{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Tutorial for Box-X

\n", "
\n", " Tool-box for Efficient Build and Debug in Python. Especially for Scientific Computing and Computer Vision.\n", "
\n", "\n", "----\n", "We use [Binder](https://mybinder.org) to run this notebooks in an executable interactive online environment. That mean you can **run those cells rightnow** in your browser without download repository.\n", "\n", "\n", "This tutorial is divided into 2 parts by wether the tool is general:\n", "1. [**General Python Tool**](#1.-General-Python-Tool). The tools could be used anywhere in Python\n", "2. [**Scientific Computing and Computer Vision Tool**](#2.-Scientific-Computing-and-Computer-Vision-Tool). Those tools only be useful in Scientific Computing and Computer Vision field\n", "\n", "\n", "*P.S. This notebook compatible with Python 2/3*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

\n", "

\n", "\n", "

Table of Contents

\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. General Python Tool" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `p` is a better way to do `print`\n", "----\n", "#### 1. `p/x` will `print(x)` and return `x`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p/x will print(x) and return x\n" ] }, { "data": { "text/plain": [ "'p/x will print(x) and return x'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import p \n", "\n", "s = 'p/x will print(x) and return x'\n", "p/s" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the output of randint(0, 3) is :\n", "1\n" ] }, { "data": { "text/plain": [ "'B'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import p \n", "from random import randint\n", "\n", "s = 'ABCD'\n", "print('the output of randint(0, 3) is :')\n", "\n", "sample = s[p/randint(0, 3)]\n", "\n", "sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see, `p/x` is easy to print value in expression while debugging. \n", "\n", "💡 **Note:** \n", "\n", "`p/randint(0, 3)` print the value of `randint(0, 3)` and return the value itself, which won't influences the program.\n", "\n", "---\n", "↓ Use pow operator for highest evaluation order. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# try run this cell online\n", "from boxx import p \n", "from random import randint \n", "tenx = 10 * p**randint(0,9) \n", "tenx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. `p()` to pretty print all variables in function or module with thier name" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\u001b[36mFile: \"\u001b[32m\u001b[0m\", line \u001b[32m3\u001b[0m, in \u001b[35mf\u001b[0m\u001b[0m\n", "\u001b[36mStacks: \u001b[0m\u001b[31mf <-\u001b[36mipython-input\u001b[0m\u001b[0m\n", "\u001b[36mLocals: \u001b[0m\n", "└── /: \u001b[36mdict 2\u001b[0m\n", " ├── l: \u001b[36mlist 2\u001b[0m\n", " │ ├── 0: \u001b[31m1\u001b[0m\n", " │ └── 1: \u001b[31m2\u001b[0m\n", " └── arg: \u001b[31m517\u001b[0m\n" ] } ], "source": [ "from boxx import p \n", "\n", "def f(arg=517):\n", " l = [1, 2]\n", " p()\n", "f()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`p()` will pretty print all variables in `locals()` and some infomation about the frame. \n", "\n", "BTW, `import boxx.p` has the same effect." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. `with p:` will pretty print mulit variables under \"with statement\"\n", "Only interested variables are printed which is under \"with statement\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\u001b[36mwithprint from \u001b[36mFile: \"\u001b[32m\u001b[0m\", line \u001b[32m3\u001b[0m, in \u001b[35mf\u001b[0m\u001b[0m\u001b[0m\n", "\u001b[36mAll Vars's Values :\u001b[0m\n", "└── /: \u001b[36mdict 2\u001b[0m\n", " ├── l: \u001b[36mlist 2\u001b[0m\n", " │ ├── 0: \u001b[31m1\u001b[0m\n", " │ └── 1: \u001b[31m2\u001b[0m\n", " └── a: \u001b[31m1\u001b[0m\n" ] } ], "source": [ "from boxx import p\n", "from random import randint\n", "def f():\n", " other_vars = \"No need to pay attention\"\n", " with p:\n", " a = randint(1, 9)\n", " l = [a, a*2]\n", " others = \"No need to pay attention\"\n", "f()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `g` and `gg` could transport variable to Python interactive console\n", "----\n", "#### 1. Use `g.name=x` or `g.name/x` to transport variable that in function or module to console.\n", "The meaning of `g`,`gg` are \"to Global\", \"to Global and log\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import g\n", "\n", "def f():\n", " listt = [1,2]\n", " g.l = listt # `listt` is transported to console as `l`\n", "f()\n", "\n", "l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`g.l = listt` create new var `l` In Python interactive console and transport `listt` assign to `l`. \n", "\n", "💡 **Note:** if variable name exists in console before, the variable's value will be covered by new value. \n", "\n", "---\n", "`gg` is same usage as `g`, but `gg` will print the transported variable. \n", "Use `g.name/x` to convenient transport value in expression." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[31mgg.l:\"\u001b[0m[1, 2]\u001b[31m\"\u001b[0m\n" ] }, { "data": { "text/plain": [ "([1, 2], [1, 2], [1, 2], True, True)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import g, gg\n", "\n", "def f():\n", " listt = [1,2]\n", " gg.l = listt\n", " return g.by_div/listt\n", "\n", "listt = f()\n", "\n", "# l, by_div are transported to console\n", "(listt, l, by_div, l is listt, by_div is listt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:**\n", " * In Python interactive console, variable `l`, `by_div` are created.\n", "\n", " * All of they are `listt` has the same `id`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. `g()` to transport all variables that in the function to Python interactive console\n", "`g()` in a function ,can transport all variables that in the function (or module) to console. It's a useful tool for debugging." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(517, [1, 2])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import g\n", "def f(arg=517):\n", " l = [1, 2]\n", " g()\n", "f()\n", "\n", "# transport all variables in function to console \n", "arg, l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:**\n", "\n", " * `g()` only transport the `locals()` to console, the `globals()` will save to `boxx.p`\n", " \n", " * `gg()` is a print version of `g`, `gg()` will pretty print all variable with thier name and some infomation about the frame.\n", "\n", " * `import boxx.g` is convenient way to use `g()` instead of `from boxx import g;g()`(`import boxx.gg` is avaliable too)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# try run this cell online\n", "\n", "def f(arg):\n", " a = 2\n", " import boxx.gg\n", " \n", "inp = [5 ,1 , 7]\n", "f(inp)\n", "\n", "# gg will pretty print all variables in f\n", "# and `a` and `arg` are transported to console \n", "a, arg, arg is inp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. `with g:` will transport mulit variables under \"with statement\"\n", "`with g` will transport the interested variables to Python interactive console under \"with statement\"(`with gg:` is avaliable too)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "In console: 9 [9, 18] False\n" ] } ], "source": [ "from boxx import g\n", "from random import randint\n", "def f():\n", " other_vars = \"No need to pay attention\"\n", " with g: # only transport a, l\n", " a = randint(1, 9)\n", " l = [a, a*2]\n", " others = \"No need to pay attention\"\n", "f()\n", "\n", "print('In console:',a , l, 'others' in locals())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:** \n", "\n", "1 . `with p`, `with g`, `with gg` only act on assignment variables under \"with statement\". \n", "\n", "2 . If variable's name exists in `locals()` before and `id(variable)` not change ,variable may not be detected \n", "Especially following cases:\n", " 1. var is int and < 256\n", " 2. `id(var)` not change" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ Summary for debug tools\n", "---\n", "\n", "
\n", "

\n", " boxx debug tool matrix\n", "

\n", "
\n", "\n", "| How many vars \\ Operation | print | transport | print & transport |\n", "| :---- | :---- | :---- | :---- |\n", "| Single variable | `p/x` | `g.name/x` | `gg.name/x`|\n", "|Multi variables | `with p:` | `with g:` | `with gg:` |\n", "|All `locals()`| `p()` | `g()` | `gg()` |\n", "|All `locals()`\\_2 | `import boxx.p` | `import boxx.g` | `import boxx.gg` | \n", "\n", " 💡 **Note:** \n", " * **transport** mean \"transport variable to Python interactive console\"\n", " * **All `locals()`** mean operation will act on all variables in the function or module\n", " * **All `locals()`\\_2 :** when `boxx` are not imported, `import boxx.{operation}` is a convenient way to execution operation " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `timeit` is convenient timing tool " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36m\"timeit\" spend time: 0.01011705\u001b[0m\n", "\u001b[36m\"sleep\" spend time: 0.1004236\u001b[0m\n" ] } ], "source": [ "from boxx import timeit\n", "from time import sleep\n", "\n", "with timeit():\n", " sleep(0.01) # simulation timing code\n", "\n", "with timeit(name='sleep'):\n", " sleep(0.1) # simulation timing code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`timeit` will timing code block under \"with statement\" and print spend time in blue color." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `mapmt` is Multi Threading version of `map`\n", "`mapmt` is the meaning of \"MAP for Multi Threading\", has almost same usage as `map`" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36m\"map\" spend time: 1.00472\u001b[0m\n", "\u001b[36m\"mapmt\" spend time: 0.204875\u001b[0m\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import mapmt, timeit\n", "from time import sleep\n", "\n", "def io_block(x): # simulation io block\n", " sleep(0.1)\n", " return x\n", "xs = range(10)\n", "\n", "with timeit('map'):\n", " resoult_1 = list(map(io_block, xs))\n", "with timeit('mapmt'):\n", " resoult_2 = mapmt(io_block, xs, pool=10)\n", " # pool=10 mean 10 threadings\n", "\n", "resoult_1 == resoult_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `mapmp` is Multi Process version of `map`\n", "`mapmp` is the meaning of \"MAP for Multi Process\", has the same usage as `map` and `mapmt` but faster." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36m\"map\" spend time: 2.870245\u001b[0m\n", "\u001b[36m\"mapmp\" spend time: 1.291457\u001b[0m\n" ] } ], "source": [ "from boxx import mapmp, timeit\n", "def bad_fibonacci(x): # simulation Complex calculations\n", " return x<=1 or x*bad_fibonacci(x-1)\n", "\n", "xs = [800]*10000\n", "\n", "if __name__ == '__main__':\n", " with timeit('map'):\n", " resoult_1 = list(map(bad_fibonacci, xs))\n", "\n", " with timeit('mapmp'):\n", " resoult_2 = mapmp(bad_fibonacci, xs)\n", " \n", " resoult_1 == resoult_2\n", " # the time printed below is run on a Intel i5 CPU on Ubuntu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:** \n", "\n", "`mapmp` and `mapmt` has same usage, they both support two parameters\n", "\n", "**pool** : int, default None \n", "> the number of Process or Threading, the default is the number of CPUs in the system \n", "\n", "**printfreq** : int or float, default None \n", "> the meaning of `print frequent`, auto print program progress in `mapmt` and `mapmp` \n", "> if `printfreq < 1` then `printfreq = len(iterables[0])*printfreq`\n", "\n", "* It's better to run multi process under `if __name__ == '__main__':`, [see multiprocessing programming guidelines](https://docs.python.org/3/library/multiprocessing.html#multiprocessing-programming)\n", "\n", "* `multiprocessing` may not work on Windows\n", "\n", "---\n", "\n", "In multi process programs, display processing progress is troublesome. \n", "**printfreq** parameter in `mapmp` can handle this problem" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# try run this cell\n", "from boxx import mapmp\n", "from operator import add\n", "\n", "xs = list(range(100))\n", "double_xs = mapmp(add, xs, xs, pool=2, printfreq=.2)\n", "\n", "double_xs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `x_` to quick build function without `lambda x:`" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 4, 9)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import x_\n", "f = x_**2\n", "\n", "f(1), f(2), f(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`x_` often used with map, reduce, filter " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# try run this cell\n", "xs = range(5)\n", "powx = map(x_**x_, xs, xs)\n", "list(powx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `mf` to quick add magic method to function\n", "`mf` is the meaning of \"Magic Method\", to wrap the function that often used while debugging." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- : [5, 1, 7]\n", "* : [5, 1, 7]\n", "**: [5, 1, 7]\n", "/ : (5, 1, 7)\n" ] } ], "source": [ "from boxx import mf\n", "\n", "l = mf(list)\n", "tuplee = (5, 1, 7)\n", "print('- :', l-tuplee)\n", "print('* :', l*tuplee)\n", "print('**:', l**tuplee)\n", "print('/ :', l/tuplee)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:** \n", "\n", " * when `-`, `*`, `**` as magic method: do `f(x)` and return `f(x)`\n", "\n", " * when `/` as magic metho: do `f(x)` but return `x`\n", "\n", " * **Functions that wraps by `mf` in `boxx`**: `stdout`, `log`, `logc`, `printt`, `pblue`, `pred`, `pdanger`, `perr`, `pinfo`, `typestr`, `getfathers`, `getfather`, `nextiter`, `mf`, `plot`, `show`, `showb`, `shows`, `loga`, `tree`, `treem`, `treea`, `dira`, `what`, `wtf`, `tprgb`, `torgb`, `normalizing`, `norma`, `npa`, `histEqualize`, `boolToIndex`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `tree` to visualization complex struct in tree format" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "└── /: \u001b[36mdict 2\u001b[0m\n", " ├── key: \u001b[36mlist 3\u001b[0m\n", " │ ├── 0: \u001b[31m0\u001b[0m\n", " │ ├── 1: \u001b[31mstr\u001b[0m\n", " │ └── 2: \u001b[36mtuple 2\u001b[0m\n", " │ ├── 0: \u001b[31min_tuple\u001b[0m\n", " │ └── 1: \u001b[31mNone\u001b[0m\n", " └── tree: \u001b[31mFunAddMagicMethod(\u001b[0m\n", " ├── \u001b[31m__class__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__context__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__doc__\u001b[0m: \u001b[36mBase class for lookup errors.\u001b[0m\n", " ├── \u001b[31m__eq__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__format__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__ge__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__getattribute__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__hash__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__init__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__init_subclass__\u001b[0m: \u001b[36mbuiltin-method : \u001b[30mThis method is called whe...\u001b[0m\n", " ├── \u001b[31m__le__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__lt__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__ne__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__new__\u001b[0m: \u001b[36mbuiltin-method : \u001b[30mCreate and return a new o...\u001b[0m\n", " ├── \u001b[31m__reduce__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__reduce_ex__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__repr__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__sizeof__\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__str__\u001b[0m: \u001b[36m\u001b[0m\n", " └── \u001b[31mwith_traceback\u001b[0m: \u001b[36m\"\n", "\n", "\u001b[36mClasses: \n", "\u001b[0m└── \u001b[31mInstance\u001b[0m of \u001b[35mmodule\u001b[0m <-\u001b[35mobject\u001b[0m\n", "\n", "\u001b[36mDocument: \n", "\u001b[0m└── \n", " A module provide system info and Python Info for boxx\n", " \n", " @author: yanglei\n", " \n", "\n", "\n", "\u001b[36mAttrs: \u001b[0m\n", "└── \u001b[31mmodule\u001b[0m: \u001b[36m31 attrs, \u001b[30mA module provide system info and Python...\u001b[0m\n", " ├── \u001b[31mPythonInfo\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31mSystemInfo\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__TmpboxxWithCall\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31m__builtins__\u001b[0m: \u001b[36m\u001b[36m【builtins-dict 152 omitted】\u001b[0m\u001b[0m\n", " ├── \u001b[31m__cached__\u001b[0m: \u001b[36m/home/yanglei/mygit/Box-X/boxx/__pycache__/ylsy...\u001b[0m\n", " ├── \u001b[31m__doc__\u001b[0m: \u001b[36m↳A module provide system info and Python Info f...\u001b[0m\n", " ├── \u001b[31m__file__\u001b[0m: \u001b[36m/home/yanglei/mygit/Box-X/boxx/ylsys.py\u001b[0m\n", " ├── \u001b[31m__loader__\u001b[0m: \u001b[36m<_frozen_importlib_external.SourceFileLoader ob...\u001b[0m\n", " ├── \u001b[31m__module\u001b[0m: \u001b[36m\u001b[0m\n", " ├── \u001b[31msysi\u001b[0m: \u001b[36m└── \u001b[31mboxx.ylsys.SystemInfo\u001b[0m: \u001b[36m12 att...\u001b[0m\n", " ├── \u001b[31mtmpYl\u001b[0m: \u001b[36m/tmp/\u001b[0m\n", " ├── \u001b[31mtmpboxx\u001b[0m: \u001b[36m/tmp/boxxTmp/\u001b[0m\n", " ├── \u001b[31musecuda\u001b[0m: \u001b[36mauto\u001b[0m\n", " └── \u001b[31mwinYl\u001b[0m: \u001b[36mFalse\u001b[0m\n", "\n", "----------\u001b[36mend of what(\u001b[0m\u001b[35m\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from boxx import heatmap\n", "\n", "heatmap('./yllab.py')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`heatmap` also support python code string." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from boxx import heatmap\n", "\n", "code = '''\n", "def bad_fibonacci(x): # simulation Complex calculations\n", " if x<=1 :\n", " return 1\n", " return x*bad_fibonacci(x-1)\n", "bad_fibonacci(3)\n", "'''\n", "heatmap(code)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `performance` could statistic function calls and visualize code performance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "from boxx import performance\n", "\n", "performance('./yllab.py') \n", "# broswer will open a web page to visualization code perfomance if possible" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:** if you are runing this Notebook on [Binder](https://mybinder.org/v2/gh/DIYer22/boxx/master?filepath=tutorial_for_boxx.ipynb), Browser won't open the web page. Please see demo here [performance demo.gif](https://raw.githubusercontent.com/DIYer22/boxx/master/other/gif/performance.gif)\n", "\n", "---\n", "\n", "`performance` also support python code string." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "code = '''\n", "def bad_fibonacci(x): # simulation Complex calculations\n", " if x<=1 :\n", " return 1\n", " return x*bad_fibonacci(x-1)\n", "bad_fibonacci(5)\n", "'''\n", "performance(code) \n", "# broswer will open a web page to visualization code perfomance if possible" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `dicto` is a convenient version of `dict`\n", "`dicto` is the meaning of \"dict that like Object\"" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "22\n" ] }, { "data": { "text/plain": [ "{'a': 22, 'b': 517}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import dicto\n", "d = {'a':22}\n", "\n", "dd = dicto(d)\n", "print(dd.a)\n", "\n", "dd.b = 517\n", "dd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:** `dicto` is sub-class of `dict` that is easy to use, allows to get and set `dict` values as attributes. \n", "\n", "BTW, `boxx.cf` is a `dicto` instance that could save your global config, and it could be used at all your `.py` files by `from boxx import cf` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `ll` is a convenient tool for `list`\n", "`ll` is the meaning of \"List tooL\"" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n", "[(0,), (1,)]\n" ] }, { "data": { "text/plain": [ "[0, 1]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import ll\n", "\n", "print(ll * 5) # instead of list(range(5))\n", "\n", "print(ll/zip([0, 1])) # quick way to do `list(x)` when x iterable\n", "\n", "ll # BTW, ll self is a list " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `sysi` include many infomation about operating environment" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36mClasses: \n", "\u001b[0m└── \u001b[31mInstance\u001b[0m of \u001b[35mboxx.ylsys.SystemInfo\u001b[0m <-\u001b[35mobject\u001b[0m\n", "\u001b[36mAttrs: \u001b[0m\n", "Filter by pattern: \"\u001b[31m^[^_]\u001b[0m\"\n", "└── \u001b[31mboxx.ylsys.SystemInfo\u001b[0m: \u001b[36m12 attrs, \u001b[30msys info\u001b[0m\u001b[0m\n", " ├── \u001b[31mcpun\u001b[0m: \u001b[36m4\u001b[0m\n", " ├── \u001b[31mcuda\u001b[0m: \u001b[36mFalse\u001b[0m\n", " ├── \u001b[31mdisplay\u001b[0m: \u001b[36m:0\u001b[0m\n", " ├── \u001b[31mgui\u001b[0m: \u001b[36mTrue\u001b[0m\n", " ├── \u001b[31mhost\u001b[0m: \u001b[36myanglei\u001b[0m\n", " ├── \u001b[31mlinux\u001b[0m: \u001b[36mTrue\u001b[0m\n", " ├── \u001b[31mos\u001b[0m: \u001b[36mlinux\u001b[0m\n", " ├── \u001b[31mosx\u001b[0m: \u001b[36mFalse\u001b[0m\n", " ├── \u001b[31mpyv\u001b[0m: \u001b[36m3\u001b[0m\n", " ├── \u001b[31mtmp\u001b[0m: \u001b[36m/tmp/\u001b[0m\n", " ├── \u001b[31muser\u001b[0m: \u001b[36myanglei\u001b[0m\n", " └── \u001b[31mwin\u001b[0m: \u001b[36mFalse\u001b[0m\n" ] } ], "source": [ "from boxx import dira, sysi\n", "\n", "dira(sysi, pattern='^[^_]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `sysi.cpun`, `sysi.user`, `sysi.host` to let code know wether the environment is local or remote." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Scientific Computing and Computer Vision Tool\n", "\n", "The tools introduced in [**General Python Tool**](#1.-General-Python-Tool) are also useful in Scientific Computing and Computer Vision(SC&CV) field. \n", "\n", "In this section we will introduce tools that only uesed in SC&CV field.\n", "\n", "BTW. Those tools support many array-like types include `numpy`, `torch.tensor`, `mxnet.ndarray`, `PIL.Image`.etc " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `loga` for visualization matrix and tensor\n", "`loga` is the meaning of \"log array\"" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shape:\u001b[31m(5, 3, 244, 244)\u001b[0m type:(\u001b[31mfloat64\u001b[0m of \u001b[31mnumpy.ndarray\u001b[0m) max: \u001b[31m4.9569\u001b[0m, min: \u001b[31m-4.4856\u001b[0m, mean: \u001b[31m-6.047e-4\u001b[0m\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "array = np.random.normal(size=(5,3, 244, 244))\n", "\n", "from boxx import loga\n", "loga(array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:**\n", "\n", "* `loga` analysis the `numpy.ndarray` by it's shape, max, min, mean, and distribute. \n", "\n", "* `loga` support other array-like types include list, `numpy`, `torch.tensor`, `mxnet.ndarray`, `PIL.Image`.etc\n", "\n", "* `loga` will tell you how many `nan`, `inf` in the array if array include `nan`, `inf`:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/yanglei/miniconda3/envs/boxx/lib/python3.6/site-packages/numpy/core/_methods.py:70: RuntimeWarning: invalid value encountered in reduce\n", " ret = umr_sum(arr, axis, dtype, out, keepdims)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "shape:\u001b[31m(5, 3, 244, 244)\u001b[0m type:(\u001b[31mfloat64\u001b[0m of \u001b[31mnumpy.ndarray\u001b[0m) max: \u001b[31mnan\u001b[0m, min: \u001b[31mnan\u001b[0m, mean: \u001b[31mnan\u001b[0m\u001b[31m\n", "\n", "Notice: \u001b[0m\u001b[35m\"nan\":36600 (4.10%), \u001b[0m\u001b[35m\"inf\":70200 (7.86%), \u001b[0m finite max: 4.9569, finite min: -4.4856, finite mean: -9.357e-4\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "array[...,:10] = np.inf\n", "array[...,-10:] = -np.inf\n", "array[...,:10,:] = np.nan\n", "loga(array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `tree` to visualization complex struct for Scientific Computing" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "visualization the struct:\n", "└── /: \u001b[36mdict 4\u001b[0m\n", " ├── path: \u001b[36mtuple 2\u001b[0m\n", " │ ├── 0: \u001b[31mtest/imgForTest/img.jpg\u001b[0m\n", " │ └── 1: \u001b[31mtest/imgForTest/gt_seg.png\u001b[0m\n", " ├── img: \u001b[31m(300, 400, 3)uint8\u001b[0m\n", " ├── gt: \u001b[31m(300, 400)uint8\u001b[0m\n", " └── listt: \u001b[36mlist 2\u001b[0m\n", " ├── 0: \u001b[31m(300, 400, 4)uint8\u001b[0m\n", " └── 1: \u001b[31m(2, 256, 256, 3)uint8\u001b[0m\n" ] } ], "source": [ "# prepare images\n", "import numpy as np\n", "from skimage.io import imread\n", "image_path = 'test/imgForTest/img.jpg'\n", "ground_truth_path = 'test/imgForTest/gt_seg.png'\n", "Lenna = imread('test/imgForTest/Lenna.jpg')\n", "\n", "image = imread(image_path)\n", "ground_truth = imread(ground_truth_path)\n", "\n", "# complex struct\n", "batch = dict(\n", " path=(image_path, ground_truth_path),\n", " img=image, \n", " gt=ground_truth, \n", " listt=[\n", " np.append(image, ground_truth[..., None], -1),\n", " np.array([Lenna, Lenna]),\n", " ],\n", ")\n", "\n", "from boxx import tree\n", "print('visualization the struct:')\n", "tree(batch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like `tree` command in shell, `boxx.tree` could visualization complex struct (like `a batch of data`) in tree format. \n", "💡 **Note:**\n", "\n", " * Support types include `list`, `tuple`, `dict`, `numpy`, `torch.tensor`, `mxnet.ndarray`, `PIL.Image`.etc \n", "\n", " * Support sample a batch from `torch.Dataset`, `torch.DataLoader`. then visualization the batch's struct." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `show` is easy to do `imshow`, even images are in complex struct" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from skimage.io import imread\n", "Lenna = imread('test/imgForTest/Lenna.jpg')\n", "\n", "from boxx import show\n", "show(Lenna)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "↓ `show` could find every image in complex struct and `plt.imshow` they." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the struct of batch:\n", "└── /: \u001b[36mdict 4\u001b[0m\n", " ├── path: \u001b[36mtuple 2\u001b[0m\n", " │ ├── 0: \u001b[31mtest/imgForTest/img.jpg\u001b[0m\n", " │ └── 1: \u001b[31mtest/imgForTest/gt_seg.png\u001b[0m\n", " ├── img: \u001b[31m(300, 400, 3)uint8\u001b[0m\n", " ├── gt: \u001b[31m(300, 400)uint8\u001b[0m\n", " └── listt: \u001b[36mlist 2\u001b[0m\n", " ├── 0: \u001b[31m(300, 400, 4)uint8\u001b[0m\n", " └── 1: \u001b[31m(2, 256, 256, 3)uint8\u001b[0m\n", "show all images in batch:\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "# prepare images\n", "import numpy as np\n", "from skimage.io import imread\n", "image_path = 'test/imgForTest/img.jpg'\n", "ground_truth_path = 'test/imgForTest/gt_seg.png'\n", "Lenna = imread('test/imgForTest/Lenna.jpg')\n", "\n", "image = imread(image_path)\n", "ground_truth = imread(ground_truth_path)\n", "\n", "# complex struct\n", "batch = dict(\n", " path=(image_path, ground_truth_path),\n", " img=image, \n", " gt=ground_truth, \n", " listt=[\n", " np.append(image, ground_truth[..., None], -1),\n", " np.array([Lenna, Lenna]),\n", " ],\n", ")\n", "\n", "from boxx import show, tree\n", "print('the struct of batch:')\n", "tree(batch)\n", "print('show all images in batch:')\n", "show(batch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "💡 **Note:**\n", "\n", " * Support image types include `numpy`, `torch.tensor`, `mxnet.ndarray`, `PIL.Image`.etc \n", "\n", " * And Support sample a batch from `torch.Dataset`, `torch.DataLoader`, then `plt.imshow` the batch." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ▶ `npa` transform other array-like object to numpy in one way\n", "`npa` is the meaning of \"numpy.array\", use magic method to quick transform other numpy like object to numpy, suport `torch.tensor`, `mxnet.ndarray`, `PIL.Image`, `list`, `tuple` .etc" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2]\n" ] }, { "data": { "text/plain": [ "array([[0, 1, 2],\n", " [0, 1, 2]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from boxx import npa\n", "\n", "print(npa-range(3))\n", "\n", "import numpy as np\n", "r = npa-range(3)\n", "npa**[r, r]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }