API Reference

This is the API for the signac-flow application.

Command Line Interface

Some core signac-flow functions are—in addition to the Python interface—accessible directly via the $ flow command.

For more information, please see $ flow --help.

usage: flow [-h] [--debug] [--version] {init} ...

flow provides the basic components to set up workflows for projects as part of
the signac framework.

positional arguments:
  {init}
    init      Initialize a signac-flow project.

optional arguments:
  -h, --help  show this help message and exit
  --debug     Show traceback on error for debugging.
  --version   Display the version number and exit.

The FlowProject

class flow.FlowProject(config=None, environment=None)

A signac project class specialized for workflow management.

This class provides a command line interface for the definition, execution, and submission of workflows based on condition and operation functions.

This is a typical example on how to use this class:

@FlowProject.operation
def hello(job):
    print('hello', job)

FlowProject().main()

Parameters:config (A signac config object.) – A signac configuration, defaults to the configuration loaded from the environment.

Attributes

FlowProject.ALIASES	These are default aliases used within the status output.
FlowProject.add_operation(name, cmd[, pre, post])	Add an operation to the workflow.
FlowProject.classify(job)	Generator function which yields labels for job.
FlowProject.completed_operations(job)	Determine which operations have been completed for job.
FlowProject.eligible_for_submission(…)	Deprecated since version 0.8.
FlowProject.export_job_stati(collection, stati)	Export the job stati to a database collection.
FlowProject.get_job_status(job[, …])	Return a dict with detailed information about the status of a job.
FlowProject.label([label_name_or_func])	Designate a function to be a label function of this class.
FlowProject.labels(job)	Yields all labels for the given job.
FlowProject.main([parser])	Call this function to use the main command line interface.
FlowProject.next_operation(job)	Determine the next operation for this job.
FlowProject.next_operations(*jobs)	Determine the next eligible operations for jobs.
FlowProject.operation(func[, name])	Add the function func as operation function to the class workflow definition.
FlowProject.operations	The dictionary of operations that have been added to the workflow.
FlowProject.post(condition[, tag])	Specify a function of job that must evaluate to True for this operation to be considered complete.
FlowProject.post.always(func)	Returns True.
FlowProject.post.copy_from(*other_funcs)	True if and only if all post conditions of other operation-function(s) are met.
FlowProject.post.false(key)	True if the specified key is present in the job document and evaluates to False.
FlowProject.post.isfile(filename)	True if the specified file exists for this job.
FlowProject.post.never(func)	Returns False.
FlowProject.post.not_(condition)	Returns not condition(job) for the provided condition function.
FlowProject.post.true(key)	True if the specified key is present in the job document and evaluates to True.
FlowProject.pre(condition[, tag])	Specify a function of job that must be true for this operation to be eligible for execution.
FlowProject.pre.after(*other_funcs)	True if and only if all post conditions of other operation-function(s) are met.
FlowProject.pre.always(func)	Returns True.
FlowProject.pre.copy_from(*other_funcs)	True if and only if all pre conditions of other operation-function(s) are met.
FlowProject.pre.false(key)	True if the specified key is present in the job document and evaluates to False.
FlowProject.pre.isfile(filename)	True if the specified file exists for this job.
FlowProject.pre.never(func)	Returns False.
FlowProject.pre.not_(condition)	Returns not condition(job) for the provided condition function.
FlowProject.pre.true(key)	True if the specified key is present in the job document and evaluates to True.
FlowProject.run([jobs, names, pretend, np, …])	Execute all pending operations for the given selection.
FlowProject.run_operations([operations, …])	Execute the next operations as specified by the project’s workflow.
FlowProject.scheduler_jobs(scheduler)	Fetch jobs from the scheduler.
FlowProject.script(operations[, parallel, …])	Generate a run script to execute given operations.
FlowProject.submit([bundle_size, jobs, …])	Submit function for the project’s main submit interface.
FlowProject.submit_operations(operations[, …])	Submit a sequence of operations to the scheduler.
FlowProject.update_aliases(aliases)	Update the ALIASES table for this class.

class flow.FlowProject(config=None, environment=None)

Bases: signac.contrib.project.Project

A signac project class specialized for workflow management.

This class provides a command line interface for the definition, execution, and submission of workflows based on condition and operation functions.

This is a typical example on how to use this class:

@FlowProject.operation
def hello(job):
    print('hello', job)

FlowProject().main()

Parameters:config (A signac config object.) – A signac configuration, defaults to the configuration loaded from the environment.

ALIASES = {'active': 'A', 'inactive': 'I', 'queued': 'Q', 'registered': 'R', 'requires_attention': '!', 'unknown': 'U'}

These are default aliases used within the status output.

PRINT_STATUS_ALL_VARYING_PARAMETERS = True

This constant can be used to signal that the print_status() method is supposed to automatically show all varying parameters.

add_operation(name, cmd, pre=None, post=None, **kwargs)

Add an operation to the workflow.

This method will add an instance of FlowOperation to the operations-dict of this project.

See also

A Python function may be defined as an operation function directly using the operation() decorator.

Any FlowOperation is associated with a specific command, which should be a function of Job. The command (cmd) can be stated as function, either by using str-substitution based on a job’s attributes, or by providing a unary callable, which expects an instance of job as its first and only positional argument.

For example, if we wanted to define a command for a program called ‘hello’, which expects a job id as its first argument, we could construct the following two equivalent operations:

op = FlowOperation('hello', cmd='hello {job._id}')
op = FlowOperation('hello', cmd=lambda 'hello {}'.format(job._id))

Here are some more useful examples for str-substitutions:

# Substitute job state point parameters:
op = FlowOperation('hello', cmd='cd {job.ws}; hello {job.sp.a}')

Pre-requirements (pre) and post-conditions (post) can be used to trigger an operation only when certain conditions are met. Conditions are unary callables, which expect an instance of job as their first and only positional argument and return either True or False.

An operation is considered “eligible” for execution when all pre-requirements are met and when at least one of the post-conditions is not met. Requirements are always met when the list of requirements is empty and post-conditions are never met when the list of post-conditions is empty.

Please note, eligibility in this contexts refers only to the workflow pipeline and not to other contributing factors, such as whether the job-operation is currently running or queued.

Parameters:

• name (str) – A unique identifier for this operation, which may be freely chosen.
• cmd (str or callable) – The command to execute operation; should be a function of job.
• pre (sequence of callables) – Required conditions.
• post – Post-conditions to determine completion.

classify(job)

Generator function which yields labels for job.

By default, this method yields from the project’s labels() method.

Parameters:job (Job) – The signac job handle. Yields:The labels for the provided job. Yield type:str

Deprecated since version 0.8: This will be removed in 0.10. Use labels() instead.

completed_operations(job)

Determine which operations have been completed for job.

Parameters:job (Job) – The signac job handle. Returns:The name of the operations that are complete. Return type:str

detect_operation_graph()

Determine the directed acyclic graph defined by operation pre- and post-conditions.

In general, executing a given operation registered with a FlowProject just involves checking the operation’s pre- and post-conditions to determine eligibility. More generally, however, the pre- and post-conditions define a directed acyclic graph that governs the execution of all operations. Visualizing this graph can be useful for finding logic errors in the specified conditions, and having this graph computed also enables additional execution modes. For example, using this graph it is possible to determine exactly what operations need to be executed in order to make the operation eligible so that the task of executing all necessary operations can be automated.

The graph is determined by iterating over all pairs of operations and checking for equality of pre- and post-conditions. The algorithm builds an adjacency matrix based on whether the pre-conditions for one operation match the post-conditions for another. The comparison of operations is conservative; by default, conditions must be composed of identical code to be identified as equal (technically, they must be bytecode equivalent, i.e. cond1.__code__.co_code == cond2.__code__.co_code). Users can specify that conditions should be treated as equal by providing tags to the operations.

Given a FlowProject subclass defined in a module project.py, the output graph could be visualized using Matplotlib and NetworkX with the following code:

import numpy as np
import networkx as nx
from matplotlib import pyplot as plt

from project import Project

project = Project()
ops = project.operations.keys()
adj = np.asarray(project.detect_operation_graph())

plt.figure()
g = nx.DiGraph(adj)
pos = nx.spring_layout(g)
nx.draw(g, pos)
nx.draw_networkx_labels(
    g, pos,
    labels={key: name for (key, name) in
            zip(range(len(ops)), [o for o in ops])})

plt.show()

Raises a RuntimeError if a condition does not have a tag. This can occur when using functools.partial, and a manually specified condition tag has not been set.

Raises:RuntimeError

eligible_for_submission(job_operation)

Deprecated since version 0.8: This will be removed in 0.10.

export_job_stati(collection, stati)

Export the job stati to a database collection.

Deprecated since version 0.8: This will be removed in 0.10. Use export_job_statuses() instead.

export_job_statuses(collection, statuses)

Export the job statuses to a database collection.

get_job_status(job, ignore_errors=False, cached_status=None)

Return a dict with detailed information about the status of a job.

classmethod label(label_name_or_func=None)

Designate a function to be a label function of this class.

For example, we can define a label function like this:

@FlowProject.label
def foo_label(job):
    if job.document.get('foo', False):
        return 'foo-label-text'

The foo-label-text label will now show up in the status view for each job, where the foo key evaluates true.

If the label functions returns any type other than str, the label name will be the name of the function if and only if the return value evaluates to True, for example:

@FlowProject.label
def foo_label(job):
    return job.document.get('foo', False)

Finally, you can specify a different default label name by providing it as the first argument to the label() decorator.

Parameters:label_name_or_func (str or callable) – A label name or callable.

labels(job)

Yields all labels for the given job.

See also: label()

main(parser=None)

Call this function to use the main command line interface.

In most cases one would want to call this function as part of the class definition, e.g.:

 my_project.py
from flow import FlowProject

class MyProject(FlowProject):
    pass

if __name__ == '__main__':
    MyProject().main()

You can then execute this script on the command line:

$ python my_project.py --help

next_operation(job)

Determine the next operation for this job.

Parameters:job (Job) – The signac job handle. Returns:An instance of JobOperation to execute next or None, if no operation is eligible. Return type::py:class:`~.JobOperation or NoneType

Deprecated since version 0.8: This will be removed in 0.10. Use next_operations() instead.

next_operations(*jobs)

Determine the next eligible operations for jobs.

Parameters:jobs – The signac job handles. Yield:All instances of JobOperation jobs are eligible for.

classmethod operation(func, name=None)

Add the function func as operation function to the class workflow definition.

This function is designed to be used as a decorator function, for example:

@FlowProject.operation
def hello(job):
    print('Hello', job)

See also: add_operation().

operations

The dictionary of operations that have been added to the workflow.

print_status(jobs=None, overview=True, overview_max_lines=None, detailed=False, parameters=None, param_max_width=None, expand=False, all_ops=False, only_incomplete=False, dump_json=False, unroll=True, compact=False, pretty=False, file=None, err=None, ignore_errors=False, no_parallelize=False, template=None, profile=False, eligible_jobs_max_lines=None)

Print the status of the project.

Parameters:

• jobs (Sequence of instances Job.) – Only execute operations for the given jobs, or all if the argument is omitted.
• overview (bool) – Aggregate an overview of the project’ status.
• overview_max_lines (int) – Limit the number of overview lines.
• eligible_jobs_max_lines (int) – Limit the number of eligible jobs that are printed in the overview.
• detailed (bool) – Print a detailed status of each job.
• parameters (list of str) – Print the value of the specified parameters.
• param_max_width (int) – Limit the number of characters of parameter columns, see also: update_aliases().
• expand (bool) – Present labels and operations in two separate tables.
• all_ops (bool) – Include operations that are not eligible to run.
• only_incomplete (bool) – Only show jobs that have eligible operations.
• dump_json (bool) – Output the data as JSON instead of printing the formatted output.
• unroll (bool) – Separate columns for jobs and the corresponding operations.
• compact (bool) – Print a compact version of the output.
• pretty (bool) – Prettify the output.
• file (str) – Redirect all output to this file, defaults to sys.stdout.
• err (str) – Redirect all error output to this file, defaults to sys.stderr.
• ignore_errors (bool) – Print status even if querying the scheduler fails.
• no_parallelize (bool) – Do not parallelize the status update.
• template (str) – User provided Jinja2 template file.

run(jobs=None, names=None, pretend=False, np=None, timeout=None, num=None, num_passes=1, progress=False, order=None)

Execute all pending operations for the given selection.

This function will run in an infinite loop until all pending operations are executed, unless it reaches the maximum number of passes per operation or the maximum number of executions.

By default there is no limit on the total number of executions, but a specific operation will only be executed once per job. This is to avoid accidental infinite loops when no or faulty post conditions are provided.

See also: run_operations()

Parameters:

• jobs (Sequence of instances Job.) – Only execute operations for the given jobs, or all if the argument is omitted.
• names (Sequence of str) – Only execute operations that are in the provided set of names, or all, if the argument is omitted.
• pretend (bool) – Do not actually execute the operations, but show which command would have been used.
• np (int) – Parallelize to the specified number of processors. Use -1 to parallelize to all available processing units.
• timeout (int) – An optional timeout for each operation in seconds after which execution will be cancelled. Use -1 to indicate not timeout (the default).
• num (int) – The total number of operations that are executed will not exceed this argument if provided.
• num_passes (int) – The total number of one specific job-operation pair will not exceed this argument. The default is 1, there is no limit if this argument is None.
• progress – Show a progress bar during execution.
• order (str, callable, or NoneType) –

  Specify the order of operations, possible values are:

  • ’none’ or None (no specific order)
  • ’by-job’ (operations are grouped by job)
  • ’cyclic’ (order operations cyclic by job)
  • ’random’ (shuffle the execution order randomly)

  • callable (a callable returning a comparison key for an

    operation used to sort operations)

  The default value is none, which is equivalent to by-job in the current implementation.

  Note

  Users are advised to not rely on a specific execution order, as a substitute for defining the workflow in terms of pre- and post-conditions. However, a specific execution order may be more performant in cases where operations need to access and potentially lock shared resources.

run_operations(operations=None, pretend=False, np=None, timeout=None, progress=False)

Execute the next operations as specified by the project’s workflow.

See also: run()

Parameters:

• operations (Sequence of instances of JobOperation) – The operations to execute (optional).
• pretend (bool) – Do not actually execute the operations, but show which command would have been used.
• np (int) – The number of processors to use for each operation.
• timeout (int) – An optional timeout for each operation in seconds after which execution will be cancelled. Use -1 to indicate not timeout (the default).
• progress – Show a progress bar during execution.

scheduler_jobs(scheduler)

Fetch jobs from the scheduler.

This function will fetch all scheduler jobs from the scheduler and also expand bundled jobs automatically.

However, this function will not automatically filter scheduler jobs which are not associated with this project.

Parameters:scheduler (Scheduler) – The scheduler instance. Yields:All scheduler jobs fetched from the scheduler instance.

script(operations, parallel=False, template='script.sh', show_template_help=False)

Generate a run script to execute given operations.

Parameters:

• operations (Sequence of instances of JobOperation) – The operations to execute.
• parallel (bool) – Execute all operations in parallel (default is False).
• template (str) – The name of the template to use to generate the script.
• show_template_help (bool) – Show help related to the templating system and then exit.

submit(bundle_size=1, jobs=None, names=None, num=None, parallel=False, force=False, walltime=None, env=None, **kwargs)

Submit function for the project’s main submit interface.

Parameters:

• bundle_size (int) – Specify the number of operations to be bundled into one submission, defaults to 1.
• jobs (Sequence of instances Job.) – Only submit operations associated with the provided jobs. Defaults to all jobs.
• names (Sequence of str) – Only submit operations with any of the given names, defaults to all names.
• num (int) – Limit the total number of submitted operations, defaults to no limit.
• parallel (bool) – Execute all bundled operations in parallel. Does nothing with the default behavior or bundle_size=1.
• force (bool) – Ignore all warnings or checks during submission, just submit.
• walltime – Specify the walltime in hours or as instance of datetime.timedelta.

submit_operations(operations, _id=None, env=None, parallel=False, flags=None, force=False, template='script.sh', pretend=False, show_template_help=False, **kwargs)

Submit a sequence of operations to the scheduler.

Parameters:

• operations (A sequence of instances of JobOperation) – The operations to submit.
• _id (str) – The _id to be used for this submission.
• parallel (bool) – Execute all bundled operations in parallel.
• flags (list) – Additional options to be forwarded to the scheduler.
• force (bool) – Ignore all warnings or checks during submission, just submit.
• template (str) – The name of the template file to be used to generate the submission script.
• pretend (bool) – Do not actually submit, but only print the submission script to screen. Useful for testing the submission workflow.
• show_template_help (bool) – Show information about available template variables and filters and exit.
• **kwargs – Additional keyword arguments to be forwarded to the scheduler.

Returns:

Returns the submission status after successful submission or None.

classmethod update_aliases(aliases)

Update the ALIASES table for this class.

Deprecated since version 0.8: This will be removed in 0.10.

FlowProject.post(tag=None)

Specify a function of job that must evaluate to True for this operation to be considered complete. For example:

@Project.operation
@Project.post(lambda job: job.doc.get('bye'))
def bye(job):
    print('bye' job)
    job.doc.bye = True

The bye-operation would be considered complete and therefore no longer eligible for execution once the ‘bye’ key in the job document evaluates to True.

An optional tag may be associated with the condition. These tags are used by detect_operation_graph() when comparing conditions for equality. The tag defaults to the bytecode of the function.

classmethod post.always(func)

Returns True.

Deprecated since version 0.9: This will be removed in 0.11. This condition decorator is obsolete.

classmethod post.copy_from(*other_funcs)

True if and only if all post conditions of other operation-function(s) are met.

classmethod post.false(key)

True if the specified key is present in the job document and evaluates to False.

classmethod post.isfile(filename)

True if the specified file exists for this job.

classmethod post.never(func)

Returns False.

classmethod post.not_(condition)

Returns not condition(job) for the provided condition function.

classmethod post.true(key)

True if the specified key is present in the job document and evaluates to True.

FlowProject.pre(tag=None)

Specify a function of job that must be true for this operation to be eligible for execution. For example:

@Project.operation
@Project.pre(lambda job: not job.doc.get('hello'))
def hello(job):
    print('hello', job)
    job.doc.hello = True

The hello-operation would only execute if the ‘hello’ key in the job document does not evaluate to True.

An optional tag may be associated with the condition. These tags are used by detect_operation_graph() when comparing conditions for equality. The tag defaults to the bytecode of the function.

classmethod pre.after(*other_funcs)

True if and only if all post conditions of other operation-function(s) are met.

classmethod pre.always(func)

Returns True.

Deprecated since version 0.9: This will be removed in 0.11. This condition decorator is obsolete.

classmethod pre.copy_from(*other_funcs)

True if and only if all pre conditions of other operation-function(s) are met.

classmethod pre.false(key)

True if the specified key is present in the job document and evaluates to False.

classmethod pre.isfile(filename)

True if the specified file exists for this job.

classmethod pre.never(func)

Returns False.

classmethod pre.not_(condition)

Returns not condition(job) for the provided condition function.

classmethod pre.true(key)

True if the specified key is present in the job document and evaluates to True.

@flow.cmd

flow.cmd(func)

Specifies that func returns a shell command.

If this function is an operation function defined by FlowProject, it will be interpreted to return a shell command, instead of executing the function itself.

For example:

@FlowProject.operation
@flow.cmd
def hello(job):
    return "echo {job._id}"

@flow.with_job

flow.with_job(func)

Specifies that func(arg) will use arg as a context manager.

If this function is an operation function defined by FlowProject, it will be the same as using with job:.

For example:

@FlowProject.operation
@flow.with_job
def hello(job):
    print("hello {}".format(job))

Is equivalent to:

@FlowProject.operation
def hello(job):
    with job:
        print("hello {}".format(job))

This also works with the @cmd decorator:

@FlowProject.operation
@with_job
@cmd
def hello(job):
    return "echo 'hello {}'".format(job)

Is equivalent to:

@FlowProject.operation
@cmd
def hello_cmd(job):
    return 'trap "cd `pwd`" EXIT && cd {} && echo "hello {job}"'.format(job.ws)

@flow.directives

class flow.directives(**kwargs)

Decorator for operation functions to provide additional execution directives.

Directives can for example be used to provide information about required resources such as the number of processes required for execution of parallelized operations.

In addition, you can use the @directives(fork=True) directive to enforce that a particular operation is always executed within a subprocess and not within the Python interpreter’s process even if there are no other reasons that would prevent that. .. note:

Setting `fork=False` will not prevent forking if there are other reasons for forking,
such as a timeout.

flow.run()

flow.run(parser=None)

Access to the “run” interface of an operations module.

Executing this function within a module will start a command line interface, that can be used to execute operations defined within the same module. All top-level unary functions will be interpreted as executable operation functions.

For example, if we have a module as such:

# operations.py

def hello(job):
    print('hello', job)

if __name__ == '__main__':
    import flow
    flow.run()

Then we can execute the hello operation for all jobs from the command like like this:

$ python operations.py hello

Note

You can control the degree of parallelization with the --np argument.

For more information, see:

$ python operations.py --help

flow.init()

flow.init(alias=None, template=None, root=None, out=None)

Initialize a templated FlowProject module.

flow.get_environment()

flow.get_environment(test=False, import_configured=True)

Attempt to detect the present environment.

This function iterates through all defined ComputeEnvironment classes in reversed order of definition and returns the first environment where the is_present() method returns True.

Parameters:test (bool) – Whether to return the TestEnvironment. Returns:The detected environment class.