Core

---

The core data structures for starting up studies.

This module contains all of the core data structures that are needed for constructing and representing studies and the moving parts that they require. These moving parts include but are not limited to:

• Classes for representing the abstract flow of a study. These objects at their core are the Study and StudyStep classes that are used to construct a DAG for the flow.
• Classes that represent the items in a study's environment such as variables, scripts, and dependencies (paths, git repos, etc.)
• Classes for managing the environment and that know how to apply the environment to an abstract flow.
• A set of classes for managing parameters and generating combinations of parameters in a clean Pythonic way.

Combination

Bases: object

Class representing a combination of parameters.

This class represents a combination of parameters generated by a class of type ParameterGenerator. The only time a user should ever get an instance of a Combination from the ParameterGenerator is when a combination of parameters is VALID.

Source code in maestrowf/datastructures/core/parameters.py

class Combination(object):
    """
    Class representing a combination of parameters.

    This class represents a combination of parameters generated by a class of
    type ParameterGenerator. The only time a user should ever get an instance
    of a Combination from the ParameterGenerator is when a combination of
    parameters is VALID.
    """

    def __init__(self, token="$"):
        """
        Initialize an empty Combination class.

        A Combination comes packed with the following:
            - Corresponding values for each parameter in the instance.
            - A name for each parameter in instance.
            - Labels for each parameter-value combination in the instance.

        The 'token' method parameter defines the character(s) that are expected
        in front of user parameterized values in strings when an instance of
        a Combination is applied. For example, assume that we have an instance
        'c' of the Combination class that has had the parameter 'PARAM1' added.
        'PARAM1' is named 'COMPONENT1' and 'token' is left at its default value
        of '$'. 'PARAM1' has some arbitrary value that was set. In order to
        substitute the different variations of 'PARAM1' in a string when the
        apply method is called, the user would include the following mark up:

        * The value of 'PARAM1': '$(PARAM1)'
        * The label of 'PARAM1': '$(PARAM1.label)'
        * The name of 'PARAM1':  '$(PARAM1.name)'

        :param token: Token expected to be found in front of a parameter.
        """
        self._params = {}
        self._labels = OrderedDict()
        self._names = {}
        self._token = token

    def add(self, key, name, value, label):
        """
        Add a parameter to the Combination object.

        :param key: Parameter key that identifies a replacement.
        :param name: Custom name that identifies a parameter.
        :param value: Value of the parameter in this combination.
        :param label: Value of the parameter label for this combination.
        """
        # For the combination being added, assign the expected parameterized
        # strings that the user would substitute in for.
        # Parameterized value:  <self.token>(<key>)
        logger.debug("Adding parameter value to Combination with args: %s",
                     [key, name, value, label])
        var = "{}({})".format(self._token, key)
        logger.debug('Parameter value: %s = %s', var, value)
        self._params[var] = value
        # Parameterized label: <self.token>(<key>.label)
        var = "{}({}.label)".format(self._token, key)
        logger.debug('Label value: %s = %s', var, label)
        self._labels[var] = label
        # Parameterized name: <self.token>(<key>.name)
        var = "{}({}.name)".format(self._token, key)
        logger.debug('Name value: %s = %s', var, name)
        self._names[var] = name

    def __str__(self):
        """
        Generate the string representation of a Combination object.

        :returns: A string representing the combination.
        """
        return ".".join(self._labels.values())

    def get_param_string(self, params):
        """
        Get the combination string for the specified parameters.

        :param params: A set of parameters to be used in the string.
        :returns: A string containing the labels for the parameters in params.
        """
        combo_str = []
        for item in sorted(params):
            var = "{}({}.label)".format(self._token, item)
            combo_str.append(self._labels[var])

        return ".".join(combo_str)

    def apply(self, item):
        """
        Apply the combination to an item.

        :param item: String that may contain parameters to be substituted.
        :returns: String equal to item, except with parameters replaced.
        """
        # Apply the Combination's labels to the item.
        # These are substrings within item that are represented by the format
        # <self.token>(<key>.label)
        for key, value in self._labels.items():
            item = item.replace(key, str(value))

        # apply the Combination's values to the item.
        # These are substrings within item that are represented by the format
        # <self.token>(<key>)
        for key, value in self._params.items():
            item = item.replace(key, str(value))

        # Apply the Combination's names to the item.
        # These are substrings within item that are represented by the format
        # <self.token>(<key>.name)
        for key, name in self._names.items():
            item = item.replace(key, str(name))

        # Return the item after the Combination has applied itself to it. The
        # parameter item is simply reused since all we're doing is replacing
        # substrings.
        return item

    def get_param_values(self, params):
        """
        Get the values for the specified parameters.

        :param params: A set of parameters to be used in the string.
        :yields: Tuples of param names and values.
        """
        for key in params:
            var = "{}({})".format(self._token, key)
            yield key, self._params[var]

__init__(token='$')

Initialize an empty Combination class.

A Combination comes packed with the following: - Corresponding values for each parameter in the instance. - A name for each parameter in instance. - Labels for each parameter-value combination in the instance.

The 'token' method parameter defines the character(s) that are expected in front of user parameterized values in strings when an instance of a Combination is applied. For example, assume that we have an instance 'c' of the Combination class that has had the parameter 'PARAM1' added. 'PARAM1' is named 'COMPONENT1' and 'token' is left at its default value of '$'. 'PARAM1' has some arbitrary value that was set. In order to substitute the different variations of 'PARAM1' in a string when the apply method is called, the user would include the following mark up:

• The value of 'PARAM1': '$(PARAM1)'
• The label of 'PARAM1': '$(PARAM1.label)'
• The name of 'PARAM1': '$(PARAM1.name)'

Parameters:

Name	Type	Description	Default
token		Token expected to be found in front of a parameter.	'$'

Source code in maestrowf/datastructures/core/parameters.py

def __init__(self, token="$"):
    """
    Initialize an empty Combination class.

    A Combination comes packed with the following:
        - Corresponding values for each parameter in the instance.
        - A name for each parameter in instance.
        - Labels for each parameter-value combination in the instance.

    The 'token' method parameter defines the character(s) that are expected
    in front of user parameterized values in strings when an instance of
    a Combination is applied. For example, assume that we have an instance
    'c' of the Combination class that has had the parameter 'PARAM1' added.
    'PARAM1' is named 'COMPONENT1' and 'token' is left at its default value
    of '$'. 'PARAM1' has some arbitrary value that was set. In order to
    substitute the different variations of 'PARAM1' in a string when the
    apply method is called, the user would include the following mark up:

    * The value of 'PARAM1': '$(PARAM1)'
    * The label of 'PARAM1': '$(PARAM1.label)'
    * The name of 'PARAM1':  '$(PARAM1.name)'

    :param token: Token expected to be found in front of a parameter.
    """
    self._params = {}
    self._labels = OrderedDict()
    self._names = {}
    self._token = token

__str__()

Generate the string representation of a Combination object.

Returns:

Type	Description
	A string representing the combination.

Source code in maestrowf/datastructures/core/parameters.py

def __str__(self):
    """
    Generate the string representation of a Combination object.

    :returns: A string representing the combination.
    """
    return ".".join(self._labels.values())

add(key, name, value, label)

Add a parameter to the Combination object.

Parameters:

Name	Type	Description	Default
key		Parameter key that identifies a replacement.	required
name		Custom name that identifies a parameter.	required
value		Value of the parameter in this combination.	required
label		Value of the parameter label for this combination.	required

Source code in maestrowf/datastructures/core/parameters.py

def add(self, key, name, value, label):
    """
    Add a parameter to the Combination object.

    :param key: Parameter key that identifies a replacement.
    :param name: Custom name that identifies a parameter.
    :param value: Value of the parameter in this combination.
    :param label: Value of the parameter label for this combination.
    """
    # For the combination being added, assign the expected parameterized
    # strings that the user would substitute in for.
    # Parameterized value:  <self.token>(<key>)
    logger.debug("Adding parameter value to Combination with args: %s",
                 [key, name, value, label])
    var = "{}({})".format(self._token, key)
    logger.debug('Parameter value: %s = %s', var, value)
    self._params[var] = value
    # Parameterized label: <self.token>(<key>.label)
    var = "{}({}.label)".format(self._token, key)
    logger.debug('Label value: %s = %s', var, label)
    self._labels[var] = label
    # Parameterized name: <self.token>(<key>.name)
    var = "{}({}.name)".format(self._token, key)
    logger.debug('Name value: %s = %s', var, name)
    self._names[var] = name

apply(item)

Apply the combination to an item.

Parameters:

Name	Type	Description	Default
item		String that may contain parameters to be substituted.	required

Returns:

Type	Description
	String equal to item, except with parameters replaced.

Source code in maestrowf/datastructures/core/parameters.py

def apply(self, item):
    """
    Apply the combination to an item.

    :param item: String that may contain parameters to be substituted.
    :returns: String equal to item, except with parameters replaced.
    """
    # Apply the Combination's labels to the item.
    # These are substrings within item that are represented by the format
    # <self.token>(<key>.label)
    for key, value in self._labels.items():
        item = item.replace(key, str(value))

    # apply the Combination's values to the item.
    # These are substrings within item that are represented by the format
    # <self.token>(<key>)
    for key, value in self._params.items():
        item = item.replace(key, str(value))

    # Apply the Combination's names to the item.
    # These are substrings within item that are represented by the format
    # <self.token>(<key>.name)
    for key, name in self._names.items():
        item = item.replace(key, str(name))

    # Return the item after the Combination has applied itself to it. The
    # parameter item is simply reused since all we're doing is replacing
    # substrings.
    return item

get_param_string(params)

Get the combination string for the specified parameters.

Parameters:

Name	Type	Description	Default
params		A set of parameters to be used in the string.	required

Returns:

Type	Description
	A string containing the labels for the parameters in params.

Source code in maestrowf/datastructures/core/parameters.py

def get_param_string(self, params):
    """
    Get the combination string for the specified parameters.

    :param params: A set of parameters to be used in the string.
    :returns: A string containing the labels for the parameters in params.
    """
    combo_str = []
    for item in sorted(params):
        var = "{}({}.label)".format(self._token, item)
        combo_str.append(self._labels[var])

    return ".".join(combo_str)

get_param_values(params)

Get the values for the specified parameters.

:yields: Tuples of param names and values.

Parameters:

Name	Type	Description	Default
params		A set of parameters to be used in the string.	required

Source code in maestrowf/datastructures/core/parameters.py

def get_param_values(self, params):
    """
    Get the values for the specified parameters.

    :param params: A set of parameters to be used in the string.
    :yields: Tuples of param names and values.
    """
    for key in params:
        var = "{}({})".format(self._token, key)
        yield key, self._params[var]

ExecutionGraph

Bases: DAG, PickleInterface

Datastructure that tracks, executes, and reports on study execution.

The ExecutionGraph is used to manage, monitor, and interact with tasks and the scheduler. This class searches its graph for tasks that are ready to run, marks tasks as complete, and schedules ready tasks.

The Execution class is where functionality for checking task status, logic for managing and automatically directing and manipulating the workflow should go. Essentially, if logic is needed to automatically manipulate the workflow in some fashion or additional monitoring is needed, this class is where that would go.

Source code in maestrowf/datastructures/core/executiongraph.py

class ExecutionGraph(DAG, PickleInterface):
    """
    Datastructure that tracks, executes, and reports on study execution.

    The ExecutionGraph is used to manage, monitor, and interact with tasks and
    the scheduler. This class searches its graph for tasks that are ready to
    run, marks tasks as complete, and schedules ready tasks.

    The Execution class is where functionality for checking task status, logic
    for managing and automatically directing and manipulating the workflow
    should go. Essentially, if logic is needed to automatically manipulate the
    workflow in some fashion or additional monitoring is needed, this class is
    where that would go.
    """

    def __init__(self, submission_attempts=1, submission_throttle=0,
                 use_tmp=False, dry_run=False):
        """
        Initialize a new instance of an ExecutionGraph.

        :param submission_attempts: Number of attempted submissions before
            marking a step as failed.
        :param submission_throttle: Maximum number of scheduled in progress
        submissions.
        :param use_tmp: A Boolean value that when set to 'True' designates
        that ExecutionGraph should use temporary files for output.
        """
        super(ExecutionGraph, self).__init__()
        # Member variables for execution.
        self._adapter = None
        self._description = OrderedDict()

        # Generate tempdir (if specfied)
        if use_tmp:
            self._tmp_dir = tempfile.mkdtemp()
        else:
            self._tmp_dir = ""

        # Sets to track progress.
        self.completed_steps = set([SOURCE])
        self.in_progress = set()
        self.failed_steps = set()
        self.cancelled_steps = set()
        self.ready_steps = deque()
        self.is_canceled = False

        self._status_order = 'bfs'  # Set status order type
        self._status_subtree = None  # Cache bfs_subtree for status writing

        # Values for management of the DAG. Things like submission attempts,
        # throttling, etc. should be listed here.
        self._submission_attempts = submission_attempts
        self._submission_throttle = submission_throttle
        self.dry_run = dry_run

        # A map that tracks the dependencies of a step.
        # NOTE: I don't know how performant the Python dict structure is, but
        # we'll use it for now. I think this may want to be changed to an AVL
        # tree or something of that nature to guarantee worst case performance.
        self._dependencies = {}

        LOGGER.info(
            "\n------------------------------------------\n"
            "Submission attempts =       %d\n"
            "Submission throttle limit = %d\n"
            "Use temporary directory =   %s\n"
            "Tmp Dir = %s\n"
            "------------------------------------------",
            submission_attempts, submission_throttle, use_tmp, self._tmp_dir
        )

        # Error check that the submission values are valid.
        if self._submission_attempts < 1:
            _msg = "Submission attempts should always be greater than 0. " \
                   "Received a value of {}.".format(self._submission_attempts)
            LOGGER.error(_msg)
            raise ValueError(_msg)

        if self._submission_throttle < 0:
            _msg = "Throttling should be 0 for unthrottled or a positive " \
                   "integer for the number of allowed inflight jobs. " \
                   "Received a value of {}.".format(self._submission_throttle)
            LOGGER.error(_msg)
            raise ValueError(_msg)

    def _check_tmp_dir(self):
        """Check and recreate the tempdir should it have been erased."""
        # If we've specified a tmp dir and the previous tmp dir doesn't exist
        # recreate it.
        if self._tmp_dir and not os.path.exists(self._tmp_dir):
            self._tmp_dir = tempfile.mkdtemp()

    def add_step(self, name, step, workspace, restart_limit, params=None):
        """
        Add a StepRecord to the ExecutionGraph.

        :param name: Name of the step to be added.
        :param step: StudyStep instance to be recorded.
        :param workspace: Directory path for the step's working directory.
        :param restart_limit: Upper limit on the number of restart attempts.
        :param params: Iterable of tuples of step parameter names, values
        """
        data = {
                    "step":          step,
                    "state":         State.INITIALIZED,
                    "workspace":     workspace,
                    "restart_limit": restart_limit,
                }
        record = _StepRecord(**data)
        if params:
            record.add_params(params)

        self._dependencies[name] = set()
        super(ExecutionGraph, self).add_node(name, record)

    def add_connection(self, parent, step):
        """
        Add a connection between two steps in the ExecutionGraph.

        :param parent: The parent step that is required to execute 'step'
        :param step: The dependent step that relies on parent.
        """
        self.add_edge(parent, step)
        self._dependencies[step].add(parent)

    def set_adapter(self, adapter):
        """
        Set the adapter used to interface for scheduling tasks.

        :param adapter: Adapter name to be used when launching the graph.
        """
        if not adapter:
            # If we have no adapter specified, assume sequential execution.
            self._adapter = None
            return

        if not isinstance(adapter, dict):
            msg = "Adapter settings must be contained in a dictionary."
            LOGGER.error(msg)
            raise TypeError(msg)

        # Check to see that the adapter type is something the
        if adapter["type"] not in ScriptAdapterFactory.get_valid_adapters():
            msg = "'{}' adapter must be specfied in ScriptAdapterFactory." \
                  .format(adapter)
            LOGGER.error(msg)
            raise TypeError(msg)

        self._adapter = adapter

    def add_description(self, name, description, **kwargs):
        """
        Add a study description to the ExecutionGraph instance.

        :param name: Name of the study.
        :param description: Description of the study.
        """
        self._description["name"] = name
        self._description["description"] = description
        self._description.update(kwargs)

    @property
    def name(self):
        """
        Return the name for the study in the ExecutionGraph instance.

        :returns: A string of the name of the study.
        """
        return self._description["name"]

    @name.setter
    def name(self, value):
        """
        Set the name for the study in the ExecutionGraph instance.

        :param name: A string of the name for the study.
        """
        self._description["name"] = value

    @property
    def description(self):
        """
        Return the description for the study in the ExecutionGraph instance.

        :returns: A string of the description for the study.
        """
        return self._description["description"]

    @description.setter
    def description(self, value):
        """
        Set the description for the study in the ExecutionGraph instance.

        :param value: A string of the description for the study.
        """
        self._description["description"] = value

    def log_description(self):
        """Log the description of the ExecutionGraph."""
        desc = ["{}: {}".format(key, value)
                for key, value in self._description.items()]
        desc = "\n".join(desc)
        LOGGER.info(
            "\n==================================================\n"
            "%s\n"
            "==================================================\n",
            desc
        )

    def generate_scripts(self):
        """
        Generate the scripts for all steps in the ExecutionGraph.

        The generate_scripts method scans the ExecutionGraph instance and uses
        the stored adapter to write executable scripts for either local or
        scheduled execution. If a restart command is specified, a restart
        script will be generated for that record.
        """
        # An adapter must be specified
        if not self._adapter:
            msg = "Adapter not found. Specify a ScriptAdapter using " \
                  "set_adapter."
            LOGGER.error(msg)
            raise ValueError(msg)

        # Set up the adapter.
        LOGGER.info("Generating scripts...")
        adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
        adapter = adapter(**self._adapter)

        self._check_tmp_dir()
        for key, record in self.values.items():
            if key == SOURCE:
                continue

            # Record generates its own script.
            record.setup_workspace()
            record.generate_script(adapter, self._tmp_dir)

    def _execute_record(self, record, adapter, restart=False):
        """
        Execute a StepRecord.

        :param record: The StepRecord to be executed.
        :param adapter: An instance of the adapter to be used for cluster
        submission.
        :param restart: True if the record needs restarting, False otherwise.
        """
        # Logging for debugging.
        LOGGER.info("Calling execute for StepRecord '%s'", record.name)

        num_restarts = 0    # Times this step has temporally restarted.
        retcode = None      # Execution return code.

        # While our submission needs to be submitted, keep trying:
        # 1. If the JobStatus is not OK.
        # 2. num_restarts is less than self._submission_attempts
        self._check_tmp_dir()

        # Only set up the workspace the initial iteration.
        if not restart:
            LOGGER.debug("Setting up workspace for '%s' at %s",
                         record.name, str(datetime.now()))
            # Generate the script for execution on the fly.
            record.setup_workspace()    # Generate the workspace.
            record.generate_script(adapter, self._tmp_dir)

        if self.dry_run:
            record.mark_end(State.DRYRUN)
            self.completed_steps.add(record.name)
            return

        while retcode != SubmissionCode.OK and \
                num_restarts < self._submission_attempts:
            LOGGER.info("Attempting submission of '%s' (attempt %d of %d)...",
                        record.name, num_restarts + 1,
                        self._submission_attempts)

            # We're not restarting -- submit as usual.
            if not restart:
                LOGGER.debug("Calling 'execute' on '%s' at %s",
                             record.name, str(datetime.now()))
                retcode = record.execute(adapter)
            # Otherwise, it's a restart.
            else:
                # If the restart is specified, use the record restart script.
                LOGGER.debug("Calling 'restart' on '%s' at %s",
                             record.name, str(datetime.now()))
                # Generate the script for execution on the fly.
                record.generate_script(adapter, self._tmp_dir)
                retcode = record.restart(adapter)

            # Increment the number of restarts we've attempted.
            LOGGER.debug("Completed submission attempt %d", num_restarts)
            num_restarts += 1
            sleep((random.random() + 1) * num_restarts)

        if retcode == SubmissionCode.OK:
            self.in_progress.add(record.name)

            if record.is_local_step:
                LOGGER.info("Local step %s executed with status OK. Complete.",
                            record.name)
                record.mark_end(State.FINISHED)
                self.completed_steps.add(record.name)
                self.in_progress.remove(record.name)
        else:
            # Find the subtree, because anything dependent on this step now
            # failed.
            LOGGER.warning("'%s' failed to submit properly. "
                           "Step failed.", record.name)
            path, parent = self.bfs_subtree(record.name)
            for node in path:
                self.failed_steps.add(node)
                self.values[node].mark_end(State.FAILED)

        # After execution state debug logging.
        LOGGER.debug("After execution of '%s' -- New state is %s.",
                     record.name, record.status)

    @property
    def status_subtree(self):
        """Cache the status ordering to improve scaling"""
        if not self._status_subtree:
            if self._status_order == 'bfs':
                subtree, _ = self.bfs_subtree("_source")

            elif self._status_order == 'dfs':
                subtree, _ = self.dfs_subtree("_source", par="_source")

            self._status_subtree = [key for key in subtree
                                    if key != '_source']

        return self._status_subtree

    def write_status(self, path):
        """Write the status of the DAG to a CSV file."""
        header = "Step Name,Job ID,Workspace,State,Run Time,Elapsed Time," \
                 "Start Time,Submit Time,End Time,Number Restarts,Params"
        status = [header]

        for key in self.status_subtree:
            value = self.values[key]

            jobid_str = "--"
            if value.jobid:
                jobid_str = str(value.jobid[-1])

            # Include step root in workspace when parameterized
            if list(value.params.items()):
                ws = os.path.join(
                    * os.path.normpath(
                        value.workspace.value).split(os.sep)[-2:]
                )
            else:
                ws = os.path.split(value.workspace.value)[1]

            _ = [
                    value.name, jobid_str,
                    ws,
                    str(value.status.name), value.run_time, value.elapsed_time,
                    value.time_start, value.time_submitted, value.time_end,
                    str(value.restarts),
                    ";".join(["{}:{}".format(param, value)
                              for param, value in value.params.items()])
                ]
            _ = ",".join(_)
            status.append(_)

        stat_path = os.path.join(path, "status.csv")
        lock_path = os.path.join(path, ".status.lock")
        lock = FileLock(lock_path)
        try:
            with lock.acquire(timeout=10):
                with open(stat_path, "w+") as stat_file:
                    stat_file.write("\n".join(status))
        except Timeout:
            pass

    def _check_study_completion(self):
        # We cancelled, return True marking study as complete.
        if self.is_canceled and not self.in_progress:
            LOGGER.info("Cancelled -- completing study.")
            return StudyStatus.CANCELLED

        # check for completion of all steps
        resolved_set = \
            self.completed_steps | self.failed_steps | self.cancelled_steps
        if not set(self.values.keys()) - resolved_set:
            # some steps were cancelled and is_canceled wasn't set
            if len(self.cancelled_steps) > 0:
                logging.info("'%s' was cancelled. Returning.", self.name)
                return StudyStatus.CANCELLED

            # some steps were failures indicating failure
            if len(self.failed_steps) > 0:
                logging.info("'%s' is complete with failures. Returning.",
                             self.name)
                return StudyStatus.FAILURE

            # everything completed were are done
            logging.info("'%s' is complete. Returning.", self.name)
            return StudyStatus.FINISHED

        return StudyStatus.RUNNING

    def execute_ready_steps(self):
        """
        Execute any steps whose dependencies are satisfied.

        The 'execute_ready_steps' method is the core of how the ExecutionGraph
        manages execution. This method does the following:

        * Checks the status of existing jobs that are executing and updates
          the state if changed.
        * Finds steps that are initialized and determines what can be run
          based on satisfied dependencies and executes steps whose
          dependencies are met.

        :returns: True if the study has completed, False otherwise.
        """
        # TODO: We may want to move this to a singleton somewhere
        # so we can guarantee that all steps use the same adapter.
        adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
        adapter = adapter(**self._adapter)

        if not self.dry_run:
            LOGGER.debug("Checking status check...")
            retcode, job_status = self.check_study_status()
        else:
            LOGGER.debug("DRYRUN: Skipping status check...")
            retcode = JobStatusCode.OK
            job_status = {}

        LOGGER.debug("Checked status (retcode %s)-- %s", retcode, job_status)

        # For now, if we can't check the status something is wrong.
        # Don't modify the DAG.
        if retcode == JobStatusCode.ERROR:
            msg = "Job status check failed -- Aborting."
            LOGGER.error(msg)
            raise RuntimeError(msg)
        elif retcode == JobStatusCode.OK:
            # For the status of each currently in progress job, check its
            # state.
            cleanup_steps = set()  # Steps that are in progress showing failed.
            cancel_steps = set()   # Steps that have dependencies to mark cancelled
            for name, status in job_status.items():
                LOGGER.debug("Checking job '%s' with status %s.", name, status)
                record = self.values[name]

                if status == State.FINISHED:
                    # Mark the step complete and notate its end time.
                    record.mark_end(State.FINISHED)
                    LOGGER.info("Step '%s' marked as finished. Adding to "
                                "complete set.", name)
                    self.completed_steps.add(name)
                    self.in_progress.remove(name)

                elif status == State.RUNNING:
                    # When detect that a step is running, mark it.
                    LOGGER.info("Step '%s' found to be running.", record.name)
                    record.mark_running()

                elif status == State.TIMEDOUT:
                    # Execute the restart script.
                    # If a restart script doesn't exist, re-run the command.
                    # If we're under the restart limit, attempt a restart.
                    if record.can_restart:
                        if record.mark_restart():
                            LOGGER.info(
                                "Step '%s' timed out. Restarting (%s of %s).",
                                name, record.restarts, record.restart_limit
                            )
                            self._execute_record(record, adapter, restart=True)
                        else:
                            LOGGER.info("'%s' has been restarted %s of %s "
                                        "times. Marking step and all "
                                        "descendents as failed.",
                                        name,
                                        record.restarts,
                                        record.restart_limit)
                            self.in_progress.remove(name)
                            cleanup_steps.update(self.bfs_subtree(name)[0])
                    # Otherwise, we can't restart so mark the step timed out.
                    else:
                        LOGGER.info("'%s' timed out, but cannot be restarted."
                                    " Marked as TIMEDOUT.", name)
                        # Mark that the step ended due to TIMEOUT.
                        record.mark_end(State.TIMEDOUT)
                        # Remove from in progress since it no longer is.
                        self.in_progress.remove(name)
                        # Add the subtree to the clean up steps
                        cleanup_steps.update(self.bfs_subtree(name)[0])
                        # Remove the current step, clean up is used to mark
                        # steps definitively as failed.
                        cleanup_steps.remove(name)
                        # Add the current step to failed.
                        self.failed_steps.add(name)

                elif status == State.HWFAILURE:
                    # TODO: Need to make sure that we do this a finite number
                    # of times.
                    # Resubmit the cmd.
                    LOGGER.warning("Hardware failure detected. Attempting to "
                                   "resubmit step '%s'.", name)
                    # We can just let the logic below handle submission with
                    # everything else.
                    self.ready_steps.append(name)

                elif status == State.FAILED:
                    LOGGER.warning(
                        "Job failure reported. Aborting %s -- flagging all "
                        "dependent jobs as failed.",
                        name
                    )
                    self.in_progress.remove(name)
                    record.mark_end(State.FAILED)
                    cleanup_steps.update(self.bfs_subtree(name)[0])

                elif status == State.UNKNOWN:
                    record.mark_end(State.UNKNOWN)
                    LOGGER.info(
                        "Step '%s' found in UNKNOWN state. Step was found "
                        "in '%s' state previously, marking as UNKNOWN. "
                        "Adding to failed steps.",
                        name, record.status)
                    cleanup_steps.update(self.bfs_subtree(name)[0])
                    self.in_progress.remove(name)

                elif status == State.CANCELLED:
                    LOGGER.info("Step '%s' was cancelled.", name)
                    self.in_progress.remove(name)
                    record.mark_end(State.CANCELLED)
                    cancel_steps.update(self.bfs_subtree(name)[0])

            # Let's handle all the failed steps in one go.
            for node in cleanup_steps:
                self.failed_steps.add(node)
                self.values[node].mark_end(State.FAILED)

            # Handle dependent steps that need cancelling
            for node in cancel_steps:
                self.cancelled_steps.add(node)
                self.values[node].mark_end(State.CANCELLED)

        # Now that we've checked the statuses of existing jobs we need to make
        # sure dependencies haven't been met.
        for key in self.values.keys():
            # We MUST dereference from the key. If we use values.items(), a
            # generator gets produced which will give us a COPY of a record and
            # not the actual record.
            record = self.values[key]

            # A completed step by definition has had its dependencies met.
            # Skip it.
            if key in self.completed_steps:
                LOGGER.debug("'%s' in completed set, skipping.", key)
                continue

            LOGGER.debug("Checking %s -- %s", key, record.jobid)
            # If the record is only INITIALIZED, we have encountered a step
            # that needs consideration.
            if record.status == State.INITIALIZED:
                LOGGER.debug("'%s' found to be initialized. Checking "
                             "dependencies. ", key)

                LOGGER.debug(
                    "Unfulfilled dependencies: %s",
                    self._dependencies[key])

                s_completed = list(filter(
                    lambda x: x in self.completed_steps,
                    self._dependencies[key]))
                self._dependencies[key] = \
                    self._dependencies[key] - set(s_completed)
                LOGGER.debug(
                    "Completed dependencies: %s\n"
                    "Remaining dependencies: %s",
                    s_completed, self._dependencies[key])

                # If the gating dependencies set is empty, we can execute.
                if not self._dependencies[key]:
                    if key not in self.ready_steps:
                        LOGGER.debug("All dependencies completed. Staging.")
                        self.ready_steps.append(key)
                    else:
                        LOGGER.debug("Already staged. Passing.")
                        continue

        # We now have a collection of ready steps. Execute.
        # If we don't have a submission limit, go ahead and submit all.
        if self._submission_throttle == 0:
            LOGGER.info("Launching all ready steps...")
            _available = len(self.ready_steps)
        # Else, we have a limit -- adhere to it.
        else:
            # Compute the number of available slots we have for execution.
            _available = self._submission_throttle - len(self.in_progress)
            # Available slots should never be negative, but on the off chance
            # we are in a slot deficit, then we will just say none are free.
            _available = max(0, _available)
            # Now, we need to take the min of the length of the queue and the
            # computed number of slots. We could have free slots, but have less
            # in the queue.
            _available = min(_available, len(self.ready_steps))
            LOGGER.info("Found %d available slots...", _available)

        for i in range(0, _available):
            # Pop the record and execute using the helper method.
            _record = self.values[self.ready_steps.popleft()]

            # If we get to this point and we've cancelled, cancel the record.
            if self.is_canceled:
                LOGGER.info("Cancelling '%s' -- continuing.", _record.name)
                _record.mark_end(State.CANCELLED)
                self.cancelled_steps.add(_record.name)
                continue

            LOGGER.debug("Launching job %d -- %s", i, _record.name)
            self._execute_record(_record, adapter)

        # check the status of the study upon finishing this round of execution
        completion_status = self._check_study_completion()
        return completion_status

    def check_study_status(self):
        """
        Check the status of currently executing steps in the graph.

        This method is used to check the status of all currently in progress
        steps in the ExecutionGraph. Each ExecutionGraph stores the adapter
        used to generate and execute its scripts.
        """
        # Set up the job list and the map to get back to step names.
        joblist = []
        jobmap = {}
        for step in self.in_progress:
            jobid = self.values[step].jobid[-1]
            joblist.append(jobid)
            jobmap[jobid] = step

        # Grab the adapter from the ScriptAdapterFactory.
        adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
        adapter = adapter(**self._adapter)
        # Use the adapter to grab the job statuses.
        retcode, job_status = adapter.check_jobs(joblist)
        # Map the job identifiers back to step names.
        step_status = {jobmap[jobid]: status
                       for jobid, status in job_status.items()}

        # Based on return code, log something different.
        if retcode == JobStatusCode.OK:
            LOGGER.info("Jobs found for user '%s'.", getpass.getuser())
            return retcode, step_status
        elif retcode == JobStatusCode.NOJOBS:
            LOGGER.info("No jobs found.")
            return retcode, step_status
        else:
            msg = "Unknown Error (Code = {})".format(retcode)
            LOGGER.error(msg)
            return retcode, step_status

    def cancel_study(self):
        """Cancel the study."""
        joblist = []
        for step in self.in_progress:
            jobid = self.values[step].jobid[-1]
            joblist.append(jobid)

        # Grab the adapter from the ScriptAdapterFactory.
        adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
        adapter = adapter(**self._adapter)

        # cancel our jobs
        crecord = adapter.cancel_jobs(joblist)
        self.is_canceled = True

        if crecord.cancel_status == CancelCode.OK:
            LOGGER.info("Successfully requested to cancel all jobs.")
        elif crecord.cancel_status == CancelCode.ERROR:
            LOGGER.error(
                "Failed to cancel jobs. (Code = %s)", crecord.return_code)
        else:
            LOGGER.error("Unknown Error (Code = %s)", crecord.return_code)

        return crecord.cancel_status

    def cleanup(self):
        """Clean up output produced by the ExecutionGraph."""
        if self._tmp_dir:
            shutil.rmtree(self._tmp_dir, ignore_errors=True)

description property writable

Return the description for the study in the ExecutionGraph instance.

Returns:

Type	Description
	A string of the description for the study.

name property writable

Return the name for the study in the ExecutionGraph instance.

Returns:

Type	Description
	A string of the name of the study.

status_subtree property

Cache the status ordering to improve scaling

__init__(submission_attempts=1, submission_throttle=0, use_tmp=False, dry_run=False)

Initialize a new instance of an ExecutionGraph.

Parameters:

Name	Type	Description	Default
submission_attempts		Number of attempted submissions before marking a step as failed.	1
submission_throttle		Maximum number of scheduled in progress submissions.	0
use_tmp		A Boolean value that when set to 'True' designates that ExecutionGraph should use temporary files for output.	False

Source code in maestrowf/datastructures/core/executiongraph.py

def __init__(self, submission_attempts=1, submission_throttle=0,
             use_tmp=False, dry_run=False):
    """
    Initialize a new instance of an ExecutionGraph.

    :param submission_attempts: Number of attempted submissions before
        marking a step as failed.
    :param submission_throttle: Maximum number of scheduled in progress
    submissions.
    :param use_tmp: A Boolean value that when set to 'True' designates
    that ExecutionGraph should use temporary files for output.
    """
    super(ExecutionGraph, self).__init__()
    # Member variables for execution.
    self._adapter = None
    self._description = OrderedDict()

    # Generate tempdir (if specfied)
    if use_tmp:
        self._tmp_dir = tempfile.mkdtemp()
    else:
        self._tmp_dir = ""

    # Sets to track progress.
    self.completed_steps = set([SOURCE])
    self.in_progress = set()
    self.failed_steps = set()
    self.cancelled_steps = set()
    self.ready_steps = deque()
    self.is_canceled = False

    self._status_order = 'bfs'  # Set status order type
    self._status_subtree = None  # Cache bfs_subtree for status writing

    # Values for management of the DAG. Things like submission attempts,
    # throttling, etc. should be listed here.
    self._submission_attempts = submission_attempts
    self._submission_throttle = submission_throttle
    self.dry_run = dry_run

    # A map that tracks the dependencies of a step.
    # NOTE: I don't know how performant the Python dict structure is, but
    # we'll use it for now. I think this may want to be changed to an AVL
    # tree or something of that nature to guarantee worst case performance.
    self._dependencies = {}

    LOGGER.info(
        "\n------------------------------------------\n"
        "Submission attempts =       %d\n"
        "Submission throttle limit = %d\n"
        "Use temporary directory =   %s\n"
        "Tmp Dir = %s\n"
        "------------------------------------------",
        submission_attempts, submission_throttle, use_tmp, self._tmp_dir
    )

    # Error check that the submission values are valid.
    if self._submission_attempts < 1:
        _msg = "Submission attempts should always be greater than 0. " \
               "Received a value of {}.".format(self._submission_attempts)
        LOGGER.error(_msg)
        raise ValueError(_msg)

    if self._submission_throttle < 0:
        _msg = "Throttling should be 0 for unthrottled or a positive " \
               "integer for the number of allowed inflight jobs. " \
               "Received a value of {}.".format(self._submission_throttle)
        LOGGER.error(_msg)
        raise ValueError(_msg)

add_connection(parent, step)

Add a connection between two steps in the ExecutionGraph.

Parameters:

Name	Type	Description	Default
parent		The parent step that is required to execute 'step'	required
step		The dependent step that relies on parent.	required

Source code in maestrowf/datastructures/core/executiongraph.py

def add_connection(self, parent, step):
    """
    Add a connection between two steps in the ExecutionGraph.

    :param parent: The parent step that is required to execute 'step'
    :param step: The dependent step that relies on parent.
    """
    self.add_edge(parent, step)
    self._dependencies[step].add(parent)

add_description(name, description, **kwargs)

Add a study description to the ExecutionGraph instance.

Parameters:

Name	Type	Description	Default
name		Name of the study.	required
description		Description of the study.	required

Source code in maestrowf/datastructures/core/executiongraph.py

def add_description(self, name, description, **kwargs):
    """
    Add a study description to the ExecutionGraph instance.

    :param name: Name of the study.
    :param description: Description of the study.
    """
    self._description["name"] = name
    self._description["description"] = description
    self._description.update(kwargs)

add_step(name, step, workspace, restart_limit, params=None)

Add a StepRecord to the ExecutionGraph.

Parameters:

Name	Type	Description	Default
name		Name of the step to be added.	required
step		StudyStep instance to be recorded.	required
workspace		Directory path for the step's working directory.	required
restart_limit		Upper limit on the number of restart attempts.	required
params		Iterable of tuples of step parameter names, values	None

Source code in maestrowf/datastructures/core/executiongraph.py

def add_step(self, name, step, workspace, restart_limit, params=None):
    """
    Add a StepRecord to the ExecutionGraph.

    :param name: Name of the step to be added.
    :param step: StudyStep instance to be recorded.
    :param workspace: Directory path for the step's working directory.
    :param restart_limit: Upper limit on the number of restart attempts.
    :param params: Iterable of tuples of step parameter names, values
    """
    data = {
                "step":          step,
                "state":         State.INITIALIZED,
                "workspace":     workspace,
                "restart_limit": restart_limit,
            }
    record = _StepRecord(**data)
    if params:
        record.add_params(params)

    self._dependencies[name] = set()
    super(ExecutionGraph, self).add_node(name, record)

cancel_study()

Cancel the study.

Source code in maestrowf/datastructures/core/executiongraph.py

def cancel_study(self):
    """Cancel the study."""
    joblist = []
    for step in self.in_progress:
        jobid = self.values[step].jobid[-1]
        joblist.append(jobid)

    # Grab the adapter from the ScriptAdapterFactory.
    adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
    adapter = adapter(**self._adapter)

    # cancel our jobs
    crecord = adapter.cancel_jobs(joblist)
    self.is_canceled = True

    if crecord.cancel_status == CancelCode.OK:
        LOGGER.info("Successfully requested to cancel all jobs.")
    elif crecord.cancel_status == CancelCode.ERROR:
        LOGGER.error(
            "Failed to cancel jobs. (Code = %s)", crecord.return_code)
    else:
        LOGGER.error("Unknown Error (Code = %s)", crecord.return_code)

    return crecord.cancel_status

check_study_status()

Check the status of currently executing steps in the graph.

This method is used to check the status of all currently in progress steps in the ExecutionGraph. Each ExecutionGraph stores the adapter used to generate and execute its scripts.

Source code in maestrowf/datastructures/core/executiongraph.py

def check_study_status(self):
    """
    Check the status of currently executing steps in the graph.

    This method is used to check the status of all currently in progress
    steps in the ExecutionGraph. Each ExecutionGraph stores the adapter
    used to generate and execute its scripts.
    """
    # Set up the job list and the map to get back to step names.
    joblist = []
    jobmap = {}
    for step in self.in_progress:
        jobid = self.values[step].jobid[-1]
        joblist.append(jobid)
        jobmap[jobid] = step

    # Grab the adapter from the ScriptAdapterFactory.
    adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
    adapter = adapter(**self._adapter)
    # Use the adapter to grab the job statuses.
    retcode, job_status = adapter.check_jobs(joblist)
    # Map the job identifiers back to step names.
    step_status = {jobmap[jobid]: status
                   for jobid, status in job_status.items()}

    # Based on return code, log something different.
    if retcode == JobStatusCode.OK:
        LOGGER.info("Jobs found for user '%s'.", getpass.getuser())
        return retcode, step_status
    elif retcode == JobStatusCode.NOJOBS:
        LOGGER.info("No jobs found.")
        return retcode, step_status
    else:
        msg = "Unknown Error (Code = {})".format(retcode)
        LOGGER.error(msg)
        return retcode, step_status

cleanup()

Clean up output produced by the ExecutionGraph.

Source code in maestrowf/datastructures/core/executiongraph.py

def cleanup(self):
    """Clean up output produced by the ExecutionGraph."""
    if self._tmp_dir:
        shutil.rmtree(self._tmp_dir, ignore_errors=True)

execute_ready_steps()

Execute any steps whose dependencies are satisfied.

The 'execute_ready_steps' method is the core of how the ExecutionGraph manages execution. This method does the following:

• Checks the status of existing jobs that are executing and updates the state if changed.
• Finds steps that are initialized and determines what can be run based on satisfied dependencies and executes steps whose dependencies are met.

Returns:

Type	Description
	True if the study has completed, False otherwise.

Source code in maestrowf/datastructures/core/executiongraph.py

def execute_ready_steps(self):
    """
    Execute any steps whose dependencies are satisfied.

    The 'execute_ready_steps' method is the core of how the ExecutionGraph
    manages execution. This method does the following:

    * Checks the status of existing jobs that are executing and updates
      the state if changed.
    * Finds steps that are initialized and determines what can be run
      based on satisfied dependencies and executes steps whose
      dependencies are met.

    :returns: True if the study has completed, False otherwise.
    """
    # TODO: We may want to move this to a singleton somewhere
    # so we can guarantee that all steps use the same adapter.
    adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
    adapter = adapter(**self._adapter)

    if not self.dry_run:
        LOGGER.debug("Checking status check...")
        retcode, job_status = self.check_study_status()
    else:
        LOGGER.debug("DRYRUN: Skipping status check...")
        retcode = JobStatusCode.OK
        job_status = {}

    LOGGER.debug("Checked status (retcode %s)-- %s", retcode, job_status)

    # For now, if we can't check the status something is wrong.
    # Don't modify the DAG.
    if retcode == JobStatusCode.ERROR:
        msg = "Job status check failed -- Aborting."
        LOGGER.error(msg)
        raise RuntimeError(msg)
    elif retcode == JobStatusCode.OK:
        # For the status of each currently in progress job, check its
        # state.
        cleanup_steps = set()  # Steps that are in progress showing failed.
        cancel_steps = set()   # Steps that have dependencies to mark cancelled
        for name, status in job_status.items():
            LOGGER.debug("Checking job '%s' with status %s.", name, status)
            record = self.values[name]

            if status == State.FINISHED:
                # Mark the step complete and notate its end time.
                record.mark_end(State.FINISHED)
                LOGGER.info("Step '%s' marked as finished. Adding to "
                            "complete set.", name)
                self.completed_steps.add(name)
                self.in_progress.remove(name)

            elif status == State.RUNNING:
                # When detect that a step is running, mark it.
                LOGGER.info("Step '%s' found to be running.", record.name)
                record.mark_running()

            elif status == State.TIMEDOUT:
                # Execute the restart script.
                # If a restart script doesn't exist, re-run the command.
                # If we're under the restart limit, attempt a restart.
                if record.can_restart:
                    if record.mark_restart():
                        LOGGER.info(
                            "Step '%s' timed out. Restarting (%s of %s).",
                            name, record.restarts, record.restart_limit
                        )
                        self._execute_record(record, adapter, restart=True)
                    else:
                        LOGGER.info("'%s' has been restarted %s of %s "
                                    "times. Marking step and all "
                                    "descendents as failed.",
                                    name,
                                    record.restarts,
                                    record.restart_limit)
                        self.in_progress.remove(name)
                        cleanup_steps.update(self.bfs_subtree(name)[0])
                # Otherwise, we can't restart so mark the step timed out.
                else:
                    LOGGER.info("'%s' timed out, but cannot be restarted."
                                " Marked as TIMEDOUT.", name)
                    # Mark that the step ended due to TIMEOUT.
                    record.mark_end(State.TIMEDOUT)
                    # Remove from in progress since it no longer is.
                    self.in_progress.remove(name)
                    # Add the subtree to the clean up steps
                    cleanup_steps.update(self.bfs_subtree(name)[0])
                    # Remove the current step, clean up is used to mark
                    # steps definitively as failed.
                    cleanup_steps.remove(name)
                    # Add the current step to failed.
                    self.failed_steps.add(name)

            elif status == State.HWFAILURE:
                # TODO: Need to make sure that we do this a finite number
                # of times.
                # Resubmit the cmd.
                LOGGER.warning("Hardware failure detected. Attempting to "
                               "resubmit step '%s'.", name)
                # We can just let the logic below handle submission with
                # everything else.
                self.ready_steps.append(name)

            elif status == State.FAILED:
                LOGGER.warning(
                    "Job failure reported. Aborting %s -- flagging all "
                    "dependent jobs as failed.",
                    name
                )
                self.in_progress.remove(name)
                record.mark_end(State.FAILED)
                cleanup_steps.update(self.bfs_subtree(name)[0])

            elif status == State.UNKNOWN:
                record.mark_end(State.UNKNOWN)
                LOGGER.info(
                    "Step '%s' found in UNKNOWN state. Step was found "
                    "in '%s' state previously, marking as UNKNOWN. "
                    "Adding to failed steps.",
                    name, record.status)
                cleanup_steps.update(self.bfs_subtree(name)[0])
                self.in_progress.remove(name)

            elif status == State.CANCELLED:
                LOGGER.info("Step '%s' was cancelled.", name)
                self.in_progress.remove(name)
                record.mark_end(State.CANCELLED)
                cancel_steps.update(self.bfs_subtree(name)[0])

        # Let's handle all the failed steps in one go.
        for node in cleanup_steps:
            self.failed_steps.add(node)
            self.values[node].mark_end(State.FAILED)

        # Handle dependent steps that need cancelling
        for node in cancel_steps:
            self.cancelled_steps.add(node)
            self.values[node].mark_end(State.CANCELLED)

    # Now that we've checked the statuses of existing jobs we need to make
    # sure dependencies haven't been met.
    for key in self.values.keys():
        # We MUST dereference from the key. If we use values.items(), a
        # generator gets produced which will give us a COPY of a record and
        # not the actual record.
        record = self.values[key]

        # A completed step by definition has had its dependencies met.
        # Skip it.
        if key in self.completed_steps:
            LOGGER.debug("'%s' in completed set, skipping.", key)
            continue

        LOGGER.debug("Checking %s -- %s", key, record.jobid)
        # If the record is only INITIALIZED, we have encountered a step
        # that needs consideration.
        if record.status == State.INITIALIZED:
            LOGGER.debug("'%s' found to be initialized. Checking "
                         "dependencies. ", key)

            LOGGER.debug(
                "Unfulfilled dependencies: %s",
                self._dependencies[key])

            s_completed = list(filter(
                lambda x: x in self.completed_steps,
                self._dependencies[key]))
            self._dependencies[key] = \
                self._dependencies[key] - set(s_completed)
            LOGGER.debug(
                "Completed dependencies: %s\n"
                "Remaining dependencies: %s",
                s_completed, self._dependencies[key])

            # If the gating dependencies set is empty, we can execute.
            if not self._dependencies[key]:
                if key not in self.ready_steps:
                    LOGGER.debug("All dependencies completed. Staging.")
                    self.ready_steps.append(key)
                else:
                    LOGGER.debug("Already staged. Passing.")
                    continue

    # We now have a collection of ready steps. Execute.
    # If we don't have a submission limit, go ahead and submit all.
    if self._submission_throttle == 0:
        LOGGER.info("Launching all ready steps...")
        _available = len(self.ready_steps)
    # Else, we have a limit -- adhere to it.
    else:
        # Compute the number of available slots we have for execution.
        _available = self._submission_throttle - len(self.in_progress)
        # Available slots should never be negative, but on the off chance
        # we are in a slot deficit, then we will just say none are free.
        _available = max(0, _available)
        # Now, we need to take the min of the length of the queue and the
        # computed number of slots. We could have free slots, but have less
        # in the queue.
        _available = min(_available, len(self.ready_steps))
        LOGGER.info("Found %d available slots...", _available)

    for i in range(0, _available):
        # Pop the record and execute using the helper method.
        _record = self.values[self.ready_steps.popleft()]

        # If we get to this point and we've cancelled, cancel the record.
        if self.is_canceled:
            LOGGER.info("Cancelling '%s' -- continuing.", _record.name)
            _record.mark_end(State.CANCELLED)
            self.cancelled_steps.add(_record.name)
            continue

        LOGGER.debug("Launching job %d -- %s", i, _record.name)
        self._execute_record(_record, adapter)

    # check the status of the study upon finishing this round of execution
    completion_status = self._check_study_completion()
    return completion_status

generate_scripts()

Generate the scripts for all steps in the ExecutionGraph.

The generate_scripts method scans the ExecutionGraph instance and uses the stored adapter to write executable scripts for either local or scheduled execution. If a restart command is specified, a restart script will be generated for that record.

Source code in maestrowf/datastructures/core/executiongraph.py

def generate_scripts(self):
    """
    Generate the scripts for all steps in the ExecutionGraph.

    The generate_scripts method scans the ExecutionGraph instance and uses
    the stored adapter to write executable scripts for either local or
    scheduled execution. If a restart command is specified, a restart
    script will be generated for that record.
    """
    # An adapter must be specified
    if not self._adapter:
        msg = "Adapter not found. Specify a ScriptAdapter using " \
              "set_adapter."
        LOGGER.error(msg)
        raise ValueError(msg)

    # Set up the adapter.
    LOGGER.info("Generating scripts...")
    adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
    adapter = adapter(**self._adapter)

    self._check_tmp_dir()
    for key, record in self.values.items():
        if key == SOURCE:
            continue

        # Record generates its own script.
        record.setup_workspace()
        record.generate_script(adapter, self._tmp_dir)

log_description()

Log the description of the ExecutionGraph.

Source code in maestrowf/datastructures/core/executiongraph.py

def log_description(self):
    """Log the description of the ExecutionGraph."""
    desc = ["{}: {}".format(key, value)
            for key, value in self._description.items()]
    desc = "\n".join(desc)
    LOGGER.info(
        "\n==================================================\n"
        "%s\n"
        "==================================================\n",
        desc
    )

set_adapter(adapter)

Set the adapter used to interface for scheduling tasks.

Parameters:

Name	Type	Description	Default
adapter		Adapter name to be used when launching the graph.	required

Source code in maestrowf/datastructures/core/executiongraph.py

def set_adapter(self, adapter):
    """
    Set the adapter used to interface for scheduling tasks.

    :param adapter: Adapter name to be used when launching the graph.
    """
    if not adapter:
        # If we have no adapter specified, assume sequential execution.
        self._adapter = None
        return

    if not isinstance(adapter, dict):
        msg = "Adapter settings must be contained in a dictionary."
        LOGGER.error(msg)
        raise TypeError(msg)

    # Check to see that the adapter type is something the
    if adapter["type"] not in ScriptAdapterFactory.get_valid_adapters():
        msg = "'{}' adapter must be specfied in ScriptAdapterFactory." \
              .format(adapter)
        LOGGER.error(msg)
        raise TypeError(msg)

    self._adapter = adapter

write_status(path)

Write the status of the DAG to a CSV file.

Source code in maestrowf/datastructures/core/executiongraph.py

def write_status(self, path):
    """Write the status of the DAG to a CSV file."""
    header = "Step Name,Job ID,Workspace,State,Run Time,Elapsed Time," \
             "Start Time,Submit Time,End Time,Number Restarts,Params"
    status = [header]

    for key in self.status_subtree:
        value = self.values[key]

        jobid_str = "--"
        if value.jobid:
            jobid_str = str(value.jobid[-1])

        # Include step root in workspace when parameterized
        if list(value.params.items()):
            ws = os.path.join(
                * os.path.normpath(
                    value.workspace.value).split(os.sep)[-2:]
            )
        else:
            ws = os.path.split(value.workspace.value)[1]

        _ = [
                value.name, jobid_str,
                ws,
                str(value.status.name), value.run_time, value.elapsed_time,
                value.time_start, value.time_submitted, value.time_end,
                str(value.restarts),
                ";".join(["{}:{}".format(param, value)
                          for param, value in value.params.items()])
            ]
        _ = ",".join(_)
        status.append(_)

    stat_path = os.path.join(path, "status.csv")
    lock_path = os.path.join(path, ".status.lock")
    lock = FileLock(lock_path)
    try:
        with lock.acquire(timeout=10):
            with open(stat_path, "w+") as stat_file:
                stat_file.write("\n".join(status))
    except Timeout:
        pass

ParameterGenerator

Class for containing parameters and generating combinations.

The goal of this class is to provide one centralized location for managing and storing parameters. This implementation of the ParameterGenerator, currently, is very basic. It takes lists of parameters and uses those to construct combinations, meaning that if you were to view this as an Excel table, you would have a row for each valid combination you wanted to study.

The other goal is to make it so that by having the ParameterGenerator manage parameters, functionality can be added without affecting how the end user interacts with this class. The ParameterGenerator has an Iterator defined and will generate each combination one by one. The end user should NEVER SEE AN INVALID COMBINATION. Because this class generates the combinations as specified by the parameters added (eventually with types or enforced inheritance), and eventually constraints, it opens up being able to quietly change how this class generates its combinations.

Easily convert studies to other types of studies. Because the API doesn't change from its nice Pythonic style, you can in theory swap out a ParameterGenerator that performs completely differently. All of a sudden, you can get the following for simply deriving from this class:

• Uncertainty Quantification (UQ): Add the ability to statistically sample parameters behind the scenes. Let the ParameterGenerator constraint solve behind the scenes and return the Combination objects it was going to return in the first place. If you can't find a valid sampling, just return nothing and the study won't run.
• Boundary and constraint testing: Like UQ above, hide the solving from the user. Simply add parameters to be constraint solved on behind the API and all the user sees is combinations on the frontend.

Ideally, all parameter generation schemes should boil down as follows:

1. Derive from this class, add constraint solving.
2. Construct a study how you would otherwise do so, just use the new ParameterGenerator and add parameters.
3. Setup, stage, and execute your study.
4. Profit.

Source code in maestrowf/datastructures/core/parameters.py

class ParameterGenerator:
    """
    Class for containing parameters and generating combinations.

    The goal of this class is to provide one centralized location for managing
    and storing parameters. This implementation of the ParameterGenerator,
    currently, is very basic. It takes lists of parameters and uses those to
    construct combinations, meaning that if you were to view this as an Excel
    table, you would have a row for each valid combination you wanted to study.

    The other goal is to make it so that by having the ParameterGenerator
    manage parameters, functionality can be added without affecting how the
    end user interacts with this class. The ParameterGenerator has an Iterator
    defined and will generate each combination one by one. The end user should
    NEVER SEE AN INVALID COMBINATION. Because this class generates the
    combinations as specified by the parameters added (eventually with types
    or enforced inheritance), and eventually constraints, it opens up being
    able to quietly change how this class generates its combinations.

    Easily convert studies to other types of studies. Because the API doesn't
    change from its nice Pythonic style, you can in theory swap out a
    ParameterGenerator that performs completely differently. All of a sudden,
    you can get the following for simply deriving from this class:

    * Uncertainty Quantification (UQ): Add the ability to statistically
      sample parameters behind the scenes. Let the ParameterGenerator
      constraint solve behind the scenes and return the Combination
      objects it was going to return in the first place. If you can't
      find a valid sampling, just return nothing and the study won't run.
    * Boundary and constraint testing: Like UQ above, hide the solving
      from the user. Simply add parameters to be constraint solved on
      behind the API and all the user sees is combinations on the frontend.

    Ideally, all parameter generation schemes should boil down as follows:

    1. Derive from this class, add constraint solving.
    2. Construct a study how you would otherwise do so, just use
       the new ParameterGenerator and add parameters.
    3. Setup, stage, and execute your study.
    4. Profit.
    """

    def __init__(self, token="$", ltoken="%%"):
        """
        Initialize an empty ParameterGenerator object.

        The ParameterGenerator is instantiated with two token values, one for
        parameters and one for labels. The 'token' parameter represents the
        character(s) expected in front of parameterized strings. For example,
        if 'token' is left at its default of '$' and we have a parameter named
        'COMP1', then the instance of the ParameterGenerator will replace the
        value '$(COMP1)' in any item passed to the apply method. The 'ltoken'
        parameter functions in much the same way, except that instead of
        substituting for a parameter, this character(s) is what is found in a
        parameter label. The label for the parameter 'COMP1' is specified as
        '$(COMP.label)' where the label may have a value of 'COMP1.%%' (where
        %% is the default value of ltoken). For any combination, '%%' will be
        replaced by the value of the parameter 'COMP1' for that given instance
        when the label is specified in a item.

        :param token: Leading token that denotes a parameter (Default: '$').
        :param ltoken: Token that represents where to place a value in a label
            (Default: '%%').
        """
        self.parameters = OrderedDict()
        self.labels = {}
        self.names = {}
        self.label_token = ltoken
        self.token = token

        self.length = 0

    def add_parameter(self, key, values, label=None, name=None):
        """
        Add a parameter to the ParameterGenerator.

        Currently, all parameters added to a ParameterGenerator instance must
        have a list of values that are the same length. Future improvements
        will add the ability to specify either types of parameters or provide
        different ParameterGenerators derivations that have unique behavior.

        :param key: Parameter key to find for replacement.
        :param values: List of values the parameter can take.
        :param label: Label string for labeling the parameter.
        :param name: Custom name for identifying parameter.
        """
        if key in self.parameters:
            logger.warning("'%s' already in parameter set. Overriding.", key)

        self.parameters[key] = values
        if self.length == 0:
            self.length = len(values)

        elif len(values) != self.length:
            error = "Length of values list must be the same size as " \
                    "the other parameters that exist in the " \
                    "generators. Length of '{}' is {}. Aborting." \
                    .format(name, len(values))
            logger.exception(error)
            raise ValueError(error)

        if label:
            self.labels[key] = label
        else:
            self.labels[key] = "{}.{}".format(key, self.label_token)

        if name:
            self.names[key] = name
        else:
            self.names[key] = key

    def __iter__(self):
        """
        Return the iterator for the ParameterGenerator.

        :returns: Iterator for walking parameter combinations.
        """
        return self.get_combinations()

    def __bool__(self):
        """
        Override for the __bool__ operator.

        :returns: True if the ParameterGenerator instance has values, False
            otherwise.
        """
        return bool(self.parameters)

    __nonzero__ = __bool__

    def get_combinations(self):
        """
        Generate all combinations of parameters.

        :returns: A generator with all combinations of parameters.
        """
        for i in range(0, self.length):
            combo = Combination()
            for key in self.parameters.keys():
                pvalue = self.parameters[key][i]
                if isinstance(self.labels[key], list):
                    tlabel = self.labels[key][i]
                else:
                    tlabel = self.labels[key].replace(self.label_token,
                                                      str(pvalue))
                name = self.names[key]
                combo.add(key, name, pvalue, tlabel)
            yield combo

    def _get_used_parameters(self, item, params):
        """
        Find the parameters used by an item in a StudyStep.

        :param item: The item to search for parameters.
        :param params: The current set of found parameters.
        """
        if not item:
            return
        elif isinstance(item, str):
            for key in self.parameters.keys():
                _ = r"\{}\({}\.*\w*\)".format(self.token, key)
                matches = re.findall(_, item)
                if matches:
                    params.add(key)
        elif isinstance(item, list):
            for each in item:
                self._get_used_parameters(each, params)
        elif isinstance(item, dict):
            for each in item.values():
                self._get_used_parameters(each, params)
        else:
            msg = \
                "Encountered an object of type '{}'. Passing."\
                .format(type(item))
            logger.debug(msg)
            return

    def get_used_parameters(self, step):
        """
        Return the parameters used by a StudyStep.

        :param step: A StudyStep instance to be checked.
        :returns: A set of the parameter names used within the step parameter.
        """
        params = set()
        self._get_used_parameters(step.__dict__, params)
        return params

    def get_metadata(self):
        """
        Produce metadata for the parameters in a generator instance.

        :returns: A dictionary containing metadata about the instance.
        """
        meta = {}
        for combo in self.get_combinations():
            meta[str(combo)] = {}
            meta[str(combo)]["params"] = combo._params
            meta[str(combo)]["labels"] = combo._labels

        return meta

__bool__()

Override for the bool operator.

Returns:

Type	Description
	True if the ParameterGenerator instance has values, False otherwise.

Source code in maestrowf/datastructures/core/parameters.py

def __bool__(self):
    """
    Override for the __bool__ operator.

    :returns: True if the ParameterGenerator instance has values, False
        otherwise.
    """
    return bool(self.parameters)

__init__(token='$', ltoken='%%')

Initialize an empty ParameterGenerator object.

The ParameterGenerator is instantiated with two token values, one for parameters and one for labels. The 'token' parameter represents the character(s) expected in front of parameterized strings. For example, if 'token' is left at its default of '$' and we have a parameter named 'COMP1', then the instance of the ParameterGenerator will replace the value '$(COMP1)' in any item passed to the apply method. The 'ltoken' parameter functions in much the same way, except that instead of substituting for a parameter, this character(s) is what is found in a parameter label. The label for the parameter 'COMP1' is specified as '$(COMP.label)' where the label may have a value of 'COMP1.%%' (where %% is the default value of ltoken). For any combination, '%%' will be replaced by the value of the parameter 'COMP1' for that given instance when the label is specified in a item.

Parameters:

Name	Type	Description	Default
token		Leading token that denotes a parameter (Default: '$').	'$'
ltoken		Token that represents where to place a value in a label (Default: '%%').	'%%'

Source code in maestrowf/datastructures/core/parameters.py

def __init__(self, token="$", ltoken="%%"):
    """
    Initialize an empty ParameterGenerator object.

    The ParameterGenerator is instantiated with two token values, one for
    parameters and one for labels. The 'token' parameter represents the
    character(s) expected in front of parameterized strings. For example,
    if 'token' is left at its default of '$' and we have a parameter named
    'COMP1', then the instance of the ParameterGenerator will replace the
    value '$(COMP1)' in any item passed to the apply method. The 'ltoken'
    parameter functions in much the same way, except that instead of
    substituting for a parameter, this character(s) is what is found in a
    parameter label. The label for the parameter 'COMP1' is specified as
    '$(COMP.label)' where the label may have a value of 'COMP1.%%' (where
    %% is the default value of ltoken). For any combination, '%%' will be
    replaced by the value of the parameter 'COMP1' for that given instance
    when the label is specified in a item.

    :param token: Leading token that denotes a parameter (Default: '$').
    :param ltoken: Token that represents where to place a value in a label
        (Default: '%%').
    """
    self.parameters = OrderedDict()
    self.labels = {}
    self.names = {}
    self.label_token = ltoken
    self.token = token

    self.length = 0

__iter__()

Return the iterator for the ParameterGenerator.

Returns:

Type	Description
	Iterator for walking parameter combinations.

Source code in maestrowf/datastructures/core/parameters.py

def __iter__(self):
    """
    Return the iterator for the ParameterGenerator.

    :returns: Iterator for walking parameter combinations.
    """
    return self.get_combinations()

add_parameter(key, values, label=None, name=None)

Add a parameter to the ParameterGenerator.

Currently, all parameters added to a ParameterGenerator instance must have a list of values that are the same length. Future improvements will add the ability to specify either types of parameters or provide different ParameterGenerators derivations that have unique behavior.

Parameters:

Name	Type	Description	Default
key		Parameter key to find for replacement.	required
values		List of values the parameter can take.	required
label		Label string for labeling the parameter.	None
name		Custom name for identifying parameter.	None

Source code in maestrowf/datastructures/core/parameters.py

def add_parameter(self, key, values, label=None, name=None):
    """
    Add a parameter to the ParameterGenerator.

    Currently, all parameters added to a ParameterGenerator instance must
    have a list of values that are the same length. Future improvements
    will add the ability to specify either types of parameters or provide
    different ParameterGenerators derivations that have unique behavior.

    :param key: Parameter key to find for replacement.
    :param values: List of values the parameter can take.
    :param label: Label string for labeling the parameter.
    :param name: Custom name for identifying parameter.
    """
    if key in self.parameters:
        logger.warning("'%s' already in parameter set. Overriding.", key)

    self.parameters[key] = values
    if self.length == 0:
        self.length = len(values)

    elif len(values) != self.length:
        error = "Length of values list must be the same size as " \
                "the other parameters that exist in the " \
                "generators. Length of '{}' is {}. Aborting." \
                .format(name, len(values))
        logger.exception(error)
        raise ValueError(error)

    if label:
        self.labels[key] = label
    else:
        self.labels[key] = "{}.{}".format(key, self.label_token)

    if name:
        self.names[key] = name
    else:
        self.names[key] = key

get_combinations()

Generate all combinations of parameters.

Returns:

Type	Description
	A generator with all combinations of parameters.

Source code in maestrowf/datastructures/core/parameters.py

def get_combinations(self):
    """
    Generate all combinations of parameters.

    :returns: A generator with all combinations of parameters.
    """
    for i in range(0, self.length):
        combo = Combination()
        for key in self.parameters.keys():
            pvalue = self.parameters[key][i]
            if isinstance(self.labels[key], list):
                tlabel = self.labels[key][i]
            else:
                tlabel = self.labels[key].replace(self.label_token,
                                                  str(pvalue))
            name = self.names[key]
            combo.add(key, name, pvalue, tlabel)
        yield combo

get_metadata()

Produce metadata for the parameters in a generator instance.

Returns:

Type	Description
	A dictionary containing metadata about the instance.

Source code in maestrowf/datastructures/core/parameters.py

def get_metadata(self):
    """
    Produce metadata for the parameters in a generator instance.

    :returns: A dictionary containing metadata about the instance.
    """
    meta = {}
    for combo in self.get_combinations():
        meta[str(combo)] = {}
        meta[str(combo)]["params"] = combo._params
        meta[str(combo)]["labels"] = combo._labels

    return meta

get_used_parameters(step)

Return the parameters used by a StudyStep.

Parameters:

Name	Type	Description	Default
step		A StudyStep instance to be checked.	required

Returns:

Type	Description
	A set of the parameter names used within the step parameter.

Source code in maestrowf/datastructures/core/parameters.py

def get_used_parameters(self, step):
    """
    Return the parameters used by a StudyStep.

    :param step: A StudyStep instance to be checked.
    :returns: A set of the parameter names used within the step parameter.
    """
    params = set()
    self._get_used_parameters(step.__dict__, params)
    return params

Study

Bases: DAG, PickleInterface

Collection of high level objects to perform study construction.

The Study class is part of the meat and potatoes of this whole package. A Study object is where the intersection of the major moving parts are collected. These moving parts include:

• ParameterGenerator for getting combinations of user parameters
• StudyEnvironment for managing and applying the environment to studies
• Study flow, which is a DAG of the abstract workflow

The class is responsible for a number of the major key steps in study setup as well. Those responsibilities include (but are not limited to):

• Setting up the workspace where a simulation campaign will be run.

• Applying the StudyEnvionment to the abstract flow DAG:

  • Creating the global workspace for a study.
  • Setting up the parameterized workspaces for each combination.
  • Acquiring dependencies as specified in the StudyEnvironment.

• Intelligently constructing the expanded DAG to be able to:

  • Recognize when a step executes in a parameterized workspace
  • Recognize when a step executes in the global workspace

• Expanding the abstract flow to the full set of specified parameters.

Future functionality that makes sense to add here:

• Metadata collection. If we're setting things up here, collect the general information. We might even want to venture to say that a set of directives may be useful so that they could be placed into Dependency classes as hooks for dumping that data automatically.
• A way of packaging an instance of the class up into something that is easy to store in the ExecutionDAG class so that an API can be designed in whatever class ends up managing all of this to have machine learning applications pipe messages to spin up new studies using the same environment.

Source code in maestrowf/datastructures/core/study.py

class Study(DAG, PickleInterface):
    """
    Collection of high level objects to perform study construction.

    The Study class is part of the meat and potatoes of this whole package. A
    Study object is where the intersection of the major moving parts are
    collected. These moving parts include:

    * ParameterGenerator for getting combinations of user parameters
    * StudyEnvironment for managing and applying the environment to studies
    * Study flow, which is a DAG of the abstract workflow

    The class is responsible for a number of the major key steps in study setup
    as well. Those responsibilities include (but are not limited to):

    * Setting up the workspace where a simulation campaign will be run.
    * Applying the StudyEnvionment to the abstract flow DAG:
        * Creating the global workspace for a study.
        * Setting up the parameterized workspaces for each combination.
        * Acquiring dependencies as specified in the StudyEnvironment.

    * Intelligently constructing the expanded DAG to be able to:
        * Recognize when a step executes in a parameterized workspace
        * Recognize when a step executes in the global workspace

    * Expanding the abstract flow to the full set of specified parameters.

    Future functionality that makes sense to add here:

    * Metadata collection. If we're setting things up here, collect the
      general information. We might even want to venture to say that a set
      of directives may be useful so that they could be placed into
      Dependency classes as hooks for dumping that data automatically.
    * A way of packaging an instance of the class up into something that is
      easy to store in the ExecutionDAG class so that an API can be
      designed in whatever class ends up managing all of this to have
      machine learning applications pipe messages to spin up new studies
      using the same environment.
    """

    def __init__(self, name, description,
                 studyenv=None, parameters=None, steps=None, out_path="./"):
        """
        Study object used to represent the full workflow of a study.

        Derived from the DAG data structure. Contains everything that a study
        requires to be expanded with the appropriate substitutions and with
        parameters inserted. This data structure should be the instance the
        future daemon loads in to track progress on a workflow.

        :param name: String representing the name of the Study.
        :param description: A text description of what the study does.
        :param steps: A list of StudySteps in proper workflow order.
        :param studyenv: A populated StudyEnvironment instance.
        :param parameters: A populated Parameters instance.
        :param outpath: The path where the output of the study is written.
        """
        # The basic study information
        self.name = name
        self.description = description

        # Initialized the DAG so we have those structures to be used.
        super(Study, self).__init__()

        # We want deep copies so that properties don't change out from under
        # the Sudy data structure.
        self.environment = studyenv
        self.parameters = parameters
        self._out_path = out_path
        self._meta_path = os.path.join(out_path, "meta")

        LOGGER.debug("OUTPUT_PATH = %s", out_path)
        # Flag the study as not having been set up and add the source node.
        self._issetup = False
        self.is_configured = False
        self.add_node(SOURCE, None)

        # Settings for handling restarts and submission attempts.
        self._restart_limit = 0
        self._submission_attempts = 0
        self._use_tmp = False
        self._dry_run = False

        # Management structures
        # The workspace used by each step.
        self.workspaces = {SOURCE: self._out_path}
        # Parameter independent dependencies by step.
        self.hub_depends = {SOURCE: set()}
        # Other dependencies per step.
        self.depends = {SOURCE: set()}
        # Parameters that each step depends on.
        self.used_params = {SOURCE: set()}
        # Combinations seen per step.
        self.step_combos = {SOURCE: set()}

        # If the user specified a flow in the form of steps, copy those into
        # into the Study object.
        if steps:
            for step in steps:
                # Deep copy because it prevents modifications after the fact.
                self.add_step(step)

    @property
    def output_path(self):
        """
        Property method for the OUTPUT_PATH specified for the study.

        :returns: The string path stored in the OUTPUT_PATH variable.
        """
        return self._out_path

    def store_metadata(self):
        """Store metadata related to the study."""
        # Create the metadata directory.
        create_parentdir(self._meta_path)

        # Store the environment object in order to preserve it.
        path = os.path.join(self._meta_path, "study")
        create_parentdir(path)
        path = os.path.join(path, "env.pkl")
        with open(path, 'wb') as pkl:
            pickle.dump(self, pkl)

        # Construct other metadata related to study construction.
        _workspaces = {}
        for key, value in self.workspaces.items():
            if key == "_source":
                _workspaces[key] = value
            elif key in self.step_combos:
                _workspaces[key] = os.path.split(value)[-1]
            else:
                _workspaces[key] = \
                    os.path.sep.join(value.rsplit(os.path.sep)[-2:])

        # Construct relative paths for the combinations and nest them in the
        # same way as the step combinations dictionary.
        _step_combos = {}
        for key, value in self.step_combos.items():
            if key == SOURCE:
                _step_combos[key] = self.workspaces[key]
            elif not self.used_params[key]:
                _ws = self.workspaces[key]
                _step_combos[key] = {key: os.path.split(_ws)[-1]}
            else:
                _step_combos[key] = {}
                for combo in value:
                    _ws = self.workspaces[combo]
                    _step_combos[key][combo] = \
                        os.path.sep.join(_ws.rsplit(os.path.sep)[-2:])

        metadata = {
            "dependencies": self.depends,
            "hub_dependencies": self.hub_depends,
            "workspaces": _workspaces,
            "used_parameters": self.used_params,
            "step_combinations": _step_combos,
        }
        # Write out the study construction metadata.
        path = os.path.join(self._meta_path, "metadata.yaml")
        with open(path, "wb") as metafile:
            metafile.write(yaml.dump(metadata).encode("utf-8"))

        # Write out parameter metadata.
        metadata = self.parameters.get_metadata()
        path = os.path.join(self._meta_path, "parameters.yaml")
        with open(path, "wb") as metafile:
            metafile.write(yaml.dump(metadata).encode("utf-8"))

        # Write out environment metadata
        path = os.path.join(self._meta_path, "environment.yaml")
        with open(path, "wb") as metafile:
            metafile.write(yaml.dump(os.environ.copy()).encode("utf-8"))

    def load_metadata(self):
        """Load metadata for the study."""
        if not os.path.exists(self._meta_path):
            return

        path = os.path.join(self._meta_path, "study", "env.pkl")
        with open(path, 'rb') as pkl:
            env = pickle.load(pkl)

        if not isinstance(env, type(self)):
            msg = "Object loaded from {path} is of type {type}. Expected an" \
                  " object of type '{cls}.'".format(path=path, type=type(env),
                                                    cls=type(self))
            LOGGER.error(msg)
            raise TypeError(msg)

        metapath = os.path.join(self._meta_path, "metadata.yaml")
        with open(metapath, "rb") as metafile:
            metadata = yaml.load(metafile)

        self.depends = metadata["dependencies"]
        self.hub_depends = metadata["hub_dependencies"]
        self.workspaces = metadata["workspaces"]
        self.used_params = metadata["used_parameters"]
        self.step_combos = metadata["step_combinations"]

    def add_step(self, step):
        """
        Add a step to a study.

        For this helper to be most effective, it recommended to apply steps in
        the order that they will be encountered. The method attempts to be
        intelligent and make the intended edge based on the 'depends' entry in
        a step. When adding steps out of order it's recommended to just use the
        base class DAG functionality and manually make connections.

        :param step: A StudyStep instance to be added to the Study instance.
        """
        # Add the node to the DAG.
        self.add_node(step.real_name, step)
        LOGGER.info(
            "Adding step '%s' to study '%s'...", step.name, self.name)
        # Apply the environment to the incoming step.
        step.__dict__ = \
            apply_function(step.__dict__, self.environment.apply_environment)

        # If the step depends on a prior step, create an edge.
        if "depends" in step.run and step.run["depends"]:
            for dependency in step.run["depends"]:
                LOGGER.info("{0} is dependent on {1}. Creating edge ("
                            "{1}, {0})...".format(step.real_name, dependency))
                if "*" not in dependency:
                    self.add_edge(dependency, step.real_name)
                else:
                    self.add_edge(
                        re.sub(ALL_COMBOS, "", dependency),
                        step.real_name
                    )
        else:
            # Otherwise, if no other dependency, just execute the step.
            self.add_edge(SOURCE, step.real_name)

    def walk_study(self, src=SOURCE):
        """
        Walk the study and create a spanning tree.

        :param src: Source node to start the walk.
        :returns: A generator of (parent, node name, node value) tuples.
        """
        # Get a DFS spanning tree of the study. This method should always
        # return a complete tree because _source is flagged as a dependency
        # if a step is added without one.
        # TODO: This method should be fixed to return both parents and nodes.
        path, parents = self.dfs_subtree(src)
        for node in path:
            yield parents[node], node, self.values[node]

    def setup_workspace(self):
        """Set up the study's main workspace directory."""
        try:
            LOGGER.debug("Setting up study workspace in '%s'", self._out_path)
            create_parentdir(self._out_path)
        except Exception as e:
            LOGGER.error(e.args)
            return False

    def setup_environment(self):
        """Set up the environment by acquiring outside dependencies."""
        # Set up the environment if it hasn't been already.
        if not self.environment.is_set_up:
            LOGGER.debug("Environment is setting up.")
            self.environment.acquire_environment()

    def configure_study(self, submission_attempts=1, restart_limit=1,
                        throttle=0, use_tmp=False, hash_ws=False,
                        dry_run=False):
        """
        Perform initial configuration of a study. \

        The method is used for going through and actually acquiring each \
        dependency, substituting variables, sources and labels. \

        :param submission_attempts: Number of attempted submissions before \
        marking a step as failed. \
        :param restart_limit: Upper limit on the number of times a step with \
        a restart command can be resubmitted before it is considered failed. \
        :param throttle: The maximum number of in-progress jobs allowed. [0 \
        denotes no cap].\
        :param use_tmp: Boolean value specifying if the generated \
        ExecutionGraph dumps its information into a temporary directory. \
        :param dry_run: Boolean value that toggles dry run to just generate \
        study workspaces and scripts without execution or status checking. \
        :returns: True if the Study is successfully setup, False otherwise. \
        """

        self._submission_attempts = submission_attempts
        self._restart_limit = restart_limit
        self._submission_throttle = throttle
        self._use_tmp = use_tmp
        self._hash_ws = hash_ws
        self._dry_run = dry_run

        LOGGER.info(
            "\n------------------------------------------\n"
            "Submission attempts =       %d\n"
            "Submission restart limit =  %d\n"
            "Submission throttle limit = %d\n"
            "Use temporary directory =   %s\n"
            "Hash workspaces =           %s\n"
            "Dry run enabled =           %s\n"
            "Output path =               %s\n"
            "------------------------------------------",
            submission_attempts, restart_limit, throttle,
            use_tmp, hash_ws, dry_run, self._out_path
        )

        self.is_configured = True

    def _stage(self, dag):
        """
        Set up the ExecutionGraph of a parameterized study.

        :param throttle: Maximum number of in progress jobs allowed.
        :returns: The path to the study's global workspace and an expanded
            ExecutionGraph based on the parameters and parameterized workflow
            steps.
        """
        # Items to store that should be reset.
        LOGGER.info(
            "\n==================================================\n"
            "Constructing parameter study '%s'\n"
            "==================================================\n",
            self.name
        )

        # Topological sorted list of steps.
        t_sorted = self.topological_sort()

        # For each step, we need to assess what type of step it is.
        # So far we've seen five types of steps:
        # 1. Linear - The step uses no parameters, so we can add it as it is.
        # 2. Parameterized - The step uses or is dependent on steps that use
        # parameters.
        # 3. Parameter Independent - A step who only uses hub dependencies; or
        # phrased more concisely, is not directly dependent on the parameters
        # of a parent step but simply makes use of all of its combinations.
        # 4. Parameter Dependent - A step that may or may not be parameterized
        # itself, but whose combinations also depend on the combinations of its
        # parents.
        # 5. Parameterized and Parameter Independent - A step that is a combo
        # of #2 and #3 which requires the step to be expanded based on the
        # used parameters of the step, and then adding all parameterized
        # combinations of funneled steps.
        for step in t_sorted:
            LOGGER.info(
                "\n==================================================\n"
                "Processing step '%s'\n"
                "==================================================\n",
                step
            )
            # If we encounter SOURCE, just add it and continue.
            if step == SOURCE:
                LOGGER.info("Encountered '%s'. Adding and continuing.", SOURCE)
                dag.add_node(SOURCE, None)
                continue

            # We're dealing with an actual step. So we have to:
            # Update our management structures.
            node = self.values[step]
            self.hub_depends[step] = set()
            self.depends[step] = set()
            self.step_combos[step] = set()

            s_params = self.parameters.get_used_parameters(node)
            p_params = set()    # Used parameters excluding the current step.
            # Iterate through dependencies to update the p_params
            LOGGER.debug("\n*** Processing dependencies ***")
            for parent in node.run["depends"]:
                # If we have a dependency that is parameter independent, add
                # it to the hub dependency set.
                if "*" in parent:
                    LOGGER.debug("Found funnel dependency -- %s", parent)
                    self.hub_depends[step].add(re.sub(ALL_COMBOS, "", parent))
                else:
                    LOGGER.debug("Found dependency -- %s", parent)
                    # Otherwise, just note the parameters used by the step.
                    self.depends[step].add(parent)
                    p_params |= self.used_params[parent]

            # Search for workspace matches. These affect the expansion of a
            # node because they may use parameters. These are likely to cause
            # a node to fall into the 'Parameter Dependent' case.
            used_spaces = re.findall(
                WSREGEX, "{} {}".format(node.run["cmd"], node.run["restart"]))
            for ws in used_spaces:
                if ws not in self.used_params:
                    msg = "Workspace for '{}' is being used before it would" \
                          " be generated.".format(ws)
                    LOGGER.error(msg)
                    raise Exception(msg)

                # We have the case that if we're using a workspace of a step
                # that is a parameter independent dependency, we can skip it.
                # The parameters don't affect the combinations.
                if ws in self.hub_depends[step]:
                    LOGGER.info(
                        "'%s' parameter independent association found. "
                        "Skipping.", ws)
                    continue

                LOGGER.debug(
                    "Found workspace '%s' using parameters %s",
                    ws, self.used_params[ws])
                p_params |= self.used_params[ws]

            # Total parameters used for this step are the union of each parent
            # and the union of the parameters used by this step.
            self.used_params[step] = p_params | s_params

            # Check for a restart and set the rlimit accordingly.
            if node.run["restart"]:
                rlimit = self._restart_limit
            else:
                rlimit = 0

            # 1. The step and all its preceding parents use no parameters.
            if not self.used_params[step]:
                LOGGER.info(
                    "\n-------------------------------------------------\n"
                    "Adding step '%s' (No parameters used)\n"
                    "-------------------------------------------------\n",
                    step
                )
                # If we're not using any parameters at all, we do:
                # Copy the step and set to not modified.
                self.step_combos[step].add(step)

                workspace = make_safe_path(self._out_path, *[step])
                self.workspaces[step] = workspace
                LOGGER.debug("Workspace: %s", workspace)

                # NOTE: I don't think it's valid to have a specific workspace
                # since a step with no parameters operates at the global level.
                # NOTE: Opting to save the old command for provenence reasons.
                cmd = node.run["cmd"]
                r_cmd = node.run["restart"]
                LOGGER.info("Searching for workspaces...\ncmd = %s", cmd)
                for match in used_spaces:
                    LOGGER.info("Workspace found -- %s", match)
                    workspace_var = "$({}.workspace)".format(match)
                    if match in self.hub_depends[step]:
                        # If we're looking at a parameter independent match
                        # the workspace is the folder that contains all of
                        # the outputs of all combinations for the step.
                        ws = make_safe_path(self._out_path, *[match])
                        LOGGER.info("Found funnel workspace -- %s", ws)
                    else:
                        ws = self.workspaces[match]
                    cmd = cmd.replace(workspace_var, ws)
                    r_cmd = r_cmd.replace(workspace_var, ws)
                # We have to deepcopy the node, otherwise when we modify it
                # here, it's reflected in the ExecutionGraph.
                node = copy.deepcopy(node)
                node.run["cmd"] = cmd
                node.run["restart"] = r_cmd
                LOGGER.debug("New cmd = %s", cmd)
                LOGGER.debug("New restart = %s", r_cmd)
                dag.add_step(step, node, workspace, rlimit)

                if self.depends[step] or self.hub_depends[step]:
                    # So, because we don't have used parameters, we can just
                    # loop over the dependencies and add them.
                    LOGGER.debug("Processing regular dependencies.")
                    for parent in self.depends[step]:
                        LOGGER.info("Adding edge (%s, %s)...", parent, step)
                        dag.add_connection(parent, step)

                    # We can still have a case where we have steps that do
                    # funnel into this one even though this particular step
                    # is not parameterized.
                    LOGGER.debug("Processing hub dependencies.")
                    for parent in self.hub_depends[step]:
                        for item in self.step_combos[parent]:
                            LOGGER.info("Adding edge (%s, %s)...", item, step)
                            dag.add_connection(item, step)
                else:
                    # Otherwise, just add source since we're not dependent.
                    LOGGER.debug("Adding edge (%s, %s)...", SOURCE, step)
                    dag.add_connection(SOURCE, step)

            # 2. The step has used parameters.
            else:
                LOGGER.info(
                    "\n==================================================\n"
                    "Expanding step '%s'\n"
                    "==================================================\n"
                    "-------- Used Parameters --------\n"
                    "%s\n"
                    "---------------------------------",
                    step, self.used_params[step]
                )
                # Now we iterate over the combinations and expand the step.
                for combo in self.parameters:
                    LOGGER.info("\n**********************************\n"
                                "Combo [%s]\n"
                                "**********************************",
                                str(combo))
                    # Compute this step's combination name and workspace.
                    nickname = None
                    combo_str = combo.get_param_string(self.used_params[step])
                    # We must encode explicitly to utf-8
                    # combo_str = combo_str.encode("utf-8")
                    if self._hash_ws:
                        nickname = md5(combo_str.encode("utf-8")).hexdigest()
                        workspace = make_safe_path(
                                        self._out_path,
                                        *[step, nickname])
                    else:
                        workspace = \
                            make_safe_path(self._out_path, *[step, combo_str])
                        LOGGER.debug("Workspace: %s", workspace)
                    combo_str = "{}_{}".format(step, combo_str)
                    self.workspaces[combo_str] = workspace

                    # Check if the step combination has been processed.
                    if combo_str in self.step_combos:
                        continue
                    # Add this step to the combinations seen.
                    self.step_combos[step].add(combo_str)

                    modified, step_exp = node.apply_parameters(combo)
                    step_exp.name = combo_str
                    step_exp.nickname = nickname

                    # Substitute workspaces into the combination.
                    cmd = step_exp.run["cmd"]
                    r_cmd = step_exp.run["restart"]
                    LOGGER.info("Searching for workspaces...\ncmd = %s", cmd)
                    for match in used_spaces:
                        # Construct the workspace variable.
                        LOGGER.info("Workspace found -- %s", ws)
                        workspace_var = "$({}.workspace)".format(match)
                        if match in self.hub_depends[step]:
                            # If we're looking at a parameter independent match
                            # the workspace is the folder that contains all of
                            # the outputs of all combinations for the step.
                            ws = make_safe_path(self._out_path, *[match])
                            LOGGER.info("Found funnel workspace -- %s", ws)
                        elif not self.used_params[match]:
                            # If it's not a funneled dependency and the match
                            # is not parameterized, then the workspace is just
                            # the unparameterized match.
                            ws = self.workspaces[match]
                            LOGGER.info(
                                "Found unparameterized workspace -- %s", match)
                        else:
                            # Otherwise, we're dealing with a combination.
                            ws = "{}_{}".format(
                                match,
                                combo.get_param_string(self.used_params[match])
                            )
                            LOGGER.info(
                                "Found parameterized workspace -- %s", ws)
                            ws = self.workspaces[ws]

                        # Replace in both the command and restart command.
                        cmd = cmd.replace(workspace_var, ws)
                        r_cmd = r_cmd.replace(workspace_var, ws)
                    LOGGER.info("New cmd = %s", cmd)

                    step_exp.run["cmd"] = cmd
                    step_exp.run["restart"] = r_cmd
                    # Add to the step to the DAG.
                    dag.add_step(
                        step_exp.real_name, step_exp, workspace, rlimit,
                        params=combo.get_param_values(self.used_params[step]))

                    if self.depends[step] or self.hub_depends[step]:
                        # So, because we don't have used parameters, we can
                        # just loop over the dependencies and add them.
                        LOGGER.info("Processing regular dependencies.")
                        for p in self.depends[step]:
                            if self.used_params[p]:
                                p = "{}_{}".format(
                                    p,
                                    combo.get_param_string(self.used_params[p])
                                )
                            LOGGER.info(
                                "Adding edge (%s, %s)...", p, combo_str
                            )
                            dag.add_connection(p, combo_str)

                        # We can still have a case where we have steps that do
                        # funnel into this one even though this particular step
                        # is not parameterized.
                        LOGGER.debug("Processing hub dependencies.")
                        for parent in self.hub_depends[step]:
                            for item in self.step_combos[parent]:
                                LOGGER.info(
                                    "Adding edge (%s, %s)...", item, combo_str
                                )
                                dag.add_connection(item, combo_str)
                    else:
                        # Otherwise, just add source since we're not dependent.
                        LOGGER.debug(
                            "Adding edge (%s, %s)...", SOURCE, combo_str
                        )
                        dag.add_connection(SOURCE, combo_str)

        return dag

    def _stage_linear(self, dag):
        """
        Execute a linear workflow without parameters.

        :param throttle: Maximum number of in progress jobs allowed.
        :returns: The path to the study's global workspace and an
            ExecutionGraph based on linear steps in the study.
        """
        # For each step in the Study
        # Walk the study and add the steps to the ExecutionGraph.
        t_sorted = self.topological_sort()
        for step in t_sorted:
            # If we find the source node, we can just add it and continue.
            if step == SOURCE:
                LOGGER.debug("Source node found.")
                dag.add_node(SOURCE, None)
                continue

            # Initialize management structures.
            ws = make_safe_path(self._out_path, *[step])
            self.workspaces[step] = ws
            self.depends[step] = set()
            # Hub dependencies are not possible in linear studies. Empty set
            # for completion.
            self.hub_depends[step] = set()
            self.used_params[step] = set()
            self.step_combos[step] = set([step])

            node = self.values[step]
            # If the step has a restart cmd, set the limit.
            if node.run["restart"]:
                rlimit = self._restart_limit
            else:
                rlimit = 0

            cmd = node.run["cmd"]
            r_cmd = node.run["restart"]
            LOGGER.info("Searching for workspaces...\ncmd = %s", cmd)
            used_spaces = re.findall(WSREGEX, cmd)
            for match in used_spaces:
                # In this case we don't need to look for any parameters, or
                # combination dependent ("funnel") steps. It's a simple sub.
                LOGGER.info("Workspace found -- %s", match)
                workspace_var = "$({}.workspace)".format(match)
                ws = self.workspaces[match]
                cmd = cmd.replace(workspace_var, ws)
                r_cmd = r_cmd.replace(workspace_var, ws)
            node.run["cmd"] = cmd
            node.run["restart"] = r_cmd

            # Add the step
            dag.add_step(step, node, ws, rlimit)
            # If the node does not depend on any other steps, make it so that
            # if connects to SOURCE.
            if not node.run["depends"]:
                dag.add_connection(SOURCE, step)
            else:
                # In this case, since our step names are not parameterized,
                # and due to topological sort, we can guarantee that our
                # dependencies have been added. Go through and add each edge.
                for parent in node.run["depends"]:
                    self.depends[step].add(parent)
                    dag.add_connection(parent, step)

        return dag

    def stage(self):
        """
        Generate the execution graph for a Study.

        Staging creates an ExecutionGraph based on the combinations generated
        by the ParameterGeneration object stored in an instance of a Study.
        The stage method also sets up individual working directories (or
        workspaces) for each node in the workflow that requires it.

        :returns: An ExecutionGraph object with the expanded workflow.
        """
        # If the workspace doesn't exist, raise an exception.
        if not os.path.exists(self._out_path):
            msg = "Study {} is not set up for staging. Workspace does not " \
                  "exists (Output Dir = {}).".format(self.name, self._out_path)
            LOGGER.error(msg)
            raise Exception(msg)

        # If the environment isn't set up, raise an exception.
        if not self.environment.is_set_up:
            msg = "Study {} is not set up for staging. Environment is not " \
                  "set up. Aborting.".format(self.name)
            LOGGER.error(msg)
            raise Exception(msg)

        # After substituting, we should start getting combinations and
        # iterating. Two options here:
        # 1. Just create a new DAG with the step names altered to reflect the
        #    parameters they use.
        # 2. Create a derived DAG that has look up tables for each node based
        #    parameters. This might reduce to the same as 1, but retains the
        #    same original naming. Though that doesn't matter since the actual
        #    object has the original name.
        # NOTE: fdinatal - 2/6/17: Looks like 1 is the easiest to get done,
        # going with that for now.
        # NOTE: fdinatal - 3/28/17: Revisiting this method.. my previous logic
        # was flawed.
        # NOTE: fdinatal - 5/17/17: There is the strong possibility this method
        # will need to be reworked some in the future. It likely won't need to
        # be a complete gutting of the method, but there may need to be logic
        # for different styles of launching since it's the ExecutionGraph that
        # will need to be formatted properly so that scripts get generated in
        # the appropriate fashion.

        # Construct ExecutionGraph
        dag = ExecutionGraph(
            submission_attempts=self._submission_attempts,
            submission_throttle=self._submission_throttle,
            use_tmp=self._use_tmp, dry_run=self._dry_run)
        dag.add_description(**self.description)
        dag.log_description()

        # Because we're working within a Study class whose steps have already
        # been verified to not contain a cycle, we can override the check for
        # the execution graph. Because the execution graph is constructed from
        # the study steps, it won't contain a cycle.
        def _pass_detect_cycle(self):
            pass

        dag.detect_cycle = MethodType(_pass_detect_cycle, dag)

        return self._out_path, self._stage(dag)

output_path property

Property method for the OUTPUT_PATH specified for the study.

Returns:

Type	Description
	The string path stored in the OUTPUT_PATH variable.

__init__(name, description, studyenv=None, parameters=None, steps=None, out_path='./')

Study object used to represent the full workflow of a study.

Derived from the DAG data structure. Contains everything that a study requires to be expanded with the appropriate substitutions and with parameters inserted. This data structure should be the instance the future daemon loads in to track progress on a workflow.

Parameters:

Name	Type	Description	Default
name		String representing the name of the Study.	required
description		A text description of what the study does.	required
steps		A list of StudySteps in proper workflow order.	None
studyenv		A populated StudyEnvironment instance.	None
parameters		A populated Parameters instance.	None
outpath		The path where the output of the study is written.	required

Source code in maestrowf/datastructures/core/study.py

def __init__(self, name, description,
             studyenv=None, parameters=None, steps=None, out_path="./"):
    """
    Study object used to represent the full workflow of a study.

    Derived from the DAG data structure. Contains everything that a study
    requires to be expanded with the appropriate substitutions and with
    parameters inserted. This data structure should be the instance the
    future daemon loads in to track progress on a workflow.

    :param name: String representing the name of the Study.
    :param description: A text description of what the study does.
    :param steps: A list of StudySteps in proper workflow order.
    :param studyenv: A populated StudyEnvironment instance.
    :param parameters: A populated Parameters instance.
    :param outpath: The path where the output of the study is written.
    """
    # The basic study information
    self.name = name
    self.description = description

    # Initialized the DAG so we have those structures to be used.
    super(Study, self).__init__()

    # We want deep copies so that properties don't change out from under
    # the Sudy data structure.
    self.environment = studyenv
    self.parameters = parameters
    self._out_path = out_path
    self._meta_path = os.path.join(out_path, "meta")

    LOGGER.debug("OUTPUT_PATH = %s", out_path)
    # Flag the study as not having been set up and add the source node.
    self._issetup = False
    self.is_configured = False
    self.add_node(SOURCE, None)

    # Settings for handling restarts and submission attempts.
    self._restart_limit = 0
    self._submission_attempts = 0
    self._use_tmp = False
    self._dry_run = False

    # Management structures
    # The workspace used by each step.
    self.workspaces = {SOURCE: self._out_path}
    # Parameter independent dependencies by step.
    self.hub_depends = {SOURCE: set()}
    # Other dependencies per step.
    self.depends = {SOURCE: set()}
    # Parameters that each step depends on.
    self.used_params = {SOURCE: set()}
    # Combinations seen per step.
    self.step_combos = {SOURCE: set()}

    # If the user specified a flow in the form of steps, copy those into
    # into the Study object.
    if steps:
        for step in steps:
            # Deep copy because it prevents modifications after the fact.
            self.add_step(step)

add_step(step)

Add a step to a study.

For this helper to be most effective, it recommended to apply steps in the order that they will be encountered. The method attempts to be intelligent and make the intended edge based on the 'depends' entry in a step. When adding steps out of order it's recommended to just use the base class DAG functionality and manually make connections.

Parameters:

Name	Type	Description	Default
step		A StudyStep instance to be added to the Study instance.	required

Source code in maestrowf/datastructures/core/study.py

def add_step(self, step):
    """
    Add a step to a study.

    For this helper to be most effective, it recommended to apply steps in
    the order that they will be encountered. The method attempts to be
    intelligent and make the intended edge based on the 'depends' entry in
    a step. When adding steps out of order it's recommended to just use the
    base class DAG functionality and manually make connections.

    :param step: A StudyStep instance to be added to the Study instance.
    """
    # Add the node to the DAG.
    self.add_node(step.real_name, step)
    LOGGER.info(
        "Adding step '%s' to study '%s'...", step.name, self.name)
    # Apply the environment to the incoming step.
    step.__dict__ = \
        apply_function(step.__dict__, self.environment.apply_environment)

    # If the step depends on a prior step, create an edge.
    if "depends" in step.run and step.run["depends"]:
        for dependency in step.run["depends"]:
            LOGGER.info("{0} is dependent on {1}. Creating edge ("
                        "{1}, {0})...".format(step.real_name, dependency))
            if "*" not in dependency:
                self.add_edge(dependency, step.real_name)
            else:
                self.add_edge(
                    re.sub(ALL_COMBOS, "", dependency),
                    step.real_name
                )
    else:
        # Otherwise, if no other dependency, just execute the step.
        self.add_edge(SOURCE, step.real_name)

configure_study(submission_attempts=1, restart_limit=1, throttle=0, use_tmp=False, hash_ws=False, dry_run=False)

Perform initial configuration of a study. The method is used for going through and actually acquiring each dependency, substituting variables, sources and labels.

Parameters:

Name	Type	Description	Default
submission_attempts		Number of attempted submissions before marking a step as failed. :param restart_limit: Upper limit on the number of times a step with a restart command can be resubmitted before it is considered failed. :param throttle: The maximum number of in-progress jobs allowed. [0 denotes no cap]. :param use_tmp: Boolean value specifying if the generated ExecutionGraph dumps its information into a temporary directory. :param dry_run: Boolean value that toggles dry run to just generate study workspaces and scripts without execution or status checking. :returns: True if the Study is successfully setup, False otherwise.	1

Source code in maestrowf/datastructures/core/study.py

def configure_study(self, submission_attempts=1, restart_limit=1,
                    throttle=0, use_tmp=False, hash_ws=False,
                    dry_run=False):
    """
    Perform initial configuration of a study. \

    The method is used for going through and actually acquiring each \
    dependency, substituting variables, sources and labels. \

    :param submission_attempts: Number of attempted submissions before \
    marking a step as failed. \
    :param restart_limit: Upper limit on the number of times a step with \
    a restart command can be resubmitted before it is considered failed. \
    :param throttle: The maximum number of in-progress jobs allowed. [0 \
    denotes no cap].\
    :param use_tmp: Boolean value specifying if the generated \
    ExecutionGraph dumps its information into a temporary directory. \
    :param dry_run: Boolean value that toggles dry run to just generate \
    study workspaces and scripts without execution or status checking. \
    :returns: True if the Study is successfully setup, False otherwise. \
    """

    self._submission_attempts = submission_attempts
    self._restart_limit = restart_limit
    self._submission_throttle = throttle
    self._use_tmp = use_tmp
    self._hash_ws = hash_ws
    self._dry_run = dry_run

    LOGGER.info(
        "\n------------------------------------------\n"
        "Submission attempts =       %d\n"
        "Submission restart limit =  %d\n"
        "Submission throttle limit = %d\n"
        "Use temporary directory =   %s\n"
        "Hash workspaces =           %s\n"
        "Dry run enabled =           %s\n"
        "Output path =               %s\n"
        "------------------------------------------",
        submission_attempts, restart_limit, throttle,
        use_tmp, hash_ws, dry_run, self._out_path
    )

    self.is_configured = True

load_metadata()

Load metadata for the study.

Source code in maestrowf/datastructures/core/study.py

def load_metadata(self):
    """Load metadata for the study."""
    if not os.path.exists(self._meta_path):
        return

    path = os.path.join(self._meta_path, "study", "env.pkl")
    with open(path, 'rb') as pkl:
        env = pickle.load(pkl)

    if not isinstance(env, type(self)):
        msg = "Object loaded from {path} is of type {type}. Expected an" \
              " object of type '{cls}.'".format(path=path, type=type(env),
                                                cls=type(self))
        LOGGER.error(msg)
        raise TypeError(msg)

    metapath = os.path.join(self._meta_path, "metadata.yaml")
    with open(metapath, "rb") as metafile:
        metadata = yaml.load(metafile)

    self.depends = metadata["dependencies"]
    self.hub_depends = metadata["hub_dependencies"]
    self.workspaces = metadata["workspaces"]
    self.used_params = metadata["used_parameters"]
    self.step_combos = metadata["step_combinations"]

setup_environment()

Set up the environment by acquiring outside dependencies.

Source code in maestrowf/datastructures/core/study.py

def setup_environment(self):
    """Set up the environment by acquiring outside dependencies."""
    # Set up the environment if it hasn't been already.
    if not self.environment.is_set_up:
        LOGGER.debug("Environment is setting up.")
        self.environment.acquire_environment()

setup_workspace()

Set up the study's main workspace directory.

Source code in maestrowf/datastructures/core/study.py

def setup_workspace(self):
    """Set up the study's main workspace directory."""
    try:
        LOGGER.debug("Setting up study workspace in '%s'", self._out_path)
        create_parentdir(self._out_path)
    except Exception as e:
        LOGGER.error(e.args)
        return False

stage()

Generate the execution graph for a Study.

Staging creates an ExecutionGraph based on the combinations generated by the ParameterGeneration object stored in an instance of a Study. The stage method also sets up individual working directories (or workspaces) for each node in the workflow that requires it.

Returns:

Type	Description
	An ExecutionGraph object with the expanded workflow.

Source code in maestrowf/datastructures/core/study.py

def stage(self):
    """
    Generate the execution graph for a Study.

    Staging creates an ExecutionGraph based on the combinations generated
    by the ParameterGeneration object stored in an instance of a Study.
    The stage method also sets up individual working directories (or
    workspaces) for each node in the workflow that requires it.

    :returns: An ExecutionGraph object with the expanded workflow.
    """
    # If the workspace doesn't exist, raise an exception.
    if not os.path.exists(self._out_path):
        msg = "Study {} is not set up for staging. Workspace does not " \
              "exists (Output Dir = {}).".format(self.name, self._out_path)
        LOGGER.error(msg)
        raise Exception(msg)

    # If the environment isn't set up, raise an exception.
    if not self.environment.is_set_up:
        msg = "Study {} is not set up for staging. Environment is not " \
              "set up. Aborting.".format(self.name)
        LOGGER.error(msg)
        raise Exception(msg)

    # After substituting, we should start getting combinations and
    # iterating. Two options here:
    # 1. Just create a new DAG with the step names altered to reflect the
    #    parameters they use.
    # 2. Create a derived DAG that has look up tables for each node based
    #    parameters. This might reduce to the same as 1, but retains the
    #    same original naming. Though that doesn't matter since the actual
    #    object has the original name.
    # NOTE: fdinatal - 2/6/17: Looks like 1 is the easiest to get done,
    # going with that for now.
    # NOTE: fdinatal - 3/28/17: Revisiting this method.. my previous logic
    # was flawed.
    # NOTE: fdinatal - 5/17/17: There is the strong possibility this method
    # will need to be reworked some in the future. It likely won't need to
    # be a complete gutting of the method, but there may need to be logic
    # for different styles of launching since it's the ExecutionGraph that
    # will need to be formatted properly so that scripts get generated in
    # the appropriate fashion.

    # Construct ExecutionGraph
    dag = ExecutionGraph(
        submission_attempts=self._submission_attempts,
        submission_throttle=self._submission_throttle,
        use_tmp=self._use_tmp, dry_run=self._dry_run)
    dag.add_description(**self.description)
    dag.log_description()

    # Because we're working within a Study class whose steps have already
    # been verified to not contain a cycle, we can override the check for
    # the execution graph. Because the execution graph is constructed from
    # the study steps, it won't contain a cycle.
    def _pass_detect_cycle(self):
        pass

    dag.detect_cycle = MethodType(_pass_detect_cycle, dag)

    return self._out_path, self._stage(dag)

store_metadata()

Store metadata related to the study.

Source code in maestrowf/datastructures/core/study.py

def store_metadata(self):
    """Store metadata related to the study."""
    # Create the metadata directory.
    create_parentdir(self._meta_path)

    # Store the environment object in order to preserve it.
    path = os.path.join(self._meta_path, "study")
    create_parentdir(path)
    path = os.path.join(path, "env.pkl")
    with open(path, 'wb') as pkl:
        pickle.dump(self, pkl)

    # Construct other metadata related to study construction.
    _workspaces = {}
    for key, value in self.workspaces.items():
        if key == "_source":
            _workspaces[key] = value
        elif key in self.step_combos:
            _workspaces[key] = os.path.split(value)[-1]
        else:
            _workspaces[key] = \
                os.path.sep.join(value.rsplit(os.path.sep)[-2:])

    # Construct relative paths for the combinations and nest them in the
    # same way as the step combinations dictionary.
    _step_combos = {}
    for key, value in self.step_combos.items():
        if key == SOURCE:
            _step_combos[key] = self.workspaces[key]
        elif not self.used_params[key]:
            _ws = self.workspaces[key]
            _step_combos[key] = {key: os.path.split(_ws)[-1]}
        else:
            _step_combos[key] = {}
            for combo in value:
                _ws = self.workspaces[combo]
                _step_combos[key][combo] = \
                    os.path.sep.join(_ws.rsplit(os.path.sep)[-2:])

    metadata = {
        "dependencies": self.depends,
        "hub_dependencies": self.hub_depends,
        "workspaces": _workspaces,
        "used_parameters": self.used_params,
        "step_combinations": _step_combos,
    }
    # Write out the study construction metadata.
    path = os.path.join(self._meta_path, "metadata.yaml")
    with open(path, "wb") as metafile:
        metafile.write(yaml.dump(metadata).encode("utf-8"))

    # Write out parameter metadata.
    metadata = self.parameters.get_metadata()
    path = os.path.join(self._meta_path, "parameters.yaml")
    with open(path, "wb") as metafile:
        metafile.write(yaml.dump(metadata).encode("utf-8"))

    # Write out environment metadata
    path = os.path.join(self._meta_path, "environment.yaml")
    with open(path, "wb") as metafile:
        metafile.write(yaml.dump(os.environ.copy()).encode("utf-8"))

walk_study(src=SOURCE)

Walk the study and create a spanning tree.

Parameters:

Name	Type	Description	Default
src		Source node to start the walk.	SOURCE

Returns:

Type	Description
	A generator of (parent, node name, node value) tuples.

Source code in maestrowf/datastructures/core/study.py

def walk_study(self, src=SOURCE):
    """
    Walk the study and create a spanning tree.

    :param src: Source node to start the walk.
    :returns: A generator of (parent, node name, node value) tuples.
    """
    # Get a DFS spanning tree of the study. This method should always
    # return a complete tree because _source is flagged as a dependency
    # if a step is added without one.
    # TODO: This method should be fixed to return both parents and nodes.
    path, parents = self.dfs_subtree(src)
    for node in path:
        yield parents[node], node, self.values[node]

StudyEnvironment

StudyEnvironment for managing a study environment.

The StudyEnvironment provides the context where all study steps can find variables, sources, dependencies, etc.

Source code in maestrowf/datastructures/core/studyenvironment.py

class StudyEnvironment:
    """
    StudyEnvironment for managing a study environment.

    The StudyEnvironment provides the context where all study
    steps can find variables, sources, dependencies, etc.
    """

    def __init__(self):
        """Initialize an empty StudyEnvironment."""
        # Types of environment objects.
        self.substitutions = {}
        self.labels = {}
        self.sources = []
        self.dependencies = {}

        # Private members
        self._tokens = set()
        self._names = set()
        # Boolean that tracks if dependencies have been acquired.
        self._is_set_up = False

        LOGGER.debug("Initialized an empty StudyEnvironment.")

    def __bool__(self):
        """
        Override for the __bool__ operator.

        :returns: True if the StudyEnvironment instance has values, False
            otherwise.
        """
        return bool(self._names)

    @property
    def is_set_up(self):
        """
        Check that the StudyEnvironment is set up.

        :returns: True is the instance is set up, False otherwise.
        """
        return self._is_set_up

    def add(self, item):
        """
        Add the item parameter to the StudyEnvironment.

        :param item: EnvObject to be added to the environment.
        """
        # TODO: Need to revist this to make this better. A label can get lost
        # because the necessary variable could have not been added yet
        # and there's too much of a need to process a dependency first.
        name = None
        LOGGER.debug("Calling add with %s", str(item))
        if isinstance(item, Dependency):
            LOGGER.debug("Adding %s of type %s.", item.name, type(item))
            LOGGER.debug("Value: %s.", item.__dict__)
            self.dependencies[item.name] = item
            name = item.name
            self._is_set_up = False
        elif isinstance(item, Substitution):
            LOGGER.debug("Value: %s", item.value)
            LOGGER.debug("Tokens: %s", self._tokens)
            name = item.name
            LOGGER.debug("Adding %s of type %s.", item.name, type(item))
            if (
                    isinstance(item.value, str) and
                    any(token in item.value for token in self._tokens)):
                LOGGER.debug("Label detected. Adding %s to labels", item.name)
                self.labels[item.name] = item
            else:
                self._tokens.add(item.token)
                self.substitutions[item.name] = item
        elif isinstance(item, Source):
            LOGGER.debug("Adding source %s", item.source)
            LOGGER.debug("Item source: %s", item.source)
            self.sources.append(item)
        else:
            error = "Received an item of type {}. Expected an item of base " \
                    "type Substitution, Source, or Dependency." \
                    .format(type(item))
            LOGGER.exception(error)
            raise TypeError(error)

        if name and name in self._names:
            error = "A duplicate name '{}' has been detected. All names " \
                    "must be unique. Aborting.".format(name)
            LOGGER.exception(error)
            raise ValueError(error)
        else:
            LOGGER.debug("{} added to set of names.".format(name))
            self._names.add(name)

    def find(self, key):
        """
        Find the environment object labeled by the specified key.

        :param key: Name of the environment object to find.
        :returns: The environment object labeled by key, None if key is not
            found.
        """
        LOGGER.debug("Looking for '%s'...", key)
        if key in self.dependencies:
            LOGGER.debug("Found '%s' in environment dependencies.", key)
            return self.dependencies[key]

        if key in self.substitutions:
            LOGGER.debug("Found '%s' in environment substitutions.", key)
            return self.substitutions[key]

        if key in self.labels:
            LOGGER.debug("Found '%s' in environment labels.", key)
            return self.labels[key]

        LOGGER.debug("'%s' not found -- \n%s", key, self)
        return None

    def remove(self, key):
        """
        Remove the environment object labeled by the specified key.

        :param key: Name of the environment object to remove.
        :returns: The environment object labeled by key.
        """
        LOGGER.debug("Looking to remove '%s'...", key)

        if key not in self._names:
            return None

        _ = self.dependencies.pop(key, None)
        if _ is not None:
            self._names.remove(key)
            return _

        _ = self.substitutions.pop(key, None)
        if _ is not None:
            self._names.remove(key)
            return _

        _ = self.labels.pop(key, None)
        if _ is not None:
            self._names.remove(key)
            return _

        LOGGER.debug("'%s' not found -- \n%s", key, self)
        return None

    def acquire_environment(self):
        """Acquire any environment items that may be stored remotely."""
        if self._is_set_up:
            LOGGER.info("Environment already set up. Returning.")
            return

        LOGGER.debug("Acquiring dependencies")
        for dependency, value in self.dependencies.items():
            LOGGER.info("Acquiring -- %s", dependency)
            value.acquire(substitutions=self.substitutions.values())

        self._is_set_up = True

    def apply_environment(self, item):
        """
        Apply the environment to the specified item.

        :param item: String to apply environment to.
        :returns: String with the environment applied.
        """
        if not item:
            return item

        LOGGER.debug("Applying environment to %s", item)
        LOGGER.debug("Processing labels...")
        for label, value in self.labels.items():
            LOGGER.debug("Looking for %s in %s", label, item)
            item = value.substitute(item)
            LOGGER.debug("After substitution: %s", item)

        LOGGER.debug("Processing dependencies...")
        for label, dependency in self.dependencies.items():
            LOGGER.debug("Looking for %s in %s", label, item)
            item = dependency.substitute(item)
            LOGGER.debug("After substitution: %s", item)
            LOGGER.debug("Acquiring %s.", label)

        LOGGER.debug("Processing substitutions...")
        for substitution, value in self.substitutions.items():
            LOGGER.debug("Looking for %s in %s", substitution, item)
            item = value.substitute(item)
            LOGGER.debug("After substitution: %s", item)

        return item

is_set_up property

Check that the StudyEnvironment is set up.

Returns:

Type	Description
	True is the instance is set up, False otherwise.

__bool__()

Override for the bool operator.

Returns:

Type	Description
	True if the StudyEnvironment instance has values, False otherwise.

Source code in maestrowf/datastructures/core/studyenvironment.py

def __bool__(self):
    """
    Override for the __bool__ operator.

    :returns: True if the StudyEnvironment instance has values, False
        otherwise.
    """
    return bool(self._names)

__init__()

Initialize an empty StudyEnvironment.

Source code in maestrowf/datastructures/core/studyenvironment.py

def __init__(self):
    """Initialize an empty StudyEnvironment."""
    # Types of environment objects.
    self.substitutions = {}
    self.labels = {}
    self.sources = []
    self.dependencies = {}

    # Private members
    self._tokens = set()
    self._names = set()
    # Boolean that tracks if dependencies have been acquired.
    self._is_set_up = False

    LOGGER.debug("Initialized an empty StudyEnvironment.")

acquire_environment()

Acquire any environment items that may be stored remotely.

Source code in maestrowf/datastructures/core/studyenvironment.py

def acquire_environment(self):
    """Acquire any environment items that may be stored remotely."""
    if self._is_set_up:
        LOGGER.info("Environment already set up. Returning.")
        return

    LOGGER.debug("Acquiring dependencies")
    for dependency, value in self.dependencies.items():
        LOGGER.info("Acquiring -- %s", dependency)
        value.acquire(substitutions=self.substitutions.values())

    self._is_set_up = True

add(item)

Add the item parameter to the StudyEnvironment.

Parameters:

Name	Type	Description	Default
item		EnvObject to be added to the environment.	required

Source code in maestrowf/datastructures/core/studyenvironment.py

def add(self, item):
    """
    Add the item parameter to the StudyEnvironment.

    :param item: EnvObject to be added to the environment.
    """
    # TODO: Need to revist this to make this better. A label can get lost
    # because the necessary variable could have not been added yet
    # and there's too much of a need to process a dependency first.
    name = None
    LOGGER.debug("Calling add with %s", str(item))
    if isinstance(item, Dependency):
        LOGGER.debug("Adding %s of type %s.", item.name, type(item))
        LOGGER.debug("Value: %s.", item.__dict__)
        self.dependencies[item.name] = item
        name = item.name
        self._is_set_up = False
    elif isinstance(item, Substitution):
        LOGGER.debug("Value: %s", item.value)
        LOGGER.debug("Tokens: %s", self._tokens)
        name = item.name
        LOGGER.debug("Adding %s of type %s.", item.name, type(item))
        if (
                isinstance(item.value, str) and
                any(token in item.value for token in self._tokens)):
            LOGGER.debug("Label detected. Adding %s to labels", item.name)
            self.labels[item.name] = item
        else:
            self._tokens.add(item.token)
            self.substitutions[item.name] = item
    elif isinstance(item, Source):
        LOGGER.debug("Adding source %s", item.source)
        LOGGER.debug("Item source: %s", item.source)
        self.sources.append(item)
    else:
        error = "Received an item of type {}. Expected an item of base " \
                "type Substitution, Source, or Dependency." \
                .format(type(item))
        LOGGER.exception(error)
        raise TypeError(error)

    if name and name in self._names:
        error = "A duplicate name '{}' has been detected. All names " \
                "must be unique. Aborting.".format(name)
        LOGGER.exception(error)
        raise ValueError(error)
    else:
        LOGGER.debug("{} added to set of names.".format(name))
        self._names.add(name)

apply_environment(item)

Apply the environment to the specified item.

Parameters:

Name	Type	Description	Default
item		String to apply environment to.	required

Returns:

Type	Description
	String with the environment applied.

Source code in maestrowf/datastructures/core/studyenvironment.py

def apply_environment(self, item):
    """
    Apply the environment to the specified item.

    :param item: String to apply environment to.
    :returns: String with the environment applied.
    """
    if not item:
        return item

    LOGGER.debug("Applying environment to %s", item)
    LOGGER.debug("Processing labels...")
    for label, value in self.labels.items():
        LOGGER.debug("Looking for %s in %s", label, item)
        item = value.substitute(item)
        LOGGER.debug("After substitution: %s", item)

    LOGGER.debug("Processing dependencies...")
    for label, dependency in self.dependencies.items():
        LOGGER.debug("Looking for %s in %s", label, item)
        item = dependency.substitute(item)
        LOGGER.debug("After substitution: %s", item)
        LOGGER.debug("Acquiring %s.", label)

    LOGGER.debug("Processing substitutions...")
    for substitution, value in self.substitutions.items():
        LOGGER.debug("Looking for %s in %s", substitution, item)
        item = value.substitute(item)
        LOGGER.debug("After substitution: %s", item)

    return item

find(key)

Find the environment object labeled by the specified key.

Parameters:

Name	Type	Description	Default
key		Name of the environment object to find.	required

Returns:

Type	Description
	The environment object labeled by key, None if key is not found.

Source code in maestrowf/datastructures/core/studyenvironment.py

def find(self, key):
    """
    Find the environment object labeled by the specified key.

    :param key: Name of the environment object to find.
    :returns: The environment object labeled by key, None if key is not
        found.
    """
    LOGGER.debug("Looking for '%s'...", key)
    if key in self.dependencies:
        LOGGER.debug("Found '%s' in environment dependencies.", key)
        return self.dependencies[key]

    if key in self.substitutions:
        LOGGER.debug("Found '%s' in environment substitutions.", key)
        return self.substitutions[key]

    if key in self.labels:
        LOGGER.debug("Found '%s' in environment labels.", key)
        return self.labels[key]

    LOGGER.debug("'%s' not found -- \n%s", key, self)
    return None

remove(key)

Remove the environment object labeled by the specified key.

Parameters:

Name	Type	Description	Default
key		Name of the environment object to remove.	required

Returns:

Type	Description
	The environment object labeled by key.

Source code in maestrowf/datastructures/core/studyenvironment.py

def remove(self, key):
    """
    Remove the environment object labeled by the specified key.

    :param key: Name of the environment object to remove.
    :returns: The environment object labeled by key.
    """
    LOGGER.debug("Looking to remove '%s'...", key)

    if key not in self._names:
        return None

    _ = self.dependencies.pop(key, None)
    if _ is not None:
        self._names.remove(key)
        return _

    _ = self.substitutions.pop(key, None)
    if _ is not None:
        self._names.remove(key)
        return _

    _ = self.labels.pop(key, None)
    if _ is not None:
        self._names.remove(key)
        return _

    LOGGER.debug("'%s' not found -- \n%s", key, self)
    return None

StudyStep

Class that represents the data and API for a single study step.

This class is primarily a 1:1 mapping of a study step in the YAML spec in terms of data. The StudyStep's class API should capture all functions that a step can be expected to perform, including:

• Applying a combination of parameters to itself.
• Tests for equality and non-equality to check for changes.
• Other -- WIP

Source code in maestrowf/datastructures/core/study.py

class StudyStep:
    """
    Class that represents the data and API for a single study step.

    This class is primarily a 1:1 mapping of a study step in the YAML spec in
    terms of data. The StudyStep's class API should capture all functions that
    a step can be expected to perform, including:

    * Applying a combination of parameters to itself.
    * Tests for equality and non-equality to check for changes.
    * Other -- WIP
    """

    def __init__(self):
        """Object that represents a single workflow step."""
        self._name = ""
        self.description = ""
        self.nickname = ""
        self.run = {
                        "cmd":              "",
                        "depends":          "",
                        "pre":              "",
                        "post":             "",
                        "restart":          "",
                        "nodes":            "",
                        "procs":            "",
                        "gpus":             "",
                        "cores per task":   "",
                        "walltime":         "",
                        "reservation":      ""
                    }

    def apply_parameters(self, combo):
        """
        Apply a parameter combination to the StudyStep.

        :param combo: A Combination instance to be applied to a StudyStep.
        :returns: A new StudyStep instance with combo applied to its members.
        """
        # Create a new StudyStep and populate it with substituted values.
        tmp = StudyStep()
        tmp.__dict__ = apply_function(self.__dict__, combo.apply)
        # Return if the new step is modified and the step itself.

        return self.__ne__(tmp), tmp

    @property
    def name(self):
        """
        Get the name to assign to a task for this step.

        :returns: A utf-8 formatted string of the task name.
        """
        if self.nickname:
            return self.nickname
        return self._name

    @name.setter
    def name(self, value):
        """
        Set the name of a StudyStep instance.

        :param value: A string value representing the name to give the step.
        """
        self._name = value

    @property
    def real_name(self):
        """
        Get the real name of the step (ignore nickname).

        :returns: A string of the true name of a StudyStep instance.
        """
        return self._name

    def __eq__(self, other):
        """
        Equality operator for the StudyStep class.

        :param other: Object to compare self to.
        : returns: True if other is equal to self, False otherwise.
        """
        if isinstance(other, self.__class__):
            # This works because the classes are currently interfaces over
            # internals that are all based on Python builtin classes.
            # NOTE: This method will need to be reworked if something more
            # complex is done with the class.
            return self.__dict__ == other.__dict__

        return False

    def __ne__(self, other):
        """
        Non-equality operator for the StudyStep class.

        :param other: Object to compare self to.
        : returns: True if other is not equal to self, False otherwise.
        """
        return not self.__eq__(other)

name property writable

Get the name to assign to a task for this step.

Returns:

Type	Description
	A utf-8 formatted string of the task name.

real_name property

Get the real name of the step (ignore nickname).

Returns:

Type	Description
	A string of the true name of a StudyStep instance.

__eq__(other)

Equality operator for the StudyStep class.

: returns: True if other is equal to self, False otherwise.

Parameters:

Name	Type	Description	Default
other		Object to compare self to.	required

Source code in maestrowf/datastructures/core/study.py

def __eq__(self, other):
    """
    Equality operator for the StudyStep class.

    :param other: Object to compare self to.
    : returns: True if other is equal to self, False otherwise.
    """
    if isinstance(other, self.__class__):
        # This works because the classes are currently interfaces over
        # internals that are all based on Python builtin classes.
        # NOTE: This method will need to be reworked if something more
        # complex is done with the class.
        return self.__dict__ == other.__dict__

    return False

__init__()

Object that represents a single workflow step.

Source code in maestrowf/datastructures/core/study.py

def __init__(self):
    """Object that represents a single workflow step."""
    self._name = ""
    self.description = ""
    self.nickname = ""
    self.run = {
                    "cmd":              "",
                    "depends":          "",
                    "pre":              "",
                    "post":             "",
                    "restart":          "",
                    "nodes":            "",
                    "procs":            "",
                    "gpus":             "",
                    "cores per task":   "",
                    "walltime":         "",
                    "reservation":      ""
                }

__ne__(other)

Non-equality operator for the StudyStep class.

: returns: True if other is not equal to self, False otherwise.

Parameters:

Name	Type	Description	Default
other		Object to compare self to.	required

Source code in maestrowf/datastructures/core/study.py

def __ne__(self, other):
    """
    Non-equality operator for the StudyStep class.

    :param other: Object to compare self to.
    : returns: True if other is not equal to self, False otherwise.
    """
    return not self.__eq__(other)

apply_parameters(combo)

Apply a parameter combination to the StudyStep.

Parameters:

Name	Type	Description	Default
combo		A Combination instance to be applied to a StudyStep.	required

Returns:

Type	Description
	A new StudyStep instance with combo applied to its members.

Source code in maestrowf/datastructures/core/study.py

def apply_parameters(self, combo):
    """
    Apply a parameter combination to the StudyStep.

    :param combo: A Combination instance to be applied to a StudyStep.
    :returns: A new StudyStep instance with combo applied to its members.
    """
    # Create a new StudyStep and populate it with substituted values.
    tmp = StudyStep()
    tmp.__dict__ = apply_function(self.__dict__, combo.apply)
    # Return if the new step is modified and the step itself.

    return self.__ne__(tmp), tmp