{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive\n"
     ]
    }
   ],
   "source": [
    "% cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive\n"
     ]
    }
   ],
   "source": [
    "% cd ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.md\r\n",
      "\u001b[34mSpeech\u001b[m\u001b[m\r\n",
      "The Python Challenge.webloc\r\n",
      "Untitled.ipynb\r\n",
      "Untitled1.ipynb\r\n",
      "Untitled2.ipynb\r\n",
      "Untitled3.ipynb\r\n",
      "\u001b[34maaa\u001b[m\u001b[m\r\n",
      "assignment-1.ipynb\r\n",
      "assignment-2.ipynb\r\n",
      "assignment-3.ipynb\r\n",
      "assignment-4.ipynb\r\n",
      "assignment-5.ipynb\r\n",
      "\u001b[34mexample\u001b[m\u001b[m\r\n",
      "\u001b[34mfiles\u001b[m\u001b[m\r\n",
      "\u001b[34mimages\u001b[m\u001b[m\r\n",
      "module_test.py\r\n",
      "music\r\n",
      "mymath.py\r\n",
      "mymath.pyc\r\n",
      "pickle.txt\r\n",
      "pickle2.txt\r\n",
      "python01.ipynb\r\n",
      "python02.ipynb\r\n",
      "python03.ipynb\r\n",
      "python04.ipynb\r\n",
      "python05.ipynb\r\n",
      "python06.ipynb\r\n",
      "python07.ipynb\r\n",
      "python08.ipynb\r\n",
      "python09.ipynb\r\n",
      "python10.ipynb\r\n",
      "python11.ipynb\r\n",
      "python12.ipynb\r\n",
      "python13.ipynb\r\n",
      "python14.ipynb\r\n",
      "python15.ipynb\r\n",
      "python16.ipynb\r\n",
      "python17.ipynb\r\n",
      "python18.ipynb\r\n",
      "python19.ipynb\r\n",
      "python20.ipynb\r\n",
      "python21.ipynb\r\n",
      "python22.ipynb\r\n",
      "python23.ipynb\r\n",
      "\u001b[34mpython3.6\u001b[m\u001b[m\r\n",
      "readme.txt\r\n",
      "removeme.txt\r\n",
      "\u001b[31msample.txt\u001b[m\u001b[m\r\n",
      "sample_new.txt\r\n",
      "supplement-2016-09.ipynb\r\n",
      "supplement.ipynb\r\n",
      "t.txt\r\n",
      "t1.txt\r\n",
      "t2.txt\r\n",
      "turtle_example.png\r\n",
      "turtle_method-1.png\r\n",
      "turtle_method-2.png\r\n",
      "turtle_method-3.png\r\n",
      "범이의 데이터 아키텍처 -- [Python] [개념을 잡아주는 프로그래밍 정석] 4.8 연습문제.webloc\r\n"
     ]
    }
   ],
   "source": [
    "!ls ./script_programming/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.md\r\n",
      "\u001b[34mSpeech\u001b[m\u001b[m/\r\n",
      "The Python Challenge.webloc\r\n",
      "Untitled.ipynb\r\n",
      "Untitled1.ipynb\r\n",
      "Untitled2.ipynb\r\n",
      "Untitled3.ipynb\r\n",
      "\u001b[34maaa\u001b[m\u001b[m/\r\n",
      "assignment-1.ipynb\r\n",
      "assignment-2.ipynb\r\n",
      "assignment-3.ipynb\r\n",
      "assignment-4.ipynb\r\n",
      "assignment-5.ipynb\r\n",
      "\u001b[34mexample\u001b[m\u001b[m/\r\n",
      "\u001b[34mfiles\u001b[m\u001b[m/\r\n",
      "\u001b[34mimages\u001b[m\u001b[m/\r\n",
      "module_test.py\r\n",
      "music\r\n",
      "mymath.py\r\n",
      "mymath.pyc\r\n",
      "pickle.txt\r\n",
      "pickle2.txt\r\n",
      "python01.ipynb\r\n",
      "python02.ipynb\r\n",
      "python03.ipynb\r\n",
      "python04.ipynb\r\n",
      "python05.ipynb\r\n",
      "python06.ipynb\r\n",
      "python07.ipynb\r\n",
      "python08.ipynb\r\n",
      "python09.ipynb\r\n",
      "python10.ipynb\r\n",
      "python11.ipynb\r\n",
      "python12.ipynb\r\n",
      "python13.ipynb\r\n",
      "python14.ipynb\r\n",
      "python15.ipynb\r\n",
      "python16.ipynb\r\n",
      "python17.ipynb\r\n",
      "python18.ipynb\r\n",
      "python19.ipynb\r\n",
      "python20.ipynb\r\n",
      "python21.ipynb\r\n",
      "python22.ipynb\r\n",
      "python23.ipynb\r\n",
      "\u001b[34mpython3.6\u001b[m\u001b[m/\r\n",
      "readme.txt\r\n",
      "removeme.txt\r\n",
      "\u001b[31msample.txt\u001b[m\u001b[m*\r\n",
      "sample_new.txt\r\n",
      "supplement-2016-09.ipynb\r\n",
      "supplement.ipynb\r\n",
      "t.txt\r\n",
      "t1.txt\r\n",
      "t2.txt\r\n",
      "turtle_example.png\r\n",
      "turtle_method-1.png\r\n",
      "turtle_method-2.png\r\n",
      "turtle_method-3.png\r\n",
      "범이의 데이터 아키텍처 -- [Python] [개념을 잡아주는 프로그래밍 정석] 4.8 연습문제.webloc\r\n"
     ]
    }
   ],
   "source": [
    "%ls ./script_programming/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_+ 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def square(x):\n",
    "    return x+x\n",
    "def cube(x):\n",
    "    return x*x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "funcs = {\n",
    "    'square' : square,\n",
    "    'cube' : cube,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "8\n",
      "cube 8\n",
      "square 4\n"
     ]
    }
   ],
   "source": [
    "x = 2\n",
    "print(square(x))\n",
    "print(cube(x))\n",
    "\n",
    "for func in sorted(funcs):\n",
    "    print(func, funcs[func](x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cube', 'square']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(funcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Errno 2] No such file or directory: 'TEST'\n",
      "/Users/do-hyungkwon/GoogleDrive\n"
     ]
    }
   ],
   "source": [
    "%cd TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/jupyter_notebook\n"
     ]
    }
   ],
   "source": [
    "%cd /Users/do-hyungkwon/GoogleDrive/jupyter_notebook/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN 랩세미나.ipynb\r\n",
      "Data_preprocessing_작업_1.ipynb\r\n",
      "Data_preprocessing_작업_2.ipynb\r\n",
      "\u001b[34mMNIST_data\u001b[m\u001b[m\r\n",
      "MNIST_load_관련.ipynb\r\n",
      "MODEL_작업.ipynb\r\n",
      "One Hidden Layer with Backpropagation.ipynb\r\n",
      "\u001b[34mRP_data\u001b[m\u001b[m\r\n",
      "\u001b[34mRP_data_2\u001b[m\u001b[m\r\n",
      "\u001b[34mRP_data_3\u001b[m\u001b[m\r\n",
      "\u001b[34mRP_data_4\u001b[m\u001b[m\r\n",
      "Two Hidden Layers with Backward Propagation.ipynb\r\n",
      "Two_hidden_layers_tf_all_in_one (1).ipynb\r\n",
      "Two_hidden_layers_tf_all_in_one.ipynb\r\n",
      "cvt_img.jpg\r\n",
      "data_preprocessing.ipynb\r\n",
      "data_preprocessing_only_code.ipynb\r\n",
      "\u001b[34mdataset\u001b[m\u001b[m\r\n",
      "\u001b[34mdataset 복사본\u001b[m\u001b[m\r\n",
      "dataset.zip\r\n",
      "\u001b[34mdataset2_gray\u001b[m\u001b[m\r\n",
      "\u001b[34mdataset_jpg\u001b[m\u001b[m\r\n",
      "\u001b[34mdataset_jpg_gray\u001b[m\u001b[m\r\n",
      "\u001b[34mdataset_old\u001b[m\u001b[m\r\n",
      "\u001b[34mgithub_reference\u001b[m\u001b[m\r\n",
      "image.jpg\r\n",
      "image.png\r\n",
      "image2.jpg\r\n",
      "image2.png\r\n",
      "\u001b[34mimage_dataset_origin\u001b[m\u001b[m\r\n",
      "image_grayscale.jpg\r\n",
      "image_grayscale.png\r\n",
      "image_jpg.jpg\r\n",
      "image_resolution.ipynb\r\n",
      "image_resolution.jpg\r\n",
      "image_resolution.png\r\n",
      "image_resolution_gray.jpg\r\n",
      "\u001b[34mimdata\u001b[m\u001b[m\r\n",
      "jpg_image.jpg\r\n",
      "mnist.pkl\r\n",
      "out.txt\r\n",
      "\u001b[34moutput\u001b[m\u001b[m\r\n",
      "resized_image.jpg\r\n",
      "t10k-images-idx3-ubyte.gz\r\n",
      "t10k-images.idx3-ubyte\r\n",
      "t10k-images.idx3-ubyte.textClipping\r\n",
      "t10k-labels-idx1-ubyte.gz\r\n",
      "test-input-image-large.jpg\r\n",
      "test_img_dataset.txt\r\n",
      "test_label.txt\r\n",
      "train-images-idx3-ubyte.gz\r\n",
      "train-labels-idx1-ubyte.gz\r\n",
      "train_image_1\r\n",
      "train_img_dataset (1).txt\r\n",
      "train_img_dataset.txt\r\n",
      "train_label.txt\r\n",
      "transpose 시키기.ipynb\r\n",
      "validation_img_dataset.txt\r\n",
      "validation_label.txt\r\n",
      "\u001b[34m무제 폴더\u001b[m\u001b[m\r\n",
      "\u001b[34m압축된 파일\u001b[m\u001b[m\r\n",
      "\u001b[34m압축된 파일 2\u001b[m\u001b[m\r\n",
      "압축된 파일 2.zip\r\n",
      "압축된 파일.zip\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing test.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile test.txt\n",
    "Hello World!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "with open('test.txt', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/sh: ㅣㄴ: command not found\r\n"
     ]
    }
   ],
   "source": [
    "!ㅣㄴ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!cd ../deeplink\\ 복사본\\(170830\\)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/jupyter_notebook\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\n"
     ]
    }
   ],
   "source": [
    "%cd /Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mdeep_convnet.py\u001b[m\u001b[m       \u001b[34mdeeplink\u001b[m\u001b[m              \u001b[31mtrain_deepnet.py\u001b[m\u001b[m\r\n",
      "\u001b[31mdeep_convnet_large.py\u001b[m\u001b[m \u001b[31mrealtime_deepnet.py\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run deep_convnet.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/script_programming\n"
     ]
    }
   ],
   "source": [
    "%cd /Users/do-hyungkwon/GoogleDrive/script_programming/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/script_programming\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.md\r\n",
      "\u001b[34mSpeech\u001b[m\u001b[m\r\n",
      "The Python Challenge.webloc\r\n",
      "Untitled.ipynb\r\n",
      "Untitled1.ipynb\r\n",
      "Untitled2.ipynb\r\n",
      "Untitled3.ipynb\r\n",
      "\u001b[34maaa\u001b[m\u001b[m\r\n",
      "assignment-1.ipynb\r\n",
      "assignment-2.ipynb\r\n",
      "assignment-3.ipynb\r\n",
      "assignment-4.ipynb\r\n",
      "assignment-5.ipynb\r\n",
      "\u001b[34mexample\u001b[m\u001b[m\r\n",
      "\u001b[34mfiles\u001b[m\u001b[m\r\n",
      "\u001b[34mimages\u001b[m\u001b[m\r\n",
      "module_test.py\r\n",
      "music\r\n",
      "mymath.py\r\n",
      "mymath.pyc\r\n",
      "pickle.txt\r\n",
      "pickle2.txt\r\n",
      "python01.ipynb\r\n",
      "python02.ipynb\r\n",
      "python03.ipynb\r\n",
      "python04.ipynb\r\n",
      "python05.ipynb\r\n",
      "python06.ipynb\r\n",
      "python07.ipynb\r\n",
      "python08.ipynb\r\n",
      "python09.ipynb\r\n",
      "python10.ipynb\r\n",
      "python11.ipynb\r\n",
      "python12.ipynb\r\n",
      "python13.ipynb\r\n",
      "python14.ipynb\r\n",
      "python15.ipynb\r\n",
      "python16.ipynb\r\n",
      "python17.ipynb\r\n",
      "python18.ipynb\r\n",
      "python19.ipynb\r\n",
      "python20.ipynb\r\n",
      "python21.ipynb\r\n",
      "python22.ipynb\r\n",
      "python23.ipynb\r\n",
      "\u001b[34mpython3.6\u001b[m\u001b[m\r\n",
      "readme.txt\r\n",
      "removeme.txt\r\n",
      "\u001b[31msample.txt\u001b[m\u001b[m\r\n",
      "sample_new.txt\r\n",
      "supplement-2016-09.ipynb\r\n",
      "supplement.ipynb\r\n",
      "t.txt\r\n",
      "t1.txt\r\n",
      "t2.txt\r\n",
      "turtle_example.png\r\n",
      "turtle_method-1.png\r\n",
      "turtle_method-2.png\r\n",
      "turtle_method-3.png\r\n",
      "범이의 데이터 아키텍처 -- [Python] [개념을 잡아주는 프로그래밍 정석] 4.8 연습문제.webloc\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/script_programming/files\n"
     ]
    }
   ],
   "source": [
    "%cd files/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/do-hyungkwon/GoogleDrive/script_programming/files'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cal.py\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   November 2014\n",
      "Mo Tu We Th Fr Sa Su\n",
      "                1  2\n",
      " 3  4  5  6  7  8  9\n",
      "10 11 12 13 14 15 16\n",
      "17 18 19 20 21 22 23\n",
      "24 25 26 27 28 29 30\n"
     ]
    }
   ],
   "source": [
    "run cal.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from random import random\n",
    "list1 = [random() for _ in range(1000)]\n",
    "list2 = [random() for _ in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       " 0.18145865993017674,\n",
       " 0.09134103190575293,\n",
       " 0.3231558991782195,\n",
       " 0.8446415920601732,\n",
       " 0.4321187683248696,\n",
       " 0.40087210145386787,\n",
       " 0.7834198704566174,\n",
       " 0.29055149263098945,\n",
       " 0.6518876180976012,\n",
       " 0.6099160154393811,\n",
       " 0.9870452328370816,\n",
       " 0.008320756235908888,\n",
       " 0.2773119610829933,\n",
       " 0.870675552187072,\n",
       " 0.961880190034377,\n",
       " 0.9568629317594524,\n",
       " 0.04779194428477007,\n",
       " 0.12545196673393544]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "arr1 = np.array(list1)\n",
    "arr2 = np.array(list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8,)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.strides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/do-hyungkwon/GoogleDrive/script_programming/files'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/Downloads\n"
     ]
    }
   ],
   "source": [
    "cd /Users/do-hyungkwon/Downloads/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017011014840263686501382.jpg\r\n",
      "2017011014840269617281400.jpg\r\n",
      "2017011014840273545251412.jpg\r\n",
      "2017012014849019897551623.jpg\r\n",
      "2017012014849025086141638.jpg\r\n",
      "2017012314851700225172162.jpg\r\n",
      "2017012314851719908292210.jpg\r\n",
      "2017년도 이공분야 대학중점연구소지원사업 연구개발과제계획서(신청용).pdf\r\n",
      "< 2016 첨단기술연구소 정기 학술 워크샵 개최 >.jpg\r\n",
      "< 연구소 발행 학술지 표지 >3.jpg\r\n",
      "< 산학협력관 202호 세미나실 >1.jpg\r\n",
      "< 산학협력관 202호 세미나실 >2..jpg\r\n",
      "School_Bus_Picture4.hwp\r\n",
      "java-json.jar.zip\r\n",
      "★ ATRC Image Works IV.pdf\r\n",
      "\u001b[34m무제 폴더\u001b[m\u001b[m/\r\n",
      "붙임3_안전교육신청 방법.pdf\r\n",
      "붙임4_연구활동종사자 교육ㆍ훈련의 시간 및 내용(제9조제1항 관련).hwp\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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ci2S7sxE0sjhmjkOMncwyCR9ePxry6tBwldPc6IyuSRSW0ULskaYQlI2jYkMT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HFJK8+0KMx4JB7HuSRVKOxuLJfMwIySVw6dKxnF0p2KTui3FaTpeSrJdxW0ikCTcN2Se4Iq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\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sn5Qv0AqpJpura7pPnajeMn2eI/ZoW785wfTiuOeElOfvGqnZHYaFHFqF0923lBDENxb5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mu3d5OibI2gL4MpYA4+nWty5tY7yJNxOFO5GHY1cainG8QsY7+IilxhggjBwV/i/P0rTt797q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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"2017011014840273545251412.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPython.core.display.Image"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img = np.asarray(Image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-02e97476df11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "img[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/y1f-YHw1uNs\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x111d82d68>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"y1f-YHw1uNs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/y1f-YHw1uNs\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x111daae80>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"y1f-YHw1uNs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "files = !ls -l | grep ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total 23088',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   105121  8 31 21:08 2017011014840263686501382.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   313258  8 31 21:09 2017011014840269617281400.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   251477  8 31 21:09 2017011014840273545251412.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   135565  8 31 21:10 2017012014849019897551623.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   235959  8 31 21:10 2017012014849025086141638.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   128405  8 31 21:11 2017012314851700225172162.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   209520  8 31 21:12 2017012314851719908292210.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  2599836  8 19 15:47 2017년도 이공분야 대학중점연구소지원사업 연구개발과제계획서(신청용).pdf',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  1116459  8 31 12:32 < 2016 첨단기술연구소 정기 학술 워크샵 개최 >.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   438798  8 31 12:33 < 연구소 발행 학술지 표지 >3.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   220185  8 31 12:33 < 산학협력관 202호 세미나실 >1.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   203689  8 31 12:33 < 산학협력관 202호 세미나실 >2..jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  2450432  8 31 11:47 School_Bus_Picture4.hwp',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff    81955  8 31 16:19 java-json.jar.zip',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  2823105  8 19 15:47 ★ ATRC Image Works IV.pdf',\n",
       " 'drwxr-xr-x  5 do-hyungkwon  staff      170  8 31 21:16 무제 폴더',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   447686  8 31 11:33 붙임3_안전교육신청 방법.pdf',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff    16896  8 31 11:33 붙임4_연구활동종사자 교육ㆍ훈련의 시간 및 내용(제9조제1항 관련).hwp']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total 23088',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  2823105  8 19 15:47 ★ ATRC Image Works IV.pdf',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  2599836  8 19 15:47 2017년도 이공분야 대학중점연구소지원사업 연구개발과제계획서(신청용).pdf',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  2450432  8 31 11:47 School_Bus_Picture4.hwp',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff  1116459  8 31 12:32 < 2016 첨단기술연구소 정기 학술 워크샵 개최 >.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   447686  8 31 11:33 붙임3_안전교육신청 방법.pdf',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   438798  8 31 12:33 < 연구소 발행 학술지 표지 >3.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   313258  8 31 21:09 2017011014840269617281400.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   251477  8 31 21:09 2017011014840273545251412.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   235959  8 31 21:10 2017012014849025086141638.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   220185  8 31 12:33 < 산학협력관 202호 세미나실 >1.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   209520  8 31 21:12 2017012314851719908292210.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   203689  8 31 12:33 < 산학협력관 202호 세미나실 >2..jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   135565  8 31 21:10 2017012014849019897551623.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   128405  8 31 21:11 2017012314851700225172162.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff   105121  8 31 21:08 2017011014840263686501382.jpg',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff    81955  8 31 16:19 java-json.jar.zip',\n",
       " '-rw-r--r--@ 1 do-hyungkwon  staff    16896  8 31 11:33 붙임4_연구활동종사자 교육ㆍ훈련의 시간 및 내용(제9조제1항 관련).hwp',\n",
       " 'drwxr-xr-x  5 do-hyungkwon  staff      170  8 31 21:16 무제 폴더']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = !ls -l -S | grep .\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "profile": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% cd ..\n",
      "% .\n",
      "% ..\n",
      "% cd .\n",
      "!ls *.ipynb\n",
      "!ls *.ipynb\n",
      "%ls *.ipynb\n",
      "!ls\n",
      "!ls ./deeplink/5.DoHyung/\n",
      "!ls ./\n",
      "!ls ./script_programming/\n",
      "%ls ./script_programming/\n",
      "list?\n",
      "1 + 1\n",
      "_+ 10\n",
      "def square(x):\n",
      "    return x+x\n",
      "def cube(x):\n",
      "    return x*x*x\n",
      "funcs = {\n",
      "    'square' : square,\n",
      "    'cube' : cube,\n",
      "}\n",
      "x = 2\n",
      "print(square(x))\n",
      "print(cube(x))\n",
      "\n",
      "for func in sorted(funcs):\n",
      "    print(func, funcs[func](x))\n",
      "sorted(funcs)\n",
      "x = 2\n",
      "print(square(x))\n",
      "print(cube(x))\n",
      "\n",
      "for func, val in sorted(funcs):\n",
      "    print(func)\n",
      "    print(val)\n",
      "#     print(func, funcs[func](x))\n",
      "x = 2\n",
      "print(square(x))\n",
      "print(cube(x))\n",
      "\n",
      "for func, val in sorted(funcs):\n",
      "    print(func, funcs[func](x))\n",
      "x = 2\n",
      "print(square(x))\n",
      "print(cube(x))\n",
      "\n",
      "for func, val in sorted(funcs):\n",
      "    print(func, funcs[func](x))\n",
      "x = 2\n",
      "print(square(x))\n",
      "print(cube(x))\n",
      "\n",
      "for funcin sorted(funcs):\n",
      "    print(func, funcs[func](x))\n",
      "x = 2\n",
      "print(square(x))\n",
      "print(cube(x))\n",
      "\n",
      "for func in sorted(funcs):\n",
      "    print(func, funcs[func](x))\n",
      "%cd TEST\n",
      "%cd /Users/do-hyungkwon/GoogleDrive/jupyter_notebook/\n",
      "!ls\n",
      "%%writefile test.txt\n",
      "%%writefile test.txt\n",
      "Hello World!\n",
      "with open('test.txt', 'r') as f:\n",
      "    print(f.read())\n",
      "!ㅣㄴ\n",
      "!ls\n",
      "!ls *.py\n",
      "!pwd\n",
      "!cd ../deeplink\\ 복사본\\(170830\\)\n",
      "!pwd\n",
      "! cd ../deeplink/5.DoHyung/raspberrypi4/\n",
      "!pwd\n",
      "!cd /Users/do-hyungkwon/GoogleDrive/deeplink\\ 복사본\\(170830\\)/5.DoHyung/raspberrypi5/\n",
      "!pwd\n",
      "!pwd\n",
      "!cd /Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\n",
      "!pwd\n",
      "%cd /Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\n",
      "!pwd\n",
      "!ls\n",
      "%run deep_convnet.py\n",
      "%cd /Users/do-hyungkwon/GoogleDrive/script_programming/\n",
      "!pwd\n",
      "!ls\n",
      "%cd files/\n",
      "pwd\n",
      "ls\n",
      "run cal.py\n",
      "from random import random\n",
      "list1 = [random() for _ in range(1000)]\n",
      "list2 = [random() for _ in range(1000)]\n",
      "list1\n",
      "import numpy as np\n",
      "arr1 = np.array(list1)\n",
      "arr2 = np.array(list2)\n",
      "arr1.shape\n",
      "arr1.dtype\n",
      "arr1.strides\n",
      "arr1.ndim\n",
      "from IPython.display import Image\n",
      "pwd\n",
      "cd /Users/do-hyungkwon/Downloads/\n",
      "ls\n",
      "Image(filename=2017011014840273545251412.jpg)\n",
      "Image(filename=\"2017011014840273545251412.jpg\")\n",
      "type(Image)\n",
      "Image\n",
      "img = np.asarray(Image)\n",
      "type(img)\n",
      "img.shape\n",
      "img\n",
      "img[0]\n",
      "from IPython.display import YouTubeVideo\n",
      "YouTubeVideo(\"lmoNmY-cmSI\")\n",
      "YouTubeVideo(\"y1f-YHw1uNs\")\n",
      "YouTubeVideo(\"y1f-YHw1uNs\")\n",
      "YouTubeVideo(\"y1f-YHw1uNs\")\n",
      "YouTubeVideo(\"y1f-YHw1uNs\")\n",
      "YouTubeVideo(\"y1f-YHw1uNs\")\n",
      "files = !ls -l -S | grep .\n",
      "files\n",
      "files\n",
      "files = !ls -l | grep .\n",
      "files\n",
      "files = !ls -l -S | grep .\n",
      "files\n",
      "lsmagic\n",
      "history\n"
     ]
    }
   ],
   "source": [
    "history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: %%capture is a cell magic, but the cell body is empty.\n"
     ]
    }
   ],
   "source": [
    "%%capture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable                      Type                          Data/Info\n",
      "---------------------------------------------------------------------\n",
      "AdaGrad                       type                          <class 'deeplink.optimizers.AdaGrad'>\n",
      "Adam                          type                          <class 'deeplink.optimizers.Adam'>\n",
      "Affine                        type                          <class 'deeplink.layers.Affine'>\n",
      "BatchNormalization            type                          <class 'deeplink.layers.BatchNormalization'>\n",
      "Convolution                   type                          <class 'deeplink.layers.Convolution'>\n",
      "DeepConvNet                   type                          <class '__main__.DeepConvNet'>\n",
      "Dropout                       type                          <class 'deeplink.layers.Dropout'>\n",
      "Image                         type                          <class 'IPython.core.display.Image'>\n",
      "Momentum                      type                          <class 'deeplink.optimizers.Momentum'>\n",
      "Nesterov                      type                          <class 'deeplink.optimizers.Nesterov'>\n",
      "Pooling                       type                          <class 'deeplink.layers.Pooling'>\n",
      "RMSprop                       type                          <class 'deeplink.optimizers.RMSprop'>\n",
      "ReLU                          type                          <class 'deeplink.layers.ReLU'>\n",
      "SGD                           type                          <class 'deeplink.optimizers.SGD'>\n",
      "Sigmoid                       type                          <class 'deeplink.layers.Sigmoid'>\n",
      "SoftmaxWithCrossEntropyLoss   type                          <class 'deeplink.layers.S<...>maxWithCrossEntropyLoss'>\n",
      "YouTubeVideo                  type                          <class 'IPython.lib.display.YouTubeVideo'>\n",
      "arr1                          ndarray                       1000: 1000 elems, type `float64`, 8000 bytes\n",
      "arr2                          ndarray                       1000: 1000 elems, type `float64`, 8000 bytes\n",
      "calendar                      module                        <module 'calendar' from '<...>b/python3.6/calendar.py'>\n",
      "col2im                        function                      <function col2im at 0x111c84048>\n",
      "cross_entropy_error           function                      <function cross_entropy_error at 0x111bceae8>\n",
      "cube                          function                      <function cube at 0x1112f3950>\n",
      "f                             TextIOWrapper                 <_io.TextIOWrapper name='<...>ode='r' encoding='UTF-8'>\n",
      "files                         SList                         ['total 23088', '-rw-r--r<...>  8 31 21:16 무제 폴더']\n",
      "func                          str                           square\n",
      "funcs                         dict                          n=2\n",
      "identity_function             function                      <function identity_function at 0x111bce620>\n",
      "im2col                        function                      <function im2col at 0x111bcef28>\n",
      "img                           ndarray                       : 1 elems, type `object`, 8 bytes\n",
      "list1                         list                          n=1000\n",
      "list2                         list                          n=1000\n",
      "mean_squared_error            function                      <function mean_squared_error at 0x111bcea60>\n",
      "np                            module                        <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>\n",
      "optimizers                    dict                          n=6\n",
      "os                            module                        <module 'os' from '/Users<...>nda/lib/python3.6/os.py'>\n",
      "pickle                        module                        <module 'pickle' from '/U<...>lib/python3.6/pickle.py'>\n",
      "random                        builtin_function_or_method    <built-in method random o<...>object at 0x7fadcf042618>\n",
      "relu                          function                      <function relu at 0x111bce8c8>\n",
      "relu_grad                     function                      <function relu_grad at 0x111bce950>\n",
      "sigmoid                       function                      <function sigmoid at 0x111bce7b8>\n",
      "sigmoid_grad                  function                      <function sigmoid_grad at 0x111bce840>\n",
      "softmax                       function                      <function softmax at 0x111bce9d8>\n",
      "softmax_loss                  function                      <function softmax_loss at 0x111bceb70>\n",
      "square                        function                      <function square at 0x1112f3a60>\n",
      "step_function                 function                      <function step_function at 0x111bce730>\n",
      "sys                           module                        <module 'sys' (built-in)>\n",
      "x                             int                           2\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%magic ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ᅦᄌᄋ' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-d8574cd7073a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mㅔㅈㅇ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'ᅦᄌᄋ' is not defined"
     ]
    }
   ],
   "source": [
    "ㅔㅈㅇ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/do-hyungkwon/GoogleDrive/script_programming'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\n"
     ]
    }
   ],
   "source": [
    "cd /Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 64\r\n",
      "-rwxr-xr-x   1 do-hyungkwon  staff   9605  8 31 10:24 \u001b[31mdeep_convnet.py\u001b[m\u001b[m*\r\n",
      "-rwxr-xr-x   1 do-hyungkwon  staff  11667  8 31 10:24 \u001b[31mdeep_convnet_large.py\u001b[m\u001b[m*\r\n",
      "drwxr-xr-x  12 do-hyungkwon  staff    408  9  1 10:36 \u001b[34mdeeplink\u001b[m\u001b[m/\r\n",
      "-rwxr-xr-x   1 do-hyungkwon  staff    430  8 31 10:24 \u001b[31mrealtime_deepnet.py\u001b[m\u001b[m*\r\n",
      "-rwxr-xr-x   1 do-hyungkwon  staff   3830  8 31 10:24 \u001b[31mtrain_deepnet.py\u001b[m\u001b[m*\r\n"
     ]
    }
   ],
   "source": [
    "ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %load deep_convnet.py\n",
    "import sys, os\n",
    "import pickle\n",
    "import numpy as np\n",
    "from deeplink.layers import *\n",
    "from deeplink.optimizers import *\n",
    "\n",
    "optimizers = {\n",
    "    \"SGD\": SGD,\n",
    "    \"Momentum\": Momentum,\n",
    "    \"Nesterov\": Nesterov,\n",
    "    \"AdaGrad\": AdaGrad,\n",
    "    \"RMSprop\": RMSprop,\n",
    "    \"Adam\": Adam\n",
    "}\n",
    "\n",
    "class DeepConvNet:\n",
    "    \"\"\"\n",
    "        conv - relu - poll - conv- relu - pool -\n",
    "        affine - relu - dropout - affine - dropout - softmax\n",
    "    \"\"\"\n",
    "    def __init__(self, input_dim=(1, 320, 240),\n",
    "                 conv_param_1 = {'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1},\n",
    "                 pool_param_1 = {'size': 2, 'stride': 2},\n",
    "                 conv_param_2 = {'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1},\n",
    "                 pool_param_2 = {'size': 2, 'stride': 2},\n",
    "                 hidden_size=128, output_size=3, optimizer='AdaGrad', learning_rate=0.01, train_flg=False):\n",
    "\n",
    "        channel_num = input_dim[0]\n",
    "        layer_input_width = input_dim[1]\n",
    "        layer_input_height = input_dim[2]\n",
    "\n",
    "        self.last_conv_pool_input_width, self.last_conv_pool_input_height = self.get_conv_pool_last_output_size(input_dim[1], input_dim[2], conv_param_1, pool_param_1, conv_param_2, pool_param_2, train_flg)\n",
    "\n",
    "        pre_node_nums = np.array([\n",
    "            channel_num * conv_param_1['filter_size'] * conv_param_1['filter_size'],\n",
    "            conv_param_1['filter_num'] * conv_param_2['filter_size'] * conv_param_2['filter_size'],\n",
    "            conv_param_2['filter_num'] * self.last_conv_pool_input_width * self.last_conv_pool_input_height,\n",
    "            hidden_size])\n",
    "\n",
    "        weight_init_scales = np.sqrt(2.0 / pre_node_nums)\n",
    "\n",
    "        self.params = {}\n",
    "\n",
    "        for idx, conv_param in enumerate([conv_param_1, conv_param_2]):\n",
    "            self.params['W' + str(idx+1)] = weight_init_scales[idx] * np.random.randn(conv_param['filter_num'], channel_num, conv_param['filter_size'], conv_param['filter_size'])\n",
    "            self.params['b' + str(idx+1)] = np.zeros(conv_param['filter_num'])\n",
    "            channel_num = conv_param['filter_num']\n",
    "\n",
    "        self.params['W3'] = weight_init_scales[2] * np.random.randn(16 * self.last_conv_pool_input_width * self.last_conv_pool_input_height, hidden_size)\n",
    "        self.params['b3'] = np.zeros(hidden_size)\n",
    "        self.params['W4'] = weight_init_scales[3] * np.random.randn(hidden_size, output_size)\n",
    "        self.params['b4'] = np.zeros(output_size)\n",
    "\n",
    "        self.layers = []\n",
    "        self.layers.append(Convolution(self.params['W1'], self.params['b1'],\n",
    "                                       conv_param_1['stride'], conv_param_1['pad']))\n",
    "        self.layers.append(ReLU())\n",
    "        self.layers.append(Pooling(pool_h=pool_param_1['size'], pool_w=pool_param_1['size'], stride=pool_param_1['stride']))\n",
    "        self.layers.append(Convolution(self.params['W2'], self.params['b2'],\n",
    "                                       conv_param_2['stride'], conv_param_2['pad']))\n",
    "        self.layers.append(ReLU())\n",
    "        self.layers.append(Pooling(pool_h=pool_param_2['size'], pool_w=pool_param_2['size'], stride=pool_param_2['stride']))\n",
    "        self.layers.append(Affine(self.params['W3'], self.params['b3']))\n",
    "        self.layers.append(ReLU())\n",
    "        self.layers.append(Dropout(0.5))\n",
    "        self.layers.append(Affine(self.params['W4'], self.params['b4']))\n",
    "        self.layers.append(Dropout(0.5))\n",
    "\n",
    "        self.last_layer = SoftmaxWithCrossEntropyLoss()\n",
    "\n",
    "        # Optimizer Initialization\n",
    "        self.optimizer = optimizers[optimizer](lr=learning_rate)\n",
    "\n",
    "    def get_conv_pool_last_output_size(self, layer_input_width, layer_input_height, conv_param_1, pool_param_1, conv_param_2, pool_param_2, train_flg):\n",
    "        layer_input_width, layer_input_height = self.conv_layer_output_size(layer_input_width,\n",
    "                                                                            layer_input_height,\n",
    "                                                                            conv_param_1['filter_size'],\n",
    "                                                                            conv_param_1['pad'], conv_param_1['stride'])\n",
    "\n",
    "        if train_flg: print(\"Shape of Conv-1 Output: ({:d}, {:d}, {:d})\".format(conv_param_1['filter_num'], layer_input_width, layer_input_height))\n",
    "\n",
    "        layer_input_width, layer_input_height = self.pool_layer_output_size(layer_input_width,\n",
    "                                                                            layer_input_height,\n",
    "                                                                            pool_param_1['size'],\n",
    "                                                                            pool_param_1['size'],\n",
    "                                                                            pool_param_1['stride'])\n",
    "        if train_flg: print(\"Shape of Pool-1 Output: ({:d}, {:d}, {:d})\".format(conv_param_1['filter_num'], layer_input_width, layer_input_height))\n",
    "\n",
    "        layer_input_width, layer_input_height = self.conv_layer_output_size(layer_input_width,\n",
    "                                                                            layer_input_height,\n",
    "                                                                            conv_param_2['filter_size'],\n",
    "                                                                            conv_param_2['pad'], conv_param_2['stride'])\n",
    "        if train_flg: print(\"Shape of Conv-2 Output: ({:d}, {:d}, {:d})\".format(conv_param_2['filter_num'], layer_input_width, layer_input_height))\n",
    "\n",
    "        layer_input_width, layer_input_height = self.pool_layer_output_size(layer_input_width,\n",
    "                                                                            layer_input_height,\n",
    "                                                                            pool_param_2['size'],\n",
    "                                                                            pool_param_2['size'],\n",
    "                                                                            pool_param_2['stride'])\n",
    "        if train_flg: print(\"Shape of Pool-2 Output: ({:d}, {:d}, {:d})\".format(conv_param_2['filter_num'], layer_input_width, layer_input_height))\n",
    "        return int(layer_input_width), int(layer_input_height)\n",
    "\n",
    "    def conv_layer_output_size(self, layer_input_width, layer_input_height, filter_size, pad, stride):\n",
    "        size_w, size_h = (layer_input_width - filter_size + 2 * pad) / stride + 1, (layer_input_height - filter_size + 2 * pad) / stride + 1\n",
    "        if size_w != int(size_w):\n",
    "            raise ValueError(\"Conv_Layer output size (size_w) is not integer\")\n",
    "        if size_h != int(size_h):\n",
    "            raise ValueError(\"Conv_Layer output size (size_h) is not integer\")\n",
    "        return int(size_w), int(size_h)\n",
    "\n",
    "    def pool_layer_output_size(self, layer_input_width, layer_input_height, filter_width, filter_height, stride):\n",
    "        size_w, size_h = (layer_input_width - filter_width) / stride + 1, (layer_input_height - filter_height) / stride + 1\n",
    "        if size_w != int(size_w):\n",
    "            raise ValueError(\"Pool_Layer output size (size_w) is not integer\")\n",
    "        if size_h != int(size_h):\n",
    "            raise ValueError(\"Pool_Layer output size (size_h) is not integer\")\n",
    "        return int(size_w), int(size_h)\n",
    "\n",
    "    def predict(self, x, train_flg=False):\n",
    "        isFirstAffine = False\n",
    "        for layer in self.layers:\n",
    "            if isinstance(layer, Affine) and not isFirstAffine:\n",
    "                isFirstAffine = True\n",
    "                x = x.reshape(-1, 16 * self.last_conv_pool_input_width * self.last_conv_pool_input_height)\n",
    "\n",
    "            if isinstance(layer, Dropout):\n",
    "                x = layer.forward(x, train_flg)\n",
    "            else:\n",
    "                x = layer.forward(x)\n",
    "        return x\n",
    "\n",
    "    def loss(self, x, t):\n",
    "        y = self.predict(x, train_flg=True)\n",
    "        return self.last_layer.forward(y, t)\n",
    "\n",
    "    def accuracy(self, x, t, batch_size=10):\n",
    "        if t.ndim != 1 : t = np.argmax(t, axis=1)\n",
    "\n",
    "        acc = 0.0\n",
    "\n",
    "        for i in range(int(x.shape[0] / batch_size)):\n",
    "            tx = x[i*batch_size:(i+1)*batch_size]\n",
    "            tt = t[i*batch_size:(i+1)*batch_size]\n",
    "            y = self.predict(tx, train_flg=False)\n",
    "            y = np.argmax(y, axis=1)\n",
    "            acc += np.sum(y == tt)\n",
    "\n",
    "        return acc / x.shape[0]\n",
    "\n",
    "    def backpropagation_gradient(self, x, t):\n",
    "        # forward\n",
    "        self.loss(x, t)\n",
    "\n",
    "        # backward\n",
    "        dout = 1\n",
    "        dout = self.last_layer.backward(dout)\n",
    "\n",
    "        tmp_layers = self.layers.copy()\n",
    "        tmp_layers.reverse()\n",
    "\n",
    "        isFirstPooling = False\n",
    "        for layer in tmp_layers:\n",
    "            if isinstance(layer, Pooling) and not isFirstPooling:\n",
    "                isFirstPooling = True\n",
    "                dout = dout.reshape(-1, 16, self.last_conv_pool_input_width, self.last_conv_pool_input_height)\n",
    "            dout = layer.backward(dout)\n",
    "\n",
    "        # 設定\n",
    "        grads = {}\n",
    "        for i, layer_idx in enumerate((0, 3, 6, 9)):\n",
    "            grads['W' + str(i+1)] = self.layers[layer_idx].dW\n",
    "            grads['b' + str(i+1)] = self.layers[layer_idx].db\n",
    "\n",
    "        return grads\n",
    "\n",
    "    def learning(self, x_batch, t_batch):\n",
    "        grads = self.backpropagation_gradient(x_batch, t_batch)\n",
    "        self.optimizer.update(self.params, grads)\n",
    "\n",
    "    def save_params(self, file_name=\"params.pkl\"):\n",
    "        with open(file_name, 'wb') as f:\n",
    "            pickle.dump(self.params, f)\n",
    "\n",
    "    def load_params(self, file_name=\"params.pkl\"):\n",
    "        with open(file_name, 'rb') as f:\n",
    "            params = pickle.load(f)\n",
    "\n",
    "        for key, val in params.items():\n",
    "            self.params[key] = val\n",
    "\n",
    "        for i, layer_idx in enumerate((0, 3, 6, 9)):\n",
    "            self.layers[layer_idx].W = self.params['W' + str(i+1)]\n",
    "            self.layers[layer_idx].b = self.params['b' + str(i+1)]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.random(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 312.49 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 4.5 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit np.sum(x*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = np.random.random(500)\n",
      "import numpy as np\n",
      "x = np.random.random(500)\n",
      "%timeit np.sum(x)\n",
      "%timeit np.sum(x*x)\n"
     ]
    }
   ],
   "source": [
    "history -l 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory history (kept in _dh)\n",
      "0: /Users/do-hyungkwon/GoogleDrive/script_programming\n",
      "1: /Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\n"
     ]
    }
   ],
   "source": [
    "dhist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "contents = !ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['deep_convnet.py', 'deep_convnet_large.py']\n"
     ]
    }
   ],
   "source": [
    "print(contents[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['deep_convnet.py', 'deep_convnet_large.py', 'deeplink', 'realtime_deepnet.py', 'train_deepnet.py']\n"
     ]
    }
   ],
   "source": [
    "print(contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'IPython.utils.text.SList'>\n"
     ]
    }
   ],
   "source": [
    "print(type(contents))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = !cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "message = \"hello from Python\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello from Python\r\n"
     ]
    }
   ],
   "source": [
    "!echo {message}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TERM_PROGRAM=Apple_Terminal\r\n",
      "VIRTUALENVWRAPPER_PROJECT_FILENAME=.project\r\n",
      "VIRTUALENVWRAPPER_SCRIPT=/usr/local/bin/virtualenvwrapper.sh\r\n",
      "rvm_bin_path=/Users/do-hyungkwon/.rvm/bin\r\n",
      "GEM_HOME=/Users/do-hyungkwon/.rvm/gems/ruby-2.2.0\r\n",
      "SHELL=/bin/bash\r\n",
      "TERM=xterm-color\r\n",
      "CLICOLOR=1\r\n",
      "TMPDIR=/var/folders/v5/h58bg81s0jx2y4hqbgk_dgw40000gn/T/\r\n",
      "IRBRC=/Users/do-hyungkwon/.rvm/rubies/ruby-2.2.0/.irbrc\r\n",
      "Apple_PubSub_Socket_Render=/private/tmp/com.apple.launchd.mBPj2NVg19/Render\r\n",
      "TERM_PROGRAM_VERSION=388.1.1\r\n",
      "TERM_SESSION_ID=7B5A326D-51E5-452E-A071-495CE7E7FF42\r\n",
      "MY_RUBY_HOME=/Users/do-hyungkwon/.rvm/rubies/ruby-2.2.0\r\n",
      "USER=do-hyungkwon\r\n",
      "_system_type=Darwin\r\n",
      "rvm_path=/Users/do-hyungkwon/.rvm\r\n",
      "SSH_AUTH_SOCK=/private/tmp/com.apple.launchd.JHJriDNVjz/Listeners\r\n",
      "__CF_USER_TEXT_ENCODING=0x1F5:0x3:0x33\r\n",
      "JPY_PARENT_PID=96449\r\n",
      "PAGER=cat\r\n",
      "WORKON_HOME=/Users/do-hyungkwon/.virtualenvs\r\n",
      "rvm_prefix=/Users/do-hyungkwon\r\n",
      "PATH=/Users/do-hyungkwon/anaconda/bin:/Users/do-hyungkwon/.rvm/gems/ruby-2.2.0/bin:/Users/do-hyungkwon/.rvm/gems/ruby-2.2.0@global/bin:/Users/do-hyungkwon/.rvm/rubies/ruby-2.2.0/bin:/Users/do-hyungkwon/anaconda/bin:/usr/local/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Users/do-hyungkwon/.rvm/bin\r\n",
      "_=/usr/bin/env\r\n",
      "VIRTUALENVWRAPPER_HOOK_DIR=/Users/do-hyungkwon/.virtualenvs\r\n",
      "PWD=/Users/do-hyungkwon/GoogleDrive/deeplink/5.DoHyung/raspberrypi4\r\n",
      "MPLBACKEND=module://ipykernel.pylab.backend_inline\r\n",
      "LANG=ko_KR.UTF-8\r\n",
      "XPC_FLAGS=0x0\r\n",
      "_system_arch=x86_64\r\n",
      "_system_version=10.12\r\n",
      "XPC_SERVICE_NAME=0\r\n",
      "rvm_version=1.29.2 (latest)\r\n",
      "SHLVL=2\r\n",
      "HOME=/Users/do-hyungkwon\r\n",
      "LOGNAME=do-hyungkwon\r\n",
      "GEM_PATH=/Users/do-hyungkwon/.rvm/gems/ruby-2.2.0:/Users/do-hyungkwon/.rvm/gems/ruby-2.2.0@global\r\n",
      "VIRTUALENVWRAPPER_WORKON_CD=1\r\n",
      "GIT_PAGER=cat\r\n",
      "SECURITYSESSIONID=186a7\r\n",
      "RUBY_VERSION=ruby-2.2.0\r\n",
      "_system_name=OSX\r\n"
     ]
    }
   ],
   "source": [
    "!env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-09-01 11:24:20--  https://raw.githubusercontent.com/ipython-books/minbook-2nd-data/master/nyc_taxi.zip\n",
      "Resolving raw.githubusercontent.com... 151.101.72.133\n",
      "Connecting to raw.githubusercontent.com|151.101.72.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 404 Not Found\n",
      "2017-09-01 11:24:20 ERROR 404: Not Found.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/ipython-books/minbook-2nd-data/master/nyc_taxi.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Password:\n"
     ]
    }
   ],
   "source": [
    "!sudo apt-get install wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "<class 'int'>\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "a = int(1)\n",
    "\n",
    "print(a)\n",
    "print(type(a))\n",
    "print(isinstance(a, int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lt = [1, 's', ['b', 'c']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4437881032 <class 'list'>\n"
     ]
    }
   ],
   "source": [
    "print(id(lt), type(lt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4395628544 <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "print(id(lt[0]), type(lt[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4398017312 <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(id(lt[1]), type(lt[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "L = list(range(10))\n",
    "A = array.array('i', L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n"
     ]
    }
   ],
   "source": [
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'array.array'>\n"
     ]
    }
   ],
   "source": [
    "print(type(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i\n"
     ]
    }
   ],
   "source": [
    "print(A.typecode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "print(A.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(A.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}