def __init__(self, params, experiments):
'''
Initialise the algorithm.
:param params: The input parameters
:param experiments: The list of experiments
'''
from libtbx.phil import parse
from dials.algorithms.background.simple import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.simple
# Create some keyword parameters
kwargs = {
'model' : params.model.algorithm,
'outlier' : params.outlier.algorithm,
'min_pixels' : params.min_pixels
}
# Create all the keyword parameters
if params.outlier.algorithm == 'null':
pass
elif params.outlier.algorithm == 'truncated':
kwargs['lower'] = params.outlier.truncated.lower
kwargs['upper'] = params.outlier.truncated.upper
elif params.outlier.algorithm == 'nsigma':
kwargs['lower'] = params.outlier.nsigma.lower
kwargs['upper'] = params.outlier.nsigma.upper
elif params.outlier.algorithm == 'normal':
kwargs['min_pixels'] = params.outlier.normal.min_pixels
elif params.outlier.algorithm == 'plane':
kwargs['fraction'] = params.outlier.plane.fraction
kwargs['n_sigma'] = params.outlier.plane.n_sigma
elif params.outlier.algorithm == 'tukey':
kwargs['lower'] = params.outlier.tukey.lower
kwargs['upper'] = params.outlier.tukey.upper
# Create the algorithm
self._algorithm = BackgroundAlgorithm(experiments, **kwargs)
def __init__(self, params, experiments):
"""
Initialise the algorithm.
:param params: The input parameters
:param experiments: The list of experiments
"""
from libtbx.phil import parse
from dials.algorithms.background.simple import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse("")).extract()
else:
params = params.integration.background.simple
# Create some keyword parameters
kwargs = {
"model": params.model.algorithm,
"outlier": params.outlier.algorithm,
"min_pixels": params.min_pixels,
}
# Create all the keyword parameters
if params.outlier.algorithm == "null":
pass
elif params.outlier.algorithm == "truncated":
kwargs["lower"] = params.outlier.truncated.lower
kwargs["upper"] = params.outlier.truncated.upper
elif params.outlier.algorithm == "nsigma":
kwargs["lower"] = params.outlier.nsigma.lower
kwargs["upper"] = params.outlier.nsigma.upper
elif params.outlier.algorithm == "normal":
kwargs["min_pixels"] = params.outlier.normal.min_pixels
elif params.outlier.algorithm == "plane":
kwargs["fraction"] = params.outlier.plane.fraction
kwargs["n_sigma"] = params.outlier.plane.n_sigma
elif params.outlier.algorithm == "tukey":
kwargs["lower"] = params.outlier.tukey.lower
kwargs["upper"] = params.outlier.tukey.upper
# Create the algorithm
self._algorithm = BackgroundAlgorithm(experiments, **kwargs)
def __init__(self, params, experiments):
'''
Initialise the algorithm.
:param params: The input parameters
:param experiments: The list of experiments
'''
from libtbx.phil import parse
from dials.algorithms.background.simple import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.simple
# Create some keyword parameters
kwargs = {
'model' : params.model.algorithm,
'outlier' : params.outlier.algorithm,
'min_pixels' : params.min_pixels
}
# Create all the keyword parameters
if params.outlier.algorithm == 'null':
pass
elif params.outlier.algorithm == 'truncated':
kwargs['lower'] = params.outlier.truncated.lower
kwargs['upper'] = params.outlier.truncated.upper
elif params.outlier.algorithm == 'nsigma':
kwargs['lower'] = params.outlier.nsigma.lower
kwargs['upper'] = params.outlier.nsigma.upper
elif params.outlier.algorithm == 'normal':
kwargs['min_pixels'] = params.outlier.normal.min_pixels
elif params.outlier.algorithm == 'plane':
kwargs['fraction'] = params.outlier.plane.fraction
kwargs['n_sigma'] = params.outlier.plane.n_sigma
elif params.outlier.algorithm == 'tukey':
kwargs['lower'] = params.outlier.tukey.lower
kwargs['upper'] = params.outlier.tukey.upper
# Create the algorithm
self._algorithm = BackgroundAlgorithm(experiments, **kwargs)
class SimpleBackgroundExt(BackgroundIface):
''' An extension class implementing simple background subtraction. '''
name = 'simple'
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse('''
outlier
.help = "Outlier rejection prior to background fit"
{
algorithm = *null nsigma truncated normal plane tukey
.help = "The outlier rejection algorithm."
.type = choice
nsigma
.help = "Parameters for nsigma outlier rejector"
.expert_level = 1
{
lower = 3
.help = "Lower n sigma"
.type = float
upper = 3
.help = "Upper n sigma"
.type = float
}
truncated
.help = "Parameters for truncated outlier rejector"
.expert_level = 1
{
lower = 0.01
.help = "Lower bound"
.type = float
upper = 0.01
.help = "Upper bound"
.type = float
}
normal
.help = "Parameters for normal outlier rejector"
.expert_level = 1
{
min_pixels = 10
.help = "The minimum number of pixels to use in calculating the"
"background intensity."
.type = int
}
plane
.help = "Parameters for mosflm-like outlier rejector. This algorithm"
"is mainly used in conjunction with a linear 2d background."
.expert_level = 1
{
fraction = 1.0
.help = "The fraction of pixels to use in determining the initial"
"plane used for outlier rejection."
.type = float
n_sigma = 4.0
.help = "The number of standard deviations above the threshold plane"
"to use in rejecting outliers from background calculation."
.type = float
}
tukey
.help = "Parameters for tukey outlier rejector"
.expert_level = 1
{
lower = 1.5
.help = "Lower IQR multiplier"
.type = float
upper = 1.5
.help = "Upper IQR multiplier"
.type = float
}
}
model
.help = "Background model"
{
algorithm = constant2d *constant3d linear2d linear3d
.help = "The choice of background model"
.type = choice
}
min_pixels = 10
.type = int(value_min=1)
.help = "The minimum number of pixels to compute the background"
''')
return phil
def __init__(self, params, experiments):
'''
Initialise the algorithm.
:param params: The input parameters
:param experiments: The list of experiments
'''
from libtbx.phil import parse
from dials.algorithms.background.simple import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.simple
# Create some keyword parameters
kwargs = {
'model' : params.model.algorithm,
'outlier' : params.outlier.algorithm,
'min_pixels' : params.min_pixels
}
# Create all the keyword parameters
if params.outlier.algorithm == 'null':
pass
elif params.outlier.algorithm == 'truncated':
kwargs['lower'] = params.outlier.truncated.lower
kwargs['upper'] = params.outlier.truncated.upper
elif params.outlier.algorithm == 'nsigma':
kwargs['lower'] = params.outlier.nsigma.lower
kwargs['upper'] = params.outlier.nsigma.upper
elif params.outlier.algorithm == 'normal':
kwargs['min_pixels'] = params.outlier.normal.min_pixels
elif params.outlier.algorithm == 'plane':
kwargs['fraction'] = params.outlier.plane.fraction
kwargs['n_sigma'] = params.outlier.plane.n_sigma
elif params.outlier.algorithm == 'tukey':
kwargs['lower'] = params.outlier.tukey.lower
kwargs['upper'] = params.outlier.tukey.upper
# Create the algorithm
self._algorithm = BackgroundAlgorithm(experiments, **kwargs)
def compute_background(self, reflections, image_volume=None):
'''
Compute the background.
:param reflections: The list of reflections
'''
return self._algorithm.compute_background(
reflections, image_volume=image_volume)
class SimpleBackgroundExt(BackgroundIface):
''' An extension class implementing XDS background subtraction. '''
name = 'simple'
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse('''
outlier
.help = "Outlier rejection prior to background fit"
{
algorithm = null *nsigma truncated normal mosflm tukey
.help = "The outlier rejection algorithm."
.type = choice
nsigma
.help = "Parameters for nsigma outlier rejector"
.expert_level = 1
{
lower = 3
.help = "Lower n sigma"
.type = float
upper = 3
.help = "Upper n sigma"
.type = float
}
truncated
.help = "Parameters for truncated outlier rejector"
.expert_level = 1
{
lower = 0.01
.help = "Lower bound"
.type = float
upper = 0.01
.help = "Upper bound"
.type = float
}
normal
.help = "Parameters for normal outlier rejector"
.expert_level = 1
{
min_pixels = 10
.help = "The minimum number of pixels to use in calculating the"
"background intensity."
.type = int
}
mosflm
.help = "Parameters for mosflm-like outlier rejector. This algorithm"
"is mainly used in conjunction with a linear 2d background."
.expert_level = 1
{
fraction = 1.0
.help = "The fraction of pixels to use in determining the initial"
"plane used for outlier rejection."
.type = float
n_sigma = 4.0
.help = "The number of standard deviations above the threshold plane"
"to use in rejecting outliers from background calculation."
.type = float
}
tukey
.help = "Parameters for tukey outlier rejector"
.expert_level = 1
{
lower = 1.5
.help = "Lower IQR multiplier"
.type = float
upper = 1.5
.help = "Upper IQR multiplier"
.type = float
}
}
model
.help = "Background model"
{
algorithm = constant2d *constant3d linear2d linear3d
.help = "The choice of background model"
.type = choice
}
''')
return phil
def __init__(self, params, experiments):
'''
Initialise the algorithm.
:param params: The input parameters
:param experiments: The list of experiments
'''
from libtbx.phil import parse
from dials.algorithms.background.simple import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.simple
# Create some keyword parameters
kwargs = {
'model' : params.model.algorithm,
'outlier' : params.outlier.algorithm
}
# Create all the keyword parameters
if params.outlier.algorithm == 'null':
pass
elif params.outlier.algorithm == 'truncated':
kwargs['lower'] = params.outlier.truncated.lower
kwargs['upper'] = params.outlier.truncated.upper
elif params.outlier.algorithm == 'nsigma':
kwargs['lower'] = params.outlier.nsigma.lower
kwargs['upper'] = params.outlier.nsigma.upper
elif params.outlier.algorithm == 'normal':
kwargs['min_pixels'] = params.outlier.normal.min_pixels
elif params.outlier.algorithm == 'mosflm':
kwargs['fraction'] = params.outlier.mosflm.fraction
kwargs['n_sigma'] = params.outlier.mosflm.n_sigma
elif params.outlier.algorithm == 'tukey':
kwargs['lower'] = params.outlier.tukey.lower
kwargs['upper'] = params.outlier.tukey.upper
# Create the algorithm
self._algorithm = BackgroundAlgorithm(experiments, **kwargs)
def compute_background(self, reflections, image_volume=None):
'''
Compute the background.
:param reflections: The list of reflections
'''
return self._algorithm.compute_background(
reflections, image_volume=image_volume)