boilercv_pipeline.sync_dvc.validators

`boilercv_pipeline.sync_dvc.validators`#

Validators for syncing dvc.yaml and params.yaml with pipeline specification.

Constants.

`dvc_prepare_stage`	Prepare a pipeline stage for `dvc.yaml`.
`dvc_add_param`	Add param to global parameters and stage command for `dvc.yaml`.
`dvc_set_stage_path`	Set stage path as a stage dep, plot, or out for `dvc.yaml`.
`dvc_append_plot_name`	Append plot name for `dvc.yaml`.
`dvc_set_only_sample`	Set the only sample for `dvc.yaml` if `only_sample` is enabled.
`dvc_extend_with_timestamp_suffixed_plots`	Extend stage plots for `dvc.yaml` with timestamp-suffixed plots.
`dvc_extend_with_named_plots_if_missing`	Extend stage plots for `dvc.yaml` with named plots if plots haven’t been set.

class boilercv_pipeline.sync_dvc.validators.Constants(

/,

**data: typing.Any,

)#

Bases: pydantic.BaseModel

Constants.

Initialization

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

table_key: str#

None

Key for the global parameters table.

‘OutFlags(…)’

Default dvc.yaml configuration for outs.

skip_cloud: list[str]#

[‘data/cines’, ‘data/large_sources’]

These paths are too large and unwieldy to cache or push to cloud storage.

‘OutFlags(…)’

Default dvc.yaml configuration for outs that skip the cloud.

boilercv_pipeline.sync_dvc.validators.dvc_prepare_stage( data: dict[str, Any], handler: pydantic.functional_validators.ModelWrapValidatorHandler[boilercv_pipeline.sync_dvc.types.Model], info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, model: type[boilercv_pipeline.sync_dvc.types.Model], ) → boilercv_pipeline.sync_dvc.types.Model#: Prepare a pipeline stage for dvc.yaml.

boilercv_pipeline.sync_dvc.validators.dvc_add_param( value: Any, info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, fields: dict[str, pydantic.fields.FieldInfo], ) → Any#: Add param to global parameters and stage command for dvc.yaml.

boilercv_pipeline.sync_dvc.validators.dvc_set_stage_path( path: pathlib.Path, info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, kind: Literal[deps, outs], ) → pathlib.Path#: Set stage path as a stage dep, plot, or out for dvc.yaml.

boilercv_pipeline.sync_dvc.validators.dvc_append_plot_name( figure: matplotlib.figure.Figure, info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, ) → matplotlib.figure.Figure#: Append plot name for dvc.yaml.

boilercv_pipeline.sync_dvc.validators.dvc_set_only_sample( only_sample: bool, info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, sample_field: str, ) → bool#: Set the only sample for dvc.yaml if only_sample is enabled.

boilercv_pipeline.sync_dvc.validators.dvc_extend_with_timestamp_suffixed_plots( times: list[str], info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, ) → list[str]#: Extend stage plots for dvc.yaml with timestamp-suffixed plots.

boilercv_pipeline.sync_dvc.validators.dvc_extend_with_named_plots_if_missing( model: pydantic.BaseModel, info: boilercv_pipeline.sync_dvc.types.DvcValidationInfo, ) → Any#: Extend stage plots for dvc.yaml with named plots if plots haven’t been set.