Each row of the content index may now contain a list of tags.
main.py takes an optional argument --tags specifying tags to filter the rows of the content index by. This argument is a sequence of lists, each list starting with an integer indicating the index of the tag, followed by tags to filter by for this index. If a tag in the content index is empty, is it considered to be matching. For a row to be processed, the tags need to match for ALL of the specified indices.
Example: Take a look at tests/input/example1/content_index.csv
If we supply --tags 1 advanced basic 2 type1 to main.py it means:
Only process rows of the content index where the first tag (tags.1) is either ‘advanced’, ‘basic’ or empty, and where the second tag (tags.1) is either ‘type1’ or empty.
You can try this out via
main.py create_flows tests/input/example1/content_index.csv out.json --format=csv --datamodels=tests.input.example1.nestedmodel --tags 1 advanced basic 2 type1
Each row of the content index may now contain a list of tags.
main.pytakes an optional argument--tagsspecifying tags to filter the rows of the content index by. This argument is a sequence of lists, each list starting with an integer indicating the index of the tag, followed by tags to filter by for this index. If a tag in the content index is empty, is it considered to be matching. For a row to be processed, the tags need to match for ALL of the specified indices.Example: Take a look at
tests/input/example1/content_index.csvIf we supply--tags 1 advanced basic 2 type1tomain.pyit means: Only process rows of the content index where the first tag (tags.1) is either ‘advanced’, ‘basic’ or empty, and where the second tag (tags.1) is either ‘type1’ or empty.You can try this out via
main.py create_flows tests/input/example1/content_index.csv out.json --format=csv --datamodels=tests.input.example1.nestedmodel --tags 1 advanced basic 2 type1