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Gijs-Koot/advanced_python.ipynb

Last active March 18, 2017 11:02
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advanced_python.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Some advanced python tricks\n",
"\n",
"* Using `yield`, `yield from`\n",
"* Using `**kwargs`, `*args`\n",
"* Using decorators\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### `yield` and `yield from`\n",
"\n",
"* `yield` is a keyword that is related to `return` \n",
"* It turns the function into a generator"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<generator object generate_even_numbers at 0x7fc28c655f68>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def generate_even_numbers(until=10):\n",
" for i in range(until):\n",
" if not(i % 2):\n",
" yield i\n",
" \n",
"generate_even_numbers(10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"2\n",
"4\n",
"6\n",
"8\n"
]
}
],
"source": [
"for n in generate_even_numbers(10):\n",
" print(n)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n"
]
}
],
"source": [
"from itertools import islice\n",
"\n",
"for i in islice(range(100), 10):\n",
" print(10)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n",
"10\n"
]
}
],
"source": [
"## Exercise 1: reimplement islice\n",
"\n",
"from itertools import islice\n",
"\n",
"for i in islice(range(100), 10):\n",
" print(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Exercise\n",
"\n",
"* Implement `fibonacci`: a generator for fibonacci numbers"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"0\n",
"1\n",
"2\n"
]
}
],
"source": [
"def range_twice(x):\n",
" yield from range(x)\n",
" yield from range(x)\n",
" \n",
"for i in range_twice(3):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"## Exercise 3: flattening\n",
"\n",
"d = {\n",
" \"first\": {\n",
" \"second\": {\n",
" \"a\": \"b\",\n",
" \"x\": \"y\"\n",
" },\n",
" \"second_other\": \"c\"\n",
" }\n",
"}\n",
"\n",
"a = {\n",
" \"first.second.a\": \"b\",\n",
" \"first.second_other.x\": \"y\",\n",
" \"first.second_other\": \"c\"\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Unpacking arguments with `*` and `**`\n",
"\n",
"* You can use `*` to unpack lists that are meant as single arguments"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/gijs/Desktop'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"\n",
"os.path.join(\"/\", \"home/\", \"gijs\", \"Desktop\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"ename": "TypeError",
"evalue": "join() argument must be str or bytes, not 'list'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-22-d74f0b1bcfa4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"home\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"gijs\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Desktop\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/usr/share/anaconda/anaconda/lib/python3.5/posixpath.py\u001b[0m in \u001b[0;36mjoin\u001b[1;34m(a, *p)\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0msep\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBytesWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m \u001b[0mgenericpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_arg_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'join'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 90\u001b[0m \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/share/anaconda/anaconda/lib/python3.5/genericpath.py\u001b[0m in \u001b[0;36m_check_arg_types\u001b[1;34m(funcname, *args)\u001b[0m\n\u001b[0;32m 141\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 142\u001b[0m raise TypeError('%s() argument must be str or bytes, not %r' %\n\u001b[1;32m--> 143\u001b[1;33m (funcname, s.__class__.__name__)) from None\n\u001b[0m\u001b[0;32m 144\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasstr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasbytes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Can't mix strings and bytes in path components\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: join() argument must be str or bytes, not 'list'"
]
}
],
"source": [
"path = [\"home\", \"gijs\", \"Desktop\"]\n",
"os.path.join(path)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'home/gijs/Desktop'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = [\"home\", \"gijs\", \"Desktop\"]\n",
"os.path.join(*path)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fc252a3dc88>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RrMjUTJ+wYx5hbsBB4PplPxvwPeCqFvtvAZaAZ4b4HcqBkMScyGu4pZ7XcEsHORDt9+2n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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc252b1e5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%pylab inline\n",
"\n",
"x = stats.norm().rvs(100)\n",
"y = .7 * stats.norm().rvs(100) + .3 * x\n",
"\n",
"plt.scatter(x=x, y=y)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fc252a0a3c8>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RrMjUTJ+wYx5hbsBB4PplPxvwPeCqFvtvAZaAZ4b4HcqBkMScyGu4pZ7XcEsHORDt9+2n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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc252b15fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = {\n",
" \"x\": x,\n",
" \"y\": y\n",
"}\n",
"\n",
"plt.scatter(**data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## `**kwargs` and `*args` as function arguments\n",
"\n",
"* These work the other way around; `kwargs` will be a `dict` of keyword arguments, and `args` a `tuple`"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('a', 'b')\n"
]
}
],
"source": [
"def f(*args):\n",
" print(args)\n",
" \n",
"f(\"a\", \"b\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'x': 'x', 'y': 'y'}\n"
]
}
],
"source": [
"def g(**kwargs):\n",
" print(kwargs)\n",
" \n",
"g(x=\"x\", y=\"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Exercises\n",
"\n",
"* Write a function takes multiple lists and prints them all on a new line separated by pipes"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"# print_lists([1, 2, 3], [3, 4, 5, 6])\n",
"\n",
"# > 1 | 2 | 3\n",
"# > 3 | 4 | 5 | 6"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* Write a function that takes multiple keywords and prints a markdown table\n",
"\n",
" md_table(x=[3, 4, 5], y=[3, 2, 1], z=[5, 3, 2])\n",
" \n",
"x | y | z\n",
"--- | --- | ---\n",
"3 | 3 | 5\n",
"4 | 2 | 3\n",
"5 | 1 | 2\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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h06b07+bNnsLZD18Iz4akaA7Eu4EP91jnhj7bcjMgYH9W9kLsD3y1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\nWWEOIMzMzKwwBxBmZmZW2P8Ff0/ywgx9k78AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc2527ead30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import datetime\n",
"\n",
"def scatter_dated(*args, **kwargs):\n",
" plt.scatter(*args, **kwargs)\n",
" plt.title(datetime.datetime.now().isoformat())\n",
" \n",
"scatter_dated(**data, color=\"red\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Exercise \n",
"\n",
"Write a function `scatter_enhanced` that takes a filename and figure size"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "Unknown property save_as",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-55-596c098cd64c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_as\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"./test.png\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/usr/share/anaconda/anaconda/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[0;32m 3256\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3257\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3258\u001b[1;33m edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[0;32m 3259\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3260\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/share/anaconda/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1817\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m 1818\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1819\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1820\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1821\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/share/anaconda/anaconda/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[0;32m 3867\u001b[0m )\n\u001b[0;32m 3868\u001b[0m \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmtransforms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIdentityTransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3869\u001b[1;33m \u001b[0mcollection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3870\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3871\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcolors\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/share/anaconda/anaconda/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m 857\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'set_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 858\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown property %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 860\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 861\u001b[0m \u001b[0mchanged\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: Unknown property save_as"
]
},
{
"data": {
"image/png": 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A6BILAECXWAAAusQCANAlFgCALrEAAHSJBQCgSywAAF1iAQDoEgsAQJdYAAC6xAIA0CUW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\nAOgSCwBA1/8DBAe+bZJxNlwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc25262ac18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y, figsize=(8, 6), save_as=\"./test.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## decorators\n",
"\n",
"* A decorator is a wrapper for a function"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I'm f.\n"
]
}
],
"source": [
"def identity(func):\n",
" return func\n",
"\n",
"@identity\n",
"def f(): print(\"I'm f.\")\n",
"\n",
"## is equivalent to\n",
"\n",
"def f(): print(\"I'm f.\")\n",
"wrapped = identity(f)\n",
"\n",
"wrapped()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before decorated function\n",
"my function asdf\n",
"After decorated function\n",
"return value\n"
]
}
],
"source": [
"from functools import wraps\n",
"\n",
"def mydecorator(f):\n",
" @wraps(f)\n",
" def wrapped(*args, **kwargs):\n",
" print(\"Before decorated function\")\n",
" r = f(*args, **kwargs)\n",
" print(\"After decorated function\")\n",
" return r\n",
" return wrapped\n",
"\n",
"@mydecorator\n",
"def myfunc(myarg):\n",
" print(\"my function\", myarg)\n",
" return \"return value\"\n",
"\n",
"r = myfunc('asdf')\n",
"print(r)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Exercises\n",
"\n",
"* Write a wrapper `twice` that repeats a function call\n",
"* Write a wrapper `debug` that prints all arguments\n",
"* Write a function `retry` that repeats a function until it works"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"## the rabbithole; if you want to add arguments you \n",
"## need three functions\n",
"def mydecorator_not_actually(count):\n",
" def true_decorator(f):\n",
" @wraps(f)\n",
" def wrapped(*args, **kwargs):\n",
" for i in range(count):\n",
" print(\"Before decorated function\")\n",
" r = f(*args, **kwargs)\n",
" for i in range(count):\n",
" print(\"After decorated function\")\n",
" return r\n",
" return wrapped\n",
" return true_decorator\n",
"\n",
"@mydecorator_not_actually(count=5)\n",
"def myfunc(myarg):\n",
" print(\"my function {myarg}\")\n",
" return \"return value\"\n",
"\n",
"r = myfunc('asdf')\n",
"print(r)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Exercises\n",
"\n",
"* Write a wrapper `repeat` that repeats a function `count` times\n",
"* Write a wrapper `memoize` that remembers function calls up to a specified number of calls"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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