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Simple. Use the PointOnAreaOverlayer transformer. In the parameters dialog set a list name. The polygon features will emerge with a list of which point features fell inside, including their attributes. Then use a ListSummer transformer to add up the attribute X in that list. I put an example workspace (template) on Dropbox at: ...


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You can open the properties dialog (for the original writer) and use the Writer option there to move that table from one writer to the other. That way you don't have to copy from one table to another. As the above screenshot shows, it doesn't even need to be the same format. FME will automatically adjust the data types to match the new format. However, ...


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Step 1 Connect your writer with transformer or any reader feature type Step 2 Right click your writer and then choose writers properties Choose User attributes tab and then select Automatic all attributes will come into your writer. if you want to edit then select manual mode.


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Add an AttributeExposer, expose the fme_feature_type attribute Connect the AttributeExposer to a AttributeCreator to push fme_feature_type out an attribute (named fme_feature_type below) In the PythonCaller, get your attribute by: layer_name = feature.getAttribute("fme_feature_type")


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I'm going to assume your text file is like a CSV with a header called "LayerName" and each row is a different layer. In that case, your function would be something like this: import fmeobjects def processFeature(feature): # Get which layer is currently being processed layer_name = feature.getAttribute("LayerName") # do some logical processing ...


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The RasterDEMGenerator transformer might be a better fit here. It will interpolate values, whereas the NumericRasterizer is just setting a cell on/off depending on the presence of points in it. Alternatively you could increase the spacing in the NumericRasterizer - to about 2x larger than the average spacing of points in the point cloud. Hopefully one of ...


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As Fetzer says, it's going to be prone to error because the RGB values will vary. However - for what it's worth - here is an FME workspace that you can probably adapt to your requirements. I made this workspace to detect swimming pools in aerial imagery. Basically it extracts the RGB values and compares them to each other. I was looking for cells where blue ...



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