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  • I am new to fme python scripting and looking to create a python caller that will produce the cartesian product of the list of routes at an intersection (points feature data). The code below produces what I am looking foR. Thank you!

I was able to implement this in the python caller which produces the result but when I substitute the hard coded data for "myList" it does not work for me.

import itertools

 

myList =

   'a1', 'b1', 'c1'],

  >'a1', 'b1', 'c1']

]

for x in itertools.product(*myList):

  results = list(itertools.product(myList))

  print (x)

 

How do I pass the output in my list out? How do I substitute myList with the data from my DB.

CODE in Python Caller which lists results in the translation log

import fme

import fmeobjects

import itertools

# Template Function interface:

# When using this function, make sure its name is set as the value of

# the 'Class or Function to Process Features' transformer parameter

def processFeature(feature):

  pass

 

# Template Class Interface:

# When using this class, make sure its name is set as the value of

# the 'Class or Function to Process Features' transformer parameter

class FeatureProcessor(object):

  def __init__(self):

    pass

  def input(self,feature):

    somelists = p>'a1', 'b1', 'c1'],3'a1', 'b1', 'c1']]

    for elements in itertools.product(*somelists):

      print (elements)

     

    self.pyoutput(feature)

     

  def close(self):

    pass

Can you share how the list of routes is stored in your data?

@ebygomm please find attached a screenshot of the sample data i am using.

 

@ebygomm please find attached a screenshot of the sample data i am using.

 

And are you hoping to get 9 features resulting from your python caller in this example where there are 3 routes at an intersection?

 

So including A1-A1, B1-B1, C1-C1 even though presumably these wouldn't be valid movements across an intersection?

 

If you can explain exactly what you are looking to achieve there are probably ways to get this without resorting to python

And are you hoping to get 9 features resulting from your python caller in this example where there are 3 routes at an intersection?

 

So including A1-A1, B1-B1, C1-C1 even though presumably these wouldn't be valid movements across an intersection?

 

If you can explain exactly what you are looking to achieve there are probably ways to get this without resorting to python

Yes I am hoping to get 9 features including the duplicates a1-a1, b1-b1, c1-c1 which i plan to eliminate using the transformer in fme. You can factor taking the duplicates out though so having 6 features excluding a1-a1, b1-b1, c1-c1 also works for me. FYI The 6 features or 9 feature must have its intersect features within the attributes too differentiating it with a prefix.

So you can do this quite easily in an inline querier

 

image

So you can do this quite easily in an inline querier

 

image

I was trying to stay away from doing this using SQL since i have thousands of features to look through and felt python will be faster to run.

So you can do this quite easily in an inline querier

 

image

The excel sheet was just a sample to show what my data looks like. My datasource will be MS SQL Server

So you have thousands of features, but presumably each intersection has a much smaller number of possible routes associated with it?

 

The first step I think would be to look at creating a feature for each intersection with a list of routes, e.g. with a list builder group by intersection and add the selected attribute identifying the route

 

You could then do something like this in a python caller

import fme
import fmeobjects
import itertools
 
class FeatureProcessor(object):
    def __init__(self):
        pass
    def input(self,feature):
        mylist=feature.getAttribute('_list{}.NLF_ID')
        for elements in set(itertools.permutations(mylist,2)):
           feature.setAttribute("NLF_ID", elementsÂ0])
           feature.setAttribute("NLF_ID_1",elements 1])
           self.pyoutput(feature)
        
    def close(self):
        pass

 

So you have thousands of features, but presumably each intersection has a much smaller number of possible routes associated with it?

 

The first step I think would be to look at creating a feature for each intersection with a list of routes, e.g. with a list builder group by intersection and add the selected attribute identifying the route

 

You could then do something like this in a python caller

import fme
import fmeobjects
import itertools
 
class FeatureProcessor(object):
    def __init__(self):
        pass
    def input(self,feature):
        mylist=feature.getAttribute('_list{}.NLF_ID')
        for elements in set(itertools.permutations(mylist,2)):
           feature.setAttribute("NLF_ID", elementsÂ0])
           feature.setAttribute("NLF_ID_1",elements 1])
           self.pyoutput(feature)
        
    def close(self):
        pass

 

Thank you for your time @ebygomm​ . Doing a group by using this field which is same id for all intersecting routes, I get a single feature instead of 3 features as seen in the sample data.  The only unique and PK field here is the Cross_Route_ID. 

NLF_ID	| Measure|	|RT_Name| 	|Cross_RouteID|	 | Inter_ID
a1	         8.666	              A-1	                 111111	            987654321
b1	         4.217	              B-1	               222222	            987654321
c1	           9.624                C-1	               333333	             987654321

 

Thank you for your time @ebygomm​ . Doing a group by using this field which is same id for all intersecting routes, I get a single feature instead of 3 features as seen in the sample data.  The only unique and PK field here is the Cross_Route_ID. 

NLF_ID	| Measure|	|RT_Name| 	|Cross_RouteID|	 | Inter_ID
a1	         8.666	              A-1	                 111111	            987654321
b1	         4.217	              B-1	               222222	            987654321
c1	           9.624                C-1	               333333	             987654321

 

That's expected, it is a single feature which contains a list of the routes. The python script then uses this list to get the permutations, and outputs 6 features, with the two routes in separate attributes

Thank you for your time @ebygomm​ . Doing a group by using this field which is same id for all intersecting routes, I get a single feature instead of 3 features as seen in the sample data.  The only unique and PK field here is the Cross_Route_ID. 

NLF_ID	| Measure|	|RT_Name| 	|Cross_RouteID|	 | Inter_ID
a1	         8.666	              A-1	                 111111	            987654321
b1	         4.217	              B-1	               222222	            987654321
c1	           9.624                C-1	               333333	             987654321

 

Thank you! That worked for me! However, I would want to include all the other attributes in my data instead of just exposing NLF_ID, NLF_ID_1. 

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