{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 以10步为例实现随机漫步"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, -1],\n",
       "       [ 1,  1],\n",
       "       [-1,  0],\n",
       "       [ 0, -1],\n",
       "       [ 1,  1],\n",
       "       [ 0,  1],\n",
       "       [-1,  0],\n",
       "       [ 0,  0],\n",
       "       [-1,  0],\n",
       "       [ 0,  1]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义函数，生成随机漫步的步长\n",
    "\n",
    "## 方法一：x、y轴分开生成，再合并\n",
    "x_arr = np.random.randint(-1,2,10)\n",
    "y_arr = np.random.randint(-1,2,10)\n",
    "step = np.column_stack((x_arr,y_arr))\n",
    "step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, -1])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, -1],\n",
       "       [ 1,  0],\n",
       "       [ 1,  0],\n",
       "       [ 0,  1],\n",
       "       [ 0,  0],\n",
       "       [ 0,  1],\n",
       "       [ 1, -1],\n",
       "       [ 0,  0],\n",
       "       [ 0,  1],\n",
       "       [ 0,  0]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 方法二：直接生成\n",
    "np.random.randint(-1,2,size = (10,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 把随机数变成坐标\n",
    "coordinate = np.empty([10,2],dtype = int)\n",
    "coordinate[0] = [0,0]\n",
    "for i in range (0,9):\n",
    "    coordinate[i+1] = coordinate[i] + step[i] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0],\n",
       "       [ 1, -1],\n",
       "       [ 2,  0],\n",
       "       [ 1,  0],\n",
       "       [ 1, -1],\n",
       "       [ 2,  0],\n",
       "       [ 2,  1],\n",
       "       [ 1,  1],\n",
       "       [ 1,  1],\n",
       "       [ 0,  1]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x103cf0ad0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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piLhuOlPrtJVuSPTYXLvVbvD0+FxdAM9Xlt+g/7X4qjUfm9M81bMBOFlZPsW5N3Mtd8PX\n6QnPrWtG2ZevA9sy8/2I+BhwKCK2ZObb05rknPHYHC+PzxFk5ncXn0fErwG/ATw3NGzNx+Y0G/8J\n4Fcry0vdzHU6Ii5cZYxG25cXZOb7AJn5MnAMuHY605tLHpvj5fG5BoP7ox4BbhsKeahxbE4z+P8V\n2FpZvg3454jYFBGLv+jtW8DNcPbi0BuZaaM61yj78v6I+EOAiDiP/kWfH0x3mp109mYYj82xWG5/\nenyOKCJ+C/gz4A+AUxGxo+mxOdXv8UfEDuAW4Gf0J/eIN3zVM8K+3Ag8DLwKrAP+JTOH/4uogaVu\nSPTYrG+E/enxOYKIuJX+bzX+Af1/RD8MfBb4cxocm97AJUmF8QYuSSqMwS9JhTH4JakwBr8kFcbg\nl6TCGPySVBiDX5IKY/BLUmH+H4qGDDIrgZejAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a378750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据随机数坐标画图\n",
    "coordinate = coordinate.T\n",
    "plt.plot(coordinate[0],coordinate[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实现z步随机漫步作图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j4srMPDznZbeFN+1Vq8zx9Jys1lTnZyUDPjOfBZ6d8duPRsSZmXniX61e3yS12rGMiD8D\nPlx4aq1jdMaJ45iZr0bEy8AVwHPzWW3rHGLycfR8LK/M8fScrNZU5+eiPkVzygf0I2JzRJw12vw2\ncO3o+a0MP2dvLZ3qO0DxP3jbCfwzvO9Y3hcRfzp6/jSGv4R5ZZELbbhVj6Pn48zKHE/PydmcnJkb\nOT/n+jn4iLgU+Evgj4C7M3Pf6HlvkprSajebjZ4vHsuLgIcY3mV8DvBUZn63rjU3kTftVavE8fSc\nnMJqM3Mj56c3OklSR3mjkyR1lANekjrKAS9JHeWAl6SOcsBLUkc54CWpoxzwktRRDnhJ6qj/B5AE\nP1NKa1/VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a378590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = 10\n",
    "x_arr = np.random.randint(-1,2,z)\n",
    "y_arr = np.random.randint(-1,2,z)\n",
    "steps = np.vstack((x_arr,y_arr))\n",
    "steps = steps.T\n",
    "random_coordinate = np.empty([z+1,2],dtype=int)\n",
    "random_coordinate[0] = [0,0]\n",
    "for i in range(0,z):\n",
    "    random_coordinate[i+1] = random_coordinate[i] + steps[i]\n",
    "\n",
    "random_coordinate = random_coordinate.T\n",
    "plt.plot(random_coordinate[0],random_coordinate[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  0,  0,  0,  0,  0, -1,  0,  1,  1],\n",
       "       [ 0,  1,  1,  2,  2,  1,  2,  2,  2,  1,  0]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_coordinate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 函数化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 输出随机游走的坐标函数\n",
    "def random_coordinate(z):\n",
    "    x_arr = np.random.randint(-1,2,z)\n",
    "    y_arr = np.random.randint(-1,2,z)\n",
    "    steps = np.vstack((x_arr,y_arr))\n",
    "    steps = steps.T\n",
    "    random_coordinate = np.empty([z+1,2],dtype=int)\n",
    "    random_coordinate[0] = [0,0]\n",
    "    for i in range(0,z):\n",
    "        random_coordinate[i+1] = random_coordinate[i] + steps[i]\n",
    "    random_coordinate = random_coordinate.T\n",
    "    return random_coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0, -1, -2, -3, -4, -5, -6, -6, -7, -6, -7, -7, -8, -8, -9, -8,\n",
       "        -7, -8, -7, -7, -7, -6, -7, -7, -8, -7, -6, -5, -6, -7, -8, -8, -8,\n",
       "        -9, -8, -9, -8, -8, -7, -7, -6, -6, -6, -6, -6, -5, -4, -3, -2, -3,\n",
       "        -3, -2, -2, -2, -1, -2, -1,  0,  0,  0,  1,  0,  1,  1,  0,  0, -1,\n",
       "         0,  0, -1, -1, -1,  0,  1,  1,  1,  0,  1,  0,  1,  2,  2,  3,  4,\n",
       "         3,  4,  4,  5,  5,  4,  3,  2,  3,  3,  3,  2,  3,  4,  5,  4],\n",
       "       [ 0, -1, -1,  0,  0,  0,  1,  2,  3,  4,  3,  4,  4,  4,  4,  4,  4,\n",
       "         5,  5,  6,  6,  7,  8,  9, 10, 11, 11, 12, 11, 12, 12, 13, 12, 12,\n",
       "        12, 11, 11, 11, 11, 10, 11, 12, 12, 12, 11, 11, 10, 10, 11, 10, 11,\n",
       "        11, 11, 10, 10, 11, 11, 12, 13, 14, 15, 16, 16, 17, 17, 16, 17, 17,\n",
       "        16, 15, 15, 14, 14, 15, 15, 15, 15, 14, 13, 14, 13, 12, 12, 11, 10,\n",
       "         9,  8,  7,  7,  8,  7,  7,  7,  8,  8,  9, 10,  9, 10,  9, 10]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_coordinate(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 输出画图的样式(测试)\n",
    "def plot_style(ax, data1, data2, param_dict):\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    out = ax.plot(data1, data2, **param_dict)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a3b9d90>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD7EHt29OU4KuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a3b99d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "plot_style(ax, y, y, {'marker':'x'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10fa8d9d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0C7QkzdfEu5BHxJnAFzPz98aP/xHYnZn3FY5Z/C3IJakDytyFfOJCDRARVwNXAv8LPJKZ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\nGs6FWpIazoVakhrOhVqSGu7/AVs51tasCz/jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca05e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.plot(y,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 输出随机游走的图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def random_walk_test(z):\n",
    "    x = random_coordinate(z)\n",
    "    ax = plt.subplot()\n",
    "    out = ax.plot(x[0],x[1])\n",
    "    return out\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110078f90>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kr6mHEbr7g+5+srtPd/cbshpKpF2GDoX774c5c+CrXw2PQFm/vv1zLFsWjscvXQrLl8Px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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fbc9690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_walk_test(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 图像展示的优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2, 9)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAf/4h56k3HOP3VqSkXEN/IUXbFdwJGOAHj3sH9ThunfXTY1HjtR89+TJ+pY3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110192c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = 100\n",
    "x_arr = np.random.randint(-1,2,z)\n",
    "y_arr = np.random.randint(-1,2,z)\n",
    "steps = np.vstack((x_arr,y_arr))\n",
    "steps = steps.T\n",
    "random_coordinate = np.empty([z+1,2],dtype=int)\n",
    "random_coordinate[0] = [0,0]\n",
    "for i in range(0,z):\n",
    "    random_coordinate[i+1] = random_coordinate[i] + steps[i]\n",
    "\n",
    "random_coordinate = random_coordinate.T\n",
    "plt.plot(random_coordinate[0],random_coordinate[1]);\n",
    "plt.plot([0],[0],marker = \"o\",color = 'green')\n",
    "plt.plot(random_coordinate[0,z],random_coordinate[1,z],marker = 'o',color = 'red')\n",
    "\n",
    "plt.plot([0,0],[random_coordinate[1].min()-1,random_coordinate[1].max()+1],color = 'black',lw = 0.5,ls = '--')\n",
    "plt.plot([random_coordinate[0].min()-1,random_coordinate[0].max()+1],np.zeros_like([random_coordinate[0].min()-1,random_coordinate[0].max()+1]),color = 'black',lw = 0.5,ls = '--')\n",
    "plt.xlim(random_coordinate[0].min()-1,random_coordinate[0].max()+1)\n",
    "plt.ylim(random_coordinate[1].min()-1,random_coordinate[1].max()+1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 展示图像优化后的函数化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_walk_final(z):\n",
    "    x = random_coordinate(z)\n",
    "    ax = plt.subplot()\n",
    "    out = ax.plot(x[0],x[1])\n",
    "    out = ax.plot([0],[0],marker = \"o\",color = 'green')\n",
    "    out = ax.plot(x[0,z],x[1,z],marker = 'o',color = 'red')\n",
    "    out = ax.plot([0,0],[x[1].min()-1,x[1].max()+1],color = 'black',lw = 0.5,ls = '--')\n",
    "    out = ax.plot([x[0].min()-1,x[0].max()+1],np.zeros_like([x[0].min()-1,x[0].max()+1]),color = 'black',lw = 0.5,ls = '--')\n",
    "    out = plt.xlim(x[0].min()-1,x[0].max()+1)\n",
    "    out = plt.ylim(x[1].min()-1,x[1].max()+1)\n",
    "    out = ax.get_xaxis().set_visible(False)\n",
    "    out = ax.get_yaxis().set_visible(False)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'numpy.ndarray' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-bf51fe67be55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrandom_walk_final\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-21-1456debb40d5>\u001b[0m in \u001b[0;36mrandom_walk_final\u001b[0;34m(z)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrandom_walk_final\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_coordinate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmarker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"o\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'green'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"
     ]
    }
   ],
   "source": [
    "random_walk_final(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 生成“九宫格”函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig1,ax = plt.subplots(3,3,figsize=(12,6))\n",
    "flag = 0\n",
    "for i in range(0,3):\n",
    "    for j in range(0,3):\n",
    "        flag += 1\n",
    "        ax[i][j].get_xaxis().set_visible(False)\n",
    "        ax[i][j].get_yaxis().set_visible(False)\n",
    "        ax[i][j].set_title('Random Walk %d' %flag )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rand_walk_9_test(x):\n",
    "    fig1,ax = plt.subplots(3,3,figsize=(12,6))\n",
    "    flag = 0\n",
    "    for i in range(0,3):\n",
    "        for j in range(0,3):\n",
    "            flag += 1\n",
    "            out = ax[i][j].get_xaxis().set_visible(False)\n",
    "            out = ax[i][j].get_yaxis().set_visible(False)\n",
    "            out = ax[i][j].set_title('Random Walk %d' %flag )\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def rand_walk_9_test2(y):\n",
    "    fig1,ax = plt.subplots(3,3,figsize=(12,6))\n",
    "    flag = 0\n",
    "    for i in range(0,3):\n",
    "        for j in range(0,3):\n",
    "            x = random_coordinate(y)\n",
    "            flag += 1\n",
    "            out = ax[i][j].get_xaxis().set_visible(False)\n",
    "            out = ax[i][j].get_yaxis().set_visible(False)\n",
    "            out = ax[i][j].set_title('Random Walk %d' %flag )\n",
    "            out = ax[i][j].plot(x[0],x[1])\n",
    "            out = ax[i][j].plot([0],[0],marker = \"o\",color = 'green')\n",
    "            out = ax[i][j].plot(x[0,y],x[1,y],marker = 'o',color = 'red')\n",
    "            out = ax[i][j].plot([0,0],[x[1].min()-1,x[1].max()+1],color = 'black',lw = 0.5,ls = '--')\n",
    "            out = ax[i][j].plot([x[0].min()-1,x[0].max()+1],np.zeros_like([x[0].min()-1,x[0].max()+1]),color = 'black',lw = 0.5,ls = '--')\n",
    "            out = plt.xlim(x[0].min()-1,x[0].max()+1)\n",
    "            out = plt.ylim(x[1].min()-1,x[1].max()+1)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rand_walk_9_test2(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rand_walk_9_test3(y):\n",
    "    fig1,ax = plt.subplots(3,3,figsize=(12,6))\n",
    "    flag = 0\n",
    "    for i in range(0,3):\n",
    "        for j in range(0,3):\n",
    "            flag += 1\n",
    "            out = ax[i][j].set_title('Random Walk %d' %flag )\n",
    "            out = random_walk_final(y)\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rand_walk_9_test3(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax2 = plt.subplot()\n",
    "# ax2.set_axis_off()\n",
    "\n",
    "# ax2.get_xaxis().set_ticks([]);\n",
    "# ax2.get_yaxis().set_ticks([]);\n",
    "\n",
    "ax2.get_xaxis().set_visible(False)\n",
    "ax2.get_yaxis().set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rand_walk_9_test4(y):\n",
    "    fig1,ax = plt.subplots(3,3,figsize=(12,6))\n",
    "    flag = 0\n",
    "    for i in range(0,3):\n",
    "        for j in range(0,3):\n",
    "            flag += 1\n",
    "#           out = ax[i][j].set_title('Random Walk %d' %flag )\n",
    "            ax[i][j] = random_walk_final(y)\n",
    "#           out = random_walk_final(y)\n",
    "            out = ax[i][j]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rand_walk_9_test4(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_walk_final(z):\n",
    "    random_coordinate \n",
    "    x = random_coordinate(z)\n",
    "    ax = plt.subplot()\n",
    "    out = ax.plot(x[0],x[1])\n",
    "    out = ax.plot([0],[0],marker = \"o\",color = 'green')\n",
    "    out = ax.plot(x[0,z],x[1,z],marker = 'o',color = 'red')\n",
    "    out = ax.plot([0,0],[x[1].min()-1,x[1].max()+1],color = 'black',lw = 0.5,ls = '--')\n",
    "    out = ax.plot([x[0].min()-1,x[0].max()+1],np.zeros_like([x[0].min()-1,x[0].max()+1]),color = 'black',lw = 0.5,ls = '--')\n",
    "    out = plt.xlim(x[0].min()-1,x[0].max()+1)\n",
    "    out = plt.ylim(x[1].min()-1,x[1].max()+1)\n",
    "    out = ax.get_xaxis().set_visible(False)\n",
    "    out = ax.get_yaxis().set_visible(False)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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