We want to work with the most specific type possible. However, we deduce the type of data sources from the most general corresponding input type of the tools that process it (cf graph.py#L202). This is not necessarily the most specific type of the data, and so the final output type may not be the most specific, either.
It would be helpful to be able to annotate data sources with their specific type. The only thing that would be needed would be for the library to understand <source node> ta:type <type node> statements in the workflow specification and to instantiate corresponding source expression with the corresponding type.
Deduction via tool specification is still a pretty good method if such annotations aren't available. Note that it's not a problem at all that a data source can be conceptualized in multiple ways (see also issue #87). In this case, there would simply be multiple ta:type predicates on the source, and the tool would pick the one it likes via the standard polymorphic mechanisms.
We want to work with the most specific type possible. However, we deduce the type of data sources from the most general corresponding input type of the tools that process it (cf graph.py#L202). This is not necessarily the most specific type of the data, and so the final output type may not be the most specific, either.
It would be helpful to be able to annotate data sources with their specific type. The only thing that would be needed would be for the library to understand
<source node> ta:type <type node>statements in the workflow specification and to instantiate corresponding source expression with the corresponding type.Deduction via tool specification is still a pretty good method if such annotations aren't available. Note that it's not a problem at all that a data source can be conceptualized in multiple ways (see also issue #87). In this case, there would simply be multiple
ta:typepredicates on the source, and the tool would pick the one it likes via the standard polymorphic mechanisms.