def _load_workflow_def(self):
# load geneflow definition file
gf_def = Definition()
if not gf_def.load(str(self._workflow_yaml)):
Log.an().error('invalid geneflow definition: %s', self._workflow_yaml)
return False
# make sure there is a workflow definition in the file
if not gf_def.workflows():
Log.an().error('no workflows in geneflow definition')
return False
# extract the workflow definition
self._workflow = next(iter(gf_def.workflows().values()))
return True
def help_func(args, other_args, subparser=None):
"""
GeneFlow workflow help.
Args:
args.workflow: workflow definition or package directory.
Returns:
On success: True.
On failure: False.
"""
# get absolute path to workflow
workflow_yaml = resolve_workflow_path(args.workflow)
if workflow_yaml:
Log.some().info('workflow definition found: %s', workflow_yaml)
else:
Log.an().error('cannot find workflow definition: %s', args.workflow)
return False
# load workflow
gf_def = Definition()
if not gf_def.load(workflow_yaml):
Log.an().error('workflow definition load failed: %s', workflow_yaml)
return False
# get first workflow dict
workflow_dict = next(iter(gf_def.workflows().values()))
print()
print('{}: {}'.format(workflow_dict['name'], workflow_dict['description']))
print()
print('Execution Command:')
print('\tgf [--log-level LOG_LEVEL] [--log-file LOG_FILE] run WORKFLOW_PATH')
print('\t\t-o OUTPUT [-n NAME] [INPUTS] [PARAMETERS] [-w WORK_DIR [WORK_DIR ...]]')
print('\t\t[--ec CONTEXT [CONTEXT ...]] [--em METHOD [METHOD ...]] [--ep PARAM [PARAM ...]]')
print()
print('\tWORKFLOW_PATH: Path to directory that contains workflow definition')
print()
print('Job Configuration:')
print('\t-o,--output: Output directory')
print('\t-n,--name: Job name, a directory with this name will be created in the output directory')
print('\t\tdefault: geneflow-job')
print('\t-w,--work: Work directories, for temporary or intermediate data')
print('\t\tdefault: ~/.geneflow/work')
print('\t--no-output-hash: Flag indicating that the output directory should NOT include a random hash')
print('\t\tdefault: not set, output will include random hash')
print()
print('Inputs: Workflow-Specific Files or Folders')
for input_key in workflow_dict['inputs']:
print(
'\t--in.{}: {}: {}'.format(
input_key,
workflow_dict['inputs'][input_key]['label'],
workflow_dict['inputs'][input_key]['description']
)
)
print(
'\t\ttype: {}, default: {}'.format(
workflow_dict['inputs'][input_key]['type'],
workflow_dict['inputs'][input_key]['default']
)
)
print()
print('Parameters: Workflow-Specific Values')
for param_key in workflow_dict['parameters']:
print(
'\t--param.{}: {}: {}'.format(
param_key,
workflow_dict['parameters'][param_key]['label'],
workflow_dict['parameters'][param_key]['description']
)
)
print(
'\t\ttype: {}, default: {}'.format(
workflow_dict['parameters'][param_key]['type'],
workflow_dict['parameters'][param_key]['default']
)
)
print()
print('Execution Configuration:')
print('\t--ec,--exec-context: Execution contexts, e.g., local, agave, gridengine.')
print('\t\tThese can be specified for all workflow steps with "default:[CONTEXT]"')
print('\t\tor for specific steps with "step-name:[CONTEXT]".')
print('\t--em,--exec-method: Exeuction methods, e.g., singularity, docker, environment.')
print('\t\tThese can be specified for all workflow steps with "default:[METHOD]"')
print('\t\tor for specific steps with "step-name:[METHOD]". By default each app associated')
print('\t\twith a workflow step tries to automatically detect the execution method.')
print('\t--ep,--exec-param: Execution parameters, e.g., slots, mem, or other.')
print('\t\tThese can be specified for all workflow steps with "default.slots:[VALUE]"')
print('\t\tor for specific steps with "step-name.slots:[VALUE]". Execution parameters')
print('\t\tdepend on the execution context.')
return True
def help_func(args):
"""
GeneFlow workflow help.
Args:
args.workflow: workflow definition or package directory.
Returns:
On success: True.
On failure: False.
"""
# get absolute path to workflow
workflow_yaml = resolve_workflow_path(args.workflow)
if workflow_yaml:
Log.some().info('workflow definition found: %s', workflow_yaml)
else:
Log.an().error('cannot find workflow definition: %s', args.workflow)
return False
# load workflow
gf_def = Definition()
if not gf_def.load(workflow_yaml):
Log.an().error('workflow definition load failed: %s', workflow_yaml)
return False
# get first workflow dict
workflow_dict = next(iter(gf_def.workflows().values()))
print()
print('GeneFlow: {}'.format(workflow_dict['name']))
print()
print('{}'.format(workflow_dict['description']))
print()
print('Inputs:')
for input_key in workflow_dict['inputs']:
print(
'\t--{}: {}: {}'.format(
input_key,
workflow_dict['inputs'][input_key]['label'],
workflow_dict['inputs'][input_key]['description']
)
)
print(
'\t\ttype: {}, default: {}'.format(
workflow_dict['inputs'][input_key]['type'],
workflow_dict['inputs'][input_key]['default']
)
)
print()
print('Parameters:')
for param_key in workflow_dict['parameters']:
print(
'\t--{}: {}: {}'.format(
param_key,
workflow_dict['parameters'][param_key]['label'],
workflow_dict['parameters'][param_key]['description']
)
)
print(
'\t\ttype: {}, default: {}'.format(
workflow_dict['parameters'][param_key]['type'],
workflow_dict['parameters'][param_key]['default']
)
)
return True