mask)
grid = transformed.profile()
# Get the sums and ensure they're the same
eps = 1e-7
sum_grid = flex.sum(grid)
sum_image = flex.sum(flex.double(flex.select(image, flags=mask)))
assert abs(sum_grid - sum_image) <= eps
mask = flex.bool(flex.grid(image.all()), True)
transformed = transform.TransformForwardNoModel(
spec, cs, bbox, 0, image.as_double(), mask)
grid = transformed.profile()
# Boost the bbox to make sure all intensity is included
x0, x1, y0, y1, z0, z1 = bbox
bbox2 = (x0 - 10, x1 + 10, y0 - 10, y1 + 10, z0 - 10, z1 + 10)
# Do the reverse transform
transformed = transform.TransformReverseNoModel(
spec, cs, bbox2, 0, grid)
image2 = transformed.profile()
# Check the sum of pixels are the same
sum_grid = flex.sum(grid)
sum_image = flex.sum(image2)
assert abs(sum_grid - sum_image) <= eps
# Do the reverse transform
transformed = transform.TransformReverseNoModel(
spec, cs, bbox, 0, grid)
image2 = transformed.profile()
from dials.algorithms.statistics import pearson_correlation_coefficient
cc = pearson_correlation_coefficient(image.as_1d().as_double(),
image2.as_1d())
assert cc >= 0.99
def tst_conservation_of_counts(self):
from scitbx import matrix
from random import uniform, seed
from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem
from dials.algorithms.profile_model.gaussian_rs import transform
from scitbx.array_family import flex
seed(0)
assert len(self.detector) == 1
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
s0_length = matrix.col(self.beam.get_s0()).length()
# Create an s1 map
s1_map = transform.beam_vector_map(self.detector[0], self.beam, True)
for i in range(100):
# Get random x, y, z
x = uniform(300, 1800)
y = uniform(300, 1800)
z = uniform(500, 600)
# Get random s1, phi, panel
s1 = matrix.col(self.detector[0].get_pixel_lab_coord((x, y))).normalize() * s0_length
phi = self.scan.get_angle_from_array_index(z, deg=False)
panel = 0
# Calculate the bounding box
bbox = self.calculate_bbox(s1, z, panel)
x0, x1, y0, y1, z0, z1 = bbox
# Create the coordinate system
cs = CoordinateSystem(m2, s0, s1, phi)
if abs(cs.zeta()) < 0.1:
continue
# The grid index generator
step_size = self.delta_divergence / self.grid_size
grid_index = transform.GridIndexGenerator(cs, x0, y0, (step_size, step_size), self.grid_size, s1_map)
# Create the image
# image = flex.double(flex.grid(z1 - z0, y1 - y0, x1 - x0), 1)
image = gaussian((z1 - z0, y1 - y0, x1 - x0), 10.0, (z - z0, y - y0, x - x0), (2.0, 2.0, 2.0))
mask = flex.bool(flex.grid(image.all()), False)
for j in range(y1 - y0):
for i in range(x1 - x0):
inside = False
gx00, gy00 = grid_index(j, i)
gx01, gy01 = grid_index(j, i + 1)
gx10, gy10 = grid_index(j + 1, i)
gx11, gy11 = grid_index(j + 1, i + 1)
mingx = min([gx00, gx01, gx10, gx11])
maxgx = max([gx00, gx01, gx10, gx11])
mingy = min([gy00, gy01, gy10, gy11])
maxgy = max([gy00, gy01, gy10, gy11])
if mingx >= 0 and maxgx < 2 * self.grid_size + 1 and mingy >= 0 and maxgy < 2 * self.grid_size + 1:
inside = True
for k in range(1, z1 - z0 - 1):
mask[k, j, i] = inside
# Transform the image to the grid
transformed = transform.TransformForwardNoModel(self.spec, cs, bbox, 0, image.as_double(), mask)
grid = transformed.profile()
# Get the sums and ensure they're the same
eps = 1e-7
sum_grid = flex.sum(grid)
sum_image = flex.sum(flex.double(flex.select(image, flags=mask)))
assert abs(sum_grid - sum_image) <= eps
mask = flex.bool(flex.grid(image.all()), True)
transformed = transform.TransformForwardNoModel(self.spec, cs, bbox, 0, image.as_double(), mask)
grid = transformed.profile()
# Boost the bbox to make sure all intensity is included
x0, x1, y0, y1, z0, z1 = bbox
bbox2 = (x0 - 10, x1 + 10, y0 - 10, y1 + 10, z0 - 10, z1 + 10)
# Do the reverse transform
transformed = transform.TransformReverseNoModel(self.spec, cs, bbox2, 0, grid)
image2 = transformed.profile()
# Check the sum of pixels are the same
sum_grid = flex.sum(grid)
sum_image = flex.sum(image2)
assert abs(sum_grid - sum_image) <= eps
# Do the reverse transform
transformed = transform.TransformReverseNoModel(self.spec, cs, bbox, 0, grid)
image2 = transformed.profile()
from dials.algorithms.statistics import pearson_correlation_coefficient
cc = pearson_correlation_coefficient(image.as_1d().as_double(), image2.as_1d())
assert cc >= 0.99
# if cc < 0.99:
# print cc, bbox
# from matplotlib import pylab
# pylab.plot(image.as_numpy_array()[(z1-z0)/2,(y1-y0)/2,:])
# pylab.show()
# pylab.plot(image2.as_numpy_array()[(z1-z0)/2,(y1-y0)/2,:])
# pylab.show()
# pylab.plot((image.as_double()-image2).as_numpy_array()[(z1-z0)/2,(y1-y0)/2,:])
# pylab.show()
# Test passed
print "OK"
