Loading in 5 sec....

Tekoa: A Domain-Specific Language for Defining Opus VariablesPowerPoint Presentation

Tekoa: A Domain-Specific Language for Defining Opus Variables

- 99 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Tekoa: A Domain-Specific Language for Defining Opus Variables' - wyoming-gallegos

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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 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 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

% 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 Variables

% 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 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 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 nm 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 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 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?

Download Presentation

Connecting to Server..