def test_that_create_manage_non_child_algorithm_is_called_once_in_centre_finder_new(
self, make_algorithm_mock, load_data_mock):
r_min = 5
r_max = 10
position_1_start = 300
position_2_start = -300
tolerance = 0.001
find_direction = FindDirectionEnum.All
iterations = 10
verbose = False
load_data_mock.return_value = {
SANSDataType.SampleScatter: [mock.MagicMock()]
}, {
SANSDataType.SampleScatter: [mock.MagicMock()]
}
beam_centre_finder = "SANSBeamCentreFinder"
beam_centre_finder_options = {
"Component": 'LAB',
"Iterations": iterations,
"RMin": r_min / 1000,
"RMax": r_max / 1000,
"Position1Start": position_1_start,
"Position2Start": position_2_start,
"Tolerance": tolerance,
"Direction": FindDirectionEnum.to_string(find_direction),
"Verbose": verbose
}
centre_finder_new(self.state,
r_min=r_min,
r_max=r_max,
iterations=iterations,
position_1_start=position_1_start,
position_2_start=position_2_start,
tolerance=tolerance,
find_direction=find_direction,
verbose=verbose,
component=DetectorType.LAB)
make_algorithm_mock.assert_called_once_with(
beam_centre_finder, **beam_centre_finder_options)
def test_that_create_manage_non_child_algorithm_is_called_once_in_centre_finder_new(self, make_algorithm_mock, load_data_mock):
r_min = 5
r_max = 10
position_1_start = 300
position_2_start = -300
tolerance = 0.001
find_direction = FindDirectionEnum.All
iterations = 10
verbose = False
load_data_mock.return_value = {SANSDataType.SampleScatter: [mock.MagicMock()]}, {
SANSDataType.SampleScatter: [mock.MagicMock()]}
beam_centre_finder = "SANSBeamCentreFinder"
beam_centre_finder_options = {"Component":'LAB', "Iterations": iterations, "RMin": r_min / 1000, "RMax": r_max / 1000,
"Position1Start": position_1_start, "Position2Start": position_2_start,
"Tolerance": tolerance, "Direction": FindDirectionEnum.to_string(find_direction),
"Verbose": verbose}
centre_finder_new(self.state, r_min=r_min, r_max=r_max, iterations=iterations, position_1_start=position_1_start
,position_2_start=position_2_start, tolerance=tolerance, find_direction=find_direction
,verbose=verbose, component=DetectorType.LAB)
make_algorithm_mock.assert_called_once_with(beam_centre_finder, **beam_centre_finder_options)
def centre_finder_new(state, r_min = 0.06, r_max = 0.26, iterations = 10, position_1_start = 0.0, position_2_start = 0.0
, tolerance = 0.0001251, find_direction = FindDirectionEnum.All, verbose=False, component=DetectorType.LAB):
"""
Finds the beam centre from a good initial guess.
This function finds the centre of the beam by splitting the workspace up into 4 quadrants and running a reduction on
each. The (left, right) and (up, down) reductions are then compared producing residuals which are minimised through
repeated iteration.
:param state: This is a sans state, to find the beam centre for.
:param r_min: This is the inner radius of the quartile mask.
:param r_max: This is the outer radius of the quartile mask.
:param max_iter: This is the maximum number of iterations.
:param position_1_start: This is the starting position of the search on the x axis.
:param position_2_start: This is the starting position of the search on the y axis.
:param tolerance: This is the tolerance for the search.
:param find_direction: This is an enumerator controlling which axis or both should be searched.
"""
# ------------------------------------------------------------------------------------------------------------------
# Load the data
# ------------------------------------------------------------------------------------------------------------------
workspace_to_name = {SANSDataType.SampleScatter: "SampleScatterWorkspace",
SANSDataType.SampleTransmission: "SampleTransmissionWorkspace",
SANSDataType.SampleDirect: "SampleDirectWorkspace",
SANSDataType.CanScatter: "CanScatterWorkspace",
SANSDataType.CanTransmission: "CanTransmissionWorkspace",
SANSDataType.CanDirect: "CanDirectWorkspace"}
workspace_to_monitor = {SANSDataType.SampleScatter: "SampleScatterMonitorWorkSpace",
SANSDataType.CanScatter: "CanScatterMonitorWorkspace"}
workspaces, monitors = provide_loaded_data(state, False, workspace_to_name, workspace_to_monitor)
# ------------------------------------------------------------------------------------------------------------------
# Get reduction settings
# Split into individual bundles which can be reduced individually. We split here if we have multiple periods or
# sliced times for example. For the beam centre finder we only use the first period.
# ------------------------------------------------------------------------------------------------------------------
reduction_packages = get_reduction_packages(state, workspaces, monitors)
reduction_package = reduction_packages[0]
# ------------------------------------------------------------------------------------------------------------------
# Setup the beam centre finder algorithm.
# ------------------------------------------------------------------------------------------------------------------
beam_centre_finder = "SANSBeamCentreFinder"
beam_centre_finder_options = {"Iterations": iterations, "RMin": r_min/1000, "RMax": r_max/1000,
"Position1Start": position_1_start, "Position2Start": position_2_start,
"Tolerance": tolerance, "Direction" : FindDirectionEnum.to_string(find_direction),
"Verbose": verbose, "Component": DetectorType.to_string(component)}
beam_centre_alg = create_managed_non_child_algorithm(beam_centre_finder, **beam_centre_finder_options)
beam_centre_alg.setChild(False)
set_properties_for_beam_centre_algorithm(beam_centre_alg, reduction_package,
workspace_to_name, workspace_to_monitor)
# -----------------------------------
# Run the beam centre finder algorithm.
# -----------------------------------
beam_centre_alg.execute()
# -----------------------------------
# Get the outputs
# -----------------------------------
centre1 = beam_centre_alg.getProperty("Centre1").value
centre2 = beam_centre_alg.getProperty("Centre2").value
return {"pos1": centre1, "pos2": centre2}
def centre_finder_new(state,
r_min=0.06,
r_max=0.26,
iterations=10,
position_1_start=0.0,
position_2_start=0.0,
tolerance=0.0001251,
find_direction=FindDirectionEnum.All):
"""
Finds the beam centre from a good initial guess.
This function finds the centre of the beam by splitting the workspace up into 4 quadrants and running a reduction on
each. The (left, right) and (up, down) reductions are then compared producing residuals which are minimised through
repeated iteration.
:param state: This is a sans state, to find the beam centre for.
:param r_min: This is the inner radius of the quartile mask.
:param r_max: This is the outer radius of the quartile mask.
:param max_iter: This is the maximum number of iterations.
:param position_1_start: This is the starting position of the search on the x axis.
:param position_2_start: This is the starting position of the search on the y axis.
:param tolerance: This is the tolerance for the search.
:param fine_direction: This is an enumerator controlling which axis or both should be searched.
"""
# ------------------------------------------------------------------------------------------------------------------
# Load the data
# ------------------------------------------------------------------------------------------------------------------
workspace_to_name = {
SANSDataType.SampleScatter: "SampleScatterWorkspace",
SANSDataType.SampleTransmission: "SampleTransmissionWorkspace",
SANSDataType.SampleDirect: "SampleDirectWorkspace",
SANSDataType.CanScatter: "CanScatterWorkspace",
SANSDataType.CanTransmission: "CanTransmissionWorkspace",
SANSDataType.CanDirect: "CanDirectWorkspace"
}
workspace_to_monitor = {
SANSDataType.SampleScatter: "SampleScatterMonitorWorkSpace",
SANSDataType.CanScatter: "CanScatterMonitorWorkspace"
}
workspaces, monitors = provide_loaded_data(state, False, workspace_to_name,
workspace_to_monitor)
# ------------------------------------------------------------------------------------------------------------------
# Get reduction settings
# Split into individual bundles which can be reduced individually. We split here if we have multiple periods or
# sliced times for example. For the beam centre finder we only use the first period.
# ------------------------------------------------------------------------------------------------------------------
reduction_packages = get_reduction_packages(state, workspaces, monitors)
reduction_package = reduction_packages[0]
# ------------------------------------------------------------------------------------------------------------------
# Setup the beam centre finder algorithm.
# ------------------------------------------------------------------------------------------------------------------
beam_centre_finder = "SANSBeamCentreFinder"
beam_centre_finder_options = {
"Iterations": iterations,
"RMin": r_min / 1000,
"RMax": r_max / 1000,
"Position1Start": position_1_start,
"Position2Start": position_2_start,
"Tolerance": tolerance,
"Direction": FindDirectionEnum.to_string(find_direction)
}
beam_centre_alg = create_managed_non_child_algorithm(
beam_centre_finder, **beam_centre_finder_options)
beam_centre_alg.setChild(False)
set_properties_for_beam_centre_algorithm(beam_centre_alg,
reduction_package,
workspace_to_name,
workspace_to_monitor)
# -----------------------------------
# Run the beam centre finder algorithm.
# -----------------------------------
beam_centre_alg.execute()
# -----------------------------------
# Get the outputs
# -----------------------------------
centre1 = beam_centre_alg.getProperty("Centre1").value
centre2 = beam_centre_alg.getProperty("Centre2").value
create_output_table(centre1, centre2)
return {"pos1": centre1, "pos2": centre2}
def PyInit(self):
# ----------
# INPUT
# ----------
# Workspace which is to be cropped
self.declareProperty(
PropertyManagerProperty('SANSState'),
doc='A property manager which fulfills the SANSState contract.')
self.declareProperty(MatrixWorkspaceProperty(
"SampleScatterWorkspace",
'',
optional=PropertyMode.Mandatory,
direction=Direction.Input),
doc='The sample scatter data')
self.declareProperty(MatrixWorkspaceProperty(
"SampleScatterMonitorWorkspace",
'',
optional=PropertyMode.Mandatory,
direction=Direction.Input),
doc='The sample scatter monitor data')
self.declareProperty(MatrixWorkspaceProperty(
"SampleTransmissionWorkspace",
'',
optional=PropertyMode.Optional,
direction=Direction.Input),
doc='The sample transmission data')
self.declareProperty(MatrixWorkspaceProperty(
"SampleDirectWorkspace",
'',
optional=PropertyMode.Optional,
direction=Direction.Input),
doc='The sample direct data')
self.declareProperty(MatrixWorkspaceProperty(
"CanScatterWorkspace",
'',
optional=PropertyMode.Optional,
direction=Direction.Input),
doc='The can scatter data')
self.declareProperty(MatrixWorkspaceProperty(
"CanScatterMonitorWorkspace",
'',
optional=PropertyMode.Optional,
direction=Direction.Input),
doc='The can scatter monitor data')
self.declareProperty(MatrixWorkspaceProperty(
"CanTransmissionWorkspace",
'',
optional=PropertyMode.Optional,
direction=Direction.Input),
doc='The can transmission data')
self.declareProperty(MatrixWorkspaceProperty(
"CanDirectWorkspace",
'',
optional=PropertyMode.Optional,
direction=Direction.Input),
doc='The can direct data')
# The component, i.e. HAB or LAB
allowed_detectors = StringListValidator([
DetectorType.to_string(DetectorType.LAB),
DetectorType.to_string(DetectorType.HAB)
])
self.declareProperty(
"Component",
DetectorType.to_string(DetectorType.LAB),
validator=allowed_detectors,
direction=Direction.Input,
doc="The component of the instrument which is to be reduced.")
self.declareProperty("Iterations",
10,
direction=Direction.Input,
doc="The maximum number of iterations.")
self.declareProperty("RMin",
0.6,
direction=Direction.Input,
doc="The inner radius of the quartile mask")
self.declareProperty('RMax',
0.28,
direction=Direction.Input,
doc="The outer radius of the quartile mask")
self.declareProperty('Position1Start',
0.0,
direction=Direction.Input,
doc="The search start position1")
self.declareProperty('Position2Start',
0.0,
direction=Direction.Input,
doc="The search start position2")
self.declareProperty('Tolerance',
0.0001251,
direction=Direction.Input,
doc="The search tolerance")
self.declareProperty(
'Direction',
FindDirectionEnum.to_string(FindDirectionEnum.All),
direction=Direction.Input,
doc=
"The search direction is an enumerable which can be either All, LeftRight or UpDown"
)
self.declareProperty(
'Verbose',
False,
direction=Direction.Input,
doc="Whether to keep workspaces from each iteration in ADS.")
# ----------
# Output
# ----------
# Workspace which is to be cropped
self.declareProperty(
'Centre1',
0.0,
direction=Direction.Output,
doc="The centre position found in the first dimension")
self.declareProperty(
'Centre2',
0.0,
direction=Direction.Output,
doc="The centre position found in the second dimension")
def PyExec(self):
state = self._get_state()
state_serialized = state.property_manager
logger = Logger("CentreFinder")
logger.notice("Starting centre finder routine...")
progress = self._get_progress()
self.scale_1 = 1000
self.scale_2 = 1000
verbose = self.getProperty('Verbose').value
x_start = self.getProperty("Position1Start").value
y_start = self.getProperty("Position2Start").value
sample_scatter = self._get_cloned_workspace("SampleScatterWorkspace")
sample_scatter_monitor = self._get_cloned_workspace(
"SampleScatterMonitorWorkspace")
sample_transmission = self._get_cloned_workspace(
"SampleTransmissionWorkspace")
sample_direct = self._get_cloned_workspace("SampleDirectWorkspace")
instrument = sample_scatter.getInstrument()
if instrument.getName() == 'LARMOR':
self.scale_1 = 1.0
can_scatter = self._get_cloned_workspace("CanScatterWorkspace")
can_scatter_monitor = self._get_cloned_workspace(
"CanScatterMonitorWorkspace")
can_transmission = self._get_cloned_workspace(
"CanTransmissionWorkspace")
can_direct = self._get_cloned_workspace("CanDirectWorkspace")
component = self.getProperty("Component").value
tolerance = self.getProperty("Tolerance").value
max_iterations = self.getProperty("Iterations").value
r_min = self.getProperty("RMin").value
r_max = self.getProperty("RMax").value
instrument_file = get_instrument_paths_for_sans_file(
state.data.sample_scatter)
position_1_step = get_named_elements_from_ipf_file(
instrument_file[1], "centre-finder-step-size",
float)['centre-finder-step-size']
try:
position_2_step = get_named_elements_from_ipf_file(
instrument_file[1], "centre-finder-step-size2",
float)['centre-finder-step-size2']
except:
position_2_step = position_1_step
find_direction = self.getProperty("Direction").value
if find_direction == FindDirectionEnum.to_string(
FindDirectionEnum.Left_Right):
position_2_step = 0.0
elif find_direction == FindDirectionEnum.to_string(
FindDirectionEnum.Up_Down):
position_1_step = 0.0
centre1 = x_start
centre2 = y_start
residueLR = []
residueTB = []
centre_1_hold = x_start
centre_2_hold = y_start
for j in range(0, max_iterations + 1):
if (j != 0):
centre1 += position_1_step
centre2 += position_2_step
progress.report("Reducing ... Pos1 " + str(centre1) + " Pos2 " +
str(centre2))
sample_quartiles = self._run_quartile_reduction(
sample_scatter, sample_transmission, sample_direct, "Sample",
sample_scatter_monitor, component, state_serialized, centre1,
centre2, r_min, r_max)
if can_scatter:
can_quartiles = self._run_quartile_reduction(
can_scatter, can_transmission, can_direct, "Can",
can_scatter_monitor, component, state_serialized, centre1,
centre2, r_min, r_max)
for key in sample_quartiles:
sample_quartiles[key] = perform_can_subtraction(
sample_quartiles[key], can_quartiles[key], self)
if mantidplot:
output_workspaces = self._publish_to_ADS(sample_quartiles)
if verbose:
self._rename_and_group_workspaces(j, output_workspaces)
residueLR.append(
self._calculate_residuals(
sample_quartiles[MaskingQuadrant.Left],
sample_quartiles[MaskingQuadrant.Right]))
residueTB.append(
self._calculate_residuals(
sample_quartiles[MaskingQuadrant.Top],
sample_quartiles[MaskingQuadrant.Bottom]))
if (j == 0):
logger.notice("Itr " + str(j) + ": (" +
str(self.scale_1 * centre1) + ", " +
str(self.scale_2 * centre2) + ") SX=" +
str(residueLR[j]) + " SY=" + str(residueTB[j]))
if mantidplot:
self._plot_quartiles(output_workspaces,
state.data.sample_scatter)
else:
# have we stepped across the y-axis that goes through the beam center?
if residueLR[j] > residueLR[j - 1]:
# yes with stepped across the middle, reverse direction and half the step size
position_1_step = -position_1_step / 2
if residueTB[j] > residueTB[j - 1]:
position_2_step = -position_2_step / 2
logger.notice("Itr " + str(j) + ": (" +
str(self.scale_1 * centre1) + ", " +
str(self.scale_2 * centre2) + ") SX=" +
str(residueLR[j]) + " SY=" + str(residueTB[j]))
if (residueLR[j] + residueTB[j]) < (
residueLR[j - 1] + residueTB[j - 1]
) or state.compatibility.use_compatibility_mode:
centre_1_hold = centre1
centre_2_hold = centre2
if abs(position_1_step) < tolerance and abs(
position_2_step) < tolerance:
# this is the success criteria, we've close enough to the center
logger.notice(
"Converged - check if stuck in local minimum! ")
break
if j == max_iterations:
logger.notice(
"Out of iterations, new coordinates may not be the best")
self.setProperty("Centre1", centre_1_hold)
self.setProperty("Centre2", centre_2_hold)
logger.notice("Centre coordinates updated: [{}, {}]".format(
centre_1_hold * self.scale_1, centre_2_hold * self.scale_2))
def PyInit(self):
# ----------
# INPUT
# ----------
# Workspace which is to be cropped
self.declareProperty(PropertyManagerProperty('SANSState'),
doc='A property manager which fulfills the SANSState contract.')
self.declareProperty(MatrixWorkspaceProperty("SampleScatterWorkspace", '',
optional=PropertyMode.Mandatory, direction=Direction.Input),
doc='The sample scatter data')
self.declareProperty(MatrixWorkspaceProperty("SampleScatterMonitorWorkspace", '',
optional=PropertyMode.Mandatory, direction=Direction.Input),
doc='The sample scatter monitor data')
self.declareProperty(MatrixWorkspaceProperty("SampleTransmissionWorkspace", '',
optional=PropertyMode.Optional, direction=Direction.Input),
doc='The sample transmission data')
self.declareProperty(MatrixWorkspaceProperty("SampleDirectWorkspace", '',
optional=PropertyMode.Optional, direction=Direction.Input),
doc='The sample direct data')
self.declareProperty(MatrixWorkspaceProperty("CanScatterWorkspace", '',
optional=PropertyMode.Optional, direction=Direction.Input),
doc='The can scatter data')
self.declareProperty(MatrixWorkspaceProperty("CanScatterMonitorWorkspace", '',
optional=PropertyMode.Optional, direction=Direction.Input),
doc='The can scatter monitor data')
self.declareProperty(MatrixWorkspaceProperty("CanTransmissionWorkspace", '',
optional=PropertyMode.Optional, direction=Direction.Input),
doc='The can transmission data')
self.declareProperty(MatrixWorkspaceProperty("CanDirectWorkspace", '',
optional=PropertyMode.Optional, direction=Direction.Input),
doc='The can direct data')
# The component, i.e. HAB or LAB
allowed_detectors = StringListValidator([DetectorType.to_string(DetectorType.LAB),
DetectorType.to_string(DetectorType.HAB)])
self.declareProperty("Component", DetectorType.to_string(DetectorType.LAB),
validator=allowed_detectors, direction=Direction.Input,
doc="The component of the instrument which is to be reduced.")
self.declareProperty("Iterations", 10, direction=Direction.Input, doc="The maximum number of iterations.")
self.declareProperty("RMin", 0.6, direction=Direction.Input, doc="The inner radius of the quartile mask")
self.declareProperty('RMax', 0.28, direction=Direction.Input, doc="The outer radius of the quartile mask")
self.declareProperty('Position1Start', 0.0, direction=Direction.Input, doc="The search start position1")
self.declareProperty('Position2Start', 0.0, direction=Direction.Input, doc="The search start position2")
self.declareProperty('Tolerance', 0.0001251, direction=Direction.Input, doc="The search tolerance")
self.declareProperty('Direction', FindDirectionEnum.to_string(FindDirectionEnum.All), direction=Direction.Input,
doc="The search direction is an enumerable which can be either All, LeftRight or UpDown")
self.declareProperty('Verbose', False, direction=Direction.Input,
doc="Whether to keep workspaces from each iteration in ADS.")
# ----------
# Output
# ----------
# Workspace which is to be cropped
self.declareProperty('Centre1', 0.0, direction=Direction.Output,
doc="The centre position found in the first dimension")
self.declareProperty('Centre2', 0.0, direction=Direction.Output,
doc="The centre position found in the second dimension")
def PyExec(self):
state = self._get_state()
state_serialized = state.property_manager
logger = Logger("CentreFinder")
logger.notice("Starting centre finder routine...")
progress = self._get_progress()
self.scale_1 = 1000
self.scale_2 = 1000
verbose = self.getProperty('Verbose').value
x_start = self.getProperty("Position1Start").value
y_start = self.getProperty("Position2Start").value
sample_scatter = self._get_cloned_workspace("SampleScatterWorkspace")
sample_scatter_monitor = self._get_cloned_workspace("SampleScatterMonitorWorkspace")
sample_transmission = self._get_cloned_workspace("SampleTransmissionWorkspace")
sample_direct = self._get_cloned_workspace("SampleDirectWorkspace")
instrument = sample_scatter.getInstrument()
if instrument.getName() == 'LARMOR':
self.scale_1 = 1.0
can_scatter = self._get_cloned_workspace("CanScatterWorkspace")
can_scatter_monitor = self._get_cloned_workspace("CanScatterMonitorWorkspace")
can_transmission = self._get_cloned_workspace("CanTransmissionWorkspace")
can_direct = self._get_cloned_workspace("CanDirectWorkspace")
component = self.getProperty("Component").value
tolerance = self.getProperty("Tolerance").value
max_iterations = self.getProperty("Iterations").value
r_min = self.getProperty("RMin").value
r_max = self.getProperty("RMax").value
instrument_file = get_instrument_paths_for_sans_file(state.data.sample_scatter)
position_1_step = get_named_elements_from_ipf_file(
instrument_file[1], ["centre-finder-step-size"], float)['centre-finder-step-size']
try:
position_2_step = get_named_elements_from_ipf_file(
instrument_file[1], ["centre-finder-step-size2"], float)['centre-finder-step-size2']
except:
position_2_step = position_1_step
find_direction = self.getProperty("Direction").value
if find_direction == FindDirectionEnum.to_string(FindDirectionEnum.Left_Right):
position_2_step = 0.0
elif find_direction == FindDirectionEnum.to_string(FindDirectionEnum.Up_Down):
position_1_step = 0.0
centre1 = x_start
centre2 = y_start
residueLR = []
residueTB = []
centre_1_hold = x_start
centre_2_hold = y_start
for j in range(0, max_iterations + 1):
if(j != 0):
centre1 += position_1_step
centre2 += position_2_step
progress.report("Reducing ... Pos1 " + str(centre1) + " Pos2 " + str(centre2))
sample_quartiles = self._run_quartile_reduction(sample_scatter, sample_transmission, sample_direct,
"Sample", sample_scatter_monitor, component,
state_serialized, centre1, centre2, r_min, r_max)
if can_scatter:
can_quartiles = self._run_quartile_reduction(can_scatter, can_transmission, can_direct, "Can",
can_scatter_monitor, component, state_serialized, centre1,
centre2, r_min, r_max)
for key in sample_quartiles:
sample_quartiles[key] = perform_can_subtraction(sample_quartiles[key], can_quartiles[key], self)
if mantidplot:
output_workspaces = self._publish_to_ADS(sample_quartiles)
if verbose:
self._rename_and_group_workspaces(j, output_workspaces)
residueLR.append(self._calculate_residuals(sample_quartiles[MaskingQuadrant.Left],
sample_quartiles[MaskingQuadrant.Right]))
residueTB.append(self._calculate_residuals(sample_quartiles[MaskingQuadrant.Top],
sample_quartiles[MaskingQuadrant.Bottom]))
if(j == 0):
logger.notice("Itr {0}: ( {1}, {2} ) SX={3:.5g} SY={4:.5g}".
format(j, self.scale_1 * centre1, self.scale_2 * centre2, residueLR[j], residueTB[j]))
if mantidplot:
self._plot_quartiles(output_workspaces, state.data.sample_scatter)
else:
# have we stepped across the y-axis that goes through the beam center?
if residueLR[j] > residueLR[j-1]:
# yes with stepped across the middle, reverse direction and half the step size
position_1_step = - position_1_step / 2
if residueTB[j] > residueTB[j-1]:
position_2_step = - position_2_step / 2
logger.notice("Itr {0}: ( {1}, {2} ) SX={3:.5g} SY={4:.5g}".
format(j, self.scale_1 * centre1, self.scale_2 * centre2, residueLR[j], residueTB[j]))
if (residueLR[j]+residueTB[j]) < (residueLR[j-1]+residueTB[j-1]) or state.compatibility.use_compatibility_mode:
centre_1_hold = centre1
centre_2_hold = centre2
if abs(position_1_step) < tolerance and abs(position_2_step) < tolerance:
# this is the success criteria, we've close enough to the center
logger.notice("Converged - check if stuck in local minimum! ")
break
if j == max_iterations:
logger.notice("Out of iterations, new coordinates may not be the best")
self.setProperty("Centre1", centre_1_hold)
self.setProperty("Centre2", centre_2_hold)
logger.notice("Centre coordinates updated: [{}, {}]".format(centre_1_hold*self.scale_1, centre_2_hold*self.scale_2))