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If I say that an input attribute is required in the input schema, then if I don't specify a value for that attribute in the CSV file, I expect looper to not submit the job.
But what happens is, looper still submits the job.
I am putting an example in the hello_looper repo.
The text was updated successfully, but these errors were encountered:
Confirming: the usa sample fails when running the hello_looper example input_schema_example because there is no data at the file_path. However, if I add the data, the usa sample should still fail because there is no area_type provided in the sample table, but it passes and looper submits the job without issue.
During sample validation, sample_schema_dict looks appropriate:
But the usa sample.to_dict() has an empty string for area type:
And this appears to allow the sample to pass without issue during eido's _validate_object call: validator.is_valid(obj)
Deleting the area_type key from the obj and re-running is_valid does cause validation failure as expected.
It appears that peppy's sample.to_dict() should remove keys if values are empty? Or perhaps eido should preprocess the sample dict to remove empty items before validation?
If I say that an input attribute is
required
in the input schema, then if I don't specify a value for that attribute in the CSV file, I expect looper to not submit the job.But what happens is, looper still submits the job.
I am putting an example in the hello_looper repo.
The text was updated successfully, but these errors were encountered: