class ManagerImage(object):
'''
A class to manage processing book-keeping
'''
def __init__(self, experiments, reflections, params):
'''
Initialise the manager.
:param experiments: The list of experiments
:param reflections: The list of reflections
:param params: The phil parameters
'''
# Initialise the callbacks
self.executor = None
# Save some data
self.experiments = experiments
self.reflections = reflections
# Save some parameters
self.params = params
# Set the finalized flag to False
self.finalized = False
# Initialise the timing information
self.time = TimingInfo()
def initialize(self):
'''
Initialise the processing
'''
from dials_algorithms_integration_integrator_ext import ReflectionManagerPerImage
from time import time
# Get the start time
start_time = time()
# Ensure the reflections contain bounding boxes
assert "bbox" in self.reflections, "Reflections have no bbox"
# Split the reflections into partials
self._split_reflections()
# Create the reflection manager
frames = self.experiments[0].scan.get_array_range()
self.manager = ReflectionManagerPerImage(frames, self.reflections)
# Parallel reading of HDF5 from the same handle is not allowed. Python
# multiprocessing is a bit messed up and used fork on linux so need to
# close and reopen file.
from dxtbx.imageset import SingleFileReader
for exp in self.experiments:
if isinstance(exp.imageset.reader(), SingleFileReader):
exp.imageset.reader().nullify_format_instance()
# Set the initialization time
self.time.initialize = time() - start_time
def task(self, index):
'''
Get a task.
'''
return Task(index=index,
frames=self.manager.frames(index),
reflections=self.manager.split(index),
experiments=self.experiments,
params=self.params,
executor=self.executor)
def tasks(self):
'''
Iterate through the tasks.
'''
for i in range(len(self)):
yield self.task(i)
def accumulate(self, result):
'''
Accumulate the results.
'''
self.manager.accumulate(result.index, result.reflections)
if result.data is not None:
self.executor.accumulate(result.index, result.data)
self.time.read += result.read_time
self.time.process += result.process_time
self.time.total += result.total_time
def finalize(self):
'''
Finalize the processing and finish.
'''
from time import time
# Get the start time
start_time = time()
# Check manager is finished
assert self.manager.finished(), "Manager is not finished"
# Update the time and finalized flag
self.time.finalize = time() - start_time
self.finalized = True
def result(self):
'''
Return the result.
:return: The result
'''
assert self.finalized, "Manager is not finalized"
return self.reflections
def finished(self):
'''
Return if all tasks have finished.
:return: True/False all tasks have finished
'''
return self.finalized and self.manager.finished()
def __len__(self):
'''
Return the number of tasks.
:return: the number of tasks
'''
return len(self.manager)
def summary(self):
return ''
def _split_reflections(self):
'''
Split the reflections into partials or over job boundaries
'''
# Optionally split the reflection table into partials, otherwise,
# split over job boundaries
num_full = len(self.reflections)
self.reflections.split_partials()
num_partial = len(self.reflections)
assert num_partial >= num_full, "Invalid number of partials"
if num_partial > num_full:
logger.info(' Split %d reflections into %d partial reflections\n' %
(num_full, num_partial))
class ManagerImage:
"""
A class to manage processing book-keeping
"""
def __init__(self, experiments, reflections, params):
"""
Initialise the manager.
:param experiments: The list of experiments
:param reflections: The list of reflections
:param params: The phil parameters
"""
# Initialise the callbacks
self.executor = None
# Save some data
self.experiments = experiments
self.reflections = reflections
# Save some parameters
self.params = params
# Set the finalized flag to False
self.finalized = False
# Initialise the timing information
self.time = dials.algorithms.integration.TimingInfo()
def initialize(self):
"""
Initialise the processing
"""
# Get the start time
start_time = time()
# Ensure the reflections contain bounding boxes
assert "bbox" in self.reflections, "Reflections have no bbox"
# Split the reflections into partials
self._split_reflections()
# Create the reflection manager
frames = self.experiments[0].scan.get_array_range()
self.manager = ReflectionManagerPerImage(frames, self.reflections)
# Set the initialization time
self.time.initialize = time() - start_time
def task(self, index):
"""
Get a task.
"""
return Task(
index=index,
frames=self.manager.frames(index),
reflections=self.manager.split(index),
experiments=self.experiments,
params=self.params,
executor=self.executor,
)
def tasks(self):
"""
Iterate through the tasks.
"""
for i in range(len(self)):
yield self.task(i)
def accumulate(self, result):
"""
Accumulate the results.
"""
self.manager.accumulate(result.index, result.reflections)
if result.data is not None:
self.executor.accumulate(result.index, result.data)
self.time.read += result.read_time
self.time.process += result.process_time
self.time.total += result.total_time
def finalize(self):
"""
Finalize the processing and finish.
"""
# Get the start time
start_time = time()
# Check manager is finished
assert self.manager.finished(), "Manager is not finished"
# Update the time and finalized flag
self.time.finalize = time() - start_time
self.finalized = True
def result(self):
"""
Return the result.
:return: The result
"""
assert self.finalized, "Manager is not finalized"
return self.reflections
def finished(self):
"""
Return if all tasks have finished.
:return: True/False all tasks have finished
"""
return self.finalized and self.manager.finished()
def __len__(self):
"""
Return the number of tasks.
:return: the number of tasks
"""
return len(self.manager)
def summary(self):
return ""
def _split_reflections(self):
"""
Split the reflections into partials or over job boundaries
"""
# Optionally split the reflection table into partials, otherwise,
# split over job boundaries
num_full = len(self.reflections)
self.reflections.split_partials()
num_partial = len(self.reflections)
assert num_partial >= num_full, "Invalid number of partials"
if num_partial > num_full:
logger.info(
" Split %d reflections into %d partial reflections\n",
num_full,
num_partial,
)
class ManagerImage(object):
'''
A class to manage processing book-keeping
'''
def __init__(self, experiments, reflections, params):
'''
Initialise the manager.
:param experiments: The list of experiments
:param reflections: The list of reflections
:param params: The phil parameters
'''
# Initialise the callbacks
self.executor = None
# Save some data
self.experiments = experiments
self.reflections = reflections
# Save some parameters
self.params = params
# Set the finalized flag to False
self.finalized = False
# Initialise the timing information
self.time = TimingInfo()
def initialize(self):
'''
Initialise the processing
'''
from dials_algorithms_integration_integrator_ext import ReflectionManagerPerImage
from time import time
# Get the start time
start_time = time()
# Ensure the reflections contain bounding boxes
assert "bbox" in self.reflections, "Reflections have no bbox"
# Split the reflections into partials
self._split_reflections()
# Create the reflection manager
frames = self.experiments[0].scan.get_array_range()
self.manager = ReflectionManagerPerImage(frames, self.reflections)
# Parallel reading of HDF5 from the same handle is not allowed. Python
# multiprocessing is a bit messed up and used fork on linux so need to
# close and reopen file.
from dxtbx.imageset import SingleFileReader
for exp in self.experiments:
if isinstance(exp.imageset.reader(), SingleFileReader):
exp.imageset.reader().nullify_format_instance()
# Set the initialization time
self.time.initialize = time() - start_time
def task(self, index):
'''
Get a task.
'''
return Task(
index = index,
frames = self.manager.frames(index),
reflections = self.manager.split(index),
experiments = self.experiments,
params = self.params,
executor = self.executor)
def tasks(self):
'''
Iterate through the tasks.
'''
for i in range(len(self)):
yield self.task(i)
def accumulate(self, result):
'''
Accumulate the results.
'''
self.manager.accumulate(result.index, result.reflections)
if result.data is not None:
self.executor.accumulate(result.index, result.data)
self.time.read += result.read_time
self.time.process += result.process_time
self.time.total += result.total_time
def finalize(self):
'''
Finalize the processing and finish.
'''
from time import time
# Get the start time
start_time = time()
# Check manager is finished
assert self.manager.finished(), "Manager is not finished"
# Update the time and finalized flag
self.time.finalize = time() - start_time
self.finalized = True
def result(self):
'''
Return the result.
:return: The result
'''
assert self.finalized, "Manager is not finalized"
return self.reflections
def finished(self):
'''
Return if all tasks have finished.
:return: True/False all tasks have finished
'''
return self.finalized and self.manager.finished()
def __len__(self):
'''
Return the number of tasks.
:return: the number of tasks
'''
return len(self.manager)
def summary(self):
return ''
def _split_reflections(self):
'''
Split the reflections into partials or over job boundaries
'''
# Optionally split the reflection table into partials, otherwise,
# split over job boundaries
num_full = len(self.reflections)
self.reflections.split_partials()
num_partial = len(self.reflections)
assert num_partial >= num_full, "Invalid number of partials"
if num_partial > num_full:
logger.info(' Split %d reflections into %d partial reflections\n' % (
num_full,
num_partial))