hyperspy.api.samfire#

hyperspy.api.samfire.fit_tests

hyperspy.api.samfire.global_strategies

hyperspy.api.samfire.local_strategies

hyperspy.api.samfire.SamfirePool(**kwargs)

Creates and manages a pool of SAMFire workers.

Submodules

SAMFire modules

The samfire module contains the following submodules:

fit_tests

Tests to check fit convergence when running SAMFire

global_strategies

Available global strategies to use in SAMFire

local_strategies

Available global strategies to use in SAMFire

SamfirePool

The parallel pool, customized to run SAMFire.

class hyperspy.api.samfire.SamfirePool(**kwargs)#

Bases: ParallelPool

Creates and manages a pool of SAMFire workers. For based on ParallelPool - either creates processes using multiprocessing, or connects and sets up ipyparallel load_balanced_view.

Ipyparallel is managed directly, but multiprocessing pool is managed via three of queues:

  • Shared by all (master and workers) for distributing “load-balanced” work.

  • Shared by all (master and workers) for sending results back to the master

  • Individual queues from master to each worker. For setting up and addressing individual workers in general. This one is checked with higher priority in workers.

Attributes:
has_poolbool

Returns True if the pool is ready and set-up else False.

poolipyparallel.LoadBalancedView or multiprocessing.pool.Pool

The pool object

ipython_kwargsdict

The dictionary with Ipyparallel connection arguments.

timeoutfloat

Timeout for either pool when waiting for results

num_workersint

The number of workers actually created (may be less than requested, but can’t be more)

timestepfloat

The timestep between “ticks” that the result queues are checked. Higher timestep means less frequent checking, which may reduce CPU load for difficult fits that take a long time to finish.

pingdict

If recorded, stores one-way trip time of each worker

piddict

If available, stores the process-id of each worker

Creates a ParallelPool with additional methods for SAMFire. All arguments are passed to ParallelPool

add_jobs(needed_number=None)#

Adds jobs to the job queue that is consumed by the workers.

Parameters:
needed_number: {None, int}

The number of jobs to add. If None (default), adds need_pixels

collect_results(timeout=None)#

Collects and parses all results, currently not processed due to being in the queue.

Parameters:
timeout: {None, flaot}

the time to wait when collecting results. If None, the default timeout is used

property need_pixels#

Returns the number of pixels that should be added to the processing queue. At most is equal to the number of workers.

parse(value)#

Parse the value returned from the workers.

Parameters:
value: tuple of the form (keyword, the_rest)

Keyword currently can be one of [‘pong’, ‘Error’, ‘result’]. For each of the keywords, “the_rest” is a tuple of different elements, but generally the first one is always the worker_id that the result came from. In particular:

  • (‘pong’, (worker_id, pid, pong_time, optional_message_str))

  • (‘Error’, (worker_id, error_message_string))

  • (‘result’, (worker_id, pixel_index, result_dict, bool_if_result_converged))

ping_workers(timeout=None)#

Ping the workers and record one-way trip time and the process_id pid of each worker if available.

Parameters:
timeout: {None, flaot}

the time to wait when collecting results after sending out the ping. If None, the default timeout is used

prepare_workers(samfire)#

Given SAMFire object, populate the workers with the required information. In case of multiprocessing, start worker listening to the queues.

Parameters:
samfireSamfire

The SAMFire object that will be using the pool.

run()#

Run the full process of adding jobs to the processing queue, listening to the results and updating SAMFire as needed. Stops when timed out or no pixels are left to run.

Run the full procedure until no more pixels are left to run in the SAMFire.

stop()#

Stops the appropriate pool and (if ipyparallel) clears the memory and history.

update_parameters()#

Updates various worker parameters.

Currently updates:

  • Optional components (that can be switched off by the worker)

  • Parameter boundaries

  • Goodness test