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Source code for galaxy.workflow.modules
"""
Modules used in building workflows
"""
import logging
from json import loads
from xml.etree.ElementTree import (
Element,
XML
)
from galaxy import (
exceptions,
model,
web
)
from galaxy.dataset_collections import matching
from galaxy.exceptions import ToolMissingException
from galaxy.jobs.actions.post import ActionBox
from galaxy.model import PostJobAction
from galaxy.tools import (
DefaultToolState,
ToolInputsNotReadyException
)
from galaxy.tools.execute import execute, MappingParameters, PartialJobExecution
from galaxy.tools.parameters import (
check_param,
params_to_incoming,
visit_input_values
)
from galaxy.tools.parameters.basic import (
BooleanToolParameter,
DataCollectionToolParameter,
DataToolParameter,
parameter_types,
RuntimeValue,
SelectToolParameter,
TextToolParameter,
workflow_building_modes
)
from galaxy.tools.parameters.wrapped import make_dict_copy
from galaxy.util.bunch import Bunch
from galaxy.util.json import safe_loads
from galaxy.util.odict import odict
from tool_shed.util import common_util
log = logging.getLogger(__name__)
# Key into Tool state to describe invocation-specific runtime properties.
RUNTIME_STEP_META_STATE_KEY = "__STEP_META_STATE__"
# Key into step runtime state dict describing invocation-specific post job
# actions (i.e. PJA specified at runtime on top of the workflow-wide defined
# ones.
RUNTIME_POST_JOB_ACTIONS_KEY = "__POST_JOB_ACTIONS__"
NO_REPLACEMENT = object()
[docs]class WorkflowModule(object):
[docs] def __init__(self, trans, content_id=None, **kwds):
self.trans = trans
self.content_id = content_id
self.state = DefaultToolState()
# ---- Creating modules from various representations ---------------------
[docs] @classmethod
def from_dict(Class, trans, d, **kwds):
module = Class(trans, **kwds)
module.recover_state(d.get("tool_state"))
module.label = d.get("label")
return module
[docs] @classmethod
def from_workflow_step(Class, trans, step, **kwds):
module = Class(trans, **kwds)
module.recover_state(step.tool_inputs)
module.label = step.label
return module
# ---- Saving in various forms ------------------------------------------
# ---- General attributes -----------------------------------------------
[docs] def get_content_id(self):
""" If this component has an identifier external to the step (such
as a tool or another workflow) return the identifier for that content.
"""
return None
# ---- Configuration time -----------------------------------------------
[docs] def get_state(self, nested=True):
""" Return a serializable representation of the persistable state of
the step.
"""
inputs = self.get_inputs()
if inputs:
return self.state.encode(Bunch(inputs=inputs), self.trans.app, nested=nested)
else:
return self.state.inputs
[docs] def recover_state(self, state, **kwds):
""" Recover state `dict` from simple dictionary describing configuration
state (potentially from persisted step state).
Sub-classes should supply a `default_state` method which contains the
initial state `dict` with key, value pairs for all available attributes.
"""
self.state = DefaultToolState()
inputs = self.get_inputs()
if inputs:
self.state.decode(state, Bunch(inputs=inputs), self.trans.app)
else:
self.state.inputs = safe_loads(state) or {}
[docs] def get_errors(self):
""" This returns a step related error message as string or None """
return None
[docs] def check_and_update_state(self):
"""
If the state is not in sync with the current implementation of the
module, try to update. Returns a list of messages to be displayed
"""
pass
[docs] def add_dummy_datasets(self, connections=None, steps=None):
""" Replace connected inputs with placeholder/dummy values. """
pass
[docs] def get_config_form(self):
""" Serializes input parameters of a module into input dictionaries. """
return {
'title' : self.name,
'inputs': [param.to_dict(self.trans) for param in self.get_inputs().values()]
}
# ---- Run time ---------------------------------------------------------
[docs] def get_runtime_inputs(self, **kwds):
""" Used internally by modules and when displaying inputs in workflow
editor and run workflow templates.
"""
return {}
[docs] def compute_runtime_state(self, trans, step_updates=None):
""" Determine the runtime state (potentially different from self.state
which describes configuration state). This (again unlike self.state) is
currently always a `DefaultToolState` object.
If `step_updates` is `None`, this is likely for rendering the run form
for instance and no runtime properties are available and state must be
solely determined by the default runtime state described by the step.
If `step_updates` are available they describe the runtime properties
supplied by the workflow runner.
"""
state = self.get_runtime_state()
step_errors = {}
if step_updates:
def update_value(input, context, prefixed_name, **kwargs):
if prefixed_name in step_updates:
value, error = check_param(trans, input, step_updates.get(prefixed_name), context)
if error is not None:
step_errors[prefixed_name] = error
return value
return NO_REPLACEMENT
visit_input_values(self.get_runtime_inputs(), state.inputs, update_value, no_replacement_value=NO_REPLACEMENT)
return state, step_errors
[docs] def encode_runtime_state(self, runtime_state):
""" Takes the computed runtime state and serializes it during run request creation. """
return runtime_state.encode(Bunch(inputs=self.get_runtime_inputs()), self.trans.app)
[docs] def decode_runtime_state(self, runtime_state):
""" Takes the serialized runtime state and decodes it when running the workflow. """
state = DefaultToolState()
state.decode(runtime_state, Bunch(inputs=self.get_runtime_inputs()), self.trans.app)
return state
[docs] def execute(self, trans, progress, invocation_step, use_cached_job=False):
""" Execute the given workflow invocation step.
Use the supplied workflow progress object to track outputs, find
inputs, etc....
Return a False if there is additional processing required to
on subsequent workflow scheduling runs, None or True means the workflow
step executed properly.
"""
raise TypeError("Abstract method")
[docs] def do_invocation_step_action(self, step, action):
""" Update or set the workflow invocation state action - generic
extension point meant to allows users to interact with interactive
workflow modules. The action object returned from this method will
be attached to the WorkflowInvocationStep and be available the next
time the workflow scheduler visits the workflow.
"""
raise exceptions.RequestParameterInvalidException("Attempting to perform invocation step action on module that does not support actions.")
[docs] def recover_mapping(self, invocation_step, progress):
""" Re-populate progress object with information about connections
from previously executed steps recorded via invocation_steps.
"""
outputs = {}
for output_dataset_assoc in invocation_step.output_datasets:
outputs[output_dataset_assoc.output_name] = output_dataset_assoc.dataset
for output_dataset_collection_assoc in invocation_step.output_dataset_collections:
outputs[output_dataset_collection_assoc.output_name] = output_dataset_collection_assoc.dataset_collection
progress.set_step_outputs(invocation_step, outputs, already_persisted=True)
[docs]class SubWorkflowModule(WorkflowModule):
# Two step improvements to build runtime inputs for subworkflow modules
# - First pass verify nested workflow doesn't have an RuntimeInputs
# - Second pass actually turn RuntimeInputs into inputs if possible.
type = "subworkflow"
name = "Subworkflow"
[docs] @classmethod
def from_dict(Class, trans, d, **kwds):
module = super(SubWorkflowModule, Class).from_dict(trans, d, **kwds)
if "subworkflow" in d:
module.subworkflow = d["subworkflow"]
elif "content_id" in d:
from galaxy.managers.workflows import WorkflowsManager
module.subworkflow = WorkflowsManager(trans.app).get_owned_workflow(trans, d["content_id"])
else:
raise Exception("Step associated subworkflow could not be found.")
return module
[docs] @classmethod
def from_workflow_step(Class, trans, step, **kwds):
module = super(SubWorkflowModule, Class).from_workflow_step(trans, step, **kwds)
module.subworkflow = step.subworkflow
return module
[docs] def get_name(self):
if hasattr(self.subworkflow, 'name'):
return self.subworkflow.name
return self.name
[docs] def get_data_inputs(self):
""" Get configure time data input descriptions. """
# Filter subworkflow steps and get inputs
step_to_input_type = {
"data_input": "dataset",
"data_collection_input": "dataset_collection",
}
inputs = []
if hasattr(self.subworkflow, 'input_steps'):
for step in self.subworkflow.input_steps:
name = step.label
if not name:
step_module = module_factory.from_workflow_step(self.trans, step)
name = "%s:%s" % (step.order_index, step_module.get_name())
step_type = step.type
assert step_type in step_to_input_type
input = dict(
input_subworkflow_step_id=step.order_index,
name=name,
label=name,
multiple=False,
extensions=["data"],
input_type=step_to_input_type[step_type],
)
inputs.append(input)
return inputs
[docs] def get_data_outputs(self):
outputs = []
if hasattr(self.subworkflow, 'workflow_outputs'):
for workflow_output in self.subworkflow.workflow_outputs:
if workflow_output.workflow_step.type in {'data_input', 'data_collection_input'}:
# It is just confusing to display the input data as output data in subworkflows
continue
output_step = workflow_output.workflow_step
label = workflow_output.label
target_output_name = workflow_output.output_name
if not label:
label = "%s:%s" % (output_step.order_index, target_output_name)
output_module = module_factory.from_workflow_step(self.trans, output_step)
data_outputs = output_module.get_data_outputs()
target_output = {}
for data_output in data_outputs:
if data_output["name"] == target_output_name:
target_output = data_output
output = dict(
name=label,
label=label,
extensions=target_output.get('extensions', ['input']),
)
if target_output.get("collection"):
output["collection"] = True
output["collection_type"] = target_output["collection_type"]
# TODO: collection_type_source should be set here to be more precise/correct -
# but it can't be passed through as is since it would reference something
# the editor can't see. Since we fix input collection types to workflows
# the more correct thing to do would be to walk the subworkflow and
# determine the effective collection type if collection_type_source
# is set.
outputs.append(output)
return outputs
[docs] def execute(self, trans, progress, invocation_step, use_cached_job=False):
""" Execute the given workflow step in the given workflow invocation.
Use the supplied workflow progress object to track outputs, find
inputs, etc...
"""
step = invocation_step.workflow_step
subworkflow_invoker = progress.subworkflow_invoker(trans, step, use_cached_job=use_cached_job)
subworkflow_invoker.invoke()
subworkflow = subworkflow_invoker.workflow
subworkflow_progress = subworkflow_invoker.progress
outputs = {}
for workflow_output in subworkflow.workflow_outputs:
workflow_output_label = workflow_output.label or "%s:%s" % (workflow_output.workflow_step.order_index, workflow_output.output_name)
replacement = subworkflow_progress.get_replacement_workflow_output(workflow_output)
outputs[workflow_output_label] = replacement
progress.set_step_outputs(invocation_step, outputs)
return None
[docs]class InputModule(WorkflowModule):
[docs] def get_runtime_state(self):
state = DefaultToolState()
state.inputs = dict(input=None)
return state
[docs] def execute(self, trans, progress, invocation_step, use_cached_job=False):
invocation = invocation_step.workflow_invocation
step = invocation_step.workflow_step
step_outputs = dict(output=step.state.inputs['input'])
# Web controller may set copy_inputs_to_history, API controller always sets
# inputs.
if invocation.copy_inputs_to_history:
for input_dataset_hda in list(step_outputs.values()):
content_type = input_dataset_hda.history_content_type
if content_type == "dataset":
new_hda = input_dataset_hda.copy()
invocation.history.add_dataset(new_hda)
step_outputs['input_ds_copy'] = new_hda
elif content_type == "dataset_collection":
new_hdca = input_dataset_hda.copy()
invocation.history.add_dataset_collection(new_hdca)
step_outputs['input_ds_copy'] = new_hdca
else:
raise Exception("Unknown history content encountered")
# If coming from UI - we haven't registered invocation inputs yet,
# so do that now so dependent steps can be recalculated. In the future
# everything should come in from the API and this can be eliminated.
if not invocation.has_input_for_step(step.id):
content = next(iter(step_outputs.values()))
if content:
invocation.add_input(content, step.id)
progress.set_outputs_for_input(invocation_step, step_outputs)
[docs] def recover_mapping(self, invocation_step, progress):
progress.set_outputs_for_input(invocation_step)
[docs]class InputDataModule(InputModule):
type = "data_input"
name = "Input dataset"
[docs] def get_filter_set(self, connections=None):
filter_set = []
if connections:
for oc in connections:
for ic in oc.input_step.module.get_data_inputs():
if 'extensions' in ic and ic['extensions'] != 'input' and ic['name'] == oc.input_name:
filter_set += ic['extensions']
if not filter_set:
filter_set = ['data']
return ', '.join(filter_set)
[docs] def get_runtime_inputs(self, connections=None):
return dict(input=DataToolParameter(None, Element("param", name="input", label=self.label, multiple=False, type="data", format=self.get_filter_set(connections)), self.trans))
[docs]class InputDataCollectionModule(InputModule):
type = "data_collection_input"
name = "Input dataset collection"
default_collection_type = "list"
collection_type = default_collection_type
[docs] def get_inputs(self):
collection_type = self.state.inputs.get("collection_type", self.default_collection_type)
input_collection_type = TextToolParameter(None, XML(
'''
<param name="collection_type" label="Collection type" type="text" value="%s">
<option value="list">List of Datasets</option>
<option value="paired">Dataset Pair</option>
<option value="list:paired">List of Dataset Pairs</option>
</param>
''' % collection_type))
return dict(collection_type=input_collection_type)
[docs] def get_runtime_inputs(self, **kwds):
collection_type = self.state.inputs.get("collection_type", self.default_collection_type)
input_element = Element("param", name="input", label=self.label, type="data_collection", collection_type=collection_type)
return dict(input=DataCollectionToolParameter(None, input_element, self.trans))
[docs] def get_data_outputs(self):
return [
dict(
name='output',
extensions=['input_collection'],
collection=True,
collection_type=self.state.inputs.get('collection_type', self.default_collection_type)
)
]
[docs]class InputParameterModule(WorkflowModule):
type = "parameter_input"
name = "Input parameter"
default_parameter_type = "text"
default_optional = False
parameter_type = default_parameter_type
optional = default_optional
[docs] def get_inputs(self):
# TODO: Use an external xml or yaml file to load the parameter definition
parameter_type = self.state.inputs.get("parameter_type", self.default_parameter_type)
optional = self.state.inputs.get("optional", self.default_optional)
input_parameter_type = SelectToolParameter(None, XML(
'''
<param name="parameter_type" label="Parameter type" type="select" value="%s">
<option value="text">Text</option>
<option value="integer">Integer</option>
<option value="float">Float</option>
<option value="boolean">Boolean (True or False)</option>
<option value="color">Color</option>
</param>
''' % parameter_type))
return odict([("parameter_type", input_parameter_type),
("optional", BooleanToolParameter(None, Element("param", name="optional", label="Optional", type="boolean", value=optional)))])
[docs] def get_runtime_inputs(self, **kwds):
parameter_type = self.state.inputs.get("parameter_type", self.default_parameter_type)
optional = self.state.inputs.get("optional", self.default_optional)
if parameter_type not in ["text", "boolean", "integer", "float", "color"]:
raise ValueError("Invalid parameter type for workflow parameters encountered.")
parameter_class = parameter_types[parameter_type]
parameter_kwds = {}
if parameter_type in ["integer", "float"]:
parameter_kwds["value"] = str(0)
# TODO: Use a dict-based description from YAML tool source
element = Element("param", name="input", label=self.label, type=parameter_type, optional=str(optional), **parameter_kwds)
input = parameter_class(None, element)
return dict(input=input)
[docs] def get_runtime_state(self):
state = DefaultToolState()
state.inputs = dict(input=None)
return state
[docs] def execute(self, trans, progress, invocation_step, use_cached_job=False):
step = invocation_step.workflow_step
step_outputs = dict(output=step.state.inputs['input'])
progress.set_outputs_for_input(invocation_step, step_outputs)
[docs]class PauseModule(WorkflowModule):
""" Initially this module will unconditionally pause a workflow - will aim
to allow conditional pausing later on.
"""
type = "pause"
name = "Pause for dataset review"
[docs] def get_data_inputs(self):
input = dict(
name="input",
label="Dataset for Review",
multiple=False,
extensions='input',
input_type="dataset",
)
return [input]
[docs] def get_data_outputs(self):
return [dict(name="output", label="Reviewed Dataset", extensions=['input'])]
[docs] def execute(self, trans, progress, invocation_step, use_cached_job=False):
step = invocation_step.workflow_step
progress.mark_step_outputs_delayed(step, why="executing pause step")
[docs] def recover_mapping(self, invocation_step, progress):
if invocation_step:
step = invocation_step.workflow_step
action = invocation_step.action
if action:
connection = step.input_connections_by_name["input"][0]
replacement = progress.replacement_for_connection(connection)
progress.set_step_outputs(invocation_step, {'output': replacement})
return
elif action is False:
raise CancelWorkflowEvaluation()
delayed_why = "workflow paused at this step waiting for review"
raise DelayedWorkflowEvaluation(why=delayed_why)
[docs] def do_invocation_step_action(self, step, action):
""" Update or set the workflow invocation state action - generic
extension point meant to allows users to interact with interactive
workflow modules. The action object returned from this method will
be attached to the WorkflowInvocationStep and be available the next
time the workflow scheduler visits the workflow.
"""
return bool(action)
[docs]class ToolModule(WorkflowModule):
type = "tool"
name = "Tool"
[docs] def __init__(self, trans, tool_id, tool_version=None, exact_tools=False, **kwds):
super(ToolModule, self).__init__(trans, content_id=tool_id, **kwds)
self.tool_id = tool_id
self.tool_version = tool_version
self.tool = trans.app.toolbox.get_tool(tool_id, tool_version=tool_version, exact=exact_tools)
if self.tool and tool_version and exact_tools and str(self.tool.version) != str(tool_version):
log.info("Exact tool specified during workflow module creation for [%s] but couldn't find correct version [%s]." % (tool_id, tool_version))
self.tool = None
self.post_job_actions = {}
self.runtime_post_job_actions = {}
self.workflow_outputs = []
self.version_changes = []
# ---- Creating modules from various representations ---------------------
[docs] @classmethod
def from_dict(Class, trans, d, exact_tools=False, **kwds):
tool_id = d.get('content_id') or d.get('tool_id')
if tool_id is None:
raise exceptions.RequestParameterInvalidException("No tool id could be located for step [%s]." % d)
tool_version = d.get('tool_version')
if tool_version:
tool_version = str(tool_version)
module = super(ToolModule, Class).from_dict(trans, d, tool_id=tool_id, tool_version=tool_version, exact_tools=exact_tools)
module.post_job_actions = d.get('post_job_actions', {})
module.workflow_outputs = d.get('workflow_outputs', [])
if module.tool:
message = ""
if tool_id != module.tool_id:
message += "The tool (id '%s') specified in this step is not available. Using the tool with id %s instead." % (tool_id, module.tool_id)
if d.get('tool_version', 'Unspecified') != module.get_version():
message += "%s: using version '%s' instead of version '%s' specified in this workflow." % (tool_id, module.get_version(), d.get('tool_version', 'Unspecified'))
if message:
log.debug(message)
module.version_changes.append(message)
return module
[docs] @classmethod
def from_workflow_step(Class, trans, step, **kwds):
tool_id = trans.app.toolbox.get_tool_id(step.tool_id) or step.tool_id
tool_version = step.tool_version
module = super(ToolModule, Class).from_workflow_step(trans, step, tool_id=tool_id, tool_version=tool_version)
module.workflow_outputs = step.workflow_outputs
module.post_job_actions = {}
for pja in step.post_job_actions:
module.post_job_actions[pja.action_type] = pja
if module.tool:
message = ""
if step.tool_id != module.tool_id: # This means the exact version of the tool is not installed. We inform the user.
old_tool_shed = step.tool_id.split("/repos/")[0]
if old_tool_shed not in tool_id: # Only display the following warning if the tool comes from a different tool shed
old_tool_shed_url = common_util.get_tool_shed_url_from_tool_shed_registry(trans.app, old_tool_shed)
if not old_tool_shed_url: # a tool from a different tool_shed has been found, but the original tool shed has been deactivated
old_tool_shed_url = "http://" + old_tool_shed # let's just assume it's either http, or a http is forwarded to https.
old_url = old_tool_shed_url + "/view/%s/%s/" % (module.tool.repository_owner, module.tool.repository_name)
new_url = module.tool.sharable_url + '/%s/' % module.tool.changeset_revision
new_tool_shed_url = new_url.split("/view")[0]
message += "The tool \'%s\', version %s by the owner %s installed from <a href=\"%s\" target=\"_blank\">%s</a> is not available. " % (module.tool.name, tool_version, module.tool.repository_owner, old_url, old_tool_shed_url)
message += "A derivation of this tool installed from <a href=\"%s\" target=\"_blank\">%s</a> will be used instead. " % (new_url, new_tool_shed_url)
if step.tool_version and (step.tool_version != module.tool.version):
message += "<span title=\"tool id '%s'\">Using version '%s' instead of version '%s' specified in this workflow. " % (tool_id, module.tool.version, step.tool_version)
if message:
log.debug(message)
module.version_changes.append(message)
else:
log.warning("The tool '%s' is missing. Cannot build workflow module." % tool_id)
return module
# ---- Saving in various forms ------------------------------------------
[docs] def save_to_step(self, step):
super(ToolModule, self).save_to_step(step)
step.tool_id = self.tool_id
step.tool_version = self.get_version()
for k, v in self.post_job_actions.items():
pja = self.__to_pja(k, v, step)
self.trans.sa_session.add(pja)
# ---- General attributes ------------------------------------------------
[docs] def get_tooltip(self, static_path=''):
if self.tool and self.tool.help:
return self.tool.help.render(host_url=web.url_for('/'), static_path=static_path)
# ---- Configuration time -----------------------------------------------
[docs] def get_data_inputs(self):
data_inputs = []
if self.tool:
def callback(input, prefixed_name, prefixed_label, **kwargs):
if not hasattr(input, 'hidden') or not input.hidden:
if isinstance(input, DataToolParameter):
data_inputs.append(dict(
name=prefixed_name,
label=prefixed_label,
multiple=input.multiple,
extensions=input.extensions,
input_type="dataset", ))
elif isinstance(input, DataCollectionToolParameter):
data_inputs.append(dict(
name=prefixed_name,
label=prefixed_label,
multiple=input.multiple,
input_type="dataset_collection",
collection_types=input.collection_types,
extensions=input.extensions,
))
visit_input_values(self.tool.inputs, self.state.inputs, callback)
return data_inputs
[docs] def get_data_outputs(self):
data_outputs = []
if self.tool:
for name, tool_output in self.tool.outputs.items():
extra_kwds = {}
if tool_output.collection:
extra_kwds["collection"] = True
extra_kwds["collection_type"] = tool_output.structure.collection_type
extra_kwds["collection_type_source"] = tool_output.structure.collection_type_source
formats = ['input'] # TODO: fix
elif tool_output.format_source is not None:
formats = ['input'] # default to special name "input" which remove restrictions on connections
else:
formats = [tool_output.format]
for change_elem in tool_output.change_format:
for when_elem in change_elem.findall('when'):
format = when_elem.get('format', None)
if format and format not in formats:
formats.append(format)
data_outputs.append(
dict(
name=name,
extensions=formats,
**extra_kwds
)
)
return data_outputs
[docs] def get_config_form(self):
if self.tool:
self.add_dummy_datasets()
incoming = {}
params_to_incoming(incoming, self.tool.inputs, self.state.inputs, self.trans.app)
return self.tool.to_json(self.trans, incoming, workflow_building_mode=True)
[docs] def check_and_update_state(self):
if self.tool:
return self.tool.check_and_update_param_values(self.state.inputs, self.trans, workflow_building_mode=True)
[docs] def add_dummy_datasets(self, connections=None, steps=None):
if self.tool:
if connections:
# Store connections by input name
input_connections_by_name = dict((conn.input_name, conn) for conn in connections)
else:
input_connections_by_name = {}
# Any input needs to have value RuntimeValue or obtain the value from connected steps
def callback(input, prefixed_name, context, **kwargs):
if isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter):
if connections is not None and steps is not None and self.trans.workflow_building_mode is workflow_building_modes.USE_HISTORY:
if prefixed_name in input_connections_by_name:
connection = input_connections_by_name[prefixed_name]
output_step = next(output_step for output_step in steps if connection.output_step_id == output_step.id)
if output_step.type.startswith('data'):
output_inputs = output_step.module.get_runtime_inputs(connections=connections)
output_value = output_inputs['input'].get_initial_value(self.trans, context)
if isinstance(input, DataToolParameter) and isinstance(output_value, self.trans.app.model.HistoryDatasetCollectionAssociation):
return output_value.to_hda_representative()
return output_value
return RuntimeValue()
else:
return input.get_initial_value(self.trans, context)
elif connections is None or prefixed_name in input_connections_by_name:
return RuntimeValue()
visit_input_values(self.tool.inputs, self.state.inputs, callback)
else:
raise ToolMissingException("Tool %s missing. Cannot add dummy datasets." % self.tool_id)
# ---- Run time ---------------------------------------------------------
[docs] def get_runtime_state(self):
state = DefaultToolState()
state.inputs = self.state.inputs
return state
[docs] def compute_runtime_state(self, trans, step_updates=None):
# Warning: This method destructively modifies existing step state.
if self.tool:
step_errors = {}
state = self.state
self.runtime_post_job_actions = {}
if step_updates:
state, step_errors = super(ToolModule, self).compute_runtime_state(trans, step_updates)
self.runtime_post_job_actions = step_updates.get(RUNTIME_POST_JOB_ACTIONS_KEY, {})
step_metadata_runtime_state = self.__step_meta_runtime_state()
if step_metadata_runtime_state:
state.inputs[RUNTIME_STEP_META_STATE_KEY] = step_metadata_runtime_state
return state, step_errors
else:
raise ToolMissingException("Tool %s missing. Cannot compute runtime state." % self.tool_id)
[docs] def decode_runtime_state(self, runtime_state):
""" Take runtime state from persisted invocation and convert it
into a DefaultToolState object for use during workflow invocation.
"""
if self.tool:
state = super(ToolModule, self).decode_runtime_state(runtime_state)
if RUNTIME_STEP_META_STATE_KEY in runtime_state:
self.__restore_step_meta_runtime_state(loads(runtime_state[RUNTIME_STEP_META_STATE_KEY]))
return state
else:
raise ToolMissingException("Tool %s missing. Cannot recover runtime state." % self.tool_id)
[docs] def execute(self, trans, progress, invocation_step, use_cached_job=False):
invocation = invocation_step.workflow_invocation
step = invocation_step.workflow_step
tool = trans.app.toolbox.get_tool(step.tool_id, tool_version=step.tool_version)
tool_state = step.state
# Not strictly needed - but keep Tool state clean by stripping runtime
# metadata parameters from it.
if RUNTIME_STEP_META_STATE_KEY in tool_state.inputs:
del tool_state.inputs[RUNTIME_STEP_META_STATE_KEY]
collections_to_match = self._find_collections_to_match(tool, progress, step)
# Have implicit collections...
if collections_to_match.has_collections():
collection_info = self.trans.app.dataset_collections_service.match_collections(collections_to_match)
else:
collection_info = None
param_combinations = []
if collection_info:
iteration_elements_iter = collection_info.slice_collections()
else:
iteration_elements_iter = [None]
for iteration_elements in iteration_elements_iter:
execution_state = tool_state.copy()
# TODO: Move next step into copy()
execution_state.inputs = make_dict_copy(execution_state.inputs)
expected_replacement_keys = set(step.input_connections_by_name.keys())
found_replacement_keys = set()
# Connect up
def callback(input, prefixed_name, **kwargs):
replacement = NO_REPLACEMENT
if isinstance(input, DataToolParameter) or isinstance(input, DataCollectionToolParameter):
if iteration_elements and prefixed_name in iteration_elements:
if isinstance(input, DataToolParameter):
# Pull out dataset instance from element.
replacement = iteration_elements[prefixed_name].dataset_instance
if hasattr(iteration_elements[prefixed_name], u'element_identifier') and iteration_elements[prefixed_name].element_identifier:
replacement.element_identifier = iteration_elements[prefixed_name].element_identifier
else:
# If collection - just use element model object.
replacement = iteration_elements[prefixed_name]
else:
replacement = progress.replacement_for_tool_input(step, input, prefixed_name)
else:
replacement = progress.replacement_for_tool_input(step, input, prefixed_name)
if replacement is not NO_REPLACEMENT:
found_replacement_keys.add(prefixed_name)
return replacement
try:
# Replace DummyDatasets with historydatasetassociations
visit_input_values(tool.inputs, execution_state.inputs, callback, no_replacement_value=NO_REPLACEMENT)
except KeyError as k:
message_template = "Error due to input mapping of '%s' in '%s'. A common cause of this is conditional outputs that cannot be determined until runtime, please review your workflow."
message = message_template % (tool.name, k.message)
raise exceptions.MessageException(message)
unmatched_input_connections = expected_replacement_keys - found_replacement_keys
if unmatched_input_connections:
log.warn("Failed to use input connections for inputs [%s]" % unmatched_input_connections)
param_combinations.append(execution_state.inputs)
complete = False
completed_jobs = {}
for i, param in enumerate(param_combinations):
if use_cached_job:
completed_jobs[i] = tool.job_search.by_tool_input(
trans=trans,
tool_id=tool.id,
tool_version=tool.version,
param=param,
param_dump=tool.params_to_strings(param, trans.app, nested=True),
job_state=None,
)
else:
completed_jobs[i] = None
try:
mapping_params = MappingParameters(tool_state.inputs, param_combinations)
max_num_jobs = progress.maximum_jobs_to_schedule_or_none
execution_tracker = execute(
trans=self.trans,
tool=tool,
mapping_params=mapping_params,
history=invocation.history,
collection_info=collection_info,
workflow_invocation_uuid=invocation.uuid.hex,
invocation_step=invocation_step,
max_num_jobs=max_num_jobs,
job_callback=lambda job: self._handle_post_job_actions(step, job, invocation.replacement_dict),
completed_jobs=completed_jobs
)
complete = True
except PartialJobExecution as pje:
execution_tracker = pje.execution_tracker
except ToolInputsNotReadyException:
delayed_why = "tool [%s] inputs are not ready, this special tool requires inputs to be ready" % tool.id
raise DelayedWorkflowEvaluation(why=delayed_why)
progress.record_executed_job_count(len(execution_tracker.successful_jobs))
if collection_info:
step_outputs = dict(execution_tracker.implicit_collections)
else:
step_outputs = dict(execution_tracker.output_datasets)
step_outputs.update(execution_tracker.output_collections)
progress.set_step_outputs(invocation_step, step_outputs, already_persisted=not invocation_step.is_new)
if collection_info:
step_inputs = mapping_params.param_template
step_inputs.update(collection_info.collections)
self._handle_mapped_over_post_job_actions(step, step_inputs, step_outputs, invocation.replacement_dict)
if execution_tracker.execution_errors:
message = "Failed to create one or more job(s) for workflow step."
raise Exception(message)
return complete
[docs] def recover_mapping(self, invocation_step, progress):
outputs = {}
for output_dataset_assoc in invocation_step.output_datasets:
outputs[output_dataset_assoc.output_name] = output_dataset_assoc.dataset
for output_dataset_collection_assoc in invocation_step.output_dataset_collections:
outputs[output_dataset_collection_assoc.output_name] = output_dataset_collection_assoc.dataset_collection
progress.set_step_outputs(invocation_step, outputs)
def _find_collections_to_match(self, tool, progress, step):
collections_to_match = matching.CollectionsToMatch()
def callback(input, prefixed_name, **kwargs):
is_data_param = isinstance(input, DataToolParameter)
if is_data_param and not input.multiple:
data = progress.replacement_for_tool_input(step, input, prefixed_name)
if isinstance(data, model.HistoryDatasetCollectionAssociation):
collections_to_match.add(prefixed_name, data)
is_data_collection_param = isinstance(input, DataCollectionToolParameter)
if is_data_collection_param and not input.multiple:
data = progress.replacement_for_tool_input(step, input, prefixed_name)
history_query = input._history_query(self.trans)
subcollection_type_description = history_query.can_map_over(data)
if subcollection_type_description:
collections_to_match.add(prefixed_name, data, subcollection_type=subcollection_type_description.collection_type)
visit_input_values(tool.inputs, step.state.inputs, callback)
return collections_to_match
def _effective_post_job_actions(self, step):
effective_post_job_actions = step.post_job_actions[:]
for key, value in self.runtime_post_job_actions.items():
effective_post_job_actions.append(self.__to_pja(key, value, None))
return effective_post_job_actions
def _handle_mapped_over_post_job_actions(self, step, step_inputs, step_outputs, replacement_dict):
effective_post_job_actions = self._effective_post_job_actions(step)
for pja in effective_post_job_actions:
if pja.action_type in ActionBox.mapped_over_output_actions:
ActionBox.execute_on_mapped_over(self.trans, self.trans.sa_session, pja, step_inputs, step_outputs, replacement_dict)
def _handle_post_job_actions(self, step, job, replacement_dict):
# Create new PJA associations with the created job, to be run on completion.
# PJA Parameter Replacement (only applies to immediate actions-- rename specifically, for now)
# Pass along replacement dict with the execution of the PJA so we don't have to modify the object.
# Combine workflow and runtime post job actions into the effective post
# job actions for this execution.
flush_required = False
effective_post_job_actions = self._effective_post_job_actions(step)
for pja in effective_post_job_actions:
if pja.action_type in ActionBox.immediate_actions:
ActionBox.execute(self.trans.app, self.trans.sa_session, pja, job, replacement_dict)
else:
pjaa = model.PostJobActionAssociation(pja, job_id=job.id)
self.trans.sa_session.add(pjaa)
flush_required = True
if flush_required:
self.trans.sa_session.flush()
def __restore_step_meta_runtime_state(self, step_runtime_state):
if RUNTIME_POST_JOB_ACTIONS_KEY in step_runtime_state:
self.runtime_post_job_actions = step_runtime_state[RUNTIME_POST_JOB_ACTIONS_KEY]
def __step_meta_runtime_state(self):
""" Build a dictionary a of meta-step runtime state (state about how
the workflow step - not the tool state) to be serialized with the Tool
state.
"""
return {RUNTIME_POST_JOB_ACTIONS_KEY: self.runtime_post_job_actions}
def __to_pja(self, key, value, step):
if 'output_name' in value:
output_name = value['output_name']
else:
output_name = None
if 'action_arguments' in value:
action_arguments = value['action_arguments']
else:
action_arguments = None
return PostJobAction(value['action_type'], step, output_name, action_arguments)
[docs]class WorkflowModuleFactory(object):
[docs] def from_dict(self, trans, d, **kwargs):
"""
Return module initialized from the data in dictionary `d`.
"""
type = d['type']
assert type in self.module_types, "Unexpected workflow step type [%s] not found in [%s]" % (type, self.module_types.keys())
return self.module_types[type].from_dict(trans, d, **kwargs)
[docs] def from_workflow_step(self, trans, step):
"""
Return module initializd from the WorkflowStep object `step`.
"""
type = step.type
return self.module_types[type].from_workflow_step(trans, step)
module_types = dict(
data_input=InputDataModule,
data_collection_input=InputDataCollectionModule,
parameter_input=InputParameterModule,
pause=PauseModule,
tool=ToolModule,
subworkflow=SubWorkflowModule,
)
module_factory = WorkflowModuleFactory(module_types)
[docs]def load_module_sections(trans):
""" Get abstract description of the workflow modules this Galaxy instance
is configured with.
"""
module_sections = {}
module_sections['inputs'] = {
"name": "inputs",
"title": "Inputs",
"modules": [
{
"name": "data_input",
"title": "Input Dataset",
"description": "Input dataset"
},
{
"name": "data_collection_input",
"title": "Input Dataset Collection",
"description": "Input dataset collection"
},
],
}
if trans.app.config.enable_beta_workflow_modules:
module_sections['experimental'] = {
"name": "experimental",
"title": "Experimental",
"modules": [
{
"name": "pause",
"title": "Pause Workflow for Dataset Review",
"description": "Pause for Review"
},
{
"name": "parameter_input",
"title": "Parameter Input",
"description": "Simple inputs used for workflow logic"
},
],
}
return module_sections
[docs]class WorkflowModuleInjector(object):
""" Injects workflow step objects from the ORM with appropriate module and
module generated/influenced state. """
[docs] def __init__(self, trans, allow_tool_state_corrections=False):
self.trans = trans
self.allow_tool_state_corrections = allow_tool_state_corrections
[docs] def inject(self, step, step_args=None, steps=None):
""" Pre-condition: `step` is an ORM object coming from the database, if
supplied `step_args` is the representation of the inputs for that step
supplied via web form.
Post-condition: The supplied `step` has new non-persistent attributes
useful during workflow invocation. These include 'upgrade_messages',
'state', 'input_connections_by_name', and 'module'.
If step_args is provided from a web form this is applied to generate
'state' else it is just obtained from the database.
"""
step_errors = None
step.upgrade_messages = {}
# Make connection information available on each step by input name.
step.setup_input_connections_by_name()
# Populate module.
module = step.module = module_factory.from_workflow_step(self.trans, step)
# Any connected input needs to have value DummyDataset (these
# are not persisted so we need to do it every time)
module.add_dummy_datasets(connections=step.input_connections, steps=steps)
state, step_errors = module.compute_runtime_state(self.trans, step_args)
step.state = state
# Fix any missing parameters
step.upgrade_messages = module.check_and_update_state()
# Populate subworkflow components
if step.type == "subworkflow":
subworkflow = step.subworkflow
populate_module_and_state(self.trans, subworkflow, param_map={})
return step_errors
[docs]def populate_module_and_state(trans, workflow, param_map, allow_tool_state_corrections=False, module_injector=None):
""" Used by API but not web controller, walks through a workflow's steps
and populates transient module and state attributes on each.
"""
if module_injector is None:
module_injector = WorkflowModuleInjector(trans, allow_tool_state_corrections)
for step in workflow.steps:
step_args = param_map.get(step.id, {})
step_errors = module_injector.inject(step, step_args=step_args)
if step_errors:
raise exceptions.MessageException(step_errors, err_data={step.order_index: step_errors})
if step.upgrade_messages:
if allow_tool_state_corrections:
log.debug('Workflow step "%i" had upgrade messages: %s', step.id, step.upgrade_messages)
else:
raise exceptions.MessageException(step.upgrade_messages, err_data={step.order_index: step.upgrade_messages})