On-demand constraint-tailoring to dataset arguments #260
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This exposes a, so far dormant, feature of the `Constraint` class to parameter validation: constraint tailoring for particular datasets. `EnsureCommandParameterization` learned a `tailor_for_dataset` parameter that can be used to identify which parameters' constraints should be tailored for which `Dataset` instances. Tailoring will only be actually done under the following conditions: - the dataset-providing parameters need to evaluate to a DatasetParameter instance, typically via `EnsureDataset` (all regular conditions apply, e.g. a default `None` not being processed etc) - the to-be-tailored parameter `Constraint` needs to implement its `for_dataset()` method to perform a tailoring. A test with some custom-made constraints is included. At the moment, no production-ready constraints do implement `for_dataset()`. However, now that a consumer for that is available, it makes more sense to address related issues, such as: - datalad#193 - datalad#131 Closes datalad#200
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This exposes a, so far dormant, feature of the
Constraintclass toparameter validation: constraint tailoring for particular datasets.
EnsureCommandParameterizationlearned atailor_for_datasetparameterthat can be used to identify which parameters' constraints should be
tailored for which
Datasetinstances. Tailoring will only be actuallydone under the following conditions:
DatasetParameter instance, typically via
EnsureDataset(all regular conditions apply, e.g. a default
Nonenot being processedetc)
Constraintneeds to implement itsfor_dataset()method to perform a tailoring.A test with some custom-made constraints is included. At the moment,
no production-ready constraints do implement
for_dataset(). However,now that a consumer for that is available, it makes more sense to
address related issues, such as:
EnsureRemoteName#193EnsureCredential#131Closes #200