def test_identical_partial():
np.random.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)
intensity = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
# Test intensity is the same
eps = 1e-7
assert intensity[0] == pytest.approx(flex.sum(p), abs=eps)
assert V[0] == pytest.approx(flex.sum(p), abs=eps)
def test_with_flat_background_partial():
np.random.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, :, :]
# Fit
fit = ProfileFitter(cp, bp, mp, pp)
intensity = 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 intensity[0] == pytest.approx(Iknown, abs=eps)
assert V[0] == pytest.approx(Vknown, abs=eps)
def test_zero(): 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 = flex.double(flex.grid(9,9,9), 0) 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)
def test_with_flat_background():
np.random.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)
intensity = 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 intensity[0] == pytest.approx(Iknown, abs=eps)
assert V[0] == pytest.approx(Vknown, abs=eps)
def test_with_flat_background(): 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)
def test_identical_partial(): 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)
def test_deconvolve_7_with_flat_background():
np.random.seed(0)
I0 = [1000, 1500, 2000, 2500, 3000, 3500, 4000]
# Create profile
p = generate_7_profiles()
Ical = [[], [], [], [], [], [], []]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 40, 40))
for i in range(p.all()[0]):
pp = p[i:i + 1, :, :, :]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 40, 40), 1)
m = flex.bool(flex.grid(40, 40, 40), True)
c += add_poisson_noise(b)
# Fit
fit = ProfileFitter(c, b, m, p)
intensity = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(7):
Ical[i].append(intensity[i])
for i in range(7):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [
1042.4904898451261,
1447.6413897568257,
2053.2524102387893,
2485.2789614429703,
3063.867598583333,
3445.9230910807582,
3977.6744651425543,
]
Vknown = [
65042.49048984377,
65447.6413897533,
66053.25241023625,
66485.27896144328,
67063.86759858252,
67445.92309107889,
67977.67446514373,
]
# Test intensity is the same
eps = 1e-7
for i in range(7):
assert intensity[i] == pytest.approx(Iknown[i], abs=eps)
assert V[i] == pytest.approx(Vknown[i], abs=eps)
def test_deconvolve_7_with_no_background():
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
I0 = [1000, 1500, 2000, 2500, 3000, 3500, 4000]
# Create profile
p = generate_7_profiles()
Ical = [[], [], [], [], [], [], []]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 40, 40))
for i in range(p.all()[0]):
pp = p[i : i + 1, :, :, :]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 40, 40), 0)
m = flex.bool(flex.grid(40, 40, 40), True)
# Fit
fit = ProfileFitter(c, b, m, p)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(7):
Ical[i].append(I[i])
for i in range(7):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [
1012.4193633595916,
1472.3322059495797,
2072.136413825826,
2486.4902438469253,
3012.6132119521126,
3409.2053517072773,
3952.803209358826,
]
# Test intensity is the same
eps = 1e-7
for i in range(7):
assert abs(I[i] - Iknown[i]) < eps
assert abs(V[i] - Iknown[i]) < eps
def tst_deconvolve_7_with_flat_background(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
I0 = [1000, 1500, 2000, 2500, 3000, 3500, 4000]
# Create profile
p = self.generate_7_profiles()
Ical = [[],[],[],[],[],[],[]]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 40, 40))
for i in range(p.all()[0]):
pp = p[i:i+1,:,:,:]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 40, 40), 1)
m = flex.bool(flex.grid(40,40,40), True)
c += add_poisson_noise(b)
# Fit
fit = ProfileFitter(c, b, m, p)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(7):
Ical[i].append(I[i])
for i in range(7):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [1042.4904898451261, 1447.6413897568257, 2053.2524102387893,
2485.2789614429703, 3063.867598583333, 3445.9230910807582,
3977.6744651425543]
Vknown = [65042.49048984377, 65447.6413897533, 66053.25241023625,
66485.27896144328, 67063.86759858252, 67445.92309107889,
67977.67446514373]
# Test intensity is the same
eps = 1e-7
for i in range(7):
assert(abs(I[i] - Iknown[i]) < eps)
assert(abs(V[i] - Vknown[i]) < eps)
print 'OK'
def tst_deconvolve_3_with_flat_background(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
I0 = [1000, 2000, 3000]
# Create profile
p = self.generate_3_profiles()
Ical = [[],[],[]]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 9, 9))
for i in range(p.all()[0]):
pp = p[i:i+1,:,:,:]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 9, 9), 1)
m = flex.bool(flex.grid(40,9,9), True)
c += add_poisson_noise(b)
# Fit
fit = ProfileFitter(c, b, m, p)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(3):
Ical[i].append(I[i])
for i in range(3):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [1030.7018033805357, 1948.7152700952695, 2972.983204218213]
Vknown = [4270.701803380534, 5188.715270095279, 6212.983204218214]
# Test intensity is the same
eps = 1e-7
for i in range(3):
assert(abs(I[i] - Iknown[i]) < eps)
assert(abs(V[i] - Vknown[i]) < eps)
print 'OK'
def tst_deconvolve_3_with_no_background(self):
from dials.algorithms.integration.fit import ProfileFitter
from scitbx.array_family import flex
from numpy.random import seed
seed(0)
I0 = [1000, 2000, 3000]
# Create profile
p = self.generate_3_profiles()
Ical = [[],[],[]]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 9, 9))
for i in range(p.all()[0]):
pp = p[i:i+1,:,:,:]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 9, 9), 0)
m = flex.bool(flex.grid(40,9,9), True)
# Fit
fit = ProfileFitter(c, b, m, p)
I = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(3):
Ical[i].append(I[i])
for i in range(3):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [1048.3221116842406, 1920.9035376774107, 2938.7743506383745]
# Test intensity is the same
eps = 1e-7
for i in range(3):
assert(abs(I[i] - Iknown[i]) < eps)
assert(abs(V[i] - Iknown[i]) < eps)
print 'OK'
def test_deconvolve_3_with_flat_background():
np.random.seed(0)
I0 = [1000, 2000, 3000]
# Create profile
p = generate_3_profiles()
Ical = [[], [], []]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 9, 9))
for i in range(p.all()[0]):
pp = p[i:i + 1, :, :, :]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 9, 9), 1)
m = flex.bool(flex.grid(40, 9, 9), True)
c += add_poisson_noise(b)
# Fit
fit = ProfileFitter(c, b, m, p)
intensity = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(3):
Ical[i].append(intensity[i])
for i in range(3):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [1030.7018033805357, 1948.7152700952695, 2972.983204218213]
Vknown = [4270.701803380534, 5188.715270095279, 6212.983204218214]
# Test intensity is the same
eps = 1e-7
for i in range(3):
assert intensity[i] == pytest.approx(Iknown[i], abs=eps)
assert V[i] == pytest.approx(Vknown[i], abs=eps)
def test_deconvolve_3_with_no_background():
np.random.seed(0)
I0 = [1000, 2000, 3000]
# Create profile
p = generate_3_profiles()
Ical = [[], [], []]
for it in range(1):
# Copy profile
c = flex.double(flex.grid(40, 9, 9))
for i in range(p.all()[0]):
pp = p[i:i + 1, :, :, :]
pp.reshape(c.accessor())
cc = add_poisson_noise(I0[i] * pp)
c += cc
b = flex.double(flex.grid(40, 9, 9), 0)
m = flex.bool(flex.grid(40, 9, 9), True)
# Fit
fit = ProfileFitter(c, b, m, p)
intensity = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
for i in range(3):
Ical[i].append(intensity[i])
for i in range(3):
Ical[i] = sum(Ical[i]) / len(Ical[i])
Iknown = [1048.3221116842406, 1920.9035376774107, 2938.7743506383745]
# Test intensity is the same
eps = 1e-7
for i in range(3):
assert intensity[i] == pytest.approx(Iknown[i], abs=eps)
assert V[i] == pytest.approx(Iknown[i], abs=eps)
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'
def test_mask():
np.random.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)
m[4, 4, 4] = False
# Fit
fit = ProfileFitter(c, b, m, p)
intensity = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
# Test intensity is approximately the same
assert intensity[0] == pytest.approx(flex.sum(c), rel=0.01)
assert intensity[0] == pytest.approx(V[0], abs=1e-3)
def test_zero():
np.random.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 = flex.double(flex.grid(9, 9, 9), 0)
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)
intensity = fit.intensity()
V = fit.variance()
assert fit.niter() < fit.maxiter()
# Test intensity is the same
eps = 1e-3
assert intensity[0] == pytest.approx(flex.sum(c), abs=eps)
assert intensity[0] == pytest.approx(V[0], abs=eps)
def test_with_no_background_partial():
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)
# 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()
Iknown = 94.67151440319306
Vknown = 94.67151440319306
# Test intensity is the same
eps = 1e-7
assert abs(I[0] - Iknown) < eps
assert abs(V[0] - Vknown) < eps