I have a script to do a closest facility analysis using Network Analyst. The script works very well until the last line where I want to save the layer as a feature class.
I get an error message.
Excerpt of the script;
###############
print "Solving"
#Solve the closest facility layer
arcpy.na.Solve(layer_object)
# Get the output layer and save it to a feature class
arcpy.management.CopyFeatures(layer_object, outRoutesFC)
#############
This is the error message:
Processing T1 routes
Solving
An error occured on line 85
Failed to execute. Parameters are not valid.
ERROR 000840: The value is not a Feature Layer.
ERROR 000840: The value is not a Raster Catalog Layer.
Failed to execute (CopyFeatures).
I will appreciate any help. Thanks.
Dionisio
Solved! Go to Solution.
If you want to save the entire Closest Facility Layer as a layer file, use SaveToLayerFile as Dan suggests. If you just want to save the output Closest Facility routes sublayer to a feature class, you will have to retrieve the sublayer. An example of retrieving and saving sublayers can be found in the last code sample on this page: Make OD Cost Matrix Layer—Help | ArcGIS Desktop
The example is for OD Cost Matrix, but the same principles hold for Closest Facility. Essentially you have to grab the sublayer object and use that in CopyFeatures.
You want to use SaveToLayerFile as documented in the code example at the bottom of the help topic for Solve
http://desktop.arcgis.com/en/arcmap/latest/tools/network-analyst-toolbox/solve.htm
If you want to save the entire Closest Facility Layer as a layer file, use SaveToLayerFile as Dan suggests. If you just want to save the output Closest Facility routes sublayer to a feature class, you will have to retrieve the sublayer. An example of retrieving and saving sublayers can be found in the last code sample on this page: Make OD Cost Matrix Layer—Help | ArcGIS Desktop
The example is for OD Cost Matrix, but the same principles hold for Closest Facility. Essentially you have to grab the sublayer object and use that in CopyFeatures.
Thank you very mu