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

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

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

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)

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.

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

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.income scales 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

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

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

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.

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?