hpcflow.app.make_demo_workflow#

hpcflow.app.make_demo_workflow(workflow_name, template_format=None, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, variables=None, status=True)#

Generate a new hpcFlow workflow from a builtin demo workflow template.

Parameters:

  • workflow_name (str) – Name of the demo workflow to make.

  • template_format (Literal['json', 'yaml'] | None) – If specified, one of “json” or “yaml”. This forces parsing from a particular format.

  • path (PathLike | None) – The directory in which the workflow will be generated. The current directory if not specified.

  • name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the workflow template name will be used, in combination with a date-timestamp.

  • overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

  • store (str) – The persistent store type to use.

  • ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

  • ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

  • store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

  • variables (dict[str, str] | None) – String variables to substitute in the demo workflow template file.

  • status (bool) – If True, display a live status to track workflow creation progress.

Returns:

The created workflow.

Return type:

Workflow