This documentation covers all the technical details of the Coconut Programming Language, and is intended as a reference specification, not a tutorialized introduction. For a full introduction and tutorial of Coconut, see HELP.
Coconut is a variant of Python built for simple, elegant, Pythonic functional programming. Coconut syntax is a strict superset of Python 3 syntax. That means users familiar with Python will already be familiar with most of Coconut.
The Coconut compiler turns Coconut code into Python code. The primary method of accessing the Coconut compiler is through the Coconut command-line utility, which also features an interpreter for real-time compilation. In addition to the command-line utility, Coconut also supports the use of IPython / Jupyter notebooks.
While most of Coconut gets its inspiration simply from trying to make functional programming work in Python, additional inspiration came from Haskell, CoffeeScript, F#, and patterns.py.
Since Coconut is hosted on the Python Package Index, it can be installed easily using pip
. Simply install Python, open up a command-line prompt, and enter:
python -m pip install coconut
coconut [-h] [-v] [source] [dest] [-t version] [-s] [-l] [-p] [-a] [-f] [-d] [-r] [-n] [-m] [-i] [-q] [-c code] [--jupyter ...] [--autopep8 ...] [--recursionlimit limit] [--color color] [--verbose]
source path to the coconut file/folder to compile
dest destination directory for compiled files (defaults to the source directory)
-h, --help show this help message and exit
-v, --version print Coconut and Python version information
-t, --target specify target Python version (defaults to universal)
-s, --strict enforce code cleanliness standards
-l, --linenumbers add line number comments for ease of debugging
-p, --package compile source as part of a package (defaults to only if source is a directory)
-a, --standalone compile source as standalone files (defaults to only if source is a single file)
-f, --force force overwriting of compiled Python (otherwise only overwrites when source code or compilation parameters change)
-d, --display print compiled Python
-r, --run run compiled Python (often used with --nowrite)
-n, --nowrite disable writing compiled Python
-m, --minify compress compiled Python
-i, --interact force the interpreter to start (otherwise starts if no other command is given)
-q, --quiet suppress all informational output (combine with --display to write runnable code to stdout)
-c code, --code code run a line of Coconut passed in as a string (can also be passed into stdin)
--jupyter, --ipython run Jupyter/IPython with Coconut as the kernel (remaining args passed to Jupyter)
--autopep8 ... use autopep8 to format compiled code (remaining args passed to autopep8)
--recursionlimit set maximum recursion depth (default is system dependent)
--tutorial open the Coconut tutorial in the default web browser
--documentation open the Coconut documentation in the default web browser
--color color show all Coconut messages in the given color
--verbose print verbose debug output
Coconut source files should, so the compiler can recognize them, use the extension .coc
. When Coconut compiles a .coc
file, it will compile to another file with the same name, except with .py
instead of .coc
, which will hold the compiled code. If an extension other than .py
is desired for the compiled files, such as .pyde
for Python Processing, then that extension can be put before .coc
in the source file name, and it will be used instead of .py
for the compiled files. For example, name.coc
will compile to name.py
, whereas name.pyde.coc
will compile to name.pyde
.
Files compiled by the coconut
command-line utility will vary based on compilation parameters. If an entire directory of files is compiled (which the compiler will search recursively for any folders containing .coc
files), a __coconut__.py
file will be created to house necessary functions (package mode), whereas if only a single file is compiled, that information will be stored within a header inside the file (standalone mode). Standalone mode is better for single files because it gets rid of the overhead involved in importing __coconut__.py
, but package mode is better for large packages because it gets rid of the need to run the same Coconut header code again in every file, since it can just be imported from __coconut__.py
.
By default, if the source
argument to the command-line utility is a file, it will perform standalone compilation on it, whereas if it is a directory, it will recursively search for all .coc
files and perform package compilation on them. Thus, in most cases, the mode chosen by Coconut automatically will be the right one. But if it is very important that no additional files like __coconut__.py
be created, for example, then the command-line utility can also be forced to use a specific mode with the --package
(-p
) and --standalone
(-a
) flags.
While Coconut syntax is based off of Python 3, Coconut code compiled in universal mode (the default --target
), and the Coconut compiler, should run on any Python version >= 2.6
on the 2.x
branch or >= 3.2
on the 3.x
branch.
Note: The tested against implementations are CPython 2.6, 2.7, 3.2, 3.3, 3.4, 3.5
and PyPy 2.7, 3.2
.
As part of Coconut’s cross-compatibility efforts, Coconut adds in new Python 3 built-ins and overwrites Python 2 built-ins to use the Python 3 versions where possible. If access to the Python 2 versions is desired, the old built-ins can be retrieved by prefixing them with py2_
. The old built-ins available are:
py2_chr
py2_filter
py2_hex
py2_input
py2_int
py2_map
py2_oct
py2_open
py2_print
py2_range
py2_raw_input
py2_str
py2_xrange
py2_zip
Additionally, since Coconut also overrides some Python 3 built-ins for optimization purposes, those can be retrieved by prefixing them with py3_
. The overwritten built-ins available are:
py3_map
py3_zip
Finally, while Coconut will try to compile Python-3-specific syntax to its universal equivalent, the follow constructs have no equivalent in Python 2, and require a target of at least 3
to be specified to be used:
*
s (use Coconut pattern-matching instead),nonlocal
keyword,@
as matrix multiplication (requires --target 3.5
),async
and await
statements (requires --target 3.5
), andf
strings (requires --target 3.6
).If the version of Python that the compiled code will be running on is known ahead of time, a target should be specified with --target
. The given target will only affect the compiled code and whether or not certain Python-3-specific syntax is allowed, detailed below. Where Python 3 and Python 2 syntax standards differ, Coconut syntax will always follow Python 3 across all targets. The supported targets are:
2
, 26
(will work on any Python >= 2.6
but < 3
),27
(will work on any Python >= 2.7
but < 3
),3
, 32
(will work on any Python >= 3.2
),33
, 34
(will work on any Python >= 3.3
),35
(will work on any Python >= 3.5
),36
(will work on any Python >= 3.6
),sys
(chooses the specific target corresponding to the current version).Note: Periods are ignored in target specifications, such that the target 2.7
is equivalent to the target 27
.
--strict
Mode¶If the --strict
or -s
flag is enabled, Coconut will throw errors on various style problems. These are
--strict
Coconut just shows a Warning),lambda
statement,u
to denote Unicode strings,--strict
Coconut just shows a Warning),It is recommended that you use the --strict
or -s
flag if you are starting a new Coconut project, as it will help you write cleaner code.
If you prefer IPython (the python kernel for the Jupyter framework) to the normal Python shell, Coconut can be used as an IPython extension or Jupyter kernel.
If Coconut is used as an extension, a special magic command will send snippets of code to be evaluated using Coconut instead of IPython, but IPython will still be used as the default. The line magic %load_ext coconut
will load Coconut as an extension, adding the %coconut
and %%coconut
magics. The %coconut
line magic will run a line of Coconut with default parameters, and the %%coconut
block magic will take command-line arguments on the first line, and run any Coconut code provided in the rest of the cell with those parameters.
If Coconut is used as a kernel, all code in the console or notebook will be sent directly to Coconut instead of Python to be evaluated. The command coconut --jupyter notebook
(or coconut --ipython notebook
) will launch an IPython / Jupyter notebook using Coconut as the kernel and the command coconut --jupyter console
(or coconut --ipython console
) will launch an IPython / Jupyter console using Coconut as the kernel. Additionally, the command coconut --jupyter
(or coconut --ipython
) will add Coconut as a language option inside of all IPython / Jupyter notebooks, even those not launched with Coconut. This command may need to be re-run when a new version of Coconut is installed.
Coconut provides the simple, clean ->
operator as an alternative to Python’s lambda
statements. The operator has the same precedence as the old statement.
In Python, lambdas are ugly and bulky, requiring the entire word lambda
to be written out every time one is constructed. This is fine if in-line functions are very rarely needed, but in functional programming in-line functions are an essential tool.
Lambda forms (lambda expressions) have the same syntactic position as expressions. They are a shorthand to create anonymous functions; the expression (arguments) -> expression
yields a function object. The unnamed object behaves like a function object defined with:
def <lambda>(arguments):
return expression
Note that functions created with lambda forms cannot contain statements or annotations.
Coconut uses a $
sign right after a function’s name but before the open parenthesis used to call the function to denote partial application. It has the same precedence as subscription.
Partial application, or currying, is a mainstay of functional programming, and for good reason: it allows the dynamic customization of functions to fit the needs of where they are being used. Partial application allows a new function to be created out of an old function with some of its arguments pre-specified.
Return a new partial
object which when called will behave like func called with the positional arguments args and keyword arguments keywords. If more arguments are supplied to the call, they are appended to args. If additional keyword arguments are supplied, they extend and override keywords. Roughly equivalent to:
def partial(func, *args, **keywords):
def newfunc(*fargs, **fkeywords):
newkeywords = keywords.copy()
newkeywords.update(fkeywords)
return func(*(args + fargs), **newkeywords)
newfunc.func = func
newfunc.args = args
newfunc.keywords = keywords
return newfunc
The partial
object is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature.
Coconut uses pipe operators for pipeline-style function application. All the operators have a precedence in-between infix calls and comparisons and are left-associative. All operators also support in-place versions. The different operators are:
(|>) => pipe forward
(|*>) => multiple-argument pipe forward
(<|) => pipe backward
(<*|) => multiple-argument pipe backward
Coconut uses the ..
operator for function composition. It has a precedence in-between subscription and exponentiation. The in-place operator is ..=
.
Coconut uses the ::
operator for iterator chaining. Coconut’s iterator chaining is done lazily, in that the arguments are not evaluated until they are needed. It has a precedence in-between bitwise or and infix calls. The in-place operator is ::=
.
A useful tool to make working with iterators as easy as working with sequences is the ability to lazily combine multiple iterators together. This operation is called chain, and is equivalent to addition with sequences, except that nothing gets evaluated until it is needed.
Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Chained inputs are evaluated lazily. Roughly equivalent to:
def chain(*iterables):
# chain('ABC', 'DEF') --> A B C D E F
for it in iterables:
for element in it:
yield element
Coconut uses a $
sign right after an iterator before a slice to perform iterator slicing. Coconut’s iterator slicing works much the same as Python’s sequence slicing, and looks much the same as Coconut’s partial application, but with brackets instead of parentheses. It has the same precedence as subscription.
Iterator slicing works just like sequence slicing, including support for negative indices and slices, and support for slice
objects in the same way as can be done with normal slicing. Iterator slicing makes no guarantee, however, that the original iterator passed to it be preserved (to preserve the iterator, use Coconut’s tee
function).
Coconut’s iterator slicing is very similar to Python’s itertools.islice
, but unlike itertools.islice
, Coconut’s iterator slicing supports negative indices, and is optimized to play nicely with custom or built-in sequence types as well as Coconut’s map
, zip
, range
, and count
objects, only computing the elements of each that are actually necessary to extract the desired slice. This behavior can also be extended to custom objects if they define their __getitem__
method lazily and set __coconut_is_lazy__
to True
.
Coconut supports Unicode alternatives to many different operator symbols. The Unicode alternatives are relatively straightforward, and chosen to reflect the look and/or meaning of the original symbol.
→ (\u2192) => "->"
↦ (\u21a6) => "|>"
*↦ (*\u21a6) => "|*>"
↤ (\u21a4) => "<|"
↤* (\u21a4*) => "<*|"
⋅ (\u22c5) => "*"
↑ (\u2191) => "**"
÷ (\xf7) => "/"
÷/ (\xf7/) => "//"
− (\u2212) => "-" (only subtraction)
⁻ (\u207b) => "-" (only negation)
¬ (\xac) => "~"
≠ (\u2260) or ¬= (\xac=) => "!="
≤ (\u2264) => "<="
≥ (\u2265) => ">="
∧ (\u2227) or ∩ (\u2229) => "&"
∨ (\u2228) or ∪ (\u222a) => "|"
⊻ (\u22bb) or ⊕ (\u2295) => "^"
« (\xab) => "<<"
» (\xbb) => ">>"
… (\u2026) => "..."
× (\xd7) => "@" (only matrix multiplication)
data
¶The syntax for data
blocks is a cross between the syntax for functions and the syntax for classes. The first line looks like a function definition, but the rest of the body looks like a class, usually containing method definitions. This is because while data
blocks actually end up as classes in Python, Coconut automatically creates a special, immutable constructor based on the given arguments.
Coconut data
blocks create immutable classes derived from collections.namedtuple
and made immutable with __slots__
. Coconut data statement syntax looks like:
data <name>(<args>):
<body>
<name>
is the name of the new data type, <args>
are the arguments to its constructor as well as the names of its attributes, and <body>
contains the data type’s methods.
Subclassing data
types can be done easily by inheriting from them in a normal Python class
, although to make the new subclass immutable, the line
__slots__ = ()
will need to be added to the subclass before any method or attribute definitions.
A mainstay of functional programming that Coconut improves in Python is the use of values, or immutable data types. Immutable data can be very useful because it guarantees that once you have some data it won’t change, but in Python creating custom immutable data types is difficult. Coconut makes it very easy by providing data
blocks.
Returns a new tuple subclass. The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with type names and field names) and a helpful __repr__()
method which lists the tuple contents in a name=value
format.
Any valid Python identifier may be used for a field name except for names starting with an underscore. Valid identifiers consist of letters, digits, and underscores but do not start with a digit or underscore and cannot be a keyword such as class, for, return, global, pass, or raise.
Named tuple instances do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples.
data vector(x, y):
def __abs__(self):
return (self.x**2 + self.y**2)**.5
v = vector(3, 4)
v |> print # all data types come with a built-in __repr__
v |> abs |> print
v.x = 2 # this will fail because data objects are immutable
import collections
class vector(collections.namedtuple("vector", "x, y")):
__slots__ = ()
def __abs__(self):
return (self.x**2 + self.y**2)**.5
v = vector(3, 4)
print(v)
print(abs(v))
v.x = 2
match
¶Coconut provides fully-featured, functional pattern-matching through its match
statements.
Match statements follow the basic syntax match <pattern> in <value>
. The match statement will attempt to match the value against the pattern, and if successful, bind any variables in the pattern to whatever is in the same position in the value, and execute the code below the match statement. Match statements also support, in their basic syntax, an if <cond>
that will check the condition after executing the match before executing the code below, and an else
statement afterwards that will only be executed if the match
statement is not. What is allowed in the match statement’s pattern has no equivalent in Python, and thus the specifications below are provided to explain it.
Coconut match statement syntax is
match <pattern> in <value> [if <cond>]:
<body>
[else:
<body>]
where <value>
is the item to match against, <cond>
is an optional additional check, and <body>
is simply code that is executed if the header above it succeeds. <pattern>
follows its own, special syntax, defined roughly like so:
pattern ::= (
"(" pattern ")" # parentheses
| "None" | "True" | "False" # constants
| "=" NAME # check
| NUMBER # numbers
| STRING # strings
| [pattern "as"] NAME # capture
| NAME "(" patterns ")" # data types
| "(" patterns ")" # sequences can be in tuple form
| "[" patterns "]" # or in list form
| "(|" patterns "|)" # lazy lists
| "{" pattern_pairs "}" # dictionaries
| ["s"] "{" pattern_consts "}" # sets
| ( # head-tail splits
"(" patterns ")"
| "[" patterns "]"
) "+" pattern
| pattern "+" ( # init-last splits
"(" patterns ")"
| "[" patterns "]"
)
| ( # head-last splits
"(" patterns ")"
| "[" patterns "]"
) "+" pattern "+" (
"(" patterns ")" # this match must be the same
| "[" patterns "]" # construct as the first match
)
| ( # iterator splits
"(" patterns ")"
| "[" patterns "]"
| "(|" patterns "|)" # lazy lists
) "::" pattern
| pattern "is" exprs # type-checking
| pattern "and" pattern # match all
| pattern "or" pattern # match any
)
match
statements will take their pattern and attempt to “match” against it, performing the checks and deconstructions on the arguments as specified by the pattern. The different constructs that can be specified in a pattern, and their function, are:
_
is used, nothing will be bound and everything will always match to it.<pattern> as <var>
): will bind <var>
to <pattern>
.=<var>
): will check that whatever is in that position is equal to the previously defined variable <var>
.<var> is <types>
): will check that whatever is in that position is of type(s) <types>
before binding the <var>
.<name>(<args>)
): will check that whatever is in that position is of data type <name>
and will match the attributes to <args>
.[<patterns>]
), Tuples ((<patterns>)
), or Lazy lists ((|<patterns>|)
): will only match a sequence (collections.abc.Sequence
) of the same length, and will check the contents against <patterns>
.{<pairs>}
): will only match a mapping (collections.abc.Mapping
) of the same length, and will check the contents against <pairs>
.{<constants>}
): will only match a set (collections.abc.Set
) of the same length and contents.<list/tuple> + <var>
): will match the beginning of the sequence against the <list/tuple>
, then bind the rest to <var>
, and make it the type of the construct used.<var> + <list/tuple>
): exactly the same as head-tail splits, but on the end instead of the beginning of the sequence.<list/tuple> + <var> + <list/tuple>
): the combination of a head-tail and an init-last split.<list/tuple/lazy list> :: <var>
, or <lazy list>
): will match the beginning of an iterable (collections.abc.Iterable
) against the <list/tuple/lazy list>
, then bind the rest to <var>
or check that the iterable is done.When checking whether or not an object can be matched against in a particular fashion, Coconut makes use of Python’s abstract base classes. Therefore, to enable proper matching for a custom object, register it with the proper abstract base classes.
def factorial(value):
match 0 in value:
return 1
else: match n is int in value if n > 0: # possible because of Coconut's
return n * factorial(n-1) # enhanced else statements
else:
raise TypeError("invalid argument to factorial of: "+repr(value))
3 |> factorial |> print
Showcases else
statements, which work much like else
statements in Python: the code under an else
statement is only executed if the corresponding match fails.
data point(x, y):
def transform(self, other):
match point(x, y) in other:
return point(self.x + x, self.y + y)
else:
raise TypeError("arg to transform must be a point")
def __eq__(self, other):
match point(=self.x, =self.y) in other:
return True
else:
return False
point(1,2) |> point(3,4).transform |> print
point(1,2) |> point(1,2).__eq__ |> print
Showcases matching to data types. Values defined by the user with the data
statement can be matched against and their contents accessed by specifically referencing arguments to the data type’s constructor.
data empty(): pass
data leaf(n): pass
data node(l, r): pass
tree = (empty, leaf, node)
def depth(t):
match tree() in t:
return 0
match tree(n) in t:
return 1
match tree(l, r) in t:
return 1 + max([depth(l), depth(r)])
empty() |> depth |> print
leaf(5) |> depth |> print
node(leaf(2), node(empty(), leaf(3))) |> depth |> print
Showcases how the combination of data types and match statements can be used to powerful effect to replicate the usage of algebraic data types in other functional programming languages.
def duplicate_first(value):
match [x] + xs as l in value:
return [x] + l
else:
raise TypeError()
[1,2,3] |> duplicate_first |> print
Showcases head-tail splitting, one of the most common uses of pattern-matching, where a + <var>
(or :: <var>
for any iterable) at the end of a list or tuple literal can be used to match the rest of the sequence.
Can’t be done without a long series of checks for each match
statement. See the compiled code for the Python syntax.
case
¶Coconut’s case
statement is an extension of Coconut’s match
statement for performing multiple match
statements against the same value, where only one of them should succeed. Unlike lone match
statements, only one match statement inside of a case
block will ever succeed, and thus more general matches should be put below more specific ones.
Each pattern in a case block is checked until a match is found, and then the corresponding body is executed, and the case block terminated. The syntax for case blocks is
case <value>:
match <pattern> [if <cond>]:
<body>
match <pattern> [if <cond>]:
<body>
...
[else:
<body>]
where <pattern>
is any match
pattern, <value>
is the item to match against, <cond>
is an optional additional check, and <body>
is simply code that is executed if the header above it succeeds. Note the absence of an in
in the match
statements: that’s because the <value>
in case <value>
is taking its place.
def classify_sequence(value):
out = "" # unlike with normal matches, only one of the patterns
case value: # will match, and out will only get appended to once
match ():
out += "empty"
match (_,):
out += "singleton"
match (x,x):
out += "duplicate pair of "+str(x)
match (_,_):
out += "pair"
match _ is (tuple, list):
out += "sequence"
else:
raise TypeError()
return out
[] |> classify_sequence |> print
() |> classify_sequence |> print
[1] |> classify_sequence |> print
(1,1) |> classify_sequence |> print
(1,2) |> classify_sequence |> print
(1,1,1) |> classify_sequence |> print
Can’t be done without a long series of checks for each match
statement. See the compiled code for the Python syntax.
In Coconut, the keywords data
, match
, case
, async
(keyword in Python 3.5), and await
(keyword in Python 3.5) are also valid variable names. While Coconut can disambiguate these two use cases, when using one of these keywords as a variable name, a backslash is allowed in front to be explicit about using a keyword as a variable name.
In Coconut, all variable names starting with _coconut
are reserved. The Coconut compiler will modify and reference these variables with the assumption that the code being compiled does not modify them in any way. If your code does modify any such variables, your code is unlikely to work properly.
Coconut supports the creation of lazy lists, where the contents in the list will be treated as an iterator and not evaluated until they are needed. Lazy lists can be created in Coconut simply by simply surrounding a comma-seperated list of items with (|
and |)
(so-called “banana brackets”) instead of [
and ]
for a list or (
and )
for a tuple.
Lazy lists use the same machinery as iterator chaining to make themselves lazy, and thus the lazy list (| x, y |)
is equivalent to the iterator chaining expression (x,) :: (y,)
, although the lazy list won’t construct any intermediate tuples.
Lazy lists, where sequences are only evaluated when their contents are requested, are a mainstay of functional programming, allowing for dynamic evaluation of the list’s contents.
Coconut supports a number of different syntactical aliases for common partial application use cases. These are:
.name => operator.attrgetter("name")
obj. => getattr$(obj)
func$ => ($)$(func)
seq[] => operator.__getitem__$(seq)
iter$[] => # the equivalent of seq[] for iterators
Coconut allows an optional s
to be prepended in front of Python set literals. While in most cases this does nothing, in the case of the empty set it lets Coconut know that it is an empty set and not an empty dictionary. Additionally, an f
is also supported, in which case a Python frozenset
will be generated instead of a normal set.
In addition to Python’s <num>j
or <num>J
notation for imaginary literals, Coconut also supports <num>i
or <num>I
, to make imaginary literals more readable if used in a mathematical context.
Imaginary literals are described by the following lexical definitions:
imagnumber ::= (floatnumber | intpart) ("j" | "J" | "i" | "I")
An imaginary literal yields a complex number with a real part of 0.0. Complex numbers are represented as a pair of floating point numbers and have the same restrictions on their range. To create a complex number with a nonzero real part, add a floating point number to it, e.g., (3+4i)
. Some examples of imaginary literals:
3.14i 10.i 10i .001i 1e100i 3.14e-10i
Coconut uses a simple operator function short-hand: surround an operator with parentheses to retrieve its function. Similarly to iterator comprehensions, if the operator function is the only argument to a function, the parentheses of the function call can also serve as the parentheses for the operator function.
A very common thing to do in functional programming is to make use of function versions of built-in operators: currying them, composing them, and piping them. To make this easy, Coconut provides a short-hand syntax to access operator functions.
(|>) => # pipe forward
(|*>) => # multi-arg pipe forward
(<|) => # pipe backward
(|*>) => # multi-arg pipe backward
(..) => # function composition
(.) => (getattr)
(::) => (itertools.chain) # will not evaluate its arguments lazily
($) => (functools.partial)
(+) => (operator.__add__)
(-) => # 1 arg: operator.__neg__, 2 args: operator.__sub__
(*) => (operator.__mul__)
(**) => (operator.__pow__)
(/) => (operator.__truediv__)
(//) => (operator.__floordiv__)
(%) => (operator.__mod__)
(&) => (operator.__and__)
(^) => (operator.__xor__)
(|) => (operator.__or__)
(<<) => (operator.__lshift__)
(>>) => (operator.__rshift__)
(<) => (operator.__lt__)
(>) => (operator.__gt__)
(==) => (operator.__eq__)
(<=) => (operator.__le__)
(>=) => (operator.__ge__)
(!=) => (operator.__ne__)
(~) => (operator.__inv__)
(@) => (operator.__matmul__)
(not) => (operator.__not__)
(and) => # boolean and
(or) => # boolean or
(is) => (operator.is_)
(in) => (operator.__contains__)
Coconut allows for shorthand in-line function definition, where the body of the function is assigned directly to the function call. The syntax for shorthand function definition is
def <name>(<args>) = <expr>
where <name>
is the name of the function, <args>
are the functions arguments, and <expr>
evaluates the value that the function should return.
Note: Shorthand function definition can be combined with infix and pattern-matching function definition.
Coconut’s shorthand function definition is as easy to write as assignment to a lambda, but will appear named in tracebacks, as it compiles to normal Python function definition.
Coconut allows for infix function calling, where a function is surrounded by backticks and then can have arguments placed in front of or behind it. Backtick calling has a precedence in-between chaining and piping.
Coconut also supports infix function definition to make defining functions that are intended for infix usage simpler. The syntax for infix function definition is
def <arg> `<name>` <arg>:
<body>
where <name>
is the name of the function, the <arg>
s are the function arguments, and <body>
is the body of the function. If an <arg>
includes a default, the <arg>
must be surrounded in parentheses.
Note: Infix function definition can be combined with shorthand and pattern-matching function definition.
A common idiom in functional programming is to write functions that are intended to behave somewhat like operators, and to call and define them by placing them between their arguments. Coconut’s infix syntax makes this possible.
Coconut supports pattern-matching / destructuring assignment syntax inside of function definition. The syntax for pattern-matching function definition is
[match] def <name>(<match>, <match>, ...):
<body>
where <name>
is the name of the function, <body>
is the body of the function, and <pattern>
is defined by Coconut’s match
statement. The match
keyword at the beginning is optional, but is sometimes necessary to disambiguate pattern-matching function definition from normal function definition, which will always take precedence. Coconut’s pattern-matching function definition is equivalent to a destructuring assignment that looks like:
def <name>(*args):
match [<match>, <match>, ...] = args
<body>
If pattern-matching function definition fails, it will raise a [MatchError
]((#matcherror) object just like destructuring assignment.
Note: Pattern-matching function definition can be combined with shorthand and infix function definition.
def last_two(_ + [a, b]):
return a, b
def xydict_to_xytuple({"x":x is int, "y":y is int}):
return x, y
range(5) |> last_two |> print
{"x":1, "y":2} |> xydict_to_xytuple |> print
Can’t be done without a long series of checks at the top of the function. See the compiled code for the Python syntax.
Coconut supports significantly enhanced destructuring assignment, similar to Python’s tuple/list destructuring, but much more powerful. The syntax for Coconut’s destructuring assignment is
[match] <pattern> = <value>
where <value>
is any expression and <pattern>
is defined by Coconut’s match
statement. The match
keyword at the beginning is optional, but is sometimes necessary to disambiguate destructuring assignment from normal assignment, which will always take precedence. Coconut’s destructuring assignment is equivalent to a match statement that follows the syntax:
match <pattern> in <value>:
pass
else:
err = MatchError(<error message>)
err.pattern = "<pattern>"
err.value = <value>
raise err
If a destructuring assignment statement fails, then instead of continuing on as if a match
block had failed, a MatchError
object will be raised describing the failure.
Unlike Python, which only supports a single variable or function call in a decorator, Coconut supports any expression.
else
Statements¶Coconut supports the compound statements try
, if
, and match
on the end of an else
statement like any simple statement would be. This is most useful for mixing match
and if
statements together, but also allows for compound try
statements.
except
Statements¶Python 3 requires that if multiple exceptions are to be caught, they must be placed inside of parentheses, so as to disallow Python 2’s use of a comma instead of as
. Coconut allows commas in except statements to translate to catching multiple exceptions without the need for parentheses.
Coconut allows for the more elegant parenthetical continuation instead of the less elegant backslash continuation in import
, del
, global
, and nonlocal
statements.
Coconut supports the ability to pass arbitrary code through the compiler without being touched, for compatibility with other variants of Python, such as Cython or Mython. Anything placed between \(
and the corresponding close parenthesis will be passed through, as well as any line starting with \\
, which will have the additional effect of allowing indentation under it.
reduce
¶Coconut re-introduces Python 2’s reduce
built-in, using the functools.reduce
version.
reduce(function, iterable[, initializer])
Apply function of two arguments cumulatively to the items of sequence, from left to right, so as to reduce the sequence to a single value. For example, reduce((x, y) -> x+y, [1, 2, 3, 4, 5])
calculates ((((1+2)+3)+4)+5)
. The left argument, x, is the accumulated value and the right argument, y, is the update value from the sequence. If the optional initializer is present, it is placed before the items of the sequence in the calculation, and serves as a default when the sequence is empty. If initializer is not given and sequence contains only one item, the first item is returned.
takewhile
¶Coconut provides itertools.takewhile
as a built-in under the name takewhile
.
takewhile(predicate, iterable)
Make an iterator that returns elements from the iterable as long as the predicate is true. Equivalent to:
def takewhile(predicate, iterable):
# takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
for x in iterable:
if predicate(x):
yield x
else:
break
dropwhile
¶Coconut provides itertools.dropwhile
as a built-in under the name dropwhile
.
dropwhile(predicate, iterable)
Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note: the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Equivalent to:
def dropwhile(predicate, iterable):
# dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x
tee
¶Coconut provides itertools.tee
as a built-in under the name tee
.
tee(iterable, n=2)
Return n independent iterators from a single iterable. Equivalent to:
def tee(iterable, n=2):
it = iter(iterable)
deques = [collections.deque() for i in range(n)]
def gen(mydeque):
while True:
if not mydeque: # when the local deque is empty
newval = next(it) # fetch a new value and
for d in deques: # load it to all the deques
d.append(newval)
yield mydeque.popleft()
return tuple(gen(d) for d in deques)
Once tee()
has made a split, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed.
This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list()
instead of tee()
.
consume
¶Coconut provides the consume
function to efficiently exhaust an iterator and thus perform any lazy evaluation contained within it. consume
takes one optional argument, keep_last
, that defaults to 0 and specifies how many, if any, items from the end to return as an iterable (None
will keep all elements). Equivalent to:
def consume(iterable, keep_last=0):
"""Fully exhaust iterable and return the last keep_last elements."""
return collections.deque(iterable, maxlen=keep_last) # fastest way to exhaust an iterator
In the process of lazily applying operations to iterators, eventually a point is reached where evaluation of the iterator is necessary. To do this efficiently, Coconut provides the consume
function, which will fully exhaust the iterator given to it.
count
¶Coconut provides a modified version of itertools.count
that supports in
, normal slicing, optimized iterator slicing, count
and index
sequence methods, repr
, and _start
and _step
attributes as a built-in under the name count
.
count(start=0, step=1)
Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to map()
to generate consecutive data points. Also, used with zip()
to add sequence numbers. Roughly equivalent to:
def count(start=0, step=1):
# count(10) --> 10 11 12 13 14 ...
# count(2.5, 0.5) -> 2.5 3.0 3.5 ...
n = start
while True:
yield n
n += step
map
and zip
¶Coconut’s map
and zip
objects are enhanced versions of their Python equivalents that support normal slicing, optimized iterator slicing (through __coconut_is_lazy__
), reversed
, len
, repr
, and have added attributes which subclasses can make use of to get at the original arguments to the object (map
supports _func
and _iters
attributes and zip
supports the _iters
attribute).
datamaker
¶Coconut provides the datamaker
function to allow direct access to the base constructor of data types created with the Coconut data
statement. This is particularly useful when writing alternative constructors for data types by overwriting __new__
. Equivalent to:
def datamaker(data_type):
"""Returns base data constructor of data_type."""
return super(data_type, data_type).__new__$(data_type)
recursive
¶Coconut provides a recursive
decorator to perform tail recursion optimization on a function written in a tail-recursive style, where it directly returns all calls to itself. Do not use this decorator on a function not written in a tail-recursive style or the function will likely break.
@recursive
def factorial(n, acc=1):
case n:
match 0:
return acc
match _ is int if n > 0:
return factorial(n-1, acc*n)
else:
raise TypeError("the argument to factorial must be an integer >= 0")
Can’t be done without a long decorator definition. The full definition of the decorator in Python can be found in the Coconut header.
parallel_map
¶Coconut provides a parallel version of map
under the name parallel_map
. parallel_map
makes use of multiple processes, and is therefore often much faster than map
. Use of parallel_map
requires concurrent.futures
, which exits in the Python 3 standard library, but under Python 2 will require python -m pip install futures
to function.
Because parallel_map
uses multiple processes for its execution, it is necessary that all of its arguments be pickleable. Only objects defined at the module level, and not lambdas, objects defined inside of a function, or objects defined inside of the interpreter, are pickleable. Furthermore, on Windows, it is necessary that all calls to parallel_map
occur inside of an if __name__ == "__main__"
guard.
parallel_map(_func, *iterables_)
Equivalent to map(func, *iterables)
except func is executed asynchronously and several calls to func may be made concurrently. If a call raises an exception, then that exception will be raised when its value is retrieved from the iterator.
MatchError
¶A MatchError
is raised when a destructuring assignment statement fails, and thus MatchError
is provided as a built-in for catching those errors. MatchError
objects support two attributes, pattern
, which is a string describing the failed pattern, and value
, which is the object that failed to match that pattern.
There are currently three options for Coconut syntax highlighting:
Instructions on how to set up syntax highlighting for SublimeText and Pygments are included below. If you don’t like SublimeText and your chosen alternative text editor doesn’t have pygments support, however, it should be sufficient to set up your editor so it interprets all .coc
files as Python code, as this should highlight most of your code well enough.
Coconut syntax highlighting for SublimeText requires that Package Control, the standard package manager for SublimeText, be installed. Once that is done, simply open the SublimeText command palette by entering Ctrl+Shift+P
, enter Package Control: Install Package
, and then Coconut
. To make sure everything is working properly, open a .coc
file, and make sure Coconut
appears in the bottom right-hand corner. If something else appears, like Plain Text
, click on it, select Open all with current extension as...
at the top of the resulting menu, and then select Coconut
.
The same pip install coconut
command that installs the Coconut command-line utility will also install the coconut
Pygments lexer. How to use this lexer depends on the Pygments-enabled application being used, but in general simply enter coconut
as the language being highlighted and/or use the file extension .coc
and Pygments should be able to figure it out. For example, this documentation is generated with Sphinx, with the syntax highlighting you see created by adding the line
highlight_language = "coconut"
to Coconut’s conf.py
.
coconut.convenience
¶It is sometimes useful to be able to use the Coconut compiler from code, instead of from the command line. The recommended way to do this is to use from coconut.convenience import
and import whatever convenience functions you’ll be using. Specifications of the different convenience functions are as follows.
parse
¶coconut.convenience.parse(code, [mode])
Likely the most useful of the convenience functions, parse
takes Coconut code as input and outputs the equivalent compiled Python code. The second argument, mode, is used to indicate the context for the parsing. Possible values of mode are:
"exec"
: code for use in exec
(the default)"file"
: a stand-alone file"single"
: a single line of code"module"
: a file in a folder or module"block"
: any number of lines of code"eval"
: a single expression"debug"
: lines of code with no headersetup
¶coconut.convenience.setup(target, strict, minify, linenumbers, quiet, color)**
If --target
, --strict
, --minify
, --linenumbers
, --quiet
, or --color
are desired for parse
, the arguments to setup
will each set the value of the corresponding flag. The possible values for each flag are:
None
(default), or any allowable targetFalse
(default) or True
False
(default) or True
False
(default) or True
False
(default) or True
None
(default) or str
cmd
¶coconut.convenience.cmd(args, [interact])
Executes the given args as if they were fed to coconut
on the command-line, with the exception that unless interact is true or -i
is passed, the interpreter will not be started. Additionally, since parse
and cmd
share the same convenience parsing object, any changes made to the parsing with cmd
will work just as if they were made with setup
.
version
¶coconut.convenience.version([which])
Retrieves a string containing information about the Coconut version. The optional argument which is the type of version information desired. Possible values of which are:
"num"
: the numerical version (the default)"name"
: the version codename"spec"
: the numerical version with the codename attached"tag"
: the version tag used in GitHub and documentation URLs"-v"
: the full string printed by coconut -v
CoconutException
¶If an error is encountered in a convenience function, a CoconutException
instance may be raised. coconut.convenience.CoconutException
is provided to allow catching such errors.
coconut.__coconut__
¶It is sometimes useful to be able to access Coconut built-ins from pure Python. To accomplish this, Coconut provides coconut.__coconut__
, which behaves exactly like the __coconut__.py
header file included when Coconut is compiled in package mode.
All Coconut built-ins are accessible from coconut.__coconut__
. The recommended way to import them is to use from coconut.__coconut__ import
and import whatever built-ins you’ll be using.