# Geometric Constraint Solvers Part 1: Algebraic Expressions

A really powerful tool in Computer Aided Design (CAD) is the ability to apply “constraints” to your drawing. Constraints are a really powerful tool, allowing the drafter to declare how parts of their drawing are related, then letting the CAD program figure out how parameters can be manipulated in such a way that

You can think of a constraint as some sort of mathematical relationship between two or more parameters.

Some examples are:

• “This interior angle is 45°”
• “That line is vertical”
• “Side A is perpendicular to side B”

Graphically they’ll be displayed something like this:

These constraints are declared mathematically, so a “This line is vertical” constraint may be written as $line.start.x - line.end.x = 0$ and $line.start.z - line.end.z = 0$ (assuming the $x$ axis is to the right and the $z$ comes out of the page).

In response to input from the user (e.g. they click on the line and drag it to the left), a constraint system will feed the perturbation into the system of equations (e.g. the $line.start.y$ changes by $-0.1$ units) and based on the available constraints and tie-breaking heuristics, it will figure out how much each remaining variable must change. Execute at 60 FPS and you’ve got an interactive, parametric CAD application.

A constraint solver is a fairly complex system, but the first step is teaching it how to manipulate and evaluate abstract maths.

## The Expression Tree

For this constraint solver we need a way to represent algebraic expressions like $x - 5$ or $sin(x) + y*y - x*y$, and the most natural form is the Abstract Syntax Tree.

In this language, an expression can be:

• A (possibly unnamed) parameter
• A floating-point constant
• A call to a function with a single argument
• Negation, and
• A binary operation (e.g. x + y, where x and y are the left and right operands, and we’re using the + operator)

Our constraint solver is considerably less complex than a general purpose programming language, so most of our Abstract Syntax Tree can fit into a couple simple types.

// src/expr.rs

use std::rc::Rc;
use smol_str::SmolStr;

/// An expression.
#[derive(Debug, Clone, PartialEq)]
pub enum Expression {
/// A free variable (e.g. x).
Parameter(Parameter),
/// A fixed constant (e.g. 3.14).
Constant(f64),
/// An expression involving two operands.
Binary {
/// The left operand.
left: Rc<Expression>,
/// The right operand.
right: Rc<Expression>,
/// The binary operation being executed.
op: BinaryOperation,
},
/// Negate the expression.
Negate(Rc<Expression>),
/// Invoke a builtin function.
FunctionCall {
/// The name of the function being called.
name: SmolStr,
/// The argument passed to this function call.
argument: Rc<Expression>,
},
}

/// A free variable.
#[derive(Debug, Clone, PartialEq, PartialOrd, Ord, Eq, Hash)]
pub enum Parameter {
/// A variable with associated name.
Named(SmolStr),
/// An anonymous variable generated by the system.
Anonymous { number: usize },
}

/// An operation that can be applied to two arguments.
#[derive(Debug, Copy, Clone, PartialEq)]
pub enum BinaryOperation {
Plus,
Minus,
Times,
Divide,
}


This should be fairly straightforward if you are familiar with Rust, although I’d like to direct your attention to two details…

1. Text fields use a smol_str::SmolStr instead of a normal String
2. Each child node is behind a reference-counted pointer

We’re using SmolStr for something called the Small String Optimisation. This is where small amounts of text can be stored inline on the stack (in place of the data pointer, length, and capacity fields in a normal String) to skip a heap allocation and a layer of indirection.

The small string optimisation is useful when you’re dealing with a large number of small strings (like programming language identifiers) because it helps avoid heap fragmentation and locality. Cloning a SmolStr is also quite cheap because even if it isn’t small, a reference-counted string (analogous to Arc<str>) is used when we need to store large strings on the heap.

A conscious design decision is to make the AST immutable. Most operations applied to an Expression will leave part of the tree unchanged, and seeing as we already need to add a layer of indirection to prevent infinitely sized types, we can use reference-counted pointers to share common sub-expressions.

This should reduce memory usage and hopefully increase performance because shared nodes are more likely to be in cache.

## Pretty-Printing and Other Useful Methods

To make our Expression type easier to work with we can implement several traits from the standard library.

Arguably the most useful of these is std::fmt::Display, a mechanism for getting the Expression’s human-readable form.

### Writing the Display Implementation

To help make writing parentheses and spaces in Display a bit easier, let’s create a temporary type for handling operator precedence.

// src/expr.rs

#[derive(Debug, Copy, Clone, PartialEq, PartialOrd)]
enum Precedence {
Bi,
Md,
As,
}


We also define some helper methods for getting an Expression’s precedence. In this case parameters, constants, and function calls all have the highest possible precedence level.

// src/expr.rs

impl Expression {
fn precedence(&self) -> Precedence {
match self {
Expression::Parameter(_)
| Expression::Constant(_)
| Expression::FunctionCall { .. } => Precedence::Bi,
Expression::Negate(_) => Precedence::Md,
Expression::Binary { op, .. } => op.precedence(),
}
}
}

impl BinaryOperation {
fn precedence(self) -> Precedence {
match self {
BinaryOperation::Plus | BinaryOperation::Minus => Precedence::As,
BinaryOperation::Times | BinaryOperation::Divide => Precedence::Md,
}
}
}


Printing a Parameter works by either printing the name as-is, or the parameter number preceded by a $ if it is anonymous (i.e. $2).

// src/expr.rs

use std::fmt::{self, Display, Formatter};

impl Display for Parameter {
fn fmt(&self, f: &mut Formatter<'_>) -> fmt::Result {
match self {
Parameter::Named(name) => write!(f, "{}", name),
Parameter::Anonymous { number } => write!(f, "${}", number), } } }  In Expression’s Display implementation we just need to do a match and use the write!() macro to generate the desired string for each variant. // src/expr.rs impl Display for Expression { fn fmt(&self, f: &mut Formatter<'_>) -> fmt::Result { match self { Expression::Parameter(p) => write!(f, "{}", p), Expression::Constant(value) => write!(f, "{}", value), Expression::Binary { left, right, op } => { write_with_precedence(op.precedence(), left, f)?; let middle = match op { BinaryOperation::Plus => " + ", BinaryOperation::Minus => " - ", BinaryOperation::Times => "*", BinaryOperation::Divide => "/", }; write!(f, "{}", middle)?; write_with_precedence(op.precedence(), right, f)?; Ok(()) }, Expression::Negate(inner) => { write!(f, "-")?; write_with_precedence( BinaryOperation::Times.precedence(), inner, f, )?; Ok(()) }, Expression::FunctionCall { name, argument } => { write!(f, "{}({})", name, argument) }, } } } fn write_with_precedence( current_precedence: Precedence, expr: &Expression, f: &mut Formatter<'_>, ) -> fmt::Result { if expr.precedence() > current_precedence { // we need parentheses to maintain ordering write!(f, "({})", expr) } else { write!(f, "{}", expr) } }  The write_with_precedence() helper is used to make sure our pretty-printer respects the precedence of its operands by adding parentheses when necessary. This prevents awkward bugs like -(x + 3) being printed as -x + 3. We also deliberately add space before and after operators with the Precedence::As precedence to help visually separate the different terms in an expression. The Display implementation is really easy to test. Just create a list of tuples containing an Expression and its expected string representation, then loop over asserting each gets formatted as desired. Long table-based pretty-printer test // src/expr.rs #[test] fn pretty_printing_works_similarly_to_a_human() { let inputs = vec![ (Expression::Constant(3.0), "3"), ( Expression::FunctionCall { name: "sin".into(), argument: Rc::new(Expression::Constant(5.0)), }, "sin(5)", ), ( Expression::Negate(Rc::new(Expression::Constant(5.0))), "-5", ), ( Expression::Negate(Rc::new(Expression::FunctionCall { name: "sin".into(), argument: Rc::new(Expression::Constant(5.0)), })), "-sin(5)", ), ( Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(1.0)), op: BinaryOperation::Plus, }, "1 + 1", ), ( Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(1.0)), op: BinaryOperation::Minus, }, "1 - 1", ), ( Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(1.0)), op: BinaryOperation::Times, }, "1*1", ), ( Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(1.0)), op: BinaryOperation::Divide, }, "1/1", ), ( Expression::Negate(Rc::new(Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(1.0)), op: BinaryOperation::Plus, })), "-(1 + 1)", ), ( Expression::Negate(Rc::new(Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(1.0)), op: BinaryOperation::Times, })), "-1*1", ), ( Expression::Binary { left: Rc::new(Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(2.0)), op: BinaryOperation::Plus, }), right: Rc::new(Expression::Constant(3.0)), op: BinaryOperation::Divide, }, "(1 + 2)/3", ), ( Expression::Binary { left: Rc::new(Expression::Constant(3.0)), right: Rc::new(Expression::Binary { left: Rc::new(Expression::Constant(1.0)), right: Rc::new(Expression::Constant(2.0)), op: BinaryOperation::Times, }), op: BinaryOperation::Minus, }, "3 - 1*2", ), ]; for (expr, should_be) in inputs { let got = expr.to_string(); assert_eq!(got, should_be); } }  ### Operator Overloads The use of operator overloads can often be quite controversial, but in this case I think it could help add some level of syntactic sugar to make constructing Expressions more readable. In this case, we’ll overload the normal binary operators so they wrap the left and right operands with an Expression::Binary. // src/expr.rs use std::ops::{Add, Div, Mul, Sub}; // define some operator overloads to make constructing an expression easier. impl Add for Expression { type Output = Expression; fn add(self, rhs: Expression) -> Expression { Expression::Binary { left: Rc::new(self), right: Rc::new(rhs), op: BinaryOperation::Plus, } } } impl Sub for Expression { type Output = Expression; fn sub(self, rhs: Expression) -> Expression { Expression::Binary { left: Rc::new(self), right: Rc::new(rhs), op: BinaryOperation::Minus, } } } impl Mul for Expression { type Output = Expression; fn mul(self, rhs: Expression) -> Expression { Expression::Binary { left: Rc::new(self), right: Rc::new(rhs), op: BinaryOperation::Times, } } } impl Div for Expression { type Output = Expression; fn div(self, rhs: Expression) -> Expression { Expression::Binary { left: Rc::new(self), right: Rc::new(rhs), op: BinaryOperation::Divide, } } }  While we’re at it, let’s add a Neg impl so you can write things like -x. // src/expr.rs use std::ops::Neg; impl Neg for Expression { type Output = Expression; fn neg(self) -> Self::Output { Expression::Negate(Rc::new(self)) } }  You can see none of these operator overloads are particularly interesting, they just let us avoid a bunch of typing. ### Iterators A useful building block for working with the Expression tree is being able to iterate over every node in the tree. Imagine scanning through a big expression to figure out which parameters it references or the functions that it calls. To do this, we’ll create an iter() method which returns something implementing Iterator<Item = &Expression>. // src/expr.rs impl Expression { /// Iterate over all [Expression]s in this [Expression] tree. pub fn iter(&self) -> impl Iterator<Item = &Expression> + '_ { Iter { to_visit: vec![self], } } } /// A depth-first iterator over the sub-[Expression]s in an [Expression]. #[derive(Debug)] struct Iter<'expr> { to_visit: Vec<&'expr Expression>, }  The Iter type works by maintaining a list of &Expression references for the nodes it still needs to visit. Getting the next_item is then just a case of popping a reference from the list, queueing any sub-expressions under next_item itself before returning it. // src/expr.rs impl<'expr> Iterator for Iter<'expr> { type Item = &'expr Expression; fn next(&mut self) -> Option<Self::Item> { let next_item = self.to_visit.pop()?; match next_item { Expression::Binary { left, right, .. } => { self.to_visit.push(right); self.to_visit.push(left); }, Expression::Negate(inner) => self.to_visit.push(inner), Expression::FunctionCall { argument, .. } => { self.to_visit.push(argument) }, _ => {}, } Some(next_item) } }  This enables nice things like an iterator over all referenced parameters or functions, and checking whether an expression depends on a parameter. // src/expr.rs impl Expression { /// Iterate over all [Parameter]s mentioned in this [Expression]. pub fn params(&self) -> impl Iterator<Item = &Parameter> + '_ { self.iter().filter_map(|expr| match expr { Expression::Parameter(p) => Some(p), _ => None, }) } /// Does this [Expression] involve a particular [Parameter]? pub fn depends_on(&self, param: &Parameter) -> bool { self.params().any(|p| p == param) } /// Iterate over all functions used by this [Expression]. pub fn functions(&self) -> impl Iterator<Item = &str> + '_ { self.iter().filter_map(|expr| match expr { Expression::FunctionCall { name, .. } => Some(name.as_ref()), _ => None, }) } }  ## Parsing Now you may be wondering why we’d mention parsing in an article about creating a geometric constraints system, and the answer is quite simple… Constructing a full Expression tree using object literals is really verbose and annoying. In turn, this increases the effort required to write and maintain tests, It’s also useful if we want to provide users with a text box so they can enter their own custom constraints. SolidWorks uses this to let an engineer specify entire families of designs who’s final dimensions are driven by one or two parameters (this is often referred to as a “Configuration”). For example, imagine having a proprietary *bolt design and being able to say “I want this as an M6x1.0" (initial parameters are outside_diameter = 6.0 millimetres, thread_pitch = 1.0 threads per millimetre) and the constraints system figuring out all the other dimensions. Besides letting the end user or developer input enter an expression into the computer in textual form, it can also be a powerful tool during development. In combination with our earlier pretty-printer, you can use strings to concisely say what an Expression should look like before and after a particular operation. Throw in a macro or two and a suite of unit tests for testing various expression operations could look like this: expr_test!(simplify_multiplication, "2*2 + x" => fold_constants => "4 + x"); expr_test!(simplify_sine_90_degrees, "sin(90)" => fold_constants => "1"); expr_test!(differentiate_cos, "cos(t)" => partial_derivative(t) => "-sin(t)"); expr_test!(evaluate_tricky_expression, "10 - 2*x + x*x" => evaluate(x: 5.0) => "25");  The process of turning unstructured text into a more structured form, an Expression tree in our case, is commonly referred to as “parsing”. While you could hack something together using string operations or even a regular expression or two, this approach will often fall over the moment you give it more complex expressions (e.g. using parentheses to nest expressions inside expressions) or if different levels of precedence are involved. ### Tokenising Often the first step in turning an unstructured string of characters into a structured Expression tree is Tokenisation. This is where the string is broken up into the “atoms” of our language (aka Tokens), so things like identifiers ("foo"), numbers ("3.14"), punctuation ("("), and operators ("+"). We also keep track of where each Token occurred in the source text. That way we’ve got somewhere to point to when reporting errors to the user. By pre-processing a stream of text into a slightly more high-level representation your parser can operate at the granularity of tokens (e.g. a number, plus symbol, or an identifier) instead of needing to process individual characters. // src/parse.rs use std::ops::Range; #[derive(Debug, Clone, PartialEq)] struct Token<'a> { text: &'a str, span: Range<usize>, kind: TokenKind, } /// The kinds of token that can appear in an [Expression]'s text form. #[derive(Debug, Copy, Clone, PartialEq)] pub enum TokenKind { Identifier, Number, OpenParen, CloseParen, Plus, Minus, Times, Divide, }  We use the Iterator trait to represent a stream of tokens. For our purposes, a Tokens stream wraps a &str and uses a cursor variable to keep track of where it has read up to. I’ve attached a couple helper methods for seeing what text is remaining. // src/parse.rs #[derive(Debug, Clone, PartialEq)] struct Tokens<'a> { src: &'a str, cursor: usize, } impl<'a> Tokens<'a> { fn new(src: &'a str) -> Self { Tokens { src, cursor: 0 } } fn rest(&self) -> &'a str { &self.src[self.cursor..] } fn peek(&self) -> Option<char> { self.rest().chars().next() } }  While it’s nice to see what text is remaining, we also need a way to advance the cursor along the string. // src/parse.rs impl<'a> Tokens<'a> { fn advance(&mut self) -> Option<char> { let c = self.peek()?; self.cursor += c.len_utf8(); Some(c) } }  If you wanted to, you could implement the rest of the Tokens type purely using calls to peek() and advance(), but sometimes it’s easier to have a more high-level primitive. For this sort of thing I’ll typically use some sort of take_while() method which accepts a predicate and will keep advancing the cursor until the predicate doesn’t like the next character in line. // src/parse.rs impl<'a> Tokens<'a> { fn take_while<P>( &mut self, mut predicate: P, ) -> Option<(&'a str, Range<usize>)> where P: FnMut(char) -> bool, { let start = self.cursor; while let Some(c) = self.peek() { if !predicate(c) { break; } self.advance(); } let end = self.cursor; if start != end { let text = &self.src[start..end]; Some((text, start..end)) } else { None } } }  Reading a token from the stream is just a case of peeking at the next character, and executing a match statement based on what we find. Something to keep in mind is we don’t care about whitespace, so the entire thing is executed in a loop which will keep skipping whitespace until we encounter something else. // src/parse.rs impl<'a> Iterator for Tokens<'a> { type Item = Result<Token<'a>, ParseError>; fn next(&mut self) -> Option<Self::Item> { loop { return match self.peek()? { space if space.is_whitespace() => { self.advance(); continue; }, '(' => self.chomp(TokenKind::OpenParen), ')' => self.chomp(TokenKind::CloseParen), '+' => self.chomp(TokenKind::Plus), '-' => self.chomp(TokenKind::Minus), '*' => self.chomp(TokenKind::Times), '/' => self.chomp(TokenKind::Divide), '_' | 'a'..='z' | 'A'..='Z' => { Some(Ok(self.chomp_identifier())) }, '0'..='9' => Some(Ok(self.chomp_number())), other => Some(Err(ParseError::InvalidCharacter { character: other, index: self.cursor, })), }; } } }  The next() method makes use of several helper methods, the simplest of which is chomp() for reading a single character as a Token with the specified TokenKind. // src/parse.rs impl<'a> Tokens<'a> { fn chomp( &mut self, kind: TokenKind, ) -> Option<Result<Token<'a>, ParseError>> { let start = self.cursor; self.advance()?; let end = self.cursor; let tok = Token { text: &self.src[start..end], span: start..end, kind, }; Some(Ok(tok)) }  We’re also introducing chomp_identifier() for consuming an entire identifier (i.e. anything that would match the regex, [\w_][\w\d_]*), and chomp_number() for extracting a floating-point number (which I’m just defining as the regex \d+(\.\d*)?, because I’m too lazy to worry about the 1e-5 notation for now). It’s overkill to actually pull in the regex crate for simple patterns like these though, so we leverage our existing take_while() function and the fact that closures can use variables from an outside scope to change their behaviour. First, let’s look at how chomp_number() is implemented. // src/parse.rs impl<'a> Tokens<'a> { fn chomp_number(&mut self) -> Token<'a> { let mut seen_decimal_point = false; let (text, span) = self .take_while(|c| match c { '.' if !seen_decimal_point => { seen_decimal_point = true; true }, '0'..='9' => true, _ => false, }) .expect("We know there is at least one digit in the input"); Token { text, span, kind: TokenKind::Number, } } }  The idea is we want to keep consuming digits, but only accept the first . character we see (hence the seen_decimal_point flag). That allows us to recognise input like 1234, 12.34, and .1234 as numbers, but reject things like 1.2.3.4 or 1..23. We do something very similar in chomp_identifier(), where we want to make sure the first character is either a letter or _, but all characters after that can be also contain digits. // src/parse.rs impl<'a> Tokens<'a> { fn chomp_identifier(&mut self) -> Token<'a> { let mut seen_first_character = false; let (text, span) = self .take_while(|c| { if seen_first_character { c.is_alphabetic() || c.is_ascii_digit() || c == '_' } else { seen_first_character = true; c.is_alphabetic() || c == '_' } }) .expect("We know there should be at least 1 character"); Token { text, span, kind: TokenKind::Identifier, } } }  ### Parsing Using Recursive Descent Arguably one of the most intuitive ways to parse text is a technique called Recursive Descent. This is a top-down method that uses recursive functions to turn a stream of tokens into a tree. I like using recursive descent because once you’ve figured out your grammar (a file is zero or more statements, a statement maybe an assignment or if-statement, etc.) turning that into code just becomes a case of writing one function per rule and matching different things depending on what the rule asks for. I’ll often create a Parser type which wraps a Tokens stream. You don’t need to do it this way, but I like how I can put the grammar in the *Parser’s doc-comment so if I need to revisit the code 6 months for now I can pull up the API docs and immediately get a feel for how an Expression is parsed. // src/parse.rs use std::iter::Peekable; /// A simple recursive descent parser (LL(1)) for converting a string into an /// expression tree. /// /// The grammar: /// /// text /// expression := term "+" expression /// | term "-" expression /// | term /// /// term := factor "*" term /// | factor "/" term /// | factor /// /// factor := "-" term /// | IDENTIFIER "(" expression ")" /// | IDENTIFIER /// | "(" expression ")" /// | NUMBER ///  #[derive(Debug, Clone)] pub(crate) struct Parser<'a> { tokens: Peekable<Tokens<'a>>, }  To help provide better error messages than just “this expression is invalid”, we’ve defined a ParseError type representing the various errors that may occur. For example, we could encounter an InvalidCharacter while tokenising or run out of tokens when we are expecting more (e.g. 42 + would result in a ParseError::UnexpectedEndOfInput), or we could run into a token that isn’t valid in that context (imagine seeing a + when we’re expecting a number or identifier). // src/parse.rs /// Possible errors that may occur while parsing. #[derive(Debug, Clone, PartialEq)] pub enum ParseError { InvalidCharacter { character: char, index: usize, }, UnexpectedEndOfInput, UnexpectedToken { found: TokenKind, span: Range<usize>, expected: &'static [TokenKind], }, }  The key to writing a Recursive Descent parser is to write down the rules so you know what to expect. For example, lets look at the top-level expression rule. expression := term "+" expression | term "-" expression | term  Now, to parse an expression we need to first parse a term (which we’ll define shortly) then look ahead to check whether it is followed by a + or - so we know whether we need to parse the right side. // src/parse.rs impl<'a> Parser<'a> { fn expression(&mut self) -> Result<Expression, ParseError> { let left = self.term()?; match self.peek() { // term + expression Some(TokenKind::Add) => { let _plus_sign = self.advance(); let right = self.expression()?; Ok(left + right) }, // term - expression Some(TokenKind::Sub) => { let _minus_sign = self.advance(); let right = self.expression()?; Ok(left - right) }, // term _ => Ok(left) } } fn term(&mut self) -> Result<Expression, ParseError> { todo!() } /// What kind of token is the next one in our Tokens stream? fn peek(&mut self) -> Option<TokenKind> { ... } /// Advance the Tokens stream by one, returning the token that was /// consumed. fn advance(&mut self) -> Option<Token> { ... } }  You can see how this code almost exactly reflects the expression. You’ll see that there’s a lot you can do to remove code duplication, but for our purposes it’s not necessary. I chose to reuse our operator overloads, though. They make constructing binary expressions slightly less verbose. Next comes the term rule. term := factor "*" term | factor "/" term | factor  You can see that the term rule is almost identical, except it looks for * and /. There’s no point copy-pasting the expression() method here, so let’s jump straight into factor. factor := "-" factor | IDENTIFIER "(" expression ")" | IDENTIFIER | "(" expression ")" | NUMBER  Finally we reach something interesting. This rule matches a bunch of things which all have the same precedence level: • Negated factors (i.e. -(x + y)) • Function calls • Variables • Nested expressions inside parentheses, or • Number The code is fairly similar to before, except we’ve got a larger match statement. // src/parse.rs impl<'a> Parser<'a> { fn factor(&mut self) -> Result<Expression, ParseError> { match self.peek() { // NUMBER Some(TokenKind::Number) => { return self.number(); }, // "-" factor Some(TokenKind::Minus) => { let _ = self.advance()?; let operand = self.factor()?; return Ok(-operand); }, // IDENTIFIER | IDENTIFIER "(" expression ")" Some(TokenKind::Identifier) => { return self.variable_or_function_call() }, // "(" expression ")" Some(TokenKind::OpenParen) => { let _ = self.advance()?; let expr = self.expression()?; let close_paren = self.advance()?; if close_paren.kind == TokenKind::CloseParen { return Ok(expr); } else { return Err(ParseError::UnexpectedToken { found: close_paren.kind, span: close_paren.span, expected: &[TokenKind::CloseParen], }); } }, // something unexpected _ => {}, } // We couldn't parse the factor so try to return a nice error // indicating what types of tokens we were expecting match self.tokens.next() { // "we found an XXX but were expecting a number, identifier, or minus" Some(Ok(Token { span, kind, .. })) => { Err(ParseError::UnexpectedToken { found: kind, expected: &[TokenKind::Number, TokenKind::Identifier, TokenKind::Minus], span, }) }, // the underlying tokens stream encountered an error (e.g. unknown // character) Some(Err(e)) => Err(e), // there were just no tokens available to parse as a factor None => Err(ParseError::UnexpectedEndOfInput), } } }  We are also in a better position to report parse errors here because we’ve reached several terminals (branches/rules which don’t recurse). If possible, we try to let the user know what type of token we were expecting to see. To keep the match statement from growing out of control I’ve pulled matching either a variable or function call (both of which start with an identifier) out into its own function. However if you look at the rule for parenthesised expressions you can probably figure out how it goes. Sorry if it feels like I’ve rushed this section. I’ve written enough recursive descent parsers that it tends to be a mechanical process and once you’ve seen how to write one or two rules, you can write pretty much anything. ### Testing Something I can’t overstate enough when writing a parser by hand is to have a reasonably large test suite with lots of edge cases. To make the process easier I’ll often create my own macro_rules macro to make writing tests easier. For example, to test that we parse something correctly I’ll generate a test that parses a string into an Expression then immediately use the pretty-printer created earlier to turn it back into a string. If the round-tripped version matches the original you can be fairly confident your parser is correct without having to write out verbose parse trees by hand. // src/parse.rs #[cfg(test)] mod parser_tests { use super::*; macro_rules! parser_test { ($name:ident, $src:expr) => { parser_test!($name, $src,$src);
};
($name:ident,$src:expr, $should_be:expr) => { #[test] fn$name() {
let got = Parser::new($src).parse().unwrap(); let round_tripped = got.to_string(); assert_eq!(round_tripped,$should_be);
}
};
}
}


And here are some of my parser tests:

// src/parse.rs

#[cfg(test)]
mod parser_tests {
...

parser_test!(simple_integer, "1");
parser_test!(one_plus_one, "1 + 1");
parser_test!(one_plus_one_plus_negative_one, "1 + -1");
parser_test!(one_plus_one_times_three, "1 + 1*3");
parser_test!(one_plus_one_all_times_three, "(1 + 1)*3");
parser_test!(negative_one, "-1");
parser_test!(negative_one_plus_one, "-1 + 1");
parser_test!(negative_one_plus_x, "-1 + x");
parser_test!(number_in_parens, "(1)", "1");
parser_test!(bimdas, "1*2 + 3*4/(5 - 2)*1 - 3");
parser_test!(function_call, "sin(1)");
parser_test!(function_call_with_expression, "sin(1/0)");
parser_test!(
function_calls_function_calls_function_with_variable,
"foo(bar(baz(pi)))"
);
}


The tokeniser tests are quite similar, except we also need to make sure spans are correct otherwise the user will get dodgy error messages.

// src/parse.rs

#[cfg(test)]
mod tokenizer_tests {
use super::*;

macro_rules! tokenize_test {
($name:ident,$src:expr, $should_be:expr) => { #[test] fn$name() {
let mut tokens = Tokens::new($src); let got = tokens.next().unwrap().unwrap(); let Range { start, end } = got.span; assert_eq!(start, 0); assert_eq!(end,$src.len());
assert_eq!(got.kind, \$should_be);

assert!(
tokens.next().is_none(),
"{:?} should be empty",
tokens
);
}
};
}
}


And this is what they look like in action.

// src/parse.rs

impl tokenizer_tests {
...

tokenize_test!(open_paren, "(", TokenKind::OpenParen);
tokenize_test!(close_paren, ")", TokenKind::CloseParen);
tokenize_test!(plus, "+", TokenKind::Plus);
tokenize_test!(minus, "-", TokenKind::Minus);
tokenize_test!(times, "*", TokenKind::Times);
tokenize_test!(divide, "/", TokenKind::Divide);
tokenize_test!(single_digit_integer, "3", TokenKind::Number);
tokenize_test!(multi_digit_integer, "31", TokenKind::Number);
tokenize_test!(number_with_trailing_dot, "31.", TokenKind::Number);
tokenize_test!(simple_decimal, "3.14", TokenKind::Number);
tokenize_test!(simple_identifier, "x", TokenKind::Identifier);
tokenize_test!(longer_identifier, "hello", TokenKind::Identifier);
tokenize_test!(
identifiers_can_have_underscores,
"hello_world",
TokenKind::Identifier
);
tokenize_test!(
identifiers_can_start_with_underscores,
"_hello_world",
TokenKind::Identifier
);
tokenize_test!(
identifiers_can_contain_numbers,
"var5",
TokenKind::Identifier
);
}


## Conclusions

While our main focus is implementing a geometric constraints solver, this article mainly focused on defining our Expression tree’s structure and converting to/from its string representation.

Now we’ve got a way to represent Expressions, enter them into a program, and print them out for debugging, we’ve created a solid foundation that the rest of the solver can be built on.

As an aside, the code and techniques used here are almost identical to those used when implementing a programming language. Indeed, that’s where I initially learned things like Backus–Naur form (the syntax used to represent rules), Recursive Descent parsers, and LL(1) grammars.