def select_modeller():
if model == 'constant2d':
return Constant2dModeller()
elif model == 'constant3d':
return Constant3dModeller()
elif model == 'linear2d':
return Linear2dModeller()
elif model == 'linear3d':
return Linear3dModeller()
raise RuntimeError("Unexpected background model: %s" % model)
def select_modeller():
if model == "constant2d":
return Constant2dModeller()
elif model == "constant3d":
return Constant3dModeller()
elif model == "linear2d":
return Linear2dModeller()
elif model == "linear3d":
return Linear3dModeller()
raise RuntimeError(f"Unexpected background model: {model}")
def test_linear3d_modeller(self):
from dials.algorithms.background.simple import Linear3dModeller
modeller = Linear3dModeller()
eps = 1e-7
for i in range(10):
p, data, mask = self.generate_linear_background_3d(self.size, 0, 100)
model = modeller.create(data, mask)
assert(len(model.params()) == 4)
for j in range(4):
assert(abs(model.params()[j] - p[j]) < eps)
def run(self):
from dials.algorithms.background.simple import Creator
from dials.algorithms.background.simple import Linear3dModeller
from dials.algorithms.background.simple import TruncatedOutlierRejector
from dials.algorithms.background.simple import NSigmaOutlierRejector
from dials.algorithms.background.simple import NormalOutlierRejector
modeller = Linear3dModeller()
outlier_rejector = [
None,
TruncatedOutlierRejector(0.01, 0.01),
NSigmaOutlierRejector(3.0, 3.0),
NormalOutlierRejector(10),
]
for rejector in outlier_rejector:
self.tst(Creator(modeller, rejector))
def tst_linear3d_modeller(self):
from dials.algorithms.background.simple import Linear3dModeller
from dials.array_family import flex
modeller = Linear3dModeller()
# Generate shoeboxes
ma = 100
mb = 1
mc = 2
md = 3
sboxes, masks = self.generate_background(self.size, 1000, ma, mb, mc,
md)
# Compute model
a, b, c, d = [], [], [], []
va, vb, vc, vd = [], [], [], []
pa = []
pv = []
for i in range(1000):
model = modeller.create(sboxes[i], masks[i])
assert (len(model.params()) == 4)
assert (len(model.variances()) == 4)
p = model.params()
v = model.variances()
pa.append(tuple(p))
pv.append(tuple(v))
a, b, c, d = zip(*pa)
va, vb, vc, vd = zip(*pv)
# Compute Z for each parameter
za = (flex.double(a) - ma) / flex.sqrt(flex.double(va))
zb = (flex.double(b) - mb) / flex.sqrt(flex.double(vb))
zc = (flex.double(c) - mc) / flex.sqrt(flex.double(vc))
zd = (flex.double(d) - md) / flex.sqrt(flex.double(vd))
# Check it looks standard normal
self.assert_std_norm(za)
self.assert_std_norm(zb)
self.assert_std_norm(zc)
self.assert_std_norm(zd)
print 'OK'
