Tekoa: A Domain-Specific Language for Defining Opus Variables

1. Tekoa: A Domain-Specific Language for Defining Opus Variables • The variable concept in Opus • Problems with defining Opus variables in Python • Tekoa examples • Syntax • Status and Plans for Further Work • User discussion & wish list

2. The Variable Concept in Opus • A model variable (or just variable) is an attribute of actors or geographies used in a model. • Variables are properties of datasets, e.g. a gridcell dataset or a parcel dataset • Examples: • Population density • Land cost • Travel time to city center • Two kinds: • Primary attribute • Derived attribute • Not the same as “variable” as used in programming languages

3. Implementing Variables • Opus implements a model variable as a subclass of the Python class Variable • Uses lazy evaluation • Methods • dependencies() • compute() • This has worked very well from the point of view of accessing and computing variables • However, defining a new variable (even a simple one) requires writing a new Python class, ideally including a unit test

4. Variables in Python vs. Tekoa % definition of zone.average_income in Python from opus_core.variables.variable import Variable class average_income(Variable): def dependencies(self): return ["household.income", "zone.zone_id”, "urbansim_parcel.household.zone_id”] def compute(self, dataset_pool): households = dataset_pool.get_dataset("household”) return self.get_dataset().aggregate_dataset_over_ids( households, "mean", "income") % *** code for unit tests omitted *** ______________________________________________ % Tekoa definition average_income = zone.aggregate(household.income, function=mean)

5. Tekoa - Aggregation through multiple geographies % employment in the ‘large_area’ geography employment=large_area.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel, zone, faz]) Explanation: • number_of_jobs is an attribute of building. We then aggregate this up to the parcel level, then the zone level, then the faz level, and finally the large_area level, to find the employment in the large_area. • The ‘employment=’ part gives an alias for the expression, so that it displays nicely in the resulting indicator.

6. Tekoa - More Complex Example % definition of parcel.is_pre_1940 % is the average building age for a parcel % older than 1940? is_pre_1940 = parcel.aggregate(building.year_built *numpy.ma.masked_where(urbansim_parcel.building.has_valid_year_built==0, 1), function=mean) < 1940

7. Syntax • Syntax is a subset of Python • An expression can be: • The name of a variable • A function or operator applied to other expressions • All of the numpy functions and operators are available, e.g. exp, sqrt, +, -, ==, < • numpy-style array and matrix operations — for example, 1.2*household.incomescales all the elements of the array of incomes • Aggregation • Intermediates argument -- list of intermediate datasets • Function - can be sum, mean, median, min, max • Disaggregation also supported

8. Interaction Sets and Expressions • InteractionDataset is a subclass of Dataset, which stores its data as a 2-d array • For example, for household location choice we are interested in the interaction between household income and cost per residential unit • The expression ln(household.income) * zone.average_housing_cost)returns an nm array where n is the number of households and m is the number of zones

9. Implementation • When a new Tekoa expression is encountered, the system: • parses it (using the Python parser) • analyzes the expression for dependencies on other variables and special methods (e.g. aggregate, disaggregate) • compiles a new Python class that defines the variable, including a dependencies() and a compute() method • Recursively compiles a new variable when aggregating/disaggretating an expression • Consequence: efficiency of expressions is the same as for the old-style definitions • The system maintains a cache of expressions that have already been compiled, so that if the same expression is encountered again the previously-compiled class is just returned

10. More Examples and Documentation • For lots of examples, see the aliases.py for various datasets in the urbansim_parcel package, e.g. • urbansim_parcel/buildings/aliases.py • urbansim_parcel/job/aliases.py • … • The language is described in Section 6.4 of the Opus/Urbansim User Manual • Also see: Alan Borning, Hana Sevcikova, and Paul Waddell, “A Domain-Specific Language for Urban Simulation Variables”, to appear, International Conference on Digital Government Research, Montreal, Canada, May 2008.

11. Tekoa Status and Future Work • Benefits: • significantly reduced code size (factor of 7 for urbansim gridcell vs urbansim parcel) • increased modeler productivity • Additional features to implement: • Parameterized expressions. For example is_pre_1940 should really be is_pre(1940) • Better error detection and messages • Tutorial & advanced techniques • Replace old variable definitions in the code base for gridcell model system with expressions (big job) • Integration of expressions with GUI • User discussion & wish list?