ch08-SciPy模块/08-02插值模块.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b240de8e",
   "metadata": {},
   "source": [
    "# 插值模块"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5296cc",
   "metadata": {},
   "source": [
    "scipy.interpolate是SciPy中负责插值操作的子模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4ef6bd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5eed2d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12b96e17",
   "metadata": {},
   "source": [
    "插值（Interpolation）是通过已知的离散数据点求未知数据的过程或方法。例如，考虑这样一组离散数据点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bbdadd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 2*np.pi, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8aba3b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66495975",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1755997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12bacdb20>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(x, y, \"o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fabfb9",
   "metadata": {},
   "source": [
    "这些数据点x的维度为1，因此是个一维插值问题。一维插值问题对应的函数为`interpolate.interp1d()`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43c53f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = interpolate.interp1d(x, y)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1bd51e60",
   "metadata": {},
   "source": [
    "返回值f可以像函数一样被调用。当输入值为数字时，f返回一个NumPy数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5e88976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dcfec593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.48209071, 0.95511217])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f([np.pi / 6, np.pi / 2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4e9ca4b6",
   "metadata": {},
   "source": [
    "默认情况下，插值函数允许的定义域范围由输入数据的范围决定。对于超出范围的数据，调用f会抛出异常。在上面的例子中，x的范围为0到2倍圆周率，因此输入负数会报错。\n",
    "\n",
    "可以通过在调用插值函数时，加入bounds_error参数来允许超出范围的输入："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "443a7134",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = interpolate.interp1d(x, y, bounds_error=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f93f5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(nan)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(-np.pi)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4fcd2a8c",
   "metadata": {},
   "source": [
    "还可以加入fill_value参数指定超出范围的默认返回值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba4a2d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = interpolate.interp1d(x, y, bounds_error=False, fill_value=-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ed2c222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(-100.)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(-np.pi)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d032805e",
   "metadata": {},
   "source": [
    "线性插值对应的函数如图8-2所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49164d5e",
   "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": [
    "t = np.linspace(0, 2 * np.pi, 200)\n",
    "\n",
    "f = interpolate.interp1d(x, y)\n",
    "plt.plot(x, y, 'o', t, f(t))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7742c5d1",
   "metadata": {},
   "source": [
    "可以通过加入`kind`参数，实现不同的插值方法。\n",
    "\n",
    "- 'nearest'：最近邻插值，x对应的值为离x最近的点对应的值。\n",
    "- 'zero'：零阶插值，使用一个常数分段函数进行插值。\n",
    "- 'linear'：线性插值，默认插值方法。\n",
    "- 'quadratic'：二次函数插值。\n",
    "- 'cubic'：三次函数插值。\n",
    "- 数字4，5，6，7：更高次数的函数插值。\n",
    "\n",
    "例如最近邻："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9519b943",
   "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": [
    "t = np.linspace(0, 2 * np.pi, 200)\n",
    "\n",
    "f = interpolate.interp1d(x, y, kind=\"nearest\")\n",
    "plt.plot(x, y, 'o', t, f(t))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5fec4c36",
   "metadata": {},
   "source": [
    "三次插值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca1d57b6",
   "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": [
    "t = np.linspace(0, 2 * np.pi, 200)\n",
    "\n",
    "f = interpolate.interp1d(x, y, kind=\"cubic\")\n",
    "plt.plot(x, y, 'o', t, f(t))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28057662",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.10"
  }
 },
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