def tst_with_flat_background(self):
from dials.algorithms.integration.fit import fit_profile
from scitbx.array_family import flex
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c0 = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
b = flex.double(flex.grid(9, 9, 9), 5)
m = flex.bool(flex.grid(9, 9, 9), True)
c = c0 + b
# Fit
fit = fit_profile(p, m, c, b)
I = fit.intensity()
V = fit.variance()
# Test intensity is the same
eps = 1e-7
assert (abs(I - flex.sum(c0)) < eps)
assert (abs(V - (flex.sum(c0) + flex.sum(b))) < eps)
print 'OK'
def tst_with_noisy_flat_background_partial(self):
from dials.algorithms.integration.fit import fit_profile
from scitbx.array_family import flex
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c0 = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
n = flex.random_double(9 * 9 * 9)
b = flex.double(flex.grid(9, 9, 9), 5) + n
m = flex.bool(flex.grid(9, 9, 9), True)
c = c0 + b
# Get the partial profiles
pp = p[0:5, :, :]
mp = m[0:5, :, :]
cp = c[0:5, :, :]
bp = b[0:5, :, :]
c0p = c0[0:5, :, :]
# Fit
fit = fit_profile(pp, mp, cp, bp)
I = fit.intensity()
V = fit.variance()
# Test intensity is the same
eps = 1e-7
assert (abs(I - flex.sum(c0)) < eps)
assert (abs(V - (flex.sum(c0p) + flex.sum(bp))) < eps)
print 'OK'
def tst_scaled_partial(self):
from dials.algorithms.integration.fit import fit_profile
from scitbx.array_family import flex
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
b = flex.double(flex.grid(9, 9, 9), 0)
m = flex.bool(flex.grid(9, 9, 9), True)
# Get the partial profiles
pp = p[0:5, :, :]
mp = m[0:5, :, :]
cp = c[0:5, :, :]
bp = b[0:5, :, :]
# Fit
fit = fit_profile(pp, mp, cp, bp)
I = fit.intensity()
V = fit.variance()
# Test intensity is the same
eps = 1e-7
assert (abs(I - flex.sum(c)) < eps)
assert (abs(V - flex.sum(cp)) < eps)
print 'OK'
def tst_with_no_background(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c = add_poisson_noise(100 * p)
b = flex.double(flex.grid(9, 9, 9), 0)
m = flex.bool(flex.grid(9,9,9), True)
# Fit
fit = ProfileFitter(c, b, m, p)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
# Test intensity is the same
eps = 1e-3
assert(abs(I[0] - flex.sum(c)) < eps)
assert(abs(V[0] - I[0]) < eps)
print 'OK'
def generate_3_profiles(self): from dials.array_family import flex p1 = gaussian((40, 9, 9), 1, (10.5, 4, 4), (2, 2, 2)) p2 = gaussian((40, 9, 9), 1, (20.5, 4, 4), (2, 2, 2)) p3 = gaussian((40, 9, 9), 1, (30.5, 4, 4), (2, 2, 2)) p1 = p1 / flex.sum(p1) p2 = p2 / flex.sum(p2) p3 = p3 / flex.sum(p3) p1.reshape(flex.grid(1, 40, 9, 9)) p2.reshape(flex.grid(1, 40, 9, 9)) p3.reshape(flex.grid(1, 40, 9, 9)) p = flex.double(flex.grid(3, 40, 9, 9)) p[0:1,:,:,:] = p1 p[1:2,:,:,:] = p2 p[2:3,:,:,:] = p3 return p
def tst_identical_partial(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c = p.deep_copy()
b = flex.double(flex.grid(9, 9, 9), 0)
m = flex.bool(flex.grid(9,9,9), True)
# Get the partial profiles
pp = p[0:5,:,:]
mp = m[0:5,:,:]
cp = c[0:5,:,:]
bp = b[0:5,:,:]
# Fit
fit = ProfileFitter(cp, bp, mp, pp)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
# Test intensity is the same
eps = 1e-7
assert(abs(I[0] - flex.sum(p)) < eps)
assert(abs(V[0] - flex.sum(p)) < eps)
print 'OK'
def tst_with_flat_background(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c0 = add_poisson_noise(100 * p)
b = flex.double(flex.grid(9, 9, 9), 10)
m = flex.bool(flex.grid(9,9,9), True)
b0 = add_poisson_noise(b)
c = c0 + b0
# Fit
fit = ProfileFitter(c, b, m, p)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
Iknown = 201.67417836585147
Vknown = 7491.6743173001205
# Test intensity is the same
eps = 1e-3
assert(abs(I[0] - Iknown) < eps)
assert(abs(V[0] - Vknown) < eps)
print 'OK'
def tst_identical(self):
from dials.algorithms.integration.fit import fit_profile
from scitbx.array_family import flex
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c = p.deep_copy()
b = flex.double(flex.grid(9, 9, 9), 0)
m = flex.bool(flex.grid(9, 9, 9), True)
# Fit
fit = fit_profile(p, m, c, b)
I = fit.intensity()
V = fit.variance()
# Test intensity is the same
eps = 1e-7
assert (abs(I - flex.sum(p)) < eps)
assert (abs(V - I) < eps)
print 'OK'
def generate_7_profiles(self): from dials.array_family import flex p1 = gaussian((40, 40, 40), 1, (10.5, 20.5, 20.5), (2, 2, 2)) p2 = gaussian((40, 40, 40), 1, (20.5, 20.5, 20.5), (2, 2, 2)) p3 = gaussian((40, 40, 40), 1, (30.5, 20.5, 20.5), (2, 2, 2)) p4 = gaussian((40, 40, 40), 1, (20.5, 10.5, 20.5), (2, 2, 2)) p5 = gaussian((40, 40, 40), 1, (20.5, 30.5, 20.5), (2, 2, 2)) p6 = gaussian((40, 40, 40), 1, (20.5, 20.5, 10.5), (2, 2, 2)) p7 = gaussian((40, 40, 40), 1, (20.5, 20.5, 30.5), (2, 2, 2)) p1 = p1 / flex.sum(p1) p2 = p2 / flex.sum(p2) p3 = p3 / flex.sum(p3) p4 = p4 / flex.sum(p4) p5 = p5 / flex.sum(p5) p6 = p6 / flex.sum(p6) p7 = p7 / flex.sum(p7) p1.reshape(flex.grid(1, 40, 40, 40)) p2.reshape(flex.grid(1, 40, 40, 40)) p3.reshape(flex.grid(1, 40, 40, 40)) p4.reshape(flex.grid(1, 40, 40, 40)) p5.reshape(flex.grid(1, 40, 40, 40)) p6.reshape(flex.grid(1, 40, 40, 40)) p7.reshape(flex.grid(1, 40, 40, 40)) p = flex.double(flex.grid(7, 40, 40, 40)) p[0:1,:,:,:] = p1 p[1:2,:,:,:] = p2 p[2:3,:,:,:] = p3 p[3:4,:,:,:] = p4 p[4:5,:,:,:] = p5 p[5:6,:,:,:] = p6 p[6:7,:,:,:] = p7 return p
def generate_single_central_non_negative_profiles(self):
from dials.array_family import flex
rlist = flex.reflection_table(1)
profile = gaussian(self.grid_size, 1000, (4, 4, 4), (1.5, 1.5, 1.5))
x = 500
y = 500
z = 5
xyz = flex.vec3_double(1)
xyz[0] = (x, y, z)
profiles = [ profile.deep_copy() ]
rlist['xyzcal.px'] = xyz
return rlist, profiles, profile
def generate_identical_non_negative_profiles(self):
from dials.array_family import flex
from random import uniform
rlist = flex.reflection_table(1000)
profile = gaussian(self.grid_size, 1000, (4, 4, 4), (1.5, 1.5, 1.5))
xyz = flex.vec3_double(1000)
profiles = []
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(0, 10)
xyz[i] = (x, y, z)
profiles.append(profile.deep_copy())
rlist['xyzcal.px'] = xyz
return rlist, profiles, profile
def generate_systematically_offset_profiles(self):
from dials.array_family import flex
from random import uniform
rlist = flex.reflection_table(1000)
xyz = flex.vec3_double(1000)
profiles = []
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(0, 10)
offset = -4.5 + 9 * x / 1000.0
profile = gaussian(self.grid_size, 1000,
(4 + offset, 4, 4), (1.5, 1.5, 1.5))
xyz[i] = (x, y, z)
profiles.append(profile)
rlist['xyzcal.px'] = xyz
return rlist, profiles
def tst_with_flat_background_partial(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
# Create profile
p = gaussian((9, 9, 9), 1, (4, 4, 4), (2, 2, 2))
s = flex.sum(p)
p = p / s
# Copy profile
c0 = add_poisson_noise(100 * p)
b = flex.double(flex.grid(9, 9, 9), 1)
m = flex.bool(flex.grid(9,9,9), True)
c = c0 + add_poisson_noise(b)
# Get the partial profiles
pp = p[0:5,:,:]
mp = m[0:5,:,:]
cp = c[0:5,:,:]
bp = b[0:5,:,:]
c0p = c0[0:5,:,:]
# Fit
fit = ProfileFitter(cp, bp, mp, pp)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
Iknown = 99.06932141277105
Vknown = 504.06932141277105
# Test intensity is the same
eps = 1e-7
assert(abs(I[0] - Iknown) < eps)
assert(abs(V[0] - Vknown) < eps)
print 'OK'
