Tekoa a domain specific language for defining opus variables
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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.

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Tekoa a domain specific language for defining opus variables
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
The Variable Concept in Opus Variables

  • 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
Implementing Variables 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
Variables in Python vs. Tekoa Variables

% 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”,


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
Tekoa - Aggregation through multiple geographies Variables

% employment in the ‘large_area’ geography


intermediates=[parcel, zone, faz])


  • 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
Tekoa - More Complex Example Variables

% 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 Variables

  • 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

Interaction sets and expressions
Interaction Sets and Expressions Variables

  • 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 Variables

  • 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
More Examples and Documentation Variables

  • 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
Tekoa Status and Future Work Variables

  • 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?