class SimpleBackgroundExt(BackgroundIface):
""" An extension class implementing XDS background subtraction. """
name = "glm"
default = True
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse(
"""
robust {
tuning_constant = 1.345
.type = float
.help = "The tuning constant for robust estimation"
}
model {
algorithm = constant2d *constant3d loglinear2d loglinear3d
.type = choice
.help = "The background model to fit"
}
"""
)
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.glm import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse("")).extract()
else:
params = params.integration.background.glm
# Create the algorithm
self._algorithm = BackgroundAlgorithm(
experiments, tuning_constant=params.robust.tuning_constant, model=params.model.algorithm
)
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)
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.glm import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.glm
# Create the algorithm
self._algorithm = BackgroundAlgorithm(
experiments,
tuning_constant=params.robust.tuning_constant,
model=params.model.algorithm,
min_pixels=params.min_pixels)
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.glm import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse("")).extract()
else:
params = params.integration.background.glm
# Create the algorithm
self._algorithm = BackgroundAlgorithm(
experiments, tuning_constant=params.robust.tuning_constant, model=params.model.algorithm
)
class GLMBackgroundExt(object):
''' An extension class implementing a robust GLM background algorithm. '''
name = 'glm'
default = True
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse('''
robust {
tuning_constant = 1.345
.type = float
.help = "The tuning constant for robust estimation"
}
model {
algorithm = constant2d *constant3d loglinear2d loglinear3d
.type = choice
.help = "The background model to fit"
}
min_pixels = 10
.type = int(value_min=1)
.help = "The minimum number of pixels required"
''')
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.glm import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.glm
# Create the algorithm
self._algorithm = BackgroundAlgorithm(
experiments,
tuning_constant=params.robust.tuning_constant,
model=params.model.algorithm,
min_pixels=params.min_pixels)
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 a robust GLM background algorithm. '''
name = 'glm'
default=True
@classmethod
def phil(cls):
from libtbx.phil import parse
phil = parse('''
robust {
tuning_constant = 1.345
.type = float
.help = "The tuning constant for robust estimation"
}
model {
algorithm = constant2d *constant3d loglinear2d loglinear3d
.type = choice
.help = "The background model to fit"
}
min_pixels = 10
.type = int(value_min=1)
.help = "The minimum number of pixels required"
''')
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.glm import BackgroundAlgorithm
# Create some default parameters
if params is None:
params = self.phil().fetch(parse('')).extract()
else:
params = params.integration.background.glm
# Create the algorithm
self._algorithm = BackgroundAlgorithm(
experiments,
tuning_constant=params.robust.tuning_constant,
model=params.model.algorithm,
min_pixels=params.min_pixels)
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)