reexamine.ipynb · OSCHINA-MIRROR/thales-ucas-postgrad

OSCHINA-MIRROR/thales-ucas-postgrad

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  {
   "source": [
    "# 考研复试通过率\n",
    "\n",
    "\n",
    "\n",
    "## 概率\n",
    "\n",
    "\n",
    "比如20人过线，招12人，那么每个人的几率"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "0.55"
      ]
     },
     "metadata": {},
     "execution_count": 1
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   ],
   "source": [
    "11/20"
   ]
  },
  {
   "source": [
    "# 复试\n",
    "\n",
    "\n",
    "## 复试占比\n",
    "\n",
    "\n",
    "一个人初试成绩350，复试占比50%，比如复试成绩是75，那么——"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "50 * (350/500) + 50 * (75/100)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "72.5"
      ]
     },
     "metadata": {},
     "execution_count": 2
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   ]
  },
  {
   "source": [
    "## 复试分时"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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T+nPGoK4aetmKKfRFJCh7Dhxm3kcFvLqqgMWb9lHrMCgrnRlTRzBtXF+6pKdEu0QJgkJfRBrk7qzfU8rbHxfy2qoC8rcW4Q4Ds9K5PncQU0b14pTeHTFTr74tUeiLyBE7isv514a9/GvDXt7duI/Cg3VzHg3v2YGbvjCUKaN7MqR7hoK+DVPoi8Spw1U1rNl1gBXbS1i+vZilW4rYvO8QAN0yUjhjUDfOHNyVMwZ1o1+X9lGuVlqKQl8kxrk7e0sr2bCnlI2FpUeCfm3BAapq6ubT6JbRjrH9Mrly0gDOHNyVYT06qDcfoxT6IjGguqaW3Qcr2Flczs7icnYUl7N5bxkbC8vYsKeUkvKqI207tEtidN9Mrj1rIGP7ZTKmbyd6ZaYq5ONEyKFvZrOBkcAr7n5HsG2C2U4knlVW11JWUU1p4FFWUc3Bw9XsL6usexyqZH9pJfvKKtlfVkFByWEKDhym9phJ8LplpDAoK4OLxvRiUFYGg7vXPRTw8S2k0DezaUCiu08ysz+Z2RB3X99UG2B0U9uJNJe7c6iyhoOHPxuUZRXVlFfVUFldS0V1beBnDRXVtVTX1FJd69S6U1Prdcu1To3XveeRDHVwnMZmFvXA6/XLBNo6tQ41tV7/+HRfNUfX8+mjhoqqWiprao97vMmJRuf2KXRJT6FrRgoTB3WlT6c0egcefTql0iszjfR2+kNePivUfxW51E+VOB84Czg2vBtqc2pT25nZdGA6QP/+/UMsT2JBba2zt7SCHcXl7Cw+zM7icnaVHGZfWUV9r7esrsdbWX38oDxaUoLRLimBpMQEkhKMhASr+2lGUmLdTwMw+LQ/bIF1jXWQDfs/r33aPiEBEhMSSDRITDASE4zk5AQS2yWRmpRASlIC7ZISAz8TaJecQEZKEhmpSaS3S6JDu7qfGalJdGmfQpeMFDq0S1JPXUIWauinAzsCy/uBcUG2aXI7d58FzIK6idFDrE/aCHdnX1n9l4wb9tQ9tuw7xK6S8iNfNH6qfUoi3TLa0SU9hR4dUxnRqyNd01PonJ5Cx9Rk0tsl0iE1ifRAcGa0SyItOfFIsKYkJZCoq0UljoUa+qVAWmA5A0gIsk0w20mMcne2F5WzYnsJK7YX8+G2YtbtPkjxofovGdOSExnUPZ3sfp2YOqYXvTNTj5y26N0pjY6p6uWKnIhQQz+fulMzi4FsYF2QbbYHsZ3EiKqaWpZvK+bdjftYtrWIFdtL2FdWCUBKYgIjendkyqheR75gHNw9g14dU3XfFpEwCjX0XwQWmVlvYArwDTO7w91nHKfNROq+4zp2ncQId2dtwcEjV3O+v2kfZZU1mMGQ7hl8fnh3xvTrRHbfTIb37EhKkv7QE4m0kELf3Q+YWS4wGbjb3QuA5U20KQFoaJ20XVU1tby3cR+vrirg9dW72Vtad9n+yd3S+cq4Ppw5qBuTBnWlU3vdjEukNQh5TJe7F1E/EifoNsFsJ61bRXUN76zfeyToS8qraJ+SyLnDuvO5YVmcObgbfTqlNf1GIhJxGsgrQXF3lm8v4ekPtvKPFbsoraimQ2oSk0f04IJRPTlnaBapyYnRLlNEmqDQl+MqOVTF35Zt5+kl21hbcJC05EQuHN2Li7J7ceagbjovL9LGKPSlQcu2FvGXdzczd1UBldW1jO6TyZ1fGcWXs3vTQVPgibRZCn05wt156+NCZi7cyPuf1E1s/fWcvnzjtP6M6pMZ7fJEpAUo9IXqmlpeWbmLP7y1iTW7DtArM5UZU0fwzQn9df8WkRij/6PjWHVNLc/lb2fmwo1s3X+IQVnp3HPpGC4e20fn6kVilEI/Drk7C9bs4devrmFjYRlj+3Xi1qkjmDyih66GFYlxCv048+G2Yu6au4YPPtnPwKx0Zl05nskje+h+NiJxQqEfJ7buO8Td89byjxW76JaRwh2XjOKy0/qRnKjTOCLxRKEf4yqra/nDWxv5/T83kJhgfP8LQ5h+zkAy9AWtSFzS//kxLH9LET99YQUf7y5l6phe/PyikfTomBrtskQkihT6Mejg4SrumbeOOYu30KtjKrO/k8MXRvSIdlki0goo9GPM66t3c9uLq9h98DDfmTSA//fFYTqVIyJHKA1ixKHKan710mqeydvG8J4dmPmtcZzav3O0yxKRVkahHwPW7DrA955cyqa9ZXw3dxA/mDxUo3JEpEEK/TbM3ZmzeAt3vLKGTmnJPH7t6Zw5uFu0yxKRVqzZoW9ms4GRwCvufkcjbTKBp4FEoAy4DKgFNgUeADe6+8pQihYoPlTJj/+6gvmrd3PusCzu/Vo2XTPaRbssEWnlmhX6ZjYNSHT3SWb2JzMb4u7rG2h6BfBbd3/dzGYCF1A3KfpT7v6TEy87vi3dWsQNTyxlb2kFM6aO4JozT9btE0QkKM3t6edSP9XhfOAs4DOh7+4PHfU0C9hD3SToF5nZucBK4Dp3rz52WzObDkwH6N+/fzPLi30vLN3OLc+vpGdmKi9cfyaj++qWxyISvOOGvpk9DAw7atXngNmB5f3AuCa2nwR0dvfFZlYDnOfuu8zsMeBC4KVjt3H3WcAsgJycHA/2QGJdba1z97x1/OGtjUwc2IWZV4ync7omGxeR5jlu6Lv7dUc/N7P7gU9nvM4AGh0iYmZdgAeArwZWrXD3isByHjAklILjUWlFNTc9/SEL1uzm8tP786svn6LROSISkuYmRz51p3QAsoHNDTUysxTgOeCn7r4lsHqOmWWbWSJwCbC82dXGoe1Fh7h05rv8c+1ufvmlkdx5ySgFvoiErLnn9F8EFplZb2AKMNHMRgKXu/uMo9pdS92pn1vN7FZgJnA78CRgwEvuvuBEi491+VuKmP5YHpU1tTx69QTOGZoV7ZJEpI0z9+adNjezzsBk4G13LwhLVQE5OTmel5cXzl20Wm99XMh1c/Lo2TGV2VedxqCsjGiXJCJthJnlu3tOQ681e5y+uxdRP4JHwmDuyl3859PLGNK9A49dO4FuGn8vIi1EV+S2Ms8u2cYtL6xgXP/OzL7qNDLTkqNdkojEEIV+KzL7nU/4r3+s5uwh3Xj4yvG0T9GvR0RallKlFXB37n9jPfctWM+UUT257xtjaZeUGO2yRCQGKfSjzN2585U1/PGdT7h0fF9+PW00SRqSKSJhotCPsnvnr+OP73zCVWcM4OcXjdQ9dEQkrBT6UfTgmxt48M2NfHNCf37xpZGYKfBFJLx0HiFKHv3XJ9wzbx0Xj+3NHZeMUuCLSEQo9KPg2bxt/PLl1Zw/sgf3fi2bRJ3SEZEIUehH2D9W7OSW51dw9pBuPHD5qbqPjohElBIngt5Ys5ubnv6Q8Sd1ZtaVORqWKSIRp9CPkPwtRVz/xFJG9OrI7KtOIy1FgS8ikafQj4Bt+w8x/bE8emWm8pdrJtAxVbdWEJHoUOiHWUl5FVc/uoTqWudPV51GF812JSJRpNAPo6qaWm54Yimb95bxh2+N1+2RRSTqdHFWmLg7P//7Kt7ZsJd7Lh3DpEFdo12SiEjze/pmNtvM3jOzGcdpk2RmW81sYeAxOthtY8Ujizbx1AfbuOHcQXwtp1+0yxERAZoZ+mY2DUh090nAQDNrbHLzMcBT7p4beKxsxrZt3murCvjvV9cydXQvbp48LNrliIgc0dyefi71s2bNp36S9GNNBC4ysw8CvfukYLc1s+lmlmdmeYWFhc0sL/o+2lnCTc8sI7tvJ37z9WzdQE1EWpXjhr6ZPXzUKZqFwI3AjsDL+4EejWy6BDjP3ScAycCFQHow27r7LHfPcfecrKy2NRF4SXkV1z++lE5pKTzy7RxSkzUWX0Ral+N+kevu1x393MzuB9ICTzNo/ENjhbtXBJbzgCFAaZDbtkm1tc7Nzy5nZ3E5z1w3iawOmtdWRFqf5gZvPvWnZbKBzY20m2Nm2WaWCFwCLG/Gtm3Sw29vYsGa3dw6dQTjT+oc7XJERBrU3CGbLwKLzKw3MAWYaGYjgcvd/egRObcDTwIGvOTuC8ys47HbnnD1rcR7G/dxz7y1TB3Ti6vOGBDtckREGmXu3rwNzDoDk4G33b0gnNvm5OR4Xl5es+qLtN0HDjP1f98hMy2Jv3/vLDLa6dIHEYkuM8t395yGXmt2Qrl7EfWjcCK2bWtUVVPL955cSllFNU/+++kKfBFp9ZRSJ+Du19ayZHMR939jLEN7dIh2OSIiTYqpETSRNO+jAh5Z9AnfnnQSF4/tE+1yRESCotAPwZ4Dh7nl+RWM7pPJrVNHRLscEZGgKfSbyd358fMrKK+q4XeXjdXsVyLSpij0m+nxxVtYuK6Qn104gsHddatkEWlbFPrNsLGwlDvnruFzQ7O4cuJJ0S5HRKTZFPpBqqqp5QfPfEhqciL3XDoGM91ITUTaHg3ZDNIDb6xnxfYSZl4xju4dU6NdjohISNTTD0L+liJ+/+YGvjquL1NG94p2OSIiIVPoN6GsopofPvshvTul8csvj4x2OSIiJ0Snd5pw59w1bN1/iGemT6JDanK0yxEROSHq6R/H4k37ePL9rfzbWScz4eQu0S5HROSEKfQbcbiqhp+9sJJ+XdL4oea5FZEYodM7jXjwzQ1s2lvGnGsnkJaiq25FJDaop9+AdQUHmblwI9NO7cPZQ9rWPL0iIsej0D9GTa1zywsr6JiWzIyLNFpHRGJLs0PfzGab2XtmNuM4ba43s4WBx4dm9rCZJZnZ1qPWjz6x0sPj8cVbWLa1mNsuGkGX9JRolyMi0qKaFfpmNg1IdPdJwEAzG9JQO3ef6e657p4LLAIeAcYAT3263t1XnmDtLW5ncTl3v7aWc4ZmcYnukS8iMai5Pf1c6qc7nA+cdbzGZtYH6OHuedRNhH6RmX0Q+GuhwS+RzWy6meWZWV5hYWEzywudu/Pzv6+i1uHOS0bp3joiEpOOG/qB0zKfno5ZCNwI7Ai8vB/o0cT73wDMDCwvAc5z9wlAMnBhQxu4+yx3z3H3nKysyH2JOndlAQvW7OGHk4f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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.family'] = ['SimHei'] # 设置中文显示\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "# ax.set_xlim(-1, 1)\n",
    "# ax.set_ylim(-1, 1)\n",
    "x = np.linspace(0, 1)\n",
    "y = (((x*2 - 1)) ** (3))\n",
    "# y = 2 * x\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "source": [
    "原始"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2244d14ea88>]"
      ]
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n\r\n<svg height=\"301.325pt\" version=\"1.1\" viewBox=\"0 0 325.4 301.325\" width=\"325.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-23T23:46:40.243477</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.hlines(0, -1, 1, 'grey', '--')\n",
    "ax.vlines(0, -1, 1, 'grey', '--')\n",
    "x = np.linspace(-1, 1)\n",
    "y = x ** 3\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "source": [
    "调整"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2244d1d8908>]"
      ]
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n\r\n<svg height=\"301.325pt\" version=\"1.1\" viewBox=\"0 0 325.4 301.325\" width=\"325.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-23T23:46:40.491477</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.hlines(0, -1, 1, 'grey', '--')\n",
    "ax.vlines(0, -1, 1, 'grey', '--')\n",
    "x = np.linspace(0, 1)\n",
    "y = (x*2 - 1) ** 3\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "source": [
    "分数匹配"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2244d261c08>]"
      ]
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n\r\n<svg height=\"301.325pt\" version=\"1.1\" viewBox=\"0 0 315.4 301.325\" width=\"315.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-23T23:46:40.700979</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.hlines(70, 0, 1, 'grey', '--')\n",
    "ax.hlines(90, 0, 1, 'grey', '--')\n",
    "ax.vlines(0.1031497370079501, 60, 100, 'red', '--')\n",
    "ax.vlines(0.8968502629920498, 60, 100, 'red', '--')\n",
    "x = np.linspace(0, 1)\n",
    "y = ((x*2 - 1) ** 3)*20 + 80\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "source": [
    "尝试更大幂"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2244d2c7648>]"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n\r\n<svg height=\"301.325pt\" version=\"1.1\" viewBox=\"0 0 315.4 301.325\" width=\"315.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2021-03-23T23:46:40.935977</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.2, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n  <style 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.hlines(70, 0, 1, 'grey', '--')\n",
    "ax.hlines(90, 0, 1, 'grey', '--')\n",
    "ax.vlines(0.1031497370079501, 60, 100, 'red', '--')\n",
    "ax.vlines(0.8968502629920498, 60, 100, 'red', '--')\n",
    "ax.vlines(0.0370626438563548, 60, 100, 'purple', '--')\n",
    "ax.vlines(0.9629373561436452, 60, 100, 'purple', '--')\n",
    "x = np.linspace(0, 1)\n",
    "y = ((x*2 - 1) ** 9)*20 + 80\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "source": [
    "随机复试分总分"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "76.46821278016364"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "import random\n",
    "def total(score):\n",
    "    x = random.random()\n",
    "    y = ((x*2 - 1) ** 3)*20 + 80\n",
    "    return 50 * (score/500) + 50 * (y/100)\n",
    "total(350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.524"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "import pandas as pd\n",
    "class Aggregate:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def preliminary(self, xls, id, rank):\n",
    "        \"\"\"\n",
    "        初试成绩\n",
    "        xls 成绩表\n",
    "        id  参考人id\n",
    "        rank    录取名次\n",
    "        \"\"\"\n",
    "        self.df = pd.read_excel(xls)\n",
    "        self.id = id\n",
    "        self.rank = rank\n",
    "    def reexam(self, seed=1):\n",
    "        \"\"\"\n",
    "        模拟复试分数\n",
    "        \"\"\"\n",
    "        random.seed(seed)\n",
    "        self.df['复试'] = self.df['分数'].map(lambda x:((random.random()*2 - 1) ** 3)*20 + 80)\n",
    "        self.df['总分'] = 50 * (self.df['分数']/500) + 50 * (self.df['复试']/100)\n",
    "        self.df = self.df.sort_values(by=\"总分\", ascending=False)\n",
    "        return self.df\n",
    "    def sim(self):\n",
    "        \"\"\"\n",
    "        模拟\n",
    "        \"\"\"\n",
    "        enroll = 0\n",
    "        times = 500 # 模拟500次\n",
    "        for n in range(1, times):\n",
    "            df = self.reexam(n)\n",
    "            target = df[df['编号'] == self.id]\n",
    "            rank = df[df['总分'] > target['总分'].values[0]]\n",
    "            if rank.count()[0] <= 12:\n",
    "                enroll += 1\n",
    "        return enroll / times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       编号   分数\n",
       "0   10001  400\n",
       "1   10002  381\n",
       "2   10003  380\n",
       "3   10004  376\n",
       "4   10005  373\n",
       "5   10006  372\n",
       "6   10007  370\n",
       "7   10008  362\n",
       "8   10009  361\n",
       "9   10010  359\n",
       "10  10011  353\n",
       "11  10012  351\n",
       "12  10013  350\n",
       "13  10014  350\n",
       "14  10015  350\n",
       "15  10016  349\n",
       "16  10017  349\n",
       "17  10018  348\n",
       "18  10019  348\n",
       "19  10020  348"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>编号</th>\n      <th>分数</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10001</td>\n      <td>400</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10002</td>\n      <td>381</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10003</td>\n      <td>380</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10004</td>\n      <td>376</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10005</td>\n      <td>373</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>10006</td>\n      <td>372</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>10007</td>\n      <td>370</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>10008</td>\n      <td>362</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>10009</td>\n      <td>361</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10010</td>\n      <td>359</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>10011</td>\n      <td>353</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>10012</td>\n      <td>351</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>10013</td>\n      <td>350</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>10014</td>\n      <td>350</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>10015</td>\n      <td>350</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>10016</td>\n      <td>349</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>10017</td>\n      <td>349</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>10018</td>\n      <td>348</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>10019</td>\n      <td>348</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>10020</td>\n      <td>348</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "agg = Aggregate()\n",
    "agg.preliminary('./preliminary.xlsx', 10014, 12)\n",
    "agg.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.524"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "agg.sim()"
   ]
  },
  {
   "source": [
    "第一名"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.896"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "agg = Aggregate()\n",
    "agg.preliminary('./preliminary.xlsx', 10001, 12)\n",
    "agg.sim()"
   ]
  },
  {
   "source": [
    "最后一名"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.424"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "agg = Aggregate()\n",
    "agg.preliminary('./preliminary.xlsx', 10020, 12)\n",
    "agg.sim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

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