Language Primer

If you know Python, you already know 95% of Seq. The following primer assumes some familiarity with Python or at least one “modern” programming language (QBASIC doesn’t count).


print('hello world')

from sys import stderr
print('hello world', end='', file=stderr)


# Seq comments start with "# 'and go until the end of the line

There are no multi-line comments. You can (ab)use the docstring operator (''')
if you really need them.


Seq is a strongly typed language like C++, Java, or C#. That means each expression must have a type that can be inferred at the compile-time.

# Booleans
True  # type: bool

# Numbers
a = 1  # type: int. It's 64-bit signed integer.
b = 1.12  # type: float. Seq's float is identical to C's double.
c = 5u  # type: int, but unsigned
d = Int[8](12)  # 8-bit signed integer; you can go all the way to Int[2048]
e = UInt[8](200)  # 8-bit unsigned integer
f = byte(3)  # a byte is C's char; equivalent to Int[8]

h = 0x12AF  # hexadecimal integers are also welcome
h = 0XAF12
g = 3.11e+9  # scientific notation is also supported
g = .223  # and this is also float
g = .11E-1  # and this as well

# Strings
s = 'hello! "^_^" '  # type: str.
t = "hello there! \t \\ '^_^' "  # \t is a tab character; \\ stands for \
raw = r"hello\n"  # raw strings do not escape slashes; this would print "hello\n"
fstr = f"a is {a + 1}"  # an F-string; prints "a is 2"
fstr = f"hi! {a+1=}"  # an F-string; prints "hi! a+1=2"
t = """
multiline string

# The following escape sequences are supported:
#   \\, \', \", \a, \b, \f, \n, \r, \t, \v,
#   \xHHH (HHH is hex code), \OOO (OOO is octal code)

# Sequence types
dna = s"ACGT"  # type: seq. These are DNA sequences.
prt = p"MYX"  # type: bio.pseq. These are protein sequences.
kmer = k"ACGT"  # type: Kmer[4]. Note that Kmer[5] is different than Kmer[12].


# Tuples
t = (1, 2.3, 'hi')  # type: Tuple[int, float, str].
t[1]  # type: float
u = (1, )  # type: Tuple[int]

As all types must be known at compile time, tuple indexing works only if a tuple is homogenous (all types are the same) or if the value of the index is known at compile-time.

You can, however, iterate over heterogenous tuples in Seq. This is achieved by unrolling the loop to accommodate the different types.

t = (1, 2.3, 'hi')
t[1]  # works because 1 is a constant int

x = int(argv[1])
t[x]  # compile error: x is not known at compile time

# This is a homogenous tuple (all member types are the same)
u = (1, 2, 3)  # type: Tuple[int, int, int].
u[x]  # works because tuple members share the same type regardless of x
for i in u:  # works

# Also works
v = (42, 'x', 3.14)
for i in v:


Tuples are immutable. a = (1, 2); a[1] = 1 will not compile.


l = [1, 2, 3]  # type: List[int]; a list of integers
s = {1.1, 3.3, 2.2, 3.3}  # type: Set[float]; a set of floats
d = {1: 'hi', 2: 'ola', 3: 'zdravo'}  # type: Dict[int, str]; a simple dictionary

ln = []  # an empty list whose type is inferred based on usage
ln = List[int]()  # an empty list with explicit element types
dn = {}  # an empty dict whose type is inferred based on usage
dn = Dict[int, float]()  # an empty dictionary with explicit element types

Because Seq is strongly typed, these won’t compile:

l = [1, 's']  # is it a List[int] or List[str]? you cannot mix-and-match types
d = {1: 'hi'}
d[2] = 3  # d is a Dict[int, str]; the assigned value must be a str

t = (1, 2.2)  # Tuple[int, float]
lt = list(t)  # compile error: t is not homogenous

lp = [1, 2.1, 3, 5]  # compile error: Seq will not automatically cast a float to an int

This will work, though:

u = (1, 2, 3)
lu = list(u)  # works: u is homogenous


Dictionaries and sets are unordered and are based on klib.

Assignments and operators

a = 1 + 2  # this is 3
a = (1).__add__(2)  # you can use a function call instead of an operator; this is also 3
a = int.__add__(1, 2)  # this is equivalent to the previous line
b = 5 / 2.0  # this is 2.5
c = 5 // 2  # this is 2; // is an integer division
a *= 2  # a is now 6

This is the list of binary operators and their magic methods:


Magic method













float (true) division



integer (floor) division









matrix multiplication;

sequence alignment



bitwise and



bitwise or



bitwise xor



left bit shift



right bit shift



less than



less or equal than



greater than



greater or equal than



equal to



not equal to



belongs to



boolean and (short-circuits)



boolean or (short-circuits)

Seq also has the following unary operators:


Magic method




bitwise inversion;

reverse complement; Optional[T] unpacking



unary positive



unary negation



boolean negation

Tuple unpacking

Seq supports most of Python’s tuple unpacking syntax:

x, y = 1, 2  # x is 1, y is 2
(x, (y, z)) = 1, (2, 3)  # x is 1, y is 2, z is 3
[x, (y, z)] = (1, [2, 3])  # x is 1, y is 2, z is 3

l = range(1, 8)  # l is [1, 2, 3, 4, 5, 6, 7]
a, b, *mid, c = l  # a is 1, b is 2, mid is [3, 4, 5, 6], c is 7
a, *end = l  # a is 1, end is [2, 3, 4, 5, 6, 7]
*beg, c = l  # c is 7, beg is [1, 2, 3, 4, 5, 6]
(*x, ) = range(3)  # x is [0, 1, 2]
*x = range(3)  # error: this does not work

*sth, a, b = (1, 2, 3, 4)  # sth is (1, 2), a is 3, b is 4
*sth, a, b = (1.1, 2, 3.3, 4)  # error: this only works on homogenous tuples for now

(x, y), *pff, z = [1, 2], 'this'
print(x, y, pff, z) # x is 1, y is 2, pff is an empty tuple --- () ---, and z is "this"

s, *q = 'XYZ'  # works on strings as well; s is "X" and q is "YZ"

Control flow


Seq supports the standard Python conditional syntax:

if a or b or some_cond():
elif whatever() or 1 < a <= b < c < 4:  # chained comparisons are supported
    print('lo and behold!')

if x: y()

a = b if sth() else c  # ternary conditional operator

Seq extends the Python conditional syntax with a match statement, which is inspired by Rust’s:

match a + some_heavy_expr():  # assuming that the type of this expression is int
    case 1:  # is it 1?
    case 2 ... 10:  # is it 2, 3, 4, 5, 6, 7, 8, 9 or 10?
    case _:  # "default" case

match bool_expr():  # now it's a bool expression
    case True: ...
    case False: ...

match str_expr():  # now it's a str expression
    case 'abc': print("it's ABC time!")
    case 'def' | 'ghi':  # you can chain multiple rules with the "|" operator
        print("it's not ABC time!")
    case s if len(s) > 10: print("so looong!")  # conditional match expression
    case _: assert False

match some_tuple:  # assuming type of some_tuple is Tuple[int, int]
    case (1, 2): ...
    case (a, _) if a == 42:  # you can do away with useless terms with an underscore
    case (a, 50 ... 100) | (10 ... 20, b):  # you can nest match expressions

match list_foo():
    case []:  # [] matches an empty list
    case [1, 2, 3]:  # make sure that list_foo() returns List[int] though!
    case [1, 2, ..., 5]:  # matches any list that starts with 1 and 2 and ends with 5
    case [..., 6] | [6, ...]:  # matches a list that starts or ends with 6
    case [..., w] if w < 0:  # matches a list that ends with a negative integer
    case [...]:  # any other list

match sequence:  # of type seq
    case 'ACGT': ...
    case 'AC_T': ...  # _ is a wildcard character and it can be anything
    case 'A_C_T_': ...  # a spaced k-mer AxCxTx
    case 'AC*T': ...  # matches a sequence that starts with AC and ends with T

You can mix, match and chain match rules as long as the match type matches the expression type.


Standard fare:

a = 10
while a > 0:  # prints even numbers from 9 to 1
    a -= 1
    if a % 2 == 1:

for i in range(10):  # prints numbers from 0 to 7, inclusive
    if i > 6: break

for construct can iterate over any generator, which means any object that implements the __iter__ magic method. In practice, generators, lists, sets, dictionaries, homogenous tuples, ranges, and many more types implement this method, so you don’t need to worry. If you need to implement one yourself, just keep in mind that __iter__ is a generator and not a function.


Technically, comprehensions are not statements (they are expressions). Comprehensions are a nifty, Pythonic way to create collections:

l = [i for i in range(5)]  # type: List[int]; l is [0, 1, 2, 3, 4]
l = [i for i in range(15) if i % 2 == 1 if i > 10]  # type: List[int]; l is [11, 13]
l = [i * j for i in range(5) for j in range(5) if i == j]  # l is [0, 1, 4, 9, 16]

s = {abs(i - j) for i in range(5) for j in range(5)}  # s is {0, 1, 2, 3, 4}
d = {i: f'item {i+1}' for i in range(3)}  # d is {0: "item 1", 1: "item 2", 2: "item 3"}

You can also use collections to create generators (more about them later on):

g = (i for i in range(10))
print(list(g))  # prints number from 0 to 9, inclusive

Exception handling

Again, if you know how to do this in Python, you know how to do it in Seq:

def throwable():
     raise ValueError("doom and gloom")

except ValueError as e:
    print("we caught the exception")
    print("ouch, we're in deep trouble")
    print("whatever, it's done")


Right now, Seq cannot catch multiple exceptions in one statement. Thus catch (Exc1, Exc2, Exc3) as var will not compile.

If you have an object that implements __enter__ and __exit__ methods to manage its lifetime (say, a File), you can use a with statement to make your life easy:

with open('foo.txt') as f, open('foo_copy.txt', 'w') as fo:
    for l in f:

Variables and scoping

You have probably noticed by now that blocks in Seq are defined by their indentation level (as in Python). We recommend using 2 or 4 spaces to indent blocks. Do not mix indentation levels, and do not mix tabs and spaces; stick to any consistent way of indenting your code.

One of the major differences between Seq and Python lies in variable scoping rules. Seq variables cannot leak to outer blocks. Every variable is accessible only within its own block (after the variable is defined, of course), and within any block that is nested within the variable’s own block.

That means that the following Pythonic pattern won’t compile:

if cond():
     x = 1
     x = 2
print(x)  # x is defined separately in if/else blocks; it is not accessible here!

for i in range(10):
print(i)  # error: i is only accessible within the for loop block

In Seq, you can rewrite that as:

x = 2
if cond():
     x = 1

# or even better
x = 1 if cond() else 2


Another important difference between Seq and Python is that, in Seq, variables cannot change types. So this won’t compile:

a = 's'
a = 1  # error: expected string, but got int

A note about function scoping: functions cannot modify variables that are not defined within the function block. You need to use global to modify such variables:

g = 5
def foo():
foo()  # works, prints 5

def foo2():
    g += 2  # error: cannot access g

def foo3():
    global g
    g += 2
foo3()  # works, prints 7
foo3()  # works, prints 9

As a rule, use global whenever you need to access variables that are not defined within the function.


You can import functions and classes from another Seq module by doing:

# Create foo.seq with a bunch of useful methods
import foo

p = foo.FooType()

# Create bar.seq with a bunch of useful methods
from bar import x, y

from bar import z as bar_z

import foo looks for foo.seq or foo/__init__.seq in the current directory.


Functions are defined as follows:

def foo(a, b, c):
    return a + b + c
print(foo(1, 2, 3))  # prints 6

How about procedures? Well, don’t return anything meaningful:

def proc(a, b):
    print(a, 'followed by', b)
proc(1, 's')

def proc2(a, b):
    if a == 5:
    print(a, 'followed by', b)
proc2(1, 's')
proc2(5, 's')  # this prints nothing

Seq is a strongly-typed language, so you can restrict argument and return types:

def fn(a: int, b: float):
    return a + b  # this works because int implements __add__(float)
fn(1, 2.2)  # 3.2
fn(1.1, 2)  # error: 1.1. is not an int

def fn2(a: int, b):
    return a - b
fn2(1, 2)  # -1
fn2(1, 1.1)  # -0.1; works because int implements __sub__(float)
fn2(1, 's')  # error: there is no int.__sub__(str)!

def fn3(a, b) -> int:
    return a + b
fn3(1, 2)  # works, as 1 + 2 is integer
fn3('s', 'u')  # error: 's'+'u' returns 'su' which is str,
               # but the signature indicates that it must return int

Default arguments? Named arguments? You bet:

def foo(a, b: int, c: float = 1.0, d: str = 'hi'):
    print(a, b, c, d)
foo(1, 2)  # prints "1 2 1 hi"
foo(1, d='foo', b=1)  # prints "1 1 1 foo"

How about optional arguments? We support that too:

# type of b promoted to Optional[int]
def foo(a, b: int = None):
    print(a, b + 1)

foo(1, 2)  # prints "1 3"
foo(1)  # raises ValueError, since b is None


As we’ve said several times: Seq is a strongly typed language. As such, it is not as flexible as Python when it comes to types (e.g. you can’t have lists with elements of different types). However, Seq tries to mimic Python’s “I don’t care about types until I do” attitude as much as possible by utilizing a technique known as monomorphization. If there is a function that has an argument without a type definition, Seq will consider it a generic function, and will generate different functions for each invocation of that generic function:

def foo(x):
    print(x)  # print relies on typeof(x).__str__(x) method to print the representation of x
foo(1)  # Seq automatically generates foo(x: int) and calls int.__str__ when needed
foo('s')  # Seq automatically generates foo(x: str) and calls str.__str__ when needed
foo([1, 2])  # Seq automatically generates foo(x: List[int]) and calls List[int].__str__ when needed

But what if you need to mix type definitions and generic types? Say, your function can take a list of anything? Well, you can use generic specifiers:

def foo(x: List[T], T: type):
foo([1, 2])           # prints [1, 2]
foo(['s', 'u'])       # prints [s, u]
foo(5)                # error: 5 is not a list!
foo(['s', 'u'], int)  # fails: T is int, so foo expects List[int] but it got List[str]

def foo(x, R: type) -> R:
    return 1
foo(4, int)  # prints 4, returns 1
foo(4, str)  # error: return type is str, but foo returns int!


Coming from C++? foo(x: List[T], T: type): ... is roughly the same as template<typename T, typename U> U foo(T x) { ... }.


Seq supports generators, and they are fast! Really, really fast!

def gen(i):
    while i < 10:
        yield i
        i += 1
print(list(gen(0)))  # prints [0, 1, ..., 9]
print(list(gen(10)))  # prints []

You can also use yield to implement coroutines: yield suspends the function, while (yield) (yes, parentheses are required) receives a value, as in Python.

def mysum[T](start: T):
    m = start
    while True:
        a = (yield)  # receives the input of coroutine.send() call
        if a == -1:
            break  # exits the coroutine
        m += a
    yield m
iadder = mysum(0)  # assign a coroutine
next(iadder)  # activate it
for i in range(10):
    iadder.send(i)  # send a value to coroutine
print(iadder.send(-1))  # prints 45


Seq extends the core Python language with a pipe operator, which is similar to bash pipes (or F#’s |> operator). You can chain multiple functions and generators to form a pipeline:

def add1(x):
    return x + 1

2 |> add1  # 3; equivalent to add1(2)

def calc(x, y):
    return x + y**2
2 |> calc(3)  # 11; equivalent to calc(2, 3)
2 |> calc(..., 3)  # 11; equivalent to calc(2, 3)
2 |> calc(3, ...)  # 7; equivalent to calc(3, 2)

def gen(i):
    for i in range(i):
        yield i
5 |> gen |> print # prints 0 1 2 3 4 separated by newline
range(1, 4) |> iter |> gen |> print(end=' ')  # prints 0 0 1 0 1 2 without newline
[1, 2, 3] |> print   # prints [1, 2, 3]
range(100000000) |> print  # prints range(0, 100000000)
range(100000000) |> iter |> print  # not only prints all those numbers, but it uses almost no memory at all

Seq will chain anything that implements __iter__; however, use generators as much as possible because the compiler will optimize out generators whenever possible. Combinations of pipes and generators can be used to implement efficient streaming pipelines.

Seq can also execute pipelines in parallel via the parallel pipe (||>) operator:

range(100000) |> iter ||> print  # prints all these numbers, probably in random order
range(100000) |> iter ||> process ||> clean  # runs process in parallel, and then cleans data in parallel

In the last example, Seq will automatically schedule the process and clean functions to execute as soon as possible. You can control the number of threads via the OMP_NUM_THREADS environment variable.

Foreign function interface (FFI)

Seq can easily call functions from C and Python.

Let’s import some C functions:

from C import pow(float) -> float
pow(2.0)  # 4.0

# Import and rename function
from C import puts(cobj) -> void as print_line  # type cobj is C's pointer (void*, char*, etc.)
print_line("hi!".c_str())  # prints "hi!".
                           # Note .ptr at the end of string--- needed to cast Seq's string to char*.

from C import only works if the symbol is available to the program. If you are running your programs via seqc, you can link dynamic libraries by running seqc run -l path/to/dynamic/ ....

Hate linking? You can also use dyld library loading as follows:

from C import LIBRARY.mymethod(int, float) -> cobj
from C import LIBRARY.myothermethod(int, float) -> cobj as my2
foo = mymethod(1, 2.2)
foo2 = my2(4, 3.2)


When importing external non-Seq functions, you must explicitly specify argument and return types.

How about Python? If you have set the SEQ_PYTHON environment variable as described in the first section, you can do:

from python import mymodule.myfunction(str) -> int as foo

Often you want to execute more complex Python code within Seq. To that end, you can use Seq’s @python annotation:

def scipy_here_i_come(i: List[List[float]]) -> List[float]:
    # Code within this block is executed by the Python interpreter,
    # and as such it must be valid Python code
    import scipy.linalg
    import numpy as np
    data = np.array(i)
    eigenvalues, _ = scipy.linalg.eig(data)
    return list(eigenvalues)
print(scipy_here_i_come([[1.0, 2.0], [3.0, 4.0]]))  # [-0.372281, 5.37228] with some warnings...

Seq will automatically bridge any object that implements the __to_py__ and __from_py__ magic methods. All standard Seq types already implement these methods.

Classes and types

Of course, Seq supports classes! However, you must declare class members and their types in the preamble of each class (like you would do with Python’s dataclasses).

class Foo:
    x: int
    y: int

    def __init__(self, x: int, y: int):  # constructor
        self.x, self.y = x, y

    def method(self):
        print(self.x, self.y)

f = Foo(1, 2)
f.method()  # prints "1 2"


Seq does not (yet!) support inheritance and polymorphism.

Unlike Python, Seq supports method overloading:

class Foo:
    x: int
    y: int

    def __init__(self, x: int, y: int):  # constructor
        self.x, self.y = 0, 0
    def __init__(self, x: int, y: int):  # another constructor
        self.x, self.y = x, y
    def __init__(self, x: int, y: float):  # another constructor
        self.x, self.y = x, int(y)
    def __init__(self):
        self.x, self.y = 0, 0

    def method(self: Foo):
        print(self.x, self.y)

Foo().method()  # prints "0 0"
Foo(1, 2).method()  # prints "1 2"
Foo(1, 2.3).method()  # prints "1 2"
Foo(1.1, 2.3).method()  # error: there is no Foo.__init__(float, float)

Classes can also be generic:

class Container[T]:
    l: List[T]
    def __init__(self, l: List[T]):
        self.l = l

Classes create objects that are passed by reference:

class Point:
    x: int
    y: int

p = Point(1, 2)
q = p  # this is a reference!
p.x = 2
print((p.x, p.y), (q.x, q.y))  # (2, 2), (2, 2)

If you need to copy an object’s contents, implement the __copy__ magic method and use q = copy(p) instead.

Seq also supports pass-by-value types via the @tuple annotation:

class Point:
    x: int
    y: int

p = Point(1, 2)
q = p  # this is a copy!
print((p.x, p.y), (q.x, q.y))  # (1, 2), (1, 2)

However, by-value objects are immutable!. The following code will not compile:

p = Point(1, 2)
p.x = 2  # error! immutable type

Under the hood, types are basically named tuples (equivalent to Python’s collections.namedtuple).

You can also add methods to types:

class Point:
    x: int
    y: int

    def __new__():          # types are constructed via __new__, not __init__
        return Point(0, 1) # and __new__ returns a tuple representation of type's members

    def some_method(self):
        return self.x + self.y

p = Point()  # p is (0, 1)
print(p.some_method())  # 1

Type extensions

Suppose you have a class that lacks a method or an operator that might be really useful. You can extend that class and add the method at compile time:

class Foo:

f = Foo(...)

# we need but it does not exist... not a problem for Seq
class Foo:
    def cool(self: Foo):
        ...  # works!

# how about we add support for adding integers and strings?
class int:
    def __add__(self: int, other: str) -> int:
        return self + int(other)

print(5 + '4')  # 9

Magic methods

Here is a list of useful magic methods that you might want to add and overload:

Magic method



overload unary and binary operators (see Assignments and operators)


copy-constructor for copy method


for len method


for bool method and condition checking


overload obj[key]


overload obj[key] = value


overload del obj[key]


support iterating over the object


support printing and str method

LLVM functions

In certain cases, you might want to use LLVM features that are not directly accessible with Seq. This can be done with the @llvm attribute:

def llvm_add[T](a: T, b: T) -> T:
    %res = add {=T} %a, %b
    ret {=T} %res

print(llvm_add(3, 4))  # 7
print(llvm_add(i8(5), i8(6)))  # 11

Issues, feedback, or comments regarding this tutorial? Let us know on GitHub.