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A library providing zero-cost chaining for functional abstractions in Nim.

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zero-functional

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A library providing (almost) zero-cost chaining for functional abstractions in Nim.

Table of Contents

Initial Example

The example:

var n = zip(a, b) -->
            map(f(it[0], it[1])).
            filter(it mod 4 > 1).
            map(it * 2).
            all(it > 4)

is expanded on compile time - additional compile-time checks omitted - to the equivalent of:

(proc (): auto =
  var minHigh134598 = min([high(a), high(b)])
  var empty = true
  for z in low(a) .. minHigh134598:
    var it0 = (a[z], b[z])
    var it1 = f(it0[0], it0[1])
    if it1 mod 4 > 1:
      var it2 = it1
      var it3 = it2 * 2
      result = true
      if not(it3 > 4):
        return false)()

Compared to:

import sequtils

var n = zip(a, b).
            mapIt(f(it[0], it[1])).
            filterIt(it mod 4 > 1).
            mapIt(it * 2).
            allIt(it > 4)

which is roughly equivalent to:

var m = min(a.len, b.len)
var result0: seq[(int, int)]
newSeq(result0, m)
for i in 0 .. <m:
  result0[i] = (m[i], m[i])
var result1: seq[int]
let t0 = result0
var i0 = 0
result1 = newSeq[int](result0.len)
for it in t0:
  result1[i0] = f(it[0], it[1])
  i0 += 1
var result2 = newSeq[int]()
for it in items(result1):
  if it mod 4 > 1:
    result.add(it)
var result3: seq[int]
let t1 = result1
var i1 = 0
result3 = newSeq[int](result2.len)
for it in t1:
  result3[i1] = it * 2
result = true
for it in items(result3):
  if not (it > 4):
    return false

Installation

nimble install zero_functional

Note: the correct name is zero_functional (with an underscore).

Rationale

Functional style handling of sequences is awesome, and Nim is supposed to be fast and smart. Allocating new sequences on each method in a chain can be extremely wasteful and there are not a lot of technical reasons to punish functional style like that.

This library can expand functional chains to simple loops fusing the method bodies one after another. It is still very experimental, but it shows that a purely metaprogramming approach can be used to optimize functional Nim code.

Variable names

The supported variable names (can be changed at the beginning of the zero_functional.nim file) are:

  • it is used for the iterator variable
  • idx is used as integer index of current iteration
  • a is used as the accumulator in fold

Seq and arrays

All supported methods work on finite indexable types and arrays.

If a handler returns a collection, it will be of the same shape as the input for seq-s, arrays and DoublyLinkedList-s. Other collections are mapped to seq if it cannot be automatically converted. (e.g. array.map returns an array).

You can always get a seq if you use <handler>Seq, e.g. mapSeq - or to(seq). Some of the supported methods default to seq-output, e.g. map when changing the result type, flatten and indexedMap.

We can describe the supported types as

type
  FiniteIndexable[T] = concept a
    a.low is int
    a.high is int
    a[int] is T

Other types

Enums are supported and mapped to seq[enumtype].

Generic objects are supported if they are of any type:

  • FiniteIndexable - contains high/low and []-access (see above)
  • FiniteIndexableLen - contains len and []

Collection types that will be generated as a result type need to implement either one of

  • Appendable (contains the append function as in DoublyLinkedList)
  • Addable (contains the add function as in seq)
  • []= operator

Some of the supported methods will only work when the []= operator is defined - except when using DoublyLinkedList or SinglyLinkedList types. This is needed for zip, combinations and foreach when changing elements.

For the creation of a generic type as result, the type needs to implement

proc zfInit(a: MyType): MyType =
  MyType(...)
  # the `a` is not actually used but is needed for overloading.

Supported methods

These are not exactly the functions from sequtils, they have the same naming and almost the same behavior.

The macros work with -->, zfun or connect. Multiple --> may be used or ..

sequence --> map(..) --> all(..)

or

zip(a, b, c) --> map(..).
                 all(..)

You can also use the call with sections - with the above calls omitting the dots or arrows or with several statements applied to the same function in one section.

let res = sequence.zfun:
  map:
    f1(it)
    f2(it)
  all(...)

or as simple arguments to a function:

connect(collection, map(..), all(..))

The methods work with the auto it variable.

map

collection --> map(op)

Map each item in the collection to a new value. Example:

let x = [1,2,3] --> map(it * 2)
check(x == [2,4,6])

Map also supports converting the type of iterator item and thus of the collection.

Map is (currently) the only command supporting value definitions inside the map call that may be used instead or additionally to the it value in subsequent calls. Assignment to simple values or tuple assignments are possible as well.

let idx = rows --> map(row = it) --> index(row > someValue)
let posCoords = coords --> map((x,y) = it) --> filter(x > 0 and y > 0)

A shortcut syntax is also supported - actually replacing it with the given variable names:

# a iterates on x
check(x --> (a) --> exists(y --> exists(a == it)))
# this is equivalent to
check(x --> map(a = it) --> exists(y --> exists(a == it)))

zfun also supports this shortcut either as single line (a) or as a parameter to zfun:

let b = x.zfun(a):
  exists:
    z.zfun(b):
      exists(a == b)
check(not b)

filter

sequence --> filter(cond)

Filter the collection items with the given condition. Example:

let x = @[-1,2,-3] --> filter(it > 0)
check(x == @[2])

zip

zip can work with n sequences. zip is (roughly) internally translated to:

zip(a,b,c) <=>
a --> zip(b,c) <~>
let minHigh = min([a.high(), b.high(), c.high()])
a --> filter(idx <= minHigh) --> map(a[idx], b[idx], c[idx])

On the right side of --> (or as 2nd and later command) the left side of --> is added to the zip result. For zip in order to work properly all arguments have to support access with [] and the high procedure. If those procedures are not available the macro tries to call the procedure mkIndexable on that parameter. Using this helper the parameter can be wrapped with a new type that supports [] and high.

split

split is kind of the opposite of zip. It works with a collection of n-tuples and splits it up into an n-tuple of sequences.

check([(1, "one"), (2, "two")] --> split() == (@[1,2], @["one","two"])

exists

Check if the given condition is true for at least one element of the collection.

exists can be used only at the end of the command chain.

sequence --> otherOperations(..) --> exists(cond): bool

all

Check if the given condition is true for all the elements of the collection.

all can be used only at the end of the command chain.

sequence --> otherOperations(..) --> all(cond): bool

index

Get the first index of the item in the collection, where the given condition is true.

index can be used only at the end of the command chain.

sequence --> otherOperations(..) --> index(cond): int

indexedMap

Adds or prepends the index of each element in the collection to the element itself and generates a named tuple (idx: index, elem: it) for each element in the collection.

var n = zip(a, b, c) -->
            indexedMap(f(it[0], it[1])).
            filter(it.idx < 10 and it.elem mod 4 > 1).
            map(it.elem * 2).
            all(it > 4)

enumerate

Prepends the index to the value of the current iteration (unlike the indexedMap, which indexes the initial collection/iteration) and generates a named tuple (idx: index, elem: it) for each element in the iterable. Does not take any parameters.

Is similar to enumerate in Python or enumerate in Rust.

0..10 --> filter(it mod 2 == 0).enumerate().map(it.idx) == @[0, 1, 2, 3, 4, 5]
# enumerate vs indexedMap
(1..7 --> filter(it mod 2 == 0) --> enumerate())    == @[(0, 2), (1, 4), (2, 6)])
(1..7 --> filter(it mod 2 == 0) --> indexedMap(it)) == @[(1, 2), (3, 4), (5, 6)])

fold

Currently a left fold (as easier to combine with the implementation).

The sequtils a is a, b is it.

var n = zip(a, b) --> map(it[0] + it[1]) --> fold(0, a + it)

reduce

Same as fold, but with the iterator converted to a tuple where it[0] or it.accu is the accumulated result and it[1] or it.elem the actual iterator on the collection.

The first item of the collection is used as initial value - the other items are then accumulated to it. This is also useful when a type does not define the neutral element for the given operation. E.g. for integers and + the neutral element is 0 but for user defined types the neutral element might not exist.

var n = a --> reduce(it.accu + it.elem)

There are a few commands that are simply mapped to reduce

max

Return the maximum value in the collection (> is needed)

min

Return the minimum value in the collection (< is needed)

product

Return the product of the (filtered) elements (*)

sum

Return the sum of the (filtered) elements (+).

indexedReduce

By adding the indexed prefix to reduce or to the reduce commands above, the index of the last value that was used for the result and the actual result of the operation are returned.

For sum and product this is not actually helpful but it can be used to find the indices of the min and max elements.

check(@[11,2,0,-2,1,3,-1] --> indexedMin() == (3,-2))
check(@[11,2,0,-2,1,3,-1] --> indexedMax() == (0,11))

Note that a named tuple is created and the index is also accessible via .idx and the actual element with .elem.

foreach

Can only be used with functions that have side effects. When used as the last command in the chain the result is void. As in-between element the code is simply executed on each element.

changing in-place

The iterator content may be changed in foreach resulting in changing the original collection. However there are a few restrictions (see test.nim):

  • the []= operator has to be available for the underlying collection type (exception: the std LinkedList types)
  • functions that alter the collection elements may not be used in the chain before (e.g. map is not allowed, but filter is).
@[1,2,3] -->
    foreach(echo($it))

var a = @[1,2,3]
a --> foreach(it = it * 2)
check (a == @[2,4,6]

sub

Works on a part of the input collection - sub(fromIndex, toIndex) or drop(fromIndex) - similarly to ranges, starting with fromIndex and ending (inclusive) with endIndex or runs til the end, when endIndex is not given.

check((1..10) --> sub(2,5) --> to(list) == @[2,3,4,5])

The endIndex may be a BackwardsIndex like ^1, but then the collection has to have a len.

sub is similar to the filter function working on the idx variable, however sub uses an internal index that is not affected by the outcome of preceding filtering functions.

# in filter `idx` counts the iterated items
check(@[-1,2,-3,4,-5,6,-7,8] --> filter(it > 0) --> filter(idx >= 3) == @[4,6,8])
# sub increments its own index when `it > 0`
check(@[-1,2,-3,4,-5,6,-7,8] --> filter(it > 0) --> sub(3) == @[8])

Similar commands like sub that result in parts of the lists being iterated on or generated are: drop, dropWhile, take and takeWhile.

drop

drop(n) drops n items before working on the collection. This is equivalent to sub(n).

dropWhile

dropWhile(cond) drops the items as long as the condition in cond is met - it starts working on the collection when the condition is not fulfilled any more. As opposed to filter the condition in drop is ignored, once it was not true any more.

check(@[-1,2,-3,4,-5] --> dropWhile(it < 0) --> sum() == -2)
check(@[-1,2,-3,4,-5] --> filter(it >= 0)   --> sum() == 6)

take

take(n) works on n items of the collection and then breaking. This is useful for very large (infinite) collections or iterators - the same for takeWhile.

takeWhile

takeWhile(cond) works on the collection as long as the condition in cond is met. Otherwise it breaks the processing.

flatten

Working on a collection of iterable items, the flatten function flattens out the elements of the collection.

check(@[@[1,2],@[3],@[4,5,6]] --> flatten() == @[1,2,3,4,5,6])

indexedFlatten

Is similar to flatten, except that it returns the index inside original sub-lists with the actual content.

check(@[@[1,2],@[3],@[4,5,6]] --> indexedFlatten()            == @[(0,1),(1,2),(0,3),(0,4),(1,5),(2,6)])
check(@[@[1,2],@[3],@[4,5,6]] --> flatten() --> map((idx,it)) == @[(0,1),(1,2),(2,3),(3,4),(4,5),(5,6)])

Note that as in the other indexed commands the tuples are named tuples and the index is also accessible via .idx and the actual element with .elem.

combinations

Combines each element of the original collection with each other - the resulting variable is an array of 2 containing the combined iterator values. In case no other collection is supplied the combinations are done on the input collection - only combining different elements with each other.

# combine collection with itself results in unordered combinations - only different elements are combined
check(@[1,2,3] --> combinations() == @[[1,2],[1,3],[2,3])
# combine collection with the same collection again as a parameter results in comparing all elements in 
# all possible ordered combinations
check(@[1,2,3] --> combinations(@[1,2,3]) == @[[1,1],[1,2],[1,3],[2,1],[2,2],[2,3],[3,1],[3,2],[3,3]])

# combine all elements of first collection with elements of the second collections
check(@[1,2,3] --> combinations(@[4,5]) == @[[1,4],[1,5],[2,4],[2,5],[3,4],[3,5]])

indexedCombinations

Same as combinations with the additional indices of the resulting combined elements. The resulting iterator is a named tuple with the combined elements and their indices (idx: [idx1, idx2], elem: [combined_element1, combined_element2]).

# find the indices of the elements in the collection, where the diff to the other element is 1
check(@[11,2,7,3,4] --> indexedCombinations() --> filter(abs(it.elem[1]-it.elem[0]) == 1) --> map(it.idx) == @[[1,3],[3,4]])
#          ^   ^ ^

concat

Concatenates the given arguments to one iterator. Can only be used as first command preceeding -->.

check(concat([1,2],[3,4]) --> to(seq) == @[1,2,3,4])

Alternatively zf_concat can be used to create an iterator that concatenates the given iterators to one.

zf_concat(con, @[1], (2,3,4))
check(con() --> to(seq) == @[1,2,3,4])

group

Creates a table containing result of each element applied to a discriminator function as keys and the corresponding elements in sequences as values.

# taking the last character of the strings as key
let m = @[(1,"one"),(2,"two"),(3,"three")] --> group(it[1][^1])
check(m['e'] == @[(1, "one"), (3, "three")])
check(m['o'] == @[(2, "two")])

partition

Simplified version of group and alternative version to filter that returns a tuple with all elements that match the filter condition as first tuple element named yes and all non-matching as the second named no.

proc isEven(i: int): bool = (i and 1) == 0
  let a = @[1,2,3,4,5,6]
let p = a --> partition(it.isEven())
check(p.yes == @[2,4,6])
check(p.no == @[1,3,5])

to

Finally, it is possible to force the result type to the type given in to - which is only allowed as last argument when generating collection results (e.g. map or filter are the last arguments before to). This method is handled differently from the others and removed internally so the command before to is the actual last command.

When the result type is given as seq, array or list (the latter is mapped to DoublyLinkedList) then the template argument can be determined automatically. However, when all auto detection fails, the result type may be given explicitly here - the resulting code is also a bit more efficient for the compilation process.

check([1,2,3]) --> to(seq) == @[1,2,3])
var l = @[1,2,3] --> map($it) --> to(list)
let l2: DoublyLinkedList[string] = l
echo(l2)

to supports a second parameter autoConvert. This actually only suppresses a warning when the actual result type and the added type are not identical but can be cast to another. A bit more complex example:

# doing a complex conversion from `seq[seq[uint8]]` to `seq[seq[int]]`
check(@[@[1u8, 2u8], @[3u8]] --> map(it --> to(seq[int],true)) --> to(seq[seq[int]]) == @[@[1,2], @[3]])
  • the map call itself contains a new --> call, i.e.: each internal list is converted from seq[uint8] to seq[int]
  • the parameter true is necessary to suppress a warning that the conversion is done by casting (each element)
  • the final result is a seq[seq[int]] - this conversion is optional (maybe to document) and can be determined automatically.

createIter

When using createIter(name:string,closure:bool=false) as last function then an iterator name is created which can be used for further processing with zero-functional with only a small overhead. Similar to to this is also a virtual function which is internally replaced and only used to check the output type. The generated iterator is inline by default and can not be returned from a proc or given to another proc (see Nim: Iterators).

To create a closure iterator, the optional argument closure has to be set to true. A closure iterator is created which can be used as a return result from a procedure or as a parameter to a procedure. This does not work with JS backend!

import zero_functional
import strutils
# filter all lines containing the word hint in the iterator
lines("nim.cfg") --> filter("hint" in it.string) --> createIter(errorLines)
errorLines() --> foreach(echo it)

Extending zero-functional

Extending zero-functional with own functions is probably more complicated than with other fp-libraries as the functions have to be implemented with macros producing inlined imperative code. Some good examples from basic to more complicated can be found in test.nim: registerExtension and in the source code of zero-functional

Extending with plain nim

When adding your own foo implementation you can write your own inlineFoo proc and register it with zero_functional. It should look like this:

import macros # do not forget to import macros when extending zero_functional 

proc inlineFoo*(ext: ExtNimNode) {.compileTime.} =
  # do some parameter checks
  if ext.node.len > SOME_MAX:
    zfFail("too many arguments in \'$1\', got $2 but expected only $3" %
        [ext.node.repr, $(ext.node.len - 1), $SOME_MAX])
  # do some stuff
  # ...
  # the actual 'loop' section code
  ext.node = quote:
    # do something

# ....
# after the function declarations register the extensions during compile time for use with zero_functional
static:
  zfCreateExtension()
  
# use foo after the registration

The vanilla inline-proc implementations should follow certain rules.

  • ext: ExtNimNode as parameter
  • ExtNimNode should be used when implementing the functions with some helpers:
    • ext.node = place the actual code here that is being generated inside the current block initially ext.node contains the current function call and its parameters (section: loop)
    • ext.initials = add the initialization code for variable definitions (section: init)
    • ext.endLoop = add code that can be inserted at the end of the loop (section: end)
    • ext.finals = add code after the loop - e.g. to calculate a result (section: final)
    • ext.res = helper: access to the function's result
    • ext.prevItNode() = access to the iterator generated in the previous statement or loop
    • ext.nextItNode() = generates a new iterator for the current block. This is the (intermediate) result of the current operation that can be used with the next function
  • check of parameters / number of parameters has to be done in the implementation
  • use zfFail() if any checks fail
  • register functions that use neither zfInline nor zfInlineCall using zfAddFunction
  • functions that create another sequence have to be registered with zfAddSequenceHandlers
  • finally call zfCreateExtension after all zfInline... definitions and zfAddFunction calls - before using the actual function implementation

Writing extensions with Zero-DSL

Example - implement filterNot function:

import macros # do not forget to import macros when extending zero_functional

zfInline filterNot(condition: bool):
  loop:
    if not condition:
      yield it   

# ....
# after the function declarations register the extensions during compile time for use with zero_functional
static:
  zfCreateExtension()
  
# use foo after the registration

zfInline is the actual macro that takes the created function name (here: map) and its parameters and a body with different sections as input. zfInline_dbg will print the generated code proc inlineFilterMap (see example for map below).

The sections directly map to their counterparts in ExtNimNode:

  • pre prepare section: initialize variables and constants. It is possible to do the entire implementation in the pre section.
    • all variables that are used in other sections have to be defined here!
  • init variable initialization before the loop
  • loop the actual loop action (maps to ext.node)
  • delegate delegate to other functions (like map, filter, etc.)
  • endLoop added to end of the loop
  • final after the loop section - e.g. to set the result

Let's dissect the (simple) map implementation:

zfInline map(f):
  loop:
    let it = f # create the next iterator in the loop setting it to the given parameter of the map function

The above map definition will be translated to:

proc inlineMap*(ext: ExtNimNode) {.compileTime.} =
  # do some parameter checks
  if ext.node.len - 1 > 1:
    zfFail("too many arguments in \'$1\', got $2 but expected only $3" %
        [ext.node.repr, $(ext.node.len - 1), $1])
  let f = 
    if ext.node.len > 1:
      adapt(ext, 1) # replace all occurences if internal iterator "it" with __it__0, __it__1, etc.
    else: # assert that the argument 'f' is supplied
      zfFail("missing argument \'$1\' for \'$2\'" % ["f", "map"])
      newIntLitNode(0)
  let nextIdent = ext.nextItNode() # create the next iterator
  # the actual 'loop' section
  ext.node = quote:
    let `nextIdent` = `f` # here the `it` is replaced by the next iterator

The Zero-DSL map function does not set the result as opposed to the count or index definition below - hence the result type is a collection result type, which is determined automatically by the zero_functional framework. The it again is seen as keyword and the definition let it = ... will internally set the new iterator value which is consequently used by the next functions. In the generated macro it is replaced by the call ext.nextItNode.

While Zero-DSL is quite powerful, not all possibilities can be handled by it when implementing a function. For instance the foreach implementation is done completely manually and reduce and other functions use the macro zfInlineCall which provides Zero-DSL within a manual function implementation and also registers the function name. The signature for creating an inline function is as in the inlineMap example above - each function foo is implemented by its inlineFoo*(ext: ExtNimNode) counterpart.

If the Zero-DSL should fail to create an own implementation of a function then zfInline_dbg instead of zfInline can be used to print the created function to the console, copy it - remember to add the * to the name - and adapt the code.

It is possible to set parameter types for the functions - for example in the index implementation:

zfInline index(cond: bool):
  init:
    result = -1 # index not found
  loop:
    if cond:
      return idx

The cond: bool definition adds additional compile time checks to the generated macros, so that when using the index-function with a different type than bool a compile error with the wrong parameter and the expected type is created. In this example also the idx variable is replaced automatically with the running index that is increment during the loop.

Note: zfCreateExtension must be called in a compile time context. This could be in a static block or macro for example:

# Register above extensions during compile time
static:
  zfCreateExtension()

Special variables

Special variables for zfInline statements are:

  • it: when used is the previous iterator, when defined with let it = creates a new iterator, in the init section it refers to the first element in the underlying collection
  • idx: the running index in the loop
  • result: the overall result and return type of the operation All other variables have to be defined in the pre-section, also when automatically assigned, e.g. when overriding the idx variable or when accessing a reference to the list as listRef. See intersect or removeDoubles implementation in test.nim as an example.
  • yield it: opposed to setting the result this means that an iterator or a collection result is returned with it being added to it.

Setting the result

Example of count that sets a result:

zfInline count():
  init:
    result = 0
  loop:
    result += 1 # add one in each loop

Functions that set a result in any section are considered final functions - no other function may follow. Opposed to using yield used for iterator results:

Example of filter that returns a collection / iterator:

zfInline filter(cond: bool):
  loop:
    if cond:
      yield it # add the current iterator to the resulting collection

Defering to Zero-DSL inside plain nim

As Zero-DSL is limited in its expressiveness - e.g. there is no possibility to defer to a different loop implementation depending on the input type - it is possible to defer to different Zero-DSL implementations in the nim code using zfInlineCall. Example calling two implementations - one specialized to work on lists, the other for sequences:

proc inlineFoo*(ext: ExtNimNode) {.compileTime.} =
  if ext.isListType():
    # provide list implementation
    zfInlineCall foo(param):
      ... 
  else:
    # provide plain implementation (e.g. using [])
    zfInlineCall reduce(op):
      ...

Creating compound commands with other commands

Zero-DSL can create commands as a sequence from already existing commands using the delegate section - which essentially calls the functions in that section. Example is the removeDoubles function that returns the input sequence with unique elements. See code in test.nim.

zfInline removeDoubles():
# remove double elements. 
  pre:
    # initialize variables in `ext` that are used below
    let listRef = ext.listRef
  delegate:
    # this actually only works only on the original list / iterator
    indexedCombinations(listRef) # combine with itself - all elements
    # this is the tricky one: remove later elements that already are in the list
    # this actually translates in the inner for loop of combinations as:
    # if idx[0] > idx[1] and it[0] == it[1]: break
    takeWhile(not(it.elem[0] == it.elem[1] and it.idx[0] > it.idx[1])) 
    # go back to the original elements
    filter(it.idx[0] == it.idx[1]) 
    map(it.elem[0])

Overview Table

The result type depends on the function used as last parameter.

Command 1st Param in-between Last Param Result Type
all + bool
combinations + (+) (+) coll[Combination]
concat + coll
exists + bool
filter + + + part coll / zeroed array
find + int
flatten + + + coll
fold + *
foreach + + + void
group + table[*, seq[*]]
index + int
indexedMap + + + seq[(int,*)]
enumerate + + seq[(int,*)]
createIter virtual iterator of given type
map + + + collType[*]
partition + (yes:seq[*], no:seq[*])
reduce + *
sub + + + part coll / zeroed array
split + (seq[*],...seq[*])
zip + + + seq[(*,..,*)]
to virtual given type
  • *: any type depending on given function parameters
  • coll: is the input collection
  • collType is the input collection type (without template argument)
  • "virtual" function: can only be given as last argument, but does not count as last argument.

Debugging using -->>

As zero_functional macros are sometimes tricky to use, it can happen that the compiler crashes or that compile errors are hard to understand.

To see the actual code that is generated (provided the generation itself does not crash) you can use the -->> operator which prints the representation repr of the actual generated nim code. It is also useful for checking what the macros are generating under the hood.

let a = [1,2,3]
a -->> foreach(echo(it))

will print during compilation:

block:
  for it0 in a:
    echo(it0)

The printed code can be copied to your actual program for further investigation.

Compile flags

The following compile flags are supported:

  • -d:zf_iter defaults to generating closure iterators instead of seq outputs.
  • -d:zf_list same as above but generating DoublyLinkedList
  • -d:zf_debug_all print all generated code

LICENSE

MIT, Alexander Ivanov

Contributors

Co-maintainers and authors: Michael Schulte, Alexander Ivanov

Creator: Alexander Ivanov

About

A library providing zero-cost chaining for functional abstractions in Nim.

License:MIT License

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