The Seahorse language

Seahorse is based on Python 3, but only supports a subset of the full Python language. It has some additional constraints on what is and isn't allowed.

Limitations: a brief overview

Seahorse tries as hard as possible to do everything that Python does. However, some things that Python can do aren't very feasibly translated to Rust. For example, Rust structs know all of their fields at compile-time, but Python objects can gain additional fields at runtime - it might be possible to support Python here, but it would come at a big runtime cost.

The most important distinction is static vs. dynamic typing, discussed here.

One feature of Python's that is (nearly) fully supported by Seahorse is the way that values are passed by alias. This basically means that if you have some value in a variable, re-assigning this value to another variable will simply make a secondary alias for the same data:

a = [1, 2, 3]


# Makes an alias to the list in a, now any edits to b will be reflected in a
b = a


# Passes an alias to b which is just an alias to the list in a. If f makes
# any edits to its parameter, then they will be reflected in a and b
f(b)

Note to program authors - this behavior is achieved by using Rust's interior mutability pattern, since Rust's mutability rules are much stricter than Python's. This means that there is a small extra runtime cost associated with operations on mutable values. "Mutable values" mean any data types that are mutable by Python/Seahorse - this includes most collections (lists, arrays) and custom accounts types. Strings and integers are both immutable, so you won't see any extra cost here.

There is a lot of room for optimization on the compiler side here, and hopefully Seahorse will be able to take advantage of it in the future. Until then, just know that Seahorse programs will always output slightly less efficient code, in order to adhere to Python's programming model.

The Seahorse prelude

When you first create a Seahorse project, a Python file called prelude.py is imported via from seahorse.prelude import *. This file contains class/function definitions for everything built in to Seahorse, and is used to provide editors with autocompletion and serve as documentation for the things you can do with Seahorse. The following table briefly summarizes the classes/functions that are made available - check prelude.py for more details:

Python constructs and builtins

Only a subset of Python's language constructs and builtins are supported. Seahorse is actively trying to support as much of Python as possible.

Builtin functions all should behave how you would expect them to in Python, sometimes with some minor adjustments for typing purposes.

Classes and OOP

Classes allow you to organize your code in a more intuitive way. You've already seen classes being used to store on-chain data (by extending Account), but they can do much more than this!

Here's what defining a simple class with some data and methods looks like:

# Creates a simple class that stores some data (2 floating-point numbers).
class Point:
  x: f64
  y: f64


  # Defines the constructor for `Point`. Note the syntax - just like in
  # Python, the constructor takes self and modifies it, returning nothing.
  def __init__(self, x: f64, y: f64):
    self.x = x
    self.y = y


  # This is an instance method, since it uses self as a parameter.
  def magnitude(self) -> f64:
    return (self.x**2 + self.y**2) ** 0.5


  # This method is static, since it has no self parameter.
  def origin() -> Point:
    return Point(0, 0)

You can then use the class just like you would in Python:

p = Point(3, 4)
print(p.magnitude()) # 5.0


q = Point.origin()
print(q.magnitude()) # 0.0

One limitation of used-defined classes is that they may not be stored in accounts. This is a known issue, and it's being worked on.

Automatic constructors with @dataclass

Constructors can be automatically generated for classes with the @dataclass decorator:

@dataclass
class Datapoint:
  x: f64
  y: f64

A constructor will be generated for this class that takes a parameter for each field, in the order defined in the class. So the above Datapoint constructor can be called like this:

# Creates a Datapoint with x=2, y=3.
p = Datapoint(2, 3)

Imports

You can stretch your codebase among multiple files and then use those files with imports. Like in Python, an import can refer to a precise object (class/function) in a module, a module itself, or a package which may contain modules and subpackages.

Seahorse (.py) files make up modules, and directories make up packages. You might have a codebase structured like this:

programs_py
|\_ seahorse (contains the Seahorse editor hints files)
|\_ program.py
|\_ util
    |\_ algorithms
    |   |\_ merge_sort.py
    |\_ data_structures
    |   |\_ merkle_tree.py

Then program.py can use util like this:

# Imports util as a package, giving access to algorithms and data_structures
import util
# Imports merge_sort as a module
from util.algorithms import merge_sort
# Imports everything in merkle_tree
from util.algorithms.merkle_tree import *


# ...


merge_sort.sort(my_list)


tree = MerkleTree(my_data)

Note the lack of an __init__.py file - some Python features require this, but Seahorse doesn't - directories are treated as packages and .py files are treated as modules. This also means that if you have a package with some extra random Python code nested deeply inside it, Seahorse will attempt to read and parse it, so make sure that you know what you're doing when you import a package!

Other constructs

These are some of the weirder/more Pythonic language constructs that you can use in Seahorse:

• List comprehensions
  Seahorse fully supports list comprehensions! Just like in Python, you can do things like [i**2 for i in range(10)]. Other types of comprehension (generator, set, dict) are not supported yet, but will be in the near future.
• F-strings
  Formatted strings work mostly like in Python, but the exact string you get might be unexpected and is subject to change. Namely, if you pass in a custom class as a parameter, Seahorse will translate this to use the class's derived Debug method under the hood, which might give you weird results. For now, you should only really count on using f-strings for ad-hoc debugging and logging information. The API will stabilize eventually.
• Tuple assignment
  Seahorse supports tuple assignment exactly like Python does - you can iterate over lists of tuples with for (x, y) in ..., and you can unpack tuples with x, y = .... You can even do the Pythonic one-line swap: x, y = y, x.
• Functional programming and functions as first-class objects
  Partially supported. New in v2, you can do things that rely on functional programming - namely map and filter (see Builtins for working with iterators). Functions are not first-class objects in Seahorse, though, so you may not assign a function to a variable and pass it around that way.

General builtins

• print(...T) -> None
  Print a message. Under the hood, Seahorse uses the built-in msg! macro to log messages to Solana.
• str(T) -> str
  Construct a string.
• list(Iter[T]) -> T
  Construct a list from an iterable.

Builtins for working with numbers

• abs({Numeric} T) -> T
  Get the absolute value of a number.
• min(...{Numeric} T) -> T
  Get the minimum of some numbers. Seahorse does not support min's alternate iterable form.
• max(...{Numeric} T) -> T
  Get the maximum of some numbers, also does not support the iterable form.
• round(f64) -> i128
  Round a floating-point number to the nearest integer.

Builtins for working with iterators

Note that Iter[T] includes any type that can be iterated over, like lists and arrays.

• len(Iter[T]) -> u64
  Get the length of an iterable.
• enumerate(Iter[T]) -> Iter[(u64, T)]
  Obtain an iterator that also gives you the index of each item.
• filter((T) -> bool, Iter[T]) -> Iter[T]
  Obtain an iterator that filters out certain elements.
• map((T) -> U, Iter[T]) -> Iter[U]
  Obtain an iterator that transforms each element of the original iterable.
• range({Numeric} T, {Numeric} T?, {Numeric} T?) -> Iter[T]
  Obtain an iterator over a range of numbers. Like in Python, range(a) counts from 0 to a (exclusive), range(a, b) counts from a to b, and range(a, b, k) counts from a to b in increments of k.
• sorted(Iter[T]) -> List[T]
  Obtain a sorted list from an iterable.
• sum(Iter[{Numeric} T]) -> T
  Get the sum of the elements in an iterable.
• zip(Iter[T], Iter[U]) -> Iter[(T, U)]
  Obtain an iterator that traverses two iterators simultaneously. Seahorse does not support a variadic amount of iterators like in Python.

Type hints and static typing

In Python, you can provide optional type hints on variables assignments, class fields, and function parameters. These hints are completely ignored by Python when your code runs, but your editor might make use of them to allow autocomplete and other features that rely on knowing the types of objects.

In Seahorse, type hints are occasionally mandatory in order to allow the underlying Rust code to be statically typed - that is, typed at compile time. The following table summarizes when you have to (or might want to) use type hints:

Using Seahorse, most of your variables can be automatically typed as long as your class fields and function parameters are. Sometimes it might fail and you'll have to add a manual type or two, but for the most part you can rely on it to get you the result you expect.

Numbers and math

Rust has much stricter rules for doing simple math operations than Python - you can't add two different types of numbers, even if the only difference between them is their size (e.g. u8 vs. u64).

Seahorse preserves this while maintaining some flexibility by performing automatic numeric coercion in certain situations (mainly if you're performing arithmetic operations). Most mathematical operations will ensure the types of both operands are the same by coercing them to the less strict of the two types. In practice, this means that small integers will coerce to big integers, which will coerce to floating point numbers, and never the other way around. (This is essentially what Python does with math between ints and floats, but safely applied to more types.)

Seahorse supports every fixed-width int size and f64. The automatic coercion rules are simple:

• An unsigned int can be coerced to any wider (more bits) unsigned/signed int type
• A signed int type can be coerced to any wider signed int type
• Any int type can coerce to f64.

Like in Rust, an untyped integer is assigned a type based on usage. If you declare a variable x = 8, Seahorse only knows that it belongs to some numeric type. If later, for instance, you pass x to a function that expects a u64 as an argument, then Seahorse will assign the u64 type to x. And if you then pass x to a function that expects u16 as an argument, Seahorse will throw an error, because x is a u64.

Here are some examples to make all of that concrete:

a      = u32(2)  # u32, constructors always return their type
b: i64 = 2       # i64, since this is explicitly declared
c      = a + b   # i64, a will be coerced to an i64 in order to add it to
                 # b, producing an i64.


d: u64 = 3
e = 10 // d  # u64, integer division simply coerces both sides to the same type
f = 10 / d   # f64, regular division requires floating points (and coerces both
             # sides as such)


g: u8 = 2
h: i8 = 3
i = g + h  # ERROR - u8 cannot coerce to i8, and i8 cannot coerce to u8.

There are two special operations to remember: / (non-integer division) and ** (exponentation).

As shown above, non-integer division will always coerce both sides to floating-point (f64), and returns an f64.

Exponentiation has two modes: integer and non-integer. When doing base ** exponent, Seahorse will decide to try either integer/non-integer exponentiation based on the type of base. If base is an integer, then exponent will attempt to be coerced to a u32. Otherwise, if base is an f64, then the exponent will be cast to an f64. You can't raise an integer to a non-u32 power.

a = 2
b: u16 = 2
c: i64 = 2


x = 10    # x is some integer
y = 10.0  # y is an f64


x ** a  # This will work (and a now becomes u32 based on usage)
x ** b  # This will work - u16 can coerce to u32
x ** c  # ERROR - i64 cannot coerce to u32, even though it's technically
        # just a constant 2


y ** c  # This will work - c will get coerced to f64 to do non-integer
        # exponentiation

Scoping rules

When your code gets compiled to Rust, certain rules need to be obeyed. Each declaration of a variable must have exactly one type, and scopes delineate where variables may be accessed. Python breaks both of those rules, resulting in runnable code like this:

x = 'string'


if condition:
  x = 2
  y = 3


print(x + y)

...which will print "5" if condition was true. Attempting to compile this to valid Rust code would be a mess, so instead Seahorse imposes Rust's rules onto Seahorse in the following way:

• You may not reassign a variable to a new type if the assignment happens in a deeper scope.
• Scoping works like in Rust, variables will be dropped as soon as the scope they're declared in ends.

Note that the first rule allows you to reassign a variable to a different type while in the same scope - you can still write code like this:

x = 'string'
# ...
x = 0
# ...
x = False

Scripts vs. modules

In a Python script, every top-level statement is run in sequence, and the result of the script is just whatever happens during those statements. If you import the script as a module, then the code just runs as usual and exposes all the new names to the importer.

In Seahorse, there is no concept of a script - the code you write is used to generate a Rust library, which is analagous to a Python module with some extra limitations. Statements can not be run unless they are part of a function that gets called. The following table summarizes what can do in your Seahorse file as a top-level statement:

Directives

Although you can't put arbitrary top-level statements in your program, there are some special statements, known as directives, that allow you to control the compiler more than just generated code. (Right now the only directive is declare_id, but more will be added in a future release!)

declare_id

Anchor has a Rust macro called declare_id! that is needed to make sure your program knows its own key. When you seahorse init a new project, the resulting .py file includes a declare_id at the top:

# Default ID - every Anchor program starts with this ID, this is not your program's unique ID yet!
declare_id('Fg6PaFpoGXkYsidMpWTK6W2BeZ7FEfcYkg476zPFsLnS')

However, this ID might change when you recompile with Anchor. This is an especially common hangup when testing your code for the first time.

All you need to do is fetch the new ID from /target/idl/<program>.json:

{
  ...


  "metadata": {
    "address": "55bK1XrRae9iWca1i5CJQqH9nxSD1faBbvx6qViLmoRs"
  }
}

...and paste it into the Seahorse declare_id statement:

# New ID taken from the IDL file
declare_id('55bK1XrRae9iWca1i5CJQqH9nxSD1faBbvx6qViLmoRs')