Miscellaneous

The following functions and classes are documented not as part of the core flow library, but for completeness/reference.

Functions

flow.run_flow.run_flow(flow: Flow[RecordType]) → None

Run a flow as a main script.

Parameters:

flow (Flow) – The flow to run

When running the flow as a main program, the following flags are available:

Flow Arguments:

  • -d, --dump YAML_FILE: Dump the configuration description to the specified file

  • -r, --run YAML_FILE: Run the flow with the configurations in the given file

  • -v, --validate YAML_FILE: Validate (but do not run) the flow with the configurations in the given file

Other Arguments:

  • -l, --logfile LOGFILE: Add a path to log the flow’s output to

  • -s, --silent: Prevent the flow from outputting to the command line

  • -h, --help: Show the program’s usage and exit

records.tag_record.get_tag_headers(labs: list[str]) → list[str]

Create the headers for a TagRecords, based on its labs.

Parameters:

labs (list[str]) – The labs that a TagRecords hold.

Returns:

The corresponding spreadsheet headers that should be used to represent the TagRecords

Return type:

list[str]

github_steps.tag_repo.get_tagger(lab: str, repo_type: Literal['personal', 'group'], tag_records_type: Type[RecordType]) → Type[FlowPropagateStep[RecordType]]

Return a propagate step that tags a specific lab.

Parameters:

  • lab (str) – The lab to tag for.

  • repo_type (Literal['personal', 'group']) – The type of repo to tag

  • tag_records_type – The type of TagRagords to operate on

Returns:

The generated class to tag the lab

Return type:

Type[FlowPropagateStep[RecordType]]

For example, to add a tagger to a Flow named tag-lab1.1 that tags all personal repos for the lab1.1 submission, one could do the following:

flow.add_propagate_step(
    "tag-lab1.1", get_tagger("lab1.1", "personal", LabTagRecords)
)

See RepoTagger for information on the generated/returned class type.

Classes

flow.flow_steps.ValidConfigTypes

alias of int | bool | str | datetime

class flow.flow_steps.FlowStep

A base class representing an abstract step in our flow.

abstract class property description: str

A high-level description of the step

abstract class property config_types: list[tuple[str, Type[ValidConfigTypes], str]]

The types of the configurations for the step. Each tuple in the list should include the name, type, and description of a configuration (respectively).

Based on the config_types, an instance of the class will have an attribute configs with each described configuration. For example, if config_types is defined as

config_types = [
  ("test", int, "A test configuration")
]

then instances of the class will have an attribute configs.test of type int

abstractmethod validate() → None

Validate the configurations for the step.

This function should inspect the object’s configs attribute, and perform any static validation possible. validate() is always called on object initialization to ensure that configurations are always valid

class records.tag_record.TagRecord(name: str | None = None, time: datetime | None = None, ref_sha: str | None = None, commit_sha: str | None = None)

A (possible) representation of a tag on a repository.

A TagRecord does not have to represent a current tag; it can also serve as a placeholder for a tag that hasn’t been created yet.

- name

The name of the tag

Type:

Optional[str]

- time

The time the tag was created

Type:

Optional[datetime]

- ref_sha

The SHA-1 Hash of the Git Reference

Type:

Optional[str]

- commit_sha

The SHA-1 Hash of the commit that the tag references

Type:

Optional[str]

tagged() → bool

Return whether the repo has already been tagged.

Returns:

Whether the record represents an actual tag

Return type:

bool