def test_nsigma():
lower = 3.0
upper = 3.0
reject = NSigmaOutlierRejector(lower, upper)
size = (9, 9, 9)
ninvalid = 5
nforeground = 20
mean = 20
def assert_is_correct(data, mask):
(
invalid,
foreground,
background,
background_used,
background_valid,
) = assert_basic_mask_is_correct(mask, ninvalid, nforeground)
subdata = data.select(background_valid)
mv = flex.mean_and_variance(subdata)
m = mv.mean()
s = mv.unweighted_sample_standard_deviation()
p0 = m - lower * s
p1 = m + upper * s
mask = (subdata >= p0) & (subdata <= p1)
exp = background_valid.select(mask)
assert len(exp) == len(background_used)
assert all(ii == jj for ii, jj in zip(exp, background_used))
for i in range(10):
data, mask = generate_shoebox(size, mean, nforeground, ninvalid)
reject(data, mask)
assert_is_correct(data, mask)
def __init__(self): from dials.algorithms.background.simple import NSigmaOutlierRejector self.lower = 3.0 self.upper = 3.0 self.reject = NSigmaOutlierRejector(self.lower, self.upper) self.size = (9,9,9) self.ninvalid = 5 self.nforeground = 20 self.mean = 20
def run(self):
from dials.algorithms.background.simple import Creator
from dials.algorithms.background.simple import Constant2dModeller
from dials.algorithms.background.simple import TruncatedOutlierRejector
from dials.algorithms.background.simple import NSigmaOutlierRejector
from dials.algorithms.background.simple import NormalOutlierRejector
modeller = Constant2dModeller()
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 select_rejector():
if outlier == 'null':
return None
elif outlier == 'truncated':
return TruncatedOutlierRejector(kwargs.get("lower", 0.01),
kwargs.get("upper", 0.01))
elif outlier == 'nsigma':
return NSigmaOutlierRejector(kwargs.get("lower", 3),
kwargs.get("upper", 3))
elif outlier == 'normal':
return NormalOutlierRejector(kwargs.get("min_pixels", 10))
elif outlier == 'plane':
return MosflmOutlierRejector(kwargs.get("fraction", 1.0),
kwargs.get("n_sigma", 4.0))
elif outlier == 'tukey':
return TukeyOutlierRejector(kwargs.get("lower", 1.5),
kwargs.get("upper", 1.5))
raise RuntimeError("Unexpected outlier rejector: %s" % outlier)
def select_rejector():
if outlier == "null":
return None
elif outlier == "truncated":
return TruncatedOutlierRejector(kwargs.get("lower", 0.01),
kwargs.get("upper", 0.01))
elif outlier == "nsigma":
return NSigmaOutlierRejector(kwargs.get("lower", 3),
kwargs.get("upper", 3))
elif outlier == "normal":
return NormalOutlierRejector(kwargs.get("min_pixels", 10))
elif outlier == "plane":
return MosflmOutlierRejector(kwargs.get("fraction", 1.0),
kwargs.get("n_sigma", 4.0))
elif outlier == "tukey":
return TukeyOutlierRejector(kwargs.get("lower", 1.5),
kwargs.get("upper", 1.5))
raise RuntimeError(f"Unexpected outlier rejector: {outlier}")
