A collection of python functions for somebody's sanity
Project description
foc
fun oriented code
or francis' odd collection
.
Functions from the Python
standard library are great. But some notations are a bit painful and confusing for personal use, so I created this odd collection of functions.
Tl;dr
foc
provides a collection of higher-order functions, some helpful (pure) functions, and piper.foc
respects thepython
standard library. Never reinvented the wheel.
How to use
# install
$ pip install -U foc
# import
>>> from foc import *
To list all available functions, call
catalog()
.
Ground rules
-
No dependencies except for the
python
standard library -
No unnessary wrapping objects.
-
Most function implementations should be less than 5-lines.
-
Followed
haskell
-like function names and arguments order -
Used
python
generator first if possible. (lazy-evaluation)map
,filter
,zip
,range
,flat
... -
Provide the functions that unpack generators in
list
as well. -
Function names that end in
l
indicate the result will be unpacked in a list.mapl
,filterl
,zipl
,rangel
,flatl
,takewhilel
,dropwhilel
, ... -
Function names that end in
_
indicate that the function is a partial application builder.cf_
,f_
,ff_
,c_
,cc_
,u_
, ...
Quickstart
foc
collection consists of piper
and regular functions
. foc
's functions are mostly piper
.
As a regular function
No further explanations are needed. Just use them as we used.
>>> id("francis")
'francis'
>>> even(3)
False
>>> take(3, range(5, 10))
[5, 6, 7]
The above functions are all piper
and are:
>>> "francis" | id
'francis'
>>> 3 | even
False
>>> range(5, 10) | take(3)
[5, 6, 7]
As a piper
piper
is a special type of function that facilitate function composition in a pipeline-like manner.
To list all available
piper
, callcatalog(piper=True)
.
>>> range(10) | length
10
>>> range(10) | filter(even) | unpack # 'unpack' unpacks generators
[0, 2, 4, 6, 8]
>>> range(10) | map(f_("+", 5)) | sum # f_("+", 5) == lambda x: x+5
95
>>> rangel(11) | shuffle | sort | last
10
If you want to make a function piper
on the fly, just wrap the function with piper
. That's it.
>>> 7 | piper(lambda x: x * 6)
42
Try binding a function to a new reference:
>>> foo = piper(func)
or use piper
decorator. All the same.
>>> @piper # @piper pipefies `func`
... def func(arg): # arg | func == func(arg)
... ...
Examples
Simple Functions
>>> id("francis")
>>> "francis" | id
'francis'
>>> const(5, "no-matther-what-comes-here")
>>> 'whatever' | const(5)
5
>>> seq("only-returns-the-following-arg", 5)
>>> 5 | seq('whatever')
5
>>> void(randbytes(256))
>>> randbytes(256) | void
... (returns None)
>>> fst(["sofia", "maria", "claire"])
>>> ["sofia", "maria", "claire"] | fst
'sofia'
Likewise, the below are all piper
.
>>> snd(("sofia", "maria", "claire"))
'maria'
>>> nth(3, ["sofia", "maria", "claire"])
'claire'
>>> take(3, range(5, 10))
[5, 6, 7]
>>> drop(3, "github") | unpack
['h', 'u', 'b']
>>> head(range(1,5))
1
>>> last(range(1,5))
4
>>> init(range(1,5)) | unpack
[1, 2, 3]
>>> tail(range(1,5)) | unpack
[2, 3, 4]
>>> pred(3)
2
>>> succ(3)
4
>>> odd(3)
True
>>> even(3)
False
>>> null([]) == null(()) == null({}) == null("")
True
>>> elem(5, range(10))
True
>>> words("fun on functions")
['fun', 'on', 'functions']
>>> unwords(['fun', 'on', 'functions'])
'fun on functions'
>>> lines("fun\non\nfunctions")
['fun', 'on', 'functions']
>>> unlines(['fun', 'on', 'functions'])
"fun\non\nfunctions"
>>> take(3, repeat(5)) # repeat(5) = [5, 5, ...]
[5, 5, 5]
>>> take(5, cycle("fun")) # cycle("fun") = ['f', 'u', 'n', 'f', 'u', 'n', ...]
['f', 'u', 'n', 'f', 'u']
>>> replicate(3, 5) # the same as 'take(3, repeat(5))'
[5, 5, 5]
>>> take(3, count(2)) # count(2) = [2, 3, 4, 5, ...]
[2, 3, 4]
>>> take(3, count(2, 3)) # count(2, 3) = [2, 5, 8, 11, ...]
[2, 5, 8]
Higher-Order Functions
>>> flip(pow)(7, 3) # the same as `pow(3, 7) = 3 ** 7`
2187
>>> bimap(f_("+", 3), f_("*", 7), (5, 7)) # bimap (3+) (7*) (5, 7)
(8, 49) # (3+5, 7*7)
>>> first(f_("+", 3), (5, 7)) # first (3+) (5, 7)
(8, 7) # (3+5, 7)
>>> second(f_("*", 7), (5, 7)) # second (7*) (5, 7)
(5, 49) # (5, 7*7)
>>> take(5, iterate(lambda x: x**2, 2)) # [2, 2**2, (2**2)**2, ((2**2)**2)**2, ...]
[2, 4, 16, 256, 65536]
>>> [* takewhile(even, [2, 4, 6, 1, 3, 5]) ]
[2, 4, 6]
>>> takewhilel(even, [2, 4, 6, 1, 3, 5])
[2, 4, 6]
>>> [* dropwhile(even, [2, 4, 6, 1, 3, 5]) ]
[1, 3, 5]
>>> dropwhilel(even, [2, 4, 6, 1, 3, 5])
[1, 3, 5]
# fold with a given initial value from the left
>>> foldl("-", 10, range(1, 5)) # foldl (-) 10 [1..4]
0
# fold with a given initial value from the right
>>> foldr("-", 10, range(1, 5)) # foldr (-) 10 [1..4]
8
# `foldl` without an initial value (used first item instead)
>>> foldl1("-", range(1, 5)) # foldl1 (-) [1..4]
-8
# `foldr` without an initial value (used first item instead)
>>> foldr1("-", range(1, 5)) # foldr1 (-) [1..4]
-2
# accumulate reduced values from the left
>>> scanl("-", 10, range(1, 5)) # scanl (-) 10 [1..4]
[10, 9, 7, 4, 0]
# accumulate reduced values from the right
>>> scanr("-", 10, range(1, 5)) # scanr (-) 10 [1..4]
[8, -7, 9, -6, 10]
# `scanl` but no starting value
>>> scanl1("-", range(1, 5)) # scanl1 (-) [1..4]
[1, -1, -4, -8]
# `scanr` but no starting value
>>> scanr1("-", range(1, 5)) # scanr1 (-) [1..4]
[-2, 3, -1, 4]
>>> concatl(["sofia", "maria"])
['s', 'o', 'f', 'i', 'a', 'm', 'a', 'r', 'i', 'a']
# Note that ["sofia", "maria"] = [['s','o','f','i','a'], ['m','a','r','i','a']]
>>> concatmapl(str.upper, ["sofia", "maria"])
['S', 'O', 'F', 'I', 'A', 'M', 'A', 'R', 'I', 'A']
Real-World Example
A causal self-attention of the transformer
model based on pytorch
can be described as follows.
Somebody insists that this helps to follow the process flow without distraction.
def forward(self, x):
B, S, E = x.size() # size_batch, size_block (sequence length), size_embed
N, H = self.config.num_heads, E // self.config.num_heads # E == (N * H)
q, k, v = self.c_attn(x).split(self.config.size_embed, dim=2)
q = q.view(B, S, N, H).transpose(1, 2) # (B, N, S, H)
k = k.view(B, S, N, H).transpose(1, 2) # (B, N, S, H)
v = v.view(B, S, N, H).transpose(1, 2) # (B, N, S, H)
# Attention(Q, K, V)
# = softmax( Q*K^T / sqrt(d_k) ) * V
# // q*k^T: (B, N, S, H) x (B, N, H, S) -> (B, N, S, S)
# = attention-prob-matrix * V
# // prob @ v: (B, N, S, S) x (B, N, S, H) -> (B, N, S, H)
# = attention-weighted value (attention score)
return cf_(
self.dropout, # dropout of layer's output
self.c_proj, # linear projection
ff_(torch.Tensor.view, *rev(B, S, E)), # (B, S, N, H) -> (B, S, E)
torch.Tensor.contiguous, # contiguos in-memory tensor
ff_(torch.transpose, *rev(1, 2)), # (B, S, N, H)
ff_(torch.matmul, v), # (B, N, S, S) x (B, N, S, H) -> (B, N, S, H)
self.dropout_attn, # attention dropout
ff_(torch.masked_fill, *rev(mask == 0, 0.0)), # double-check masking
f_(F.softmax, dim=-1), # softmax
ff_(torch.masked_fill, *rev(mask == 0, float("-inf"))), # no-look-ahead
ff_("/", math.sqrt(k.size(-1))), # / sqrt(d_k)
ff_(torch.matmul, k.transpose(-2, -1)), # Q @ K^T -> (B, N, S, S)
)(q)
In Detail
Get binary functions from python
operators: sym
sym(OP)
converts python
's symbolic operators into binary functions.
The string forms of operators like +
, -
, /
, *
, **
, ==
, !=
, .. represent the corresponding binary functions.
To list all available symbol operators, call
sym()
.
>>> sym("+")(5, 2) # 5 + 2
7
>>> sym("==")("sofia", "maria") # "sofia" == "maria"
False
>>> sym("%")(123456, 83) # 123456 % 83
35
Build partial application: f_
and ff_
f_
build left-associative partial application,
where the given function's arguments partially evaluation from the left.ff_
build right-associative partial application,
where the given function's arguments partially evaluation from the right.
f_(fn, *args, **kwargs)
ff_(fn, *args, **kwargs) == f_(flip(fn), *args, **kwargs)
>>> f_("+", 5)(2) # the same as `(5+) 2` in Haskell
7 # 5 + 2
>>> ff_("+", 5)(2) # the same as `(+5) 2 in Haskell`
7 # 2 + 5
>>> f_("-", 5)(2) # the same as `(5-) 2`
3 # 5 - 2
>>> ff_("-", 5)(2) # the same as `(subtract 5) 2`
-3 # 2 - 5
Build curried functions: c_
and cc_
When currying a given function,
c_
takes the function's arguments from the left- while
cc_
takes them from the right.
c_(fn) == curry(fn)
cc_(fn) == c_(flip(fn))
See also uncurry
# currying from the left args
>>> c_("+")(5)(2) # 5 + 2
7
>>> c_("-")(5)(2) # 5 - 2
3
# currying from the right args
>>> cc_("+")(5)(2) # 2 + 5
7
>>> cc_("-")(5)(2) # 2 - 5
-3
Build composition of functions: cf_
and cfd
cf_
(composition of function) composes functions using the given list of functions.cfd
(composing-function decorator) decorates a function with the given list of functions.
cf_(*fn, rep=None)
cfd(*fn, rep=None)
>>> square = ff_("**", 2) # the same as (^2) in Haskell
>>> add5 = ff_("+", 5) # the same as (+5) in Haskell
>>> mul7 = ff_("*", 7) # the same as (*7) in Haskell
>>> cf_(mul7, add5, square)(3) # (*7) . (+5) . (^2) $ 3
98 # mul7(add5(square(3))) = ((3 ^ 2) + 5) * 7
>>> cf_(square, rep=3)(2) # cf_(square, square, square)(2) == ((2 ^ 2) ^ 2) ^ 2 = 256
256
cfd
is very handy and useful to recreate previously defined functions by composing functions. All you need is to write a basic functions to do fundamental things.
Extend and Pipefy Built-ins: map
, filter
and zip
- Extend usability while maintaining full compatibility
- Pipefied, symbolified and partially-applied automatically
- Never use these functions if you don't know what you're doing!
map(f, *xs)
mapl(f, *xs)
>>> map(abs, range(-2, 3)) | unpack
[2, 1, 0, 1, 2]
>>> map("*", [1, 2, 3], [4, 5, 6]) | unpack # the same as `zipwith`
[4, 10, 18]
>>> [4, 5, 6] | map("*", [1, 2, 3]) | unpack # pipefied
[4, 10, 18]
filter(p, xs)
filterl(p, xs)
>>> filter(f_("==", "f"))("fun-on-functions") | unpack
['f', 'f']
>>> primes = [2, 3, 5, 7, 11, 13, 17, 19]
>>> primes | filter(cf_(ff_("==", 2), ff_("%", 3))) | unpack
[2, 5, 11, 17]
Lazy Evaluation: lazy
and force
lazy
defers the evaluation of a function(or expression) and returns the deferred expression.force
forces the deferred-expression to be fully evaluated when needed.it reminds
Haskell
'sforce x = deepseq x x
.
lazy(function-name, *args, **kwargs)
force(expr)
mforce([expr])
# strictly generate a random integer between [1, 10)
>>> randint(1, 10)
# generate a lazy expression for the above
>>> deferred = lazy(randint, 1, 10)
# evaluate it when it need
>>> force(deferred)
# the same as above
>>> deferred()
Are those evaluations with lazy
really deferred?
>>> long_list = randint(1, 100000, 100000) # a list of one million random integers
>>> %timeit sort(long_list)
142 ms ± 245 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
# See the evaluation was deferred
>>> %timeit lazy(sort, long_list)
1.03 µs ± 2.68 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each
when to use
For given a function randint(low, high)
, how can we generate a list of random integers?
[ randint(1, 10) for _ in range(5) ] # exactly the same as 'randint(1, 10, 5)'
It's the simplest way but what about using replicate
?
# generate a list of random integers using 'replicate'
>>> replicate(5, randint(1, 10))
[7, 7, 7, 7, 7] # ouch, duplication of the first evaluated item.
Wrong! This result is definitely not what we want. We need to defer the function evaluation till it is replicated.
Just use lazy(randint, 1, 10)
instead of randint(1, 10)
# replicate 'deferred expression'
>>> randos = replicate(5, lazy(randint, 1, 10))
# evaluate when needed
>>> mforce(randos) # mforce = map(force), map 'force' over deferred expressions
[6, 2, 5, 1, 9] # exactly what we wanted
Here is the simple secret: if you complete f_
or ff_
with a function name and its arguments, and leave it unevaluated (not called), they will act as a deferred expression.
Not related to lazy
operation, but you do the same thing with uncurry
# replicate the tuple of arguments (1, 10) and then apply to uncurried function
>>> map(u_(randint))(replicate(5, (1,10))) # u_ == uncurry
[7, 6, 1, 7, 2]
Raise and assert with expressions: error
and guard
Raise any kinds of exception in lambda
expression as well.
error(MESSAGE, e=EXCEPTION_TO_RAISE)
>>> error("Error, used wrong type", e=TypeError)
>>> error("out of range", e=IndexError)
>>> (lambda x: x if x is not None else error("Error, got None", e=ValueError))(None)
Likewise, use guard
if there need assertion not as a statement, but as an expression.
guard(PREDICATE, MESSAGE, e=EXCEPTION_TO_RAISE)
>>> guard("Almost" == "enough", "'Almost' is never 'enough'")
>>> guard(rand() > 0.5, "Assertion error occurs with a 0.5 probability")
>>> guard(len(x := range(11)) == 10, f"length is not 10: {len(x)}")
Exception catcher builder: trap
Document will be updated
Utilities
Normalize containers: flat
flat
flattens all kinds of iterables except for string-like object (str
, bytes
).
flat(*args)
flatl(*args)
# Assume that we regenerate 'data' every time in the examples below
>>> data = [1,2,[3,4,[[[5],6],7,{8},((9),10)],range(11,13)], (x for x in [13,14,15])]
>>> flatl(data)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
# regardless of the number of arguments
>>> flatl(1,[2,{3}],[[[[[4]],5]]], "sofia", "maria")
[1, 2, 3, 4, 5, 'sofia', 'maria']
Shell Command: shell
shell
executes shell commands synchronosly and asynchronously and capture their outputs.
shell(CMD, sync=True, o=True, *, executable="/bin/bash")
--------------------------------------------------------------------
o-value | return | meaning
--------------------------------------------------------------------
o = 1 | [str] | captures stdout/stderr (2>&1)
o = -1 | None | discard (&>/dev/null)
otherwise | None | do nothing or redirection (2>&1 or &>FILE)
--------------------------------------------------------------------
shell
performs the same operation as ipython
's magic command !!
. However, it can also be used within a python
script.
>>> output = shell("ls -1 ~")
>>> output = "ls -1 ~" | shell # the same
>>> shell("find . | sort" o=-1) # run and discard the result
>>> "find . | sort") | shell(o=-1)
>>> shell("cat *.md", o=writer(FILE)) # redirect to FILE
>>> "cat *.md" | shell(o=writer(FILE)) # redirect to FILE
Neatify data structures: neatly
and nprint
neatly
generates neatly formatted string of the complex data structures of dict
and list
.
nprint
(neatly-print) prints data structures to stdout
using neatly
formatter.
nprint(...)
= print(neatly(...))
nprint(DICT, _cols=INDENT, _width=WRAP, _repr=BOOL, **kwargs)
>>> o = {
... "$id": "https://example.com/enumerated-values.schema.json",
... "$schema": "https://json-schema.org/draft/2020-12/schema",
... "title": "Enumerated Values",
... "type": "object",
... "properties": {
... "data": {
... "enum": [42, True, "hello", None, [1, 2, 3]]
... }
... }
... }
>>> nprint(o)
$id | 'https://example.com/enumerated-values.schema.json'
$schema | 'https://json-schema.org/draft/2020-12/schema'
properties | data | enum + 42
: : - True
: : - 'hello'
: : - None
: : - + 1
: : - 2
: : - 3
title | 'Enumerated Values'
type | 'object'
Dot-accessible dictionary: dmap
dmap
is a yet another dict
. It's exactly the same as dict
but it enables to access its nested structure with 'dot notations'.
dmap(DICT, **kwargs)
>>> d = dmap() # empty dict
>>> d = dmap(dict(...))
>>> d = dmap(name="yunchan lim", age=19, profession="pianist") # or dmap({"name":.., "age":..,})
# just put the value in the desired keypath
>>> d.cliburn.semifinal.mozart = "piano concerto no.22"
>>> d.cliburn.semifinal.liszt = "12 transcendental etudes"
>>> d.cliburn.final.beethoven = "piano concerto no.3"
>>> d.cliburn.final.rachmaninoff = "piano concerto no.3"
>>> nprint(d)
age | 19
cliburn | final | beethoven | 'piano concerto no.3'
: : rachmaninoff | 'piano concerto no.3'
: semifinal | liszt | '12 transcendental etudes'
: : mozart | 'piano concerto no.22'
name | 'yunchan lim'
profession | 'pianist'
>>> del d.cliburn.semifinal
>>> d.profession = "one-in-a-million talent"
>>> nprint(d)
age | 19
cliburn | final | beethoven | 'piano concerto no.3'
: : rachmaninoff | 'piano concerto no.3'
name | 'yunchan lim'
profession | 'one-in-a-million talent'
# No such keypath
>>> d.bach.chopin.beethoven
{}
Handy File Tools: ls
and grep
Use ls
and grep
in the same way you use in your terminal every day.
This is just a more intuitive alternative to os.listdir
and os.walk
. When applicable, use shell
instead.
ls(*paths, grep=REGEX, i=BOOL, r=BOOL, f=BOOL, d=BOOL, g=BOOL)
# couldn't be simpler!
>>> ls() # the same as ls("."): get contents of the curruent dir
# expands "~" automatically
>>> ls("~") # the same as `ls -a1 ~`: returns a list of $HOME
# support glob patterns (*, ?, [)
>>> ls("./*/*.py")
# with multiple filepaths
>>> ls(FILE, DIR, ...)
# list up recursively and filter hidden files out
>>> ls(".git", r=True, grep="^[^\.]")
# only files in '.git' directory
>>> ls(".git", r=True, f=True)
# only directories in '.git' directory
>>> ls(".git", r=True, d=True)
# search recursivley and matching a pattern with `grep`
>>> ls(".", r=True, i=True, grep=".Py") # 'i=True' for case-insensitive grep pattern
[ ..
'.pytest_cache/v/cache/stepwise',
'foc/__init__.py',
'foc/__pycache__/__init__.cpython-310.pyc',
'tests/__init__.py',
.. ]
# regex patterns come in
>>> ls(".", r=True, grep=".py$")
['foc/__init__.py', 'setup.py', 'tests/__init__.py', 'tests/test_foc.py']
# that's it!
>>> ls(".", r=True, grep="^(foc).*py$")
# the same as above
>>> ls("foc/*.py")
['foc/__init__.py']
grep
build a filter to select items matching REGEX
pattern from iterables.
grep(REGEX, i=BOOL)
# 'grep' builds filter with regex patterns
>>> grep(r"^(foc).*py$")(ls(".", r=True))
['foc/__init__.py']
See also: HOME
, cd
, pwd
, mkdir
, rmdir
, exists
, dirname
, and basename
.
Flexible Progress Bar: taskbar
Document will be updated
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