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Remove docs related files (#2023)

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0
.github/PULL_REQUEST_TEMPLATE/pull_request_template.md → .github/pull_request_template.md vendored
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.github/workflows/ci.yml vendored
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name: The Juvix compiler CI
name: Juvix Compiler CI
"on":
workflow_dispatch:
inputs:
@ -148,105 +148,6 @@ jobs:
cd main
make smoke
docs-linux:
needs: build-and-test-linux
runs-on: ubuntu-20.04
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
# Grant GITHUB_TOKEN the permissions required to make a Pages deployment
permissions:
pages: write # to deploy to Pages
id-token: write # to verify the deployment originates from an appropriate source
steps:
- name: Checkout our repository
uses: actions/checkout@v3
with:
path: main
submodules: recursive
- name: Install mdbook-pagetoc
uses: baptiste0928/cargo-install@v1
with:
crate: mdbook-pagetoc
- name: Install mdbook-katex
uses: baptiste0928/cargo-install@v1
with:
crate: mdbook-katex
- name: Install mdbook-linkcheck
uses: baptiste0928/cargo-install@v1
with:
crate: mdbook-linkcheck
- name: MDBook setup
uses: peaceiris/actions-mdbook@v1
with:
mdbook-version: "latest"
- name: Cache LLVM and Clang
id: cache-llvm
uses: actions/cache@v3
with:
path: |
C:/Program Files/LLVM
./llvm
key: "${{ runner.os }}-llvm-13"
- name: Install LLVM and Clang
uses: KyleMayes/install-llvm-action@v1
with:
version: "13.0"
cached: "${{ steps.cache-llvm.outputs.cache-hit }}"
- name: Make runtime
run: |
cd main
make runtime
- name: Stack setup
id: stack
uses: freckle/stack-action@v4
with:
working-directory: main
test: false
- name: Generate HTML files from examples
run: |
cd main
echo "$GITHUB_WORKSPACE/.local/bin" >> $GITHUB_PATH
make install
make html-examples
make demo-example
- name: Build the mdbook
run: |
cd main
make docs
- name: Setup Pages
uses: actions/configure-pages@v3
if: >-
github.ref == 'refs/heads/main' || github.event_name ==
'workflow_dispatch'
- name: Upload artifact
uses: actions/upload-pages-artifact@v1
if: >-
github.ref == 'refs/heads/main' || github.event_name ==
'workflow_dispatch'
with:
path: main/book/html
- name: Deploy to GitHub Pages
uses: actions/deploy-pages@v1
id: deployment
if: >-
github.ref == 'refs/heads/main' || github.event_name ==
'workflow_dispatch'
build-and-test-macos:
runs-on: macos-12
steps:

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Makefile
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@ -56,11 +56,7 @@ clean-juvix-build:
repl:
@${STACK} ghci Juvix:lib ${STACKFLAGS}
# ------------------------------------------------------------------------------
# -- The Juvix Book
# ------------------------------------------------------------------------------
# -- EXAMPLES
# -- EXAMPLES HTML OUTPUT
.PHONY: html-examples
html-examples: $(EXAMPLES)
@ -76,26 +72,6 @@ demo-example:
@mkdir -p ${OUTPUTDIR}
@${JUVIXBIN} html $(DEMO_EXAMPLE) --output-dir=$(CURDIR)/${OUTPUTDIR}
# -- MDBook
.PHONY: docs
docs: html-examples
@cp $(METAFILES) docs/
@cp -r assets/ docs/
@mdbook build
.PHONY: serve-docs
serve-docs: docs
@mdbook serve --open
cargo-dependencies:
@cargo install mdbook \
mdbook-katex \
mdbook-linkcheck \
mdbook-pagetoc
# -- Codebase Documentation
.PHONY : haddock
haddock :
@cabal --docdir=docs/ --htmldir=docs/ haddock --enable-documentation

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docs/SUMMARY.md
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# Summary
- [The Juvix project](./README.md)
- [Changelog](./CHANGELOG.md)
- [Quick start](./quick-start.md)
# Tutorials
- [Learn Juvix in minutes](./tutorials/learn.md)
- [Structuring Juvix projects](./tutorials/structure.md)
- [Juvix Emacs mode](./tutorials/emacs.md)
- [Juvix VSCode extension](./tutorials/vscode.md)
# How-to guides
- [Installing Juvix](./howto/installing.md)
- [Compiling Juvix programs](./howto/compilation.md)
- [Judoc: Juvix documentation tool](./howto/judoc.md)
# Explanations
- [Type theory](./explanations/typetheory.md)
- [Totality checking](./explanations/totality/README.md)
- [Termination](./explanations/totality/termination.md)
- [Strictly positive data
types](./explanations/totality/positive.md)
- [Coverage checking](./explanations/totality/coverage.md)
# Reference
- [Standard library](./reference/stdlib.md)
- [Language reference](./reference/language/README.md)
- [Project](./reference/language/project.md)
- [Functions](./reference/language/functions.md)
- [Infix operators](./reference/language/infix.md)
- [Data types](./reference/language/datatypes.md)
- [Modules](./reference/language/modules.md)
- [Statements](./reference/language/statement.md)
- [Local definitions](./reference/language/let.md)
- [Control structures](./reference/language/control.md)
- [Comments](./reference/language/comments.md)
- [Axioms](./reference/language/axioms.md)
- [Example programs](./reference/examples.md)
- [Benchmarks](./reference/benchmarks.md)
- [Tooling](./reference/tooling/README.md)
- [Command line interface](./reference/tooling/CLI.md)
- [Doctor](./reference/tooling/doctor.md)
- [Emacs mode](./reference/tooling/emacs.md)
- [Haskell test suite](./reference/tooling/testing.md)
- [Judoc reference](./reference/judoc.md)
# About
- [Community](./about/community.md)
- [Contributing](./CONTRIBUTING.md)
- [License](./LICENSE.md)

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# Juvix community
Join us on our [Discord server](https://discord.gg/waYhQ2Qr)
This project is part of a bigger effort called
[Anoma](https://anoma.net/). Anoma is a suite of protocols and
mechanisms for self-contained, self-sovereign coordination. Join the
[Anoma project](https://anoma.net/community).

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# The Juvix Dev Team

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## [Examples of programs written in Juvix](https://github.com/anoma/juvix/tree/main/examples/milestone)
The following links are clickable versions of their corresponding Juvix
programs. The HTML output can be generated by running
`juvix html --recursive FileName.juvix`.
- [HelloWorld.juvix](./html/examples/html/HelloWorld/HelloWorld.html)
- [Fibonacci.juvix](./html/examples/html/Fibonacci/Fibonacci.html)
- [Hanoi.juvix](./html/examples/html/Hanoi/Hanoi.html)
- [PascalsTriangle.juvix](./html/examples/html/PascalsTriangle/PascalsTriangle.html)
- [Collatz.juvix](./html/examples/html/Collatz/Collatz.html)
- [TicTacToe.juvix](./html/TicTacToe/CLI/CLI.TicTacToe.html)
The [Juvix standard library](https://anoma.github.io/juvix-stdlib/)
contains common functions that can be used in Juvix programs.

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# Totality checking

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# Coverage checking

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# Strictly positive data types

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# Termination
To not bring inconsistencies by function declarations, Juvix requires
that every function passes its termination checker. However, since this
is a strong requirement, often tricky to fulfil, we give the user the
possibility to skip this check in two different ways:
- Using the `terminating` keyword to annotate function type signatures
as terminating. The syntax is the following.
```juvix
terminating fun : A → B;
```
Note that annotating a function as `terminating` means that _all_ its
function clauses pass the termination checker's criterion. To skip the
termination checker for mutual recursive functions, all the functions
involved must be annotated as `terminating`.
- Using the CLI global flag `--no-termination`.
```juvix
juvix typecheck --no-termination MyProgram.juvix
```
In any case, be aware that our termination checker is limited as it only
accepts a subset of recursion functions. The termination checker
algorithm is a slight modification of the algorithm for checking
termination in the Foetus's language.

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# Type theory

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# Compiling simple programs
A Juvix file must declare a module whose name corresponds exactly to the
name of the file. For example, a file `Hello.juvix` must declare a
module `Hello`:
```juvix
-- Hello world example. This is a comment.
module Hello;
-- Import the standard library prelude, including the 'String' type
open import Stdlib.Prelude;
main : String;
main := "Hello world!";
```
A file compiled to an executable must define the zero-argument function
`main` of type `IO` which is evaluated when running the program.
To compile the file `Hello.juvix` type `juvix compile Hello.juvix`.
Typing `juvix compile --help` will list all options to the `compile`
command.
# Compilation targets
Since version 0.3 Juvix supports three compilation targets. The targets
are specified with the `-t` option:
`juvix compile -t target file.juvix`.
1. `native`. This is the default. Produces a native 64bit executable
for your machine.
2. `wasm32-wasi`. Produces a WebAssembly binary which uses the WASI
runtime.
3. `geb`. Produces a [GEB](https://anoma.github.io/geb/) input file.
# Compilation options
To see all compilation options type `juvix compile --help`. The most
commonly used options are:
- `-t target`: specify the target,
- `-g`: generate debug information and runtime assertions,
- `-o file`: specify the output file.
# Juvix projects
A <u>Juvix project</u> is a collection of Juvix modules inside one main
project directory containing a `juvix.yaml` metadata file. The name of
each module must coincide with the path of the file it is defined in,
relative to the project's root directory. For example, if the file is
`root/Data/List.juvix` then the module must be called `Data.List`,
assuming `root` is the project's directory.
To interactively initialize a Juvix project in the current directory,
use `juvix init`.
To check that Juvix is correctly detecting your project's root, you can
run the command `juvix dev root File.juvix`.
See also: [Modules Reference](../reference/language/modules.md).

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# Dependencies
You need [Clang / LLVM](https://releases.llvm.org/download.html) version
13 or later. Note that on macOS the preinstalled clang does not support
the wasm target, so use e.g. `brew install llvm` instead.
If you want to compile to WebAssembly, you also need:
- [wasmer](https://wasmer.io)
- [wasi-sdk](https://github.com/WebAssembly/wasi-sdk/releases)
- [wasm-ld](https://lld.llvm.org) - the LLVM linker for WASM (NB: On
Linux you may need to install the `lld` package; on macOS this is
installed as part of `llvm`).
See [below](./installing.md#installing-dependencies) for instructions on
how to install the dependencies.
# Installing Juvix
### MacOS
The easiest way to install Juvix on MacOS is by using
[Homebrew](https://brew.sh).
To install the [homebrew-juvix
tap](https://github.com/anoma/homebrew-juvix), run:
```shell
brew tap anoma/juvix
```
To install Juvix, run:
```shell
brew install juvix
```
Helpful information can also be obtained by running:
```shell
brew info juvix
```
### Linux x86<sub>64</sub>
A Juvix compiler binary executable for Linux x86<sub>64</sub> is
available on the [Juvix release
page](https://github.com/anoma/juvix/releases/latest).
To install this executable, download and unzip the linked file and move
it to a directory on your shell's `PATH`.
For example if `~/.local/bin` is on your shell's `PATH`, you can install
Juvix as follows:
```shell
cd /tmp
curl -OL https://github.com/anoma/juvix/releases/download/v0.3.2/juvix-linux_x86_64-v0.3.2.zip
unzip juvix-linux_x86_64-v0.3.2.zip
mv juvix-linux_x86_64-v0.3.2 ~/.local/bin/juvix
```
### Building Juvix from source
To install Juvix from source you must clone the [Github
repository](https://github.com/anoma/juvix.git). Then Juvix can be
installed with the following commands. We assume you have
[Stack](https://haskellstack.org) and [GNU
Make](https://www.gnu.org/software/make/) installed.
```shell
git clone --recursive https://github.com/anoma/juvix.git
cd juvix
make install
```
The C compiler and linker paths can be specified as options to the
`make install` command, e.g.
```shell
make install CC=path/to/clang LIBTOOL=path/to/llvm-ar
```
On MacOS, you can alternatively run the following command for Homebrew.
The flag `--HEAD` used below is optional – use it to build the latest
version of Juvix in the `main` branch on Github.
```shell
brew install --build-from-source --HEAD juvix --verbose
```
### Building the project with `cabal`
We recommend to use the `stack` build tool with this project.
If you prefer the `cabal` build tool instead, then you need to generate
the `juvix.cabal` file using [hpack](https://github.com/sol/hpack)
before running `cabal build`.
You also need to compile the runtime first:
```shell
make runtime
cabal build
```
# Installing dependencies
To install `wasi-sdk` you need to download `libclang_rt` and
`wasi-sysroot` precompiled archives from the [wasi-sdk release
page](https://github.com/WebAssembly/wasi-sdk/releases/) and:
1. Extract the `libclang_rt.builtins-wasm32-wasi-*.tar.gz` archive in
the `clang` installation root (for example `/usr/lib/clang/13` on
Ubuntu or `` `brew --prefix llvm` `` on macos).
For example on macos with homebrew clang:
```shell
cd `brew --prefix llvm`
curl https://github.com/WebAssembly/wasi-sdk/releases/download/wasi-sdk-15/libclang_rt.builtins-wasm32-wasi-15.0.tar.gz -OL
tar xf libclang_rt.builtins-wasm32-wasi-15.0.tar.gz
```
2. Extract the `wasi-sysroot-*.tar.gz` archive on your local system and
set `WASI_SYSROOT_PATH` to its path.
For example:
```shell
cd ~
curl https://github.com/WebAssembly/wasi-sdk/releases/download/wasi-sdk-15/wasi-sysroot-15.0.tar.gz -OL
tar xf wasi-sysroot-15.0.tar.gz
export WASI_SYSROOT_PATH=~/wasi-sysroot
```

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# Documenting Juvix programs with Judoc

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---
author: Jan Mas Rovira
title: Builtins
---
# Overview
The goal is to support builtin types and functions that get compiled to
efficient primitives. We plan on supporting primitives for the following
types of definitions:
1. Builtin inductive definitions. For example:
```juvix
builtin nat
type Nat :=
zero : Nat |
suc : Nat → Nat;
```
We will call this the canonical definition of natural numbers.
2. Builtin function definitions. For example:
```juvix
inifl 6 +;
builtin nat-plus
+ : Nat → Nat → Nat;
+ zero b := b;
+ (suc a) b := suc (a + b);
```
3. Builtin axiom definitions. For example:
```juvix
builtin nat-print
axiom printNat : Nat → Action;
```
## Collecting builtin information
The idea is that builtin definitions are treated normally throughout the
pipeline except in the backend part. There is one exception to that. We
need to collect information about the builtins that have been included
in the code and what are the terms that refer to them. For instance,
imagine that we find this definitions in a juvix module:
```juvix
builtin nat
type MyNat :=
z : MyNat |
s : MyNat → MyNat;
```
We need to take care of the following:
1. Check that the definition `MyInt` is up to renaming equal to the
canonical definition that we provide in the compiler.
2. Rember a map from concrete to canonical names: {MyNat ↦ Nat; z ↦
zero; s ↦ suc};
3. Rembember that we have a definition for builtin natural numbers.
This is necessary if later we attempt to define a builtin function
or axiom that depends on natural numbers.
In the compiler we need to know the following:
1. For inductives:
1. What is the primitive type that we will target in the backend:
E.g. {Nat ↦ int}.
2. For constructors:
1. What is the primitive constructor function: E.g. {zero ↦ 0;
suc ↦ plus<sub>one</sub>};
2. How to test if a term matches a pattern with that
constructor. E.g. {zero ↦ is<sub>zero</sub>; suc ↦
is<sub>notzero</sub>};
3. How to deconstruct/project each of the constructor
arguments. E.g. {zero ↦ ∅; suc ↦ minus<sub>one</sub>}}. Note
that if a constructor takes multiple arguments we will need
to have a projection function for each argument.
2. For functions and axioms:
1. What is the primitive function that we will target in the
backend: E.g. {+ ↦ add}.

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# LSP support
We provide a sample `hie.yaml` configuration file for both `cabal` and
`stack`.
If you prefer `stack`, run:
```shell
cp stack.hie.yaml hie.yaml
```
If you prefer `cabal`, run:
```shell
cp cabal.hie.yaml hie.yaml
```

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---
author: Jan Mas Rovira
---
# Monomorphization
(Removed in v0.2.5)
Monomorphization refers to the process of converting polymorphic code to
monomorphic code (no type variables) through static analysis.
Example:
```juvix
id : (A : Type) → A → A;
id _ a := a;
b : Bool;
b := id Bool true;
n : Nat;
n := id Nat zero;
```
Is translated into:
```juvix
id_Bool : Bool → Bool;
id_Bool a := a;
id_Nat : Nat → Nat;
id_Nat a := a;
```
# More examples
## Mutual recursion
```juvix
type List (A : Type) :=
nil : List A |
cons : A → List A → List A;
even : (A : Type) → List A → Bool;
even A nil := true ;
even A (cons _ xs) := not (odd A xs) ;
odd : (A : Type) → List A → Bool;
odd A nil := false ;
odd A (cons _ xs) := not (even A xs) ;
-- main := even Bool .. odd Nat;
```
# Collection algorithm
This section describes the algorithm to collect the list of all concrete
functions and inductive definitions that need to be generated.
## Assumptions:
1. Type abstractions only appear at the leftmost part of a type
signature.
2. All functions and constructors are fully type-applied: i.e. currying
for types is not supported.
3. There is at least one function with a concrete type signature.
4. All axioms are monomorphic.
5. No module parameters.
## Definitions
1. **Application**. An application is an expression of the form
`t₁ t₂ … tₙ` with n > 0.
2. **Sub application**. If `t₁ t₂ … tₙ` is an application then for
every `0<i<n` `t₁ t₂ … tᵢ` is a sub application.
Fix a juvix program `P`. Let `𝒲` be the set of all applications that
appear in `P`.
1. **Maximal application**. A maximal application is an application
`A∈𝒲` such that for every `A'∈𝒲` we have that `A` is **not** a sub
application of `A'`.
2. **Type application**. If
1. `t a₁ a₂ … aₙ` is a maximal application; and
2. `t` is either a function or an inductive type; and
3. `a₁, …, aₘ` are types; and
4. `aₘ₊₁` is not a type or `m = n`.
Then `t a₁, …, aₘ` is a type application.
3. **Concrete type**. A type is concrete if it involves no type
variables.
4. **Concrete type application**. A type application `t a₁ a₂ … aₙ` if
`a₁, a₂, …,
aₙ` are concrete types.
## Option 1
Let `S₀` be the set of functions with a concrete type signature. Gather
all type applications (both in the type and in the body) in each of the
functions in `S₀`. Clearly the collected type applications are all
concrete. We now have a stack `c₁, c₂, …, cₙ` of concrete type
applications.
1. If the stack is empty, we are done.
2. Otherwise pop `c₁` from the stack. It will be of the form
`t a₁ … aₘ`, where `t` is either an inductive or a function and
`a₁, …, aₘ` are concrete types.
3. If the instantiation `t a₁ … aₘ` has already been registered go to
step 1. Otherwise continue to the next step.
4. Register the instantiation `t a₁ … aₘ`.
5. If `t` is a function, then it has type variables `v₁, …, vₘ`.
Consider the substitution `σ = {v₁ ↦ a₁, …, vₘ ↦ aₘ}`. Consider the
list of type applications in the body of `t`: `d₁, …, dᵣ`. Add
`σ(d₁), …, σ(dᵣ)` to the stack and continue to step 1. It is easy to
see that for any `i`, `σ(dᵢ)` is a concrete type application.
6. If `t` is an inductive type, let `d₁, …, dᵣ` be the type
applications that appear in the type of its constructors, then
proceed as before.
### Correctness
It should be clear that the algorithm terminates and registers all
instantiations of constructors and functions.
# Generation algorithm
The input of this algorithm is the list of concrete type applications,
name it `ℒ`, produced by the collection algorithm. Example:
```juvix
List String
Pair Int Bool
Maybe String
Maybe Int
if (Maybe String)
if (Maybe Int)
if (Pair Int Bool)
```
## Name generation
Let `f â` be an arbitrary element of `ℒ`, where `â` is a list of
concrete types.
- If `f` is a function, assign a fresh name to `f â`, call it
`⋆(f â)`.
- If `f` is an inductive type, assign a fresh name to `f â`, call it
`⋆(f â)`. Then, for each constructor `cᵢ` of `f`, where `i` is the
index of the constructor, assign a fresh name to it and call it
`⋆ᵢ(f â)`.
## Function generation
Consider an arbitrary function `f` in the original program. Then
consider the list of concrete type applications involving `f`:
`f â₁, …, f âₙ`.
- If `n = 0`, then either:
1. `f` has a concrete type signature, in that case we proceed as
expected.
2. `f` is never called from the functions with a concrete type. In
this case we can safely ignore it.
- If `n > 1`. For each `âᵢ` we proceed as follows in the next
sections. Fix `m` to be the lenght of `âᵢ` with `m > 0`.
### Function name
The name of the monomorphized function is `⋆(f âᵢ)`.
### Type signature
Let `𝒮` be the type signature of `f`. Then `𝒮` has to be of the form
`(A₁ :
Type) → … → (Aₘ : Type) → Π`, where `Π` is a type with no type
abstractions. Now consider the substitution
`σ = {A₁ ↦ âᵢ[1], …, Aₘ ↦ âᵢ[m]}`. Since `âᵢ` is a list of concrete
types, it is clear that `σ(Π)` is a concrete type. Then proceed as
described in <span class="spurious-link" target="Types">_Types_</span>.
### Function clause
Let `𝒞` be a function clause of `f`. Let `p₁ … pₖ` with `k ≥ m` be the
list of patterns in `𝒞`. Clearly the first `m` patterns must be either
variables or wildcards. Wlog assume that the first `m` patterns are all
variables, namely `v₁, …, vₘ`. Let `σ = {v₁ ↦ âᵢ[1], …, Aₘ ↦ âᵢ[m]}` be
a substitution. Let `e` be the body of `𝒞`, then clearly `σ(e)` has no
type variables in it. Now, since each name id must be bound at most
once, we need to generate new ones for the local variables bound in the
patterns `pₘ₊₁, …, pₖ`. Let `w₁, …, wₛ` be the variables bound in
`pₘ₊₁, …, pₖ`. Let `w'₁, …, w'ₛ` be fresh variables. Then let
`δ = {w₁ ↦ w'₁, …, wₛ ↦ w'ₛ}`.
Now let `𝒞'` have patterns `δ(pₘ₊₁), …, δ(pₖ)` and let `e' :` (σ ∪
δ)(e)=. It should be clear that `e'` has no type variables in it and
that all local variable references in `e'` are among `w'₁, …, w'ₛ`. Note
that `e'` is not yet monomorphized. Proceed to the next step to achieve
that.
### Expressions
The input is an expression `e` that has no type variables in it. The
goal is to replace the concrete type applications by the corresponding
monomorphized expression.
The only interesting case is when we find an application. Consider the
unfolded view of the application: `f a₁ … aₘ`. Then, if `f` is either a
constructor, or a function, let `A₁, …, Aₖ` with `k ≤ m` be the list of
type parameters of `f`.
- If `f` is a function and `f a₁ … aₖ ∉ ℒ` then recurse normally,
otherwise, let `â :` a₁ … aₖ= and replace the original expression
`f a₁ … aₘ`, by `⋆(f â)
aₖ₊₁' … aₘ'` where `aₖ₊₁' … aₘ'` are the monomorphization of
`aₖ₊₁ … aₘ` respectively.
- If `f` is a constructor, let `d` be its inductive type. Then check
`d a₁ … aₖ
∈ ℒ`. Proceed analogously as before.
### Types
The input is a type `t` that has no type variables in it. The goal is to
replace the concrete type applications by the corresponding
monomorphized type. Proceed analogously to the previous section.

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# Benchmarks of the new Juvix runtime
Benchmarked version: commit 148ececb4d4259eacbb980f5992073a3ac611d82
from 31.10.2022
## Summary
We benchmark several programs manually compiled into the primitives of
the new Juvix runtime. The code corresponds closely to the code that
will be generated by the new compilation process, with basic low-level
optimisations (unboxing, untagging, etc.) but without any high-level
optimisations on JuvixCore (inlining, specialisation, constant folding,
fusion, etc.). This corresponds to the compilation process planned for
the 0.4 milestone.
We compare the running time and memory usage with analogous programs
written in Haskell, OCaml, JuvixCore (using the evaluator), current
Juvix (with the "direct" transpilation to C) and C.
The results suggest that for most first-order programs the new
compilation process will produce code with running time comparable to
the code produced by the native OCaml compiler. For higher-order
programs heavy on closure manipulation, the results are acceptable but
noticeably worse, especially with third-order functions (i.e. functions
which take functions taking functions). This could, however, be
alleviated by implementing the specialisation optimisation (see the
"specialised" column in the \`ackermann\` and \`mapfun\` benchmarks).
Besides, functional programs of order higher than two are rare.
The comparisons with OCaml and Haskell were not entirely fair because
the new Juvix runtime does not perform garbage collection. The overhead
of garbage collection is particularly visible on the \`mergesort\`
benchmark which creates many intermediate data structures that are
quickly discarded. With proper memory management, the running time
results on first-order programs for the new Juvix runtime are expected
to become slightly worse than for the native OCaml compiler.
For simple programs operating on integers which don't require any heap
memory allocation (\`fibonacci\` and \`combinations\` benchmarks), the
direct transpilation to C in the current Juvix seems to perform best
(behind only C). The reason is that for very simple programs \`clang\`
can better optimise the output of such a direct transpiler. The main
problem with the transpilation to C approach is that it cannot scale to
reliably work for more complex programs, as evidenced by the segfaults,
longer running time and higher memory use on other benchmarks.
In addition to the \`fibonacci\` and \`combinations\` benchmarks, the
advantage of direct transpilation for very simple programs is also
visible on the \`fold\` benchmark where a simple loop over a list
dominates the running time. However, this is partly because the
compilation of closures in current Juvix is incorrect allowing it to be
more efficient.
## Benchmark programs
# fibonacci: compute the Nth Fibonacci number modulo 2<sup>28</sup> (N = 100’000’000)
The Nth Fibonacci number is computed in O(N). Needs only constant stack
space and no heap memory. This benchmark tests the efficiency of tail
recursion and arithmetic operations.
# combinations: count combinations of numbers 1 to N having sum N (N = 100)
This benchmark tests the efficiency of general recursion. No heap memory
needs to be allocated. Uses stack space proportional to N. The running
time is exponential in N.
# prime: compute the Nth prime (N = 16384)
The Nth prime number is computed via the Eratosthenes sieve. A list of N
primes is created. No intermediate lists are discarded (garbage
collection not needed). This benchmark tests the efficiency of tail
recursion, arithmetic operations, list cell allocation and access.
# mergesort: merge sort a list of N integers (N = 2’000’000)
At each level of merge sort intermediate lists are created and
discarded. The running time for this benchmark largely depends on the
efficiency of memory management. Here one may observe the overhead of
garbage collection or the memory blow-up if no garbage collection is
used.
# maybe: optionally sum N integers from a binary tree K times (N = 2<sup>20</sup>, K = 100)
If a fixed number k is encountered in the tree then the result is
\`Nothing\`, otherwise it is \`Just sum\`. The computation is repeated
for values of k from 0 to K. This tests the efficiency of handling
optional values and data structure access.
# fold: fold a list of N integers K times (N = 100’000, K = 1000)
The sum of N natural numbers is computed via fold<sub>left</sub>
(tail-recursive). The computation is repeated K times. The list is
created only once, so that allocation time does not dominate. This
benchmark tests the efficiency of closure call and list cell access.
# cps: compute the Nth Fibonacci number modulo 2<sup>28</sup> with CPS (N = 100’000’000)
The function computing the Nth Fibonacci number is written in
continuation-passing style, tail-recursively calling a continuation
supplied as an argument. This benchmark tests the efficiency of closure
call and allocation.
# mapfold: map and fold a list of N integers K times (N = 10000, K = 10000)
This benchmark tests the efficiency of standard higher-order functions
on lists, closure call and memory management. The program allocates O(K)
intermediate lists of length N which are quickly discarded.
# ackermann: compute Ack(3, N) with the higher-order Ackermann function definition (N = 11)
The higher-order Ackermann function definition iterates an iteration of
function compositions. Hence, it uses a third-order invocation of an
iteration function. This benchmark tests the efficiency of creating and
calling second-order closures, and of partial application.
# mapfun: successively map K functions to a list of N integers (K = 100, N = 10000)
The benchmark stores K second-order closures in a list, maps them
successively to a list of K closures, and then successively maps the K
closures from the result to a list of N integers. This benchmark tests
the efficiency of manipulating closures and storing them in data
structures.
The benchmark programs can be found in \`tests/benchmark\` in the Juvix
source directory.
## Methodology
For each program the total running time (elapsed real time) and memory
use (maximum resident set size) were measured on an M1 iMac with no
significant background activity. Averages of several runs were taken.
The variance was negligible, unless indicated otherwise by providing a
range.
## Results
# fibonacci: compute the Nth Fibonacci number modulo 2<sup>28</sup> (N = 100’000’000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 0.26 | 0.35 | 0.35 | 0.23 | 13.15 | 10.03 | 0.39 | 0.35 | 0.94 | 0.16 | 0.22 |
| Memory use (MB, max RSS) | 1.5 | 3.8 | 1.3 | 8.8 | 21.3 | 8067.7 | 9.7 | 1.7 | 1.8 | 1.3 | 4.0 |
# combinations: count all combinations of numbers 1 to N having sum N (N = 1000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 6.67 | 11.25 | 3.22 | 5.1 | 441.71 | 5.48 | 5.48 | 6.53 | 41.08 | 2.69 | 4.80 |
| Memory use (MB, max RSS) | 1.5 | 3.9 | 1.3 | 8.9 | 22.3 | 9.6 | 9.6 | 1.7 | 1.9 | 1.3 | 4.0 |
# prime: compute the Nth prime (N = 16384)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 1.52 | 1.91 | segfault | 3.09 | 167.04 | 3.85 | 3.85 | 1.68 | 14.82 | 0.12 | 0.13 |
| Memory use (MB, max RSS) | 1.7 | 4.0 | segfault | 9.3 | 24.4 | 9.8 | 9.6 | 2.2 | 2.2 | 1.4 | 4.0 |
# mergesort: merge sort a list of N integers (N = 2’000’000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 0.40 | 0.31 | 3.55 | 1.32 | 22.45 | 2.86 | 2.90 | 1.95 | 3.52 | 0.15 | 0.15 |
| Memory use (MB, max RSS) | 1973.7 | 720.4 | 5046.7 | 2729.8 | 1728.9 | 253.6 | 253.6 | 172.6 | 343.1 | 24.4 | 26.8 |
# maybe: optionally sum N non-zero integers from a binary tree K times (N = 2<sup>20</sup>, K = 100)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 0.45 | 0.64 | 3.29 | 1.57 | 22.75 | 5.58 | 0.59 | 0.30 | 3.57 | 0.27 | 0.50 |
| Memory use (MB, max RSS) | 1.6 | 3.8 | 2646.1 | 1320.9 | 22.4 | 5560.7 | 9.7 | 3.9 | 4.0 | 1.3 | 4.1 |
# fold: fold a list of N integers K times (N = 100’000, K = 1000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 0.45 | 0.54 | 0.35 | 0.23 | 15.27 | 0.58 | 0.58 | 0.36 | 1.80 | NA | NA |
| Memory use (MB, max RSS) | 3.1 | 4.6 | 4.4 | 10.6 | 43.4 | 12.7 | 12.7 | 5.9 | 5.9 | NA | NA |
# cps: compute the Nth Fibonacci number modulo 2<sup>28</sup> with CPS (N = 100’000’000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 0.43 | 0.52 | 1.56 | stack overflow | 20.22 | 10.04 | 0.39 | 0.35 | 1.60 | 0.16 | 0.25 |
| Memory use (MB, max RSS) | 1.5 | 3.9 | 1539.3 | stack overflow | 21.3 | 8067.7 | 9.7 | 1.7 | 1.8 | 1.3 | 4.0 |
# mapfold: map and fold a list of N integers K times (N = 10000, K = 10000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 1.01 | 1.59 | 2.74 | 1.81 | 38.24 | 1.29 | 2.42 | 1.43 | 4.22 | NA | NA |
| Memory use (MB, max RSS) | 2154.5 | 893.0 | 3059.1 | 1542.0 | 26.4 | 10.6 | 10.7 | 7.5 | 10-20 | NA | NA |
# ackermann: compute Ack(3, N) with the higher-order Ackermann function definition (N = 11)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | New Juvix runtime (specialised, native) | New Juvix runtime (specialised, wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | --------------------------------------- | ----------------------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 0.92 | 1.21 | 0.30 | 0.65 | segfault | runtime error | 11.71 | 0.87 | 0.47 | 0.54 | 1.35 | 0.00 | 0.14 |
| Memory use (MB, max RSS) | 2.6 | 4.1 | 2.3 | 3.9 | segfault | runtime error | 23.3 | 13.6 | 9.6 | 2.0 | 3.6 | 1.3 | 4.0 |
# mapfun: successively map K functions to a list of N integers (K = 100, N = 10000)
| | New Juvix runtime (native) | New Juvix runtime (wasm32, wasmer) | New Juvix runtime (specialised, native) | New Juvix runtime (specialised, wasm32, wasmer) | Current Juvix (native) | Current Juvix (wasm32, wasmer) | JuvixCore evaluator | Haskell (native, ghc -O2) | Haskell (native, ghc -XStrict -O2) | OCaml (native, ocamlopt -O2) | OCaml (bytecode) | C (native, clang -O3) | C (wasm32, clang -Os, wasmer) |
| ------------------------ | -------------------------- | ---------------------------------- | --------------------------------------- | ----------------------------------------------- | ---------------------- | ------------------------------ | ------------------- | ------------------------- | ---------------------------------- | ---------------------------- | ---------------- | --------------------- | ----------------------------- |
| Time (seconds, real) | 1.27 | 1.04 | 0.39 | 0.46 | segfault | runtime error | 4.18 | 1.85 | 0.95 | 0.19 | 0.68 | NA | NA |
| Memory use (MB, max RSS) | 3209.8 | 1229.7 | 21.8 | 13.2 | segfault | runtime error | 33.0 | 13.6 | 11.6 | 5.3 | 7.9 | NA | NA |
Comments
---
# "New Juvix runtime" denotes C programs written using the primitives
of the new Juvix runtime. These programs were "manually" compiled from
the corresponding Juvix/JuvixCore programs, according to the new Juvix
compilation concept. They correspond closely to the code that will be
generated by the basic version of the new compilation process, without
any high-level optimisations (inlining, specialisation, fusion, constant
folding) but with basic low-level memory representation optimisations
(unboxing, untagging, etc). This version of the new compilation process
should be finished with the 0.4 milestone.
# The "specialised" column for "New Juvix runtime" denotes a version
of the corresponding "New Juvix runtime" benchmark program for which
specialisation of higher-order functions was manually performed (to
simulate the effects of the high-level specialisation optimisation).
# "Current Juvix" denotes Juvix programs compiled with the current
compilation process via a "direct" translation to C. For a fair
comparison, all number operations were implemented using native binary C
integers (exposed via \`foreign\` and \`compile\` blocks) without
overflow check, instead of using the unary Nat from the standard
library. For Haskell, we use the fixed-precision Int instead of the
arbitrary-precision Integer.
# For the simplest benchmark programs without heap memory allocation
(e.g. \`fibonacci\`, \`combinations\`), the performance of "Current
Juvix" is comparable to or better than that of "New Juvix runtime". This
is because \`clang\` managed to eliminate (tail) recursion and optimise
the code to essentially the same or better thing. The main problem with
the current "direct" transpilation to C approach is that it cannot scale
to reliably work for more complex programs. By "more complex" I mean
larger program size, more functions, more complex patterns of recursion
and/or the use of more functional programming features (including
functional data structures). I don't mean higher computational
complexity or more resource use.
# The segfaults and runtime errors for "Current Juvix" are
consequences of incorrectly generated code (current compilation of
partial application is not entirely correct) or stack overflows (when
\`clang\` didn't figure out how to eliminate tail recursion).
# The comparison with "Current Juvix" is not entirely fair for
benchmarks that test the manipulation and calling of closures (e.g.
\`fold\`). Current Juvix achieves good performance (when it doesn't
segfault) at the expense of correctness: partial application is not
compiled correctly and fixing this would require a fundamental change in
closure representation.
# The comparison with Haskell and OCaml compilers is not entirely
fair, because the new Juvix runtime does not perform garbage collection.
With the GC overhead, I would expect the Juvix runtime results for
native compilation of first-order programs to become a bit worse than
the native OCaml versions. The GC overhead is particularly noticeable
for the \`mergesort\` benchmark which creates many large intermediate
lists. The memory usage of the Juvix runtime is much higher on this
benchmark than the memory usage of OCaml or Haskell versions. The
relatively small time difference between the OCaml native and bytecode
versions of \`mergesort\` also indicates that GC accounts for a
significant part of the running time.
# Another small overhead will be introduced by bounds checking for
integer operations. Currently, the new Juvix runtime operates on unboxed
31-bit (or 63-bit) integers without checking for integer overflow.
# If we decide to default to transparent arbitrary-precision integers,
then another small overhead will be introduced by the need to check the
integer representation with each arithmetic operation.
# Admittedly, the programs were deliberately written in a way to make
high-level optimisations unnecessary, except specialisation for
higher-order functions (mostly in \`ackermann\` and \`mapfun\`). This
also explains the good performance of the OCaml native compiler which
doesn't do much high-level optimisation.
# In the "Current Juvix" and OCaml version of \`mergesort\`, to avoid
stack overflow the \`merge\` function was written tail-recursively with
accumulator reversal at the end. This is not necessary for the new Juvix
runtime, because the stack is dynamically extended when needed.
# As evidenced by the \`combinations\` benchmark, for non-tail-recursive
direct calls our code performs worse than the code which uses the C /
WebAssembly stack and function call mechanisms. However, in general it
is impossible to directly use the C / WebAssembly stack and call
mechanisms for a purely functional language. Since we dynamically
allocate the stack segments when needed, stack overflow is impossible.
This is convenient in an eager functional language. Otherwise, one needs
to rewrite all functions operating on large data to use tail recursion.
We pay for this convenience with a small overhead, which is the main
reason for poorer performance on \`combinations\` where stack
manipulation cost dominates.
# Haskell's laziness seems to introduce more overhead than I
expected. This would explain the comparatively better performance of the
native OCaml compiler. The problem is particularly stark when Haskell's
strictness analysis fails for some reason, as in the \`fibonacci\`
benchmark. The second "Haskell" column with the "-XStrict" flag for GHC
indicates the version of the benchmark compiled with strictness as the
default.
# The C versions of the programs were written to take advantage of C's
imperative features, e.g., using arrays instead of lists, loops instead
of recursion. No C versions are provided for some benchmarks designed to
test specifically functional language features.
# With the new Juvix runtime, the 32-bit WebAssembly version of
\`mergesort\` is faster than the 64-bit native version because it needs
roughly half as much memory (the word size is 4 bytes instead of 8). The
difference is even starker between the WebAssembly and native versions
of \`mergesort\` for "Current Juvix".
# There seems to be a memory leak in the JuvixCore evaluator. This is
what happens too often when one uses a lazy language.
# Haskell also leaks memory in the Fibonacci benchmark, despite it
being a simple tail-recursive program. It seems strictness analysis
didn't work.

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# Strictly positive data types
We follow a syntactic description of strictly positive inductive data
types.
An inductive type is said to be <u>strictly positive</u> if it does not
occur or occurs strictly positively in the types of the arguments of its
constructors. A name qualified as strictly positive for an inductive
type if it never occurs at a negative position in the types of the
arguments of its constructors. We refer to a negative position as those
occurrences on the left of an arrow in a type constructor argument.
In the example below, the type `X` occurs strictly positive in `c0` and
negatively at the constructor `c1`. Therefore, `X` is not strictly
positive.
```minijuvix
axiom B : Type;
type X :=
c0 : (B -> X) -> X
| c1 : (X -> X) -> X;
```
We could also refer to positive parameters as such parameters occurring
in no negative positions. For example, the type `B` in the `c0`
constructor above is on the left of the arrow `B->X`. Then, `B` is at a
negative position. Negative parameters need to be considered when
checking strictly positive data types as they may allow defining
non-strictly positive data types.
In the example below, the type `T0` is strictly positive. However, the
type `T1` is not. Only after unfolding the type application `T0 (T1 A)`
in the data constructor `c1`, we can find out that `T1` occurs at a
negative position because of `T0`. More precisely, the type parameter
`A` of `T0` is negative.
```minijuvix
type T0 (A : Type) := c0 : (A -> T0 A) -> T0 A;
type T1 := c1 : T0 T1 -> T1;
```
## Bypass the strict positivity condition
To bypass the positivity check, a data type declaration can be annotated
with the keyword `positive`. Another way is to use the CLI global flag
`--no-positivity` when type checking a `Juvix` File.
```juvix
$ cat tests/negative/MicroJuvix/NoStrictlyPositiveDataTypes/E5.mjuvix
module E5;
positive
type T0 (A : Type) :=
c0 : (T0 A -> A) -> T0 A;
end;
```
## Examples of non-strictly data types
- `Bad` is not strictly positive because of the negative parameter
`A` of `Tree`.
```juvix
type Tree (A : Type) :=
leaf : Tree A
| node : (A -> Tree A) -> Tree A;
type Bad :=
bad : Tree Bad -> Bad;
```
- `A` is a negative parameter.
```juvix
type B (A : Type) :=
b : (A -> B (B A -> A)) -> B A;
```

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# Quick Start
<a href="https://github.com/anoma/juvix">
<img align="left" width="200" height="200" alt="Juvix Mascot" src="assets/images/tara-teaching.svg" />
</a>
To install Juvix, follow the instructions in the [Installation
How-to](./howto/installing.md).
After installation, run `juvix --help` to see the list of commands.
Run Juvix doctor to check your system setup:
```shell
juvix doctor
```
## CLI Usage Examples
Create a new package:
```shell
juvix init
```
Compile a source file into an executable:
```shell
juvix compile path/to/source.juvix
```
Compile a source file into a WebAssembly binary:
```shell
juvix compile -t wasm path/to/source.juvix
```
Launch the REPL:
```shell
juvix repl
```
Typecheck a source file:
```shell
juvix typecheck path/to/source.juvix
```
Generate HTML representations of a source file and its imports:
```shell
juvix html --recursive path/to/source.juvix
```
## The Hello World example
This is the Juvix source code of the traditional Hello World program.
```shell
-- HelloWorld.juvix
module HelloWorld;
open import Stdlib.Prelude;
main : IO;
main := printStringLn "hello world!";
end;
```
To compile and run a binary generated by Juvix, save the source code to
a file called `HelloWorld.juvix` and run the following command from the
directory containing it:
```shell
juvix compile HelloWorld.juvix
./HelloWorld
```
You should see the output: `hello world!`
The source code can also be compiled to a WebAssembly binary. This
requires some additional setup. See the [Installation
How-to](https://anoma.github.io/juvix/howto/installing.html) for more
information. You can also run `juvix doctor` to check your setup.
```shell
juvix compile --target wasm HelloWorld.juvix
wasmer HelloWorld.wasm
```

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# Benchmarks

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The following links are clickable versions of their corresponding Juvix
programs. The HTML output is generated by running
`juvix html --recursive FileName.juvix`.
- [HelloWorld.juvix](./../examples/html/HelloWorld/HelloWorld.html)
- [Fibonacci.juvix](./../examples/html/Fibonacci/Fibonacci.html)
- [Hanoi.juvix](./../examples/html/Hanoi/Hanoi.html)
- [PascalsTriangle.juvix](./../examples/html/PascalsTriangle/PascalsTriangle.html)
- [Collatz.juvix](./../examples/html/Collatz/Collatz.html)
- [TicTacToe.juvix](./../examples/html/TicTacToe/CLI/CLI.TicTacToe.html)

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# Judoc syntax reference
Judoc is used to document parts of your code. You can attach _Judoc
blocks_ to the following entities:
1. A module.
2. A type definition.
3. A constructor definition.
4. A type signature of a function.
5. An axiom definition.
In order to attach documentation to any of these entities, write _blocks_ of
documentation before them:
1. For modules:
```
--- This module is cool
module Cool;
..
```
2. For type definitions:
```
--- Unary representation of natural numbers
type Nat : Type :=
| --- Nullary constructor representing number 0
zero : Nat
| --- Unary constructor representing the successor of a natural number
suc : Nat -> Nat;
```
3. For type signatures (and likewise for axioms):
```
--- The polymorphic identity function
id : {A : Type} -> A -> A;
```
Next we define the syntax of Judoc _blocks_.
## Block
A _block_ can be one of these:
1. A _paragraph_.
2. An _example_.
_Blocks_ are separated by a line with only `---`.
For instance, this is a sequence of two _blocks_:
```
--- First block
---
--- Second block
```
Note that the following is a single block since it lacks the `---` separator:
```
--- First block
--- Still first block
```
### Paragraph
A _paragraph_ is a non-empty sequence of _lines_.
For instance, the following is a paragraph with two _lines_:
```
--- First line
--- Second line
```
Note that a rendered paragraph will have have no line breaks. If you want to
have line breaks, you will need to split the paragraph. Hence, the paragraph
above will be rendered as
```
First line Second line
```
##### line
A _line_ starts with `---` and is followed by a non-empty sequence of
_atoms_.
For instance, the following is a valid _line_:
```
--- A ;Pair Int Bool; contains an ;Int; and a ;Bool;
```
##### Atom
An _atom_ is either:
1. A string of text (including spaces but not line breaks).
2. An inline Juvix expression surrounded by `;`.
For instance, the following are valid _atoms_:
1. `I am some text.`
2. `;Pair Int Bool;`
### Example
An example is of the following form
```
--- >>> someExpression ;
```
The `someExpression` can span multiple lines and it must be ended with a `;`.
For instance:
```
--- >>> 1
+ 2
+ 3;
```

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- [Functions](./functions.md)
- [Data types](./datatypes.md)
- [Modules](./modules.md)
- [Local definitions](./let.md)
- [Control structures](./control.md)
- [Comments](./comments.md)
- [Axioms](./axioms.md)

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# Axiom
Axioms or postulates can be introduced by using the `axiom` keyword. For
example, let us imagine one wants to write a program that assumes _A_ is
a type, and there exists a term _x_ that inhabits _A_. Then the program
would look like the following.
```juvix
module Example;
axiom
A : Type;
axiom
x : A;
end;
```
Terms introduced by the `axiom` keyword lack any computational content.
Programs containing axioms not marked as builtins cannot be compiled to
most targets.

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# Built-ins
Juvix has support for the built-in natural type and a few functions that
are compiled to efficient primitives.
1. Built-in inductive definitions.
```juvix
builtin nat
type Nat :=
zero : Nat |
suc : Nat → Nat;
```
2. Builtin function definitions.
```juvix
infixl 6 +;
builtin nat-plus
+ : Nat → Nat → Nat;
+ zero b := b;
+ (suc a) b := suc (a + b);
```
3. Builtin axiom definitions.
```juvix
builtin nat-print
axiom printNat : Nat → Action;
```

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# Comments
Comments follow the same syntax as in `Haskell` and `Agda`. Be aware,
Juvix has no support for nested comments.
- Inline Comment
```juvix
-- This is a comment!
```
- Region comment
```juvix
{-
This is a comment!
-}
```

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# Control structures
## Case
A case expression has the following syntax:
```juvix
case value
| pat1 := branch1
..
| patN := branchN
```
For example, one can evaluate the following expression in the REPL:
```juvix
Stdlib.Prelude> case 2 | zero := 0 | suc x := x | _ := 19
1
```
## Lazy builtins
The standard library provides several builtin functions which are
treated specially and evaluated lazily. These builtins must always be
fully applied.
- `if condition branch1 branch2`. First evaluates `condition`, if true
evaluates and returns `branch1`, otherwise evaluates and returns
`branch2`.
- `a || b`. Lazy disjunction. First evaluates `a`, if true returns
true, otherwise evaluates and returns `b`.
- `a && b`. Lazy conjunction. First evaluates `a`, if false returns
false, otherwise evaluates and returns `b`.
- `a >> b`. Sequences two IO actions. Lazy in the second argument.

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# Data types
A data type declaration consists of:
- The `type` keyword,
- a unique name for the type,
- the `:=` symbol, and
- a non-empty list of constructor declarations (functions for
building the elements of the data type).
The simplest data type is the `Unit` type with one constructor called
`unit`.
```juvix
type Unit := unit : Unit;
```
In the following example, we declare the type `Nat` – the unary
representation of natural numbers. This type comes with two
constructors: `zero` and `suc`. Example elements of type `Nat` are the
number one represented by `suc zero`, the number two represented by
`suc (suc zero)`, etc.
```juvix
type Nat :=
zero : Nat
| suc : Nat -> Nat;
```
Constructors can be used like normal functions or in patterns when
defining functions by pattern matching. For example, here is a function
adding two natural numbers:
```juvix
infixl 6 +;
+ : Nat -> Nat -> Nat;
+ zero b := b;
+ (suc a) b := suc (a + b);
```
A data type may have type parameters. A data type with a type parameter
`A` is called _polymorphic in_ `A`. A canonical example is the type
`List` polymorphic in the type of list elements.
```juvix
infixr 5 ::;
type List (A : Type) :=
nil : List A
| :: : A -> List A -> List A;
elem : {A : Type} -> (A -> A -> Bool) -> A -> List A -> Bool;
elem _ _ nil := false;
elem eq s (x :: xs) := eq s x || elem eq s xs;
```
For more examples of inductive types and how to use them, see [the Juvix
standard library](https://anoma.github.io/juvix-stdlib/).

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# Function declarations
A function declaration consists of a type signature and a group of
_function clauses_.
In the following example, we define a function `multiplyByTwo`. The
first line `multiplyByTwo : Nat -> Nat;` is the type signature and the
second line `multiplyByTwo n := 2 * n;` is a function clause.
```juvix
open import Stdlib.Prelude;
multiplyByTwo : Nat -> Nat;
multiplyByTwo n := 2 * n;
```
A function may have more than one function clause. When a function is
called, the first clause that matches the arguments is used.
The following function has two clauses.
```juvix
open import Stdlib.Prelude;
neg : Bool -> Bool;
neg true := false;
neg false := true;
```
When `neg` is called with `true`, the first clause is used and the
function returns `false`. Similarly, when `neg` is called with `false`,
the second clause is used and the function returns `true`.
## Mutually recursive functions
Function declarations can depend on each other recursively. In the
following example, we define a function that checks if a number is
`even` by calling a function that checks if a number is `odd`.
```juvix
open import Stdlib.Prelude;
odd : Nat -> Bool;
even : Nat -> Bool;
odd zero := false;
odd (suc n) := even n;
even zero := true;
even (suc n) := odd n;
```
## Anonymous functions
Anonymous functions, or _lambdas_, are introduced with the syntax:
```juvix
\{| pat1 .. patN_1 := clause1
| ..
| pat1 .. patN_M := clauseM}
```
The first pipe `|` is optional. Instead of `\` one can also use `λ`.
An anonymous function just lists all clauses of a function without
naming it. Any function declaration can be converted to use anonymous
functions:
```juvix
open import Stdlib.Prelude;
odd : Nat -> Bool;
even : Nat -> Bool;
odd := \{
| zero := false
| (suc n) := even n
};
even := \{
| zero := true
| (suc n) := odd n
};
```
## Short definitions
A function definition can be written in one line, with the body
immediately following the signature:
```juvix
multiplyByTwo : Nat -> Nat := \{n := 2 * n};
```

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# Infix operators
TODO

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# Local definitions
Local definitions are introduced with the `let` construct.
```juvix
sum : NList -> Nat;
sum lst :=
let
go : Nat -> NList -> Nat;
go acc nnil := acc;
go acc (ncons x xs) := go (acc + x) xs;
in
go 0 lst;
```
The declaractions in a `let` have the same syntax as declarations inside
a module, but they are visible only in the expression following the `in`
keyword.

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# Module system
Modules are the way in which we split our programs in separate files. Juvix also
supports local modules, which provide a way to better organize different scopes
within a file.
We call top modules those who are defined at the top of a file.
We call local modules those who are defined inside another module.
## Top modules
A module has a name and a body, which comprises a sequence of
[statements](statement.md).
In order to define a module named `Data.List` we will use the following syntax:
```juvix
module Data.List;
<body>
```
### Top module naming convention
Top modules that belong to a [project](project.md) must follow a naming
convention. That is, if `dir` is the root of a project, then the module in
`dir/Data/List.juvix` must be named `Data.List`.
## _Import_ and _open_ statements
In order to access the definitions from another modules we use an
_import_ statement. To import some module named `Data.List` we will write
```juvix
import Data.List;
```
Now, we can access the definitions in the imported module using _qualified
names_. E.g., `Data.List.sort`.
It is possible to import modules and give them a more convinent way thus:
```juvix
import Data.List as List;
```
If we want to access the contents of a module without the need to qualify the
names, we use an _open statement_. The syntax is as follows:
```juvix
open Data.List;
```
Now we can simply write `sort`. It is important to remember that when we open a
module, that module must be in scope, i.e., it must either be imported
or defined as a local module
Since importing and opening a module is done often, there is special syntax for
that. The following statement:
```juvix
open import Data.List;
```
Is equivalent to this:
```juvix
import Data.List;
open Data.List;
```
When opening a module, if we want to open an explicit subset of its definitions,
we must use the `using` keyword thus:
```juvix
open Data.List using {List; sort; reverse}
```
If we want to open all definitions of a module minus a subset, we
must use the `hiding` keyword thus:
```juvix
open Data.List hiding {head; tail}
```
All opened definitions are available under the current module, but
they are not exported by default. Meaning that if another module imports the current
module, it will only be able to access the definitions defined there but not
those which have been opened. If we want opened definitions to be exported, we
must use the `public` keyword thus:
```
module Prelude;
open import Data.List public;
```
Now, from another module we can access definitions in `Data.List` through the
`Prelude` module.
```
module MyModule;
open import Prelude;
-- List, sort, reverse, etc. are now in scope
```
## Local modules
Juvix modules have a hierarchical structure. So far we have discussed top level
modules, which have a one-to-one correspondence with files in the filesystem. On
the other hand, local modules are defined within another module. They can be
useful to group definitions within a file.
The syntax for local modules is as follows:
```
module Path.To.TopModule;
module Loc;
<body>
end;
```
After the definition of a local module, we can access its definitions by using
qualified names. Local modules can be opened by open statements in the same way
as top modules.
Local modules inherit the scope of the parent module. Some shadowing rules
apply, and they probably follow your intuition:
1. Opening or defining a symbol shadows inherited instances of that symbol.
2. Opening a symbol does _not_ shadow a defined instanance of that symbol in the
current module.
3. Conversely, defining a symbol in the current module does _not_ shadow an
opened instance of that symbol.
As a consequence of 2 and 3, using a symbol that is both defined and opened
locally will result in an ambiguity error.

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# Module system
## Defining a module
The `module` keyword starts the declaration of a module followed by its
name and body. The module declaration ends with the `end` keyword.
```juvix
-- ModuleName.juvix
module ModuleName;
end;
```
A <u>Juvix project</u> is a collection of Juvix modules inside one main
project folder containing a metadata file named `juvix.yaml`. Each Juvix
file has to define a <u>module</u> of the same name. The name of the
module must coincide with the path of the its file relative to its
project's root directory. For example, if the file is
`root/Data/List.juvix` then the module must be called `Data.List`,
assuming `root` is the project's folder.
To check that Juvix is correctly detecting your project's root, one can
run the command `juvix dev root File.juvix`.
## Importing modules
To bring into the current scope all module definitions from other
external modules, one can use the `import` keyword along with the
corresponding module name. This way, one gets all the imported names
qualified.
```juvix
-- A.juvix
module A;
axiom Nat : Type;
axiom zero : Nat;
end;
-- B.juvix
module B;
import A;
x : A.Nat;
x := A.zero;
```
Additionally, one can <u>open</u> an imported module making available
all its names by their unqualified name.
```juvix
-- A.juvix
module A;
axiom Nat : Type;
axiom zero : Nat;
end;
-- B.juvix
module B;
import A;
open A;
x : Nat;
x := zero;
```
However, opening modules may create name collisions if you already have
the imported names as definitions in the current module. In this case,
Juvix will complain with an error, letting you know which symbols are
ambiguous. For example, in module `B` below, the name `a` is ambiguous.
```juvix
-- A.juvix
module A;
axiom A : Type;
axiom a : A;
end;
-- B.juvix
module B;
import A;
open A;
axiom a : A;
x := a;
end;
```
One alternative here is hiding the name `a` as follows.
```juvix
-- B.juvix
module B;
import A;
open A hiding {a};
axiom a : A;
x := a;
end;
```
Now, we can use the `open import` syntax to simplify the `import-open`
statements.
Instead of having:
```juvix
import Prelude;
open Prelude;
```
We simplify it by the expression:
```juvix
open import Prelude;
```
The `hiding` keyword can be used within an `open-import` statement.
```juvix
-- B.juvix
module A;
open import A hiding {a};
axiom a : A;
x := a;
end;
```
## Exporting symbols
The module `C` below does not typecheck. Both symbols, originally
defined in module `A`, are not visible in module `C` after importing
`B`. The symbols `A` and `a` are not exported by the module `B`. To
export symbols from an imported module, one can use the `public` keyword
at the end of the corresponding `open` statement. For example, the
module `C` typechecks after marking the import of `A` as `public` in
module `B`.
```juvix
-- A.juvix
module A;
axiom A : Type;
axiom a : A;
end;
-- B.juvix
module B;
open import A;
end;
-- C.juvix
module C;
open import B;
x : A;
x := a;
end;
```
Fix:
```juvix
-- B.juvix
module B;
open import A public;
end;
```

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# Juvix project
A _juvix project_ is a collection of juvix modules plus some extra metadata
gathered in a `juvix.yaml` file. The most convenient way to create a juvix
project is to run the command `juvix init`.
A project is rooted in a directory. The root is set by creating a `juvix.yaml`,
which contains the following fields:
1. **name**: The name of the project. The name must not be empty and cannot
exceed 100 characters. Lower case letters, digits and hyphen `-` are
acceptable characters. The first letter must not be a hyphen. Summarizing, it
must match the following regexp: `[a-z0-9][a-z0-9-]{0,99}`.
2. **version** (_optional_): The version of the project. It must follow the
[SemVer](https://semver.org/) specification. If ommited the version will be
assumed to be _0.0.0_.
3. **dependencies** (_optional_): The dependencies of the project given as a
list. Each dependency is given as relative (or absolute) path to the root of
another juvix project. If the field is ommited, it will be assumed to contain
the juvix standard library as a dependency.
As intuition would tell, a juvix module belongs to a juvix project if it is
placed in the subtree hanging from the root directory. This rule has two
exceptions:
1. Modules in a hidden (or hanging from a hidden) directory are not part of the
project. E.g., if the root of a project is `dir`, then the module
`dir/.d/Lib.juvix` does not belong to the project rooted in `dir`.
1. A `juvix.yaml` file shadows other `juvix.yaml` files in parent
directories. E.g. if the root of a project is `dir` and the files
`dir/juvix.yaml` and `dir/nested/juvix.yaml` exist, then the module
`dir/nested/Lib.juvix` would belong to the project in `dir/nested`.
## Module naming convention
Modules that belong to a project must follow a naming convention.
See the documentation for [modules](modules.md).

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# Statement
A juvix statement is each of the components of a module.
All statements are listed below:
1. [type definition](datatypes.md).
2. [function definition](functions.md).
3. [axiom definition](axioms.md).
4. [fixity declaration](infix.md).
5. [function definition](functions.md).
6. [open](modules.md).
7. [import](modules.md).
8. [local module](modules.md).

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The [Juvix standard library](https://anoma.github.io/juvix-stdlib/)
contains common functions that can be used in Juvix programs.

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# CLI
## Usage
```shell
juvix [Global options] ((-v|--version) | (-h|--help) | COMPILER_CMD | UTILITY_CMD)
```
## Informative options
- `-v,--version` Print the version and exit
- `-h,--help` Show this help text
## Global Command flags
- `--no-colors` Disable globally ANSI formatting
- `--show-name-ids` Show the unique number of each identifier when
pretty printing
- `--only-errors` Only print errors in a uniform format (used by
juvix-mode)
- `--no-termination` Disable termination checking
- `--no-positivity` Disable positivity checking for inductive types
- `--no-stdlib` Do not use the standard library
## Main Commands
- `html` Generate HTML output from a Juvix file
- `typecheck` Typecheck a Juvix file
- `compile` Compile a Juvix file
## Utility Commands
- `doctor` Perform checks on your Juvix development environment
- `init` Interactively initialize a Juvix project in the current
directory
## Dev Commands
```shell
juvix dev COMMAND
```
- `parse` Parse a Juvix file
- `scope` Parse and scope a Juvix file
- `highlight` Highlight a Juvix file
- `core` Subcommands related to JuvixCore
- `asm` Subcommands related to JuvixAsm
- `root` Show the root path for a Juvix project
- `termination` Subcommands related to termination checking
- `internal` Subcommands related to Internal
- `minic` Translate a Juvix file to a subset of C
## CLI Auto-completion Scripts
The Juvix CLI can generate auto-completion scripts. Follow the
instructions below for your shell.
NB: You may need to restart your shell after installing the completion
script.
### Bash
Add the following line to your bash init script (for example
`~/.bashrc`).
```shell
eval "$(juvix --bash-completion-script juvix)"
```
### Fish
Run the following command in your shell:
```shell
juvix --fish-completion-script juvix
> ~/.config/fish/completions/juvix.fish
```
### ZSH
Run the following command in your shell:
```shell
juvix --zsh-completion-script juvix > $DIR_IN_FPATH/_juvix
```
where `$DIR_IN_FPATH` is a directory that is present on the [ZSH FPATH
variable](https://zsh.sourceforge.io/Doc/Release/Functions.html) (which
you can inspect by running `echo $FPATH` in the shell).

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- [Command line Interface](./CLI.md)
- [Emacs Mode](./emacs.md)
- [Test Suite](./testing.md)
- [Doctor](./doctor.md)

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# Juvix Doctor
The `juvix doctor` command can help you to troubleshoot problems with
your development environment. For each problem the doctor finds they'll
be a link to a section on this page to help you fix it.
## Could not find the clang command
The Juvix compiler uses the [Clang compiler](https://clang.llvm.org)
version 13 or later to generate binaries. You need to have Clang
available on your system `$PATH`.
Recommended installation method:
### MacOS
Use [Homebrew](https://brew.sh):
```shell
brew install llvm
```
NB: The distribution of Clang that comes with XCode does not support the
`Wasm` target so you must install the standard Clang distribution.
### Debian / Ubuntu Linux
```shell
sudo apt install clang lldb lld
```
### Arch Linux
```shell
sudo pacman -S llvm lld
```
## Could not find the wasm-ld command
The Juvix compiler required `wasm-ld` (the Wasm linker) to produce
`Wasm` binaries.
Recommended installation method:
### MacOS
`wasm-ld` is included in the [Homebrew](https://brew.sh) llvm
distribution:
```shell
brew install llvm
```
### Debian / Ubuntu Linux
```shell
sudo apt install lldb lld
```
### Arch Linux
```shell
sudo pacman -S lld
```
## Newer Clang version required
Juvix requires Clang version 13 or above. See the documentation on
[installing Clang](./doctor.md#could-not-find-the-clang-command).
## Clang does not support the wasm32 target
Juvix requires Clang version 13 or above. See the documentation on
[installing Clang](./doctor.md#could-not-find-the-clang-command).
## Clang does not support the wasm32-wasi target
Juvix uses [WASI - The Wasm System Interface](https://wasi.dev) to
produce binaries that can be executed using a Wasm runtime. The files
necessary to setup Clang with `wasm32-wasi` support are available at
[wasi-sdk](https://github.com/WebAssembly/wasi-sdk/releases).
To install the `wasm32-wasi` target for Clang you need to do two things:
### Install `libclang_rt.builtins-wasm32.a` into your Clang distribution
1. Obtain `libclang_rt.builtins-wasm32-wasi-16.0.tar.gz` from the
[wasi-sdk
releases](https://github.com/WebAssembly/wasi-sdk/releases) page.
2. Untar the file and place the file
`lib/wasi/libclang_rt.builtins-wasm32.a` into your Clang
distribution directory.
On MacOS, if you installed llvm using homebrew you can find the
Clang distribution directory using `brew --prefix llvm`. You should
then place the builtins file at
`` `brew --prefix llvm`/lib/wasi/libclang_rt.builtins-wasm32.a ``.
On Linux the Clang distribution directory will be something like
`/usr/lib/clang/13.0.1` where `13.0.1` is the version of Clang that
you have installed. You should then place the builtins file at
`/usr/lib/clang/13.0.1/lib/wasi/libclang_rt.builtins-wasm32`.
### Download the WASI sysroot and set `WASI_SYSROOT_PATH`
1. Obtain `wasi-sysroot-16.0.tar.gz` from the [wasi-sdk
releases](https://github.com/WebAssembly/wasi-sdk/releases) page.
2. Untar the file and set the environment variable `WASI_SYSROOT_PATH`
to that location.
## Environment variable `WASI_SYSROOT_PATH` is not set
Set the `WASI_SYSROOT_PATH` to the directory where you installed the
`wasi-sdk` sysroot files. See [installing the WASI
sysroot](./doctor.md#download-the-wasi-sysroot-and-set-wasi_sysroot_path).
## Could not find the wasmer command
The Juvix test suite uses [Wasmer](https://wasmer.io) as a Wasm runtime
to execute compiled Wasm binaries. See [the Wasmer
documentation](https://docs.wasmer.io/ecosystem/wasmer/getting-started)
to see how to install it.

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## Emacs Mode
There is an Emacs mode available for Juvix. Currently, it supports
syntax highlighting for well-scoped modules.
To get started, clone the Juvix Emacs mode repository:
```bash
git clone https://github.com/anoma/juvix-mode.git
```
To install it add the following lines to your Emacs configuration file:
```elisp
(push "/path/to/juvix-mode/" load-path)
(require 'juvix-mode)
```
Make sure that Juvix is installed in your `PATH`.
The Juvix major mode will be activated automatically for `.juvix` files.
### Keybindings
| Key | Function Name | Description |
| --------- | ----------------------- | ----------------------------------------------------- |
| `C-c C-l` | `juvix-load` | Runs the scoper and adds semantic syntax highlighting |
| `M-.` | `juvix-goto-definition` | Go to the definition of symbol at point |
| `C-c C-f` | `juvix-format-buffer` | Format the current buffer |
### Emacs installation
Most Linux distributions contain an Emacs package which can be installed
with your package manager (`sudo apt install emacs` on Ubuntu). On
macOS, it is recommended to install Emacs Plus via Homebrew:
`brew install emacs-plus`. Using the Emacs Homebrew casks is not
recommended.
### Common problems
- Error "Symbol's value as variable is void: sh:1:"
Make sure the juvix executable is on the Emacs' `exec-path`. Note
that `exec-path` may be different from your shell's `PATH`. This is
particularly common on macOS with Emacs launched from GUI instead of
the terminal.
The easiest way to resolve this issue is to install the
[exec-path-from-shell](https://github.com/purcell/exec-path-from-shell)
package (available on MELPA). Alternatively, one may set `exec-path`
to match shell `PATH` by following the instructions from
[EmacsWiki](https://www.emacswiki.org/emacs/ExecPath).

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# Testing
### Dependencies
See [Installing dependencies](./doctor.html) for instructions on how to
setup the testing environment for the WASM compiler tests.
### Running
Run tests using:
```shell
stack test
```
To run tests, ignoring all the WASM tests:
```shell
stack test --ta '-p "! /slow tests/"'
```

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- [NodeJS Interop](./nodejs-interop.md)

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# Juvix Emacs mode tutorial
First, follow the instructions in the [Emacs Mode
Reference](../reference/tooling/emacs.md) to install the Juvix Emacs
mode. Once you've successfully set it up, create a file `Hello.juvix`
with the following content.
```juvix
module Hello;
open import Stdlib.Prelude;
main : IO;
main := printStringLn "Hello world!";
end;
```
Type `C-c C-l` to run the scoper and highlight the syntax.
If you make a mistake in your program, it is automatically underlined in
red with the error message popping up when you hover the mouse pointer
over the underlined part.
For example, in the following program the identifier `printStringLna`
should be underlined with the error message "Symbol not in scope".
```juvix
module Hello;
open import Stdlib.Prelude;
main : IO;
main := printStringLna "Hello world!";
end;
```
If error underlining doesn't work, make sure you have the `flycheck`
mode turned on. It should be turned on automatically when loading
`juvix-mode`, but in case this doesn't work you can enable it with
`M-x flycheck-mode`.
Let's extend our program with another definition.
```juvix
module Hello;
open import Stdlib.Prelude;
print : IO;
print := printStringLn "Hello world!";
main : IO;
main := print;
end;
```
Place the cursor on the `print` call in the function clause of `main`
and press `M-.`. The cursor will jump to the definition of `print`
above. This also works across files and for definitions from the
standard library. You can try using `M-.` to jump to the definition of
`printStringLn`.
One more feature of the Juvix Emacs mode is code formatting. To format
the content of the current buffer, type `C-c C-f`. Here is the result.
```juvix
module Hello;
open import Stdlib.Prelude;
print : IO;
print := printStringLn "Hello world!";
main : IO;
main := print;
end;
```

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# Juvix tutorial
- [Juvix REPL](./learn.md#juvix-repl)
- [Basic expressions](./learn.md#basic-expressions)
- [Files, modules and
compilation](./learn.md#files-modules-and-compilation)
- [Data types and functions](./learn.md#data-types-and-functions)
- [Pattern matching](./learn.md#pattern-matching)
- [Comparisons and
conditionals](./learn.md#comparisons-and-conditionals)
- [Local definitions](./learn.md#local-definitions)
- [Recursion](./learn.md#recursion)
- [Partial application and higher-order
functions](./learn.md#partial-application-and-higher-order-functions)
- [Polymorphism](./learn.md#polymorphism)
- [Tail recursion](./learn.md#tail-recursion)
- [Totality checking](./learn.md#totality-checking)
- [Exercises](./learn.md#exercises)
## Juvix REPL
After [installing Juvix](../howto/installing.md), launch the Juvix REPL:
```shell
juvix repl
```
The response should be similar to:
```jrepl
Juvix REPL version 0.3: https://juvix.org. Run :help for help
OK loaded: ./.juvix-build/stdlib/Stdlib/Prelude.juvix
Stdlib.Prelude>
```
Currently, the REPL supports evaluating expressions but it does not yet
support adding new definitions. To see the list of available REPL
commands type `:help`.
## Basic expressions
You can try evaluating simple arithmetic expressions in the REPL:
```jrepl
Stdlib.Prelude> 3 + 4
7
Stdlib.Prelude> 1 + 3 * 7
22
Stdlib.Prelude> div 35 4
8
Stdlib.Prelude> mod 35 4
3
Stdlib.Prelude> sub 35 4
31
Stdlib.Prelude> sub 4 35
0
```
By default, Juvix operates on non-negative natural numbers. Natural number
subtraction is implemented by the function `sub`. Subtracting a bigger
natural number from a smaller one yields `0`.
You can also try boolean expressions
```jrepl
Stdlib.Prelude> true
true
Stdlib.Prelude> not true
false
Stdlib.Prelude> true && false
false
Stdlib.Prelude> true || false
true
Stdlib.Prelude> if true 1 0
1
```
and strings, pairs and lists:
```jrepl
Stdlib.Prelude> "Hello world!"
"Hello world!"
Stdlib.Prelude> (1, 2)
(1, 2)
Stdlib.Prelude> 1 :: 2 :: nil
1 :: 2 :: nil
```
In fact, you can use all functions and types from the
[Stdlib.Prelude](https://anoma.github.io/juvix-stdlib/Stdlib.Prelude.html)
module of the [standard library](https://anoma.github.io/juvix-stdlib),
which is preloaded by default.
```jrepl
Stdlib.Prelude> length (1 :: 2 :: nil)
3
Stdlib.Prelude> null (1 :: 2 :: nil)
false
Stdlib.Prelude> swap (1, 2)
(2, 1)
```
## Files, modules and compilation
Currently, the REPL does not support adding new definitions. To define
new functions or data types, you need to put them in a separate file and
either load the file in the REPL with `:load file.juvix` or compile the
file to a binary executable with the shell command
`juvix compile file.juvix`.
To conveniently edit Juvix files, an [Emacs mode](./emacs.md) and a
[VSCode extension](./vscode.md) are available.
A Juvix file must declare a module whose name corresponds exactly to the
name of the file. For example, a file `Hello.juvix` must declare a
module `Hello`:
```juvix
-- Hello world example. This is a comment.
module Hello;
-- Import the standard library prelude, including the 'String' type
open import Stdlib.Prelude;
main : String;
main := "Hello world!";
```
A file compiled to an executable must define the zero-argument
function `main` which is evaluated when running the program. The
definition of `main` can have any non-function type, e.g., `String`,
`Bool` or `Nat`. The generated executable prints the result of
evaluating `main`.
## Data types and functions
To see the type of an expression, use the `:type` REPL command:
```jrepl
Stdlib.Prelude> :type 1
Nat
Stdlib.Prelude> :type true
Bool
```
The types `Nat` and `Bool` are defined in the standard library.
The type `Bool` has two constructors `true` and `false`.
```juvix
type Bool :=
| true : Bool
| false : Bool;
```
The constructors of a data type can be used to build elements of the
type. They can also appear as patterns in function definitions. For
example, the `not` function is defined in the standard library by:
```juvix
not : Bool -> Bool;
not true := false;
not false := true;
```
The first line is the _signature_ which specifies the type of the
definition. In this case, `not` is a function from `Bool` to `Bool`. The
signature is followed by two _function clauses_ which specify the
function result depending on the shape of the arguments. When a function
call is evaluated, the first clause that matches the arguments is used.
In contrast to languages like Python, Java or C/C++, Juvix doesn't
require parentheses for function calls. All the arguments are just
listed after the function. The general pattern for function application
is: `func arg1 arg2 arg3 ...`
A more complex example of a data type is the `Nat` type from the
standard library:
```juvix
type Nat :=
| zero : Nat
| suc : Nat -> Nat;
```
The constructor `zero` represents `0` and `suc` represents the successor
function – `suc n` is the successor of `n`, i.e., `n+1`. For example,
`suc zero` represents `1`. The number literals `0`, `1`, `2`, etc., are
just shorthands for appropriate expressions built using `suc` and
`zero`.
The constructors of a data type specify how the elements of the type can
be constructed. For instance, the above definition specifies that an
element of `Nat` is either:
- `zero`, or
- `suc n` where `n` is an element of `Nat`, i.e., it is constructed by
applying `suc` to appropriate arguments (in this case the argument
of `suc` has type `Nat`).
Any element of `Nat` can be built with the constructors in this way –
there are no other elements. Mathematically, this is an inductive
definition, which is why the data type is called _inductive_.
If implemented directly, the above unary representation of natural
numbers would be extremely inefficient. The Juvix compiler uses a binary
number representation under the hood and implements arithmetic
operations using corresponding machine instructions, so the performance
of natural number arithmetic is similar to other programming languages.
The `Nat` type is a high-level presentation of natural numbers as seen
by the user who does not need to worry about low-level arithmetic
implementation details.
One can use `zero` and `suc` in pattern matching, like any other
constructors:
```juvix
infixl 6 +;
+ : Nat -> Nat -> Nat;
+ zero b := b;
+ (suc a) b := suc (a + b);
```
The `infixl 6 +` declares `+` to be an infix left-associative operator
with priority 6. The `+` is an ordinary function, except that function
application for `+` is written in infix notation. The definitions of the
clauses of `+` still need the prefix notation on the left-hand sides.
The `a` and `b` in the patterns on the left-hand sides of the clauses
are _variables_ which match arbitrary values of the corresponding type.
They can be used on the right-hand side to refer to the values matched.
For example, when evaluating
```juvix
(suc (suc zero)) + zero
```
the second clause of `+` matches, assigning `suc zero` to `a` and `zero`
to `b`. Then the right-hand side of the clause is evaluated with `a` and
`b` substituted by these values:
```juvix
suc (suc zero + zero)
```
Again, the second clause matches, now with both `a` and `b` being
`zero`. After replacing with the right-hand side, we obtain:
```juvix
suc (suc (zero + zero))
```
Now the first clause matches and finally we obtain the result
```juvix
suc (suc zero)
```
which is just `2`.
The function `+` is defined like above in the standard library, but the
Juvix compiler treats it specially and generates efficient code using
appropriate CPU instructions.
## Pattern matching
The patterns in function clauses do not have to match on a single
constructor – they may be arbitrarily deep. For example, here is an
(inefficient) implementation of a function which checks whether a
natural number is even:
```juvix
even : Nat -> Bool;
even zero := true;
even (suc zero) := false;
even (suc (suc n)) := even n;
```
This definition states that a natural number `n` is even if either `n`
is `zero` or, recursively, `n-2` is even.
If a subpattern is to be ignored, then one can use a wildcard `_`
instead of naming the subpattern.
```juvix
isPositive : Nat -> Bool;
isPositive zero := false;
isPositive (suc _) := true;
```
The above function could also be written as:
```juvix
isPositive : Nat -> Bool;
isPositive zero := false;
isPositive _ := true;
```
It is not necessary to define a separate function to perform pattern
matching. One can use the `case` syntax to pattern match an expression
directly.
```jrepl
Stdlib.Prelude> case (1, 2) | (suc _, zero) := 0 | (suc _, suc x) := x | _ := 19
1
```
## Comparisons and conditionals
To use the comparison operators on natural numbers, one needs to import
the `Stdlib.Data.Nat.Ord` module. The comparison operators are not in
`Stdlib.Prelude` to avoid clashes with user-defined operators for other
data types. The functions available in `Stdlib.Data.Nat.Org` include:
`<`, `<=`, `>`, `>=`, `==`, `/=`, `min`, `max`.
For example, one may define the function `max3` by:
```juvix
open import Stdlib.Data.Nat.Ord;
max3 : Nat -> Nat -> Nat -> Nat;
max3 x y z := if (x > y) (max x z) (max y z);
```
The conditional `if` is a special function which is evaluated lazily,
i.e., first the condition (the first argument) is evaluated, and then
depending on its truth-value one of the branches (the second or the
third argument) is evaluated and returned.
By default, evaluation in Juvix is _eager_ (or _strict_), meaning that
the arguments to a function are fully evaluated before applying the
function. Only `if`, `||` and `&&` are treated specially and evaluated
lazily. These special functions cannot be partially applied (see
[Partial application and higher-order
functions](./learn.md#partial-application-and-higher-order-functions)
below).
## Local definitions
Juvix supports local definitions with let-expressions.
```juvix
f : Nat -> Nat;
f a := let x : Nat := a + 5;
y : Nat := a * 7 + x
in
x * y;
```
The variables `x` and `y` are not visible outside `f`.
One can also use multi-clause definitions in `let`-expressions, with the
same syntax as definitions inside a module. For example:
```juvix
even : Nat -> Bool;
even :=
let
even' : Nat -> Bool;
odd' : Nat -> Bool;
even' zero := true;
even' (suc n) := odd' n;
odd' zero := false;
odd' (suc n) := even' n;
in
even';
```
The functions `even'` and `odd'` are not visible outside `even`.
## Recursion
Juvix is a purely functional language, which means that functions have
no side effects and all variables are immutable. An advantage of
functional programming is that all expressions are _referentially
transparent_ – any expression can be replaced by its value without
changing the meaning of the program. This makes it easier to reason
about programs, in particular to prove their correctness. No errors
involving implicit state are possible, because the state is always
explicit.
In a functional language, there are no imperative loops. Repetition is
expressed using recursion. In many cases, the recursive definition of a
function follows the inductive definition of a data structure the
function analyses. For example, consider the following inductive type of
lists of natural numbers:
```juvix
type NList :=
| nnil : NList
| ncons : Nat -> NList -> NList;
```
An element of `NList` is either `nnil` (empty) or `ncons x xs` where
`x : Nat` and `xs : NList` (a list with head `x` and tail `xs`).
A function computing the length of a list may be defined by:
```juvix
nlength : NList -> Nat;
nlength nnil := 0;
nlength (ncons _ xs) := nlength xs + 1;
```
The definition follows the inductive definition of `NList`. There are
two function clauses for the two constructors. The case for `nnil` is
easy – the constructor has no arguments and the length of the empty list
is `0`. For a constructor with some arguments, one typically needs to
express the result of the function in terms of the constructor
arguments, usually calling the function recursively on the constructor's
inductive arguments (for `ncons` this is the second argument). In the
case of `ncons _ xs`, we recursively call `nlength` on `xs` and add `1`
to the result.
Let's consider another example – a function which returns the maximum of
the numbers in a list or 0 for the empty list.
```juvix
open import Stdlib.Data.Nat.Ord; -- for `max`
nmaximum : NList -> Nat;
nmaximum nnil := 0;
nmaximum (ncons x xs) := max x (nmaximum xs);
```
Again, there is a clause for each constructor. In the case for `ncons`,
we recursively call the function on the list tail and take the maximum
of the result and the list head.
For an example of a constructor with more than one inductive argument,
consider binary trees with natural numbers in nodes.
```juvix
type Tree :=
| leaf : Nat -> Tree
| node : Nat -> Tree -> Tree -> Tree;
```
The constructor `node` has two inductive arguments (the second and the
third) which represent the left and the right subtree.
A function which produces the mirror image of a tree may be defined by:
```juvix
mirror : Tree -> Tree;
mirror (leaf x) := leaf x;
mirror (node x l r) := node x (mirror r) (mirror l);
```
The definition of `mirror` follows the definition of `Tree`. There are
two recursive calls for the two inductive constructors of `node` (the
subtrees).
## Partial application and higher-order functions
Strictly speaking, all Juvix functions have only one argument.
Multi-argument functions are really functions which return a function
which takes the next argument and returns a function taking another
argument, and so on for all arguments. The function type former `->`
(the arrow) is right-associative. Hence, the type, e.g.,
`Nat -> Nat -> Nat` when fully parenthesised becomes
`Nat -> (Nat -> Nat)`. It is the type of functions which given an
argument of type `Nat` return a function of type `Nat -> Nat` which
itself takes an argument of type `Nat` and produces a result of type
`Nat`. Function application is left-associative. For example, `f a b`
when fully parenthesised becomes `(f a) b`. So it is an application to
`b` of the function obtained by applying `f` to `a`.
Since a multi-argument function is just a one-argument function
returning a function, it can be _partially applied_ to a smaller number
of arguments than specified in its definition. The result is an
appropriate function. For example, `sub 10` is a function which
subtracts its argument from `10`, and `(+) 1` is a function which adds
`1` to its argument. If the function has been declared as an infix
operator (like `+`), then for partial application one needs to enclose
it in parentheses.
A function which takes a function as an argument is a _higher-order
function_. An example is the `nmap` function which applies a given
function to each element in a list of natural numbers.
```juvix
nmap : (Nat -> Nat) -> NList -> NList;
nmap _ nnil := nnil;
nmap f (ncons x xs) := ncons (f x) (nmap f xs);
```
The application
```juvix
nmap \{ x := div x 2 } lst
```
divides every element of `lst` by `2`, rounding down the result. The
expression
```juvix
\{ x := div x 2 }
```
is an unnamed function, or a _lambda_, which divides its argument by
`2`.
## Polymorphism
The type `NList` we have been working with above requires the list
elements to be natural numbers. It is possible to define lists
_polymorphically_, parameterising them by the element type. This is
similar to generics in languages like Java, C++ or Rust. Here is the
polymorphic definition of lists from the standard library:
```juvix
infixr 5 ::;
type List (A : Type) :=
| nil : List A
| :: : A -> List A -> List A;
```
The constructor `::` is declared as a right-associative infix operator
with priority 5. The definition has a parameter `A` which is the element
type.
Now one can define the `map` function polymorphically:
```juvix
map : {A B : Type} -> (A -> B) -> List A -> List B;
map f nil := nil;
map f (h :: hs) := f h :: map f hs;
```
This function has two _implicit type arguments_ `A` and `B`. These
arguments are normally omitted in function application – they are
inferred automatically during type checking. The curly braces indicate
that the argument is implicit and should be inferred.
In fact, the constructors `nil` and `::` also have an implicit argument:
the type of list elements. All type parameters of a data type definition
become implicit arguments of the constructors.
Usually, the implicit arguments in a function application can be
inferred. However, sometimes this is not possible and then the implicit
arguments need to be provided explicitly by enclosing them in braces:
```juvix
f {implArg1} .. {implArgK} arg1 .. argN
```
For example, `nil {Nat}` has type `List Nat` while `nil` by itself has
type `{A : Type} -> List A`.
## Tail recursion
Any recursive call whose result is further processed by the calling
function needs to create a new stack frame to save the calling function
environment. This means that each such call will use a constant amount
of memory. For example, a function `sum` implemented as follows will use
an additional amount of memory proportional to the length of the
processed list:
```juvix
sum : NList -> Nat;
sum nnil := 0;
sum (ncons x xs) := x + sum xs;
```
This is not acceptable if you care about performance. In an imperative
language, one would use a simple loop going over the list without any
memory allocation. In pseudocode:
```pascal
sum : Nat := 0;
while (lst /= nil) do
begin
sum := sum + head lst;
lst := tail lst;
end;
result := sum;
```
Fortunately, it is possible to rewrite this function to use _tail
recursion_. A recursive call is _tail recursive_ if its result is also
the result of the calling function, i.e., the calling function returns
immediately after it without further processing. The Juvix compiler
_guarantees_ that all tail calls will be eliminated, i.e., that they
will be compiled to simple jumps without extra memory allocation. In a
tail recursive call, instead of creating a new stack frame, the old one
is reused.
The following implementation of `sum` uses tail recursion.
```juvix
sum : NList -> Nat;
sum lst :=
let
go : Nat -> NList -> Nat;
go acc nnil := acc;
go acc (ncons x xs) := go (acc + x) xs;
in
go 0 lst;
```
The first argument of `go` is an _accumulator_ which holds the sum
computed so far. It is analogous to the `sum` variable in the imperative
loop above. The initial value of the accumulator is 0. The function `go`
uses only constant additional memory overall. The code generated for it
by the Juvix compiler is equivalent to an imperative loop.
Most imperative loops may be translated into tail recursive functional
programs by converting the locally modified variables into accumulators
and the loop condition into pattern matching. For example, here is an
imperative pseudocode for computing the nth Fibonacci number in linear
time. The variables `cur` and `next` hold the last two computed
Fibonacci numbers.
```pascal
cur : Nat := 0;
next : Nat := 1;
while (n /= 0) do
begin
tmp := next;
next := cur + next;
cur := tmp;
n := n - 1;
end;
result := cur;
```
An equivalent functional program is:
```juvix
fib : Nat -> Nat;
fib :=
let go : Nat -> Nat -> Nat -> Nat;
go cur _ zero := cur;
go cur next (suc n) := go next (cur + next) n;
in
go 0 1;
```
A naive definition of the Fibonacci function runs in exponential time:
```juvix
fib : Nat -> Nat;
fib zero := 0;
fib (suc zero) := 1;
fib (suc (suc n)) := fib n + fib (suc n);
```
Tail recursion is less useful when the function needs to allocate memory
anyway. For example, one could make the `map` function from the previous
section tail recursive, but the time and memory use would still be
proportional to the length of the input because of the need to allocate
the result list.
## Totality checking
By default, the Juvix compiler requires all functions to be total.
Totality consists of:
- [termination](../explanations/totality/termination.md),
- [coverage](../explanations/totality/coverage.md),
- [strict positivity](../explanations/totality/positive.md).
The termination check ensures that all functions are structurally
recursive, i.e., all recursive call are on structurally smaller values –
subpatterns of the matched pattern. For example, the termination checker
rejects the definition
```juvix
fact : Nat -> Nat;
fact x := if (x == 0) 1 (x * fact (sub x 1));
```
because the recursive call is not on a subpattern of a pattern matched
on in the clause. One can reformulate this definition so that it is
accepted by the termination checker:
```juvix
fact : Nat -> Nat;
fact zero := 1;
fact x@(suc n) := x * fact n;
```
Sometimes, such a reformulation is not possible. Then one can use the
`terminating` keyword to forgoe the termination check.
```juvix
terminating
log2 : Nat -> Nat;
log2 n := if (n <= 1) 0 (suc (log2 (div n 2)));
```
Coverage checking ensures that there are no unhandled patterns in
function clauses or `case` expressions. For example, the following
definition is rejected because the case `suc zero` is not handled:
```juvix
even : Nat -> Bool;
even zero := true;
even (suc (suc n)) := even n;
```
Since coverage checking forces the user to specify the function for all input values, it may be unclear how to implement functions which are typically partial. For example, the `tail` function on lists is often left undefined for the empty list. One solution is to return a default value. In the Juvix standard library, `tail` is implemented as follows, returning the empty list when the argument is empty.
```juvix
tail : {A : Type} -> List A -> List A;
tail (_ :: xs) := xs;
tail nil := nil;
```
Another solution is to wrap the result in the `Maybe` type from the standard library, which allows to represent optional values. An element of `Maybe A` is either `nothing` or `just x` with `x : A`.
```juvix
type Maybe (A : Type) :=
| nothing : Maybe A
| just : A -> Maybe A;
```
For example, one could define the tail function as:
```juvix
tail' : {A : Type} -> List A -> Maybe (List A)
tail' (_ :: xs) := just xs;
tail' nil := nothing;
```
Then the user needs to explicitly check if the result of the function contains a value or not:
```juvix
case tail' lst
| just x := ...
| nothing := ...
```
## Exercises
You have now learnt the very basics of Juvix. To consolidate your
understanding of Juvix and functional programming, try doing some of
the following exercises. To learn how to write more complex Juvix
programs, see the
[advanced tutorial](./../examples/html/Tutorial/Tutorial.html)
and the [Juvix program examples](../reference/examples.md).
1. Define a function `prime : Nat -> Nat` which checks if a given
natural number is prime.
2. What is wrong with the following definition?
```juvix
half : Nat -> Nat;
half n := if (n < 2) 0 (half (n - 2) + 1);
```
How can you reformulate this definition so that it is accepted by
Juvix?
3. Define a polymorphic function which computes the last element of a
list. What is the result of your function on the empty list?
4. A _suffix_ of a list `l` is any list which can be obtained from `l`
by removing some initial elements. For example, the suffixes of
`1 :: 2 :: 3 :: nil` are: `1 :: 2 :: 3 :: nil`, `2 :: 3 :: nil`,
`3 :: nil` and `nil`.
Define a function which computes the list of all suffixes of a given
list in the order of decreasing length.
5. Recall the `Tree` type from above.
```juvix
type Tree :=
| leaf : Nat -> Tree
| node : Nat -> Tree -> Tree -> Tree;
```
Analogously to the `map` function for lists, define a function
```juvix
tmap : (Nat -> Nat) -> Tree -> Tree;
```
which applies a function to all natural numbers stored in a tree.
6. Make the `Tree` type polymorphic in the element type and repeat the
previous exercise.
7. Write a tail recursive function which reverses a list.
8. Write a tail recursive function which computes the factorial of a
natural number.
9. Define a function `comp : {A : Type} -> List (A -> A) -> A -> A`
which composes all functions in a list. For example,
```juvix
comp (suc :: (*) 2 :: \{x := sub x 1} :: nil)
```
should be a function which given `x` computes `2(x - 1) + 1`.

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# NodeJS Interop
A Juvix module can be compiled to a Wasm module. When a Wasm module is
instantiated by a host, functions from the host can be injected into a
Wasm module and functions from the Wasm module can be called by the
host.
In this tutorial you will see how to call host functions in Juvix and
call Juvix functions from the host using the Wasm mechanism.
## The Juvix module
The following Juvix module has two functions.
The function `hostDisplayString` is an `axiom` with no corresponding
`compile` block that implements it. We will inject an implementation for
this function when we instantiate the module from NodeJS.
The function `juvixRender` is a normal Juvix function. We will call this
from NodeJS.
-- NodeJsInterop.juvix
module NodeJsInterop;
open import Stdlib.Prelude;
axiom hostDisplayString : String → IO;
juvixRender : IO;
juvixRender := hostDisplayString "Hello World from Juvix!";
end;
## Compiling the Juvix module
The Juvix module can be compiled using the following command:
juvix compile -t wasm -r standalone NodeJsInterop.juvix
This will create a file containing a Wasm module called
`NodeJsInterop.wasm`.
## The NodeJS module
The following NodeJS module demonstrates both calling a Juvix function
from NodeJS and injecting a NodeJS function into a Juvix module.
The NodeJS function `hostDisplayString` is passed to the Wasm module
`NodeJSInterop.wasm` when it is instantiated. After instantiation the
Juvix function `juvixRender` is called.
The functions `ptrToCstr` and `cstrlen` are necessary to convert the
`char` pointer passed from Juvix to a JS `String`.
// NodeJSInterop.js
const fs = require('fs');
let wasmModule = null;
function cstrlen(mem, ptr) {
let len = 0;
while (mem[ptr] != 0) {
len++;
ptr++;
}
return len;
}
function ptrToCstr(ptr) {
const wasmMemory = wasmModule.instance.exports.memory.buffer;
const mem = new Uint8Array(wasmMemory);
const len = cstrlen(mem, ptr);
const bytes = new Uint8Array(wasmMemory, ptr, len);
return new TextDecoder().decode(bytes);
}
function hostDisplayString(strPtr) {
const text = ptrToCstr(strPtr);
console.log(text);
}
const wasmBuffer = fs.readFileSync("NodeJsInterop.wasm");
WebAssembly.instantiate(wasmBuffer, {
env: {
hostDisplayString,
}
}).then((w) => {
wasmModule = w;
wasmModule.instance.exports.juvixRender();
});
## Running the Wasm module
Now you should have the files `NodeJsInterop.wasm` and
`NodeJsInterop.js` in the same directory. Run the following command to
execute the module:
node NodeJsInterop.js
You should see the following output:
Hello World from Juvix!

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# Juvix VSCode extension tutorial
To install the [Juvix VSCode extension][vscode-marketplace], click on the "Extensions" button
in the left panel and search for the "Juvix" extension by Heliax.
Once you've installed the Juvix extension, you can open a Juvix file.
For example, create a `Hello.juvix` file with the following content.
```juvix
module Hello;
open import Stdlib.Prelude;
main : IO;
main := printStringLn "Hello world!";
end;
```
Syntax should be automatically highlighted for any file with `.juvix`
extension. You can jump to the definition of an identifier by pressing
F12 or control-clicking it. To apply the Juvix code formatter to the
current file, use Shift+Ctrl+I.
In the top right-hand corner of the editor window you should see several
buttons. Hover the mouse pointer over a button to see its description.
The functions of the buttons are as follows.
- Load file in REPL (Shift+Alt+R). Launches the Juvix REPL in a
separate window and loads the current file into it. You can then
evaluate any definition from the loaded file.
- Typecheck (Shift+Alt+T). Type-checks the current file.
- Compile (Shift+Alt+C). Compiles the current file. The resulting
native executable will be left in the directory of the file.
- Run (Shift+Alt+X). Compiles and runs the current file. The output of
the executable run is displayed in a separate window.
- Html preview. Generates HTML documentation for the current file and
displays it in a separate window.
[vscode-marketplace]: https://marketplace.visualstudio.com/items?itemName=heliax.juvix-mode