08.Numpy_and_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Numpy](images/chapter08/numpy.jpg)\n",
    "\n",
    "[NumPy](https://numpy.org) 是非常著名的 Python 科学计算工具包，其中包含了大量有用的概念，比如数组对象（用来表示向量、矩阵、图像等）以及线性代数函数。NumPy 中的数组对象可以帮助你实现数组中重要的操作，比如矩阵乘积、转置、解方程系统、向量乘积和归一化，这为图像变形、图像分类、图像聚类和深度学习等提供了基础。\n",
    "\n",
    "[NumPy 官方快速入门教程 Quickstart tutorial](https://numpy.org/devdocs/user/quickstart.html)\n",
    "\n",
    "使用 import 命令导入 NumPy包："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.20.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check version\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 NumPy 数组\n",
    "\n",
    "NumPy 包的核心是 ndarray 对象，用来表示 N 维数组，它的许多方法在编译代码中执行以提高性能。\n",
    "\n",
    "NumPy 数组和标准 Python 序列之间有几个重要的区别：\n",
    "\n",
    "- NumPy 数组的大小在创建时就已经固定，不像 Python list 可以动态增长。改变 ndarray 数组的大小将会创建一个新的数组对象并销毁之前的对象。\n",
    "\n",
    "\n",
    "- NumPy 数组中的元素都需要具有相同的数据类型，因此在内存中的大小相同。\n",
    "\n",
    "\n",
    "\n",
    "- NumPy 数组对大数据量的高级数学和其他类型的操作进行了优化。与使用 Python 内置序列相比，此类操作的执行效率更高，代码更少。\n",
    "\n",
    "\n",
    "- 越来越多的基于 Python 的科学和数学软件包正在使用 NumPy 数组; 虽然这些包通常支持 Python 序列作为输入，但它们在处理之前将这些输入转换为NumPy数组，并且它们通常输出 NumPy 数组。为了有效地使用当今大多数的基于 Python 的科学/数学软件，只知道如何使用 Python 的内置序列类型是不够的 ，还需要知道如何使用 NumPy 数组。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy 数组是一个所有的元素都是一种类型、通过一个正整数元组索引的元素表格(通常是元素是数字)。在 NumPy 中维度(dimensions)叫做轴(axes)，维度(轴)的个数叫做秩(rank)。\n",
    "\n",
    "例如，3D空间中的点的坐标 \\[1, 2, 1\\] 是rank为1的数组，因为它具有一个轴。该轴的长度为3。\n",
    "\n",
    "在下面的示例中，该数组有2个轴。第一个轴（维度）的长度为2，第二个轴（维度）的长度为3。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 0.0, 0.0], [0.0, 1.0, 2.0]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[[ 1., 0., 0.],\n",
    "[ 0., 1., 2.]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用内置 dir 来查看 ndarray 的方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__array__',\n",
       " '__array_finalize__',\n",
       " '__array_function__',\n",
       " '__array_interface__',\n",
       " '__array_prepare__',\n",
       " '__array_priority__',\n",
       " '__array_struct__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__complex__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ifloordiv__',\n",
       " '__ilshift__',\n",
       " '__imatmul__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__index__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__irshift__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__matmul__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rlshift__',\n",
       " '__rmatmul__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__rpow__',\n",
       " '__rrshift__',\n",
       " '__rshift__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__xor__',\n",
       " 'all',\n",
       " 'any',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'astype',\n",
       " 'base',\n",
       " 'byteswap',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'compress',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'copy',\n",
       " 'ctypes',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'diagonal',\n",
       " 'dot',\n",
       " 'dtype',\n",
       " 'dump',\n",
       " 'dumps',\n",
       " 'fill',\n",
       " 'flags',\n",
       " 'flat',\n",
       " 'flatten',\n",
       " 'getfield',\n",
       " 'imag',\n",
       " 'item',\n",
       " 'itemset',\n",
       " 'itemsize',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'min',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'newbyteorder',\n",
       " 'nonzero',\n",
       " 'partition',\n",
       " 'prod',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'ravel',\n",
       " 'real',\n",
       " 'repeat',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'round',\n",
       " 'searchsorted',\n",
       " 'setfield',\n",
       " 'setflags',\n",
       " 'shape',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'strides',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'tobytes',\n",
       " 'tofile',\n",
       " 'tolist',\n",
       " 'tostring',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'var',\n",
       " 'view']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(np.ndarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ndarray 对象关键的属性有：\n",
    "\n",
    "- **ndarray.ndim**\n",
    "      数组的轴（维度）的个数。在 Python 世界中，维度的数量被称为 rank。\n",
    "\n",
    "- **ndarray.shape**\n",
    "      数组的维度。这是一个整数的元组，表示每个维度中数组的大小。对于有 n 行和 m 列的矩阵，shape 将是 (n,m)。因此，shape 元组的长度就是 rank 或维度的个数 ndim。\n",
    "      \n",
    "- **ndarray.size**\n",
    "      数组元素的总数，等于 shape 的元素的乘积。\n",
    "      \n",
    "- [**ndarray.dtype**](https://numpy.org/doc/stable/user/basics.types.html)\n",
    "      一个描述数组中元素类型的对象。可以使用标准的 Python 类型创建或指定 dtype。另外 NumPy 提供它自己的类型，例如 numpy.int32、numpy.int16 和 numpy.float64。\n",
    "\n",
    "- **ndarray.itemsize**\n",
    "      数组中每个元素的字节大小。例如，元素为 float64 类型的数组的 itemsize 为8（=64/8），而 int32 类型的数组的 itemsize 为4（=32/8）。它等于 ndarray.dtype.itemsize。\n",
    "      \n",
    "- **ndarray.data**\n",
    "      该缓冲区包含数组的实际元素。通常，我们不需要使用此属性，因为我们将使用索引访问数组中的元素。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 创建数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可以使用 np.array() 函数从常规 Python 列表或元组中创建数组，得到的数组的类型是从Python列表中元素的类型推导出来的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_list = [2, 3, 4]\n",
    "a = np.array(origin_list)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n",
      "(3,)\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(a.dtype)\n",
    "print(a.shape)\n",
    "print(a.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array(origin_list * 5)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2 3.5 5.1]\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "c = np.array([1.2, 3.5, 5.1])\n",
    "print(c)\n",
    "print(c.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个常见的错误在于使用多个数值参数调用 np.array() 函数，而不是提供一个数字列表（List）作为参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a = np.array(1, 2, 3, 4)    # Wrong\n",
    "a = np.array([1, 2, 3, 4])  # Right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np.array() 将序列的序列转换成二维数组，将序列的序列的序列转换成三维数组，等等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.5, 2. , 3. ],\n",
       "       [4. , 5. , 6. ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([(1.5, 2, 3), (4, 5, 6)])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 (2, 3) 2\n"
     ]
    }
   ],
   "source": [
    "print(b.dtype, b.shape, b.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数组的类型也可以在创建时明确指定："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.+0.j, 2.+0.j],\n",
       "       [3.+0.j, 4.+0.j]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.array([[1, 2], [3, 4]], dtype=complex)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complex128 (2, 2) 2\n"
     ]
    }
   ],
   "source": [
    "print(c.dtype, c.shape, c.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常，数组的元素数值最初是未定的，但它的大小是已知的。因此，NumPy 提供了几个函数来创建具有初始占位符内容的数组。这就减少了数组增长的必要，因为数组增长的操作花费很大。\n",
    "\n",
    "函数 **zeros()** 创建一个由 0 组成的数组，函数 **ones()** 创建一个由 1 组成的数组，函数 **empty()** 内容是随机的并且取决于存储器的状态，函数 **full()** 创建一个由指定数值组成的数组。默认情况下，创建的数组的 dtype 是 float64。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]]], dtype=int16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2, 3, 4), dtype=np.int16)          # dtype can also be specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5 2.  3. ]\n",
      " [4.  5.  6. ]]\n"
     ]
    }
   ],
   "source": [
    "temp = np.empty((2, 3))                     # uninitialized, output may vary\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.2, 5.2, 5.2],\n",
       "       [5.2, 5.2, 5.2]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((2, 3), 5.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "zeros_like() 函数可以生成一个和指定数组一样大小的 0 数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros_like(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类似，ones_like() 可以生成一个和指定数组一样大小的全是 1 的数组。full_like() 可以生成一个和指定数组一样大小，由指定数值组成的数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones_like(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.34, 2.34, 2.34],\n",
       "       [2.34, 2.34, 2.34]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full_like(temp, 2.34)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要创建数字序列，NumPy 提供了一个类似于 range 的函数 arange，该函数返回数组而不是列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 15, 20, 25])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10, 30, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it accepts float arguments\n",
    "np.arange(0, 2, 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当 arange 与浮点参数一起使用时，由于浮点数的精度是有限的，通常不容易预测获得的元素数量。出于这个原因，通常最好使用函数 linspace ，它接收我们想要的元素数量而不是步长作为参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9 numbers from 0 to 2\n",
    "np.linspace(0, 2, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.12822827, 0.25645654, 0.38468481, 0.51291309,\n",
       "       0.64114136, 0.76936963, 0.8975979 , 1.02582617, 1.15405444,\n",
       "       1.28228272, 1.41051099, 1.53873926, 1.66696753, 1.7951958 ,\n",
       "       1.92342407, 2.05165235, 2.17988062, 2.30810889, 2.43633716,\n",
       "       2.56456543, 2.6927937 , 2.82102197, 2.94925025, 3.07747852,\n",
       "       3.20570679, 3.33393506, 3.46216333, 3.5903916 , 3.71861988,\n",
       "       3.84684815, 3.97507642, 4.10330469, 4.23153296, 4.35976123,\n",
       "       4.48798951, 4.61621778, 4.74444605, 4.87267432, 5.00090259,\n",
       "       5.12913086, 5.25735913, 5.38558741, 5.51381568, 5.64204395,\n",
       "       5.77027222, 5.89850049, 6.02672876, 6.15495704, 6.28318531])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import pi\n",
    "\n",
    "# useful to evaluate function at lots of points\n",
    "np.linspace(0, 2*pi, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 打印数组\n",
    "\n",
    "当你打印数组时，NumPy 以与嵌套列表类似的方式显示它，但是具有以下布局：\n",
    "\n",
    "- 最后一个轴从左到右打印，\n",
    "\n",
    "- 倒数第二个从上到下打印，\n",
    "\n",
    "- 其余的也从上到下打印，每个切片与下一个用空行分开。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一维数组被打印为行、二维为矩阵和三维为矩阵列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(6)                         # 1d array\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2]\n",
      " [ 3  4  5]\n",
      " [ 6  7  8]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "b = np.arange(12).reshape(4, 3)           # 2d array\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2, 3, 4)         # 3d array\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果数组太大而无法打印，NumPy 将自动跳过数组的中心部分并仅打印角点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0    1    2 ... 9997 9998 9999]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    1    2 ...   97   98   99]\n",
      " [ 100  101  102 ...  197  198  199]\n",
      " [ 200  201  202 ...  297  298  299]\n",
      " ...\n",
      " [9700 9701 9702 ... 9797 9798 9799]\n",
      " [9800 9801 9802 ... 9897 9898 9899]\n",
      " [9900 9901 9902 ... 9997 9998 9999]]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(10000).reshape(100,100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 基本操作\n",
    "\n",
    "数组上的算术运算符按元素运算的，运算后创建一个新的数组并填充结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([20, 30, 40, 50])\n",
    "b = np.arange(4)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 29, 38, 47])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = a - b\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 4, 9], dtype=int32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.12945251, -9.88031624,  7.4511316 , -2.62374854])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 * np.sin(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用不等号运算符, 会得到一个布尔型数组\n",
    "a < 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 30])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a < 35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 6., 9.])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1.0, 2.0, 3.0])\n",
    "y = np.array([2.0, 4.0, 6.0])\n",
    "x + y                           # 对应元素的加法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  8., 18.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y                           # element-wise product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5, 0.5])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x / y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与许多矩阵语言不同，乘法运算符 * 的运算在NumPy数组中是元素级别的。矩阵乘积可以使用 dot 函数或方法执行："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0],\n",
       "       [0, 4]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array( [[1,1],\n",
    "               [0,1]] )\n",
    "B = np.array( [[2,0],\n",
    "               [3,4]] )\n",
    "A * B                           # elementwise product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 4],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)                        # matrix product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 4],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(A, B)                    # another matrix product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "某些操作（例如 += 和 \\*= ）适用于修改现有数组，而不是创建新数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3, 3],\n",
       "       [3, 3, 3]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((2, 3), dtype=int)\n",
    "a *= 3\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.7522609 , 3.36052811, 3.11088039],\n",
       "       [3.25763309, 3.48611713, 3.27355978]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.random.random((2, 3))\n",
    "b += a\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当使用不同类型的数组操作时，结果数组的类型对应于更一般或更精确的数组（称为向上转换的行为）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cannot cast ufunc add output from dtype('float64') to dtype('int32') with casting rule 'same_kind'\n",
    "# a += b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.7522609  6.36052811 6.11088039]\n",
      " [6.25763309 6.48611713 6.27355978]]\n"
     ]
    }
   ],
   "source": [
    "a = a + b\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.linspace(0, pi, 3)\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         2.57079633 4.14159265]\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "b = np.ones(3, dtype=np.int32)\n",
    "c = a + b\n",
    "print(c)\n",
    "print(c.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "许多一元运算，例如计算数组中所有元素的总和、查找最值，都是作为 ndarray 类的方法实现的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.10700376, 0.74192914, 0.72805403],\n",
       "       [0.95894351, 0.53729644, 0.45040722]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.random((2,3))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5236341037588934"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()                     # 所有元素求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10700376301357728"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min()                     # 返回数组最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9589435107259067"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.max()                     # 返回数组最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.argmax()                  # 返回数组最大值的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "默认情况下，这些操作适用于数组，就好像它是数字列表一样，无论其形状如何。但是，通过指定 axis 参数，你可以沿着数组的指定轴应用操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(12).reshape(3, 4)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 15, 18, 21])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.sum(axis=0)                   # sum of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 22, 38])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.sum(axis=1)                   # sum of each row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 8])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.min(axis=1)                   # min of each row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  3,  6],\n",
       "       [ 4,  9, 15, 22],\n",
       "       [ 8, 17, 27, 38]], dtype=int32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.cumsum(axis=1)                # cumulative sum along each row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2, 3, 4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1],\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 1, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 沿轴axis=0方向数值最大值索引\n",
    "c.argmax(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 2, 2, 2],\n",
       "       [2, 2, 2, 2]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 沿轴axis=1方向数值最大值索引\n",
    "c.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3, 3],\n",
       "       [3, 3, 3]], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 沿轴axis=2方向数值最大值索引\n",
    "c.argmax(axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 广播\n",
    "\n",
    "NumPy 中，形状不同的数组之间也可以进行运算。之前的例子中，在 2×2 的矩阵A和标量10之间进行了乘法运算。在这个过程中，标量10被扩展成了 2 × 2 的形状，然后再与矩阵A进行乘法运算。这个巧妙的功能称为广播（broadcast）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 20],\n",
       "       [30, 40]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2], [3, 4]])\n",
    "B = np.array([10])\n",
    "A * B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](images/chapter08/broadcast1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "再看一个广播的例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[10, 40],\n",
       "       [30, 80]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([[1, 2], [3, 4]])\n",
    "B = np.array([10, 20])\n",
    "print(B.shape)\n",
    "A * B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个运算中，一维数组 B 被“巧妙地”变成了和二位数组 A 相同的形状，然后再以对应元素的方式进行运算。\n",
    "\n",
    "![img](images/chapter08/broadcast2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 通用函数\n",
    "\n",
    "NumPy 提供了常见的数学函数，如 sin，cos 和 exp。在NumPy中，这些叫作“通用函数”(ufunc)。在 NumPy 中，这些函数在数组上按元素级别操作，产生一个数组作为输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = np.arange(3)\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 2.71828183, 7.3890561 ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.        , 1.41421356])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 0., 6.])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = np.array([2., -1., 4.])\n",
    "np.add(B, C)                   # 和 B + C 相等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.7 索引、切片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一维数组可以被索引，切片和迭代，就像列出和其它 Python 序列一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729], dtype=int32)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)**3\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 27, 64], dtype=int32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1000,     1, -1000,    27, -1000,   125,   216,   343,   512,\n",
       "         729], dtype=int32)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:6:2] = -1000\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  729,   512,   343,   216,   125, -1000,    27, -1000,     1,\n",
       "       -1000], dtype=int32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以使用列表作为切片的参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 27], dtype=int32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[1,3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用 for 循环对数组进行遍历："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.0\n",
      "2.0\n",
      "3.0\n",
      "3.9999999999999996\n",
      "5.0\n",
      "5.999999999999999\n",
      "6.999999999999999\n",
      "7.999999999999999\n",
      "8.999999999999998\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10)**3\n",
    "for i in a:\n",
    "    print(i**(1/3.))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多维（Multidimensional） 数组每个轴可以有一个索引。这些索引在元组中以逗号分隔给出："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [10, 11, 12, 13],\n",
       "       [20, 21, 22, 23],\n",
       "       [30, 31, 32, 33],\n",
       "       [40, 41, 42, 43]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x, y):\n",
    "    return 10 * x + y\n",
    "\n",
    "# https://numpy.org/doc/stable/reference/generated/numpy.fromfunction.html\n",
    "b = np.fromfunction(f, (5, 4), dtype=int)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 11, 21, 31, 41])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0:5, 1]                       # each row in the second column of b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 11, 21, 31, 41])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, 1]                        # equivalent to the previous example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:3, :]                      # each column in the second and third row of b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当提供比轴数更少的索引时，缺失的索引被认为是一个完整切片（但是不建议这种用法）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40, 41, 42, 43])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[-1]                           # the last row. Equivalent to b[-1,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三个点（ ... ）表示产生完整索引元组所需的冒号。例如，如果 x 是rank为的5数组（即，它具有5个轴），则\n",
    "\n",
    "- x[1,2,...] 等于 x[1, 2, :, :, :]\n",
    "\n",
    "\n",
    "- x[...,3] 等效于 x[:, :, :, :, 3]\n",
    "\n",
    "\n",
    "- x[4,..., 5, :] 等效于 x[4, :, :, 5, :]。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 3)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a 3D array (two stacked 2D arrays)\n",
    "c = np.array( [[[  0,  1,  2],\n",
    "                [ 10, 12, 13]],\n",
    "               [[100,101,102],\n",
    "                [110,112,113]]])\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 101, 102],\n",
       "       [110, 112, 113]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[1,...]                    # same as c[1,:,:] or c[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,  13],\n",
       "       [102, 113]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[...,2]                    # same as c[:,:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 3)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[...,].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.8 浅拷贝和深拷贝\n",
    "\n",
    "对 Python 内置类型 list 进行分片操作，将会得到数组对象的拷贝。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1, 3, 4, 5] [-1, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "list1 = [2, 3, 4, 5]\n",
    "list2 = list1       # 共享引用\n",
    "list2[0] = -1\n",
    "print(list1, list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5] [-1, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "list1 = [2, 3, 4, 5]\n",
    "list2 = list1[:]    # 拷贝对象\n",
    "list2[0] = -1\n",
    "print(list1, list2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于 numpy 数组，分片后的变量与原始的数据共享同一数据存储，也称为浅拷贝。这种特点是专门针对大数据的处理而定制的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "[0 1 2 3]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(12).reshape(3, 4)\n",
    "b = a[0,:]          # b = a[:]\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[99  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "b[0] = 99\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果需要进行对象深拷贝，需要调用 copy 函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "[99  1  2  3]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(12).reshape(3, 4)\n",
    "b = a[0,:].copy()\n",
    "b[0] = 99\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上面的结果可以看出，通过分片后复制操作生成的新的数据对象的操作与原始的数据是独立的。新的数据对象的变化并不会影响到原始数据的信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.9 迭代\n",
    "\n",
    "迭代 Numpy 多维数组是相对于第一个轴完成的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "\n",
      "[4 5 6 7]\n",
      "\n",
      "[ 8  9 10 11]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "b = np.arange(12).reshape(3, 4)\n",
    "for row in b:\n",
    "    print(row, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "\n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2, 3, 4)\n",
    "for row in c:\n",
    "    print(row, end='\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是，如果想要对数组中的每个元素执行操作，可以使用 flat 属性，该属性是数组中所有元素的迭代器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "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",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for element in b.flat:             # A 1-D iterator over the array\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "或者使用 flatten() 方法将数组转换为一维数组，然后进行迭代："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "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",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "# Return a copy of the array collapsed into one dimension\n",
    "for element in b.flatten():\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[numpy.nditer](https://docs.scipy.org/doc/numpy/reference/generated/numpy.nditer.html) 提供了一种迭代访问多维数组元素的方式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(10, 16).reshape(2,3)\n",
    "for x in np.nditer(a):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以使用迭代器对象追踪单个索引或多重索引："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 10\n",
      "1 11\n",
      "2 12\n",
      "3 13\n",
      "4 14\n",
      "5 15\n"
     ]
    }
   ],
   "source": [
    "it = np.nditer(a, flags=['c_index'])   # 单个索引\n",
    "while not it.finished:\n",
    "    print(it.index, it.value)\n",
    "    it.iternext()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0) 10\n",
      "(0, 1) 11\n",
      "(0, 2) 12\n",
      "(1, 0) 13\n",
      "(1, 1) 14\n",
      "(1, 2) 15\n"
     ]
    }
   ],
   "source": [
    "it = np.nditer(a, flags=['multi_index'])   # 多重索引\n",
    "while not it.finished:\n",
    "    print(it.multi_index, it.value)\n",
    "    it.iternext()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.10 形状操作\n",
    "\n",
    "一个数组具有由每个轴上的元素数量给出的形状："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(12).reshape(3,4)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数组的形状可以通过各种命令进行更改。请注意，以下三个命令（ravel、reshape、T）都返回一个修改后的数组，但不会更改原始数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ravel()                 # returns the array, flattened"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 2,  3],\n",
       "       [ 4,  5],\n",
       "       [ 6,  7],\n",
       "       [ 8,  9],\n",
       "       [10, 11]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(6, 2)            # returns the array with a modified shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  8],\n",
       "       [ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T                       # returns the array, transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果在reshape操作中将维度指定为-1，则会自动计算其他维度："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(arr.shape)\n",
    "arr.reshape(4, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ravel() 与 flatten()：\n",
    "\n",
    "- 两个函数都可以将多维数组转换为一维数组\n",
    "\n",
    "- ravel()：如果没有必要，不会产生源数据的副本\n",
    "\n",
    "- flatten()：返回源数据的副本\n",
    "\n",
    "由 ravel() 产生的数组中元素的顺序通常是“C风格”，也就是说，最右边的索引“改变最快”，所以[0,0]之后的元素是[0,1] 。如果数组被重新塑造成其他形状，数组又被视为“C-style”。NumPy通常创建按此顺序存储的数组，因此ravel()通常不需要复制其参数，但如果数组是通过切片另一个数组或使用不寻常选项创建的，则可能需要复制它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "second = arr.ravel()\n",
    "print(second)\n",
    "third = arr.flatten()\n",
    "print(third)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[88  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "second[0] = 88\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[88  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "third[0] = 99\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.11 堆叠数组\n",
    "\n",
    "几个数组可以使用 vstack()、hstack()、dstack() 等函数沿不同的轴堆叠在一起。一般来说，对于具有两个以上维度的数组，vstack 沿第一轴堆叠，hstack 沿第二轴堆叠，dstack 沿第三轴堆叠。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]]\n",
      "[[ 6  7  8]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(0,6).reshape(2,3)\n",
    "b = np.arange(6,12).reshape(2,3)\n",
    "print(a)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 3)\n",
      "[[ 0  1  2]\n",
      " [ 3  4  5]\n",
      " [ 6  7  8]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Stack arrays in sequence vertically (row wise)\n",
    "c = np.vstack((a,b))\n",
    "print(c.shape)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 6)\n",
      "[[ 0  1  2  6  7  8]\n",
      " [ 3  4  5  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Stack arrays in sequence horizontally (column wise)\n",
    "d = np.hstack((a,b))\n",
    "print(d.shape)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3, 2)\n",
      "[[[ 0  6]\n",
      "  [ 1  7]\n",
      "  [ 2  8]]\n",
      "\n",
      " [[ 3  9]\n",
      "  [ 4 10]\n",
      "  [ 5 11]]]\n"
     ]
    }
   ],
   "source": [
    "# Stack arrays in sequence depth wise (along third axis)\n",
    "e = np.dstack((a,b))\n",
    "print(e.shape)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.12 随机数函数\n",
    "\n",
    "[numpy.random](https://numpy.org/doc/stable/reference/random/index.html) 模块提供了非常全的产生随机数据方法。\n",
    "\n",
    "- 随机数是由随机种子根据一定的计算方法计算出来的数值。所以，只要计算方法一定，随机种子一定，那么产生的随机数就不会变。\n",
    "\n",
    "\n",
    "- 只要用户不设置随机种子，那么在默认情况下随机种子来自系统时钟（即定时/计数器的值）\n",
    "\n",
    "\n",
    "- 随机数产生的算法与系统有关。即便是随机种子一样，Windows 系统和 Linux 系统产生的随机数也不一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1234)    #设置随机种子为1234"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.random.rand() 产生一个给定形状的数组，数组中的值服从 \\[0, 1)之间的均匀分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.19151945, 0.62210877, 0.43772774],\n",
       "       [0.78535858, 0.77997581, 0.27259261]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.27646426, 0.80187218, 0.95813935],\n",
       "       [0.87593263, 0.35781727, 0.50099513]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.random.uniform(low=0.0, high=1.0, size=None) 返回一个在区间 \\[low, high) 中均匀分布的数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.15116642, 7.41431824],\n",
       "       [4.33225679, 6.05076567],\n",
       "       [5.52774849, 1.12391605]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(1, 10, (3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.random.randn() 生成一个指定形状的数组，数组中的值服从标准正态分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.40545341,  0.28909194],\n",
       "       [ 1.32115819, -1.54690555],\n",
       "       [-0.20264632, -0.65596934]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.random.normal(loc=0.0, scale=1.0, size=None) 返回一个由size指定形状的数组，数组中的值服从均值 $\\mu=loc$, 标准差 $\\sigma=scale$ 的正态分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.03868428, 1.11068778],\n",
       "       [1.26363031, 0.90613894],\n",
       "       [1.13511082, 0.63659455]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(1, 0.2, (3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.random.randint(low, high=None, size=None, dtype='l') 返回一个在区间 \\[low, high)中离散均匀抽样的数组，size指定形状，dtype指定数据类型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 8],\n",
       "       [5, 1],\n",
       "       [6, 2]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1, 10, (3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[numpy.random.random(size=None)](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.random.html) 返回从 \\[0, 1)之间均匀抽样的数组，size指定形状。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.32791208, 0.81352893],\n",
       "       [0.64655232, 0.04742648],\n",
       "       [0.99495757, 0.68923564]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random([3, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy.random.choice(a,size=None,replace=True,p=None) 若a为数组，则从a中选取元素；若a为单个int类型数，则选取range(a)中的数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice([0,2,4,6,8,10,12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.13 存取操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy 提供了存取数组内容的文件操作函数，存取数组数据的文件可以是二进制格式或者文本格式。\n",
    "\n",
    "### [tofile()](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.tofile.html), [fromfile()](https://numpy.org/doc/stable/reference/generated/numpy.fromfile.html)\n",
    "\n",
    "- tofile()将数组中的数据以二进制格式写进文件，写入的数据不保存数组形状和元素类型等信息\n",
    "\n",
    "\n",
    "- fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(12).reshape(3, 4)\n",
    "a.tofile('array.raw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.fromfile('array.raw', dtype=np.int32)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.fromfile('array.raw', dtype=np.int32).reshape(3, 4)\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [save()](https://numpy.org/doc/stable/reference/generated/numpy.save.html)，[load()](https://numpy.org/doc/stable/reference/generated/numpy.load.html)\n",
    "\n",
    "存取 NumPy 专用的二进制格式保存数据（默认文件后缀名.npy），它们会自动处理元素类型和形状等信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('arr.npy', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.load('arr.npy')\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [savetxt()](https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html)，[loadtxt()](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html)\n",
    "\n",
    "- 只能存取一维或二维数组的文本文件\n",
    "\n",
    "\n",
    "- 可以用来读写csv格式文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 默认按照'%.18e'格式保存数值，空格分隔\n",
    "np.savetxt('a1.txt', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.,  7.],\n",
       "       [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt('a1.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('a2.txt', a, fmt='%d', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.],\n",
       "       [ 4.,  5.,  6.,  7.],\n",
       "       [ 8.,  9., 10., 11.]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt('a2.txt', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.14 其它常用操作\n",
    "\n",
    "数据类型转换 [astype](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html) 方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n",
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.arange(24).reshape(2, 3, 4)\n",
    "print(arr.dtype)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "[[[ 0.  1.  2.  3.]\n",
      "  [ 4.  5.  6.  7.]\n",
      "  [ 8.  9. 10. 11.]]\n",
      "\n",
      " [[12. 13. 14. 15.]\n",
      "  [16. 17. 18. 19.]\n",
      "  [20. 21. 22. 23.]]]\n"
     ]
    }
   ],
   "source": [
    "b = arr.astype(np.float64)\n",
    "print(b.dtype)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "矩阵转置 [T](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.T.html)、维度排序 [transpose](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.transpose.html) 和交换轴 [swapaxes](https://numpy.org/doc/stable/reference/generated/numpy.swapaxes.html)，\n",
    "\n",
    "- transpose\n",
    "- swapaxes 交换一对轴编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 3, 2)\n",
      "[[[ 0 12]\n",
      "  [ 4 16]\n",
      "  [ 8 20]]\n",
      "\n",
      " [[ 1 13]\n",
      "  [ 5 17]\n",
      "  [ 9 21]]\n",
      "\n",
      " [[ 2 14]\n",
      "  [ 6 18]\n",
      "  [10 22]]\n",
      "\n",
      " [[ 3 15]\n",
      "  [ 7 19]\n",
      "  [11 23]]]\n"
     ]
    }
   ],
   "source": [
    "print(arr.T.shape)\n",
    "print(arr.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0, 12],\n",
       "        [ 4, 16],\n",
       "        [ 8, 20]],\n",
       "\n",
       "       [[ 1, 13],\n",
       "        [ 5, 17],\n",
       "        [ 9, 21]],\n",
       "\n",
       "       [[ 2, 14],\n",
       "        [ 6, 18],\n",
       "        [10, 22]],\n",
       "\n",
       "       [[ 3, 15],\n",
       "        [ 7, 19],\n",
       "        [11, 23]]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.transpose(2, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [16, 17, 18, 19]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.swapaxes(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ndarray.clip(a_min, a_max, out=None) 将数组中的元素限制在a_min, a_max之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a.clip(1, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.clip(3, 6, out=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. [Matplotlib](https://matplotlib.org)\n",
    "\n",
    "在深度学习的实验中，图形的绘制和数据的可视化非常重要。Matplotlib是用于绘制图形的库，使用Matplotlib 可以轻松地绘制图形和实现数据的可视化。这里，我们来介绍一下图形的绘制方法和图像的显示方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 绘制简单图形\n",
    "\n",
    "可以使用 matplotlib 的 pyplot 模块绘制图形。下面是绘制 sin 函数曲线的例子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "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": [
    "# 生成数据\n",
    "x = np.arange(0, 6, 0.1) # 以0.1为单位，生成0到6的数据\n",
    "y = np.sin(x)\n",
    "\n",
    "# 绘制图形\n",
    "plt.plot(x, y, label=\"sin\")\n",
    "plt.title('sin')        # 标题\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 pyplot的功能\n",
    "\n",
    "在刚才的 sin 函数的图形中，我们尝试追加 cos 函数的图形，并尝试使用 pyplot 添加标题和x轴标签名等其他功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4i6vC6tG4Crd0q8fnC3YwZaXnpv5UqtCyM+Cb65zT1Vd+6pxp8FI2C8hiIE5E6opIEE6RmHzySiJSEegOTDphWaiIhB2/D/QBVpdIam/T73lMtVYE/Hg7FY4m8d615xAR4h1N/HzFyL6NaFMrgocmrGR7il4PUZb99F/YvwauGAeVvLuVoLUCYozJBUYAM4C1wHhjTKKI3Coit56w6mXATGPMiX/ZMcAfIrICWARMMcZML6nsXiUwmAn1nyU3L5/xld+jWbTnJ5EpbQL9/XhjUBtEYMSXyziWq9dDlCU7F8HKr6H7g86o3F5OjDnVZYfSJz4+3iQklOyUsZ62bu9hLnlrHiOqb+DOiPnIZe9B+Uq2Y/mkGYl7ueWzJdzUuS5PXNzUdhxVVm2eA3W6edWpKxFZUlBfO+1d5sOycvK4+6vlhAcHcvX1tyJXf6PF4yz0bVaVGzrV4cN5W3U2Q1Wy8vMgeYNzv/75XlU8TkcLiA97fto61u87wpiBLYmq4Dp1dWgbfHcTZKVZzearHu7fmOY1wnno+1UkHzlmO44qK+a9Du91hv3rbCcpEi0gPmruuv18PH8bN3auw3mNqvz9RMYBSPwBpj1oLZsvKxfgz6tXtib9WC6PTlxFWTrFqyzZvRzmjnZaVEY3sp2mSLSA+KDkI8e4/7sVNK4axoMnj7AbGw/dRsKKr5xCooosLiaMkX0aMnPNPn5Yvst2HFWaZR+FCcMgtApc9BqIbzW/1wLiY4wxPDpxFYezcnl9UBuCA/3/vVK3+6F6G/jpHjii5/LdMbRLPeJrV+KJSYnsScu0HUeVVrMeh5SNcNm7EFLZdpoi0wLiY6as2sPMNfu4r3dDGlUNK3gl/0C4fCzkZMHcZ0s2YCnh7yeMGdiK3DzDgxP0VJbyAGNcwxLdDfXOs53GLb5xqV8BkJJ+jCcnJdIqtuKZZxaMioNrxkN17xuAzVfUiQrl4f6NeWJSIl8v3sng9rVsR1KliQj0esp2irOiRyA+5Okf13A4K4cX/9OKAP9C/NPV7QblKjhHIkcPnnl99S/XdqhNp/qRjPppDTsPFn06YaUK9MdrsHmu7RRnTQuIj5iZuJfJK3YzokfcqU9dFSQ/Dz7oDZPv9Fy4UszPT3jxPy0REe7/boWeylJnb+dimP0UrP3RdpKzpgXEB6Rl5vDYD6tpXDWM286rX7SN/fyh+RXOVLhr/jXUmCqE2EohPNK/CQu2HOTbhCTbcZQvyz3mzGseXsPnT1+BFhCfMHrKGlIysnnpP63cm5q24wio2hKmjoTM1GLPVxYMaleTdnUqMXrqWg6kawdD5abfxkDyOrj4NWeKWh+nBcTL/bYhmfEJSdzSrR4tYt2cC9k/AC550+lkOOvx4g1YRvj5Cc9d3oKj2bmMnrLWdhzliw5shD9egZaDnFkGSwEtIF4sMzuPR39YRb3oUO7qGXd2O6veGjreAfvWOBfVVZE1qBLGbd3rM3HZLn7fWEpmt1Qlp3J96D8G+j1nO0mx0QLixd75ZRM7D2Yy6tLmBXcYLKrzH4OhMyEw+Oz3VUbd3qMB9aJCeeyH1ToNriq8vFzw84P4G32yw+CpaAHxUpuT03nv181c1qYGnepHFc9OA8o5F9UzUmDtT8WzzzImONCfUZc1Z3vKUd74eaPtOMoXHNkLb7aBDTNtJyl2WkC8kDGGx39YTflAfx7p36T4X2DuKPj2Bjiwqfj3XQZ0qh/Ff86J5f3ftrBu72HbcZS3m/mYU0Qii9iC0gdYLSAi0k9E1ovIJhF5qIDnzxORNBFZ7ro9UdhtfdnkFbuZvzmF+/s1JjrMAzMMnvcwBJaHafc7wymoInu0fxPCywfyyPeryM/X36E6hS2/wqpvocu9WkCKk4j4A28DFwBNgcEiUtA0cL8bY1q7bs8UcVufk5aZw//9tJaWsRW52lNDZ1So4lwP2TwH1mrfEHdUCg3i0f5NWLojle+WaN8QVYDcbKfpfERtp4CUQjaPQNoDm4wxW4wx2cDXwIAS2NarvTxzPQczjjH60hb4+3lwaOf4oRDTAqY/DMfSPfc6pdjlbWsQX7sSL0xfR1pmju04yttsnAEHNjgtrwLL207jETYLSA1g5wmPk1zLTtZRRFaIyDQRaVbEbRGR4SKSICIJycne3fRyVVIany3YznXn1na/z0dh+QfAhS8784fkarNed4gITw9oxqGj2bw6a4PtOMrbNLkYhv8KDfvYTuIxNgtIQV+vTz6ZvBSobYxpBbwJ/FCEbZ2FxrxvjIk3xsRHR0e7m9Xj8vMNj/2wiqgK5bivbwnNSlarA1z5KYQWUyuvMqhZ9Ypc3aEWny3YrhfU1d/SXKc1q7e2GsPTbBaQJKDmCY9jgd0nrmCMOWyMSXfdnwoEikhUYbb1NROWJrEiKY2HL2hMeHBgyb548npncDe9oO6WkX0aER4cwJOTEnWwRQUbZ8HrrWHbH7aTeJzNArIYiBORuiISBAwC/nFFV0SqijhzPIpIe5y8KYXZ1pccycrhhenraVMrgktbF3gmzrO2/gZ/vAqJ35f8a5cCESFB3N+3MQu3HuTHlXtsx1E25WY71xUr1YbY9rbTeJy1AmKMyQVGADOAtcB4Y0yiiNwqIre6VvsPsFpEVgBvAIOMo8BtS/5dFI+35m7iQPoxnry4GX6evHB+KvE3OYMtznzcmaNZFdlV7WrSvEY4o6esIeNYru04ypbFY50pavs+BwFBttN4nNV+IMaYqcaYhsaY+saY0a5l7xlj3nPdf8sY08wY08oYc64xZv7ptvVFWw9k8OEfW7mibSyta0bYCeHnDxe8AId3wfw37WTwcf5+wtOXNGff4WO8NVc7aJZJGQfglxegQa9SfeH8RNoT3bLRU9YS5O/Hg/1K6ML5qdTuBE0vhXmvQdouu1l81Dm1K3FF21jG/b6FrQcybMdRJW37PMjPgb7P2k5SYrSAWPTbhmRmr93HiPPjqBLuBQMc9n4GOtwC5Yow46H6hwcvaESQvx/PTtUh38ucpgPg3kSItvxlsARpAbEkJy+fZ35aQ+3IEG7qUsd2HEel2s4saaVgohtbqoQFc3uPBsxas4/5mw/YjqNKgjGwa4lzvxSNtFsYWkAs+XzBdjbtT+exC5tSLqAYhmovTlt/g4m3arNeNw3tUpcaEeUZ9dNa8nScrNJv7WQYe36pHG33TLSAWHAow+m53DUuil5NqtiO82+pO2HFV7DqO9tJfFJwoD8PXtCYNXsOM0HHySrdcrKc0XarNIP659tOU+K0gFjw5pxNpB/L5bELm+Lq5uJdWg2Gaq1g9pParNdNF7esRptaEbw0c7026y3NFrwNqTucWQb9A2ynKXFaQErYtgMZfLZgG1e1q0mjql56sdrPD/o972rW+4btND5JRHj8oqYkHznGe79uth1HeUJ6Mvz+KjTqD/W6205jhRaQEvbC9HUE+vtxb++GtqOc3l/Nel+Hw9q72h1ta1XiklbVef+3LexKzbQdRxW35LXO9NC9n7GdxBotICUoYdtBpq3ey63d61MlzAua7Z5Jr6ecNu2h3jsIpbd78ILGALw0fZ3lJKrY1e3mNNuNirOdxBotICXEGMOoKWuJCS/HsK51bccpnMp1If7GMnlut7jUiCjPsK51+WH5bpbvTLUdRxWXzXMgPw8CPDBjqA/RAlJCflq5h+U7UxnZpxEhQT72gbzsC5h4m+0UPuu28xoQVSGIZ6eu1dF6S4Ntf8Bnl8GSj20nsU4LSAk4lpvHC9PX0aRaOJe3jbUdp+gykmHFl878zqrIKpQL4O5eDVm09SBz1u23HUedjfx8p9lueA1ofbXtNNZpASkBn87fTtKhTB7t38Sz09R6SodboWJN5w8nP992Gp80qF1N6kWF8vy0deTm6e/QZyV+D7uXwfmPl9ppaotCC4iHpR7N5s05G+nRKJoucT46819gsPMHs3clrPrWdhqfFOjvxwP9GrFxfzoTlmrnQp+UkwWzn4aqLaDlVbbTeAUtIB72zi+bST+Wy0MXNLEd5ey0GOh0Lpzzf86kOarI+jarSttaEbwyawOZ2Xm246iiSkty5vjoM9rpK6XsFhAR6Sci60Vkk4g8VMDz14jIStdtvoi0OuG5bSKySkSWi0hCySYvnF2pmXw8fxtXtI313k6DheXnB/3HwMWvl4mJcjxBRHi4fxP2HT7Gh/O22o6jiiqqAdyxqMx2GiyItQIiIv7A28AFQFNgsIg0PWm1rUB3Y0xL4P+A9096vocxprUxJt7jgd3w6qwNAN7fabCwaraHBj1tp/Bp7epUpnfTGN79ZTMp6cdsx1GFteUXOHbEmXxN/cXmEUh7YJMxZosxJhv4Ghhw4grGmPnGmEOuhwsAn2nCtG7vYSYsTeLGTnWoHlGKLrYZA7OegDmjbCfxWQ/2a8TR7FzenKMzF/qEw7vhy6uc//fqH2wWkBrAzhMeJ7mWncpQYNoJjw0wU0SWiMjwU20kIsNFJEFEEpKTk88qcFG8NH09YeUCuO28+iX2miVCBNL3w7w3dOZCNzWoEsZV7WrxxcLtbE/RmQu93i/PgcmHzvfYTuJ1bBaQgtqzFtjLSkR64BSQB09Y3NkY0xbnFNgdItKtoG2NMe8bY+KNMfHR0SUzJMfCLSn8vG4/t53XgIiQUni9oMcjgHH+sJRb7u0VR4CfH2NmbrAdRZ1O8npY9jnED3UmXFP/YLOAJAE1T3gcC+w+eSURaQmMAwYYY1KOLzfG7Hb93A9MxDklZp0xhuenr6NqeDA3dq5jO45nRNSCdsNg+RfOH5gqsirhwQztUpcfV+xm9a4023HUqfz8DASGQreRtpN4JZsFZDEQJyJ1RSQIGARMPnEFEakFfA9cZ4zZcMLyUBEJO34f6AOsLrHkpzEjcS/LdqRyb+84ggNL8QW3rvc5f1hz/s92Ep81vHs9IkICeWmGFmGvlJPlXDjvfDeE+mgfLg+zNiiTMSZXREYAMwB/4ENjTKKI3Op6/j3gCSASeMc18VKuq8VVDDDRtSwA+NIYM93C2/iH3Lx8Xpy+ngZVKnCFLw5ZUhShUTDgLYhubDuJzwoPDuT28+rz7NR1/Lk5hY71I21HUicKDIbrJznXP1SBpCwN7hYfH28SEjzXZeSrRTt4+PtV/O+6c+jbrKrHXkeVHlk5efQY8wsx4cFMvL2Td85QWRbtXg4hkRBR84yrlgUisqSg7hLanbKYZOXk8frsjbSpFUGfpjG245ScjBQYfz1snGU7iU8KDvTnnl5xLN+Zysw1+2zHUeCM9/bD7fDllU6zdXVKWkCKyWd/bmfv4Swe6Nu4bH2LLBcGe1Y4YwTpQItuuaJtLPWiQ3lpxnry8vUDy7rV38H+ROc6X1n6W3aDFpBicCQrh3d+2UTXuKiydx47IAh6PAb7VjkjlaoiC/D34/4+jdi0P53vdaBFu3KzYe5oZ8DEZpfbTuP1tIAUg7G/b+XQ0Rwe6FtGLyg3vwJimju90/NybKfxSf2aV6VlbEVenbWBrBwdaNGaZZ/BoW1w/hM6YGIh6G/oLB1IP8a437fQv0VVWsRWtB3HDj8/Z7j3Q1udTleqyESEB/s1ZndaFp8v2G47TtmVlgR1ukJcb9tJfIIWkLP09txNZOXk8d/ejWxHsathX+j7HDS52HYSn9W5QRRdGkT9NQWAsqDXk3DdD3rto5C0gJyFpENH+WLBDgaeU5MGVSrYjmOXCHS8XTtcnaX7+zbiYEY2H/yuw72XqMxU2LXEue9vrXucz9ECchZen70RBO7uFWc7ivfYvQw+uxyydHgOd7SqGUHfZjGM/X0LhzJ04q4SM/8NGNvTuf6hCk0LiJs27T/ChKVJXHdu7dI1XPtZE9j8M8x/y3YQn3Vfn0ZkZOfy3q+bbUcpG47sgwXvQrPLoFId22l8ihYQN70yawPlA/25vbQN1362qreGppfCn29DeskNn1+aNIwJ47LWNfh4/jb2pmXZjlP6/T4Gco/B+Y/ZTuJztIC4YVVSGlNX7WVo13pEVihnO4736fEo5GbCH6/YTuKz7u3dkHxjeHPORttRSrdD2yHhI2hzLUTql8Gi0gLihjEz1xMREsiwrnVtR/FO0Q2h9dWweByk7jzz+upfalYOYVC7WnyzeKdOOuVJ+xKhfAR0f/CMq6p/0wJSRAu3pPDrhmRu616f8OBA23G8V/eHoPczEFoyk3iVRnee34AAf+G12XoU4jGN+8O9iVDxdJOhqlPRAlIExhjGzFxPlbByXN+xju043i2iJpx7mzMktnJLlfBghnSqww/Ld7Fu72HbcUqfnYudwRID9DS0u7SAFMGvG5JZvO0Qd/aMo3xQKZ4sqjitHA8zHrWdwmfd2q0+FYICGDNDp74tVntWwAe9YNFY20l8mhaQQsrPN7w0Yz01K5fnqnidI6DQktfBn2/B3lW2k/ikSqFB3NytHrPX7mPZjkO245Qec0ZBcAS0usp2Ep9mtYCISD8RWS8im0TkoQKeFxF5w/X8ShFpW9hti9v0xL0k7j7Mvb0aEhSgdbfQOt0JwRVhzmjbSXzWTV3qUjk0iJdn6lFIsdj+J2ycCV3udf5vKrdZ+yQUEX/gbeACoCkwWESanrTaBUCc6zYceLcI2xab3Lx8Xp65nrgqFRjQWi+2FUn5StDpLtgwzTnnrIqsQrkAbj+vPn9sOsD8zQdsx/FtxsDPz0CFGGg/3HYan3fGAiIiI0Skkgdeuz2wyRizxRiTDXwNDDhpnQHAp8axAIgQkWqF3LbYTFy2i83JGdzXpxH+fjrIWpF1uNVpjTXnGdtJfNa159amangwY2aspyxNQ13sMpKdEXe73Q9BIbbT+LzCHIFUBRaLyHjXaaPi+gStAZzYSSDJtaww6xRmWwBEZLiIJIhIQnKyez2j96ZlcU7tSvRtVoamqi1O5SpAv+eh/S06RaibggP9uatnHEt3pDJn3X7bcXxXhSpwZwKcc4PtJKXCGQuIMeYxnFNIHwA3ABtF5FkROdtumwUVopM/XU61TmG2dRYa874xJt4YEx8d7V6fhDt7xjH+lo5la6ra4tbiP9DkIh0m+ywMjI+ldmQIL81YT75OfVt0BzZBTqbTbNdf+3AVh0JdAzHOMfNe1y0XqAR8JyIvnsVrJwEnNmeKBXYXcp3CbFus9NRVMcjJhLnPwoaZtpP4pEB/P+7t1ZB1e48wZdUe23F8S34efD0YvhpkO0mpUphrIHeJyBLgRWAe0MIYcxtwDnDFWbz2YiBOROqKSBAwCJh80jqTgetdrbHOBdKMMXsKua3yNn6BsHoCzH4S8vNtp/FJF7eqTqOYMF6ZtYHcPP0dFtrKb+DABoi/yXaSUqUwRyBRwOXGmL7GmG+NMTkAxph84CJ3X9gYkwuMAGYAa4HxxphEEblVRG51rTYV2AJsAsYCt59uW3ezqBLiHwA9HoH9a5xCoorM30+4r09Dth7IYMLSJNtxfENuNvzyHFRrDU0usZ2mVJGy1KIjPj7eJCQk2I5RtuXnw/+6QXY6jFis56LdYIzh0nfmk3w4i7n3n0e5AB0V4bQWjYWpI+HaCdCgl+00PklElhhj4k9erj3iVMny84Oej8OhrbDsM9tpfJKIcH+fRuxOy+KLBTtsx/F+W3+F2p2hfk/bSUodLSCq5MX1cXqo1/jXFxpVSF3iouhUP5K3524i41iu7Tje7crPYNAX2gLQA7SAqJInAn1GQbWWtpP4tJF9G5GSkc1H87bajuKdsg5D+n7n/1t5T/SFVlpAlD2pO+Gn/zp/6KrI2taqRO+mMfzvty2kHs22Hcf7zH8T3mijUyt7kBYQZU9GMiR8AAvesZ3EZ43s04j0Y7m89+sW21G8S8YB5/9Vg15QQSc18xQtIMqeGm2hycUw/y3ISLGdxic1qhrGpa1r8PH8rew/nGU7jvf4/WXIOQo9dC4aT9ICouzq8RjkZMAfr9hO4rPu6RVHbp7hzTmbbEfxDqk7YPE4aH01RDe0naZU0wKi7KrSGFoOctrqp2nHOHfUjgzlqnY1+WrRDnakHLUdx75NP4P4QXePTxNU5mkBUfb1eBjaXAP+QbaT+Ky7esbh7ye8NlsnnSL+Rrh7JUTozKGepgVE2RdRCy561RlqW7klJjyYGzrVYeLyXazfe8R2HHtSXbM8hOnUCyVBC4jyHkkJ8OtLtlP4rNvOq0+FcgG8NGO97Sh27FoCr7eENTquaknRAqK8x8aZMHcU7FpqO4lPiggJ4tbu9Zm9dh8J2w7ajlPyZj/tdBis38N2kjJDC4jyHh1HQPnKzpzVyi03dq5DdFg5Xpi+rmxNfbvlF2fMq673Qbkw22nKDC0gynsEh0O3kbBlrvOBoIosJCiAu3vGsXjbIeauLyNT3xrjHH2Ex0L8UNtpyhQtIMq7xA91Pgh+fkbnT3fTVe1qUicyhBenryevLEx9e2AD7F8L5z0EgcG205QpWkCUdwkMht5PQ+MLIV9HmXVHoL8f9/VpxLq9R5i0fJftOJ4X3QjuWgatBttOUuZYKSAiUllEZonIRtfPfw2VKSI1RWSuiKwVkUQRufuE554SkV0istx161+y70B5VIv/OOeydbIpt13YohrNa4TzyqwNHMvNsx3Hc9J2OUeq4dWcGS9VibJ1BPIQ8LMxJg742fX4ZLnAfcaYJsC5wB0i0vSE5181xrR23aZ6PrIqUcY4094mTrSdxCf5+QkP9mtM0qFMvlxYSiedys6AsT1g+sO2k5RZtgrIAOAT1/1PgEtPXsEYs8cYs9R1/wjO3Oc1Siqg8gIL/wfTHnQ+KFSRdWngTDr11pxNpJfGSacWvAPp+6DZZbaTlFm2CkiMMWYPOIUCOG0XZBGpA7QBFp6weISIrBSRDws6BXbCtsNFJEFEEpKTdV4AnyECvf/P+YD4U4d7d4eIcxSSkpHN+7+VsuHeMw7AH69D44ugVgfbacosjxUQEZktIqsLuA0o4n4qABOAe4wxx2ceeheoD7QG9gAvn2p7Y8z7xph4Y0x8dLTOC+BTanVwPiDmve58YKgia1UzgotaVmPsb1tK13Dvv41xRnHu+YTtJGWaxwqIMaaXMaZ5AbdJwD4RqQbg+llgg3URCcQpHl8YY74/Yd/7jDF5xph8YCzQ3lPvQ1nW6ylnXodfX7SdxGfd37cRufn5vFpaBlrMPQbrp0Cb65wWWMoaW6ewJgNDXPeHAJNOXkFEBPgAWGuMeeWk56qd8PAyYLWHcirbouKc9v21zrWdxGfVjgzl2nNr883inWzcVwoGWgwoB7cvdL5cKKtsFZDngd4ishHo7XqMiFQXkeMtqjoD1wHnF9Bc90URWSUiK4EewL0lnF+VpO4PQPPLbafwaXeeH0doUAAvTF9nO8rZObIPcrMhKARCKttOU+ZZaThtjEkBehawfDfQ33X/D0BOsf11Hg2ovE9OJix8D+p2d6bCVUVSOTSI23rU58Xp61mwJYVz60XajlR0xsD3Nzut8obNdhpaKKu0J7ryDfm5ztzps57QIU7cdFPnulSrGMyzU9eS74tDnGyc6QyY2GKgFg8voQVE+YZyYc61kG2/w4bpttP4pOBAf+7r04iVSWlMWbXHdpyiycuFmY9D5foQf5PtNMpFC4jyHefcAJFxMPMxyMuxncYnXdamBo2rhvHijHW+NcTJ0o/hwHro/QwE6NTH3kILiPId/oHQZxSkbIKED22n8Un+fsLD/Zuw82Amn/253Xacwls/DWp3dgbZVF5DC4jyLQ37OhNP1Yi3ncRndW8YTbeG0bzx80YOZmTbjlM4V38LV36m1z68jBYQ5VtEoO9oiD3HdhKf9tiFTcjIzuM1b+9cmL4fjh4EPz8I9cGWY17gQPoxLn9nHit2phb7vrWAKN+UkQI/3QsHS9kYTyWkYUwYV7evxRcLd3h358IZj8I7HSGnFA3DUsJenrmelUlpVAgu/l4bWkCUb8rPgRXfwKwnbSfxWff2bkhIkD+jpqy1HaVgu5bAqvHQ5hqdadBNq3el8fXinQzpVIf60RWKff9aQJRvCqsKXe6FtZNh+3zbaXxS5dAg7u4Zx68bkr1v/nRjnKOP0GjofI/tND7JGMMzP62hUkgQd/WM88hraAFRvqvjHRBeA2Y8Avn5ttP4pOs71qFuVCijflpDTp4X/Q5XT4Adf8L5j0FwuO00Pmna6r0s2nqQ//ZuSMXynpndUwuI8l1BIdDzSdi9DFZ8aTuNTwoK8OOR/k3YnJzhXTMX7lwI1Vo5I+6qIsvKyePZqWtpXDWMwe1reex1dBJh5dtaDHT6hdTtbjuJz+rVpAqdG0Ty6uwNDGhdnYgQL+io1/8lOJYOfv62k/ikcb9vcaYzvrkD/n6ea/qsRyDKt/n5wfmPQkRN20l8lojw2IVNOZyZw2uzN9oNk7oTkl1Ni8sV/0XfsmDf4Sze+WUzfZvF0Kl+lEdfSwuIKh1Sd8DnV8B+Hx+u3JIm1cIZ3L4Wny3Yzrq9h8+8gadMfwg+6OUcfSi3vDB9Hbl5hkf7N/X4a2kBUaVDYAgkLYZpD+hovW66v28jwoMDeOKHRIyN3+GWX2DdT9DpLj36cNPynal8v3QXQ7vWpVZkiMdfz0oBEZHKIjJLRDa6flY6xXrbXBNHLReRhKJur8qQ0Cjo8Zgz3PfaH22n8UkRIUE80K8xi7YdZNLy3SX74nk5MO0hqFTHGapGFVlevuGxH1ZRJawcd/RoUCKvaesI5CHgZ2NMHPCz6/Gp9DDGtDbGnDj4UVG2V2VF/E1QpZnTfyAn03Yan3RVfE1axVZk9NS1HMkqwRGPF38AyWuh77PaadBNXy7czupdh3nsoqZUKFcy7aNsFZABwCeu+58Al5bw9qo08g+A/i9C2g5n9kJVZH5+wjMDmnMg/Rivl+QF9ewjENcXGvU/87rqXw6kH+OlGevpVD+Si1tWK7HXtVVAYowxewBcP6ucYj0DzBSRJSIy3I3tVVlTpwtcPg7aDbOdxGe1qhnBoHY1+Wj+NjaU1DhZ3e6HwV/raLtuem7qOjJz8nhmQHOkBH+HHisgIjJbRFYXcBtQhN10Nsa0BS4A7hCRbm7kGC4iCSKSkJycXNTNlS9qOdCZwTA3Wy+ou+n+vo0JCw7giUmrPXtBffufsHmOc99P2/S4Y9HWg0xYmsTNXevRoErJNj7w2L+YMaaXMaZ5AbdJwD4RqQbg+lngQDzGmN2un/uBiUB711OF2t617fvGmHhjTHx0dHTxvUHl3VJ3wrudYM0k20l8UuXQIEb2acSCLQeZvMJDF9RzsmDSHTDlPp1h0k05efk8/sNqakSUZ8T5JXPh/ES2Sv5kYIjr/hDgX3/lIhIqImHH7wN9gNWF3V6VcWHVILA8THsQstJsp/FJg9vXokWNioyaspa0TA98wM97HQ5uhv5jnNkmVZF9Mn8b6/cd4YmLmxISVPIDi9gqIM8DvUVkI9Db9RgRqS4iU13rxAB/iMgKYBEwxRgz/XTbK/UX/wC4+HXI2A8//5/tND7J30949rIWpKQf4/lpxdxBM2Uz/P4yNL8CGvQs3n2XEXvTsnh11gZ6NIqmT9MYKxmsjIVljEkB/vW/xnXKqr/r/hagVVG2V+ofarSF9sNh4f+g1WCdxdANLWIrclPnuoz7YyuXtq5Oh3rFMCugMc5pq4ByTrNd5Zanf0wkJ9/w1CXNSvTC+Yn0qpUq3Xo86swdkvCB7SQ+6799GhJbqTwPT1zFsdy8s9+hMdDkYqd4hFU9+/2VQdNW7WHa6r3c0yuO2pGh1nJoAVGlW3A43DAFLnnTdhKfFRIUwOjLWrAlOYO3524++x36+UG7odBWh2p3R9rRHB6flEiz6uHc3LWe1SxaQFTpF1nfGRY8IwUO77Gdxid1bxjNpa2r8+4vm86ub8jUB2DZF8UXrAwaNWUNh45m88IVLQn0t/sRrgVElQ15OTD2PPjhNu0b4qbHXUNkPPz9KvLz3fgdbpgBi/4Hh7YWf7gy4o+NB/h2SRK3dKtH8xoVbcfRAqLKCP9AZ27tLXNhyce20/ikyArleOzCpizZfogvFm4v2sZZafDjPRDdxOl1rorsaHYuD32/knpRoR6b47yotICosiP+Jqh3Hsx8DA4V8QNQAXB52xp0jYvi+Wnr2HnwaOE3nPEopO+FS992Wl+pIhszYwNJhzJ54T8tCQ70jpkatYCoskPEdTFdYPIIyM+3ncjniDh9Q0SEkd+uKNyprH2JsOwz6Hw31NCm1O5YuuMQH83fynXn1qZdncq24/xFC4gqWyJqQd9RUC4ccorwDVr9pWblEJ64uCkLtx7kw3mFuJ4R0wyumwjdddYFd2Rm5zHy2xVUCw/mgX6NbMf5By0gquxpOwSu+lxnvTsLA8+JpVeTGF6csZ6Np2uVdbzVW/3zdZ4PNz07dS1bkjMYM7AVYcHeNeSLFhBV9og4twObYPrDeirLDSLCc5e3oEK5AO4dv5ycvAJ+hxtnw2st/h5tVxXZ3HX7+WzBdoZ1qUunBlG24/yLFhBVdu34Exa8o5NPuSk6rBzPXtaC1bsO8+acTf98Mn0//HArRMVBrY52Avq4lPRj3P/dShpXDWNkX+86dXWcFhBVdrW5Fhr2g9lPwp4VttP4pH7Nq3J52xq8PXcTy3emOgvz82HirXDsCFzxgTMqsioSYwwPfb+Kw5k5vDaotde0ujqZFhBVdonAgHcgJBK+vdH5wFNF9uTFzYgJK8d/xy/naHauc1S3+WfoMwpimtqO55O+WbyTWWv28UC/RjSuGm47zilpAVFlW2gkXDHO6R39+yu20/ikiuUDGXNlK7YeyOCxH1ZjcrOcwRJ1WmG3bD2QwdM/rqFzg0hu6lzXdpzT0gKiVJ0uMOhL7SF9FjrVj+LunnF8v3QX35a/CgZ+qvObu+FYbh73fL2MoAA/xgxshZ+fd/8OrcwHopTXaXSB8/NYOmSlQsVYq3F80V3mS7Jio3l8kh8tYivSpJr3nnrxVv/30xpWJKXx3rVtqVbR+68dWSkgIlIZ+AaoA2wDrjTGHDppnUaudY6rBzxhjHlNRJ4CbgaSXc89YoyZihtycnJISkoiKyvLnc29TnBwMLGxsQQGeld7cZ9gDHx+OeRkwrDZOuRGUaz6Dr95r3JX+7uZkNaIO75YyuQ7u1ChnH5HLazvlybx+YIdDO9Wj37Nq9mOUyhiLIxMKiIvAgeNMc+LyENAJWPMg6dZ3x/YBXQwxmx3FZB0Y8yYorxufHy8SUhI+MeyrVu3EhYWRmRkpLVZvYqLMYaUlBSOHDlC3brefe7Ua62fBl8NcmYy7P+S7TS+Ye8qGNcbqrWCG35iwfbDXD12ARe2rM4bg1r7/N9VSVi75zCXvTOPVrERfDGsAwGWh2k/mYgsMcbEn7zcVsoBwCeu+58Al55h/Z7AZmNMsY+Al5WVVSqKBziduyIjI0vN0ZQVjS6AjiNg0fuw9FPbabzf0YPw9dVQvhJc+Sn4B3JuvUju69OIH1fs5ouFO2wn9HppmTnc9vkSwoMDefPqNl5XPE7HVtIYY8weANfPKmdYfxDw1UnLRojIShH5UEQqnWpDERkuIgkikpCcnHyqdYoQ3buVpvdiTa+noV4P+Om/sGOB7TTebekncGSfMzRMWMxfi2/rXp/uDaN55sc1f/cPUf+Sn28Y+e0Kkg5l8s41bakS5lvDvXisgIjIbBFZXcBtQBH3EwRcAnx7wuJ3gfpAa2AP8PKptjfGvG+MiTfGxEdHRxf9jaiyxz8ABn4ETS6CijVtp/Fune+B4XMh9p+j7Pr5Ca9e1ZqYiuUY9kkCu1Iz7eTzcu/9tplZa/bxSP8mxHvRKLuF5bECYozpZYxpXsBtErBPRKoBuH7uP82uLgCWGmP2nbDvfcaYPGNMPjAWaO+p92HLsGHDWLNmje0YZVf5SjDwY6hYw+lZnaOnBf9h/TRI2ew01Y1pVuAqlUOD+HBIO47l5DH048Ucycop4ZDebWbiXsbMWM+FLatxY+c6tuO4xdYprMnAENf9IcCk06w7mJNOXx0vPi6XAauLNZ0XGDduHE2bai9e6/Lz4ZtrdSrcE+1eDt/eADMfP+OqcTFhvHNtWzbuT+eur5aRW9Cgi2XQsh2HuOvrZbSIjWDMf1r57KlnW23sngfGi8hQYAcwEEBEqgPjjDH9XY9DgN7ALSdt/6KItAYMTjPgk593y9M/JrJm9+Hi2NVfmlYP58mLC/6GdlxGRgZXXnklSUlJ5OXl8fjjj/Puu+8yZswY4uPjqVChAnfffTc//fQT5cuXZ9KkScTExJx2n6qY+PlBzXYw+ylnWI6y3tnw4Fb4YiCERMHFrxdqk65x0TwzoBmPTlzNqClreeqS0/89lHbbUzIY9kkCVcKC+WBIPOWDvHOcq8KwcgRijEkxxvQ0xsS5fh50Ld99vHi4Hh81xkQaY9JO2v46Y0wLY0xLY8wlxy/I+6rp06dTvXp1VqxYwerVq+nXr98/ns/IyODcc89lxYoVdOvWjbFjx1pKWkZ1vgdaXAlzRpXtllnp++GzyyA/B677HioU/priNR1qM7RLXT6ev41P/9zmuYxe7mBGNjd8tJg8Y/j4xnZEVfDtvkbay+cEZzpS8JQWLVowcuRIHnzwQS666CK6du36j+eDgoK46KKLADjnnHOYNWuWjZhllwgMeBuOpsCPdzuzGTa71Haqkjd3NKTvgyE/QnTRhxd/pH8Ttqdk8NTkRKpVLE/vpmXrKDorJ49hnyxmV2omXw7rQL1o35/QzHcaHJdiDRs2ZMmSJbRo0YKHH36YZ5555h/PBwYG/nWO1N/fn9zcXBsxy7aAILjqM2h4gTMtblnU9zmneMT+qz9Zofj7Ca8PakOLGhW544ul/LL+dG1nSpe8fMO93yxn2c5UXr+qtU+2uCqIFhAvsHv3bkJCQrj22msZOXIkS5cutR1JFSQoFAZ/CTXaOo+P7LWbpyTk58GvL0LWYQgKcbt4HBdaLoBPb+pAgyoVGP7ZEv7YeKCYgnqv3Lx87hu/nGmr9/LYhU25oIVvDFNSGFpAvMCqVato3749rVu3ZvTo0Tz22GO2I6kzWfg+vNUe9qy0ncRz8vNg8p3Oqav104pttxVDAvliWAfqRYUy7NPF/Lk5pdj27W1y8vK555vl/LB8N/f3bcTQLqVriCErY2HZUtBYWGvXrqVJkyaWEnlGaXxPXid1J3zYD3Iz4doJUL2N7UTFK/cYTBgGayfDeY/Aeaccqs5tB9KPMfj9BSQdyuSTm9rTvm7pOK1zXHZuPnd/vYxpq/fySP/GDO9W33Ykt3nbWFhK+baImjBkMgSGwscXwea5thMVn+wMZ0DJtZOd6x4eKB4AURXK8cXNHagWEcyNHy0iYdtBj7yODcdy87jjy6VMW72Xxy9q6tPF43S0gCjlrsj6MHQmRNR2PnAP+3Rr8r8dPQgHNjotzzre7tGXqhIWzFc3n0tMeDBXj1vIjyt2e/T1SkJWTh63fb6UWWv28cyAZqXutNWJtBmvUmcjvBrcOBW2/eHc92VHD0JwhHN0NWIxBJbMhEYx4cF8d1snbvksgTu/Wsb2lAzu6NHAJ3tn703L4pbPEliRlMboy5pzTYfatiN5lB6BKHW2ykc4Ay8CbJwFPz/jDIHiS3Ythf91h9lPOo9LqHgcVzk0iM+HdeDS1tUZM3MDI79dSXaub/0Ol2w/xMVv/cGm/en877pzSn3xAC0gShWvzXPg95fhi/84Pbe9nTGwaCx82BcwVjtIlgvw59WrWnNPrzgmLE3iug8Wkno021qeohi/eCeD319ASJA/E+/oTN9mVW1HKhFaQJQqTn2fhQtfge3z4N3OTkHxVseOwIShMHUk1DsPbvkNapxzxs08SUS4p1dDXruqNct2pHLhG38wf5P39hXJycvnqcmJPDBhJR3qVWbSHZ1pGBNmO1aJ0QKiVHESgXZD4ea5EFIZPrvcOT3kjQ5tg/XToecTMPgbJ6+XuLRNDb655VyCAvy4etxCnpqcSGZ2nu1Y/7AqKY1L357Hx/O3MaxLXT66oR0RIUG2Y5UovYiulCfENHWKyOrv/u4jkpECoZF2c2VnwJrJ0HowVG0B96yE0Ci7mU6hTa1KTLmrCy9OX8/H87fx64ZkxgxsxTm1TzkBaYk4mp3LKzM38OG8rURWKMe717QtVb3Li0ILyMk+uvDfy5pdCu1vhuyjzlDWJ2t9NbS5xvmAGH/9P5+7ccoZX/LTTz9lzJgxiAgtW7Zk1KhR3HTTTSQnJxMdHc1HH31ErVq1+Pbbb3n66afx9/enYsWK/Pbbb+69R1UygkKgrev/w6Ft8Pa50OI/0OPRkm+xZQys+hZmPQlHdjvFo2pzry0ex4UEBfDUJc3o0zSG+79bycD35nNT57rc3qMBlUNL/tv+L+v38+jE1exKzeTqDrV4sF9jKpYPLPEc3kILiGWJiYmMHj2aefPmERUVxcGDBxkyZAjXX389Q4YM4cMPP+Suu+7ihx9+4JlnnmHGjBnUqFGD1NRU29FVUZQLh/gbnQvWqydAxxHQ+S4oVwLny3cvg2kPws6FUK21M9Ni1eaef91i1KlBFNPv6cqzU9fywbytfLloB9d3rMPNXesS6eEh0Y0xLN52iPd/28zstfupHx3K+Fs6lrqe8+7QoUwsD/vx5ptvsnfvXkaPHv3XsqioKPbs2UNgYCA5OTlUq1aNAwcOcOutt7J582auvPJKLr/8ciIjCz4dYvs9qdM4uMVp5ps4ESrWgruXg58HJhQyxrkek58HrzQBkw89n4TW1ziTZPmwjfuO8OacTfy4cjfBAf5c37E2N3erV+xza+Tm5TM9cS9jf9vCiqQ0KoUEMrRLXW7uVo9yAb47CZQ7TjWUiZUjEBEZCDwFNAHaG2MSTrFeP+B1wB9npsLnXcsrA98AdXBmJLzSGHPI48E9wBhzxg5Tx59/7733WLhwIVOmTKF169YsX778lEVEeanK9ZwjgI4jYOuvfxePKSOhWktoOgCCK7q///3rYOknsO13GO7a/xUfOPs+m/16kbiYMN4Y3Ia7esbx1pyNjP19Cx/O20rH+lH0aRpD76YxxIQHu7XvvHzDql1p/LExma8W7WRXaiZ1o0IZdWlzrmgb69OzB3qClSMQEWkC5AP/A0YWVEBExB/YgDOlbRKwGBhsjFkjIi8CB40xz4vIQ0AlY8wZB+zxxiOQxMRELrvsMv78808iIyM5ePAgN9xwAwMHDuS6667j448/ZtKkSUycOJHNmzdTv74zpk6bNm346KOPaN269b/2afs9qSI6dgTePw9SNkFAMNQ6F6o0g+aXF2749O3zYf5bsHspHNkDfoHQ+EK46FWvalnlKZuT0/l60Q5mrdnHtpSjALSKrcj5jWOoXyWUGhHlqRFRnqgK5fDzc76MGWM4mp1HamYOB9OzWbrjEPM2HWDBlhQOZznz7bSrU4lhXevRq0kM/n6+1yu+OHnVEYgxZi1wpm/e7YFNxpgtrnW/BgYAa1w/z3Ot9wnwC+CZEd88rFmzZjz66KN0794df39/2rRpwxtvvMFNN93ESy+99NdFdID777+fjRs3YoyhZ8+etGrVynJ6VSzKhcGIBNi1BFaOd65VJHwAVZo4BWRfInx8odO7PT/379u1E6B+D6dxx4H1UKcrxLaDZpcVabpZX1c/ugKPXtiUR/o3YdP+dGau2cfMNft4dfaGf6wX5O9HTMVyZOXkk3Y0h+y8f/Z0r1m5PP1bVKNTgyg61Y/0+elmS4LVayAi8gunPgL5D9DPGDPM9fg6oIMxZoSIpBpjIk5Y95AxpsC2fSIyHBgOUKtWrXO2b9/+j+dL47f10vieypz8PKdIBJRzCsjiD8A/EPwC/r416g+xdjv+ebPDWTnsOpTJ7tRMdrlu+9KyCA70JyIkiIiQQCqFBFKxfBBNq4VTKzLEdmSvVeJHICIyGyioP/+jxphJhdlFAcuKXO2MMe8D74NzCquo2ytlhZ//39dHYprBRa/YzeODwoMDCa8WSJNq4bajlFoeKyDGmF5nuYskoOYJj2OB42M97xORasaYPSJSDfCBQYeUUqp08eb2fIuBOBGpKyJBwCBgsuu5ycAQ1/0hQGGOaE6pNDVlLk3vRSnl3awUEBG5TESSgI7AFBGZ4VpeXUSmAhhjcoERwAxgLTDeGJPo2sXzQG8R2YjTSut5d7MEBweTkpJSKj54jTGkpKQQHOxeE0allCqKMt+RMCcnh6SkJLKysiylKl7BwcHExsYSGFh2h1dQShUvr2rG600CAwOpW7f0TjmplFKe4s3XQJRSSnkxLSBKKaXcogVEKaWUW8rURXQRSQa2n3HFgkUB3ju3ZtGVpvdTmt4L6PvxZqXpvUDh309tY8y/xscpUwXkbIhIQkGtEHxVaXo/pem9gL4fb1aa3guc/fvRU1hKKaXcogVEKaWUW7SAFN77tgMUs9L0fkrTewF9P96sNL0XOMv3o9dAlFJKuUWPQJRSSrlFC4hSSim3aAEpBBHpJyLrRWSTaw52nyUiH4rIfhFZbTvL2RKRmiIyV0TWikiiiNxtO5O7RCRYRBaJyArXe3nadqbiICL+IrJMRH6yneVsicg2EVklIstF5F+zqPoSEYkQke9EZJ3r76ejW/vRayCnJyL+wAacYeOTcOYpGWyMWWM1mJtEpBuQDnxqjGluO8/ZcE0mVs0Ys1REwoAlwKW++G8jIgKEGmPSRSQQ+AO42xizwHK0syIi/wXigXBjzEW285wNEdkGxBtjfL4joYh8AvxujBnnmm8pxBiTWtT96BHImbUHNhljthhjsoGvgQGWM7nNGPMbcNB2juJgjNljjFnqun8EZ96YGnZTucc40l0PA103n/52JyKxwIXAONtZ1N9EJBzoBnwAYIzJdqd4gBaQwqgB7DzhcRI++iFVmolIHaANsNByFLe5Tvcsx5mieZYxxmffi8trwANAvuUcxcUAM0VkiYgMtx3mLNQDkoGPXKcXx4lIqDs70gJyZlLAMp/+ZljaiEgFYAJwjzHmsO087jLG5BljWgOxQHsR8dlTjCJyEbDfGLPEdpZi1NkY0xa4ALjDdTrYFwUAbYF3jTFtgAzArWu7WkDOLAmoecLjWGC3pSzqJK7rBROAL4wx39vOUxxcpxN+AfrZTXJWOgOXuK4bfA2cLyKf2410dowxu10/9wMTcU5v+6IkIOmEI9zvcApKkWkBObPFQJyI1HVdbBoETLacSfHXhecPgLXGmFds5zkbIhItIhGu++WBXsA6q6HOgjHmYWNMrDGmDs7fzBxjzLWWY7lNREJdDTVwne7pA/hkS0ZjzF5gp4g0ci3qCbjV8KTMT2l7JsaYXBEZAcwA/IEPjTGJlmO5TUS+As4DokQkCXjSGPOB3VRu6wxcB6xyXTsAeMQYM9VeJLdVAz5xtfrzA8YbY3y+6WspEgNMdL6zEAB8aYyZbjfSWbkT+ML1pXgLcKM7O9FmvEoppdyip7CUUkq5RQuIUkopt2gBUUop5RYtIEoppdyiBUQppZRbtIAopZRyixYQpZRSbtECopRFItJORFa65gMJdc0F4rNjYKmyRTsSKmWZiIwCgoHyOGMUPWc5klKFogVEKctcw0ksBrKATsaYPMuRlCoUPYWllH2VgQpAGM6RiFI+QY9AlLJMRCbjDHleF2eK3hGWIylVKDoar1IWicj1QK4x5kvXSLzzReR8Y8wc29mUOhM9AlFKKeUWvQailFLKLVpAlFJKuUULiFJKKbdoAVFKKeUWLSBKKaXcogVEKaWUW7SAKKWUcsv/AwLnPeG+eFf4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成数据\n",
    "x = np.arange(0, 6, 0.1) # 以0.1为单位，生成0到6的数据\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "# 绘制图形\n",
    "plt.plot(x, y1, label=\"sin\")\n",
    "plt.plot(x, y2, linestyle = \"--\", label=\"cos\") # 用虚线绘制\n",
    "plt.xlabel(\"x\")         # x轴标签\n",
    "plt.ylabel(\"y\")         # y轴标签\n",
    "\n",
    "plt.title('sin & cos')  # 标题\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "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": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.plot(x, x, label='linear')\n",
    "plt.plot(x, x**2, label='quadratic')\n",
    "plt.plot(x, x**3, label='cubic')\n",
    "\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "\n",
    "plt.title(\"Simple Plot\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pyplot 同样支持一幅图中绘制多个子图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randn(2, 100)\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(8, 8))\n",
    "axs[0, 0].hist(data[0])\n",
    "axs[1, 0].scatter(data[0], data[1])\n",
    "axs[0, 1].plot(data[0], data[1])\n",
    "axs[1, 1].hist2d(data[0], data[1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 显示图像\n",
    "\n",
    "pyplot 中还提供了用于显示图像的方法 imshow()。可以使用 matplotlib.image 模块的 imread() 方法读入图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "from matplotlib.image import imread\n",
    "\n",
    "img = imread('images/chapter08/lena.jpg')       # 读入图像（设定合适的路径）\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "fig.add_subplot(1, 3, 1)\n",
    "plt.imshow(img[:,:,0], cmap='gray')\n",
    "plt.title('R')\n",
    "\n",
    "fig.add_subplot(1, 3, 2)\n",
    "plt.imshow(img[:,:,1], cmap='gray')\n",
    "plt.title('G')\n",
    "\n",
    "fig.add_subplot(1, 3, 3)\n",
    "plt.imshow(img[:,:,2], cmap='gray')\n",
    "plt.title('B')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 练习\n",
    "\n",
    "1. 交换二维矩阵的第一行和第二行\n",
    "2. 使用numpy.ndarray分片操作将图像水平镜像和垂直镜像\n",
    "3. 归一化二维矩阵，将其中每个元素都量化到0-1区间\n",
    "4. 将二维矩阵的每一行的元素都减去该行的平均值"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "226px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}