#default_exp foundation
#export
from fastcore.imports import *
from fastcore.basics import *
from functools import lru_cache
from contextlib import contextmanager
from copy import copy
from configparser import ConfigParser
import random,pickle,inspect
from fastcore.test import *
from nbdev.showdoc import *
from fastcore.nb_imports import *
The
L
class and helpers for it
#export
@contextmanager
def working_directory(path):
"Change working directory to `path` and return to previous on exit."
prev_cwd = Path.cwd()
os.chdir(path)
try: yield
finally: os.chdir(prev_cwd)
#export
def add_docs(cls, cls_doc=None, **docs):
"Copy values from `docs` to `cls` docstrings, and confirm all public methods are documented"
if cls_doc is not None: cls.__doc__ = cls_doc
for k,v in docs.items():
f = getattr(cls,k)
if hasattr(f,'__func__'): f = f.__func__ # required for class methods
f.__doc__ = v
# List of public callables without docstring
nodoc = [c for n,c in vars(cls).items() if callable(c)
and not n.startswith('_') and c.__doc__ is None]
assert not nodoc, f"Missing docs: {nodoc}"
assert cls.__doc__ is not None, f"Missing class docs: {cls}"
add_docs
allows you to add docstrings to a class and its associated methods. This function allows you to group docstrings together seperate from your code, which enables you to define one-line functions as well as organize your code more succintly. We believe this confers a number of benefits which we discuss in our style guide.
Suppose you have the following undocumented class:
class T:
def foo(self): pass
def bar(self): pass
You can add documentation to this class like so:
add_docs(T, cls_doc="A docstring for the class.",
foo="The foo method.",
bar="The bar method.")
Now, docstrings will appear as expected:
test_eq(T.__doc__, "A docstring for the class.")
test_eq(T.foo.__doc__, "The foo method.")
test_eq(T.bar.__doc__, "The bar method.")
add_docs
also validates that all of your public methods contain a docstring. If one of your methods is not documented, it will raise an error:
class T:
def foo(self): pass
def bar(self): pass
f=lambda: add_docs(T, "A docstring for the class.", foo="The foo method.")
test_fail(f, contains="Missing docs")
#hide
class _T:
def f(self): pass
@classmethod
def g(cls): pass
add_docs(_T, "a", f="f", g="g")
test_eq(_T.__doc__, "a")
test_eq(_T.f.__doc__, "f")
test_eq(_T.g.__doc__, "g")
#export
def docs(cls):
"Decorator version of `add_docs`, using `_docs` dict"
add_docs(cls, **cls._docs)
return cls
Instead of using add_docs
, you can use the decorator docs
as shown below. Note that the docstring for the class can be set with the argument cls_doc
:
@docs
class _T:
def f(self): pass
def g(cls): pass
_docs = dict(cls_doc="The class docstring",
f="The docstring for method f.",
g="A different docstring for method g.")
test_eq(_T.__doc__, "The class docstring")
test_eq(_T.f.__doc__, "The docstring for method f.")
test_eq(_T.g.__doc__, "A different docstring for method g.")
For either the docs
decorator or the add_docs
function, you can still define your docstrings in the normal way. Below we set the docstring for the class as usual, but define the method docstrings through the _docs
attribute:
@docs
class _T:
"The class docstring"
def f(self): pass
_docs = dict(f="The docstring for method f.")
test_eq(_T.__doc__, "The class docstring")
test_eq(_T.f.__doc__, "The docstring for method f.")
show_doc(is_iter)
assert is_iter([1])
assert not is_iter(array(1))
assert is_iter(array([1,2]))
assert (o for o in range(3))
# export
def coll_repr(c, max_n=10):
"String repr of up to `max_n` items of (possibly lazy) collection `c`"
return f'(#{len(c)}) [' + ','.join(itertools.islice(map(repr,c), max_n)) + (
'...' if len(c)>max_n else '') + ']'
coll_repr
is used to provide a more informative __repr__
about list-like objects. coll_repr
and is used by L
to build a __repr__
that displays the length of a list in addition to a preview of a list.
Below is an example of the __repr__
string created for a list of 1000 elements:
test_eq(coll_repr(range(1000)), '(#1000) [0,1,2,3,4,5,6,7,8,9...]')
test_eq(coll_repr(range(1000), 5), '(#1000) [0,1,2,3,4...]')
test_eq(coll_repr(range(10), 5), '(#10) [0,1,2,3,4...]')
test_eq(coll_repr(range(5), 5), '(#5) [0,1,2,3,4]')
We can set the option max_n
to optionally preview a specified number of items instead of the default:
test_eq(coll_repr(range(1000), max_n=5), '(#1000) [0,1,2,3,4...]')
# export
def is_bool(x):
"Check whether `x` is a bool or None"
return isinstance(x,(bool,NoneType)) or risinstance('bool_', x)
# export
def mask2idxs(mask):
"Convert bool mask or index list to index `L`"
if isinstance(mask,slice): return mask
mask = list(mask)
if len(mask)==0: return []
it = mask[0]
if hasattr(it,'item'): it = it.item()
if is_bool(it): return [i for i,m in enumerate(mask) if m]
return [int(i) for i in mask]
test_eq(mask2idxs([False,True,False,True]), [1,3])
test_eq(mask2idxs(array([False,True,False,True])), [1,3])
test_eq(mask2idxs(array([1,2,3])), [1,2,3])
#export
def cycle(o):
"Like `itertools.cycle` except creates list of `None`s if `o` is empty"
o = listify(o)
return itertools.cycle(o) if o is not None and len(o) > 0 else itertools.cycle([None])
test_eq(itertools.islice(cycle([1,2,3]),5), [1,2,3,1,2])
test_eq(itertools.islice(cycle([]),3), [None]*3)
test_eq(itertools.islice(cycle(None),3), [None]*3)
test_eq(itertools.islice(cycle(1),3), [1,1,1])
#export
def zip_cycle(x, *args):
"Like `itertools.zip_longest` but `cycle`s through elements of all but first argument"
return zip(x, *map(cycle,args))
test_eq(zip_cycle([1,2,3,4],list('abc')), [(1, 'a'), (2, 'b'), (3, 'c'), (4, 'a')])
#export
def is_indexer(idx):
"Test whether `idx` will index a single item in a list"
return isinstance(idx,int) or not getattr(idx,'ndim',1)
You can, for example index a single item in a list with an integer or a 0-dimensional numpy array:
assert is_indexer(1)
assert is_indexer(np.array(1))
However, you cannot index into single item in a list with another list or a numpy array with ndim > 0.
assert not is_indexer([1, 2])
assert not is_indexer(np.array([[1, 2], [3, 4]]))
L
helpers¶#export
class CollBase:
"Base class for composing a list of `items`"
def __init__(self, items): self.items = items
def __len__(self): return len(self.items)
def __getitem__(self, k): return self.items[list(k) if isinstance(k,CollBase) else k]
def __setitem__(self, k, v): self.items[list(k) if isinstance(k,CollBase) else k] = v
def __delitem__(self, i): del(self.items[i])
def __repr__(self): return self.items.__repr__()
def __iter__(self): return self.items.__iter__()
ColBase
is a base class that emulates the functionality of a python list
:
class _T(CollBase): pass
l = _T([1,2,3,4,5])
test_eq(len(l), 5) # __len__
test_eq(l[-1], 5); test_eq(l[0], 1) #__getitem__
l[2] = 100; test_eq(l[2], 100) # __set_item__
del l[0]; test_eq(len(l), 4) # __delitem__
test_eq(str(l), '[2, 100, 4, 5]') # __repr__
#export
class _L_Meta(type):
def __call__(cls, x=None, *args, **kwargs):
if not args and not kwargs and x is not None and isinstance(x,cls): return x
return super().__call__(x, *args, **kwargs)
#export
class L(GetAttr, CollBase, metaclass=_L_Meta):
"Behaves like a list of `items` but can also index with list of indices or masks"
_default='items'
def __init__(self, items=None, *rest, use_list=False, match=None):
if (use_list is not None) or not is_array(items):
items = listify(items, *rest, use_list=use_list, match=match)
super().__init__(items)
@property
def _xtra(self): return None
def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
def copy(self): return self._new(self.items.copy())
def _get(self, i):
if is_indexer(i) or isinstance(i,slice): return getattr(self.items,'iloc',self.items)[i]
i = mask2idxs(i)
return (self.items.iloc[list(i)] if hasattr(self.items,'iloc')
else self.items.__array__()[(i,)] if hasattr(self.items,'__array__')
else [self.items[i_] for i_ in i])
def __setitem__(self, idx, o):
"Set `idx` (can be list of indices, or mask, or int) items to `o` (which is broadcast if not iterable)"
if isinstance(idx, int): self.items[idx] = o
else:
idx = idx if isinstance(idx,L) else listify(idx)
if not is_iter(o): o = [o]*len(idx)
for i,o_ in zip(idx,o): self.items[i] = o_
def __eq__(self,b):
if risinstance('ndarray', b): return array_equal(b, self)
if isinstance(b, (str,dict)): return False
return all_equal(b,self)
def sorted(self, key=None, reverse=False): return self._new(sorted_ex(self, key=key, reverse=reverse))
def __iter__(self): return iter(self.items.itertuples() if hasattr(self.items,'iloc') else self.items)
def __contains__(self,b): return b in self.items
def __reversed__(self): return self._new(reversed(self.items))
def __invert__(self): return self._new(not i for i in self)
def __repr__(self): return repr(self.items)
def _repr_pretty_(self, p, cycle):
p.text('...' if cycle else repr(self.items) if is_array(self.items) else coll_repr(self))
def __mul__ (a,b): return a._new(a.items*b)
def __add__ (a,b): return a._new(a.items+listify(b))
def __radd__(a,b): return a._new(b)+a
def __addi__(a,b):
a.items += list(b)
return a
@classmethod
def split(cls, s, sep=None, maxsplit=-1): return cls(s.split(sep,maxsplit))
@classmethod
def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
def filter(self, f=noop, negate=False, gen=False, **kwargs):
return self._new(filter_ex(self, f=f, negate=negate, gen=gen, **kwargs))
def enumerate(self): return L(enumerate(self))
def renumerate(self): return L(renumerate(self))
def unique(self, sort=False, bidir=False, start=None): return L(uniqueify(self, sort=sort, bidir=bidir, start=start))
def val2idx(self): return val2idx(self)
def cycle(self): return cycle(self)
def map_dict(self, f=noop, *args, gen=False, **kwargs): return {k:f(k, *args,**kwargs) for k in self}
def map_first(self, f=noop, g=noop, *args, **kwargs):
return first(self.map(f, *args, gen=False, **kwargs), g)
def itemgot(self, *idxs):
x = self
for idx in idxs: x = x.map(itemgetter(idx))
return x
def attrgot(self, k, default=None):
return self.map(lambda o: o.get(k,default) if isinstance(o, dict) else nested_attr(o,k,default))
def starmap(self, f, *args, **kwargs): return self._new(itertools.starmap(partial(f,*args,**kwargs), self))
def zip(self, cycled=False): return self._new((zip_cycle if cycled else zip)(*self))
def zipwith(self, *rest, cycled=False): return self._new([self, *rest]).zip(cycled=cycled)
def map_zip(self, f, *args, cycled=False, **kwargs): return self.zip(cycled=cycled).starmap(f, *args, **kwargs)
def map_zipwith(self, f, *rest, cycled=False, **kwargs): return self.zipwith(*rest, cycled=cycled).starmap(f, **kwargs)
def shuffle(self):
it = copy(self.items)
random.shuffle(it)
return self._new(it)
def concat(self): return self._new(itertools.chain.from_iterable(self.map(L)))
def reduce(self, f, initial=None): return reduce(f, self) if initial is None else reduce(f, self, initial)
def sum(self): return self.reduce(operator.add)
def product(self): return self.reduce(operator.mul)
def setattrs(self, attr, val): [setattr(o,attr,val) for o in self]
#export
add_docs(L,
__getitem__="Retrieve `idx` (can be list of indices, or mask, or int) items",
range="Class Method: Same as `range`, but returns `L`. Can pass collection for `a`, to use `len(a)`",
split="Class Method: Same as `str.split`, but returns an `L`",
copy="Same as `list.copy`, but returns an `L`",
sorted="New `L` sorted by `key`. If key is str use `attrgetter`; if int use `itemgetter`",
unique="Unique items, in stable order",
val2idx="Dict from value to index",
filter="Create new `L` filtered by predicate `f`, passing `args` and `kwargs` to `f`",
argwhere="Like `filter`, but return indices for matching items",
map="Create new `L` with `f` applied to all `items`, passing `args` and `kwargs` to `f`",
map_first="First element of `map_filter`",
map_dict="Like `map`, but creates a dict from `items` to function results",
starmap="Like `map`, but use `itertools.starmap`",
itemgot="Create new `L` with item `idx` of all `items`",
attrgot="Create new `L` with attr `k` (or value `k` for dicts) of all `items`.",
cycle="Same as `itertools.cycle`",
enumerate="Same as `enumerate`",
renumerate="Same as `renumerate`",
zip="Create new `L` with `zip(*items)`",
zipwith="Create new `L` with `self` zip with each of `*rest`",
map_zip="Combine `zip` and `starmap`",
map_zipwith="Combine `zipwith` and `starmap`",
concat="Concatenate all elements of list",
shuffle="Same as `random.shuffle`, but not inplace",
reduce="Wrapper for `functools.reduce`",
sum="Sum of the items",
product="Product of the items",
setattrs="Call `setattr` on all items"
)
#export
#hide
# Here we are fixing the signature of L. What happens is that the __call__ method on the MetaClass of L shadows the __init__
# giving the wrong signature (https://stackoverflow.com/questions/49740290/call-from-metaclass-shadows-signature-of-init).
def _f(items=None, *rest, use_list=False, match=None): ...
L.__signature__ = inspect.signature(_f)
#export
Sequence.register(L);
L
is a drop in replacement for a python list
. Inspired by NumPy, L
, supports advanced indexing and has additional methods (outlined below) that provide additional functionality and encourage simple expressive code. For example, the code below takes a list of pairs, selects the second item of each pair, takes its absolute value, filters items greater than 4, and adds them up:
from fastcore.utils import gt
d = dict(a=1,b=-5,d=6,e=9).items()
test_eq(L(d).itemgot(1).map(abs).filter(gt(4)).sum(), 20) # abs(-5) + abs(6) + abs(9) = 20; 1 was filtered out.
Read this overview section for a quick tutorial of L
, as well as background on the name.
You can create an L
from an existing iterable (e.g. a list, range, etc) and access or modify it with an int list/tuple index, mask, int, or slice. All list
methods can also be used with L
.
t = L(range(12))
test_eq(t, list(range(12)))
test_ne(t, list(range(11)))
t.reverse()
test_eq(t[0], 11)
t[3] = "h"
test_eq(t[3], "h")
t[3,5] = ("j","k")
test_eq(t[3,5], ["j","k"])
test_eq(t, L(t))
test_eq(L(L(1,2),[3,4]), ([1,2],[3,4]))
t
(#12) [11,10,9,'j',7,'k',5,4,3,2...]
Any L
is a Sequence
so you can use it with methods like random.sample
:
assert isinstance(t, Sequence)
import random
random.sample(t, 3)
['j', 5, 10]
#hide
# test set items with L of collections
x = L([[1,2,3], [4,5], [6,7]])
x[0] = [1,2]
test_eq(x, L([[1,2], [4,5], [6,7]]))
There are optimized indexers for arrays, tensors, and DataFrames.
arr = np.arange(9).reshape(3,3)
t = L(arr, use_list=None)
test_eq(t[1,2], arr[[1,2]])
import pandas as pd
df = pd.DataFrame({'a':[1,2,3]})
t = L(df, use_list=None)
test_eq(t[1,2], L(pd.DataFrame({'a':[2,3]}, index=[1,2]), use_list=None))
You can also modify an L
with append
, +
, and *
.
t = L()
test_eq(t, [])
t.append(1)
test_eq(t, [1])
t += [3,2]
test_eq(t, [1,3,2])
t = t + [4]
test_eq(t, [1,3,2,4])
t = 5 + t
test_eq(t, [5,1,3,2,4])
test_eq(L(1,2,3), [1,2,3])
test_eq(L(1,2,3), L(1,2,3))
t = L(1)*5
t = t.map(operator.neg)
test_eq(t,[-1]*5)
test_eq(~L([True,False,False]), L([False,True,True]))
t = L(range(4))
test_eq(zip(t, L(1).cycle()), zip(range(4),(1,1,1,1)))
t = L.range(100)
test_shuffled(t,t.shuffle())
def _f(x,a=0): return x+a
t = L(1)*5
test_eq(t.map(_f), t)
test_eq(t.map(_f,1), [2]*5)
test_eq(t.map(_f,a=2), [3]*5)
An L
can be constructed from anything iterable, although tensors and arrays will not be iterated over on construction, unless you pass use_list
to the constructor.
test_eq(L([1,2,3]),[1,2,3])
test_eq(L(L([1,2,3])),[1,2,3])
test_ne(L([1,2,3]),[1,2,])
test_eq(L('abc'),['abc'])
test_eq(L(range(0,3)),[0,1,2])
test_eq(L(o for o in range(0,3)),[0,1,2])
test_eq(L(array(0)),[array(0)])
test_eq(L([array(0),array(1)]),[array(0),array(1)])
test_eq(L(array([0.,1.1]))[0],array([0.,1.1]))
test_eq(L(array([0.,1.1]), use_list=True), [array(0.),array(1.1)]) # `use_list=True` to unwrap arrays/arrays
If match
is not None
then the created list is same len as match
, either by:
len(items)==1
then items
is replicated,match
and items
are not already the same size.test_eq(L(1,match=[1,2,3]),[1,1,1])
test_eq(L([1,2],match=[2,3]),[1,2])
test_fail(lambda: L([1,2],match=[1,2,3]))
If you create an L
from an existing L
then you'll get back the original object (since L
uses the NewChkMeta
metaclass).
test_is(L(t), t)
An L
is considred equal to a list if they have the same elements. It's never considered equal to a str
a set
or a dict
even if they have the same elements/keys.
test_eq(L(['a', 'b']), ['a', 'b'])
test_ne(L(['a', 'b']), 'ab')
test_ne(L(['a', 'b']), {'a':1, 'b':2})
L
Methods¶show_doc(L.__getitem__)
t = L(range(12))
test_eq(t[1,2], [1,2]) # implicit tuple
test_eq(t[[1,2]], [1,2]) # list
test_eq(t[:3], [0,1,2]) # slice
test_eq(t[[False]*11 + [True]], [11]) # mask
test_eq(t[array(3)], 3)
show_doc(L.__setitem__)
L.__setitem__
[source]
L.setitem
(idx
,o
)
Set idx
(can be list of indices, or mask, or int) items to o
(which is broadcast if not iterable)
t[4,6] = 0
test_eq(t[4,6], [0,0])
t[4,6] = [1,2]
test_eq(t[4,6], [1,2])
show_doc(L.unique)
test_eq(L(4,1,2,3,4,4).unique(), [4,1,2,3])
show_doc(L.val2idx)
test_eq(L(1,2,3).val2idx(), {3:2,1:0,2:1})
show_doc(L.filter)
list(t)
[0, 1, 2, 3, 1, 5, 2, 7, 8, 9, 10, 11]
test_eq(t.filter(lambda o:o<5), [0,1,2,3,1,2])
test_eq(t.filter(lambda o:o<5, negate=True), [5,7,8,9,10,11])
show_doc(L.argwhere)
L.argwhere
[source]
L.argwhere
(f
,negate
=False
, ****kwargs
**)
Like filter
, but return indices for matching items
test_eq(t.argwhere(lambda o:o<5), [0,1,2,3,4,6])
show_doc(L.map)
test_eq(L.range(4).map(operator.neg), [0,-1,-2,-3])
If f
is a string then it is treated as a format string to create the mapping:
test_eq(L.range(4).map('#{}#'), ['#0#','#1#','#2#','#3#'])
If f
is a dictionary (or anything supporting __getitem__
) then it is indexed to create the mapping:
test_eq(L.range(4).map(list('abcd')), list('abcd'))
You can also pass the same arg
params that bind
accepts:
def f(a=None,b=None): return b
test_eq(L.range(4).map(f, b=arg0), range(4))
show_doc(L.map_dict)
L.map_dict
[source]
L.map_dict
(f
=noop
, ***args
,gen
=False
, **kwargs
**)
Like map
, but creates a dict from items
to function results
test_eq(L(range(1,5)).map_dict(), {1:1, 2:2, 3:3, 4:4})
test_eq(L(range(1,5)).map_dict(operator.neg), {1:-1, 2:-2, 3:-3, 4:-4})
t = L([[1,2,3],'abc'])
test_eq(t.zip(), [(1, 'a'),(2, 'b'),(3, 'c')])
t = L([[1,2,3,4],['a','b','c']])
test_eq(t.zip(cycled=True ), [(1, 'a'),(2, 'b'),(3, 'c'),(4, 'a')])
test_eq(t.zip(cycled=False), [(1, 'a'),(2, 'b'),(3, 'c')])
show_doc(L.map_zip)
t = L([1,2,3],[2,3,4])
test_eq(t.map_zip(operator.mul), [2,6,12])
show_doc(L.zipwith)
b = [[0],[1],[2,2]]
t = L([1,2,3]).zipwith(b)
test_eq(t, [(1,[0]), (2,[1]), (3,[2,2])])
show_doc(L.map_zipwith)
L.map_zipwith
[source]
L.map_zipwith
(f
, ***rest
,cycled
=False
, **kwargs
**)
Combine zipwith
and starmap
test_eq(L(1,2,3).map_zipwith(operator.mul, [2,3,4]), [2,6,12])
show_doc(L.itemgot)
test_eq(t.itemgot(1), b)
show_doc(L.attrgot)
# Example when items are not a dict
a = [SimpleNamespace(a=3,b=4),SimpleNamespace(a=1,b=2)]
test_eq(L(a).attrgot('b'), [4,2])
#Example of when items are a dict
b =[{'id': 15, 'name': 'nbdev'}, {'id': 17, 'name': 'fastcore'}]
test_eq(L(b).attrgot('id'), [15, 17])
show_doc(L.sorted)
test_eq(L(a).sorted('a').attrgot('b'), [2,4])
show_doc(L.split)
test_eq(L.split('a b c'), list('abc'))
show_doc(L.range)
test_eq_type(L.range([1,1,1]), L(range(3)))
test_eq_type(L.range(5,2,2), L(range(5,2,2)))
show_doc(L.concat)
test_eq(L([0,1,2,3],4,L(5,6)).concat(), range(7))
t = L([0,1,2,3],4,L(5,6)).copy()
test_eq(t.concat(), range(7))
show_doc(L.map_first)
t = L(0,1,2,3)
test_eq(t.map_first(lambda o:o*2 if o>2 else None), 6)
show_doc(L.setattrs)
t = L(SimpleNamespace(),SimpleNamespace())
t.setattrs('foo', 'bar')
test_eq(t.attrgot('foo'), ['bar','bar'])
#export
def save_config_file(file, d, **kwargs):
"Write settings dict to a new config file, or overwrite the existing one."
config = ConfigParser(**kwargs)
config['DEFAULT'] = d
config.write(open(file, 'w'))
#export
def read_config_file(file, **kwargs):
config = ConfigParser(**kwargs)
config.read(file)
return config
Config files are saved and read using Python's configparser.ConfigParser
, inside the DEFAULT
section.
_d = dict(user='fastai', lib_name='fastcore', some_path='test')
try:
save_config_file('tmp.ini', _d)
res = read_config_file('tmp.ini')
finally: os.unlink('tmp.ini')
test_eq(res['DEFAULT'], _d)
#export
def _add_new_defaults(cfg, file, **kwargs):
for k,v in kwargs.items():
if cfg.get(k, None) is None:
cfg[k] = v
save_config_file(file, cfg)
#export
@lru_cache(maxsize=None)
class Config:
"Reading and writing `settings.ini`"
def __init__(self, cfg_name='settings.ini'):
cfg_path = Path.cwd()
while cfg_path != cfg_path.parent and not (cfg_path/cfg_name).exists(): cfg_path = cfg_path.parent
self.config_path,self.config_file = cfg_path,cfg_path/cfg_name
assert self.config_file.exists(), f"Could not find {cfg_name}"
self.d = read_config_file(self.config_file)['DEFAULT']
_add_new_defaults(self.d, self.config_file,
host="github", doc_host="https://%(user)s.github.io", doc_baseurl="/%(lib_name)s/")
def __setitem__(self,k,v): self.d[k] = str(v)
def __contains__(self,k): return k in self.d
def save(self): save_config_file(self.config_file,self.d)
def __getattr__(self,k): return stop(AttributeError(k)) if k=='d' or k not in self.d else self.get(k)
def get(self,k,default=None): return self.d.get(k, default)
def path(self,k,default=None):
v = self.get(k, default)
return v if v is None else self.config_path/v
Config
searches parent directories for a config file, and provides direct access to the 'DEFAULT' section. Keys ending in _path
are converted to paths in the config file's directory.
try:
save_config_file('../tmp.ini', _d)
cfg = Config('tmp.ini')
finally: os.unlink('../tmp.ini')
test_eq(cfg.user,'fastai')
test_eq(cfg.doc_baseurl,'/fastcore/')
test_eq(cfg.get('some_path'), 'test')
test_eq(cfg.path('some_path'), Path('../test').resolve())
test_eq(cfg.get('foo','bar'),'bar')
#hide
from nbdev.export import notebook2script
notebook2script()
Converted 00_test.ipynb. Converted 01_basics.ipynb. Converted 02_foundation.ipynb. Converted 03_xtras.ipynb. Converted 03a_parallel.ipynb. Converted 03b_net.ipynb. Converted 04_dispatch.ipynb. Converted 05_transform.ipynb. Converted 07_meta.ipynb. Converted 08_script.ipynb. Converted index.ipynb.