Tutorial

Metaprogramming

Writing code that writes code.

Hissp is designed with metaprogramming in mind. Unlike most programming languages, Hissp is not made of text, but data: Abstract Syntax Trees (AST).

Abstract Syntax Tree

A type of intermediate tree data structure used by most compilers after parsing a programming language.

You’ve been writing AST all along, albeit indirectly. To understand code at all, in any programming language, you must have an understanding of how to parse it, mentally.

Python itself has an AST representation used by its compiler (the ast module) which is accessible to Python programs, but because it represents all of the possible Python syntax, which is considerable, it is difficult to use effectively for metaprogramming.

The Hissp compiler, in contrast, compiles Hissp code to a simplified functional subset of Python. This sacrifice ultimately gives Hissp greater expressive power, because it makes Hissp a simpler language that is easier to manipulate programmatically.

In Hissp, you write in this parsed form far more directly: Hissp code is AST.

Installation

Hissp requires Python 3.8+ and has no other dependencies.

Install the Hissp version matching this document with:

$ pip install hissp==0.2.0

Hello World

A Hissp program is made of Python objects composed into tuples which represent the syntax tree structure.

You can invoke the Hissp compiler directly from Python.

>>> from hissp.compiler import readerless
>>> readerless(
...     ('lambda',('name',),
...      ('print',('quote','Hello'),'name',),)
... )
"(lambda name:\n  print(\n    'Hello',\n    name))"
>>> print(_)
(lambda name:
  print(
    'Hello',
    name))
>>> eval(_)('World')
Hello World

The readerless() function takes a Hissp program as input, and returns its Python translation as a string.

Let’s break this down. Notice that the first element of each tuple designates its function.

In the case of ('print',('quote','Hello'),'name',), the first element represents a call to the print() function. The remaining elements are the arguments.

The interpretation of the lambda form is a special case. It represents a lambda expression, rather than a function call. ('name',) is its parameters tuple. The remainder is its body.

Note that 'name' was used as an identifier, but the ('quote','Hello') expression was compiled to a string. That’s the interpretation of quote: its argument is seen as “data” rather than code by the compiler.

Together, lambda and quote are the only special forms known to the compiler. There are ways to define more forms with special interpretations, called “macros”. This is how Hissp gets much of its expressive power.

('quote','Hello') seems a little verbose compared to its Python translation. This could get tedious every time we need a string. Isn’t there something we can do?

Let’s try it.

>>> def q(data):
...     return 'quote', data
...
>>> q('Hello')
('quote', 'Hello')

You may not have noticed, but congratulations! We’ve just written our first metaprogram: The q() function is Python code that writes Hissp code.

Let’s use it.

>>> readerless(
...     ('lambda',('name'),
...      ('print',q('Hello'),'name',),)
... )
"(lambda n,a,m,e:\n  print(\n    'Hello',\n    name))"
>>> print(_)
(lambda n,a,m,e:
  print(
    'Hello',
    name))
>>> eval(_)('World')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: <lambda>() missing 3 required positional arguments: 'a', 'm', and 'e'

What happened?

Look at the compiled Python. Our q() worked as expected, but there are too many parameters in the lambda because we forgot the comma in ('name'). Lambda doesn’t care what kind of iterable you use for its parameters, as long as it yields appropriate elements in appropriate order. We could have used a list, for example. This flexibility can make metaprogramming easier, but mutable collections are not recommended. Python strings are iterables yielding their characters, so the characters n, a, m, and e got compiled to the parameters.

Caution

When writing Hissp tuples, it’s best to think of commas as terminators, rather than separators, to avoid this kind of problem. In Python, (except for the empty tuple ()) it is the comma that creates a tuple, not the parentheses. The parentheses only control evaluation order. There are some contexts where tuples don’t require parentheses at all.

Let’s try that again.

>>> readerless(
...     ('lambda',('name',),
...      ('print',q('Hello'),'name',),)
... )
"(lambda name:\n  print(\n    'Hello',\n    name))"
>>> print(_)
(lambda name:
  print(
    'Hello',
    name))

That’s better.

Lissp

It can feel a little tedious writing significant amounts of Hissp code in Python. You have to quote every identifier and 'quote' every string, and it’s easy to miss a comma in a tuple.

Naturally, the way to make this easier is by metaprogramming. We already saw a simple example with the q() function above.

But we can do much better than that.

Hissp is made of data structures. They’re ephemeral; they only live in memory. If Hissp is the spoken word, we need a written word. And to “speak” the written word back into Hissp, we need a reader. Hissp comes with a hissp.reader module that interprets a lightweight language called Lissp as Hissp code.

Lissp is made of text. Lissp is to the written word as Hissp is to the spoken word. When you are writing Lissp, you are still writing Hissp.

Lissp

A lightweight textual language representing Hissp, as defined by hissp.reader.

Lissp also includes reader macros, that act like the q() example: metaprogramming abbreviations.

Reader macro

An abbreviation used by the reader. These are not part of the Hissp langauge proper, but rather are functions that expand to Hissp; They run at read time and return Hissp code.

Read time

The pre-compile phase that translates Lissp to Hissp: when the reader runs.

Let’s see our “Hello World” example in Lissp:

>>> from hissp.reader import Lissp
>>> next(Lissp().reads("""
... (lambda (name)
...   (print 'Hello name))
... """))
('lambda', ('name',), ('print', ('quote', 'Hello'), 'name'))

There are no commas to miss, because there are no commas at all.

As you can see, the Hissp structure is exactly the same as before. But now you don’t have to quote identifiers either.

The ' is a built-in reader macro that acts just like the q() function we defined earlier: it wraps the next expression in a quote form.

The REPL

Hissp comes with its own interactive command-line interface, called the Lissp REPL.

REPL

Read-Evaluate-Print Loop.

You can launch the REPL from Python code (which is useful for debugging, like code.interact), but let’s start it from the command line using an appropriate Python interpreter:

$ python -m hissp

Or, if you installed the hissp package using pip, you can use the installed entry point script:

$ lissp

You should see the Lissp prompt #> appear.

You can quit with (exit) or EOF 1.

Follow along with the examples by typing them into the Lissp REPL. Try variations that occur to you.

The REPL is layered on top of the Python interpreter. You type in the part at the Lissp prompt #>, and then Lissp will compile it to Python, which it will enter into the Python interpreter >>> for you. Then Python will evaluate it and print a result as normal.

Data Elements of Lissp

Hissp has special behaviors for Python’s tuple and str types. Everything else is just data, and Hissp does its best to compile it that way.

Besides the tuple and string lexical elements, Lissp adds special behavior to reader macros. (And ignores things like whitespace and comments.) Everything else is an atom, which is passed through to the Hissp level with minimal processing.

Basic Atoms

Most literals work just like Python:

#> 1 ; Lissp comments use ';' instead of '#'.
>>> (1)
1

#> -1.0  ; float
>>> (-1.0)
-1.0

#> 1e10  ; exponent notation
>>> (10000000000.0)
10000000000.0

#> 2+3j  ; complex
>>> ((2+3j))
(2+3j)

#> ...
>>> ...
Ellipsis

#> True
>>> True
True

#> None ; These don't print.
>>> None

Comments, as one might expect, are ignored by the reader, and do not appear in the output.

#> ;; Use two ';'s if it starts the line.
>>>

Raw Strings

Hash strings and raw strings represent text data, but are lexically distinct from the other atoms, and have somewhat different behavior.

Raw strings in Lissp are double-quoted and read backslashes and newlines literally, which makes them similar to triple-quoted r-strings in Python.

#> "Two
#..lines\ntotal"
>>> ('Two\nlines\\ntotal')
'Two\nlines\\ntotal'

Do note, however, that the tokenizer expects backslashes to be paired.

#> "\"
#..\\"  ; One string, not two!
>>> ('\\"\n\\\\')
'\\"\n\\\\'

The second double-quote character didn’t end the raw string, but the backslash “escaping” it was still read literally. The third double quote did end the string despite being adjacent to a backslash, because that was already paired with another backslash. Again, this is the same as Python’s r-strings.

Hash Strings

You can enable the processing of Python’s backslash escape sequences by prefixing the raw string syntax with a hash #. These are called hash strings.

#> #"Three
#..lines\ntotal"
>>> ('Three\nlines\ntotal')
'Three\nlines\ntotal'

Recall that the str type in (readerless mode) Hissp is used to represent Python identifiers in the compiled output, and must be quoted with the quote special form to represent text data instead.

strs in Hissp can represent almost any raw Python code to inject in the compiled output, not just identifiers. So another way to represent text data in Hissp is a str that contains the Python code for a string literal. Quoting our entire example shows us how that Lissp would get translated to Hissp, because when quoted, it’s just data:

#> (quote
#..  (lambda (name)
#..    (print "Hello" name)))
>>> ('lambda', ('name',), ('print', "('Hello')", 'name'))
('lambda', ('name',), ('print', "('Hello')", 'name'))

Notice that rather than using the quote special form for "Hello", Lissp reads in a double-quoted string as a Hissp str containing a Python string literal: ('Hello').

Symbols

In our basic example:

(lambda (name)
  (print 'Hello name))

lambda, name, print, Hello, and name are symbols.

Symbols are meant for variable names and the like. Quoting our example again to see how Lissp would get read as Hissp:

#> (quote
#..  (lambda (name)
#..    (print 'Hello name)))
>>> ('lambda', ('name',), ('print', ('quote', 'Hello'), 'name'))
('lambda', ('name',), ('print', ('quote', 'Hello'), 'name'))

We see that there are no symbol objects at the Hissp level. Notice that the Lissp symbols are read in as strs

Symbols only exist as reader syntax in Lissp, where they represent the subset of Hissp strs that can act as identifiers.

These symbols in Lissp become strs in Hissp which become identifiers in Python, unless they’re quoted, like ('quote', 'Hello'), in which case they become string literals in Python.

Experiment with this process in the REPL.

Attributes

Symbols with an internal . access attributes when used as an identifier:

#> int.__name__
>>> int.__name__
'int'

#> int.__name__.__class__  ; These chain.
>>> int.__name__.__class__
<class 'str'>

Munging

Symbols have another important difference from raw strings:

#> 'foo->bar?  ; xH_ is for "Hyphen", xGT_ for "Greater Than/riGhT".
>>> 'fooxH_xGT_barxQUERY_'
'fooxH_xGT_barxQUERY_'

#> "foo->bar?"
>>> ('foo->bar?')
'foo->bar?'

Because symbols may contain special characters, but the Python identifiers they represent cannot, the reader munges symbols with forbidden characters to valid identifier strs by using xQUOTEDxWORDS_.

This format was chosen because it contains an underscore and both lower-case and upper-case letters, which makes it distinct from standard Python naming conventions: lower_case_with_underscores, UPPER_CASE_WITH_UNDERSCORES, and CapWords. This makes it easy to tell if an identifier contains munged characters, which makes demunging possible in the normal case. It also cannot introduce a leading underscore, which can have special meaning in Python. It might have been simpler to use the character’s ord(), but it’s important that the munged symbols still be human-readable.

Munging happens at read time, which means you can use a munged symbol both as an identifier and as a string representing that identifier:

#> ((lambda (spam)
#..   (setattr spam
#..            '!@%$  ; Munges and compiles to string literal.
#..            'eggs)
#..   spam.!@%$)  ; Munges and compiles to attribute identifier.
#.. (lambda ()))  ; Call with something that can take attrs.
>>> (lambda spam:(
...   setattr(
...     spam,
...     'xBANG_xAT_xPCENT_xDOLR_',
...     'eggs'),
...   spam.xBANG_xAT_xPCENT_xDOLR_)[-1])(
...   (lambda :()))
'eggs'

Spaces, double quotes, parentheses, and semicolons are allowed in atoms, but they must each be escaped with a backslash to prevent it from terminating the symbol. (Escape a backslash with another backslash.)

#> 'embedded\ space
>>> 'embeddedxSPACE_space'
'embeddedxSPACE_space'

Python does not allow some characters to start an identifier that it allows inside identifiers, such as digits. You also have to escape these if they begin a symbol to distinguish them from numbers.

#> '\108
>>> 'xDIGITxONE_08'
'xDIGITxONE_08'

Notice that only the first digit had to be munged to make it a valid Python identifier.

The munger also normalizes Unicode characters to NFKC, because Python already does this when converting identifiers to strings:

>>> ascii_a = 'A'
>>> unicode_a = '𝐀'
>>> ascii_a == unicode_a
False
>>> import unicodedata
>>> ascii_a == unicodedata.normalize('NFKC', unicode_a)
True
>>> A = unicodedata.name(ascii_a)
>>> A
'LATIN CAPITAL LETTER A'
>>> 𝐀 = unicodedata.name(unicode_a)  # Assign a unicode variable name.
>>> 𝐀  # Different, as expected.
'MATHEMATICAL BOLD CAPITAL A'
>>> A  # Huh?
'MATHEMATICAL BOLD CAPITAL A'
>>> globals()[unicode_a]  # The Unicode name does not work!
Traceback (most recent call last):
  ...
KeyError: '𝐀'
>>> globals()[ascii_a]  # Retrieve with the normalized name.
'MATHEMATICAL BOLD CAPITAL A'

The ASCII A and Unicode 𝐀 are aliases of the same identifier as far as Python is concerned. But the globals dict can only use one of them as its key, so it uses the normalized version.

Remember our first munging example?

#> ((lambda (spam)
#..   (setattr spam
#..            '𝐀  ; Munged symbol compiles to a string.
#..            'eggs)
#..   spam.𝐀)  ; Munged symbol compiles to an identifier.
#.. (lambda ()))  ; Call with something that can take attrs.
>>> (lambda spam:(
...   setattr(
...     spam,
...     'A',
...     'eggs'),
...   spam.A)[-1])(
...   (lambda :()))
'eggs'

Notice that the compiled Python is pure ASCII in this case. This example couldn’t work if the munger didn’t normalize symbols, because setattr() would store the Unicode 𝐀 in spam’s __dict__, but spam.𝐀 would do the same thing as spam.A, and there’s no such attribute.

Control Words

Atoms that begin with a colon are called control words 2. These are mainly used to give internal structure to macro invocations—You want a word distinguishable from a string at compile time, but it’s not meant to be a Python identifier. Thus, they do not get munged:

#> :foo->bar?
>>> ':foo->bar?'
':foo->bar?'

Control words compile to string literals that begin with :, so you usually don’t need to quote them, but you can:

#> ':foo->bar?
>>> ':foo->bar?'
':foo->bar?'

Note that you can do nearly the same thing with a raw string:

#> ":foo->bar?"
>>> (':foo->bar?')
':foo->bar?'

The lambda special form, as well as certain macros, use certain “active” control words as syntactic elements to control the interpretation of other elements, hence the name.

Some control words are also “active” in normal function calls, (like :** for dict unpacking, covered later.) You must quote these like ':** or ":**" to pass them as data in that context.

Macros operate at compile time (before evaluation), so they can also distinguish a raw control word from a quoted one.

Module Literals and Qualified Identifiers

You can refer to variables defined in any module by using a qualified identifier:

#> operator.  ; Module literals end in a dot and automatically import.
>>> __import__('operator')
<module 'operator' from '...operator.py'>

#> (operator..add 40 2)  ; Qualified identifiers include their module.
>>> __import__('operator').add(
...   (40),
...   (2))
42

Notice the second dot required to access a module attribute.

The translation of module literals to __import__ calls happens at compile time, so this feature is still available in readerless mode. Qualification is important for macros that are defined in one module, but used in another.

Compound Expressions

Atoms are just the basic building blocks. To do anything interesting with them, you have to combine them into syntax trees using tuples.

Empty

The empty tuple () might as well be an atom:

#> ()
>>> ()
()

Lambdas

The anonymous function special form:

(lambda <parameters>
  <body>)

The separator control word : divides the parameters tuple 3 into single and paired sections.

Hissp has all of Python’s parameter types:

#> (lambda (a :/  ; positional only
#..         b  ; positional
#..         : e 1  f 2  ; default
#..         :* args  h 4  i :?  j 1  ; kwonly
#..         :** kwargs)
#..  42)
>>> (lambda a,/,b,e=(1),f=(2),*args,h=(4),i,j=(1),**kwargs:(42))
<function <lambda> at ...>

Everything left of the colon is implicitly paired with the placeholder control word :?. You can do this explicitly by putting the colon first. Sometimes it’s easier to metaprogram this way. Notice the Python compilation is exactly the same as above.

#> (lambda (: a :?
#..         :/ :?
#..         b :?
#..         e 1
#..         f 2
#..         :* args
#..         h 4
#..         i :?
#..         j 1
#..         :** kwargs)
#..  42)
>>> (lambda a,/,b,e=(1),f=(2),*args,h=(4),i,j=(1),**kwargs:(42))
<function <lambda> at ...>

The :* and :** control words mark their parameters as taking the remainder of the positional and keyword arguments, respectively:

#> (lambda (: :* args :** kwargs)
#..  (print args)
#..  (print kwargs)  ; Body expressions evaluate in order.
#..  :return-value)  ; The last one is returned.
>>> (lambda *args,**kwargs:(
...   print(
...     args),
...   print(
...     kwargs),
...   ':return-value')[-1])
<function <lambda> at ...>

#> (_ 1 : b :c)
>>> _(
...   (1),
...   b=':c')
(1,)
{'b': ':c'}
':return-value'

You can omit the right of a pair with :? (except the final **kwargs). Also note that the body can be empty:

#> (lambda (: a 1  :/ :?  :* :?  b :?  c 2))
>>> (lambda a=(1),/,*,b,c=(2):())
<function <lambda> at ...>

Note that positional-only parameters with defaults must appear after the :, which forces the :/ into the paired side. Everything on the paired side must be paired, no exceptions. (Even though :/ can only be paired with :?, adding another special case to not require the :? would make metaprogramming more difficult.)

The : may be omitted if there are no paired parameters:

#> (lambda (a b c :))  ; No pairs after ':'.
>>> (lambda a,b,c:())
<function <lambda> at ...>

#> (lambda (a b c))  ; The ':' was omitted.
>>> (lambda a,b,c:())
<function <lambda> at ...>

#> (lambda (:))  ; Colon isn't doing anything.
>>> (lambda :())
<function <lambda> at ...>

#> (lambda ())  ; You can omit it.
>>> (lambda :())
<function <lambda> at ...>

The : is required if there are any paired parameters, even if there are no single parameters:

#> (lambda (: :** kwargs))
>>> (lambda **kwargs:())
<function <lambda> at ...>

Calls

Any tuple that is not quoted, empty, or a special form or macro is a runtime call.

Like Python, it has three parts:

(<callable> <args> : <kwargs>)

For example:

#> (print 1 2 3 : sep ":"  end #"\n.")
>>> print(
...   (1),
...   (2),
...   (3),
...   sep=(':'),
...   end=('\n.'))
1:2:3
.

Either <args> or <kwargs> may be empty:

#> (int :)
>>> int()
0

#> (print :foo :bar :)
>>> print(
...   ':foo',
...   ':bar')
:foo :bar

#> (print : end "X")
>>> print(
...   end=('X'))
X

The : is optional if the <kwargs> part is empty:

#> (int)
>>> int()
0

#> (float "inf")
>>> float(
...   ('inf'))
inf

The <kwargs> part has implicit pairs; there must be an even number.

Use the control words :* for iterable unpacking, :? to pass by position, and :** for keyword unpacking:

#> (print : :* '(1 2)  :? 3  :* '(4)  :** (dict : sep :  end #"\n."))
>>> print(
...   *(1, 2),
...   (3),
...   *(4,),
...   **dict(
...     sep=':',
...     end=('\n.')))
1:2:3:4
.

This parallels the parameter syntax for lambdas.

Unlike parameter names, these control words can be repeated, but (as in Python) a :* is not allowed to follow :**.

Method calls are similar to function calls:

(.<method name> <self> <args> : <kwargs>)

Like Clojure, a method on the first “argument” (<self>) is assumed if the function name starts with a dot:

#> (.conjugate 1j)
>>> (1j).conjugate()
-1j

Reader Macros

Besides a few builtins, reader macros in Lissp consist of a symbol ending with a #, followed by another form.

A function named by a qualified identifier is invoked on the form, and the reader embeds the resulting object into the output Hissp:

#> builtins..float#inf
>>> __import__('pickle').loads(  # inf
...     b'Finf\n.'
... )
inf

This inserts an actual float object at read time into the Hissp code.

It’s neither a str nor a tuple, so Hissp tries its best to compile this as data, but because its repr, inf, isn’t a valid Python literal, it has to compile to a pickle instead. But if it’s used by something before compile time, like another macro, then it won’t have been serialized yet.

You should normally try to avoid emitting pickles (e.g. use (float 'inf) or math..inf instead). While unpickling does have some overhead, it may be worth it if constructing the object normally has even more. Naturally, the object must be picklable to emit a pickle.

Reader macros can also be unqualified. These three macros are built into the reader: Inject .#, discard _#, and gensym $#. The reader will also check the current module’s _macro_ namespace (if it has one) when it encounters an unqualified macro name.

If you need more than one argument for a reader macro, use the built-in inject .# macro, which evaluates a form at read time:

#> .#(fractions..Fraction 1 2)
>>> __import__('pickle').loads(  # Fraction(1, 2)
...     b'cfractions\nFraction\n(V1/2\ntR.'
... )
Fraction(1, 2)

And can inject arbitrary text into the compiled output:

#> .##"{(1, 2): \"\"\"buckle my shoe\"\"\"}  # This is Python!"
>>> {(1, 2): """buckle my shoe"""}  # This is Python!
{(1, 2): 'buckle my shoe'}

Reader macros compose:

#> '.#"{(3, 4): 'shut the door'}" ; this quoted inject is a string
>>> "{(3, 4): 'shut the door'}"
"{(3, 4): 'shut the door'}"

#> '.#.#"{(5, 6): 'pick up sticks'}" ; even quoted, this double inject is a dict
>>> {(5, 6): 'pick up sticks'}
{(5, 6): 'pick up sticks'}

The discard _# macro omits the next expression, even if it’s a tuple. It’s a way to comment out code structurally:

#> (print 1 _#"I'm not here!" 3) _#(I'm not here either.)
>>> print(
...   (1),
...   (3))
1 3

Templates

Besides ', which we’ve already seen, Lissp has three other built-in reader macros that don’t require a #:

• ` template quote

• , unquote

• ,@ splice unquote

The template quote works much like a normal quote:

#> '(1 2 3)  ; quote
>>> (1, 2, 3)
(1, 2, 3)

#> `(1 2 3)  ; template quote
>>> (lambda *xAUTO0_:xAUTO0_)(
...   (1),
...   (2),
...   (3))
(1, 2, 3)

Notice the results are the same, but the template quote compiles to the code that evaluates to the result, instead of to the result itself.

This gives you the ability to interpolate data into the tuple at the time it is evaluated, much like a format string:

#> '(1 2 (operator..add 1 2))  ; normal quote
>>> (1, 2, ('operator..add', 1, 2))
(1, 2, ('operator..add', 1, 2))

#> `(1 2 ,(operator..add 1 2))  ; template and unquote
>>> (lambda *xAUTO0_:xAUTO0_)(
...   (1),
...   (2),
...   __import__('operator').add(
...     (1),
...     (2)))
(1, 2, 3)

The splice unquote is similar, but unpacks its result:

#> `(:a ,@"bcd" :e)
>>> (lambda *xAUTO0_:xAUTO0_)(
...   ':a',
...   *('bcd'),
...   ':e')
(':a', 'b', 'c', 'd', ':e')

Templates are reader syntax: because they’re reader macros, they only exist in Lissp, not Hissp. They are abbreviations for the Hissp that they return.

If you quote an example, you can see that intermediate step:

#> '`(:a ,@"bcd" ,(opearator..mul 2 3))
>>> (('lambda', (':', ':*', 'xAUTO0_'), 'xAUTO0_'),
...  ':',
...  ':?',
...  ':a',
...  ':*',
...  "('bcd')",
...  ':?',
...  ('opearator..mul', 2, 3))
(('lambda', (':', ':*', 'xAUTO0_'), 'xAUTO0_'), ':', ':?', ':a', ':*', "('bcd')", ':?', ('opearator..mul', 2, 3))

Templates are Lissp syntactic sugar based on what Hissp already has.

Judicious use of sugar can make code much easier to read and write. While all Turing-complete languages have the same theoretical power, they are not equally expressive. Metaprogramming makes a language more expressive. Reader macros are a kind of metaprogramming. Because you can make your own reader macros, you can make your own sugar.

Templates are extremely valuable tools for metaprogramming. Most compiler macros will use at least one internally.

Gensyms

The final builtin reader macro $# creates a generated symbol (gensym) based on the given symbol. Within a template, the same gensym name always makes the same gensym:

#> `($#hiss $#hiss)
>>> (lambda *xAUTO0_:xAUTO0_)(
...   '_hissxAUTO41_',
...   '_hissxAUTO41_')
('_hissxAUTO41_', '_hissxAUTO41_')

But each new template increments the counter.

#> `$#hiss
>>> '_hissxAUTO42_'
'_hissxAUTO42_'

Gensyms are mainly used to prevent accidental name collisions in generated code, which is very important for reliable compiler macros.

Collection Atoms

A subset of Python’s data structure notation works in Lissp as well:

#> [1,2,3]
>>> [1, 2, 3]
[1, 2, 3]

#> {'foo':2}
>>> {'foo': 2}
{'foo': 2}

You can nest these to create small, JSON-like data structures which can be very useful as inputs to macros, (especially reader macros, which can only take one argument).

Except for the empty tuple.

You can quote it if you want, it doesn’t change the result:

#> '()
>>> ()
()

#> ()
>>> ()
()

However, macros could distinguish these cases, because they act before evaluation.

Tuples are different. Since they normally represent code, you must quote them to use them as data.

Caution

Collection atoms are tokenized like the other basic atoms. The characters

\ \"\(\)\;\\

must be written like that, with backslash escape codes, even in nested string literals.

While a significantly more complex reader could distinguish these cases without escapes (as Python does), the Lissp reader’s source is meant to be simple and comprehensible, and Lissp doesn’t really need this capability because it can already read in arbitrary Python expressions using the inject macro .# applied to a raw string. The collection atoms are just a convenience for simple cases. If you need too many backslashes, it’s not a “simple case”. Use something else.

Unlike Python’s notation, because these collections are read in as a single atom, they may contain only static values discernible at read time. If you want to interpolate runtime data, use function calls and templates instead:

#> (list `(,@(.upper "abc") ,@[1,2,3] ,(.title "zed")))
>>> list(
...   (lambda *xAUTO0_:xAUTO0_)(
...     *('abc').upper(),
...     *[1, 2, 3],
...     ('zed').title()))
['A', 'B', 'C', 1, 2, 3, 'Zed']

If this is still too verbose for your taste, remember that you can use helper functions or metaprogramming to simplify:

#> (.__setitem__ (globals)
#..              'enlist
#..              (lambda (: :* args)
#..                (list args)))
>>> globals().__setitem__(
...   'enlist',
...   (lambda *args:
...     list(
...       args)))

#> (enlist 'A 'B 'C (enlist 1 2 3) (.title "zed"))
>>> enlist(
...   'A',
...   'B',
...   'C',
...   enlist(
...     (1),
...     (2),
...     (3)),
...   ('zed').title())
['A', 'B', 'C', [1, 2, 3], 'Zed']

You can also use the unpacking control words in these:

#> (enlist : :* (.upper "abc")  :? [1,2,3]  :? (.title "zed"))
>>> enlist(
...   *('abc').upper(),
...   [1, 2, 3],
...   ('zed').title())
['A', 'B', 'C', [1, 2, 3], 'Zed']

Macros

Hissp macros are callables that are evaluated by the compiler at compile time.

They take the Hissp code itself as arguments (unevaluated), and return Hissp code as a result, called a macroexpansion (even if it gets smaller). The compiler inserts the expansion in the macro invocation’s place in the code, and then continues as normal. If another macro invocation appears in the expansion, it is expanded as well (this pattern is known as a recursive macro), which is an ability that the reader macros lack.

The compiler recognizes a callable as a macro if it is invoked directly from a _macro_ namespace:

#> (hissp.basic.._macro_.define spam :eggs) ; qualified macro
>>> # hissp.basic.._macro_.define
... __import__('operator').setitem(
...   __import__('builtins').globals(),
...   'spam',
...   ':eggs')

#> spam
>>> spam
':eggs'

The compiler will also check the current module’s _macro_ namespace (if present) for matching macro names when compiling an unqualified invocation.

While .lissp files don’t have one until you add it, the REPL automatically includes a _macro_ namespace with all of the basic macros:

#> _macro_.define
>>> _macro_.define
<function _macro_.define at ...>

#> (define eggs :spam)  ; unqualified macro
>>> # define
... __import__('operator').setitem(
...   __import__('builtins').globals(),
...   'eggs',
...   ':spam')

#> eggs
>>> eggs
':spam'

The compiler helpfully includes a comment whenever it expands a macro. Note the shorter Python comment emitted by the unqualified expansion.

You can define your own macro by putting a callable into the _macro_ namespace. Let’s try it:

#> (setattr _macro_ 'hello (lambda () '(print 'hello)))
>>> setattr(
...   _macro_,
...   'hello',
...   (lambda :('print', ('quote', 'hello'))))

#> (hello)
>>> # hello
... print(
...   'hello')
hello

A zero-argument macro isn’t that useful.

Let’s give it one. Use a template:

#> (setattr _macro_ 'greet (lambda (name) `(print 'Hello ,name)))
>>> setattr(
...   _macro_,
...   'greet',
...   (lambda name:
...     (lambda *xAUTO0_:xAUTO0_)(
...       'builtins..print',
...       (lambda *xAUTO0_:xAUTO0_)(
...         'quote',
...         '__main__..Hello'),
...       name)))

#> (greet 'Bob)
>>> # greet
... __import__('builtins').print(
...   '__main__..Hello',
...   'Bob')
__main__..Hello Bob

Not what you expected?

A template quote automatically qualifies any unqualified symbols it contains with builtins (if applicable) or the current __name__ (which is __main__):

#> `int  ; Works directly on symbols too.
>>> 'builtins..int'
'builtins..int'

#> `(int spam)
>>> (lambda *xAUTO0_:xAUTO0_)(
...   'builtins..int',
...   '__main__..spam')
('builtins..int', '__main__..spam')

Qualified symbols are especially important when a macro expands in a module it was not defined in. This prevents accidental name collisions when the unqualified name was already in use. And the qualified identifiers in the expansion will automatically import any required helpers.

You can force an import from a particular location by using a qualified symbol yourself in the template in the first place. Qualified symbols in templates are not qualified again. Usually, if you want an unqualified symbol in the template’s result, it’s a sign that you need to use a gensym instead. Gensyms are never qualified. If you don’t think it needs to be a gensym, that’s a sign that the macro could maybe be an ordinary function instead.

If you want to capture 4 a symbol (collide on purpose), you can still put unqualified symbols into templates by interpolating in an expression that evaluates to an unqualified symbol. (Like a quoted symbol):

#> `(float inf)
>>> (lambda *xAUTO0_:xAUTO0_)(
...   'builtins..float',
...   '__main__..inf')
('builtins..float', '__main__..inf')

#> `(float ,'inf)
>>> (lambda *xAUTO0_:xAUTO0_)(
...   'builtins..float',
...   'inf')
('builtins..float', 'inf')

Let’s try again. (Yes, reader macros compose like that.):

#> (setattr _macro_ 'greet (lambda (name) `(print ','Hello ,name)))
>>> setattr(
...   _macro_,
...   'greet',
...   (lambda name:
...     (lambda *xAUTO0_:xAUTO0_)(
...       'builtins..print',
...       (lambda *xAUTO0_:xAUTO0_)(
...         'quote',
...         'Hello'),
...       name)))

#> (greet 'Bob)
>>> # greet
... __import__('builtins').print(
...   'Hello',
...   'Bob')
Hello Bob

Using a symbol here is a bit sloppy. If you really meant it to be text, rather than an identifier, a raw string might have been a better idea:

#> (setattr _macro_ 'greet (lambda (name) `(print "Hello" ,name)))
>>> setattr(
...   _macro_,
...   'greet',
...   (lambda name:
...     (lambda *xAUTO0_:xAUTO0_)(
...       'builtins..print',
...       "('Hello')",
...       name)))

#> (greet 'Bob)
>>> # greet
... __import__('builtins').print(
...   ('Hello'),
...   'Bob')
Hello Bob

While the parentheses around the ‘Hello’ don’t change the meaning of the expression in Python, it does prevent the template reader macro from qualifying it like a symbol.

There’s really no need to use a macro when a function will do. The above are for illustrative purposes only. But there are times when a function will not do:

#> (setattr _macro_ '# (lambda (: :* body) `(lambda (,'#) (,@body))))
>>> setattr(
...   _macro_,
...   'xHASH_',
...   (lambda *body:
...     (lambda *xAUTO0_:xAUTO0_)(
...       'lambda',
...       (lambda *xAUTO0_:xAUTO0_)(
...         'xHASH_'),
...       (lambda *xAUTO0_:xAUTO0_)(
...         *body))))

#> (any (map (# print (.upper #) ":" #)
#..          "abc"))
>>> any(
...   map(
...     # xHASH_
...     (lambda xHASH_:
...       print(
...         xHASH_.upper(),
...         (':'),
...         xHASH_)),
...     ('abc')))
A : a
B : b
C : c
False

This macro is a metaprogram that creates a one-argument lambda. This is an example of intentional capture. The anaphor 4 is #. Try doing that in Python. You can get pretty close with higher-order functions, but you can’t delay the evaluation of the .upper() without a lambda, which really negates the whole point of creating a shorter lambda.

One of the main uses of macros is delaying evaluation. You can do that much with a lambda in Python. But advanced macros can inject anaphors, delay evaluation, and do a find-and-replace on symbols in code all at once. You have full programmatic control over the code itself, with the full power of Python’s ecosystem.

These techniques will be covered in more detail in the macro tutorial.

Compiling Packages

It isn’t always necessary to create a compiled file. While you could compile it to Python first, you can run a .lissp file directly as the main module using hissp:

$ python -m hissp foo.lissp

Or:

$ lissp foo.lissp

But you’ll probably want to break a larger project up into smaller modules. And those must be compiled for import.

The recommended way to compile a Lissp project is to put a call to transpile() in the main module and in each __init__.py— with the name of each top-level .lissp file, or .lissp file in the corresponding package, respectively:

from hissp.reader import transpile

transpile(__package__, "spam", "eggs", "etc")

Or equivalently in Lissp, used either at the REPL or if the main module is written in Lissp:

(hissp.reader..transpile __package__ 'spam 'eggs 'etc)

This will automatically compile each named Lissp module. This approach gives you fine-grained control over what gets compiled when. If desired, you can remove a name passed to the transpile() call to stop recompiling that file. Then you can compile the file manually at the REPL as needed using transpile().

Note that you usually would want to recompile the whole project rather than only the changed files on import like Python does for .pyc files, because macros run at compile time. Changing a macro in one file normally doesn’t affect the code that uses it in other files until they are recompiled. That is why transpile() will recompile the named files unconditionally. Even if the corresponding source has not changed, the compiled output may be different due to an updated macro in another file.

Footnotes

1

End Of File. Usually Ctrl-D, but enter Ctrl-Z on Windows. This doesn’t quit Python if the REPL was launched from Python, unlike (exit).

2

The equivalent concept is called a keyword in other Lisps, but that means something else in Python.

3

The equivalent concept is called the “lambda list” in Common Lisp, and the “params vector” in Clojure, but Hissp is made of tuples, not linked-lists or vectors, hence “parameters tuple”.

4(1,2)

When symbol capture is done on purpose, these are known as anaphoric macros. (When it’s done on accident, these are known as bugs.)