Why I Like Functional Composition

After spending years developing a level of mastery over Object Oriented Design Patterns, I'm having a lot of fun understanding Functional Design Patterns.

The OO Design Patterns are helpful because they're concrete expressions of the SOLID design principles. Much of the "Gang of Four" book demonstrates the Interface Segregation, Dependency Injection, and Liskov Substitution Principles nicely. They point the way for implementing the Open/Closed and the Single Responsibility Principles.

For Functional Programming, there are some equivalent ideas, with distinct implementation techniques. The basic I, D, L, and S principles of SOLID apply, but they have a different look in a functional programming context. The Open/Closed principle takes on a radically different look because it turns into an exercise in Functional Composition.

For our example today, I'm building an Arduino device that collects GPS data. (The context for this device is the subject of many posts coming in the future.)

GPS devices generally follow the NMEA 0183 protocol and transmit their data as sentences with various kinds of formats. In particular, the GPRMC and GPVTG sentences contain speed over ground (SOG) data.

I've been collecting data in my apartment. And it's odd-looking. I've also collected data on my boat, and it doesn't seem to look quite so odd. Here's the analysis I used to make a more concrete conclusion.

def sog_study(source_path = Path("gps_data_gsa.csv")):
    with source_path.open() as source_file:
        rdr = csv.DictReader(source_file)
        sog_seq = list(map(float, filter(None, (row['SOG'] for row in rdr))))
        print("max {}\tMean {}\tStdev {}".format(
            max(sog_seq), statistics.mean(sog_seq), statistics.stdev(sog_seq)))

This is a small example of functional composition to build a sequence of SOG reports for analysis.

This code opens a CSV file with data extracted from the Arduino. There was some reformatting and normalizing done in a separate process: This resulted in a file in a format suitable for the processing shown above.

The compositional part of this is the list(map(float, filter(None, generator))) processing.

The (row['SOG'] for row in rdr) generator can iterate over all values from the SOG column. The filter(None, generator) will drop all None objects from the results, assuring that irrelevant sentences are ignored.

Given an iterable that can produce SOG values, the map(float, iterable) will convert the input strings into useful numbers. The surrounding list() creates a concrete list object to support summary statistics computations.

I'm really delighted with this kind of short, focused functional programming.

"But wait," you say. "How is that anything like the SOLID OO design?"

Remember to drop the OO notions. This is functional composition, not object composition.

ISP: The built-in functions all have well-segregated interfaces. Each one does a small, isolated job.

LSP: The concept of an iterable supports the Liskov Substitution Principle: it's easy to insert additional or different processing as long as we define functions that accept iterables as an argument and yield their values or return an iterable result.

For example.

def sog_gen(csv_reader):
    for row in csv_reader:
        yield row['SOG']

We've expanded the generator expression, (row['SOG'] for row in rdr), into a function. We can now use sog_gen(rdr) instead of the generator expression. The interfaces are the same, and the two expressions enjoy Liskov Substitution.

To be really precise, annotations with type hints can clarify this. Something like sog_gen(rdr: Iterable[Dict[str, str]]) -> Iterable[str] would clarify this.

DIP: If we want to break this down into separate assignment statements, we can see how a different function can easily be injected into the processing pipeline. We could define a higher-order function that accepted functions like sog_gen, float, statistics.mean, etc., and then created the composite expression.

OCP: Each of the component functions is closed to modification but open to extension. We might want to do something like this: map_float = lambda source: map(float, source). The map_float() function extends map() to include a float operation. We might even want to write something like this. map_float = lambda xform, source: map(xform, map(float, source)). This would look more like map(), with a float operation provided automatically.

SRP: Each of the built-in functions does one thing. The overall composition builds a complex operation from simple pieces.

The composite operation has two features which are likely to change: the column name and the transformation function. Perhaps we might rename the column from 'SOG' to 'sog'; perhaps we might use decimal() instead of float(). There are a number of less-likely changes. There might be a more complex filter rule, or perhaps a more complex transformation before computing the statistical summary. These changes would lead to a different composition of the similar underlying pieces.