In the model builder, I have different result of features on every iteration inside same model.
How can I merge all result feature to single output feature class ??
Solved! Go to Solution.
Did you collect the values with Collect Values?
no I didn't
but if I put collect value, will collect from first iteration to last iteration or last iteration only
and if the input are features, Is collect value tool output features or not?
And did you uniquely name each output in the iterator by naming it something like "tmp%n%"? Otherwise the iterator will just overwrite on each iteration! (The examples in the help demonstrate this.)
yes I did that I put %n% in the end, but I have last result only.
that is because merge tool run in every iteration, isn't that. but that is not that what I want.
I want to collect all iteration data in one merge, not many merge in every iteration
(one merge for all iteration)
I made it.
the solution is replace merge tool with append tool.
it was on that link : Problems Integrating Sub-Model into Master Model
It would have been useful since most of use were under the assumption that you wanted to produce a new output rather than appending to an existing output. Append adds to an existing output (which isn't a good idea for a variety of reasons) and Merge produces a new output, leaving the inputs to the model unaltered.
I hope you weren't producing a new output then appending to it, since that is a problem with collecting the results from iterator.
From the help...
To combine input datasets into a new output dataset, use the Merge tool.
I think Darren's suggestion is still the correct one, but we didn't get to see your model
No Dan, I want to make accumulative features from each iteration.
I tried Merge tool and Append, But Append tool is the right.
thanks for help
i guess it isn't clear what you are appending to then. Obviously you are appending to one of the elements used as input into the iterator (thereby changing it), rather than collecting the results of the iterator to produce an output. Seeing that part of the model would be interesting to see where that would be useful.