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‎nbs/000_tour.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"from nbdev.showdoc import *\n",
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"from fastcore.all import *\n",
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"import numpy as np,numbers"

‎nbs/00_test.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"#|default_exp test"
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"#| default_exp test"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"from nbdev.showdoc import *\n",
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"from fastcore.nb_imports import *"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"import pandas as pd\n",
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"import torch"
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"def _f():\n",
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" with ExceptionExpected(): 1\n",
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"test_fail(partial(_f))\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#|eval: false\n",
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"#| hide\n",
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"#| eval: false\n",
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"from nbdev import nbdev_export\n",
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"nbdev_export()"
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]

‎nbs/01_basics.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"#|default_exp basics"
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"#| default_exp basics"
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]
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},
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{

‎nbs/02_foundation.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"#|default_exp foundation"
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"#| default_exp foundation"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"from fastcore.imports import *\n",
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"from fastcore.basics import *\n",
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"from functools import lru_cache\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"from fastcore.test import *\n",
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"from nbdev.showdoc import *\n",
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"from fastcore.nb_imports import *"
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"@contextmanager\n",
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"def working_directory(path):\n",
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" \"Change working directory to `path` and return to previous on exit.\"\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def add_docs(cls, cls_doc=None, **docs):\n",
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" \"Copy values from `docs` to `cls` docstrings, and confirm all public methods are documented\"\n",
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" if cls_doc is not None: cls.__doc__ = cls_doc\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"class _T:\n",
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" def f(self): pass\n",
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" @classmethod\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def docs(cls):\n",
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" \"Decorator version of `add_docs`, using `_docs` dict\"\n",
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" add_docs(cls, **cls._docs)\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def coll_repr(c, max_n=20):\n",
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" \"String repr of up to `max_n` items of (possibly lazy) collection `c`\"\n",
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" return f'(#{len(c)}) [' + ','.join(itertools.islice(map(repr,c), max_n)) + (\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def is_bool(x):\n",
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" \"Check whether `x` is a bool or None\"\n",
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" return isinstance(x,(bool,NoneType)) or risinstance('bool_', x)"
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def mask2idxs(mask):\n",
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" \"Convert bool mask or index list to index `L`\"\n",
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" if isinstance(mask,slice): return mask\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def cycle(o):\n",
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" \"Like `itertools.cycle` except creates list of `None`s if `o` is empty\"\n",
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" o = listify(o)\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def zip_cycle(x, *args):\n",
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" \"Like `itertools.zip_longest` but `cycle`s through elements of all but first argument\"\n",
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" return zip(x, *map(cycle,args))"
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def is_indexer(idx):\n",
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" \"Test whether `idx` will index a single item in a list\"\n",
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" return isinstance(idx,int) or not getattr(idx,'ndim',1)"
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"class CollBase:\n",
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" \"Base class for composing a list of `items`\"\n",
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" def __init__(self, items): self.items = items\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"class _L_Meta(type):\n",
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" def __call__(cls, x=None, *args, **kwargs):\n",
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" if not args and not kwargs and x is not None and isinstance(x,cls): return x\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"class L(GetAttr, CollBase, metaclass=_L_Meta):\n",
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" \"Behaves like a list of `items` but can also index with list of indices or masks\"\n",
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" _default='items'\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"add_docs(L,\n",
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" __getitem__=\"Retrieve `idx` (can be list of indices, or mask, or int) items\",\n",
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" range=\"Class Method: Same as `range`, but returns `L`. Can pass collection for `a`, to use `len(a)`\",\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#|hide\n",
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"#| export\n",
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"#| hide\n",
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"# Here we are fixing the signature of L. What happens is that the __call__ method on the MetaClass of L shadows the __init__\n",
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"# giving the wrong signature (https://stackoverflow.com/questions/49740290/call-from-metaclass-shadows-signature-of-init).\n",
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"def _f(items=None, *rest, use_list=False, match=None): ...\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"Sequence.register(L);"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"# test set items with L of collections\n",
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"x = L([[1,2,3], [4,5], [6,7]])\n",
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"x[0] = [1,2]\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def save_config_file(file, d, **kwargs):\n",
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" \"Write settings dict to a new config file, or overwrite the existing one.\"\n",
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" config = ConfigParser(**kwargs)\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"def read_config_file(file, **kwargs):\n",
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" config = ConfigParser(**kwargs)\n",
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" config.read(file, encoding='utf8')\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"#| export\n",
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"class Config:\n",
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" \"Reading and writing `ConfigParser` ini files\"\n",
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" def __init__(self, cfg_path, cfg_name, create=None, save=True, extra_files=None, types=None, **cfg_kwargs):\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|hide\n",
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"#| hide\n",
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"import nbdev; nbdev.nbdev_export()"
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]
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},

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