ipython_blog_1

{
 "metadata": {
  "name": "IPython Blog Part 1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "\u09ae\u09be\u099b\u09c7\u09b0 \u09a4\u09c7\u09b2\u09c7 \u09ae\u09be\u099b \u09aa\u09b0\u09cd\u09ac \u09e7"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u09aa\u09be\u0987\u09a5\u09a8 \u0986\u09ae\u09be\u09b0 \u09b8\u09ac\u099a\u09c7\u09df\u09c7 \u09aa\u099b\u09a8\u09cd\u09a6\u09c7\u09b0 \u09aa\u09cd\u09b0\u09cb\u0997\u09cd\u09b0\u09be\u09ae\u09bf\u0982 \u09b2\u09cd\u09af\u09be\u0999\u09cd\u0997\u09c1\u09df\u09c7\u099c \u098f\u09b0 \u098f\u0995\u099f\u09bf\u0964 \u098f\u099f\u09bf \u0986\u09ac\u09be\u09b0 \u0986\u09ae\u09be\u09b0 \u099c\u09c0\u09ac\u09bf\u0995\u09be\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09cb\u0964 \u09a4\u09be\u0987 \u09ad\u09be\u09ac\u09b2\u09be\u09ae \u0986\u09ae\u09be\u09b0 \u098f\u0987 \u09a4\u09c3\u09a4\u09c0\u09df \u09ac\u09cd\u09b2\u0997 \u09aa\u09cb\u09b8\u09cd\u099f \u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u0989\u09aa\u09b0 \u09b2\u09bf\u0996\u09bf \u098f\u09ac\u0982 \u09a4\u09be\u0993 \u098f\u0995\u099f\u09c1 \u09ad\u09bf\u09a8\u09cd\u09a8\u09ad\u09be\u09ac\u09c7\u0964 \u098f\u0987 \u09aa\u09cb\u09b8\u09cd\u099f\u099f\u09bf \u09b2\u09bf\u0996\u09be \u09b9\u09ac\u09c7 IPython \u09a6\u09bf\u09df\u09c7 \u098f\u09ac\u0982 \u098f\u09b0 \u09ae\u09c2\u09b2 \u09ac\u09bf\u09b7\u09df\u0993 \u09b9\u09ac\u09c7 IPython \u09ad\u09bf\u09a4\u09cd\u09a4\u09bf\u0995 (\u0995\u09be\u099c\u09c7\u0987 \u098f\u0987 \u09b6\u09bf\u09b0\u09cb\u09a8\u09be\u09ae)\u0964 \u09a4\u09be\u09b9\u09b2\u09c7 \u09b6\u09c1\u09b0\u09c1 \u0995\u09b0\u09be \u09af\u09be\u0995\u0964"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "IPython \u0995\u09c0? \u098f\u0995 \u0995\u09a5\u09be\u09df \u09ac\u09b2\u09a4\u09c7 \u0997\u09c7\u09b2\u09c7 \u098f\u099f\u09bf \u098f\u0995\u099f\u09bf \u09aa\u09b0\u09bf\u09ac\u09c7\u09b6 \u09af\u09c7\u0996\u09be\u09a8\u09c7 \u0986\u09aa\u09a8\u09bf \u09aa\u09be\u0987\u09a5\u09a8 (\u098f\u09ac\u0982 \u0985\u09a8\u09cd\u09af\u09be\u09a8\u09cd\u09af \u0995\u09bf\u099b\u09c1 \u09b2\u09cd\u09af\u09be\u0999\u09cd\u0997\u09c1\u09df\u09c7\u099c) \u09aa\u09cd\u09b0\u09cb\u0997\u09cd\u09b0\u09be\u09ae\u09bf\u0982 \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u09aa\u09cd\u09b0\u09a5\u09ae \u09a6\u09c7\u0996\u09be\u09df \u09ae\u09a8\u09c7 \u09b9\u09ac\u09c7 \u09af\u09c7 \u098f\u099f\u09bf \u0985\u09a4\u09cd\u09af\u09a8\u09cd\u09a4 \u09b6\u0995\u09cd\u09a4\u09bf\u09b6\u09be\u09b2\u09c0 \u098f\u0995\u099f\u09bf \u09b6\u09c7\u09b2\u0964 \u0986\u09ae\u09be\u09b0 \u09aa\u09cd\u09b0\u09a5\u09ae \u09aa\u09b0\u09bf\u099a\u09df IPython \u098f\u09b0 \u09b8\u09be\u09a5\u09c7 \u099b\u09bf\u09b2 \u0985\u09a8\u09c7\u0995 \u0986\u0997\u09c7, \u09a4\u0996\u09a8\u0995\u09be\u09b0 \u09b8\u0982\u099c\u09cd\u099e\u09be\u09a8\u09c1\u09af\u09be\u09df\u09c0 \u09b9\u09df\u09a4 \"\u09b6\u0995\u09cd\u09a4\u09bf\u09b6\u09be\u09b2\u09c0 \u09b6\u09c7\u09b2\" \u09a0\u09bf\u0995 \u099b\u09bf\u09b2\u0964 \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u0995\u09df\u09c7\u0995\u09ac\u099b\u09b0 \u09a7\u09b0\u09c7 \u09af\u09c7 \u0986\u09aa\u09a1\u09c7\u099f \u0986\u09b8\u099b\u09c7 IPython \u098f, \u09a4\u09be\u09a4\u09c7 \u09ae\u09a8\u09c7 \u09b9\u09ac\u09c7 \u09af\u09c7 \u098f\u099f\u09bf \"\u09b6\u09c7\u09b2\" \u0989\u09aa\u09be\u09a7\u09bf \u099b\u09be\u09dc\u09bf\u09df\u09c7 \u09b9\u09df\u09c7 \u0989\u09a0\u09c7\u099b\u09c7 \u0985\u09a8\u09cd\u09af\u0995\u09bf\u099b\u09c1\u0964 \u098f\u0987 \u09aa\u09cb\u09b8\u09cd\u099f\u09c7 \u0986\u09ae\u09bf \u09b8\u09c7\u099f\u09bf \u09a4\u09c1\u09b2\u09c7 \u09a7\u09b0\u09ac\u0964"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u09aa\u09cd\u09b0\u09a5\u09ae\u09c7\u0987 \u0987\u09a8\u09cd\u09b8\u099f\u09b2 \u0995\u09b0\u09be \u09af\u09be\u0995\u0964 pip install ipython \u099a\u09b2\u09ac\u09c7 \u0986\u09aa\u09be\u09a4\u09a4\u0964 \u09a4\u09be\u09b9\u09b2\u09c7 \u0995\u09b0\u09be \u09af\u09be\u0995\u0964 \u0986\u09aa\u09a8\u09bf terminal \u098f \u09af\u09c7\u09df\u09c7 ipython \u099f\u09be\u0987\u09aa \u0995\u09b0\u09b2\u09c7\u0987 \u0986\u09aa\u09a8\u09bf \u099a\u09b2\u09c7 \u0986\u09b8\u09ac\u09c7\u09a8 ipython \u09b6\u09c7\u09b2\u09c7\u0964 \u09aa\u09cd\u09b0\u09a5\u09ae \u09a6\u09c7\u0996\u09be\u09a4\u09c7 \u09ae\u09a8\u09c7 \u09b9\u09ac\u09c7 \u09af\u09c7 \u098f\u099f\u09bf \u098f\u0995\u099f\u09c1 \u0989\u09a8\u09cd\u09a8\u09a4\u09ae\u09be\u09a8\u09c7\u09b0 \u09b6\u09c7\u09b2 \u09af\u09c7\u0996\u09be\u09a8\u09c7 \u099f\u09cd\u09af\u09be\u09ac \u09ac\u09cb\u09a4\u09be\u09ae \u09aa\u09cd\u09b0\u09c7\u09b8 \u0995\u09b0\u09b2\u09c7\u0987 \u0985\u099f\u09cb\u0995\u09ae\u09aa\u09cd\u09b2\u09bf\u099f \u0995\u09b0\u09c7 \u09a6\u09c7\u09df, \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 ipython \u0986\u09b0\u0993 \u0985\u09a8\u09c7\u0995 \u0995\u09bf\u099b\u09c1, \u09b8\u09c7\u099f\u09bf \u0986\u09aa\u09a8\u09bf \u09ac\u09c1\u099d\u09a4\u09c7 \u09aa\u09be\u09b0\u09ac\u09c7\u09a8 \u09af\u0996\u09a8 \u0986\u09aa\u09a8\u09bf \u0995\u09cb\u09a8 \u09aa\u09be\u0987\u09a5\u09a8 \u09b8\u0999\u09cd\u0995\u09c7\u09a4 paste \u0995\u09b0\u09ac\u09c7\u09a8\u0964 \u0986\u09aa\u09a8\u09bf \u0986\u09aa\u09a8\u09be\u09b0 \u09b8\u09bf\u09b8\u09cd\u099f\u09c7\u09ae\u09c7\u09b0 paste \u09b6\u09b0\u09cd\u099f\u0995\u09be\u099f \u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0 \u09a8\u09be \u0995\u09b0\u09c7 ipython \u098f\u09b0 magic command \u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0 \u0995\u09b0\u09ac\u09c7\u09a8 \u09af\u09be \u09b9\u09b2 %paste, \u0995\u09cb\u09a8 \u09aa\u09be\u0987\u09a5\u09a8 \u09b8\u0999\u09cd\u0995\u09c7\u09a4 \u0995\u09aa\u09bf \u0995\u09b0\u09c7 ipython \u098f \u0997\u09bf\u09df\u09c7 %paste \u09b2\u09bf\u0996\u09c7  \u09a6\u09c7\u0996\u09c1\u09a8\u09a4\u09cb? \u0986\u09b0\u0993 \u09ae\u099c\u09be\u09b0 \u09ac\u09bf\u09b7\u09df, \u09af\u09c7 \u0995\u09cb\u09a8 \u09ab\u09be\u0982\u09b6\u09a8 \u09b2\u09bf\u0996\u09c7 ? \u0985\u09a5\u09ac\u09be ?? \u09a6\u09bf\u09df\u09c7\u0987 \u09ac\u09be \u09a6\u09c7\u0996\u09c1\u09a8? \u09af\u09c7\u09ae\u09a8 (\u09af\u09a6\u09bf \u0986\u09aa\u09a8\u09bf import os \u0995\u09b0\u09c7 \u09a5\u09be\u0995\u09c7\u09a8) os? \u0985\u09a5\u09ac\u09be os.system? \u0985\u09a5\u09ac\u09be os.path??... \"?\" \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09a8\u09bf\u09df\u09c7 \u09af\u09be\u09ac\u09c7 \u09a1\u0995\u09c1\u09ae\u09c7\u09a8\u09cd\u099f\u09c7\u09b6\u09a8\u09c7 \u0986\u09b0 ?? \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u0986\u09aa\u09a8\u09bf \u0993\u0987 \u09ab\u09be\u0982\u09b6\u09a8 \u0985\u09a5\u09ac\u09be \u09ae\u09a1\u09c1\u09b2\u09c7\u09b0 \u09b8\u09cb\u09b0\u09cd\u09b8 \u09a6\u09c7\u0996\u09a4\u09c7 \u09aa\u09be\u09ac\u09c7\u09a8\u0964 \u0986\u09b0 \u09af\u09a6\u09bf \u0986\u09aa\u09a8\u09bf a*? \u09a6\u09bf\u09a8, \u09a4\u09be\u09b9\u09b2\u09c7 a \u09a6\u09bf\u09df\u09c7 \u09b6\u09c1\u09b0\u09c1 \u09b9\u09df \u098f\u09ae\u09a8 \u09b8\u0995\u09b2 \u0985\u09ac\u099c\u09c7\u0995\u09cd\u099f \u098f\u09b0 \u09b2\u09bf\u09b8\u09cd\u099f \u09a6\u09bf\u09ac\u09c7 \u0986\u09aa\u09a8\u09be\u0995\u09c7\u0964 os.system \u09a5\u09c7\u0995\u09c7 \u09ae\u09a8\u09c7 \u09aa\u09b0\u09b2, \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf ipython \u09b6\u09c7\u09b2\u09c7 \u09a5\u09be\u0995\u09be\u0995\u09be\u09b2\u09c0\u09a8 \u09b8\u09ae\u09df\u09c7 \u099a\u09be\u09a8 bash \u0995\u09ae\u09be\u09a8\u09cd\u09a1 \u099f\u09be\u0987\u09aa \u0995\u09b0\u09a4\u09c7 \u09a4\u09be\u09b9\u09b2\u09c7 \u0986\u09aa\u09a8\u09be\u09b0 ipython \u09b6\u09c7\u09b2\u09c7 ! \u09af\u09cb\u0997 \u0995\u09b0\u09c1\u09a8 \u0986\u09aa\u09a8\u09be\u09b0 \u0995\u09ae\u09be\u09a8\u09cd\u09a1\u09c7\u09b0 \u09aa\u09c2\u09b0\u09cd\u09ac\u09c7\u0964 \u0989\u09a6\u09be\u09b9\u09b0\u09a3\u09b8\u09cd\u09ac\u09b0\u09c2\u09aa\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!ls",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\u001b[34mApplications\u001b[m\u001b[m                   \u001b[34mPublic\u001b[m\u001b[m\r\n\u001b[34mApps\u001b[m\u001b[m                           \u001b[34mPycharmProjects\u001b[m\u001b[m\r\n\u001b[34mBooks\u001b[m\u001b[m                          \u001b[34mRubymineProjects\u001b[m\u001b[m\r\n\u001b[34mCodes\u001b[m\u001b[m                          \u001b[31m_vimrc\u001b[m\u001b[m\r\n\u001b[34mDesktop\u001b[m\u001b[m                        \u001b[34madt-bundle-mac-x86_64-20130917\u001b[m\u001b[m\r\n\u001b[34mDocuments\u001b[m\u001b[m                      \u001b[34mnltk_data\u001b[m\u001b[m\r\n\u001b[34mDownloads\u001b[m\u001b[m                      \u001b[34mnode_modules\u001b[m\u001b[m\r\nIPython Blog Part 1.ipynb      requirements.txt\r\n\u001b[34mLibrary\u001b[m\u001b[m                        tbl_raw.csv\r\n\u001b[34mMovies\u001b[m\u001b[m                         test.py\r\n\u001b[34mMusic\u001b[m\u001b[m                          \u001b[34mtmp\u001b[m\u001b[m\r\n\u001b[34mPictures\u001b[m\u001b[m\r\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u0986\u09aa\u09a8\u09bf \u0995\u09bf \u099c\u09be\u09a8\u09a4\u09c7 \u099a\u09be\u09a8 \u0986\u09aa\u09a8\u09be\u09b0 \u0995\u09cb\u09a1 \u099a\u09b2\u09a4\u09c7 \u0995\u09c7\u09ae\u09a8 \u09b8\u09ae\u09df\u09c7 \u09a8\u09c7\u09df? \u0986\u09aa\u09a8\u09be\u09b0 \u099c\u09a8\u09cd\u09af \u09b0\u09df\u09c7\u099b\u09c7 %timeit \u09ae\u09cd\u09af\u09be\u099c\u09bf\u0995\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import random as rd\n%timeit sorted([rd.random() for i in range(10000000)])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1 loops, best of 3: 17.2 s per loop\n"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u098f\u09b0\u0995\u09ae \u0986\u09b0\u0993 \u0985\u09a8\u09c7\u0995 \u09ae\u09cd\u09af\u09be\u099c\u09bf\u0995 \u0995\u09ae\u09cd\u09af\u09be\u09a8\u09cd\u09a1 \u09b0\u09df\u09c7\u099b\u09c7 ipython \u098f, \u09af\u09be \u09ae\u09c2\u09b2\u09a4 \u0986\u09aa\u09a8\u09be\u09b0 \u0987\u09a8\u09cd\u099f\u09be\u09b0\u200c\u09cd\u09af\u09be\u0995\u09cd\u099f\u09bf\u09ad \u09aa\u09be\u0987\u09a5\u09a8 \u09aa\u09cd\u09b0\u09cb\u0997\u09cd\u09b0\u09be\u09ae\u09bf\u0982\u0995\u09c7 \u0995\u09b0\u09c7 \u09a4\u09cb\u09b2\u09c7 \u09b8\u09b9\u099c \u0993 \u09ae\u099c\u09be\u09a6\u09be\u09b0\u0964 \u0986\u09ae\u09be\u09b0 \u098f\u0995\u099f\u09bf \u09aa\u099b\u09a8\u09cd\u09a6\u09c7\u09b0 \u09ae\u09cd\u09af\u09be\u099c\u09bf\u0995 \u09b9\u09b2\u0993 \"/\"\u0964 / \u098f\u09b0 \u09aa\u09b0 \u0986\u09aa\u09a8\u09bf \u09af\u09c7\u0987 \u09ab\u09be\u0982\u09b6\u09a8 \u09a6\u09bf\u09ac\u09c7\u09a8 \u09b8\u09c7\u099f\u09be\u09a4\u09c7 \u0995\u09cb\u09a8 \u09ac\u09cd\u09b0\u09be\u0995\u09c7\u099f\u09c7\u09b0 \u09aa\u09cd\u09b0\u09df\u09cb\u099c\u09a8 \u09aa\u09b0\u09c7 \u09a8\u09be (\u09b0\u09c1\u09ac\u09bf \u09b8\u09cd\u099f\u09be\u0987\u09b2)\u0964"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "/ord \"A\"",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": "65"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u0986\u09aa\u09a8\u09bf \u09a8\u09bf\u09b6\u09cd\u099a\u09df \u099c\u09be\u09a8\u09c7\u09a8 \u09af\u09c7 _ \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u0986\u09aa\u09a8\u09bf \u09a0\u09bf\u0995 \u0986\u0997\u09c7\u09b0 \u09b0\u09c7\u099c\u09be\u09b2\u09cd\u099f \u09aa\u09be\u099a\u09cd\u099b\u09c7\u09a8 \u09b6\u09c7\u09b2\u09c7\u0964 ipython \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u0986\u09b0\u09c7\u0995\u099f\u09c1 \u09b8\u09c1\u09ac\u09bf\u09a7\u09be \u09a6\u09bf\u09df\u09c7 \u09a5\u09be\u0995\u09c7\u0964 \u0986\u09aa\u09a8\u09bf \u09a6\u09c7\u0996\u099b\u09c7\u09a8 \u09af\u09c7 In [] \u098f\u09ac\u0982 Out [] \u09aa\u09cd\u09b0\u09a4\u09bf \u09b2\u09be\u0987\u09a8\u09c7 \u09b0\u09df\u09c7\u099b\u09c7? \u09ac\u09cb\u099d\u09be \u09b8\u09b9\u099c \u09af\u09c7 In \u09a6\u09cd\u09ac\u09be\u09b0\u09be Input \u0993 Out \u09a6\u09cd\u09ac\u09be\u09b0\u09be Output \u09ac\u09cb\u099d\u09be\u09a8\u09cb \u09b9\u09df\u09c7\u099b\u09c7 \u098f\u09ac\u0982 \u09ac\u09cd\u09b0\u09be\u0995\u09c7\u099f\u09c7\u09b0 \u09ae\u09be\u099d\u0996\u09be\u09a8\u09c7\u09b0 \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0 \u09b9\u09b2\u0993 \u09b2\u09be\u0987\u09a8 \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0\u0964 \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u09af\u09a6\u09bf \u0986\u09aa\u09a8\u09bf \u09b2\u09be\u0987\u09a8 \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0\u09c7\u09b0 \u09aa\u09be\u0987\u09a5\u09a8 \u09ae\u09be\u09a8 \u099a\u09be\u09a8 \u09a4\u09be\u09b9\u09b2\u09c7? _ \u09a6\u09bf\u09df\u09c7 \u09a4\u09cb \u09b6\u09c1\u09a7\u09c1 \u09a0\u09bf\u0995 \u0986\u0997\u09c7\u09b0 \u09ae\u09be\u09a8 \u09aa\u09be\u0993\u09df\u09be \u09af\u09be\u09df, \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 _i<line_number> \u0993 _<line_number> \u09a6\u09bf\u09df\u09c7 \u0986\u09aa\u09a8\u09bf line_numer \u098f\u09b0 \u0987\u09a8\u09aa\u09c1\u099f \u0993\u09b0 \u0985\u0989\u099f\u09aa\u09c1\u099f \u09aa\u09be\u09ac\u09c7\u09a8\u0964 %history \u09ae\u09cd\u09af\u09be\u099c\u09bf\u0995 \u09b9\u09b2 IPython \u098f\u09b0 \"history\" \u0995\u09ae\u09be\u09a8\u09cd\u09a1\u0964 \u098f\u0995\u099f\u09c1 \u09a6\u09c7\u0996\u09c1\u09a8\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "1+1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": "2"
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "2+2",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": "4"
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_i32",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": "u'1+1'"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "_33",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": "4"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%history",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "%paste\n%paste\n%help\nqs = lambda xs : ( (len(xs) <= 1 and [xs]) or\n                     [ qs( [x for x in xs[1:] if x < xs[0]] ) +\n                           [xs[0]]                            +\n                       qs( [x for x in xs[1:] if x >= xs[0]] ) ] )[0]\nqs?\nqs??\n!ls\n%cpaste\n!ls\n%\n%timeit\n%timeit [i for i in range(10000)]\nimport random\n\n%timeit (lambda xs : ( (len(xs) <= 1 and [xs]) or\n                     [ qs( [x for x in xs[1:] if x < xs[0]] ) +\n                           [xs[0]]                            +\n                       qs( [x for x in xs[1:] if x >= xs[0]] ) ] )[0])([random.random() for i in range(1000000)])\nimport random as rd\n%timeit sorted([rd.random() for i in range(10000000)])\n%pprint\n%pprint\n%pprint [1,2,3]\n%pprint\npaste\n%%paste\n%magic\n%dhist\n/str 12\n/str 420\n/sorted [random.random() for i in range(10)]\n/ord \"A\"\ni_14\n%i_14\n_i14\ntable = [[1,2,3],[4,5,6],[7,8,9]]\ntable\n1+1\n2+2\n_i32\n_33\n%history\n"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u09b9\u09be\u09b9\u09be! \u098f\u0987 \u09aa\u09cb\u09b8\u09cd\u09a4 \u09b2\u09bf\u0996\u09be\u09b0 \u09b8\u09ae\u09df\u09c7 \u0986\u09ae\u09bf \u0985\u09a8\u09c7\u0995 \u098f\u0995\u09cd\u09b8\u09aa\u09c7\u09b0\u09bf\u09ae\u09c7\u09a8\u09cd\u099f \u0995\u09b0\u09c7\u099b\u09bf, \u09a4\u09be\u0987 \u0993\u0987\u09b8\u09ac \u0985\u09a4\u09bf\u09b0\u09bf\u0995\u09cd\u09a4 \u09b2\u09be\u0987\u09a8\u0964 \n\n\u0986\u09ae\u09be\u09b0 \u098f\u09ae\u09a8 \u09aa\u09cd\u09b0\u09be\u09df\u09c7\u0987 \u09b9\u09df \u09af\u09c7 \u09b6\u09c7\u09b2\u09c7 \u098f\u0995\u09cd\u09b8\u09aa\u09c7\u09b0\u09bf\u09ae\u09c7\u09a8\u09cd\u099f \u0995\u09b0\u09a4\u09c7 \u0995\u09b0\u09a4\u09c7 \u0985\u09a8\u09c7\u0995 \u09ab\u09be\u0982\u09b6\u09a8 \u09b2\u09bf\u0996\u09c7 \u09ab\u09c7\u09b2\u09bf \u09af\u09be \u0986\u09ac\u09be\u09b0 \u0989\u09aa\u09b0\u09c7 \u0997\u09bf\u09df\u09c7 \u0995\u09aa\u09bf \u0995\u09b0\u09be \u09b2\u09be\u0997\u09c7\u0964 \u098f\u09ae\u09a8 \u09af\u09a6\u09bf \u09b9\u09df\u09c7 \u09a5\u09be\u0995\u09c7, \u09a4\u09be\u09b9\u09b2\u09c7 \u0986\u09aa\u09a8\u09bf %%write\u001bfile \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af \u09a8\u09bf\u09a8\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%%writefile ipython_html.py\nclass ListTable(list):\n    \"\"\" Overridden list class which takes a 2-dimensional list of \n        the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n        IPython Notebook. \"\"\"\n    \n    def _repr_html_(self):\n        html = [\"<table>\"]\n        for row in self:\n            html.append(\"<tr>\")\n            \n            for col in row:\n                html.append(\"<td>{0}</td>\".format(col))\n            \n            html.append(\"</tr>\")\n        html.append(\"</table>\")\n        return ''.join(html)\nimport random\ntable = ListTable()\ntable.append(['x', 'y', 'x-y', '(x-y)^2'])\nfor i in xrange(7):\n    x = random.uniform(0, 10)\n    y = random.uniform(0, 10)\n    table.append([x, y, x-y, (x-y)**2])\ntable\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Writing ipython_html.py\n"
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!ls ipython_html.py",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "ipython_html.py\r\n"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "!cat ipython_html.py",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "class ListTable(list):\r\n    \"\"\" Overridden list class which takes a 2-dimensional list of \r\n        the form [[1,2,3],[4,5,6]], and renders an HTML Table in \r\n        IPython Notebook. \"\"\"\r\n    \r\n    def _repr_html_(self):\r\n        html = [\"<table>\"]\r\n        for row in self:\r\n            html.append(\"<tr>\")\r\n            \r\n            for col in row:\r\n                html.append(\"<td>{0}</td>\".format(col))\r\n            \r\n            html.append(\"</tr>\")\r\n        html.append(\"</table>\")\r\n        return ''.join(html)\r\nimport random\r\ntable = ListTable()\r\ntable.append(['x', 'y', 'x-y', '(x-y)^2'])\r\nfor i in xrange(7):\r\n    x = random.uniform(0, 10)\r\n    y = random.uniform(0, 10)\r\n    table.append([x, y, x-y, (x-y)**2])\r\ntable"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u099c\u099f\u09bf\u09b2 \u09a8\u09be? \u0986\u09b0 \u0993\u0987 \u0995\u09cb\u09a1\u099f\u09bf \u09a8\u09c7\u0993\u09df\u09be \u09b9\u09df\u09c7\u099b\u09c7 http://calebmadrigal.com/display-list-as-table-in-ipython-notebook/ \u09a5\u09c7\u0995\u09c7\u0964"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u0985\u09a8\u09cd\u09af\u09be\u09a8\u09cd\u09af \u09ae\u09cd\u09af\u09be\u099c\u09bf\u0995 \u0995\u09ae\u09be\u09a8\u09cd\u09a1 \u09b8\u09ae\u09cd\u09aa\u09b0\u09cd\u0995\u09c7 \u099c\u09be\u09a8\u09a4\u09c7 \u0987\u099a\u09cd\u099b\u09c1\u0995 \u09a5\u09be\u0995\u09c7\u09a8 \u09a4\u09be\u09b9\u09b2\u09c7 %magic \u099f\u09be\u0987\u09aa \u0995\u09b0\u09c1\u09a8 ipython \u09b6\u09c7\u09b2\u09c7\u0964 \u0986\u09b0 \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf pdb \u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0 \u0995\u09b0\u09c7 \u09a5\u09be\u0995\u09c7\u09a8 \u09a4\u09be\u09b9\u09b2\u09c7 \u09b9\u09df\u09a4 \u0986\u09aa\u09a8\u09bf %debug, %profile, %pdb \u0987\u09a4\u09cd\u09af\u09be\u09a6\u09bf \u0995\u09ae\u09cd\u09af\u09be\u09a8\u09cd\u09a1\u09c7\u09b0 \u09aa\u09cd\u09b0\u09a4\u09bf \u09a8\u099c\u09b0 \u09b0\u09be\u0996\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0986\u09b0 \u0986\u09aa\u09a8\u09bf ? \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u09ae\u09be\u099c\u09bf\u0995 \u0995\u09ae\u09cd\u09af\u09be\u09a8\u09cd\u09a1\u09c7\u09b0 \u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0\u09ac\u09bf\u09a7\u09bf \u099c\u09c7\u09a8\u09c7 \u09a8\u09bf\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%pwd?",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u09af\u09a6\u09bf \u09b0\u09c1\u09ac\u09bf \u09aa\u09cd\u09b0\u09cb\u0997\u09cd\u09b0\u09be\u09ae\u09bf\u0982 \u0995\u09b0\u09a4\u09c7 \u0987\u099a\u09cd\u099b\u09c7 \u0995\u09b0\u09c7 \u09a4\u09be\u09b9\u09b2\u09c7\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%%ruby\nputs \"Ruby is also an awesome language\"",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Ruby is also an awesome language\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "%run \u0995\u09ae\u09be\u09a8\u09cd\u09a1 \u09a6\u09bf\u09df\u09c7 \u0986\u09aa\u09a8\u09bf \u09af\u09c7 \u0995\u09cb\u09a8 \u09aa\u09be\u0987\u09a5\u09a8 \u09ab\u09be\u0987\u09b2 \u099a\u09be\u09b2\u09be\u09a4\u09c7 \u09aa\u09be\u09b0\u09ac\u09c7\u09a8\u0964 notebook \u09aa\u09b0\u09cd\u09ac\u09c7\u09b0 \u09aa\u09c2\u09b0\u09cd\u09ac\u09c7 \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09a6\u09c7\u0996\u09bf\u09df\u09c7 \u09a6\u09bf\u099a\u09cd\u099b\u09bf \u09ae\u09cd\u09af\u09be\u099c\u09bf\u0995 \u0995\u09ae\u09be\u09a8\u09cd\u09a1\u09c7\u09b0 \u09b2\u09bf\u09b8\u09cd\u099f"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%lsmagic",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "json": "{\"cell\": {\"prun\": \"ExecutionMagics\", \"file\": \"Other\", \"!\": \"OSMagics\", \"capture\": \"ExecutionMagics\", \"timeit\": \"ExecutionMagics\", \"script\": \"ScriptMagics\", \"ruby\": \"Other\", \"system\": \"OSMagics\", \"perl\": \"Other\", \"HTML\": \"Other\", \"bash\": \"Other\", \"python\": \"Other\", \"SVG\": \"Other\", \"javascript\": \"DisplayMagics\", \"writefile\": \"OSMagics\", \"pypy\": \"Other\", \"python3\": \"Other\", \"latex\": \"DisplayMagics\", \"sx\": \"OSMagics\", \"svg\": \"DisplayMagics\", \"html\": \"DisplayMagics\", \"sh\": \"Other\", \"time\": \"ExecutionMagics\", \"debug\": \"ExecutionMagics\"}, \"line\": {\"load\": \"CodeMagics\", \"psource\": \"NamespaceMagics\", \"lsmagic\": \"BasicMagics\", \"logstate\": \"LoggingMagics\", \"logstart\": \"LoggingMagics\", \"popd\": \"OSMagics\", \"ed\": \"Other\", \"pycat\": \"OSMagics\", \"loadpy\": \"CodeMagics\", \"install_ext\": \"ExtensionMagics\", \"cd\": \"OSMagics\", \"pastebin\": \"CodeMagics\", \"clear\": \"KernelMagics\", \"colors\": \"BasicMagics\", \"prun\": \"ExecutionMagics\", \"pushd\": \"OSMagics\", \"rep\": \"Other\", \"config\": \"ConfigMagics\", \"dirs\": \"OSMagics\", \"time\": \"ExecutionMagics\", \"who_ls\": \"NamespaceMagics\", \"install_profiles\": \"DeprecatedMagics\", \"macro\": \"ExecutionMagics\", \"autocall\": \"AutoMagics\", \"alias\": \"OSMagics\", \"bookmark\": \"OSMagics\", \"connect_info\": \"KernelMagics\", \"rehashx\": \"OSMagics\", \"pprint\": \"BasicMagics\", \"system\": \"OSMagics\", \"whos\": \"NamespaceMagics\", \"hist\": \"Other\", \"install_default_config\": \"DeprecatedMagics\", \"logoff\": \"LoggingMagics\", \"env\": \"OSMagics\", \"qtconsole\": \"KernelMagics\", \"load_ext\": \"ExtensionMagics\", \"save\": \"CodeMagics\", \"tb\": \"ExecutionMagics\", \"store\": \"StoreMagics\", \"more\": \"KernelMagics\", \"profile\": \"BasicMagics\", \"doctest_mode\": \"KernelMagics\", \"pylab\": \"PylabMagics\", \"run\": \"ExecutionMagics\", \"reset_selective\": \"NamespaceMagics\", \"pfile\": \"NamespaceMagics\", \"pinfo2\": \"NamespaceMagics\", \"pdef\": \"NamespaceMagics\", \"killbgscripts\": \"ScriptMagics\", \"who\": \"NamespaceMagics\", \"precision\": \"BasicMagics\", \"matplotlib\": \"PylabMagics\", \"quickref\": \"BasicMagics\", \"pinfo\": \"NamespaceMagics\", \"pwd\": \"OSMagics\", \"psearch\": \"NamespaceMagics\", \"autosave\": \"KernelMagics\", \"less\": \"KernelMagics\", \"sc\": \"OSMagics\", \"automagic\": \"AutoMagics\", \"reset\": \"NamespaceMagics\", \"sx\": \"OSMagics\", \"magic\": \"BasicMagics\", \"dhist\": \"OSMagics\", \"timeit\": \"ExecutionMagics\", \"edit\": \"KernelMagics\", \"logstop\": \"LoggingMagics\", \"gui\": \"BasicMagics\", \"xdel\": \"NamespaceMagics\", \"xmode\": \"BasicMagics\", \"notebook\": \"BasicMagics\", \"pdb\": \"ExecutionMagics\", \"recall\": \"HistoryMagics\", \"unalias\": \"OSMagics\", \"unload_ext\": \"ExtensionMagics\", \"alias_magic\": \"BasicMagics\", \"reload_ext\": \"ExtensionMagics\", \"man\": \"KernelMagics\", \"rerun\": \"HistoryMagics\", \"debug\": \"ExecutionMagics\", \"logon\": \"LoggingMagics\", \"page\": \"BasicMagics\", \"pdoc\": \"NamespaceMagics\", \"history\": \"HistoryMagics\"}}",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": "Available line magics:\n%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %colors  %config  %connect_info  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %man  %matplotlib  %more  %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  %run  %save  %sc  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n\nAvailable cell magics:\n%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n\nAutomagic is ON, % prefix IS NOT needed for line magics."
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u098f\u09ac\u09be\u09b0 \u09af\u09be\u0993\u09df\u09be \u09b9\u09cb\u0995 notebook \u09aa\u09b0\u09cd\u09ac\u09c7\u0964 \u09af\u09a6\u09bf\u0993 \u0986\u09ae\u09bf \u09a6\u09cd\u09ac\u09bf\u09a4\u09c0\u09df \u09aa\u09b0\u09cd\u09ac\u09c7 \u0986\u09b0\u0993 \u09ac\u09bf\u09b8\u09cd\u09a4\u09be\u09b0\u09bf\u09a4 \u0986\u09b2\u09cb\u099a\u09a8\u09be \u0995\u09b0\u09ac, \u09a4\u09ac\u09c7 \u099b\u09cb\u099f \u098f\u0995\u099f\u09bf \u099f\u09bf\u099c\u09be\u09b0 \u09b9\u09df\u09c7 \u09af\u09be\u0995? \u09a0\u09bf\u0995 \u0986\u099b\u09c7, \u0986\u09aa\u09a8\u09bf \u0986\u09aa\u09a8\u09be\u09b0 \u09aa\u09cd\u09b0\u09bf\u09df (\u0986\u09ae\u09bf \u0986\u09b6\u09be \u0995\u09b0\u09bf \u09aa\u09be\u0993\u09df\u09be\u09b0 \u09b6\u09c7\u09b2 \u09a8\u09be) \u09b6\u09c7\u09b2\u09c7 \u0997\u09bf\u09df\u09c7 \u099f\u09be\u0987\u09aa \u0995\u09b0\u09c1\u09a8\u0983 ipython notebook \u098f\u09ac\u0982 \u0986\u09aa\u09a8\u09be\u09b0 \u09aa\u09cd\u09b0\u09bf\u09df (\u0986\u09ae\u09bf \u0986\u09b6\u09be \u0995\u09b0\u09bf \u0995\u09cd\u09b0\u09ae) \u09ac\u09cd\u09b0\u09be\u0989\u099c\u09be\u09b0\u09c7 \u09af\u09c7\u09df\u09c7 localhost:8888 (\u0985\u09a5\u09ac\u09be \u09af\u09c7\u0987 \u09aa\u09cb\u09b0\u09cd\u099f \u0986\u09aa\u09a8\u09be\u09b0 \u09a8\u09cb\u099f\u09ac\u09c1\u0995 \u09b8\u09be\u09aa\u09cb\u09b0\u09cd\u099f \u0995\u09b0\u09c7) \u099f\u09be\u0987\u09aa \u0995\u09b0\u09c1\u09a8 \u098f\u09ac\u0982 \u09a6\u09c7\u0996\u09c1\u09a8 \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u0995\u09cb\u09a5\u09be\u09df \u09a8\u09bf\u09df\u09c7 \u09af\u09be\u09df ipython."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "ipython notebook \u098f \u0986\u09aa\u09a8\u09bf \u0986\u09aa\u09a8\u09be\u09b0 \u09ac\u09cd\u09b0\u09be\u0989\u099c\u09be\u09b0\u09c7 \u09a6\u09c7\u0996\u09a4\u09c7 \u09aa\u09be\u09ac\u09c7\u09a8 \u0986\u09aa\u09a8\u09be\u09b0 \u0987\u09a8\u09aa\u09c1\u099f/\u0985\u0989\u099f\u09aa\u09c1\u099f, \u09b8\u09be\u09a5\u09c7 \u09af\u09a6\u09bf \u0987\u09ae\u09c7\u099c \u0985\u09a5\u09ac\u09be \u0997\u09cd\u09b0\u09be\u09ab \u09a5\u09be\u0995\u09c7, \u09a4\u09be\u0993 \u09a6\u09c7\u0996\u09a4\u09c7 \u09aa\u09be\u09ac\u09c7\u09a8\u0964 \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u098f\u0995\u099f\u09bf \u099d\u099f\u09bf\u0995\u09be \u09b8\u09ab\u09b0 \u0995\u09b0\u09be\u0987 \u0986\u09b8\u09c1\u09a8\u0983"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import YouTubeVideo, ScribdDocument, Image\nYouTubeVideo(\"99dmQGPkvog\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "\n        <iframe\n            width=\"400\"\n            height=300\"\n            src=\"http://www.youtube.com/embed/99dmQGPkvog\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        ",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": "<IPython.lib.display.YouTubeVideo at 0x1035c5390>"
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "Image(\"http://imgs.xkcd.com/comics/python.png\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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AsL/uc87dA7h3hVTN+mec/uJcj25WMF8hd0G3eAC779C1sESyB9/+5Rrc2q36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chfus4XwLliVR9SZxd4Ah9LDU4icVAxe2By3STgXUqIEnx7FXuCtEu5N5zPfwbNFDE5lXuF16rw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\nERB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DrrlMqZmlvHNLLyth9xA+6HtEwEt3GzUVZWUxL1T9LLDJw58jJmFSreUg3esAdNKO\nBGKaPAXiSm8Cf4tSx3ZoZyoSca1jbLD+IYCZ8kd5dwgQszcBiAgj5H1oZFLunUK/3oBXzafiIlW6\nBBg8AbgsXg2UlphZqxYC8aYLgB5OWaz1T+xC5W1bhwWNQFJC7rn9rSyPGtg+BMbJqtGuLAPstU6A\nRKdzjK3LXlcJ9zXvrrXktfVjINH95v8bBiNyDklN9BCqqq7jHkgAfEMASXJyxOu7t97w2kVueDws\n7UuDja5x5TLA4dWZThGfCjxuY6q7OothDsm7pK/3MParBhwUg8V5ajWP1doAMNeGttKY0m3iDYTp\nLASeZalCxyONhwCJ0dw21FoJEqeF8m6mKDBUeSOstJK2yGs8gZPCB3CexkR3TlnFQXuXKlWgtcMr\nOFks8f8Ng3m1cj760723oTnHtiRlN7vGnvi7OnyxofnpaB7hCaRVFuIwbcvSoFA4hNoMo6k0SHqF\nOAaf9KYC3Lyc+SxvxDL/2yeXemyDapWSIcG6+b51KxaP+yNtYdk0YLXSIf6SmZjWOqXB9dLvgRqz\nWeuZctYgEsJKiakQXkujTX/z2fy/YbDPnYItWTGYVDgNuKAtTjOoCG/NSqxa4lI0ksnSuMK9HQOB\nT7HASlE5iwtNKKkraZm3T4KPIcA9dRVTm0IOdg1iUqWG4WFqL9NkY/ON6v4gSQYISyYxnmN2McCr\nmaFAyokxg9YkfQ8MwqYG/zwMbv9z3IkkteBgsNQ4FmBrtwTunYC37awMWviStaI4wHN1kWX2cfLZ\nQ1dvh2S0WqmvfOOTUjIdFfxrujv89Y5sZ0sI/BYP45disFfF94v2883qE05Lyw8MAv85xbhEUnAw\neJq9bFNO4THAG0e1Uz/i6r4JA8nVGAKDw6OlmkbT6nD9cOaP8uOSc+n/P+4hM4e9s96ROeDyzaL0\nUqcv+i+du9snOvZrMJB8UyGkH47BF0ukHwUeg3in67gJdRs7ZyevZ2k2u9ilJHSLp6fBjS0uMsqd\n7hSyKYPzjErWBZi8F7hfSUv8TuqUJwC7hJapsop24aCvwECRF/HnYYDXfi6uWKDl2bZE+Ye8SX2i\n3fXR1oXpKvVK1aP90t2wPkKml7q3mmSZBDj1gUcGdvr9jX1SDJYCbLIJSX5/cfuWlK/BYFZjBQY/\nCwOPI0CYmqyji3GontlumObaHyrLau4SoSmNxwnVPrvALhVwHU94kSbJ+x6X/C1R7yjAtBJfrxtw\nOP/GMylS83DooZ6900vASR69/Y9jIB+H5oZBhI7MmxasNZi0DFXntdEz2KYmqywcbyw1977TeTHS\nE0i0msNA51hgUlFvoycAHZp/AwbhR9P+XjmU9PZ6S8qDzau3LBlzi53Np4YjTdgObO2Uee7bJZuo\nq8e311MTpWfI7SshLYTaWHjSbn5Crr+SFpdMGikJQEoqpEZF/19hMLtZbgfHWZ8CQrXkp1irUsxJ\nP91rdcxs+NprC9MT7hTpBMAdvfDBxsMXbRstemG3HuBgofNG7wDqKhWr1C/4KzH4OzlT9iZsVVWa\nTEOhrKVtprrgmkoNN/NtcLiZBFigXMkxgLgZCdeXAFzRj+oklOuv8c640Srm16453aWWmrJXENGj\npnoVLncG3uxJb4FS6eFiau1sV8TVxLVCA8/y5UrYamnezBcM7v5avv2xPOwfMSX4e2CwrkKuQz+D\nh8Al8UC+4uIvg0Yqyd/7HK/makJJyFPP1a7Em7UpbLWnhV4RQxUhLvgYvwdY3vySSSSAR6ku3Sue\n+A4Y7BY65+IcF4/xxKN6ZFREfFRqk2JI1mgu4piWP1wwv0zDDQSbvVwkVgB3TEIHqj3IfIK+Nu8g\nDD/jG8FuHbgmtHtOaqb+F3vEAz48AAgue/838euSuTMHN+rUtUNTE6MmHQfP/8PnHzCIupPFkpmw\nqV6ZdnOuxZF8LtPQe7yoMLSBGMfBk0jGHwO42v8xxIxzr78zUtbyQLz/66f37j25Fwp+aqe/BwZH\ncu11b2m9AvaJzPPogrTliboajiLs9sMy8gQr/cxT2ogF9GlKmaHEhZwXjy9axQGSyof3W8YDYTqn\nvqFT+DsJcaxl5VmKXUYMkwUQTfMC9pr6f9S9R4r7LOh3iFiHRyeEzj14bxNywyZzi35TX4r2iTJp\nXNAPniAGA508iDGexGzRyQ/SXGcFGaSPfPm16T/pBo8C4UhhoeT1a3ra0sclRfWetfSFV9paoTdI\nnj3hgFgDrHEO6FOFRAf12/DCQDSEtqoThxY2X5zELzOAxEpKyjqaurpiDUQanvguKqJbbhhc1XgL\nnBR3pTCvCgYOqsi+gWjLYwBDPGU7T1eZp1vM8pr1UVymAx+Uz103j5aqHgvsgj4FPD+idfE7YUCT\nPT017nJMJWq5zPu21x1IczmI1072F08h8byEGPsHLyyrlk0kyPbDa+sg0tJtrW0GyD7MTkClubSv\nANTpCm3bsVbD0ewODGmBZYZXeWzLf8KgTR+Oi3q713b02ihbE+PkehOI2DzkjlUPZyNj2bc1qLRU\nbWVZEfAUpcOZarxA/01DsQfS1qgNo0w/IMG51pWn3q+9z4ZDvPXx74HBHevc5tceN4wE7oo7AHxQ\nmgPBxeQBaeeNAgBW6EVIl/8UavX8rEXx5FTrzkCU3ppQreMQVuQgQ4S6sooQYvOHV2/evPTNfwxG\nzuMNXFEL2G/x7MLkM3DMKQWoNJ+Of9BKFpgX63A30PZmkZGEWD4LM/+1f7Xu8r7PWBb93nQukYfs\n6rGgSPjoimIbTKrD6eIxTQsHs7wiRbY9PyK7sAUN/gmDeeXpqZHFQHJZnJf9N98hMiClsot04N1d\nntJmmwsUnVGtFn08Igx2DFEOAKjVONFgJ0DbjDkU8TbfBQO/33PzqM63SwHOyVoD2oh5M9TryEf9\nI6VxP0eE7BkeFuIifyodJFq/BRBnNJhu5RLp0Qbertu89s/zW9Wlk73Lp+Y7BpOHAjxU87kt1ITy\ncrhuFwVUXsyvIykse2pxTreTXd/e0vdOtL0Xb6RerIn8655bRJZfv3jv1jrCwIMzjh9a17Qwfc4s\nJx5bhCW7jmR9CWoJUVg2wJxd/p8w2FeccXpZKgacSw9q9FgCBBhK6ZxoEv3y4nvgqAvJ1lsDDRd3\nr0Wx4W9U70L0IfVz95WfAwzJSJqRYPtdMMhdJtgmAPutZVlX4vo62g2S56VLdpSGjKcelDk0Lonq\nkBgAkQ3OAImFreKTyrq3dku3Pn+wHrl2zZrFm97nf2swsTvAS3H9oeHxxzGAv40PUPIQ8/pTRJYg\nNK3EJYnLHRr3SdQ5m6SfqX/tJB3j8FJTGDa/fdsi+oVjBPjpjGVbYZ6Y+rHHIHy7K9Vrn5bbwf9y\n/ScMDrqxViyaVrvTUbmpxVcMf7F2axA813sGMFP1I8BpYamqqulxhCs28YmmWzhsZteGhi2wb9K4\nkbroy0pnCcDYcrMbFZNylGh37Mdi8PgJQEzO45Pb2Vx+kWuyvt9LdyB2aqsMj5AkMu176QYz2wO8\nFafvmUq7qBCbZxDo8Jo1LWj5K0BKAl4HcLnOaePHTmtwyfRYGsicfMfVtyVCsP6ZICtfwGEMBxy5\nZRoGNUZuNqfiivQjDljG/QMGp92JrSJMmngK+ZwfOgqhLPTGpF0yjwJ4pS/VDg6OOf7+ShPVl28d\nouOMjkAfMYjeZamg5dBgydNUhkgDLRYJg5KNpQPUGOO1PxaDHyL5gcGf7gARWgvuKEk9HpGm52Gb\nRxqzy7FJ6yUwbSflhlH0MdRwMT6E44zkU4Or1jsGUP8XAALrVgRIW/oy2eo43BfbOS38LqkHwjGL\nhoWpOi+jn9fz/ycMnJJIDI6H/uk2s5SHp3w/zdMqPadwDMAjt0zfUaLNlACn6Dj97XBdDGG8DfV6\ny3piqRqytEz6M4kre1iBQY5y2DoBoHGzD0Z3SXk0ZBeFFpPsPht2/gJtzdfdaNgyjZZFY5yvw0s7\n4XtLaOsIs3odDgD0rQMQW07IH4zEeSHHrTrChzGJl8RtoIdoQK1qh4+c2dG9F/CufcQ/YHClUIw0\nV/rIdBtt76kAbwpXcXgKMExaV2T7BQCadYmw9Uux2AVpAy+xVyO2m8yo11o6BP6zTMa54yUKDHKU\nDxslANEBAd2mdLYTdv7Y1dxZrliENBYlZYaFqB0MF7ZIbHcA96dIQtfOWnZRrsHdUF0iCV9v0+Bi\neq9WSddO/Co1NIdujwRCu9yguxBCSdd9D+RUOzcbBg/1fam+BGiYPo9skk0UkFJkbb2ewEtHaRP/\nm+ZrgM4T0+yeYrNQeu4H4l4/WW7DvlbPo9+tObrJNu47G5MLPgbn+3mVG37+SyJEb1lZlx20L5Dk\nYa469TNc+TFvZIrayQ85qhbCUEtjWKY4iGMthzz+bK+op3duvomWfJlryVf7FqPFxJCZlulXEWzW\nLBnm2CZdUhry+q77BOnaJJe+gKT8JaxP4iibLh+tP2Z8FZkBXwh1ZdHrntazJO+rO1euXv1fxeBp\nC+HWrkcZZbsN/3wySXxSuiMoD03Ko407vjX8IxsGsXaXYKGG0Mqk1Z9UqXxistgERxy0VTvLh83r\nxEPYVyIZl6WUHSBvOTavqyg362yYv+U1b7SsdIWyUBLNgfXz/nsYbBDWewB8eorGlxIpqJLdp/A8\nHvA7naX1uVNWw2wFQJK3b0ar0lZv9DCNLdBjD6/kgzIJcVkDCxM6N5x9+sk7X78keKXslJjPGKS9\nOzxlyO9/7rr7hTOQkl7kFvi58Chp/kkAYeuaNL8F6VmnPq7d+emrMbgiWst9v+dthcHqfwsGOUpa\nRA7vL21TrVqH+OegzXQJLrkon1uDyCZCWBQtZW2iU3xx7gkok/4MlL/SE+qy8IxPV6RN2gPZPqmF\nV9BbjAFW6Rm0rKm2G963iwa4Yqilr/Z76tdhEGFXNUMniLsxXK1R1L8Yg3ySpLT8xeCxncmmd8mQ\nFPdxdQm1sj7Zdw1bFQtwS/l+z87d6jRZ/ixphbLsw+5dDyDNUa4Ie2v7TDIdpnMYeqq8hZmWkdw0\nDgFeqQ1KjjtsUCP6qzD4RdzPsm22jp8Cg3xWEWOKOX3MdCPl1P/Mtuse0SgROGIS6SUcG5TXF679\nbWXjKHupo2RIadmuy6o+0T9CH3GOpR5AmPFp9lnGAXdUHwOBbtVSvgKDh2Ja1m2e9ik/94HGpP3f\nYTBMLUvyq9T+IltqmbNC/AGsdkhx7QZEnLgxxUH6GLYXle6x3Uj2ldecOLAu0L0pY8sA74zvsMgZ\nIND4JkBo4flfgUE/tWzmuo1aObcGab6nr+YKyPzCFdcmAlHpEWZJT2KJPvvn8lErEyHtWhDADb8P\nd2+mG87frTnyMIcGLNz5z/zBIHnIqwKCQaxB9po8q0TWQitntAbrPYMRFSgsiw9fKPV0MFhW/cvX\nUOp+jLf+y3MLcMCZYR4zJywtXQv61QeItpM+nTnF845BlGHbbNsijBdn3z105vCuNe2UhTjPzs05\nabpbNEbPsaoF53XcV6fSfxqwRLSnvxBG1qIX7Bel/CHFXVtbR1u/9ytYf4T40kIIjUnA04YtM5H3\nTvTOHww+md0rIBi8EZ99oAdU92VevGoYVK0J1GuNhawq3BZLaeaQ+vKTlF5N0Dv4YLnE5iNwwZ5O\npcyFSQNfaDQbINJWmr5nn2V8njF4LFZm3zj8s/DmK0K/Yuu+e59eit0iRINUPk0N9Mvs6Q+2WAmh\nXudj7IfWER1SKomRpJUxt+U3a984/hSXqFLBtVoyeLnfffNps0PhKFpUZqbetec3x9WL45O9mZHW\nUnhYcX4KEK7ZJ38wuO4QU0AwuCuWfrZ/RcfMLeEV48RL6jcp/kuiqbAqVdkXjulFJS/6CEXl6fxa\njqKJ5QtOOf7pnAwcqEC9wUvFIiDZ9RRAuKM07/Y6F0meMdgvPkuHeMQ4+1Ah2GSz7E0YT7hcJpFz\nwkBFo8a+9F/bWDQZiKePJ2n3zUcPEyqrThU6rPthq1UiSDwaR2lt8haToWUpgPlWiax0SyzUS2Yz\n7m758XZ30ST+tJCWTXWtnz8Y5FJD7CdgcEls/Gz/8SJzbqf91onUbUThCaHawyd2Gh8BN7UCborZ\nhJrJA8rGNjymbVGX4U2POSQDw/tSdja/iTESvG0+AoQ6SjvBcWXy3imsF0+yb7yj4Z19VR1ZZE6r\nskguNfzoLTof29c0w8FbSVqr6KXabuBk4RbVVqjr9Yk2eHLENAaYY3VP/RJTTSOZYBgHh3RmwmXj\n57pLUt/5fErFz+gMcF6j9hsdD5VZQH371HzBoE/XgoLBC/G5urNFdMq0tMJFwgPNRfbrfY1lQ4q3\nKscnCneOWcoTeJ1xqjT2iGFoyRUPjT5BjOsNHAbCZqWBHChOYmTAmWdOF4DUgNI98o7BYvFZVYlA\n3c8CW4dK8xM+UDsG8W4tcVgPdLSW2RsiCkmjNZYUSgJiy1SvwlzHd0lmp30tAyChuecVbZ+YP8Uc\nVogJyzqLccAjowcuWlpaOqoN0rbZxgMcFYc8xs0U22GmeJ8vGNSaWlAweC+Gfrb/eVEl09JvxYAB\nQtx/YPCRtIi3i9oledharjW816eLfBd/Q3Et3nK0yT0cp8OM8rIiY+fFouUW1Q11lFQ2Fy62cEQl\nS2HxIO8YLJfHv2VIis307Kt2SIPIhrpJgO2qflUWwEsredToc2uppeOXpgD+lXuUgSTw+APbmdcn\n6avd3CdsVdSUKnBWCKGyE8BH69GVldtvPD5tPrajrI572a6/lGS48hFuiMP5gUGK0+6CgoHEufpn\n+/uKzB9tt7rAS23jpMvCyNleS9NuE3+IkbjVs8+I4apUOIqOoryEHTpzFpveJLnwJoBVxp1E4992\n7XybWtqgSMX+E/cFf4X5aKf4PKa9ePbUj7ywfPhkU6P5ntMAwnVX9RjPJg1n+Ud721nqOuzRCGBd\nm2XSoW6HatQWwmL8O3rpNV3xepdO9AO1rkWF8w7gjbosBclFVSEbEY0uu1p8oLe4nKg5Kj8wuGfg\nU2DsBv3FZ2PXEKVxmZaaNgWYO557tfuMWrLnQRL4bU5hqMhUY/DaE7jregk4W7TkdYhr+gJA0naC\nbJz1OuRvr/DvMLgtPreeO0/LvibWSUWoWM9z3AaAy+D+wlx9fLrb+IaFNPD614oA9dfuM48HWKWV\n0K7qszjAbR7wVvPWO91n3G0oekCIpuyhpZUUblJzWZ+hN8V9EioU+uTRAvy2+n0jBkMqFxzz0U3x\nmbPxnliYaenklXTzTJaR+tLsriLpcD0tFUDWJ39p1om/wyDcuHT2syRYfl6R0b3/tUAJLuMAJA4T\nBwlhn2EdfWEg1SknW8TCW4eAw4YRALeVnvevAvBR/zqQZN0nSusMsE6MJ8XmV1jVGXbrztS+BhDj\netVbnIUXep6OPaC/OPqNGLQcWXAwoIP6q890g2X5fAHfggEzxJlsG/3Vtn92ijLTALqWB/AzuNy7\n8TEz/fPpBsNCCwC69i96CNp046o0vXOY9h/T7CXAQdUPAHNXv1c+CrBAjOeXnXBILL+o9RfOowGm\nNeOV+As4qyc6gs/jb+0UGkwuQBjcFB2ybXycgy3hm0TyTRi80iyfzS54WHz+CmoPB9ii/Az8S1em\nSwN8y6unB/NP03uQfKqSjv8ig3lddJ6TZC8tBHPGb4VjKjDIQtZ8pVyJB0gcuZs0CTBSKM+HkcUT\n4ZbLG0I1xgLcrTQ1P3SDScsKEAYMF9mSiDzKbwy+qVOA38VvWTd2y8H80LANQJSL56HfdL3e07MK\nRFTQvCHvsDpqG4vKD5EsdHQ/CZw4ItsQdB9gai5mQdKuXwde6nS/NcFsO3BaOi81l7CAPGKQllaQ\nMEhqLkZmTdordhQoDFLai8lZW6uFn59ivNQU87yESsn1aXDsGPDRtXnGQVueSL66vdqipuS0859v\n49+d3yCul+icOea1j6x68HdJK7btRJ4xIK6u6JNxgVGlLHIo/pAoGxYkRmQ6MD4in67607svybT3\nb09zMUVUORsgf3zb5W3wmL++/bdT47I5k7wG5R0DkruIUgdkLyK+vsrFAvqg//XZTvZbCSWn6Xc+\nhoVdbilaSp32r0T/3N5tDMCSM/Bh46Q5l2Ue/JB9qQCpEPfyyf07928/5HxNN0O99j5wob59kXYb\nroUjcV78FRgg2WInim9+/O7jvbUlxVb+/zAIPyofTi31+XkYEH97bmVlJR0DTaHaU+a++0XkViL6\nQG2Asj25rq1RwlZUkk7PvqD2FvhQP5iuQgihLOzYIGqN+r2y7kNfNe26NayF+ItIs/NfgwEkbion\nlNWEEEUO8K/A4Hq9ACBxRkDWQjyZmriPNzI2vFC5IDeTHCNxmSwOIeDOyywfQ5Ze+uILIPruiZPX\nUiHhwoqR/Tp16tW/5dOsSk/om+d3L608yZcHqPvvF06jtsn7XW+Rq3VjQ+EUYHBFdmq+JPWVh3TE\n+cz4LfBR9WKk0dJLRy88OLqNt3ZBwGj3pDJDIfnl4Uh8TV98HQbAxyLlpm98LeHfgcE+cQ9IKbyG\nYS5dJvftcYWrSyCN1OlSE/dMWwNp+Sfp63KRlZV7aPSRXsJy0jOYV0VHV7SKIC0ZIGJuycKukzMF\nKJTqS/JgXTUdc82dcE9ZzczRslARF+tnQMBF4G6bMVOalDBRV1E2KLc+DxhwQ5hlvKQuucdErPcA\nmOPAab0IYJ1RDICP1Scg3uoUDvJnEejsD2w1SaoqVzN8LD5+NQYhBiMo0JIFg8vSpASNB8eZKnt6\nlrVvzW/lQEK89X7pcEzTZqxjRmhMLVnNkOs2qXst7q+2Vb9OIcuTAQeMyzDvV4B+ovufky21Mubr\n1GzDNLHgVXisXxCkvgtNpOMvEBgAzFQJYrdy9U7lxh6/aDHdJ0xCHjB4ZaoibGV12bmUXn7zc1lQ\nB2CVAxetYoADmqEAPtYBAMV34iLX4SJcnwHVmuM2+713OMBTx/CvxmC2UDv+nV5g4o3U/Mbglt4H\ngGb9WeIigeRQenYAiLc+BFChtV9ArHOGmjNa5qI5VDGt6ByIcmmP6RzgntqThV4A822SIHJgRrRY\n06IU75Tl92eZxjK6OrBNPH2nLa01Fu96LQ+6AcCvZttUhLssRXsjt9xNwLMaAGxzZr/2pnfJL2sU\nSQV4YRMMUGoDpQ2KVt8JEOdkVb20cYmgFAehJM2pm3O97S/CIM5NCOvn3weD225x+Y/Be4DmI9jm\nIV1TfxJAbOGLwGuDW0CfDLPVDtkNj+17wykO2Gwf734MeOYQubYswD6bBIAJ2vLmtFkxSmf1dJ4Q\nr+nfAIgxOzzATPqgI4r45A2Dj9ozsLPSa5QCcF91f+63O78hwMrybBaqaiaiqDTQ8LWevUvZ0hbi\nOC71OxerEgVEOtsLkxlBBNhsOrovBODa12NwWKm+rnmFhO+CwbyG+d4p+Ji8iLx/8XSJ2cypQMzj\nM8mUWwUQ5fwQ2OSYDGwtmn6X10smE/wByh6Z3AfghktM2ctww2Uyy2oAHLeOB/ioKvdoNW1DhyLT\ne/1yK0PPNPCm/W8Apf7sIhqcA/BxCssbBnN0Q2lbfoM0QP3XUn9zu+vqA3QbzsJa/msWn5G9mU8G\nDXs26j7P+KzE6Z7MLhfkcL+D+D2Rjw7yTvCyXczXYjDI/Ij1uoy4snyVpr/nOwaBJuYGenqaYjVj\nTSvqKxl/pNRhQFYd8Pf2AO8s5FWEeekRQ/eyXLWKqjML4HoFal1oU9JkPCxuC3DcVZoc0VpuB286\ni30u5erZmaef47beB+rOBNLcDnOylVqt13DOLTlvGJRuCpNME7uJffBY7Sh8uJ/bSKEsEO16himZ\no9v9rX2AxCLXEhzkoYIhhV+zQsXz/VsruVf600a+FoM6Nd8bvR0v9n0PDNz35TsGsbZNbviGhbtu\npkfZ3zbe9yO52BUAH9M3wPDOAH5O6dNc/T3jPxTSOtd7EJUnAyztjdfJnmIWMLstwGnp9GZfU3lo\nZnvptPgIiynycxxySKbSIeCiYwTwsa6FH5tLkicMrokDcEhMq2ht+IhRjqlwzjrnkDuOOyXD+EbQ\n99dMawOtvIFwlzcJDg8gLSpQwjuHIHhT2PWOxqFQ74urN+XWon8JBolWE+NNHlHe6DvMXPtk7ZPv\nGFB3A4D7ARpIrbLSmtYyFWpJTYAVjdL3jiwVMXHuLKtCd+jRAaDJHjxX0EH5HMzsCnBEOsdjfnoC\ntsbDpeULO7cHeBYBE5tC5Q1AO+mwI8Z0JzPr5Q2D0U5J8EBJeD229vI1GALQrmXOt/tU90zgSHEF\nRmTODRhpeQl4XjQ+3qpRN6/Cmibv8DF9Bbxt0F9JSUkoKYk7RL35WgxuiQspRqd5pvYdgjVu2Ebk\nPwbVlgIU3Ubl3wFJ9AeHcwDepttPbzjz2u4NRBbLKElC+aXV/YM1i6Wytmgq3C8bi+evJLXUvcbw\nHgBHXdOANRnJEjp345cayTC8FUDHwv03lxwD7VvBJQdpI/PM/D6DW+UJgzjrhXC0tCgTzgpRVJrG\nMcRufc73210I48PA/AGZViaVvQBcsIwPdjS28eg26ziEHZWOwwJ/m7x0/+OAd6m01wj4SgwWqgRR\naBMM0c7/ck996+e/3SDOfD2Ay2Jq1Bhbv5qzwZSSOlXr1652xUAIoXy/R3vo9kumg3uK9lB/NsS6\njiam4lqoOAwS6uj4Lqt9dfq4HlXNnl3pZmuY4egc6MJD9TaplJ8L8LKHrZrZM9itevi2xSpS7voF\nbDIcD1275AmDg3qBfmWFu2FNSPIQntLXt8nmY473G793ezBAapZ4kHAJ4LchjbC4XKcSruyS/JUY\n9HCH4gvgeSbbW35Ju9H5j8EzcRFgXwB1hEuFNkNOJM1tU6lsuQW8ePz26ZPYF4aly7hmBvqQOAOh\n8cBW4WjRG2jXE4guZ7pBRaewQ6eZqpo6ZZZnsro8OwvnNNyKNJE97NSwIEDSVZjOBB8dNbNiO4AW\nHfOEQdXmtyyN9tFfNwpWWF+Qv5/SSfwA+RIM3DpCueFAq6L5Hq7h+Vf+Y+DbSZ7srtqunHZ+1G1k\nlkS6SRnug+dzz6YCV+8ARJwMPeMHSHyu5/BN3mmzLJt9J+1tCJD65NzzNMhI+PhlGNwQuqJ+MGwQ\nLyE13TksKb2qgGDwVsyEDt2BC2JBPv98pN2l/McgI5Ql9MdGGWUzDYfmBYP2wmRpEvAiW/zndHu/\ngoHBNXES+rQE6Kqcz7bEVwaPvgMGBU2+BIMFY6ROpRjDbJ9/62LvCwQGe5TeSYugw1udRvn788+M\nnyswyCrZZ4IkdXKJLAgYDC4ODJe6tGeKK/n68zfNQ/5PMUiO+koMqmUvGiJp4HHg48/HoNQvwGzp\nPJ9Qwzr5+vPHXVP+HzF4epQLdb4Sg06m2TNRJEy01V72szF4IlYAG92kS4vErvz8+dB8encFDIO5\nJqk3TEK/DoNB5jk4mM8YN34s+akYrBTPgdO2Uv9kjL1tJAVPsmGQlycWcitfi4z7bwL8NXa9kFXF\nyTMGy3VzigsJ7Sc2/1QMujkB3NOS6XLbxfSCj8G7is+++NAt4nzHz+3sYTcg7IuNptInltT/MlxU\n8QE8pibrbPk6DPaKnLXmKZd+JgaJFr0BgrUPykb6NtMKPgaJC//uDSZn2TjPbpFhxLuu4QA70ku9\nnzYNZZDXF/56ePW7AAmmAyHS9DAwsjPLvb8Og93i1o9/fv+IwV2pBTnVTj7Pzj+24GPw93LIJbMS\nVnHU4KqckX6CXeR5JrmtdJ8W7l96xiJSp1vT8pDmMA94u/irB4x9xf4CiMFuIXVylWnxg68sIe17\nYXDcIhPJ9y0eFp3OKWnppb7VIPheLHBF3KZC2S89YyXpw/m1KFBi9jfZDaJsROMCiMFNc2n+itLi\ndT7+bMgTJCmSHLPtp0ljE9+5yzLuJN0/cerK3UtnJQCxfwWRcDcFEq8df/PFGCTdu7grNNMdFsm0\nrf+ox6a+nJQWMuzaCG8b4fQC9otXlC8OiVtGbvo8T/S7Q2kkn7kL8Ol9AtTtB8B4R8Blxjdh8Fi/\nqzhf8DAgIRXA31DkljXq/o3773Ia/SdFhKYHu3w8MLZPt54DB+0HiL+/o4luneK27pY3AZadAIg9\nlwB8OM9zr1CAFqWTgE8jG1oJoayhJMQMgDfiECfFLfxrCKG26ksx8LZQK50Jg6mZwvxSynkvaQNH\nDANCnz360Lw5bYud61M7jT91oyjnRWJLYSkyInx8u0md9QfFKxoJMQimKotCv6VWGAeEMKkYpNit\n+iYMtum/K9KgAGIglRtaRTVyjozzNxYqGsWa/xEF9+W1FVOuzRlUx1xdaMv8h6eFirkoWtHLbBRA\ndaFlJpy6DOpWtZwEKN+HDcPZJfoBG0yTw4wvAGNMXwNcKNJw5nGbNsFn7/QuAxBjsos94go9lVbd\n6ijW5IpB0sVf3UtvSibhTcDrl8GS6LhEkCwqX387MRv6muqVSY8dSLlDSCjsEwYaGirKon+o1mWS\nnNYzoQh4NGC8OBHt4J4O+TWZ+hahO3uLODFWPJ0r6q4b0NK/6Dw4qFS6iQfEW2z7JgxGOrE1f20z\n+YnBOccbSjnnEowqWt376u9VRcUYmf/pQ/xVayF0G/4yYexoWVKMkEPvwkwOy6Mkzp0KSyoyHDhm\nGQd0aMFiI2YLsQxOaIVTfAlMF5nmRbSqCqx2SAXiLbZwVlzCozYws19uGNyz0Wk1prNOK64qa+gI\n8/DHZ+HGNM16rVQn3te0Vi9S+I9sN7FdZebZe3evOP/6SmvKhlVGI/mlHmmFfsO8BzzVSK8L/FDl\nDgSHQZl2HiOIMj7QSzwDEixX8VC7YyVRDRIK7/gmDNpWgWqFgwsoBnOd+CWTQSSzTK0FcEscw2MW\nQPfWN+scHFA4ez9RdHLmpZaNgWvWkUD/atzW+nRbq47xK67oBtK8FyPU92bad1pZYGvhZCDBchOP\nxVFK1/1bFfGR2gXghc7pV2K497F9DHWFhaI3bNAKSvHXO/OZfWmtVF0oMzBOX0nLuPwTmrYiTH1d\nuNiVpQ7rW7UL0KMjeHa1vQ5uR6erhAARevtpWownwjER3sV9EwatW8MF0bKAYtC4G58sc3YvbigO\nsNk8kMKrAdbrh8Mh/ezGxipZIhmH1gDe2/gCY0rwSuthnOFOjx480vVhVLFaxllmhc4pDeyy8nl0\ncPEM9a0EqW6mmUP40x3XJLlhEGxyA8BtcYg4DTDPPIFd4ircVX2Er9LRz1G2TwNwG4dtf+IA91/4\nqLTfR9yE13oP5LvFGk2C8iUIMPvT5DZpbhd3qYcBkbr7non10Dm9AvDXY1CjHzArtyjweIkkMQej\np3+mmITkVHi7MJqjWdMTycPSoqMkX4+BxGUJ7BUHc7q0TZYJPBxncZoI3d0A57QD4LZJYLbdmg/P\nvLSsDBBoWKlnr3ZGJQk3vIzH8uPiXJrlfqYKYXUh874bHVNhmhBCqOqJlURob+aQUFXX1hiaKwY6\nx4FDSg+eSz2i8xzSWG8QAd7iDL5i72f3MNwuFcB5JJMtQnmyj+oNeSKmBypthTkmGUOFSh25rar+\naZflPf1b3CoUu103EkgwX3ZfnCK5uWckjPV68g0YpDkuAWKdiucYe/bUqb67pdvoF5Aondo0fNyE\niZP6lNLfDHhL8wy1WEJYMTGPWrI3m5gEkOzVIgmI/EVfp+jyVIg+Obp/z15v8oiBn95VoHmJnKbV\nHTSOT3IUzveJNd4GcFv1ITwzzV5sr2mW6V5/2adAvLO1W4maLs6pMaanqTOMVhWpOJH1+pfaiXWZ\n9j1sFQ+rjDaeehkRYT2fROs1cHvj3Rgf4wO5YBBqqO45rK9KE3zEWYAFJaC3fRrcERdIdat7LXu8\nfhvpXLb6NYl0tatsOp9HV0kcfYTymiv7i2Xx6YUvlhW7aNmiWFmtntjMSSo7iIUWCQBF2lBRs7a1\n43t4rPl59ts8YBBttAdgf85hnz7KbnP2Lmut2puGq4A/LS1NVISw7nEJYEADgCjdBSy0aCMGdZbN\nsmy2BMBbX7VpEowSrY9PVm6R2NtceDas6fUujxhcMQkCzomcam7dNvjEy79a65S45T4V4IY4Dq+0\nsysSLSdkaUJsk4Ayh4HtpglJFjvpVpkw++U92nPdNISpmUYBXCocC4ulthyXiaTYzefTCwCvOblg\nEKbXc7hHsfHhhKqtBBjfHFqUBTaKx7BKGGVPAB4t5eLQfnjcYfzb9PWBPYXZQe6pXZZ/DmaiimST\ncsdwlqupuwVxVVq6sXlLXv0+ZH0EME9oXfoGDHx1pOXSSnnmOCqTzp9bWijCaiEw3jKa3w3fyJr7\nAR0BPqgcpehf7NAylk5oirHYC7DH66neFFgu1sEF1X22VZ+S2QP3hRjsc0gG0rzsc5h36mdwE+B9\nkR6jqgJsEy/gg152y3iLLJ3CfpNooNw+4JR+dLLZKoYUgQ1WpXvxxvAJjBKH0vc9ZR0Hy6WD/cqt\nwGkyE60TgBrjcsHAX++a9CbTtvkC9F8AZ08AxyYnQYJPxJcrT4GR8EikDzDXDwxCEp0GXNqRoc6H\nhWVYqt1Wf4tucN5A2pluztGzEGz1lKSQa66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       "prompt_number": 49,
       "text": "<IPython.core.display.Image at 0x101a16b90>"
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": " ScribdDocument(57352264, width=800, height=400, start_page=3, view_mode=\"book\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "\n        <iframe\n            width=\"800\"\n            height=400\"\n            src=\"http://www.scribd.com/embeds/57352264/content?view_mode=book&start_page=3\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        ",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": "<IPython.lib.display.ScribdDocument at 0x1035c53d0>"
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "class ListTable(list):\n    \"\"\" Overridden list class which takes a 2-dimensional list of \n        the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n        IPython Notebook. \"\"\"\n    \n    def _repr_html_(self):\n        html = [\"<table>\"]\n        for row in self:\n            html.append(\"<tr>\")\n            \n            for col in row:\n                html.append(\"<td>{0}</td>\".format(col))\n            \n            html.append(\"</tr>\")\n        html.append(\"</table>\")\n        return ''.join(html)\n\n    \nimport random\n\n\ntable = ListTable()\ntable.append(['x', 'y', 'x-y', '(x-y)^2'])\nfor i in xrange(7):\n    x = random.uniform(0, 10)\n    y = random.uniform(0, 10)\n    table.append([x, y, x-y, (x-y)**2])\ntable\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<table><tr><td>x</td><td>y</td><td>x-y</td><td>(x-y)^2</td></tr><tr><td>7.36047422482</td><td>2.3981892483</td><td>4.96228497652</td><td>24.6242721882</td></tr><tr><td>5.80403175147</td><td>1.26840479974</td><td>4.53562695173</td><td>20.5719118453</td></tr><tr><td>4.33310361456</td><td>8.63676500911</td><td>-4.30366139455</td><td>18.5215013989</td></tr><tr><td>1.32853651288</td><td>2.44226297309</td><td>-1.11372646021</td><td>1.24038662818</td></tr><tr><td>8.20924942945</td><td>1.0305620391</td><td>7.17868739035</td><td>51.5335526484</td></tr><tr><td>7.47018921883</td><td>0.040898824834</td><td>7.42929039399</td><td>55.1943557583</td></tr><tr><td>2.67068738143</td><td>2.4374886084</td><td>0.233198773033</td><td>0.054381667744</td></tr></table>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": "[['x', 'y', 'x-y', '(x-y)^2'],\n [7.360474224818089, 2.398189248299757, 4.962284976518332, 24.624272188179546],\n [5.8040317514713236,\n  1.2684047997417935,\n  4.53562695172953,\n  20.571911845255304],\n [4.3331036145623605,\n  8.636765009108881,\n  -4.303661394546521,\n  18.521501398910104],\n [1.328536512879811,\n  2.4422629730921583,\n  -1.1137264602123473,\n  1.2403866281771252],\n [8.209249429453024, 1.030562039098285, 7.178687390354739, 51.53355264843813],\n [7.470189218827538,\n  0.04089882483402918,\n  7.429290393993509,\n  55.194355758284225],\n [2.6706873814320256,\n  2.4374886083991965,\n  0.2331987730328291,\n  0.05438166774401694]]"
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "HTML \"\"\"<iframe width=\"100%\" \n  height=\"166\" \n  scrolling=\"no\" \n  frameborder=\"no\" \n  src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/85157364&amp;color=ff6600&amp;auto_play=false&amp;show_artwork=true\">\n  </iframe>\"\"\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-64-dbb6e2022a0e>, line 6)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-64-dbb6e2022a0e>\"\u001b[0;36m, line \u001b[0;32m6\u001b[0m\n\u001b[0;31m    </iframe>\"\"\")\u001b[0m\n\u001b[0m                 \n^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Oops!!!",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import HTML\n\n\nHTML(\"\"\"<iframe width=\"100%\" \n  height=\"166\" \n  scrolling=\"no\" \n  frameborder=\"no\" \n  src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/85157364&amp;color=ff6600&amp;auto_play=false&amp;show_artwork=true\">\n  </iframe>\"\"\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe width=\"100%\" \n  height=\"166\" \n  scrolling=\"no\" \n  frameborder=\"no\" \n  src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/85157364&amp;color=ff6600&amp;auto_play=false&amp;show_artwork=true\">\n  </iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": "<IPython.core.display.HTML at 0x1035c5d90>"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u0986\u099a\u09cd\u099b\u09be \u0986\u09ae\u09be\u09b0 \u098f\u0987 \u09ae\u09c7\u09b6\u09bf\u09a8\u09c7 \u0995\u09bf matplotlib \u0986\u099b\u09c7?"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline\nimport numpy as np\n\nfrom pylab import *\nfrom matplotlib import pyplot as plt",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "n = np.random.randn(100000)\nfig, axes = plt.subplots(1, 2, figsize=(12,4))\n\naxes[0].hist(n)\naxes[0].set_title(\"Default histogram\")\naxes[0].set_xlim((min(n), max(n)))\n\naxes[1].hist(n, cumulative=True, bins=50)\naxes[1].set_title(\"Cumulative detailed histogram\")\naxes[1].set_xlim((min(n), max(n)));\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bzPbNmzcTHR2Nw+Fg3rx5ZntdXR0zZszA4XAwZswYdu7c2a39ExHxJhXOIiI9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\nhbOIiIiIiAf+PximE3snCFa4AAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x1083ca3d0>"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\nplt.plot(np.random.rand(100))\nfig = plt.gcf()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/5u6PZ9GIBFmpgq+oIB0ms/wYD6IEz5voBIh58CwFf+IE6Q8RlcRru5fgvR78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\nC4sShSV4CwsLixKFJXgLCwuLEsX/B4LtQ6mf7ehzAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x1036894d0>"
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from pylab import *\nfrom matplotlib.patches import Polygon\n\ndef func(x):\n    return (x-3)*(x-5)*(x-7)+85\n\nax = subplot(111)\n\na, b = 2, 9 # integral area\nx = arange(0, 10, 0.01)\ny = func(x)\nplot(x, y, linewidth=1)\n\n# make the shaded region\nix = arange(a, b, 0.01)\niy = func(ix)\nverts = [(a,0)] + list(zip(ix,iy)) + [(b,0)]\npoly = Polygon(verts, facecolor='0.8', edgecolor='k')\nax.add_patch(poly)\n\ntext(0.5 * (a + b), 30,\n     r\"$\\int_a^b f(x)\\mathrm{d}x$\", horizontalalignment='center',\n     fontsize=20)\n\naxis([0,10, 0, 180])\nfigtext(0.9, 0.05, 'x')\nfigtext(0.1, 0.9, 'y')\nax.set_xticks((a,b))\nax.set_xticklabels(('a','b'))\nax.set_yticks([])\nshow()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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R6Oho2rVr55E13n33XSae3rN3165dDBkypNLjQkJCGD16NB999JHb1l60aBFj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\n8v8BzWsCSB3vwgMAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x10866bad0>"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u09ac\u09bf\u09b8\u09cd\u09a4\u09be\u09b0\u09bf\u09a4\u0983 [\u098f\u0987\u0996\u09be\u09a8\u09c7](http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%203%20-%20Plotting%20with%20Matplotlib.ipynb) \u098f\u09ac\u0982 [\u098f\u0987\u0996\u09be\u09a8\u09c7](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "\u0986\u099c\u0995\u09c7 \u0986\u09aa\u09be\u09a4\u09a4 \u098f\u09a4\u099f\u09c1\u0995\u09c1 \u09a5\u09be\u0995\u09c1\u0995\u0964 \u0993 \u09ad\u09c1\u09b2\u09c7\u0987 \u0997\u09bf\u09df\u09c7\u099b\u09bf\u09b2\u09be\u09ae, \u0986\u09aa\u09a8\u09bf ipython notebook \u0995\u09c7 \u09aa\u09be\u09ac\u09b2\u09bf\u09b6 \u0995\u09bf\u09ad\u09be\u09ac\u09c7 \u0995\u09b0\u09ac\u09c7\u09a8? \u09b8\u09b9\u099c, \u0986\u09aa\u09a8\u09be\u09b0 \u09a8\u09cb\u099f\u09ac\u09c1\u0995 \u09ab\u09be\u0987\u09b2 \u09b8\u09c7\u09ad \u0995\u09b0\u09c1\u09a8, Gist \u098f \u0995\u09aa\u09bf \u0995\u09b0\u09c1\u09a8 \u0986\u09aa\u09a8\u09be\u09b0 \u09ab\u09be\u0987\u09b2\u09c7\u09b0 \u0995\u09a8\u099f\u09c7\u09a8\u09cd\u099f \u098f\u09ac\u0982 \u0993\u0987 gist \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0 \u09a6\u09bf\u09df\u09c7 \u09a6\u09bf\u09a8 http://nbviewer.ipython.org/ \u09b8\u09be\u0987\u099f\u099f\u09bf\u09a4\u09c7\u0964\n\n\u0986\u09ac\u09be\u09b0 \u09a6\u09c7\u0996\u09be \u09b9\u09ac\u09c7 [\u0986\u09ae\u09be\u09b0 \u09ac\u09cd\u09b2\u0997\u09c7](http://code-shoily.tumblr.com) \u0985\u09a5\u09ac\u09be [\u09aa\u09be\u0987\u099a\u09be\u09b0\u09ae\u09be\u09b0\u09c7](https://www.facebook.com/groups/polyglot.programmers/)\n\u0985\u09a5\u09ac\u09be [\u09aa\u09b2\u09bf\u0997\u09cd\u09b2\u099f \u09aa\u09cd\u09b0\u09cb\u0997\u09cd\u09b0\u09be\u09ae\u09be\u09b0](https://www.facebook.com/groups/pythonbd/) \u098f\u0964 \u09ad\u09be\u09b2 \u09a5\u09be\u0995\u09c1\u09a8\u0964"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}