hpcflow.app.ElementActionRun#

class hpcflow.app.ElementActionRun(id_, is_pending, element_action, index, data_idx, start_time, end_time, snapshot_start, snapshot_end, submission_idx, success, skip, exit_code, metadata)#

Bases: ElementActionRun

Methods

`compose_commands`	returns: commands
`get`
`get_EAR_dependencies`	Get EARs that this EAR depends on.
`get_IFG_input_values`
`get_OFP_inputs`
`get_OFP_output_files`
`get_OFP_outputs`
`get_data_idx`
`get_dependent_EARs`	Get downstream EARs that depend on this EAR.
`get_environment`
`get_environment_label`
`get_input_dependencies`	Get information about locally defined input, sequence, and schema-default values that this EAR depends on.
`get_input_values`	Get a dict of (optionally a subset of) inputs values for this run.
`get_input_values_direct`	Get a dict of input values that are to be passed directly to a Python script function.
`get_parameter_names`	Get parameter types associated with a given prefix.
`get_parameter_sources`
`get_resources`	Resolve specific resources for this EAR, considering all applicable scopes and template-level resources.
`write_source`

Attributes

`action`
`app`
`data_idx`
`dir_diff`	Get the changes to the EAR working directory due to the execution of this EAR.
`element`
`element_action`
`element_iteration`
`end_time`
`exit_code`
`id_`
`index`	Run index.
`input_files`
`inputs`
`is_pending`
`metadata`
`output_files`
`outputs`
`resources`
`skip`
`snapshot_end`
`snapshot_start`
`start_time`
`status`	Return the state of this EAR.
`submission_idx`
`success`
`task`
`workflow`

Parameters:

id_ (int) –
is_pending (bool) –
index (int) –
data_idx (Dict) –
start_time (Union[datetime, None]) –
end_time (Union[datetime, None]) –
snapshot_start (Union[Dict, None]) –
snapshot_end (Union[Dict, None]) –
submission_idx (Union[int, None]) –
success (Union[bool, None]) –
skip (bool) –
exit_code (Union[int, None]) –
metadata (Dict) –

property action#

app = BaseApp(name='hpcFlow', version='0.2.0a127')#

compose_commands(jobscript, JS_action_idx)#

Returns:

commands
shell_vars – Dict whose keys are command indices, and whose values are lists of tuples, where each tuple contains: (parameter name, shell variable name, “stdout”/”stderr”).

Parameters:

jobscript (Jobscript) –
JS_action_idx (int) –

Return type:

Tuple[str, List[str], List[int]]

property data_idx#

property dir_diff: DirectorySnapshotDiff#: Get the changes to the EAR working directory due to the execution of this EAR.

property element#

property element_action#

property element_iteration#

property end_time#

property exit_code#

get(path=None, default=None, raise_on_missing=False, raise_on_unset=False)#

Parameters:

path (str) –
default (Any) –
raise_on_missing (bool) –
raise_on_unset (bool) –

get_EAR_dependencies(as_objects=False)#: Get EARs that this EAR depends on.

get_IFG_input_values()#

Return type:: Dict[str, Any]

get_OFP_inputs()#

Return type:: Dict[str, str | List[str]]

get_OFP_output_files()#

Return type:: Dict[str, str | List[str]]

get_OFP_outputs()#

Return type:: Dict[str, str | List[str]]

get_data_idx(path=None)#

Parameters:: path (str) –

get_dependent_EARs(as_objects=False)#

Get downstream EARs that depend on this EAR.

Return type:: List[int | ElementActionRun]

get_environment()#

Return type:: Environment

get_environment_label()#

Return type:: str

get_input_dependencies()#: Get information about locally defined input, sequence, and schema-default values that this EAR depends on. Note this does not get values from this EAR’s task/schema, because the aim of this method is to help determine which upstream tasks this EAR depends on.

get_input_values(inputs=None, label_dict=True)#

Get a dict of (optionally a subset of) inputs values for this run.

Parameters:

inputs (List[str] | None) – If specified, a list of input parameter types to include. For schema inputs that have multiple=True, the input type should be labelled.
label_dict (bool) – If True, arrange the values of schema inputs with multiple=True as a dict whose keys are the labels. If False, labels will be included in the top level keys.

Return type:

Dict[str, Any]

get_input_values_direct(label_dict=True)#

Get a dict of input values that are to be passed directly to a Python script function.

Parameters:: label_dict (bool) –

get_parameter_names(prefix)#

Get parameter types associated with a given prefix.

For inputs, labels are ignored. See Action.get_parameter_names for more information.

Parameters:: prefix (str) – One of “inputs”, “outputs”, “input_files”, “output_files”.
Return type:: List[str]

get_parameter_sources(path=None, typ=None, as_strings=False, use_task_index=False)#

Parameters:

path (str) –
typ (str) –
as_strings (bool) –
use_task_index (bool) –

get_resources()#: Resolve specific resources for this EAR, considering all applicable scopes and template-level resources.

property id_: int#

property index#: Run index.

property input_files#

property inputs#

property is_pending: bool#

property metadata#

property output_files#

property outputs#

property resources#

property skip#

property snapshot_end#

property snapshot_start#

property start_time#

property status#: Return the state of this EAR.

property submission_idx#

property success#

property task#

property workflow#

write_source(js_idx, js_act_idx)#

Parameters:

js_idx (int) –
js_act_idx (int) –