8.2

### 29Pyret for Racketeers and Schemers

If you’ve programmed before in a language like Scheme or the student levels of Racket (or the WeScheme programming environment), or for that matter even in certain parts of OCaml, Haskell, Scala, Erlang, Clojure, or other languages, you will find many parts of Pyret very familiar. This chapter is specifically written to help you make the transition from (student) Racket/Scheme/WeScheme (abbreviated “RSW”) to Pyret by showing you how to convert the syntax. Most of what we say applies to all these languages, though in some cases we will refer specifically to Racket (and WeScheme) features not found in Scheme.

In every example below, the two programs will produce the same results.

#### 29.1Numbers, Strings, and Booleans

Numbers are very similar between the two. Like Scheme, Pyret implements arbitrary-precision numbers and rationals. Some of the more exotic numeric systems of Scheme (such as complex numbers) aren’t in Pyret; Pyret also treats imprecise numbers slightly differently.

 RSW Pyret 1 1

 RSW Pyret 1/2 1/2

 RSW Pyret #i3.14 ~3.14

Strings are also very similar, though Pyret allows you to use single-quotes as well.

 RSW Pyret "Hello, world!" "Hello, world!"

 RSW Pyret "\"Hello\", he said" "\"Hello\", he said"

 RSW Pyret "\"Hello\", he said" '"Hello", he said'

Booleans have the same names:

 RSW Pyret true true

 RSW Pyret false false

#### 29.2Infix Expressions

Pyret uses an infix syntax, reminiscent of many other textual programming languages:

 RSW Pyret (+ 1 2) 1 + 2

 RSW Pyret (* (- 4 2) 5) (4 - 2) * 5

Note that Pyret does not have rules about orders of precedence between operators, so when you mix operators, you have to parenthesize the expression to make your intent clear. When you chain the same operator you don’t need to parenthesize; chaining associates to the left in both languages:
 RSW Pyret (/ 1 2 3 4) 1 / 2 / 3 / 4
These both evaluate to 1/24.

#### 29.3Function Definition and Application

Function definition and application in Pyret have an infix syntax, more reminiscent of many other textual programming languages. Application uses a syntax familiar from conventional algebra books:
 RSW Pyret (dist 3 4) dist(3, 4)

Application correspondingly uses a similar syntax in function headers, and infix in the body:

RSW

Pyret

 (define (dist x y) (sqrt (+ (* x x) (* y y))))

fun dist(x, y):
num-sqrt((x * x) +
(y * y))
end

#### 29.4Tests

There are essentially three different ways of writing the equivalent of Racket’s check-expect tests. They can be translated into check blocks:
 RSW Pyret (check-expect 1 1) check: 1 is 1 end
Note that multiple tests can be put into a single block:

RSW

Pyret

 (check-expect 1 1) (check-expect 2 2)

check:
1 is 1
2 is 2
end

The second way is this: as an alias for check we can also write examples. The two are functionally identical, but they capture the human difference between examples (which explore the problem, and are written before attempting a solution) and tests (which try to find bugs in the solution, and are written to probe its design).

The third way is to write a where block to accompany a function definition. For instance:

fun double(n):
n + n
where:
double(0) is 0
double(10) is 20
double(-1) is -2
end

These can even be written for internal functions (i.e., functions contained inside other functions), which isn’t true for check-expect.

In Pyret, unlike in Racket, a testing block can contain a documentation string. This is used by Pyret when reporting test successes and failures. For instance, try to run and see what you get:

check "squaring always produces non-negatives":
(0 * 0) is 0
(-2 * -2) is 4
(3 * 3) is 9
end

This is useful for documenting the purpose of a testing block.

Just as in Racket, there are many testing operators in Pyret (in addition to is). See the documentation.

#### 29.5Variable Names

Both languages have a fairly permissive system for naming variables. While you can use CamelCase and under_scores in both, it is conventional to instead use what is known as kebab-case.This name is inaccurate. The word “kebab” just means “meat”. The skewer is the “shish”. Therefore, it ought to at least be called “shish kebab case”. Thus:
 RSW Pyret this-is-a-name this-is-a-name
Even though Pyret has infix subtraction, the language can unambiguously tell apart this-name (a variable) from this - name (a subtraction expression) because the - in the latter must be surrounded by spaces.

Despite this spacing convention, Pyret does not permit some of the more exotic names permitted by Scheme. For instance, one can write
 (define e^i*pi -1)
in Scheme but that is not a valid variable name in Pyret.

#### 29.6Data Definitions

Pyret diverges from Racket (and even more so from Scheme) in its handling of data definitions. First, we will see how to define a structure:
 RSW Pyret (define-struct pt (x y)) data Point: | pt(x, y) end

This might seem like a fair bit of overkill, but we’ll see in a moment why it’s useful. Meanwhile, it’s worth observing that when you have only a single kind of datum in a data definition, it feels unwieldy to take up so many lines. Writing it on one line is valid, but now it feels ugly to have the | in the middle:

data Point: | pt(x, y) end

Therefore, Pyret permits you to drop the initial |, resulting in the more readable

data Point: pt(x, y) end

Now suppose we have two kinds of points. In the student languages of Racket, we would describe this with a comment:
 ;; A Point is either ;; - (pt number number), or ;; - (pt3d number number number)
In Pyret, we can express this directly:

data Point:
| pt(x, y)
| pt3d(x, y, z)
end

In short, Racket optimizes for the single-variant case, whereas Pyret optimizes for the multi-variant case. As a result, it is difficult to clearly express the multi-variant case in Racket, while it is unwieldy to express the single-variant case in Pyret.

For structures, both Racket and Pyret expose constructors, selectors, and predicates. Constructors are just functions:
 RSW Pyret (pt 1 2) pt(1, 2)
Predicates are also functions with a particular naming scheme:
 RSW Pyret (pt? x) is-pt(x)
and they behave the same way (returning true if the argument was constructed by that constructor, and false otherwise). In contrast, selection is different in the two languages (and we will see more about selection below, with cases):
 RSW Pyret (pt-x v) v.x
Note that in the Racket case, pt-x checks that the parameter was constructed by pt before extracting the value of the x field. Thus, pt-x and pt3d-x are two different functions and neither one can be used in place of the other. In contast, in Pyret, .x extracts an x field of any value that has such a field, without attention to how it was constructed. Thus, we can use .x on a value whether it was constructed by pt or pt3d (or indeed anything else with that field). In contrast, cases does pay attention to this distinction.

#### 29.7Conditionals

There are several kinds of conditionals in Pyret, one more than in the Racket student languages.

General conditionals can be written using if, corresponding to Racket’s if but with more syntax.

RSW

Pyret

 (if full-moon "howl" "meow")

if full-moon:
"howl"
else:
"meow"
end

RSW

Pyret

 (if full-moon "howl" (if new-moon "bark" "meow"))

if full-moon:
"howl"
else if new-moon:
"bark"
else:
"meow"
end

Note that if includes else if, which makes it possible to list a collection of questions at the same level of indentation, which if in Racket does not have. The corresponding code in Racket would be written
 (cond [full-moon "howl"] [new-moon "bark"] [else "meow"])
to restore the indentation. There is a similar construct in Pyret called ask, designed to parallel cond:

| full-moon then: "howl"
| new-moon then:  "bark"
| otherwise:      "meow"
end

In Racket, we also use cond to dispatch on a datatype:
 (cond [(pt? v)   (+ (pt-x v) (pt-y v))] [(pt3d? v) (+ (pt-x v) (pt-z v))])
We could write this in close parallel in Pyret:

| is-pt(v)   then: v.x + v.y
| is-pt3d(v) then: v.x + v.z
end

or even as:

if is-pt(v):
v.x + v.y
else if is-pt3d(v):
v.x + v.z
end

(As in Racket student languages, the Pyret versions will signal an error if no branch of the conditional matched.)

However, Pyret provides a special syntax just for data definitions:

cases (Point) v:
| pt(x, y)      => x + y
| pt3d(x, y, z) => x + z
end

This checks that v is a Point, provides a clean syntactic way of identifying the different branches, and makes it possible to give a concise local name to each field position instead of having to use selectors like .x. In general, in Pyret we prefer to use cases to process data definitions. However, there are times when, for instance, there many variants of data but a function processes only very few of them. In such situations, it makes more sense to explicitly use predicates and selectors.

#### 29.8Lists

In Racket, depending on the language level, lists are created using either cons or list, with empty for the empty list. The corresponding notions in Pyret are called link, list, and empty, respectively. link is a two-argument function, just as in Racket:

 RSW Pyret (cons 1 empty) link(1, empty)

 RSW Pyret (list 1 2 3) [list: 1, 2, 3]

Note that the syntax [1, 2, 3], which represents lists in many languages, is not legal in Pyret: lists are not privileged with their own syntax. Rather, we must use an explicit constructor: just as [list: 1, 2, 3] constructs a list, [set: 1, 2, 3] constructs a set instead of a list.In fact, we can create our own constructors and use them with this syntax.

Exercise

Try typing [1, 2, 3] and see the error message.

This shows us how to construct lists. To take them apart, we use cases. There are two variants, empty and link (which we used to construct the lists):

RSW

Pyret

 (cond [(empty? l) 0] [(cons? l) (+ (first l) (g (rest l)))])

cases (List) l:
| empty      => 0
| link(f, r) => f + g(r)
end

It is conventional to call the fields f and r (for “first” and “rest”). Of course, this convention does not work if there are other things by the same name; in particular, when writing a nested destructuring of a list, we conventionally write fr and rr (for “first of the rest” and “rest of the rest”).

#### 29.9First-Class Functions

The equivalent of Racket’s lambda is Pyret’s lam:
 RSW Pyret (lambda (x y) (+ x y)) lam(x, y): x + y end

#### 29.10Annotations

In student Racket languages, annotations are usually written as comments:
 ; square: Number -> Number ; sort-nums: List -> List ; sort: List * (T * T -> Boolean) -> List
In Pyret, we can write the annotations directly on the parameters and return values. Pyret will check them to a limited extent dynamically, and can check them statically with its type checker. The corresponding annotations to those above would be written as

fun square(n :: Number) -> Number: ...

fun sort-nums(l :: List<Number>) -> List<Number>: ...

fun sort<T>(l :: List<T>, cmp :: (T, T -> Boolean)) -> List<T>: ...

Though Pyret does have a notation for writing annotations by themselves (analogous to the commented syntax in Racket), they aren’t currently enforced by the language, so we don’t include it here.

#### 29.11What Else?

If there are other parts of Scheme or Racket syntax that you would like to see translated, please let us know.

### 30Pyret vs. Python

For the curious, we offer a few examples here to justify our frustration with Python for early programming.

 Python Pyret Python exposes machine arithmetic by default. Thus, by default, 0.1 + 0.2 is not the same as 0.3. (We hope you’re not surprised to hear this.) Why this is the case is a fascinating subject of study, but we consistently find it a distraction for first-time programmers writing programs with arithmetic. And if we handwave the details of floating point aside, are we taking our claims of program reliability seriously? Pyret implements exact arithmetic, including rationals, by default. In Pyret, 0.1 + 0.2 really is equal to 0.3. Where a computation must return an inexact number, Pyret does it explicitly: a key requirement in a curriculum built on reliability.

 Python Pyret Understanding the difference between creating a variable and updating its value is a key learning outcome, along with understanding variables’ scopes. Python explicitly conflates declaration with update, and has a tangled history with scope. Pyret is statically scoped, and goes to great lengths—e.g., in the design of a query language for tables—to maintain it. There is no ambiguity in Pyret’s syntax for working with variables.

 Python Pyret Python has a weakly-defined, optional mechanism of annotations that was added late in the language’s design, which conflates values and types. Drawing on lessons learned from our several prior research projects on adding types to languages after-the-fact, Pyret was designed with typability from the start, with several subtle design choices to enable this. Pyret also has support (currently dynamic) for refinement-type annotations.

 Python Pyret Python has weak built-in support for testing. While it has extensive professional libraries to test software, these impose a non-trivial burden on learners, as a result of which most introductory curricula do not use them. First, a curriculum that proclaims reliability must put testing at its heart. Second, our pedagogy places heavy emphasis on the use of examples, and in particular the building-up of abstractions from concrete instances. For both these reasons, Pyret has extensive support in the language itself—not through optional, external libraries—for writing examples and tests, and provides direct language support for many of the interesting and tricky issues that arise when doing so.

 Python Pyret Images are not values in the language. You can write a program to produce an image, but you can’t just view it in your programming environment. Images are values. Pyret can print an image just like it can a string or a number (and why not?). Images are fun values, but they aren’t frivolous: they are especially useful for demystifying and explaining important but abstract issues like function composition.

 Python Pyret The language doesn’t have a built-in notion of reactive programs. Reactivity is a core concept in the language, and the subject of both design and implementation research.

 Python Pyret Python’s error messages are not added with novices as a primary audience. Novices make many errors. They can be especially intimidated by error reports, and can feel discouraged about causing errors. Thus, Pyret’s error messages are the result of nearly a decade of research. In fact, some educators have created pedagogic techniques that explicitly rely on the nature and presentation of information in Pyret’s errors.

 Python Pyret Python has begun to suffer from complexity creep that we believe serves professionals at the expense of novices. For example, the result of map in Python is actually a special generator value. This can lead to outcomes requiring extra explanation, like map(str, [1, 2, 3]) producing . Type hints (discussed above) are another example. Since Pyret’s target audience is novice programmers programming in the style of this book, our primary goal when adding any feature is to preserve the early experience and avoid surprises.

 Python Pyret Data definitions are central to computer science, but Python over-relies on built-in data structures (especially dictionaries) and makes user-defined ones unwieldy to create. Pyret borrows from the rich tradition of languages like Standard ML, OCaml, and Haskell to provide algebraic datatypes, whose absence often forces programmers to engage in unwieldy (and inefficient) encoding tricks.

 Python Pyret Python has several more rough corners that can lead to unexpected and undesirable outcomes. For instance, = sometimes introduces new variables and sometimes rebinds them. A function where a student forgot to return a value doesn’t result in an error but silently returns None. Python has a complicated table that describes which values are true and which are false. And so on. Pyret is designed from the ground-up to avoid all these problems.

### 31Comparing This Book to HtDP

This book (DCIC) is often compared to How to Design Programs (HtDP), from which it draws enormous inspiration. Here we briefly describe how the two books compare.

At a high level they are very similar:
• Both are built around the centrality of data structure. Both want to provide methods for designing programs. Both start with functional programming but transition to (and take very seriously) stateful imperative programming.

• Both are built around languages carefully designed with education in mind. The languages provide special support for writing examples and tests; error reporting designed for beginners; built-in images and reactivity. The languages eschew weird gotchas (in a way that Python does not: see Pyret vs. Python or, if you want to read much more, this paper.

and so on. To call these “similarities” is, however, a disservice. DCIC copied these ideas from HtDP; in some cases, HtDP even pioneered them.

Now for the differences. Note that they are differences now. Some ideas from DCIC are going to HtDP, and over time more may intermingle.
• The most obvious is that DCIC is in Pyret. HtDP has tons of good ideas, all ignored because it Racket, whose syntax some people (especially some educators) dislike. We built Pyret to embody good ideas we’d learned from the Racket student languages and other good ideas of our own, but package them in a familiar syntax. But as you can see, the two languages are not actually that far apart: Pyret for Racketeers and Schemers.

• The next most obvious thing is that DCIC also includes Python. HtDP has a (not formally published) follow-up that teaches program design in Java. In contrast, we wanted to integrate the transition to Python into DCIC itself. There’s much to be learned from the contrast! In particular, Pyret and its environment were carefully designed around pedagogic ideas for teaching state. Python was not, despite the ubiquity and difficulty of state! So there’s a lot to be gained, when introducing state, to contrast them.

• Next, DCIC has a lot algorithmic content, whereas HtDP has almost none. DCIC covers, for instance, Big-O analysis [Predicting Growth]. It even has a section on amortized analysis [Halloween Analysis]. It goes up through some graph algorithms. This is far more advanced material than HtDP covers.

Those are most of the differences. They’re visible (some even evident) from glancing through the table of contents. However, there is one very deep difference that will not be apparent to most readers, which we discuss below.

HtDP is built around a beautiful idea: the data structures shown grow in complexity in set-theoretic terms. Therefore it begins with atomic data, then has fixed-size data (structures), then unbounded collections (lists) of atomic data, pairs of lists, lists of structures, and so on. All built up, systematically, in a neat progression.

However, this has a downside. You have to imagine what the data represent (this number is an age, that string is a name, that list is of GDPs), but they’re idealized. In a way the most real data are actually images! After that (which come early), all the data are “virtualized” and imaginary.

Our view is that the most interesting data are lists of structures. (Remember those? They’re complicated and come some ways down the progression.) You might find this surprising; if so, we give you another name for them: tables. Tables are ubiquitous. Even companies process and publish them; even primary school students recognize and use them. They are perhaps our most important universal form of structured data.

Even better, lots of real-world data are provided as tables. You don’t have to imagine things or make up fake GDPs like 1, 2, and 3. You can get actual GDPs or populations or movie revenues or sports standings or whatever interests you. (Ideally, cleansed and curated.) We believe that just about every student—even every child—is a nascent data scientist (at least when it’s convenient to them). Even a child who says “I hate math” will often gladly use statistics to argue for their favorite actor or sportsperson or whatever. We just have to find what motivates them.

Buut there’s a big catch! A key feature of HtDP is that for every level of datatype, it provides a Design Recipe for programming over that datatype. Lists-of-structs are complex. So is their programming recipe. And we want to put them near the beginning! Furthermore, the Design Recipe is dangerous to ignore. Students struggle with blank pages and often fill them up with bad code, which they then get attached to. The Design Recipe provides structure, scaffolding, reviewability, and much more. It’s cognitively grounded in schemas.

So over the past few years, we’ve been working on different program design methods that address the same ends through different means. A lot of our recent education research has been putting new foundations in place. It’s very much work in progress. And DCIC is the distillation of those efforts. As we have new results, we’ll be weaving them into DCIC (and probably HtDP too). Stay tuned!

### 32Release Notes

This is a summary of updates made with each release of the book (excluding typos and other minor fixes).

Version 2022-01-25

• Consistently renamed the definitions and interactions window to the definitions and interactions pane.

• Moved the material on working with variables out of the intro to Python section and into the Programming with State section. Mutation of structured data moved before variable mutation within the Programming with State section.

• Added a comparison between DCIC and HtDP.

• The include line for the DCIC libraries at this version is

include shared-gdrive(
"dcic-2021",
"1wyQZj_L0qqV9Ekgr9au6RX2iqt2Ga8Ep")

Version 2021-08-21 – the original release

### 33Glossary

bandwidth

The bandwidth between two network nodes is the quantity of data that can be transferred in a unit of time between the nodes.

cache

A cache is an instance of a space-time tradeoff: it trades space for time by using the space to avoid recomputing an answer. The act of using a cache is called caching. The word “cache” is often used loosely; we use it only for information that can be perfectly reconstructed even if it were lost: this enables a program that needs to reverse the trade—i.e., use less space in return for more time—to do so safely, knowing it will lose no information and thus not sacrifice correctness.

coinduction

Coinduction is a proof principle for mathematical structures that are equipped with methods of observation rather than of construction. Conversely, functions over inductive data take them apart; functions over coinductive data construct them. The classic tutorial on the topic will be useful to mathematically sophisticated readers.

idempotence

An idempotent operator is one whose repeated application to any value in its domain yields the same result as a single application (note that this implies the range is a subset of the domain). Thus, a function $$f$$ is idempotent if, for all $$x$$ in its domain, $$f(f(x)) = f(x)$$ (and by induction this holds for additional applications of $$f$$).

invariants

Invariants are assertions about programs that are intended to always be true (“in-vary-ant”—never varying). For instance, a sorting routine may have as an invariant that the list it returns is sorted.

latency

The latency between two network nodes is the time it takes for packets to go between the nodes.

metasyntactic variable

A metasyntactic variable is one that lives outside the language, and ranges over a fragment of syntax. For instance, if we write “for expressions e1 and e2, the sum e1 + e2”, we do not mean the programmer literally wrote “e1” in the program; rather we are using e1 to refer to whatever the programmer might write on the left of the addition sign. Therefore, e1 is metasyntax.

packed representation

At the machine level, a packed representation is one that ignores traditional alignment boundaries (in older or smaller machines, bytes; on most contemporary machines, words) to let multiple values fit inside or even spill over the boundary.

For instance, say we wish to store a vector of four values, each of which represents one of four options. A traditional representation would store one value per alignment boundary, thereby consuming four units of memory. A packed representation would recognize that each value requires two bits, and four of them can fit into eight bits, so a single byte can hold all four values. Suppose instead we wished to store four values representing five options each, therefore requiring three bits for each value. A byte- or word-aligned representation would not fundamentally change, but the packed representation would use two bytes to store the twelve bits, even permitting the third value’s three bits to be split across a byte boundary.

Of course, packed representations have a cost. Extracting the values requires more careful and complex operations. Thus, they represent a classic space-time tradeoff: using more time to shrink space consumption. More subtly, packed representations can confound certain run-time systems that may have expected data to be aligned.

parsing

Parsing is, very broadly speaking, the act of converting content in one kind of structured input into content in another. The structures could be very similar, but usually they are quite different. Often, the input format is simple while the output format is expected to capture rich information about the content of the input. For instance, the input might be a linear sequence of characters on an input stream, and the output might be expected to be rich and tree-structured according to some datatype: most program and natural-language parsers are faced with this task.

reduction

Reduction is a relationship between a pair of situations—problems, functions, data structures, etc.—where one is defined in terms of the other. A reduction R is a function from situations of the form P to ones of the form Q if, for every instance of P, R can construct an instance of Q such that it preserves the meaning of P. Note that the converse strictly does not need to hold.