import itertools
import logging
import math
import os
import pathlib
from time import time
import psutil
import boost_adaptbx.boost.python
import libtbx
import dials.algorithms.integration
import dials.util
import dials.util.log
from dials.array_family import flex
from dials.model.data import make_image
from dials.util import tabulate
from dials.util.log import rehandle_cached_records
from dials.util.mp import available_cores, multi_node_parallel_map
from dials_algorithms_integration_integrator_ext import (
Executor,
Group,
GroupList,
Job,
JobList,
ReflectionManager,
ReflectionManagerPerImage,
ShoeboxProcessor,
)
try:
import resource
except ImportError:
# resource does not exist on non-Linux, so can't float the import
resource = None
__all__ = [
"Block",
"build_processor",
"Debug",
"Executor",
"Group",
"GroupList",
"Job",
"job",
"JobList",
"Lookup",
"MultiProcessing",
"NullTask",
"Parameters",
"Processor2D",
"Processor3D",
"ProcessorFlat3D",
"ProcessorSingle2D",
"ProcessorStills",
"ReflectionManager",
"ReflectionManagerPerImage",
"Shoebox",
"ShoeboxProcessor",
"Task",
]
logger = logging.getLogger(__name__)
def assess_available_memory(params):
# Obtain information about system memory
available_memory = psutil.virtual_memory().available
available_swap = psutil.swap_memory().free
# https://htcondor.readthedocs.io/en/latest/users-manual/services-for-jobs.html#extra-environment-variables-htcondor-sets-for-jobs
condor_job_ad = os.environ.get("_CONDOR_JOB_AD")
if condor_job_ad:
try:
job_classad = dials.util.parse_htcondor_job_classad(
pathlib.Path(condor_job_ad)
)
except Exception as e:
logger.error(
f"Error parsing _CONDOR_JOB_AD {condor_job_ad}: {e}",
exc_info=True,
)
else:
if job_classad.memory_provisioned:
# Convert MB to bytes
available_memory = min(
available_memory, job_classad.memory_provisioned * 1024 ** 2
)
available_incl_swap = available_memory + available_swap
available_limit = available_incl_swap * params.block.max_memory_usage
available_immediate_limit = available_memory * params.block.max_memory_usage
# Compile a memory report
report = [
"Memory situation report:",
]
def _report(description, numbytes):
report.append(f" {description:<50}:{numbytes/1e9:5.1f} GB")
_report("Available system memory (excluding swap)", available_memory)
_report("Available swap memory", available_swap)
_report("Available system memory (including swap)", available_incl_swap)
_report("Maximum memory for processing (including swap)", available_limit)
_report(
"Maximum memory for processing (excluding swap)",
available_immediate_limit,
)
# Check if a ulimit applies
# Note that resource may be None on non-Linux platforms.
# We can't use psutil as platform-independent solution in this instance due to
# https://github.com/conda-forge/psutil-feedstock/issues/47
rlimit = getattr(resource, "RLIMIT_VMEM", getattr(resource, "RLIMIT_AS", None))
if rlimit:
try:
ulimit = resource.getrlimit(rlimit)[0]
if ulimit <= 0 or ulimit > (2 ** 62):
report.append(" no memory ulimit set")
else:
ulimit_used = psutil.Process().memory_info().rss
_report("Memory ulimit detected", ulimit)
_report("Memory ulimit in use", ulimit_used)
available_memory = max(0, min(available_memory, ulimit - ulimit_used))
available_incl_swap = max(
0, min(available_incl_swap, ulimit - ulimit_used)
)
available_immediate_limit = (
available_memory * params.block.max_memory_usage
)
_report("Available system memory (limited)", available_memory)
_report(
"Available system memory (incl. swap; limited)",
available_incl_swap,
)
_report(
"Maximum memory for processing (exc. swap; limited)",
available_immediate_limit,
)
except Exception as e:
logger.debug(
"Could not obtain ulimit values due to %s", str(e), exc_info=True
)
return available_immediate_limit, available_incl_swap, report
def _average_bbox_size(reflections):
"""Calculate the average bbox size for debugging"""
bbox = reflections["bbox"]
sel = flex.random_selection(len(bbox), min(len(bbox), 1000))
subset_bbox = bbox.select(sel)
xmin, xmax, ymin, ymax, zmin, zmax = subset_bbox.parts()
xsize = flex.mean((xmax - xmin).as_double())
ysize = flex.mean((ymax - ymin).as_double())
zsize = flex.mean((zmax - zmin).as_double())
return xsize, ysize, zsize
@boost_adaptbx.boost.python.inject_into(Executor)
class _:
@staticmethod
def __getinitargs__():
return ()
class _Job:
def __init__(self):
self.index = 0
self.nthreads = 1
job = _Job()
class MultiProcessing:
"""
Multi processing parameters
"""
def __init__(self):
self.method = "multiprocessing"
self.nproc = 1
self.njobs = 1
self.nthreads = 1
self.n_subset_split = None
def update(self, other):
self.method = other.method
self.nproc = other.nproc
self.njobs = other.njobs
self.nthreads = other.nthreads
self.n_subset_split = other.n_subset_split
class Lookup:
"""
Lookup parameters
"""
def __init__(self):
self.mask = None
def update(self, other):
self.mask = other.mask
class Block:
"""
Block parameters
"""
def __init__(self):
self.size = libtbx.Auto
self.units = "degrees"
self.threshold = 0.99
self.force = False
self.max_memory_usage = 0.90
def update(self, other):
self.size = other.size
self.units = other.units
self.threshold = other.threshold
self.force = other.force
self.max_memory_usage = other.max_memory_usage
class Shoebox:
"""
Shoebox parameters
"""
def __init__(self):
self.flatten = False
self.partials = False
def update(self, other):
self.flatten = other.flatten
self.partials = other.partials
class Debug:
"""
Debug parameters
"""
def __init__(self):
self.output = False
self.select = None
self.split_experiments = True
self.separate_files = True
def update(self, other):
self.output = other.output
self.select = other.select
self.split_experiments = other.split_experiments
self.separate_files = other.separate_files
class Parameters:
"""
Class to handle parameters for the processor
"""
def __init__(self):
"""
Initialize the parameters
"""
self.mp = MultiProcessing()
self.lookup = Lookup()
self.block = Block()
self.shoebox = Shoebox()
self.debug = Debug()
def update(self, other):
"""
Update the parameters
"""
self.mp.update(other.mp)
self.lookup.update(other.lookup)
self.block.update(other.block)
self.shoebox.update(other.shoebox)
self.debug.update(other.debug)
def execute_parallel_task(task):
"""
Helper function to run things on cluster
"""
dials.util.log.config_simple_cached()
result = task()
handlers = logging.getLogger("dials").handlers
assert len(handlers) == 1, "Invalid number of logging handlers"
return result, handlers[0].records
class _Processor:
"""Processor interface class."""
def __init__(self, manager):
"""
Initialise the processor.
The processor requires a manager class implementing the _Manager interface.
This class executes all the workers in separate threads and accumulates the
results to expose to the user.
:param manager: The processing manager
:param params: The phil parameters
"""
self.manager = manager
@property
def executor(self):
"""
Get the executor
:return: The executor
"""
return self.manager.executor
@executor.setter
def executor(self, function):
"""
Set the executor
:param function: The executor
"""
self.manager.executor = function
def process(self):
"""
Do all the processing tasks.
:return: The processing results
"""
start_time = time()
self.manager.initialize()
mp_method = self.manager.params.mp.method
mp_njobs = self.manager.params.mp.njobs
mp_nproc = self.manager.params.mp.nproc
assert mp_nproc > 0, "Invalid number of processors"
if mp_nproc * mp_njobs > len(self.manager):
mp_nproc = min(mp_nproc, len(self.manager))
mp_njobs = int(math.ceil(len(self.manager) / mp_nproc))
logger.info(self.manager.summary())
if mp_njobs > 1:
assert mp_method != "none" and mp_method is not None
logger.info(
" Using %s with %d parallel job(s) and %d processes per node\n",
mp_method,
mp_njobs,
mp_nproc,
)
else:
logger.info(" Using multiprocessing with %d parallel job(s)\n", mp_nproc)
if mp_njobs * mp_nproc > 1:
def process_output(result):
rehandle_cached_records(result[1])
self.manager.accumulate(result[0])
multi_node_parallel_map(
func=execute_parallel_task,
iterable=list(self.manager.tasks()),
njobs=mp_njobs,
nproc=mp_nproc,
callback=process_output,
cluster_method=mp_method,
preserve_order=True,
)
else:
for task in self.manager.tasks():
self.manager.accumulate(task())
self.manager.finalize()
end_time = time()
self.manager.time.user_time = end_time - start_time
result1, result2 = self.manager.result()
return result1, result2, self.manager.time
class _ProcessorRot(_Processor):
"""Processor interface class for rotation data only."""
def __init__(self, experiments, manager):
"""
Initialise the processor.
The processor requires a manager class implementing the _Manager interface.
This class executes all the workers in separate threads and accumulates the
results to expose to the user.
:param manager: The processing manager
"""
# Ensure we have the correct type of data
if not experiments.all_sequences():
raise RuntimeError(
"""
An inappropriate processing algorithm may have been selected!
Trying to perform rotation processing when not all experiments
are indicated as rotation experiments.
"""
)
super().__init__(manager)
class NullTask:
"""
A class to perform a null task.
"""
def __init__(self, index, reflections):
"""
Initialise the task
:param index: The index of the processing job
:param experiments: The list of experiments
:param reflections: The list of reflections
"""
self.index = index
self.reflections = reflections
def __call__(self):
"""
Do the processing.
:return: The processed data
"""
return dials.algorithms.integration.Result(
index=self.index,
reflections=self.reflections,
data=None,
read_time=0,
extract_time=0,
process_time=0,
total_time=0,
)
class Task:
"""
A class to perform a processing task.
"""
def __init__(self, index, job, experiments, reflections, params, executor=None):
"""
Initialise the task.
:param index: The index of the processing job
:param experiments: The list of experiments
:param reflections: The list of reflections
:param params: The processing parameters
:param job: The frames to integrate
:param flatten: Flatten the shoeboxes
:param executor: The executor class
"""
assert executor is not None, "No executor given"
assert len(reflections) > 0, "Zero reflections given"
self.index = index
self.job = job
self.experiments = experiments
self.reflections = reflections
self.params = params
self.executor = executor
def __call__(self):
"""
Do the processing.
:return: The processed data
"""
# Get the start time
start_time = time()
# Set the global process ID
job.index = self.index
# Check all reflections have same imageset and get it
exp_id = list(set(self.reflections["id"]))
imageset = self.experiments[exp_id[0]].imageset
for i in exp_id[1:]:
assert (
self.experiments[i].imageset == imageset
), "Task can only handle 1 imageset"
# Get the sub imageset
frame0, frame1 = self.job
try:
allowed_range = imageset.get_array_range()
except Exception:
allowed_range = 0, len(imageset)
try:
# range increasing
assert frame0 < frame1
# within an increasing range
assert allowed_range[1] > allowed_range[0]
# we are processing data which is within range
assert frame0 >= allowed_range[0]
assert frame1 <= allowed_range[1]
# I am 99% sure this is implied by all the code above
assert (frame1 - frame0) <= len(imageset)
if len(imageset) > 1:
imageset = imageset[frame0:frame1]
except Exception as e:
raise RuntimeError(f"Programmer Error: bad array range: {e}")
try:
frame0, frame1 = imageset.get_array_range()
except Exception:
frame0, frame1 = (0, len(imageset))
self.executor.initialize(frame0, frame1, self.reflections)
# Set the shoeboxes (don't allocate)
self.reflections["shoebox"] = flex.shoebox(
self.reflections["panel"],
self.reflections["bbox"],
allocate=False,
flatten=self.params.shoebox.flatten,
)
# Create the processor
processor = ShoeboxProcessor(
self.reflections,
len(imageset.get_detector()),
frame0,
frame1,
self.params.debug.output,
)
# Loop through the imageset, extract pixels and process reflections
read_time = 0.0
for i in range(len(imageset)):
st = time()
image = imageset.get_corrected_data(i)
if imageset.is_marked_for_rejection(i):
mask = tuple(flex.bool(im.accessor(), False) for im in image)
else:
mask = imageset.get_mask(i)
if self.params.lookup.mask is not None:
assert len(mask) == len(
self.params.lookup.mask
), "Mask/Image are incorrect size %d %d" % (
len(mask),
len(self.params.lookup.mask),
)
mask = tuple(
m1 & m2 for m1, m2 in zip(self.params.lookup.mask, mask)
)
read_time += time() - st
processor.next(make_image(image, mask), self.executor)
del image
del mask
assert processor.finished(), "Data processor is not finished"
# Optionally save the shoeboxes
if self.params.debug.output and self.params.debug.separate_files:
output = self.reflections
if self.params.debug.select is not None:
output = output.select(self.params.debug.select(output))
if self.params.debug.split_experiments:
output = output.split_by_experiment_id()
for table in output:
i = table["id"][0]
table.as_file("shoeboxes_%d_%d.refl" % (self.index, i))
else:
output.as_file("shoeboxes_%d.refl" % self.index)
# Delete the shoeboxes
if self.params.debug.separate_files or not self.params.debug.output:
del self.reflections["shoebox"]
# Finalize the executor
self.executor.finalize()
# Return the result
return dials.algorithms.integration.Result(
index=self.index,
reflections=self.reflections,
data=self.executor.data(),
read_time=read_time,
extract_time=processor.extract_time(),
process_time=processor.process_time(),
total_time=time() - start_time,
)
class _Manager:
"""
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
# Other data
self.data = {}
# 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"
if self.params.mp.nproc is libtbx.Auto:
self.params.mp.nproc = available_cores()
logger.info(f"Setting nproc={self.params.mp.nproc}")
# Compute the block size and processors
self.compute_jobs()
self.split_reflections()
self.compute_processors()
# Create the reflection manager
self.manager = ReflectionManager(self.jobs, self.reflections)
# Set the initialization time
self.time.initialize = time() - start_time
def task(self, index):
"""
Get a task.
"""
job = self.manager.job(index)
frames = job.frames()
expr_id = job.expr()
assert expr_id[1] > expr_id[0], "Invalid experiment id"
assert expr_id[0] >= 0, "Invalid experiment id"
assert expr_id[1] <= len(self.experiments), "Invalid experiment id"
experiments = self.experiments # [expr_id[0]:expr_id[1]]
reflections = self.manager.split(index)
if len(reflections) == 0:
logger.warning("No reflections in job %d ***", index)
task = NullTask(index=index, reflections=reflections)
else:
task = Task(
index=index,
job=frames,
experiments=experiments,
reflections=reflections,
params=self.params,
executor=self.executor,
)
return task
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.data[result.index] = result.data
self.manager.accumulate(result.index, result.reflections)
self.time.read += result.read_time
self.time.extract += result.extract_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.manager.data(), self.data
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 compute_jobs(self):
"""
Sets up a JobList() object in self.jobs
"""
if self.params.block.size == libtbx.Auto:
if (
self.params.mp.nproc * self.params.mp.njobs == 1
and not self.params.debug.output
and not self.params.block.force
):
self.params.block.size = None
# calculate the block overlap based on the size of bboxes in the data
# calculate once here rather than repeated in the loop below
block_overlap = 0
if self.params.block.size is not None:
assert self.params.block.threshold > 0, "Threshold must be > 0"
assert self.params.block.threshold <= 1.0, "Threshold must be < 1"
frames_per_refl = sorted([b[5] - b[4] for b in self.reflections["bbox"]])
cutoff = int(self.params.block.threshold * len(frames_per_refl))
block_overlap = frames_per_refl[cutoff]
groups = itertools.groupby(
range(len(self.experiments)),
lambda x: (id(self.experiments[x].imageset), id(self.experiments[x].scan)),
)
self.jobs = JobList()
for key, indices in groups:
indices = list(indices)
i0 = indices[0]
i1 = indices[-1] + 1
expr = self.experiments[i0]
scan = expr.scan
imgs = expr.imageset
array_range = (0, len(imgs))
if scan is not None:
assert len(imgs) >= len(scan), "Invalid scan range"
array_range = scan.get_array_range()
if self.params.block.size is None:
block_size_frames = array_range[1] - array_range[0]
elif self.params.block.size == libtbx.Auto:
# auto determine based on nframes and overlap
nframes = array_range[1] - array_range[0]
nblocks = self.params.mp.nproc * self.params.mp.njobs
# want data to be split into n blocks with overlaps
# i.e. [x, overlap, y, overlap, y, overlap, ....,y, overlap, x]
# blocks are x + overlap, or overlap + y + overlap.
x = (nframes - block_overlap) / nblocks
block_size = int(math.ceil(x + block_overlap))
# increase the block size to be at least twice the overlap, in
# case the overlap is large e.g. if high mosaicity.
block_size_frames = max(block_size, 2 * block_overlap)
elif self.params.block.units == "radians":
_, dphi = scan.get_oscillation(deg=False)
block_size_frames = int(math.ceil(self.params.block.size / dphi))
# if the specified block size is lower than the overlap,
# reduce the overlap to be half of the block size.
block_overlap = min(block_overlap, int(block_size_frames // 2))
elif self.params.block.units == "degrees":
_, dphi = scan.get_oscillation()
block_size_frames = int(math.ceil(self.params.block.size / dphi))
# if the specified block size is lower than the overlap,
# reduce the overlap to be half of the block size.
block_overlap = min(block_overlap, int(block_size_frames // 2))
elif self.params.block.units == "frames":
block_size_frames = int(math.ceil(self.params.block.size))
block_overlap = min(block_overlap, int(block_size_frames // 2))
else:
raise RuntimeError(
f"Unknown block_size units {self.params.block.units!r}"
)
self.jobs.add(
(i0, i1),
array_range,
block_size_frames,
block_overlap,
)
assert len(self.jobs) > 0, "Invalid number of jobs"
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
if self.params.shoebox.partials:
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,
)
else:
num_full = len(self.reflections)
self.jobs.split(self.reflections)
num_partial = len(self.reflections)
assert num_partial >= num_full, "Invalid number of partials"
if num_partial > num_full:
num_split = num_partial - num_full
logger.info(
" Split %d reflections overlapping job boundaries\n", num_split
)
# Compute the partiality
self.reflections.compute_partiality(self.experiments)
def compute_processors(self):
"""
Compute the number of processors
"""
# Get the maximum shoebox memory to estimate memory use for one process
memory_required_per_process = flex.max(
self.jobs.shoebox_memory(self.reflections, self.params.shoebox.flatten)
)
(
available_immediate_limit,
available_incl_swap,
report,
) = assess_available_memory(self.params)
report.append(
f" {'Memory required per process':50}:{memory_required_per_process/1e9:5.1f} GB"
)
output_level = logging.INFO
# Limit the number of parallel processes by amount of available memory
if self.params.mp.method == "multiprocessing" and self.params.mp.nproc > 1:
# Compute expected memory usage and warn if not enough
njobs = available_immediate_limit / memory_required_per_process
if njobs >= self.params.mp.nproc:
# There is enough memory. Take no action
pass
elif njobs >= 1:
# There is enough memory to run, but not as many processes as requested
output_level = logging.WARNING
report.append(
"Reducing number of processes from %d to %d due to memory constraints."
% (self.params.mp.nproc, int(njobs))
)
self.params.mp.nproc = int(njobs)
elif (
available_incl_swap * self.params.block.max_memory_usage
>= memory_required_per_process
):
# There is enough memory to run, but only if we count swap.
output_level = logging.WARNING
report.append(
"Reducing number of processes from %d to 1 due to memory constraints."
% self.params.mp.nproc,
)
report.append("Running this process will rely on swap usage!")
self.params.mp.nproc = 1
else:
# There is not enough memory to run
output_level = logging.ERROR
report.append("")
logger.log(output_level, "\n".join(report))
if output_level >= logging.ERROR:
raise MemoryError(
"""
Not enough memory to run integration jobs. This could be caused by a
highly mosaic crystal model. Possible solutions include increasing the
percentage of memory allowed for shoeboxes or decreasing the block size.
The average shoebox size is %d x %d pixels x %d images - is your crystal
really this mosaic?
"""
% _average_bbox_size(self.reflections)
)
def summary(self):
"""
Get a summary of the processing
"""
# Compute the task table
if self.experiments.all_stills():
rows = [["#", "Group", "Frame From", "Frame To", "# Reflections"]]
for i in range(len(self)):
job = self.manager.job(i)
group = job.index()
f0, f1 = job.frames()
n = self.manager.num_reflections(i)
rows.append([str(i), str(group), str(f0), str(f1), str(n)])
elif self.experiments.all_sequences():
rows = [
[
"#",
"Group",
"Frame From",
"Frame To",
"Angle From",
"Angle To",
"# Reflections",
]
]
for i in range(len(self)):
job = self.manager.job(i)
group = job.index()
expr = job.expr()
f0, f1 = job.frames()
scan = self.experiments[expr[0]].scan
p0 = scan.get_angle_from_array_index(f0)
p1 = scan.get_angle_from_array_index(f1)
n = self.manager.num_reflections(i)
rows.append(
[str(i), str(group), str(f0 + 1), str(f1), str(p0), str(p1), str(n)]
)
else:
raise RuntimeError("Experiments must be all sequences or all stills")
# The job table
task_table = tabulate(rows, headers="firstrow")
# The format string
if self.params.block.size is None:
block_size = "auto"
else:
block_size = str(self.params.block.size)
return (
"Processing reflections in the following blocks of images:\n\n"
" block_size: {} {}\n\n{}\n"
).format(
block_size,
"" if block_size in ("auto", "Auto") else self.params.block.units,
task_table,
)
[docs]class Processor3D(_ProcessorRot):
"""Top level processor for 3D processing."""
[docs] def __init__(self, experiments, reflections, params):
"""Initialise the manager and the processor."""
# Set some parameters
params.shoebox.partials = False
params.shoebox.flatten = False
# Create the processing manager
manager = _Manager(experiments, reflections, params)
# Initialise the processor
super().__init__(experiments, manager)
[docs]class ProcessorFlat3D(_ProcessorRot):
"""Top level processor for flat 3D processing."""
[docs] def __init__(self, experiments, reflections, params):
"""Initialise the manager and the processor."""
# Set some parameters
params.shoebox.flatten = True
params.shoebox.partials = False
# Create the processing manager
manager = _Manager(experiments, reflections, params)
# Initialise the processor
super().__init__(experiments, manager)
[docs]class Processor2D(_ProcessorRot):
"""Top level processor for 2D processing."""
[docs] def __init__(self, experiments, reflections, params):
"""Initialise the manager and the processor."""
# Set some parameters
params.shoebox.partials = True
# Create the processing manager
manager = _Manager(experiments, reflections, params)
# Initialise the processor
super().__init__(experiments, manager)
[docs]class ProcessorSingle2D(_ProcessorRot):
"""Top level processor for still image processing."""
[docs] def __init__(self, experiments, reflections, params):
"""Initialise the manager and the processor."""
# Set some of the parameters
params.block.size = 1
params.block.units = "frames"
params.shoebox.partials = True
params.shoebox.flatten = False
# Create the processing manager
manager = _Manager(experiments, reflections, params)
# Initialise the processor
super().__init__(experiments, manager)
[docs]class ProcessorStills(_Processor):
"""Top level processor for still image processing."""
[docs] def __init__(self, experiments, reflections, params):
"""Initialise the manager and the processor."""
# Set some parameters
params.block.size = 1
params.block.units = "frames"
params.shoebox.partials = False
params.shoebox.flatten = False
# Ensure we have the correct type of data
if not experiments.all_stills():
raise RuntimeError(
"""
An inappropriate processing algorithm may have been selected!
Trying to perform stills processing when not all experiments
are indicated as stills experiments.
"""
)
# Create the processing manager
manager = _Manager(experiments, reflections, params)
# Initialise the processor
super().__init__(manager)
def build_processor(Class, experiments, reflections, params=None):
"""
A function to simplify building the processor
:param Class: The input class
:param experiments: The input experiments
:param reflections: The reflections
:param params: Optional input parameters
"""
_params = Parameters()
if params is not None:
_params.update(params)
return Class(experiments, reflections, _params)
