1.intro/03.python_intro.ipynb

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   "source": [
    "***\n",
    "# 数据科学的编程工具\n",
    "> # Python使用简介\n",
    "***\n",
    "\n",
    "王成军\n",
    "\n",
    "wangchengjun@nju.edu.cn\n",
    "\n",
    "计算传播网 http://computational-communication.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "## 参考资料\n",
    "- 课程代码 http://github.com/computational-class/datascience\n",
    "- 课程讲义 https://computational-class.github.io/ccbook/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "# 人生苦短，我用Python。\n",
    "\n",
    "Python（/ˈpaɪθən/）是一种面向对象、解释型计算机程序设计语言\n",
    "- 由Guido van Rossum于1989年底发明\n",
    "- 第一个公开发行版发行于1991年\n",
    "- Python语法简洁而清晰\n",
    "- 具有强大的标准库和丰富的第三方模块\n",
    "- 它常被昵称为胶水语言\n",
    "- TIOBE编程语言排行榜“2010年度编程语言”\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   "source": [
    "# 特点\n",
    "- 免费、功能强大、使用者众多\n",
    "- 与R和MATLAB相比，Python是一门更易学、更严谨的程序设计语言。使用Python编写的脚本更易于理解和维护。\n",
    "- 如同其它编程语言一样，Python语言的基础知识包括：类型、列表（list）和元组（tuple）、字典（dictionary）、条件、循环、异常处理等。\n",
    "- 关于这些，初阶读者可以阅读《Beginning Python》一书（Hetland, 2005)。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Python中包含了丰富的类库。\n",
    "- 众多开源的科学计算软件包都提供了Python的调用接口\n",
    "    - 例如著名的计算机视觉库OpenCV。\n",
    "- Python本身的科学计算类库发展也十分完善\n",
    "    - 例如NumPy、SciPy和matplotlib等。\n",
    "- 就社会网络分析而言\n",
    "    - igraph, networkx, graph-tool, Snap.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Python软件与IDE\n",
    "- 目前最新的Python版本为3.X。\n",
    "- 编译器是编写程序的重要工具。\n",
    "\n",
    "<del>免费的Python编译器有Spyder、PyCharm(免费社区版)、Ipython、Vim、 Emacs、 Eclipse(加上PyDev插件)。\n",
    "    \n",
    " - 推荐使用  **Jupyter Notebook**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Installing Anaconda Python\n",
    "- Use the Anaconda Python\n",
    "- https://www.anaconda.com/distribution/\n",
    "- https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "# 第三方包可以使用pip install的方法安装。\n",
    "- 可以点击ToolsOpen command prompt\n",
    "- 然后在打开的命令窗口中输入：\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "> pip install flownetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
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     "end_time": "2020-02-27T09:43:37.590454Z",
     "start_time": "2020-02-27T09:43:32.547946Z"
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    "slideshow": {
     "slide_type": "slide"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9"
     ]
    }
   ],
   "source": [
    "from flownetwork import flownetwork as flow\n",
    "from time import sleep\n",
    "for i in range(10):\n",
    "    sleep(0.5)\n",
    "    flow.flushPrint(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- NumPy /SciPy for scientific computing\n",
    "- pandas to make Python usable for data analysis\n",
    "- matplotlib to make graphics\n",
    "- scikit-learn for machine learning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python速成课程\n",
    "\n",
    "A crash course of Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:22.565962Z",
     "start_time": "2020-03-02T14:15:22.562753Z"
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    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import random, datetime\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import matplotlib\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import norm\n",
    "from scipy.stats.stats import pearsonr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Part 1. Variable Type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T08:45:58.562435Z",
     "start_time": "2020-02-27T08:45:58.558112Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('data science', 2020, 3.14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str, int, float\n",
    "v_str = 'data science'\n",
    "v_int = 2020\n",
    "v_float = 3.14\n",
    "\n",
    "v_str, v_int, v_float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:06:41.739260Z",
     "start_time": "2020-02-27T09:06:41.730297Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str\n",
    "v_int2str = str(v_int)\n",
    "v_int2str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T08:49:01.755605Z",
     "start_time": "2020-02-27T08:49:01.752061Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.14'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_float2str = str(v_float)\n",
    "v_float2str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:07:22.643594Z",
     "start_time": "2020-02-27T09:07:22.635184Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# int\n",
    "int(v_int2str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:07:32.594117Z",
     "start_time": "2020-02-27T09:07:32.589406Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.14"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# float\n",
    "float(v_float2str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Part2. help & dir\n",
    "\n",
    "当你想要了解对象的详细信息时使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:10:03.137762Z",
     "start_time": "2020-02-27T09:10:03.135652Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#help(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T08:52:29.617949Z",
     "start_time": "2020-02-27T08:52:29.608947Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__add__ __class__ __contains__ __delattr__ __dir__ __doc__ __eq__ __format__ __ge__ __getattribute__ __getitem__ __getnewargs__ __gt__ __hash__ __init__ __init_subclass__ __iter__ __le__ __len__ __lt__ __mod__ __mul__ __ne__ __new__ __reduce__ __reduce_ex__ __repr__ __rmod__ __rmul__ __setattr__ __sizeof__ __str__ __subclasshook__ capitalize casefold center count encode endswith expandtabs find format format_map index isalnum isalpha isascii isdecimal isdigit isidentifier islower isnumeric isprintable isspace istitle isupper join ljust lower lstrip maketrans partition replace rfind rindex rjust rpartition rsplit rstrip split splitlines startswith strip swapcase title translate upper zfill\n"
     ]
    }
   ],
   "source": [
    "dir_str = dir(str)\n",
    "print(*dir_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:11:21.998510Z",
     "start_time": "2020-02-27T09:11:21.995620Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "#help(dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T01:06:04.019465Z",
     "start_time": "2020-03-03T01:06:04.015007Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "dir?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Part3. Strings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:11:58.661838Z",
     "start_time": "2020-02-27T09:11:58.659540Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x = ' Hello WorlD  '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Type\n",
    "当你想要了解变量类型时使用type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:12:11.948902Z",
     "start_time": "2020-02-27T09:12:11.941041Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:12:34.615722Z",
     "start_time": "2020-02-27T09:12:34.610417Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' hello world  '"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lower\n",
    "x.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:07:11.489364Z",
     "start_time": "2020-02-25T01:07:11.486061Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' HELLO WORLD  '"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# upper\n",
    "x.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:13:11.835452Z",
     "start_time": "2020-02-27T09:13:11.831629Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello WorlD  '"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rstrip\n",
    "x.rstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:13:21.895986Z",
     "start_time": "2020-02-27T09:13:21.891595Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello WorlD'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# strip\n",
    "x.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:13:52.737837Z",
     "start_time": "2020-02-27T09:13:52.726821Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Hel WorlD  '"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace\n",
    "x.replace('lo', '') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:14:37.279462Z",
     "start_time": "2020-02-27T09:14:37.275666Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' Hel', ' WorlD  ']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split\n",
    "x.split('lo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:15:37.203625Z",
     "start_time": "2020-02-27T09:15:37.198620Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a, b, c'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join \n",
    "alist = ['a', 'b', 'c']\n",
    "\n",
    "', '.join(alist) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 作业\n",
    "\n",
    "编写python代码，数一下这一段话共有多少个单词？\n",
    "\n",
    "> Republic begins by posing a central question: \"What is justice, and why should we be just, especially when the wicked often seem happier and more successful?\" For Plato, the answer lies with the ways people, groups, and institutions organize and behave. A brilliant inquiry into the problems of constructing the perfect state, and the roles education, the arts, family, and religion should play in our lives, Republic employs picturesque settings, sharply outlined characters, and conversational dialogue to drive home the philosopher's often provocative arguments.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:40:28.663147Z",
     "start_time": "2020-02-27T09:40:28.657981Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84"
      ]
     },
     "execution_count": 58,
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   ],
   "source": [
    "s = '''Republic begins by posing a central question: \"What is justice, and why should we be just, especially when the wicked often seem happier and more successful?\" For Plato, the answer lies with the ways people, groups, and institutions organize and behave. A brilliant inquiry into the problems of constructing the perfect state, and the roles education, the arts, family, and religion should play in our lives, Republic employs picturesque settings, sharply outlined characters, and conversational dialogue to drive home the philosopher's often provocative arguments.'''\n",
    "\n",
    "s = s.replace(',', '').replace('.', '').replace(':', '').replace('\"', '').replace('?', '').replace(\"'s\", '')\n",
    "s = s.lower()\n",
    "s = s.split(' ')\n",
    "len(s)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Part 4. Data Structure\n",
    "- list 列表\n",
    "- tuple 元组\n",
    "- set 集合\n",
    "- dictionary 字典\n",
    "- array 数组\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:17:29.235255Z",
     "start_time": "2020-02-25T01:17:29.230096Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 3] (1, 2, 3, 3) {1, 2, 3} {'a': 1, 'b': 2, 'c': 3} [1 2 3 3]\n"
     ]
    }
   ],
   "source": [
    "l = [1, 2, 3, 3] # list 列表\n",
    "t = (1, 2, 3, 3) # tuple 元组\n",
    "s = {1, 2, 3, 3} # set 集合\n",
    "# s = set([1,2,3,3]) # set\n",
    "d = {'a':1,'b':2,'c':3} # dict 字典\n",
    "a = np.array(l) # array 数组\n",
    "print(l, t, s, d, a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:55:14.467615Z",
     "start_time": "2020-02-27T09:55:14.460598Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1,2,3,3]\n",
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:55:16.536682Z",
     "start_time": "2020-02-27T09:55:16.533101Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 3, 4]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.append(4)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T09:56:26.664528Z",
     "start_time": "2020-02-27T09:56:26.660500Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1, 2, 3] + [4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:20:47.541182Z",
     "start_time": "2020-02-25T01:20:47.537084Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:19:29.933505Z",
     "start_time": "2020-02-25T01:19:29.929760Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['a', 'b', 'c'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d.keys() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:19:54.328945Z",
     "start_time": "2020-02-25T01:19:54.324717Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([1, 2, 3])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-12T07:41:50.545469Z",
     "start_time": "2018-09-12T07:41:50.540708Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('b', 2), ('a', 1), ('c', 3)])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1,'b':2,'c':3} # dict\n",
    "d.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Part 5. 定义函数 & For 循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:24:14.775939Z",
     "start_time": "2020-02-25T01:24:14.773480Z"
    },
    "code_folding": [
     0
    ],
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def devidePlus(m, n): # 结尾是冒号\n",
    "    y = m/n + 1 # 注意：空格\n",
    "    return y          # 注意：return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:41:43.122995Z",
     "start_time": "2020-02-27T11:41:43.118766Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9\n"
     ]
    }
   ],
   "source": [
    "print(*range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:42:01.164580Z",
     "start_time": "2020-02-27T11:42:01.159741Z"
    },
    "code_folding": [],
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:24:59.738742Z",
     "start_time": "2020-02-25T01:24:59.732819Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0\n",
      "1 10 1\n",
      "2 20 4\n",
      "3 30 9\n",
      "4 40 16\n",
      "5 50 25\n",
      "6 60 36\n",
      "7 70 49\n",
      "8 80 64\n",
      "9 90 81\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(i, i*10, i**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:25:31.013871Z",
     "start_time": "2020-02-25T01:25:31.006598Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.5\n",
      "2.0\n",
      "2.5\n",
      "3.0\n",
      "3.5\n",
      "4.0\n",
      "4.5\n",
      "5.0\n",
      "5.5\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(devidePlus(i, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:43:04.798168Z",
     "start_time": "2020-02-27T11:43:04.792610Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表内部的for循环  列表推演 list comprehension\n",
    "r = [devidePlus(i, 2) for i in range(10)]\n",
    "r  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:44:14.323163Z",
     "start_time": "2020-02-27T11:44:14.319086Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 4.0 1.4\n"
     ]
    }
   ],
   "source": [
    "m1 = map(devidePlus, [4,3,2], [2, 1, 5])\n",
    "print(*m1)\n",
    "#print(*map(devidePlus, [4,3,2], [2, 1, 5]))\n",
    "# 注意： 将（4， 2)作为一个组合进行计算，将（3， 1）作为一个组合进行计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:44:57.096297Z",
     "start_time": "2020-02-27T11:44:57.091920Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0 4.0 1.4\n"
     ]
    }
   ],
   "source": [
    "m2 = map(lambda x, y: x/y+1, [4, 3, 2], [2,1,5])\n",
    "print(*m2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:45:29.597831Z",
     "start_time": "2020-02-27T11:45:29.593187Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 4 9 13 14\n"
     ]
    }
   ],
   "source": [
    "m3 = map(lambda x, y, z: x + y - z, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10], [3, 3, 2, 2, 5])\n",
    "print(*m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# if elif else 语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:47:34.604422Z",
     "start_time": "2020-02-27T11:47:34.597350Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "余数是1\n"
     ]
    }
   ],
   "source": [
    "j = 3\n",
    "if j%2 == 1:\n",
    "    print(r'余数是1')\n",
    "elif j%2 ==0:\n",
    "    print(r'余数是0')\n",
    "else:\n",
    "    print(r'余数既不是1也不是0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:48:46.384702Z",
     "start_time": "2020-02-27T11:48:46.378110Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0 10\n"
     ]
    }
   ],
   "source": [
    "x = 5\n",
    "if x < 5:\n",
    "    y = -1\n",
    "    z = 5\n",
    "elif x > 5:\n",
    "    y = 1\n",
    "    z = 11\n",
    "else:\n",
    "    y = 0\n",
    "    z = 10\n",
    "print(x, y, z) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# while循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:51:01.342343Z",
     "start_time": "2020-02-27T11:51:01.337738Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "while j <10:\n",
    "    print(j)\n",
    "    j+=1 # avoid dead loop j = j+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-25T01:34:43.361333Z",
     "start_time": "2020-02-25T01:34:43.353079Z"
    },
    "code_folding": [
     1
    ],
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "9\n",
      "25\n",
      "49\n",
      "81\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "while j <10:\n",
    "    if j%2 != 0: \n",
    "        print(j**2)\n",
    "    j+=1 # avoid dead loop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:52:33.684842Z",
     "start_time": "2020-02-27T11:52:33.679206Z"
    },
    "code_folding": [
     1
    ],
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "9\n",
      "25\n",
      "49\n",
      "81\n",
      "121\n",
      "169\n",
      "225\n",
      "289\n",
      "361\n",
      "441\n",
      "529\n",
      "625\n",
      "729\n",
      "841\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "while j <50:\n",
    "    if j == 30:\n",
    "        break\n",
    "    if j%2 != 0: \n",
    "        print(j**2)\n",
    "    j+=1 # avoid dead loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:55:07.660653Z",
     "start_time": "2020-02-27T11:55:07.656771Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "a = 4\n",
    "while a: # 0, None, False, []\n",
    "    print(a) \n",
    "    a -= 1 # a = a-1\n",
    "    if a == 2:\n",
    "        a = 0 # None # [] False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# try except 异常处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T11:58:48.970214Z",
     "start_time": "2020-02-27T11:58:48.965189Z"
    },
    "code_folding": [],
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n",
      "0 division by zero\n",
      "1.8\n"
     ]
    }
   ],
   "source": [
    "# try except\n",
    "for i in [2, 0, 5]:\n",
    "#     devidePlus(4, i)\n",
    "    try:\n",
    "        print(devidePlus(4, i))\n",
    "    except Exception as e:\n",
    "        print(i, e)\n",
    "        pass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T12:02:20.362803Z",
     "start_time": "2020-02-27T12:02:20.357826Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "10.0\n",
      "0 division by zero\n"
     ]
    }
   ],
   "source": [
    "alist = [[1,1], [0, 0, 1]]\n",
    "for aa in alist:\n",
    "    try:\n",
    "        for a in aa:\n",
    "            print(10 / a)\n",
    "    except Exception as e:\n",
    "        print(a, e)\n",
    "        pass "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T12:03:27.777107Z",
     "start_time": "2020-02-27T12:03:27.771437Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "10.0\n",
      "0 division by zero\n",
      "0 division by zero\n",
      "10.0\n"
     ]
    }
   ],
   "source": [
    "alist = [[1,1], [0, 0, 1]]\n",
    "for aa in alist:\n",
    "    for a in aa:\n",
    "        try:\n",
    "            print(10 / a)\n",
    "        except Exception as e:\n",
    "            print(a, e)\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python Crash Course\n",
    "## Part 6. Write and Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:39:22.799241Z",
     "start_time": "2020-03-02T11:39:22.793273Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0],\n",
       " [1, 1, 1],\n",
       " [2, 4, 8],\n",
       " [3, 9, 27],\n",
       " [4, 16, 64],\n",
       " [5, 25, 125],\n",
       " [6, 36, 216],\n",
       " [7, 49, 343],\n",
       " [8, 64, 512],\n",
       " [9, 81, 729]]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data =[[i, i**2, i**3] for i in range(10)] \n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:42:04.661897Z",
     "start_time": "2020-03-02T11:42:04.658237Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0\\t0\\t0'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alist = [0, 0, 0]\n",
    "# alist = [str(i) for i in alist]\n",
    "# '\\t'.join(alist)\n",
    "#print(*map(str, alist))\n",
    "'\\t'.join(map(str, alist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:42:47.206732Z",
     "start_time": "2020-03-02T11:42:47.202990Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0\t0\n",
      "1\t1\t1\n",
      "2\t4\t8\n",
      "3\t9\t27\n",
      "4\t16\t64\n",
      "5\t25\t125\n",
      "6\t36\t216\n",
      "7\t49\t343\n",
      "8\t64\t512\n",
      "9\t81\t729\n"
     ]
    }
   ],
   "source": [
    "for i in data:\n",
    "    print('\\t'.join(map(str, i))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:42:53.752460Z",
     "start_time": "2020-03-02T11:42:53.749234Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:42:58.799859Z",
     "start_time": "2020-03-02T11:42:58.796856Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:43:02.324377Z",
     "start_time": "2020-03-02T11:43:02.320971Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 8]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:43:12.704502Z",
     "start_time": "2020-03-02T11:43:12.699881Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function write:\n",
      "\n",
      "write(text, /) method of _io.TextIOWrapper instance\n",
      "    Write string to stream.\n",
      "    Returns the number of characters written (which is always equal to\n",
      "    the length of the string).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(f.write)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:44:48.087585Z",
     "start_time": "2020-03-02T11:44:48.055722Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# 保存数据\n",
    "data =[[i, i**2, i**3] for i in range(10000)] \n",
    "\n",
    "f = open(\"../data/data_write_to_file2020.txt\", \"w\")\n",
    "for i in data:\n",
    "    f.write('\\t'.join(map(str,i)) + '\\n')\n",
    "f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:45:43.669045Z",
     "start_time": "2020-03-02T11:45:43.661977Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['0\\t0\\t0\\n', '1\\t1\\t1\\n', '2\\t4\\t8\\n', '3\\t9\\t27\\n', '4\\t16\\t64\\n'], 10000)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../data/data_write_to_file2020.txt','r') as f:\n",
    "    data = f.readlines()\n",
    "data[:5], len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:46:38.001776Z",
     "start_time": "2020-03-02T11:46:37.997454Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../data/data_write_to_file2020.txt','r') as f:\n",
    "    data = f.readlines(1000) # bytes\n",
    "len(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:47:08.260794Z",
     "start_time": "2020-03-02T11:47:08.256547Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0\t0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../data/data_write_to_file2020.txt','r') as f:\n",
    "    print(f.readline())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:48:25.123942Z",
     "start_time": "2020-03-02T11:48:25.119066Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "1 2\n",
      "2 3\n",
      "3 4\n",
      "4 5\n"
     ]
    }
   ],
   "source": [
    "f = [1, 2, 3, 4, 5]\n",
    "\n",
    "for k, i in enumerate(f):\n",
    "    print(k, i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:49:41.869953Z",
     "start_time": "2020-03-02T11:49:41.861257Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 10000\n"
     ]
    }
   ],
   "source": [
    "line_num = 0\n",
    "data_list = []\n",
    "with open('../data/data_write_to_file2020.txt','r') as f:\n",
    "    for i in f:\n",
    "        line_num+=1\n",
    "        data_list.append(i)\n",
    "print(line_num, len(data_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:51:06.000490Z",
     "start_time": "2020-03-02T11:51:05.991849Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t0\t0\n",
      "\n",
      "2000\t4000000\t8000000000\n",
      "\n",
      "4000\t16000000\t64000000000\n",
      "\n",
      "6000\t36000000\t216000000000\n",
      "\n",
      "8000\t64000000\t512000000000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../data/data_write_to_file2020.txt','r') as f:\n",
    "    for k, i in enumerate(f):\n",
    "        if k%2000 == 0:\n",
    "            print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:51:59.697234Z",
     "start_time": "2020-03-02T11:51:59.692159Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "line = '0\\t0\\t0\\n'\n",
    "line = line.replace('\\n', '')\n",
    "line = line.split('\\t')\n",
    "line = [int(i) for i in line] # convert str to int\n",
    "data.append(line)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:52:22.904337Z",
     "start_time": "2020-03-02T11:52:22.870382Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[9995, 99900025, 998500749875],\n",
       " [9996, 99920016, 998800479936],\n",
       " [9997, 99940009, 999100269973],\n",
       " [9998, 99960004, 999400119992],\n",
       " [9999, 99980001, 999700029999]]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = []\n",
    "with open('../data/data_write_to_file2020.txt','r') as f:\n",
    "    for line in f:\n",
    "        line = line.replace('\\n', '').split('\\t')\n",
    "        line = [int(i) for i in line]\n",
    "        data.append(line)\n",
    "data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:28:48.204570Z",
     "start_time": "2020-03-02T11:28:48.173408Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0], [1, 1, 1], [2, 4, 8], [3, 9, 27], [4, 16, 64]]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = []\n",
    "with open('../data/data_write_to_file.txt','r') as f:\n",
    "    for line in f:\n",
    "        line = line.replace('\\n', '').split('\\t')\n",
    "        line = [int(i) for i in line]\n",
    "        data.append(line)\n",
    "data[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 使用Pandas读写数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:53:13.020445Z",
     "start_time": "2020-03-02T11:53:13.018232Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:53:33.207066Z",
     "start_time": "2020-03-02T11:53:33.197075Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:56:22.237572Z",
     "start_time": "2020-03-02T11:56:22.197896Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>99900025</td>\n",
       "      <td>998500749875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>99920016</td>\n",
       "      <td>998800479936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>99940009</td>\n",
       "      <td>999100269973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>99960004</td>\n",
       "      <td>999400119992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>99980001</td>\n",
       "      <td>999700029999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a         b             c\n",
       "9995  9995  99900025  998500749875\n",
       "9996  9996  99920016  998800479936\n",
       "9997  9997  99940009  999100269973\n",
       "9998  9998  99960004  999400119992\n",
       "9999  9999  99980001  999700029999"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/data_write_to_file.txt', \n",
    "                 sep = '\\t', names = ['a', 'b', 'c'])\n",
    "df[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 保存中间步骤产生的字典数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:59:00.083226Z",
     "start_time": "2020-03-02T11:59:00.078029Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "data_dict = {'a':1, 'b':2, 'c':3}\n",
    "with open('../data/save_dict2020.json', 'w') as f:\n",
    "    json.dump(data_dict, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T11:59:24.230622Z",
     "start_time": "2020-03-02T11:59:24.210379Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 1, 'b': 2, 'c': 3}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd = json.load(open(\"../data/save_dict2020.json\"))\n",
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## 重新读入json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 保存中间步骤产生的列表数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T07:11:56.415515Z",
     "start_time": "2018-04-27T07:11:56.411564Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "data_list = list(range(10))\n",
    "with open('../data/save_list.json', 'w') as f:\n",
    "    json.dump(data_list, f) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T07:12:32.842754Z",
     "start_time": "2018-04-27T07:12:32.837820Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl = json.load(open(\"../data/save_list.json\"))\n",
    "dl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 使用matplotlib绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T01:08:12.810646Z",
     "start_time": "2020-03-03T01:08:10.984271Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pylab as plt\n",
    "x = range(1, 100)\n",
    "y = [i**-3 for i in x]\n",
    "plt.plot(x, y, 'b-s')\n",
    "plt.ylabel('$p(k)$', fontsize = 20)\n",
    "plt.xlabel('$k$', fontsize = 20)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.title('Degree Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:01.944986Z",
     "start_time": "2020-03-02T14:15:01.784445Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# red dashes, blue squares and green triangles\n",
    "t = np.arange(0., 5., 0.2)\n",
    "plt.plot(t, t, 'r--')\n",
    "plt.plot(t, t**2, 'bs')\n",
    "plt.plot(t, t**3, 'g^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:03.268429Z",
     "start_time": "2020-03-02T14:15:03.031393Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# red dashes, blue squares and green triangles\n",
    "t = np.arange(0., 5., 0.2)\n",
    "plt.plot(t, t**2, 'b-s', label = '1')\n",
    "plt.plot(t, t**2.5, 'r-o', label = '2')\n",
    "plt.plot(t, t**3, 'g-^', label = '3')\n",
    "plt.annotate(r'$\\alpha = 3$', xy=(3.5, 40), xytext=(2, 80),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            fontsize = 20)\n",
    "plt.ylabel('$f(t)$', fontsize = 20)\n",
    "plt.xlabel('$t$', fontsize = 20)\n",
    "plt.legend(loc=2,numpoints=1,fontsize=10)\n",
    "plt.show()\n",
    "# plt.savefig('/Users/chengjun/GitHub/cjc/figure/save_figure.png',\n",
    "#             dpi = 300, bbox_inches=\"tight\",transparent = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:04.453454Z",
     "start_time": "2020-03-02T14:15:04.022863Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "plt.plot(t, t, 'r--')\n",
    "plt.text(2, 0.8*np.max(t), r'$\\alpha = 1$', fontsize = 20)\n",
    "plt.subplot(222)\n",
    "plt.plot(t, t**2, 'bs')\n",
    "plt.text(2, 0.8*np.max(t**2), r'$\\alpha = 2$', fontsize = 20)\n",
    "plt.subplot(223)\n",
    "plt.plot(t, t**3, 'g^')\n",
    "plt.text(2, 0.8*np.max(t**3), r'$\\alpha = 3$', fontsize = 20)\n",
    "plt.subplot(224)\n",
    "plt.plot(t, t**4, 'r-o')\n",
    "plt.text(2, 0.8*np.max(t**4), r'$\\alpha = 4$', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:05.221542Z",
     "start_time": "2020-03-02T14:15:05.217942Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:06.288745Z",
     "start_time": "2020-03-02T14:15:06.047015Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aSErKRhITN7B//7fA+tBjM1CyhlMK0BZojVarbYyG2DZBF+brhB4poYcA+cCB0GM/sAfYgUZu7Qg9tpd43vnri1Gq/0YkJzciIeEwDhwI95f6S9/GJCFyKPT/OhR6FAIHgVxgX+i55N/7K+mzPBJC/SWWejRvnkhmZiaTJk2KsD3FGLNERDLLfbOiO0F1H8CVwIgSr68H/lnmnBVAqxKvvwEal9PWQCAHyGnTpk2N73DRcPvtt0t6eroUFhZ62u+AAQOkQYMGkpeX52m/IiKrVq2S5ORkGTBggOt9FbuTRgoYOeWUCyQ/P99VS7t8f/leSU29RyAxZGE+FrLOaj6zKDuGUaMOStOmHwlcJ8V++PYCj8nhh38bsfshkuOlF7DDjzyBuaKJateFLOOS1nKqwLGh2cAVAgMkPf1OadDg3pAV+nuBQaHP9hKdlbQJ/Q/LWt4tBLoI9BN1Q40UTZ7bJCUXnquevdT0+EGBrQKrBOYIvC/wb4G/i86kbhHoI+oW6yI6Qzg5NP42AkeGntsJHCNwvEAHUbdjF4HzBS4X6C9wu8C9Ag8LPC06i3lDYKLo2tQsgSUhWb4T2C4JCfmiLr3SY3BiVot16VSfUaNGCSArVqzwrM/c3FxJT0+Xm2++2bM+yzJ48GABpHnzea65N4oV72hRN8Z5kpaW58nCWnlKUV09KyQczQONRBeyN9RI6erYDoUU2+0CTULtHiFwh6gfWn/kxrjrS6/IVdGoUVlFuU3gI1H3x5+k2NVygkAr0ZthHVEXUcPQmI4W6CRwoegawxCB4QKfiPrg88pVyI0aOec+ieT4r8dcrFzLvzE6dxOKdMyx4MNPQudt7ShetO1Q5pzfUnrRdnxV7fql8NesWSOADBs2zLM+x44dK4DMmDHDsz7LMmLEXjGmVeiHnuvoFzCM/rjGCSSI+plzHbNqai5P+LFI4LKQbAmSkHCuwAui4Z2HKvyRpqUVyfPPfyuNGo0WGCBq2SKQJnC1JCS8K5BfrrJxm0gWEZ1UihUpOadmL5Eer2jMTvvwnRpztLiq8LV9LgLWhlw1Q0LHHgUuCf2dCkxAwzIXAUdV1aZfCr+oqEiaNWsmffv29azP3r17S6tWreTQoUOe9VkW/VH/N6Ss7nBFMekUN1Ggq8DeX/qoSTSOE5T3Y09N3ShpaUNCN76we6K+wJmhG0I/0Wl879CxBiXOayhqIb9danxBi5N3Wyl6FQkU7Zirei9WQ0JdV/huPPxS+CIiV199tbRq1UqKiopc72vr1q2SlJQk9913n+t9VUZxJMufQsprkqPK+L333hMNH8yWsK/cS2u3Iip29YjA1wLDRKM+uov6eNuJ+nZPE/V13yHwijRvvlhKhgmWHFtQFEFVOKkULf5hFX6E/POf/xRA1q9f71ofxQtr/xBAnnzyS9f6qg7F0/Z8gVMFGgt854gynjx5siQnJ8vRR3eStLTSoZd+W7vlEakLI6zogmbJW+ITq/AjZPny5QLIyJEjXWm/tHI4U+BU35VDaZlWCTQQY06TESP21aitsNXXuPFbkpiYLB07dpSdO3fGhEVY02SZWBibpfZjFX6EHDp0SI444gi56aabXGm/2IJcG3KfPO27a0OktMJq2vQjMSZBevbsKfn5+RG1UawUnxNAEhK6ybBhO90T3AWsC8MSq1iFXwMuvfRS1wqpFfuI/yIanrjZ18XLinjttdcEkJNPPl9at95bLSWnN7O9oqn/iMYq7/f9ZmaxxAuVKXxbHrkC6tXrxrp16zDmB8dLzmptewFGA+cSrnvvVM17p7jxxhu55ZYRfPnlVDZt6orImlKbipRXlnfjxiVAJjASGAKMB1J9L79ssViwFn55jB4tUqfO4pCF+rbjC3Da/txQ+yMDvcCnFvtHoklJqaKVGb8vE2tdJPC5JCb2D81YjhSYHphIHIslnsBa+JExZAgcOHAaWtdjFuDsrlB9+0LXrqMxJg24nIwMZ+u3OIla5hei+XRXAE8Crdm+vSN5eZcDF6N1UTpy6NAEUlPvJi1tBdD9lzac3MDFYrHUHKvwy0GVXBLQFd1tqeTx6CkoKOCLL8Zx7bWXIZLOhg3BVPZQ0s3UEnVBrUV3ymoMfA38iBbhegX4ngMHnuHVVxuSkQHGEOibmcUSbyT5LUAQadMmXF2xJ7pv6kYgwzEf+8cff8yOHTvo16+fMw26yNChZSsltqdu3YdJS4Pt2399fps2qtytgrdYgoe18Mth6NDwnqm9QkemOuqWGD16NE2aNKFnz57ONOgiffuqhV7WYn/xxbL7ylrXjcUSdKyFXw5h6/TBB0/ku++OpG7dqQwffosjVuuuXbuYNGkSt912G8nJydE36AGVWexDhqirq00bVfbWsrdYgou18Cugb1/YuNEwYEAv6tSZyjXXFFb9oWowceJEDhw4EBPunKro2xc2bICiIgK9DmGxWBSr8Kvg4osvZufOncyfP9+R9kaPHs2xxx5LZmb5G9JYLBaLW1iFXwW9evUiOTmZDz74IOq2vv76a2bOnEn//v0xxlT9AYvFYnEQq/CroEGDBnTv3t0RhT98+HASExO56aabHJDMYrFYIsMq/GpwySWXsHbtWlavXl3jNvLz83n99de57LLLaNGihYPSWSwWS/WwCr8a9OnTB2MM48aNi/iz4XozaWnvsH37dtq3H+S8gBaLxVINrMKvBkceeSRnn302b7/9tpYYrSZjxmjSkiZxvQIcw4sv9nC0EJvFYrFUF6vwq8l1113HmjVrWLp0abU/M2RIOEN1BTAHuI39+xMcq8ljsVgskRCVwjfGHGGMmWqM+Tr03LCC8w4ZY5aGHtGvfvrAFVdcQUpKCiNHjqz2Z4pr7zyP7uM+oMxxi8Vi8Y5oLfz7gWki0h6tMnZ/BeftF5HTQo9LouzTFxo1asSVV17JqFGjyCsuLFMpWntnM/AmcDNacCx4de8tFkt8EK3CvxQYFfp7FHBZlO0FmkGDBrF7927Gjh1brfOHDoWkpGeAIuAewNabsVgs/hGtwm8mIj+G/v4JaFbBeanGmBxjzAJjTIU3BWPMwNB5Odu2bYtSNOfp2rUrrVufzKBBz2LMoSp3wurSZQPwMvXq3YAxbW2pYIvF4itVFk8zxnwKNC/nrVJLjyIixpiKQlgyROR7Y8xRwHRjzJci8k3Zk0RkODAcIDMzs/rhMB7x1luGLVse4uDBa4CJbNx4DQMH6nvlKfEhQ4aQlJTA6tWP0KqVp6JaLBbLr6jSwheR80TkpHIe7wNbjDEtAELPWyto4/vQ83rgM+B0x0bgIUOGQEHBlcCJwF+A/Ap3wpoyZQpvvfUW99xzD62strdYLAEgWpfOB8ANob9vAN4ve4IxpqExpk7o78ZAF2BllP36gkbXJKBRN2uBR385XnJD79att9Gv30BOOOEEhtgYTIvFEhCiVfhPAT2NMV8D54VeY4zJNMaMCJ1zApBjjFkGzACeEpGYVPjF0TW9gJuAvwH/4YgjihOsRPLYvPkytm/fyrXXjiI1NdU3eS0Wi6UkJpLMUS/JzMyUnJwcv8UoRThzVqMy96FbIC6hbt3HyMvrC6wB/gh8BYwjI+MqNmzwTVyLxRKHGGOWiEi59ddtpm0ElN7urz6tWn1Ex44Xkpd3P9AaneRsAaYAV9kEK4vFEijsFocRUnq7v4bA+7RosYCffloGNAEuAHSzV5tgZbFYgoS18B3gmWeyqFv3NuBywsreJlhZLJagYRW+A5R29WATrCwWSyCxLh2HKO3qsVgsluAR2CgdY8w2YGMUTTQGfnZInFjBjjk+sGOOD2o65gwRaVLeG4FV+NFijMmpKDSptmLHHB/YMccHbozZ+vAtFoslTrAK32KxWOKE2qzwh/stgA/YMccHdszxgeNjrrU+fIvFYrGUpjZb+BaLxWIpgVX4FovFEifUOoVvjLnAGLPGGLPOGFPRpuq1CmPMa8aYrcaYFX7L4gXGmNbGmBnGmJXGmK+MMX/wWyYvMMakGmMWGWOWhcb9iN8yeYExJtEY84UxZrLfsniFMWaDMeZLY8xSY4xjZYNrlQ/fGJOI7kzSE9gMLAaui9X6+9XFGNMNrdf8hoic5Lc8bhPaXa2FiHxujEkHlgCXxcF1NkA9EdlnjEkG5gB/EJEFPovmKsaYu4FMoIGI9PZbHi8wxmwAMkXE0WSz2mbhdwLWich6ESkAxgKX+iyT64jILGCH33J4hYj8KCKfh/7eC6wCWvorlfuIsi/0Mjn0qD0WWzkYY1oBFwMjqjrXUjW1TeG3BDaVeL2ZOFAE8Ywxpi26R/JCfyXxhpB7Yym6f/RUEant434BGAwU+S2IxwjwX2PMEmPMQKcarW0K3xJHGGPqA+8AfxSRPX7L4wUickhETgNaAZ2MMbXWhWeM6Q1sFZElfsviA11FpCNwIfDbkNs2amqbwv8e3XoqTKvQMUstI+TDfgcYIyLv+i2P14jILnSP6Av8lsVFugCXhPzZY4EexpjR/orkDSLyfeh5K/Af1F0dNbVN4S8G2htj2hljUoBrgQ98lsniMKHFy38Dq0TkOb/l8QpjTBNjzOGhv9PQ4ITV/krlHiLygIi0EpG26G95uoj081ks1zHG1AsFI2CMqQf0AhyJwKtVCl9ECoHfAZ+gC3njReQrf6VyH2PM28B84DhjzGZjzM1+y+QyXYDrUYtvaehxkd9CeUALYIYxZjlq3EwVkbgJVYwjmgFzjDHLgEXAhyIyxYmGa1VYpsVisVgqplZZ+BaLxWKpGKvwLRaLJU6wCt9isVjihMBuYt64cWNp27at32JYLBZLTLFkyZKfK9rT1jOFb4x5DQgnUlSZLNK2bVtychyrGWSxWCxxgTFmY0XveenSGUntThKxWCyWQOOZwve0wFduLvz0kyddBYKDB2HBAthY4Y299lFUBPEWUiwCK1bAsmXxNfZ162DxYjh0yG9JYp5ALdoaYwYaY3KMMTnbtm2reUMffAAtWkD//pCf75yAQWTKFB1rdja0bQsXXgi7dvktlbssXgzt2sHy5fr622/h+ziooPHAA3DyyXDaaXD88bBqld8SucuWLXDWWdC+PXTqBGvW+C2R++TluXozD5TCF5HhIpIpIplNmpS75lA9zjwT7rkH3nwTfvOb2msZrFyp42vVCsaPh8cfVyu/sNBvydxj4UI4+2wwBhIS9MdxzTWQlVX7Z3V//Su8/jr8+9+wezd07lx7lf727WrEfP45PPssTJwIJ5yg7xUU+CubW+Tn63f7scfc60NEPHsAbYEV1Tn3jDPOkKgZPlwERJ54Ivq2gsrMmSJ79hS/LijwTxa32btX5OijRTIyRH76qfj4kiUiqakiF10kUlTkm3iucPCgyN13i2zaVPr4t9+KNG4s8pe/+CKW69xwg0hyssi8eaWPv/aaSPv2Ijt3+iKWq9x1l+qrd96JqhkgRyrSwRW94cbDc4VfVCRy9dUiaWki27ZF316Q+OGHit/bvVvkmmtEZs3yTh4v+NOfRIwpf1wvvaRf5zFjvJfLTV54Qcf19tu/fm/zZu/l8Ypvvy1/zAsXiiQmitx+u+ciucrixXqd77gj6qYqU/ie1dIJFfg6B2gMbAH+KiL/ruj8zMxMcSQsc+tW2LMHjjkm+raCwtKlkJmpbpzLL//1+3l5cPTR6uedMcN7+dzijjt0Oj+inM2Piorg9NNh/351dSUFNsWk+uTmwlFHQYcOMH16xed98w00aQINGngnm1uIqLuuMu64Q78Da9fqulVt4KKL1F357bdRX0djzBIRySz3zYruBH4/HLHwy1JbpvuXXSZy2GGVT2tffFEthunTvZPLCyq7hu+/L9Kvn8iOHd7J4ybPPKPXcPbsis/57juRpCSRRx/1Ti43mTBB5MILK5+Rb9okkpIicsst3snlJps3i9StK/LUU440RxAs/EhxzMIHtRouv1ytpWefdaZNv9iwQccxZEjlizv5+dC6tUY5vBvj+4Ps26dW7Kmn+i2JdxQV6SwtIwM++6zycy+6SGd9GzdCcrIn4rnGWWfp4vvq1ZCYWPF5gwbBG29oJE96unfyucXWrVCvnj6ipDILP1BROq5hDKSmwmuv6ZQ/lnn1VR3PwCq2uUxNhZtu0iyC4CYAACAASURBVBDVH37wRja3GDNGQxGXLave+V98oTeIWCYvT42Uu++u+tzbb4cff4RJk9yXy02++grmzIHbbqtc2YOGqH7+eewr+3AEYdOmjij7qogPhQ9qEezapX7vWOXQIRg5Ei6+WK33qhg4EP7wBw1fjGWGDVPr/pRTqj533z7o2hWeesp9udykfn2djV5ySdXnXnSRfh+GDXNfLjcZNgxSUmDAgKrPzcjQNapY5/nnNYw8N9eT7mJcE0RAt266cDs6hrfETEyEnBz9klSHo49WpdG8ubtyucnKlWqx33xz1Yt5oIryqqtgwgQ4cMB9+dxg1y5dpK1u/khioirJGTNiN+musBDGjoVLL4XGjav3mQ0b4Npr9fsRq4werd9rD6x7iCeFb4wm6Eyfrv6yWKVFC1Xk1aWwEKZOhfXr3ZPJTcaN0xnKVVdV/zPXXquJSZ984p5cbvLuu3DuuZEpst//HjZvhsMPd08uNykogLvu0pl4dWnQAN55B95+2z253GTNGnVTXnutZ13Gj8IH6NdPp/qxuLBVUAB9+sCsWZF9btcuLbfw6qvuyOU2kyZp9mEks5Rzz4VGjfRmEYuMG6cL82ecUf3PNGmifuBYpW5d9cv36FH9zxxxBPTqpW7agAafVMr48WqIRmLMREl8Kfzjj4d774WGDf2WJHI+/RTeew/27o3sc40bw3nnxe6PYtasyG9Wycm64Dl1auyV1di+HaZNg6uvrp4LqySff67XOtYW6QsLdVYT6XcbdNa+cSMsWuS8XG4zYQJ06QItW3rWZXwpfNAkrHHjdMofS0yapP7p886L/LOXXaYundWrnZfLberXj8yFFWboUPXxVhXtETSmTNGbVJ8+kX82JUVvFh9+6LxcbjJ/PlxxBfz3v5F/tndvdflNnuy8XG4iAr/7ndb88pD4U/grVqjPbMoUvyWpPiL6he7VC+rUifzzF1+sz7H2oxg0SKOSakKTJuomiDU+/RSaNdNM6kjp0EGjV2LtOk+erLOynj0j/+wRR8D11+v1jiXCodWXXuppt/Gn8M88U/27sfSjWLZMF+R6967Z51u31rDGSP3/fvLzzzB8eHQ1/sNRH7Hkynr1VZg9u2ahtMbod+TTT2Mr32TyZF2nqWlJgZEj4c47HRXJdT76yBfXW/wp/MREjVv+6KPY8e/u3av1wC+6qOZtfPihrgHEClOmqKIOz05qwp49mni2cqVzcrlNUpLWf68pvXtr0lZV2blB4dtv9fpEc51BNwGKZg8NL8nN1TWmZ57xvOv4U/igrpEdO6qfuek3Z52lhZWaNat5Gy1bxpY/+9NPdSbWsWPN2+jVS5+nTXNGJrcZNkx9utHMSLp1g+7dY+dah2ed558fXTunnqo+8Vhg7lzNEYl2zDUgPhV+9+76PH++v3JUh8JC56bn994Lf/+7M225iYgmEXXvHl2WcNu2ujNWrFQMffNNdedEGp1Tkrp1NdckfLMLOv376yYu0WbNZmbqdY4F992MGTqT69rV867jU+G3bKlTyTvu8FuSqpk1S5Np5s2Lvq2lS2Mj0zg3F048MToXVpgePdS9EXT33b59OouLJA69qvZiYXtPY1TZR3OTA/2/bdum9XiCzvTpukObR9m1JYlPhQ9q/UX7JfOCGTNUWZ10UvRt9egBX34Z/Ezj+vXh44/hxhujb+uii9QltnNn9G25ydy5OptzQuEvW6a5Jh9/HH1bbrJ2rV7jr7+Ovq3wrL2yfQOCwN69sGRJsbweE78Kf8MG6NtX//lBZvp0+J//cWZzi7AyCfqCnpMRJpdfrgu31a3P4hfTp2toYpcu0bd1wgkakx90V9bUqRph48RmNRkZmp0cdIWfng7ffeebdyF+FX79+vDWW8Gut7J3r2YQOjXNP+MM/cIF+Uchoj/cBx5wtt09e5xtz2lSUrQyphO5AykpOqsJ8nUGlS8jQ9dZnODZZ3WdKugceaRvBQ3jV+E3bqwr+0H+UcyZo9N8p6Z/SUmash/k0hIrV+oGGNGEJpbl0Ud13ebgQefadJrHHoOJE51rr0cP9Wdv2eJcm05SVKQzEKeMGdCMcidmSG7yxz/C++/71n38KnxQRTp3bnAXt44/Hp58Ejp3dq7NESO0zaASvgE7qQg6dNBFzMWLnWvTSQoKnG8zbCQE1a2zbJmuqzh5nUHdREFNMNy2DV580deF5fhW+D16qLJfsMBvScqnXTu4/37nSwSIaHJOEJkxQxfUndyc+pxz9Dmos7nBg3VR3smQwo4d4bnnNLM8iPz8s87inF68/OMfg2vQzJypzz4t2EK8K/xu3eDkk9X6Cxr79ulioxsbWnTsqPXTg4aIWmdhBe0UjRqp+y78gwsaM2dqUp2TUWOJiVpf3in/uNP07KlROk5XiuzeXXMZCgudbdcJZs5U460mdZIcIr4V/mGHwfLlNa9R4yYLFmgdmIULnW+7VStn4vqdprBQ/e39+zvfdpcu+r8MWjz+vn36HXTSbRdm1y5dFwjaLlgi7iVIdemieRxffulO+9GwYIGWSPFxP474VvhhioqCl6EXdjO5MSXv3FlLJW/f7nzb0ZCcrOFqbkx5+/VTF0fQLL/Fi/X7l53tfNtffqmbawTNp/3tt7pZy0cfOd92+MYZNIOmqEi/3926+SqGVfiffqolVoNmEcyfr9mmbmxZF45kCFppicWLNT/CDbKz4ZZbalZe2k3CN/asLOfbzsxUJRM05Td/vvrw3dj4o00bDXsM2oYoCQl6HR55xF8xfO09CBx1lG6GEqQfhYgqAjesPlBFkJQUrDED3Hqr1gh3i2++CZ61e+aZ8NBDanQ4TVqartcE7TrPn69lBZzIHi+LMTref//b+bZrAVbht2unC2Zz5/otSTFr12o1T7cUft268MQTNds9yy327tVZlltjBrjvPrjhBvfarwk9emgMvlt07qwzJzdCP2vK/Pnqy3aromdGhjPZu05y/fUwYIDfUliFjzHq4giSFdS+vSYgXXaZe33ce6/zMdDRkJPjni87TOfO6jIKyp6vO3fqTc7NheTOnTX0+Isv3OsjEnJzNQbfzeu8cyf89rfqrg0CIpofUFTktyRW4QP6o1i/XjM8g0BCgtZDadTIvT4KC3XT6x9/dK+PSAivJ7gZNx5euwjKzX3yZDjlFHcTcc4/H9atU4s6COTna7hotBueVEb9+vD668HZ23fjRs14dmOdJkKswge44AJ48EG/pSjm4YdrtqFzJGzZorV1xo1zt5/qMn++Zha7Wfbh9NMhNTU4Cn/BAq1t1KGDe32kp+sm8EGpDNuoETz9tDthqGGSk/UGF5TrHDZm3JzVVBOr8EF/cEOH+lbQqBR796pP1+0va8uW6usMyo/i5ZfhjTfc7SMlRSuPBmW9xm1fdpjp03U3qCCEHq9dq7s9uU3nzjqDDUJG+fz5um528sl+S2IV/i/k5ekXxG8WLXLflx2mc2dVfkFQBK1aqTJ2m5df9rV41S/k5mrClRfXefVq+Ne/3At5rS4iWsXzttvc76tLF3VbBqF+0qmn6ppCABaSrcIP8/DD+uPzwvqoDC982WE6d9YFzE2b3O+rMmbPhpde8qaIXYcOwZjJ5eToYq1XN3bwP+/i22918x0vvttZWdC6tUa7+c3NNwdma1Gr8MNkZ2vomt9W/vz5umDrRsJVWcLKxm9F8PbbGovuRcq5iNZN/+AD9/uqjI4ddVHRi31NTzpJ4979vs7hJDMvbnKNGulGI336uN9XZezaFai9GKzCDxMU5ffzz+4uaJXklFN0cdiJvWOjYf58tfrc9mWDLl6+/DKMGuV+X5WRnq7/dyd2MquKpCRdK/D7u+1mwlVQefVVDUQIwkwDq/CLad5cS/L6/aNYuBBeecWbvpKTtWphero3/ZVHuHiYlxEM2dl6nf1auxCBZ57xti565846g/WzeFx4kdorX/ann2qphW++8aa/8pg/X/WKG5nUNcAq/JJkZQWjNr6Xiztr1mh9D782gQkXD/MyRjkrS/MPvvvOuz5Lsn69Jr7NmeNdn489pjdWL2ZRFfHCC/DXv3rXX9Omuj7llxEnon0HIBwzjKcK3xhzgTFmjTFmnTHmfi/7rhaDB8OECf5Zfg895H369apVumDt19rF11+rEvJS4fvtvvMjLjsIcfhdu8LZZ3vXX4cOmoTllxH33XeazBmPCt8Ykwj8C7gQOBG4zhhzolf9V4vTT1fF49ePY9Ik79P+/VZ+Awdq8Tovp7ynnKJ7IfgVnbRggSoiNxOuyuP22+Gmm7ztM8ysWZpZ7KUxlZjo79pFgBKuwnhp4XcC1onIehEpAMYCl3rYf/WYNEkfXrN3L6xY4f2Xo1kzLSDn59pFvXre9peUpPuL3nuvt/2G8Srhqiy5uVqD3o8Z7PPPa0kFr42p7Gyt3ZOb622/oMbjP/+pBkZA8FLhtwRKmlSbQ8d+wRgz0BiTY4zJ2bZtm4eileBvf9NKkl7j5kYYVZGd7c+0d/16XTTOyfG+b792HSoo0GxTP+qqZGVpSQ2vE7D89GVfcIEmevmRcdu2bWASrsIEatFWRIaLSKaIZDZp0sQfIbKz1Z/tdQKWlwlXZcnO1gqDXt9k587VSIqUFG/7BV27OOccd7aQrIyUFN1pbPBgb/sF/9x3GzbojcYPhd+1q2YZe61P8vM1v8Qvw7UCvFT43wOtS7xuFToWLMIJWF6Xk23WDK691t3iYRVx883qR/f6RzF/vvvFwyqiUSPdVHr2bO/7TknRNQSvOflkfxKwwv35VS3y0CGtWOklS5bA//5vcGpVhfBS4S8G2htj2hljUoBrAZ/THcvBLyvollvUIvCDtDR/pp3hTZ39CBVs2lR3O/P6Oj/wgFaL9IOkJP2eHX+8t/0uWaI3Gr+Kh912m37PvFy7COCCLXio8EWkEPgd8AmwChgvIh5mnlSTFi20iuSyZd71mZ8PBw961195/OMf3kZweFk8rCKysrxNwBKB117zNuGqLC+8oH5lL3n6aR2zX77szEyt4fPtt971OX++GhRNm3rXZzXw1IcvIh+JyLEicrSIDPWy74hYtEg3UPCK8eM1xX79eu/6LMvmzTB6tHcJWD//rD50L+Oyy5Kd7W0CVrh4mN8bYeTna1SYVyQkqBHlF2GjwqvAhAAmXIUJ1KJtYGja1Nvwsfnz1a/btq13fZYlK0tnGV6tXWRk6IKtn/vqduumURz79nnTXxCm+bt36/rByy9709/nn2uuhZ8VWTt08HbtYtMmNST8vrGXg1X45bF9O9x4o/u7ToVZsECjcxJ8vBxer1347cICjY/++GPvFo0XLFDF48cidZjDDtOywV5Zu9OmaQGx1FRv+isPr4vHtW6tkUnXXedNfxFgFX55pKfD2LHwySfu9+VH8bDy8LJ4nIha+H/+s/t9VQevknLS0nQvV7/jsr0sHjd/PhxzjPcRYGUZMgSefNKbvozR77ebe1LXEKvwyyMlRfd79UL5LVzoX8JVWS69VMND3eabb3TK26aN+31VxTPP6A/Ti7WLv/89GHsIZ2drjRe3QxVFNCzRq3LflXHuuZrk5wVDhsB773nTV4RYhV8R2dkaTuZ2Ala7dlrJMAj+vhde0FRwtwnvKRsERXDMMXqNlyxxt5+iInfbjwSvFjHXr9eEqyBcZ9C9fd024nJzNVt/0SJ3+6khVuFXRFaWNwlYRx2lVTK92OGquhQWutv+vHnqSz7hBHf7qQ5eKb8nn4TjjvN/C03QePi//11nsW7y449q0ARF4d92m/tbDYa3ruzSxd1+aohV+BWRna0/0N273eujqEgXDd3sIxJENCnH7bT/uXP1/+vnInUYr4rHzZ2rvvs6ddztpzokJWnhuPbt3e2na1e18v1KuCqLF2sX4dlrEFy05RCAX1xAOfJIWL0azj/fvT5WrtRt7t5/370+IsEYaNzYXeUnopbWrbe610ekuK0Iioq0/aBYuqC1k95/H/bv91sS78jOdr943Lx5OnMNyA5XZbEKvyq8sAaCpAjcLh5nDPz+93D55e60XxMGDNCFNrf87KtW6WbWQZrmz54Nl13m3trFrl3QsqUmFQaF8DqZmwZNbi6cdZZ77UeJVfiV8f77Gk72vUs13ubN0ySvo492p/2a4HbxuK++0qzeINGzJ9xxh3s1fcIFtIJ0Y3db+S1cqJv5BMnSDRePc7NC6owZ3iW11QCr8CujeXNNwnLrRzF3riqBIGw/F8btBKw779Twz6CxYYN71u5xx2n9Grd95pEQLh7n1mL1vHm6RuNHue+KSErSa/zMM+72E4S1qQoIrmRB4PTTdZHNDeW3ZYvGowfJ6gMtHnf//dCxo/NtFxaqdRW0MQP0769Wvht066bhrkG6sYO7axdz52omc3q6821Hw3HHubf5zR//qCWRA4xV+JURTsBywwpq3Fgrcvbt63zb0fLkk+4UNVu+XH2cQfJlh8nOVjeW0wlY+/bp4r8f2wpWhVvF48I39iBe5x9+UMXshstyyhTv6jLVEKvwqyI7W2NrnVYEiYlqAR15pLPtOkFRkSrnnTudbXfOHH0OooWfna31fZzebvHTTzVqI2AbYQBw9dW6oNy6ddXnRsL+/Vp3v3dvZ9t1guRkePFF58umbNsGa9YE87tdAqvwq+Kyy+APf3B+T8xHH9XMvyDy1Vdw6qnwgcP708yYoX7jIJRUKMtZZ6nLZcYMZ9udPl1r6PzP/zjbrhM0aaJ5F077nNPTddPyCy5wtl0naNJEi9c5fZ0/+0yfu3d3tl2HsQq/Krp21ew8J6MNduyAhx8uDssMGh06qMvJ6RvSiy/CG28426ZTNGoEp53m/JinT9ebiR/79laHzz6De+5xts3Vq93P1o6GHj10tllQ4Fyb06frjc7t7OUosQq/OhQUwNKlzrU3c6b6dHv0cK5NJ0lIUEtlxgxnfc9t2gTTrxtmxAgYM8a59rZs0dlSUK8zqOvu2WedK6R28KDOZu6+25n23KB7d52xO1nv5pRTdNHf70qoVWAVfnV45BH9Eju1IDN9OtStG8xpfpju3XUjh2++caa9SZPglVeCuXgZpmNHZ9dUwtP8ICv8sGxOuThycvR30q2bM+25wdlnazTaTz851+btt8NTTznXnktYhV8dunfXKWp40TFagj7Nh2JF4JSL4//+D156KXihiWV5+WXnNpM//3x4910N7w0qHTqoX9up6xxu55xznGnPDY44QpMpr7zSmfZ+/DHw0TlhrMKvDp076+q+Ez+K3FydTgbZ6gM49lgt7HbttdG3dfCgpvIHfcygawz/+IczbR1+OPTpE+xpvjF6XaZPd2b2NX26Lvg3bhx9W24SNjycGPODD2pSXZBnryGswq8Odetq2J4TCr9ePd3MOsg+TtAfxAUX6Obq0bJ4sd7oYkHh9+ihvt1oN/n+4Qeti/7DD87I5Sbdu+t3fPv26NrJz9dAhFi4zitWaMRYtL9pEW0jaBnzFWAVfnXp0UOLijkVmx5kqy/MTz/B449H78efPl1/DG4kczlNjx5az3z27OjamTpVM5Z//tkZudzk1lth7drorfKkJJ0VBqkSakVkZOga1bRp0bWzfr0mrsXCTQ6r8KtP//4aXVO/fnTtdO0Kzz3njExuc+CA7jv74YfRtbNxo4Y8BnCPz1/RubOurUSrCKZP1/GedJIzcrlJOA4/WpdEUpLOFoKwsU1VpKfrxuZOXGewCr/W0a6dLrRGU4dj7Vqd8rpVy8NpMjLUNzllSnTtvPqqN/sDO0Fams5Etm6teRtFRZrJee65gS6kVYphw3S7x2ji559/XmfBsULPnhpVFM0sbMoULQN9/PHOyeUiMfJtDAhLl+p2hDW1hMKWchBTziuid2+1YnJzo2snCDs9VZcPP4Q336z555cs0Rj83/zGOZncpnFjdU/UtATEjz/qulS0xoGX9O6tN+doZH7kETVoYsB/D1bhR8YXX8DQoVr0rCZMnqxhcO3aOSuXm/Tura6dmk59f/tbuP56Z2Vym/AMrKY39pUrdaYQxNICFdGrl4570qSaff6jj/Q5loyZjh3hd7+Lrmz1SSfBhRc6J5PLWIUfCRddpHfymvwodu+GWbNi6wcBuubQrFnNtoUrKoKJE3URNNYYNKjmdftvuEEjXoIemliS9HSNna+pwp88WYuwBWX/2uqQkKAhuDWt2f/22zruGMIq/Eho1kx3Cnrnncg/e+CAZuM5lezhFSkpukPVnXdG/tk5c9QXfsklzsvlNg0aaMTJjh2RfS48K0hLc14mt7nkEq34uHJlZJ/bt0/XLC65JGZcG6VYsULHHQlFRTB4MAwf7o5MLmEVfqRcfbW6dCL9gjRtqpmmmZnuyOUm4RDSSBf0xo1TxRdrsxrQ61xYCO+9F9nnnn5a6wXF4ubgV1yhteLr1Yvsc199pWs011zjjlxucuCAXq9IyyLMn6+G0NVXuyOXS1iFHylXXaVT9bVrq/+Z3bs1rtutTbLdRqR439fqUlio7pzevaMPZfWDM87QxJxx4yL73NixOvZYtPBbtNBIm4yMyD535pmasxHkwngVUaeOlkD/z39U+VeXceP0szE2e7UKP1JattQvdyQRGBMnajEpt/ZMdRtj1J01cWL1N4I5cEAXbAcOdFc2tzBGLdZp0zTipjqsXq0L+7Fo6YYR0UJqX39dvfMPHtTP1KkTOyGoZbnmGjXKPv64eucfPAgTJuianhOZ6B4So1fIZxIT1Vrfs6d65w8frskosejOCXPDDZpl/O671Tu/Xj34y1/gvPPclctNBgzQfQuqW+Tu1VfV/RXwfU0rZdcuVWTVTQ78v//TGHSnd0fzkp49oXlzvX7VYcMGvcENGOCmVK5gFX5NENHF2+q4OL74QmuzDBoUmwtaYc49F44+WkscV8WWLWoBObnBhB8ce6zmXTRsWPW5+fkwcqS6B5o3d10012jYUP3SY8ZUXQFSRL8PDRtW738UVJKTdUvG2bPV0q+K9u213MjFF7svm8NYhV8TjNG07AkTqq6p/a9/QWpq7MWilyUhQd0zs2frphmVMWyYKo2ahHIGjcJCvc7V2Z3s8cfhT39yXya3ue02LR5X1e5kM2aoG+u227yRy03uvltr6xx2WOXnbdumN/fERH3EGEYCWtIzMzNTcpzeUNpJvv5a3TS//70udJVHfr5Od3v3hn/+01v53GDHDrX8br1Vb2LlsWtXcRkKp/fE9YOCAjjuOF2oX7Qotmdp1UVEF2A3bYJ168rPkhbREhTr1qm1G4uL1OVRVKTXvKLvd79++j1YuTKwBRCNMUtEpFz/sbXwa0r79lpQ7eWXNTyrPFJTYdUqtfxqA0ccoTe41NSKs1Cfe06V/iOPeCubW6SkaAG5nJyKb2Djx6trIxYTzMrDGHj0UV2cXL26/HMWLdLZ3pAhtUfZHzigM/c//7n897/6Ct56K/h7HFSGiLj+AK4CvgKKgMzqfOaMM86QwLN+vUhamsi99/76vRUrRPLyvJfJCz74QOTcc0Xy80sfX7pUJCVF5Jpr/JHLLQoKRE44QaRlS5EdO0q/t3mzSMOGIp07ixQV+SOfGxQVVf79LSoSGTXq19+BWGfAAJHERJFFi0ofLygQ6dRJr/W2bf7IVk2AHKlAr3pl4a8ALgdmedSfN7Rrp1bOE0+UPr56tUanxLrfviKM0XDFO+4onYyVl6fb+dUG91VJkpPVn71lS+la77t3a0jfgQO6YFub3D3GqOVeWKiz2HDG8YEDWkTQGJ3hxlJRvOrw3HO6r/F112mde1A3z1136axm2LDYKplRloruBG48gM+oTRZ+STZvVsv2978XOfxwkaZNRZYv91sq93joIREQycoSuftukdxcPV6brNyyvPyyyPjx+vd774kcc4xIUpLI2LH+yuUmn32ms7a2bfU6n3aayGGHifz8s9+Suce8eTrGJk1Eli0TOXhQ5PzzdfwxAJVY+IFS+MBAIAfIadOmjYv/Ehd4802RNm1E6tQRufhidffUdkaMEDnuOJ0Cz5njtzTeMm6cTvGnTfNbEveZPVskO1skIUHk5JOLb3q1mRUrRHr2LHbhHTgQM8ZMZQrfsSgdY8ynQHkByENE5P3QOZ8B94hIleE3gY/SKQ8RXbiL1QWdmlJYGH9jjkfsdY4JKovScezqiUgMp1Q6hDHx+YOIxzHHI/Y6xzw2LNNisVjiBE8UvjGmjzFmM5ANfGiM+cSLfi0Wi8VSTGAzbY0x24CNUTTRGIhid+KYxI45PrBjjg9qOuYMEWlS3huBVfjRYozJqWjhorZixxwf2DHHB26M2frwLRaLJU6wCt9isVjihNqs8GNrd2FnsGOOD+yY4wPHx1xrffgWi8ViKU1ttvAtFovFUgKr8C0WiyVOqHUK3xhzgTFmjTFmnTHmfr/l8QJjzGvGmK3GmBV+y+IFxpjWxpgZxpiVxpivjDF/8FsmLzDGpBpjFhljloXGXUt2makcY0yiMeYLY8xkv2XxCmPMBmPMl8aYpcYYx4qK1SofvjEmEVgL9AQ2A4uB60Rkpa+CuYwxphuwD3hDRE7yWx63Mca0AFqIyOfGmHRgCXBZHFxnA9QTkX3GmGRgDvAHEVngs2iuYoy5G8gEGohIb7/l8QJjzAa0srCjyWa1zcLvBKwTkfUiUgCMBS71WSbXEZFZwA6/5fAKEflRRD4P/b0XWAW09Fcq9wlVv90XepkcetQei60cjDGtgIuBEX7LUhuobQq/JbCpxOvNxIEiiGeMMW2B04GF/kriDSH3xlJgKzBVRGr7uF8ABqPbo8YTAvzXGLPEGDPQqUZrm8K3xBHGmPrAO8AfRWSP3/J4gYgcEpHTgFZAJ2NMrXXhGWN6A1tFZInfsvhAVxHpCFwI/Dbkto2a2qbwvwdal3jdKnTMUssI+bDfAcaIyLt+y+M1IrILmAFc4LcsLtIFuCTkzx4LDF1GdgAAAQFJREFU9DDGjPZXJG8Qke9Dz1uB/6Du6qipbQp/MdDeGNPOGJMCXAt84LNMFocJLV7+G1glIs/5LY9XGGOaGGMOD/2dhgYnrPZXKvcQkQdEpJWItEV/y9NFpJ/PYrmOMaZeKBgBY0w9oBfgSARerVL4IlII/A74BF3IGy8iX/krlfsYY94G5gPHGWM2G2Nu9lsml+kCXI9afEtDj4v8FsoDWgAzjDHLUeNmqojETahiHNEMmGOMWQYsAj4UkSlONFyrwjItFovFUjG1ysK3WCwWS8VYhW+xWCxxglX4FovFEidYhW+xWCxxglX4FovFEidYhW+xWCxxglX4FovFEif8P8xiEYSx9BusAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(t1, f(t1), 'bo')\n",
    "plt.plot(t2, f(t2), 'k')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:07.846923Z",
     "start_time": "2020-03-02T14:15:07.256156Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "\n",
    "t = np.arange(0., 5., 0.2)\n",
    "\n",
    "gs = gridspec.GridSpec(3, 3)\n",
    "ax1 = plt.subplot(gs[0, :])\n",
    "plt.plot(t, t**2, 'b-s')\n",
    "ax2 = plt.subplot(gs[1,:-1])\n",
    "plt.plot(t, t**2, 'g-s')\n",
    "ax3 = plt.subplot(gs[1:, -1])\n",
    "plt.plot(t, t**2, 'r-o')\n",
    "ax4 = plt.subplot(gs[-1,0])\n",
    "plt.plot(t, t**2, 'g-^')\n",
    "ax5 = plt.subplot(gs[-1,1])\n",
    "plt.plot(t, t**2, 'b-<')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:08.525724Z",
     "start_time": "2020-03-02T14:15:08.520615Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def OLSRegressPlot(x,y,col,xlab,ylab):\n",
    "    xx = sm.add_constant(x, prepend=True)\n",
    "    res = sm.OLS(y,xx).fit()\n",
    "    constant, beta = res.params\n",
    "    r2 = res.rsquared\n",
    "    lab = r'$\\beta = %.2f, \\,R^2 = %.2f$' %(beta,r2)\n",
    "    plt.scatter(x,y,s=60,facecolors='none', edgecolors=col)\n",
    "    plt.plot(x,constant + x*beta,\"red\",label=lab)\n",
    "    plt.legend(loc = 'upper left',fontsize=16)\n",
    "    plt.xlabel(xlab,fontsize=26)\n",
    "    plt.ylabel(ylab,fontsize=26)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T14:15:31.781737Z",
     "start_time": "2020-03-02T14:15:31.513066Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.randn(50)\n",
    "y = np.random.randn(50) + 3*x\n",
    "pearsonr(x, y)\n",
    "fig = plt.figure(figsize=(10, 4),facecolor='white')\n",
    "OLSRegressPlot(x,y,'RoyalBlue',r'$x$',r'$y$')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qFI8//jjV1XKWv7Byly9DSgrY2MDUqWqnsViZwR7oNBrYuRMuXlQ7juggdqYW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114197590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 4),facecolor='white')\n",
    "data = norm.rvs(10.0, 2.5, size=5000)\n",
    "mu, std = norm.fit(data)\n",
    "plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')\n",
    "xmin, xmax = plt.xlim()\n",
    "x = np.linspace(xmin, xmax, 100)\n",
    "p = norm.pdf(x, mu, std)\n",
    "plt.plot(x, p, 'r', linewidth=2)\n",
    "title = r\"$\\mu = %.2f, \\,  \\sigma = %.2f$\" % (mu, std)\n",
    "plt.title(title,size=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:18:55.087571",
     "start_time": "2017-05-11T22:18:55.070993"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c\n",
       "0  0   0   0\n",
       "1  1   1   1\n",
       "2  2   4   8\n",
       "3  3   9  27\n",
       "4  4  16  64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('../data/data_write_to_file.txt', sep = '\\t', names = ['a', 'b', 'c'])\n",
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:21:09.150592",
     "start_time": "2017-05-11T22:21:08.103623"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3fc048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line()\n",
    "plt.yscale('log')\n",
    "plt.ylabel('$values$', fontsize = 20)\n",
    "plt.xlabel('$index$', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:32:42.105831",
     "start_time": "2017-05-11T22:32:41.789998"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J4Gp3761y/H3AGnf/TpX9ChhpSpMnT+fw4QNlrYX3jMK1LoE1a9bw0Y9qoXQJxBkwqQ6R\nuftRM7sVeIjgT6173b3XzG4Kdvs95R+SeJEidW74+ZZiuOgPMEmSlusXyTBN5st4abl+ESnR09NT\nZdkXhYvUjyycRSYiEe3t0xgYeKustbDsC0TDZdKkdg4fPoRIGtSDEckQM6sSLgCtRMNl5cqVChdJ\nlXowIhkx/HzLkcFWDYlJPVAPRqTOLVmypMoNwjTfIvVNPRiROmbWRnQp/bAV3SBMskA9GJE6FfRa\nKoULlM+3LFu2TOEidUc9GJE6s2TJEh588MEKezTfItmigBGpI5WHxKZQvCmYhsQkOzREJlIHNmzY\nUGNI7BDBisjFMDnvvPkKF6l76sGIpGz69FkcOPBqhT3RIbHDg639/f10dHQkUZrIuChgRFJU+dqW\nyRQDRUNikl0aIhNJQeVrWyDotRym/Cyxzs7ZChfJHPVgRBJWeSIfqp0ltnXrVubNm5dAZSITSz0Y\nkYTcdtttVSbyW6l1Vb7CRbJKPRiRBNTutRwjOEusOJG/ePFivvvd7yZTnEhM1IMRidFdd901wl5L\nMVzcXeEiDUE9GJGYDN9rAZ0lJo1MPRiRCfbpT3+6Sq8FSnstxTBZvny5wkUajnowIhNo+DPEQL0W\naRbqwYhMgEsvvXTUvZabb75Z4SINTT0YkXEya6U4p1KyJ/JcvRZpPurBiIzRSSedFPZaRh4uixcv\nVrhI00g9YMysy8y2mdl2M1taYf81Zvbz8LHezM5Lo06RghtuuAGzFl588cUKe41qQ2I6/ViajaX5\n15SZtQDbgUXAXmAjcJW7b4scswDodfdXzawL6Hb3BVVez/XXocRpLJP4V155JatWrYqzLJExMzPc\nvdLCeOOW9hzMhUCfu+8EMLNVwGJgMGDc/SeR438CzEm0QhGgo6ODl156qcpezbWIVJL2ENkcYFdk\neze1A+STwLpYKxKJCM4Oa6kSLlOoNhx25513Klyk6aXdgxkxM3sfcCOwsNZx3d3dg89zuRy5XC7W\nuqQxrVixgltu+QzlPZKi6J0mi8u8TJ8+nddffz3+AkXGKJ/Pk8/nE/lcac/BLCCYU+kKt28H3N2X\nlR03H3gA6HL3X9Z4Pc3ByLhVn2eBWsNhutOkZFGcczBpD5FtBM4ys9PMrB24ClgdPcDMTiUIl0/U\nCheR8Zo+fXqNiyUnU2047KKLLsLdFS4iZVIdInP3o2Z2K/AQQdjd6+69ZnZTsNvvAe4EjgeWW/Db\nP+DuF6ZXtTSa2bNn09+/n9rDYYW7TBZvBNbS0sLRo9V6OiKS6hDZRNMQmYzGOeecQ1/fL6gdLAWl\nx+guk9IoGnmITCRx8+fPx6yFvr4+KodL9YslL7/8ct1lUmSEMnMWmch4vfe97+Wpp56meo+lHRgI\nn7cQnYvp7Oxk37598RYo0mAUMNLw5s+fz5Ytz1A9WCDosQxQDJZiuGjYVWRsNEQmDWvu3LmYtbBl\nyxZqz7NEh8OKwbJ+/XqFi8g4KGCk4XR0dGDWwo4dOxh5sAy9T8sll1wSb6EiDU5DZNIwpk2bxltv\nHWT4obCC0uMWLlzIj370ozhKE2lK6sFIpm3YsAEzw6yFt956i7H0WM4++2zcXeEiMsHUg5FMWrRo\nEY8++hjFv5FGfy3Lqaeeys6dO2OoTkRAASMZM2PGDN544wDFsBj9mmFz5sxh9+7dMVQnIlEaIpO6\nd9dddw0Og73xxhsMP8dSeSjsjDPOwN0VLiIJUQ9G6laxtzLcMFhL2b7S4+bNm8fWrVsnvkARqUk9\nGKkrixYtqtBbqTYM1krQWykESuVlXRQuIulQwEjqenp6BkPl0Uf/KWwdyTDYMYIbfpUGy7Jly3B3\nHnnkkbhKFpER0BCZpKa1tZVjx5zSIbBay99XmrgP7ibZ1tbGwMDAkI8QkfSoByOJam1tHeytHDt2\njJGFSvWJ+87OTtxd4SJShxQwEqsVK1aEgVIIlWhQ1BINlcLxxY+5/vrrcXetcCxSxzREJhNu7ty5\n4TpgRvA3TCtBL2U0Q2BQHkJTp07lzTffnMBKRSRO6sHIuJX3UnbseCHcUwiUsQyBDe2tKFxEskU9\nGBm1/fv309nZGW6NtpcCMAU4FNkuvdc9wIknnsj+/fsnpF4RSYd6MDKsJUuWlPRQOjtPIggFGFkv\nBYJQKfRUDlF6enEQLu3t7bg77q5wEWkAChgpsXbt2kiYBIHy4INrCQKlcMX8SAIFSoe/ykPl8OBR\n/f39uDuHDh2q9CIiklEaImtiJ5xwAi+//HKkJTrcBSMf8ooqn6iP3tu+NFQ6OjpGVa+IZIsCpsGd\nfPLJ7Nmzp8KeiQiTwuuUi579FbzelClTwvu1iEizSH2IzMy6zGybmW03s6VVjvkrM+szs01m9p6k\na6xH7e3tZUNZlR4t7NnzIkGIRB9QOtQ1mlBpZ+iZX4Whs9Kzv+bNmzc4p6JwEWk+qQaMmbUAXwY+\nCLwbuNrMzi075kPAme5+NnAT8JXEC41ZcS2ukT5aGBg4xtDgGC5ERhsmUBomBgwAkykNlOA1J0+e\nPBgoWmRSRNIeIrsQ6HP3nQBmtgpYDGyLHLMY+HsAd3/CzGaa2Wx3b4hLuE85ZS67d79AMRSGM9ah\nrJEoX/a+oLztIBAs+3LkyJGhh4uIkP4Q2RxgV2R7d9hW65g9FY7JpJ6eHnbv3kHlXka1x0Qp75kU\nlr1vo7R3UgyXWbNmlfRQFC4iUkvaPZgJ193dPfg8l8uRy+VSq2U4999/f8yfodIEfFSl3kqwaOSk\nSZM4fPhwhf0ikmX5fJ58Pp/I5zL34RYdjPGTmy0Aut29K9y+HXB3XxY55ivAY+7+rXB7G3BZpSEy\nM/M0v57R6unp4brrrhvlR02i/Kr32ob/fnR1dbFu3bpR1iEijcDMcPfh/hod22unHDCtwHPAIuBX\nwJPA1e7eGznmw8Bn3P0jYSB9yd0XVHm9TAUMwKmnnsGuXTtH+VGFm22N3Pnnn8/PfvazUX4eEWl0\ncQZMqnMw7n4UuBV4CHgWWOXuvWZ2k5l9Kjzme8AOM/sF8FXgltQKjsELL+xg5cqvM3Teo9ajGC5r\n1qwpmRep9lC4iEjSUu3BTLQs9mBERNLUsD0YERFpXAoYERGJhQJGRERioYAREZFYKGBERCQWChgR\nEYmFAkZERGKhgBERkVgoYEREJBYKGBERiYUCRkREYqGAERGRWChgREQkFgoYERGJhQJGRERioYAR\nEZFYKGBERCQWChgREYmFAkZERGKhgBERkVgoYEREJBYKGBERiUVqAWNmbzezh8zsOTP7gZnNrHDM\nyWb2qJk9a2ZbzOw/plGriIiMXpo9mNuBh939ncCjwB0VjjkC/Fd3fzfwb4HPmNm5CdaYmHw+n3YJ\n46L606X605X1+uOSZsAsBv4ufP53wJLyA9z9RXffFD5/A+gF5iRWYYKy/gOq+tOl+tOV9frjkmbA\ndLr7PgiCBOisdbCZnQ68B3gi9spERGTc2uJ8cTP7ITA72gQ48KcVDvcarzMd+Dbwn8KejIiI1Dlz\nr/q+Hu8nNusFcu6+z8zeATzm7vMqHNcGrAXWuftfDvOa6XwxIiIZ5u4Wx+vG2oMZxmrgBmAZcD3w\nYJXj/i+wdbhwgfi+SSIiMnpp9mCOB+4HTgF2Ar/v7r82s5OAr7n7R83sEuCfgS0EQ2gO/Im7fz+V\nokVEZMRSCxgREWlsmbmS38z+t5n1mtkmM3vAzI6L7LvDzPrC/R+ItJ9vZpvNbLuZfSnS3m5mq8KP\nedzMTk3664kysy4z2xbWuTTNWgqqXeRa6wLZ0f4/JPR1tJjZU2a2Omv1m9lMM/uHsJ5nzeyijNV/\nR1j3ZjPrCX/v6rZ+M7vXzPaZ2eZI24TVG/f7TpX6033fdPdMPIArgJbw+ReAz4fP3wU8TTCfdDrw\nC4o9syeAC8Ln3wM+GD7/NLA8fH4lsCrFr6slrPk0YBKwCTi3Dr7f7wDeEz6fDjwHnEswZ/a5sH0p\n8IWx/j8k9HX8F2AlsDrczkz9wP8DbgyftwEzs1J/+PP8PNAebn+LYK61busHFhJcCrE50jZh9RLz\n+06V+lN930zklzyGH4QlwNfD57cDSyP71gEXEbxBbo20XwWsCJ9/H7gofN4K7E/xa1lAcIZcYbvk\n66mXB/Dd8Id1GzA7bHsHsG2s/w8J1Hwy8EMgRzFgMlE/cBzwywrtWan/7WGtbw/fxFZn4eeHIBij\nb9ATVm8S7zvl9ZftS/x9MzNDZGX+kCBZIbiyf1dk356wbQ6wO9K+m+IqAIMf4+5HgV9bcNJBGsrr\nj9ZZF6x4ketPCH7ZKl0gO5b/h7j9BfBZSq+xykr9ZwAvmdl94RDfPWY2jYzU7+6vAP8HeCGs5VV3\nf5iM1B9R7YLwLL7vJP6+WVcBY2Y/DMf+Co8t4b8fixzz34ABd//mRH7qCXythmJDL3ItPyukLs8S\nMbOPAPs8WGqo1v9vXdZP8Ff/+cDfuPv5wAGCvzqz8v2fSzA8eRrwG8DbzOxaMlJ/DRNZb2LvO2m9\nb6Z5HcwQ7v7+WvvN7Abgw8DlkeY9BKc6F5wctlVrj37MXjNrBY5z95fHVfzY7QGik2XROlNlwUWu\n3yboVheuU9pnZrO9eIFsf9g+lv+HOF0CfNzMPgxMBWaY2deBFzNS/25gl7v/NNx+gCBgsvL9/zfA\nhsLvlZn9I3Ax2am/YCLrTeV9J833zbrqwdRiZl0Ewx0fd/dDkV2rgavCMxzOAM4Cngy7s6+a2YVm\nZsAfULyYczXBhCPA7xGs5pyWjcBZZnaambUTjHmuTrGeqEoXuRYukIXSC2TH8v8QG3f/E3c/1d3n\nEnxPH3X3TwBrMlL/PmCXmZ0TNi0CniUj33+Ck0IWmNmU8PMuArZmoH6j9C/ziaw3ifedkvpTf9+M\na7IshsmrPoILMp8KH8sj++4gOAuiF/hApP29BBdp9gF/GWmfTHCRZx/BvMLpKX9tXQS/kH3A7Wl/\nr8OaLgGOEpzV9nT4Pe8CjgceDut9CJg11v+HBL+WyyhO8memfuA3Cf4A2QR8h+AssizV/1mCUNxM\nsGL6pHquH/gGsBc4RDB3dCPBSQoTUm/c7ztV6k/1fVMXWoqISCwyM0QmIiLZooAREZFYKGBERCQW\nChgREYmFAkZERGKhgBERkVgoYEREJBYKGBERiYUCRiRmZvaPZrYxXLz1k2nXI5IUXckvEjMzm+Xu\nvzazKQRLv/y2B8vZizQ09WBE4vefzWwTwfpNJwNnp1yPSCLqarl+kUZjZpcRLJN+kbsfMrPHgCkp\nlyWSCPVgROI1E3glDJdzCW6RLdIUFDAi8fo+MMnMngX+F/B4yvWIJEaT/CIiEgv1YEREJBYKGBER\niYUCRkREYqGAERGRWChgREQkFgoYERGJhQJGRERioYAREZFY/H8y/JUdxnh4XwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118312150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='a', y='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:26:59.893837",
     "start_time": "2017-05-11T22:26:59.528857"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7/Q/AcFX9voiMA+apapWInAo8BpxLfOhkHtC9FzFVNSYitwBziU9ZfEBVV4vI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\nwI0xpkBZgBtjTIH6/0bAIoOSa0aZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136cc310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hexbin(x='a', y='b', gridsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:28:39.025521",
     "start_time": "2017-05-11T22:28:38.630099"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138f4350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['a'].plot.kde()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:37:06.791001",
     "start_time": "2017-05-11T22:37:06.226303"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chengjun/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a708d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = df.boxplot()\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:39:16.120016",
     "start_time": "2017-05-11T22:39:15.913844"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a0b9890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['c'].diff().hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-11T22:40:14.418943",
     "start_time": "2017-05-11T22:40:14.037032"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MzKpA3p9Mng9cB8zLmg4B/m+qoszMbPjLO4L5r8AngNcBImIzMCZVUWZmNvzlDZhdERFk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11905c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(stacked=True, bins=20)\n",
    "# plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "> To be a programmer is to develop a carefully managed relationship with error. There's no getting around it. You either make your accommodations with failure, or the work will become intolerable.\n",
    "\n",
    "Ellen Ullman  （an American computer programmer and author）\n",
    "\n",
    "<a href=\"http://www.azquotes.com/quote/1385379\" title=\"Ellen Ullman quote\"><img src=\"http://www.azquotes.com/picture-quotes/quote-to-be-a-programmer-is-to-develop-a-carefully-managed-relationship-with-error-there-s-ellen-ullman-138-53-79.jpg\" alt=\"To be a programmer is to develop a carefully managed relationship with error. There's no getting around it. You either make your accommodations with failure, or the work will become intolerable. - Ellen Ullman\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# This is the end.\n",
    "> Thank you for your attention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:24:41.128412Z",
     "start_time": "2020-03-10T01:24:41.119672Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16960"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('../data/pg150.txt', 'r') as f:\n",
    "    txt = f.readlines()\n",
    "\n",
    "len(txt)\n",
    "#txt[61]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:24:42.024346Z",
     "start_time": "2020-03-10T01:24:41.968306Z"
    },
    "code_folding": [],
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "txt = ' '.join(txt)\n",
    "txt = txt.replace('\\n', '').replace('*', '').replace(\"\\'s\", '').replace(\"\\'S\", '')\n",
    "txt = txt.replace('.', ' ').replace(',','').replace(\"\\'\", '').replace('/', ' ')\n",
    "txt = txt.replace('\\ufeff', '').replace(':', ' ').replace(';', '').replace('-', '')\n",
    "txt = txt.replace('(', '').replace(')', '').replace('\"', '').replace('?', '')\n",
    "txt = txt.split(' ')\n",
    "txt = [j.lower()  for j in txt if j]\n",
    "\n",
    "#txt[50:80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:25:31.172574Z",
     "start_time": "2020-03-10T01:25:31.167352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127554"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:27:36.546310Z",
     "start_time": "2020-03-10T01:27:36.530612Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8526"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_words = set(txt)\n",
    "dic = {i:0 for i in unique_words}\n",
    "len(unique_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:27:58.171385Z",
     "start_time": "2020-03-10T01:27:58.138277Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in txt:\n",
    "    dic[i] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:18:24.333893Z",
     "start_time": "2020-03-10T01:18:24.316193Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 7840), ('and', 6029), ('of', 5196), ('to', 3518), ('is', 2691)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "dic = Counter(txt)\n",
    "dic.most_common(5)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:18:45.832511Z",
     "start_time": "2020-03-10T01:18:45.698687Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.about.ipynb              03.python_intro.ipynb.zip\r\n",
      "0.common_questions.ipynb   first_python.ipynb\r\n",
      "01.intro2datasci.ipynb     \u001b[31mhello\u001b[m\u001b[m*\r\n",
      "01.jupyter_notebook.ipynb  hello.c\r\n",
      "02.bigdata.ipynb           \u001b[34mimg\u001b[m\u001b[m/\r\n",
      "03.python_intro.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T01:21:26.594562Z",
     "start_time": "2020-03-10T01:21:26.583954Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('../data/pg150_most_common5.txt', 'a') as f:\n",
    "    for i, j in dic.most_common(1000):\n",
    "        f.write(i + '\\t'+ str(j)+ '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T02:44:20.795875Z",
     "start_time": "2020-03-03T02:44:20.789159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3518"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i.count('to')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T09:30:02.480462Z",
     "start_time": "2020-03-03T09:30:02.473788Z"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def contains(small, big):\n",
    "    for i in range(len(big)-len(small)+1):\n",
    "        if big[i:i+len(small)] == small:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def actual_entropy(l):\n",
    "    n = len(l)\n",
    "    sequence = [l[0]]\n",
    "    sum_gamma = 0\n",
    "    shortest_substrings = [[0, l[0], None]]\n",
    "    for i in range(1, n):\n",
    "        for j in range(i+1, n+1):\n",
    "            s = l[i:j]\n",
    "            if not contains(list(s), sequence): # s is not contained in previous sequence\n",
    "                shortest_substrings.append([i,l[i], list(s)])\n",
    "                sum_gamma += len(s)\n",
    "                sequence.append(l[i])\n",
    "                break\n",
    "\n",
    "    ae = 1 / (sum_gamma / n ) * math.log(n)            \n",
    "    return ae, sum_gamma, n, shortest_substrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T09:30:02.936493Z",
     "start_time": "2020-03-03T09:30:02.934200Z"
    }
   },
   "outputs": [],
   "source": [
    "D1='1212121212'\n",
    "D2='2112221121'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T09:30:03.382836Z",
     "start_time": "2020-03-03T09:30:03.378170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3544618194082623"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/1.7 * math.log(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T09:30:03.879821Z",
     "start_time": "2020-03-03T09:30:03.875819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contains(['1'], ['1', '2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T09:30:04.996311Z",
     "start_time": "2020-03-03T09:30:04.992078Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.3544618194082623,\n",
       " 17,\n",
       " 10,\n",
       " [[0, '1', None],\n",
       "  [1, '2', ['2']],\n",
       "  [2, '1', ['1', '2', '1']],\n",
       "  [3, '2', ['2', '1', '2']],\n",
       "  [4, '1', ['1', '2', '1', '2', '1']],\n",
       "  [5, '2', ['2', '1', '2', '1', '2']]])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_entropy(D1)#, actual_entropy(D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T09:30:10.907688Z",
     "start_time": "2020-03-03T09:30:10.903351Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.2118868910494978,\n",
       " 19,\n",
       " 10,\n",
       " [[0, '2', None],\n",
       "  [1, '1', ['1']],\n",
       "  [2, '1', ['1', '2']],\n",
       "  [3, '2', ['2', '2']],\n",
       "  [4, '2', ['2', '2']],\n",
       "  [5, '2', ['2', '1', '1', '2', '1']],\n",
       "  [6, '1', ['1', '1', '2', '1']],\n",
       "  [7, '1', ['1', '2', '1']]])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_entropy(D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T13:51:01.111562Z",
     "start_time": "2020-03-03T13:51:01.108266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212'"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T05:50:55.328518Z",
     "start_time": "2020-03-05T05:50:55.314374Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99\n"
     ]
    }
   ],
   "source": [
    "print(*range(10, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T05:52:46.246574Z",
     "start_time": "2020-03-05T05:52:46.240501Z"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def contains(small, big):\n",
    "    for i in range(len(big)-len(small)+1):\n",
    "        if big[i:i+len(small)] == small:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "def actual_entropy(l):\n",
    "    n = len(l)\n",
    "    sequence = [l[0]]\n",
    "    sum_gamma = 0\n",
    "    #shortest_substrings = [[0, l[0], None]]\n",
    "    for i in range(1, n):\n",
    "        for j in range(i+1, n+1):\n",
    "            s = l[i:j]\n",
    "            if not contains(list(s), sequence): # s is not contained in previous sequence\n",
    "                #shortest_substrings.append([i,l[i], list(s)])\n",
    "                sum_gamma += len(s)\n",
    "                sequence.append(l[i])\n",
    "                break\n",
    "\n",
    "    ae = 1 / (sum_gamma / n ) * math.log(n)            \n",
    "    return ae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T06:04:01.326925Z",
     "start_time": "2020-03-05T06:03:53.562708Z"
    }
   },
   "outputs": [],
   "source": [
    "import random \n",
    "import numpy as np\n",
    "\n",
    "random.seed(2020)\n",
    "ent_list = []\n",
    "\n",
    "for n in range(5, 200):\n",
    "    D2 = []\n",
    "    for i in range(n*2):\n",
    "        if random.random() >= 0.5:\n",
    "            D2.append('1')\n",
    "        else:\n",
    "            D2.append('2')\n",
    "    D2  = ''.join(D2)\n",
    "    D1 = '12'*n\n",
    "    ent_list.append([n, actual_entropy(D1), actual_entropy(D2)])  \n",
    "\n",
    "n, ent1, ent2 = np.array(ent_list).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T06:07:45.286290Z",
     "start_time": "2020-03-05T06:07:45.044296Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as plt\n",
    "\n",
    "plt.plot(2*n, ent1, label = 'regular string')\n",
    "plt.plot(2*n, ent2, label = 'random string')\n",
    "plt.axhline(y=0.8,ls=\":\",c=\"red\", alpha = 0.3)#添加水平直线\n",
    "plt.axhline(y=0.7,ls=\":\",c=\"red\", alpha = 0.3)#添加水平直线\n",
    "plt.axhline(y=0.2,ls=\":\",c=\"red\", alpha = 0.3)#添加水平直线\n",
    "plt.axhline(y=0.1,ls=\":\",c=\"red\", alpha = 0.3)#添加水平直线\n",
    "\n",
    "plt.ylim(0, 2)\n",
    "plt.legend(fontsize = 15)\n",
    "#plt.xscale('log', basex=2)\n",
    "plt.xlabel('length of string',fontsize = 15)\n",
    "plt.ylabel('actual entropy',fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T13:56:02.693004Z",
     "start_time": "2020-03-03T13:56:02.658321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.21197508967985743, 0.7645479605408423)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_entropy(D1), actual_entropy(D2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T10:34:10.690612Z",
     "start_time": "2020-03-03T10:34:10.188638Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T10:35:04.092847Z",
     "start_time": "2020-03-03T10:35:04.084396Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9639047615927534"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mpmath\n",
    "N = 201\n",
    "S = 0.5\n",
    "\n",
    "def getPredictability(N, S):\n",
    "    f = lambda x: (((1-x)/(N-1)) **(1-x))* x**x - 2**(-S)\n",
    "    root = mpmath.findroot(f, 1)\n",
    "    return float(root.real)\n",
    "\n",
    "getPredictability(N, S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T10:35:12.548246Z",
     "start_time": "2020-03-03T10:35:12.523416Z"
    }
   },
   "outputs": [],
   "source": [
    "N  = 201\n",
    "slist = np.arange(0, 1.1 ,0.1)\n",
    "plist = []\n",
    "for S in slist:\n",
    "    p = getPredictability(N, S)\n",
    "    plist.append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T10:35:20.830504Z",
     "start_time": "2020-03-03T10:35:20.636211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.plot(slist, plist, 'g-o')\n",
    "pl.xlabel('$S$', fontsize = 20)\n",
    "pl.ylabel('$\\Pi{max}$ ', fontsize = 20)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-03T10:35:35.605285Z",
     "start_time": "2020-03-03T10:35:35.219495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nlist  = np.arange(100, 1000, 200)\n",
    "slist = np.arange(0, 1.1 ,0.1)\n",
    "nsplist = []\n",
    "for N in nlist:\n",
    "    for S in slist:\n",
    "        p = getPredictability(N, S)\n",
    "        nsplist.append([N, S, p])\n",
    "\n",
    "df = pd.DataFrame(nsplist, columns = ['N', 'S', 'P'])\n",
    "groups = df.groupby('N')\n",
    "for name, group in groups:\n",
    "    pl.plot(group.S, group.P, label = \"N = \"+ str(name), marker='o', linestyle='-')\n",
    "pl.legend()\n",
    "pl.xlabel('$S$', fontsize = 20)\n",
    "pl.ylabel('$\\Pi{max}$ ', fontsize = 20)\n",
    "pl.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "48px",
    "left": "1263px",
    "top": "0px",
    "width": "159px"
   },
   "toc_section_display": false,
   "toc_window_display": true
  },
  "toc_position": {
   "height": "339px",
   "left": "1245.99px",
   "right": "20px",
   "top": "120px",
   "width": "196px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}