math-modeling-competition-CSU-2022-6/数学建模.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78042719-c377-4929-a2f4-74aeabf8e29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16bfdf9c-816b-424d-8dc9-95f973ea78c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelFile(\"/home/bobmaster/Downloads/数学建模/附件1：化学成分及力学性能.xlsx\") as origin_data:\n",
    "    pd_chemicals_raw = pd.read_excel(origin_data, \"化学成分\", usecols=[0,2,3,4,5,6,7])\n",
    "    pd_physics_raw = pd.read_excel(origin_data, \"力学性能\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40754eb4-28f6-45ba-a4ba-adc5894bdfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_chemical = pd_chemicals_raw.iloc[1:,:]\n",
    "pd_physics = pd_physics_raw.dropna(how = \"any\")\n",
    "# pd_chemical = pd_chemical.reindex(index = pd_chemical.index[::-1])\n",
    "pd_physics_ronglianhao = pd_physics.iloc[:,0].astype(\"int64\")\n",
    "pd_physics_qufu = pd_physics.iloc[:,2]\n",
    "pd_physics_kangla = pd_physics.iloc[:,3]\n",
    "pd_physics_yanshen = pd_physics.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "813c73a1-d75c-4c63-9cd8-0677be75dc98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取相同熔炼号的数据\n",
    "comp_table = pd_physics.iloc[:,0].duplicated(keep = \"last\") #比较表\n",
    "# phy_num = pd_physics.count()  # 力学表数据量 11213\n",
    "phy_num = 11213\n",
    "#phy_ronglianhao = []\n",
    "phy_dict = {}\n",
    "phy_qufu = []\n",
    "phy_kangla = []\n",
    "phy_yanshen = []\n",
    "temp = 0\n",
    "for i in range(phy_num):\n",
    "    phy_qufu.append(pd_physics_qufu[i])\n",
    "    phy_kangla.append(pd_physics_kangla[i])\n",
    "    phy_yanshen.append(pd_physics_yanshen[i])\n",
    "    if (comp_table[i] == False):\n",
    "        #phy_ronglianhao[temp] = pd_physics_ronglianhao[i]\n",
    "        phy_dict[pd_physics_ronglianhao[i]] = [phy_qufu, phy_kangla, phy_yanshen]\n",
    "        temp += 1\n",
    "        phy_qufu = []\n",
    "        phy_kangla = []\n",
    "        phy_yanshen = []\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3732856a-cb58-4d94-b3c6-f5dc8d886c33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据规约 - 力学性能数据均值和标准差\n",
    "phy_dict_qufu_mean = {}\n",
    "phy_dict_qufu_std = {}\n",
    "phy_dict_kangla_mean = {}\n",
    "phy_dict_kangla_std = {}\n",
    "phy_dict_yanshen_mean = {}\n",
    "phy_dict_yanshen_std = {}\n",
    "phy_dict_qufu_mean_list = []\n",
    "phy_dict_qufu_std_list = []\n",
    "phy_dict_kangla_mean_list = []\n",
    "phy_dict_kangla_std_list = []\n",
    "phy_dict_yanshen_mean_list = []\n",
    "phy_dict_yanshen_std_list = []\n",
    "\n",
    "for key in phy_dict:\n",
    "    np_physics_array_qufu = np.array(phy_dict[key][0])\n",
    "    np_physics_array_kangla = np.array(phy_dict[key][1])\n",
    "    np_physics_array_yanshen = np.array(phy_dict[key][2])\n",
    "    phy_dict_qufu_mean[key] = np_physics_array_qufu.mean()\n",
    "    phy_dict_qufu_std[key] = np_physics_array_qufu.std()\n",
    "    phy_dict_kangla_mean[key] = np_physics_array_kangla.mean()\n",
    "    phy_dict_kangla_std[key] = np_physics_array_kangla.std()\n",
    "    phy_dict_yanshen_mean[key] = np_physics_array_yanshen.mean()\n",
    "    phy_dict_yanshen_std[key] = np_physics_array_yanshen.std()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1945b0e-e52c-444f-b5cc-84067d4b8647",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 清洗化学成分\n",
    "# 重建索引保证在同一熔炼号的情况下与力学指标数据匹配\n",
    "pd_chem_ronglianhao = pd_chemical.iloc[:,0].astype(\"int64\")\n",
    "pd_chem_ronglianhao = pd_chem_ronglianhao.drop_duplicates().reset_index().iloc[:,1]\n",
    "pd_chem_E1_data = pd_chemical.iloc[:,1].reset_index().iloc[:,1]\n",
    "pd_chem_E2_data = pd_chemical.iloc[:,2].reset_index().iloc[:,1]\n",
    "pd_chem_E3_data = pd_chemical.iloc[:,3].reset_index().iloc[:,1]\n",
    "pd_chem_E4_data = pd_chemical.iloc[:,4].reset_index().iloc[:,1]\n",
    "pd_chem_E5_data = pd_chemical.iloc[:,5].reset_index().iloc[:,1]\n",
    "pd_chem_E6_data = pd_chemical.iloc[:,6].reset_index().iloc[:,1]\n",
    "pd_chem_E1 = {}\n",
    "pd_chem_E2 = {}\n",
    "pd_chem_E3 = {}\n",
    "pd_chem_E4 = {}\n",
    "pd_chem_E5 = {}\n",
    "pd_chem_E6 = {}\n",
    "temp = 0\n",
    "\n",
    "# 数据规约 - 化学成分\n",
    "# 0-701 清洗后得到的范围\n",
    "for i in range(702):\n",
    "    if (i%2 != 0 and temp != 321):\n",
    "        pd_chem_E1[pd_chem_ronglianhao[temp]] = (pd_chem_E1_data[i-1] + pd_chem_E1_data[i])/2\n",
    "        pd_chem_E2[pd_chem_ronglianhao[temp]] = (pd_chem_E2_data[i-1] + pd_chem_E2_data[i])/2\n",
    "        pd_chem_E3[pd_chem_ronglianhao[temp]] = (pd_chem_E3_data[i-1] + pd_chem_E3_data[i])/2\n",
    "        pd_chem_E4[pd_chem_ronglianhao[temp]] = (pd_chem_E4_data[i-1] + pd_chem_E4_data[i])/2\n",
    "        pd_chem_E5[pd_chem_ronglianhao[temp]] = (pd_chem_E5_data[i-1] + pd_chem_E5_data[i])/2\n",
    "        pd_chem_E6[pd_chem_ronglianhao[temp]] = (pd_chem_E6_data[i-1] + pd_chem_E6_data[i])/2\n",
    "        temp += 1\n",
    "\n",
    "E1_list = []\n",
    "E2_list = []\n",
    "E3_list = []\n",
    "E4_list = []\n",
    "E5_list = []\n",
    "E6_list = []\n",
    "\n",
    "# 整理出最终所需数据并保证化学成分与力学性能数据一致性\n",
    "for key in pd_chem_E1:\n",
    "    if key in phy_dict:\n",
    "        E1_list.append(pd_chem_E1[key])\n",
    "        E2_list.append(pd_chem_E2[key])\n",
    "        E3_list.append(pd_chem_E3[key])\n",
    "        E4_list.append(pd_chem_E4[key])\n",
    "        E5_list.append(pd_chem_E5[key])\n",
    "        E6_list.append(pd_chem_E6[key])\n",
    "        \n",
    "        phy_dict_qufu_mean_list.append(phy_dict_qufu_mean[key])\n",
    "        phy_dict_qufu_std_list.append(phy_dict_qufu_std[key])\n",
    "        phy_dict_kangla_mean_list.append(phy_dict_kangla_mean[key])\n",
    "        phy_dict_kangla_std_list.append(phy_dict_kangla_std[key])\n",
    "        phy_dict_yanshen_mean_list.append(phy_dict_yanshen_mean[key])\n",
    "        phy_dict_yanshen_std_list.append(phy_dict_yanshen_std[key])\n",
    "        \n",
    "np_E1 = np.array(E1_list)\n",
    "np_E2 = np.array(E2_list)\n",
    "np_E3 = np.array(E3_list)\n",
    "np_E4 = np.array(E4_list)\n",
    "np_E5 = np.array(E5_list)\n",
    "np_E6 = np.array(E6_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b37c2ff4-0b0e-4c94-997e-daf199031604",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "632a32cf-b02b-4787-ba57-1787247c5d1a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import colors\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import matplotlib.font_manager as fm\n",
    "myfont = fm.FontProperties(fname='/usr/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/wqy-zenhei.ttc')\n",
    "fig, ax = plt.subplots(figsize=(5, 5), tight_layout=True)\n",
    "\n",
    "#dist1 材料\n",
    "dist1_E1 = np_E1\n",
    "dist1_E2 = np_E2\n",
    "dist1_E3 = np_E3\n",
    "dist1_E4 = np_E4\n",
    "dist1_E5 = np_E5\n",
    "dist1_E6 = np_E6\n",
    "\n",
    "#dist2 力学性能均值\n",
    "dist2_qufu = np.array(phy_dict_qufu_mean_list)\n",
    "dist2_kangla = np.array(phy_dict_kangla_mean_list)\n",
    "dist2_yanshen = np.array(phy_dict_yanshen_mean_list)\n",
    "\n",
    "#dist3 力学性能标准差\n",
    "dist3_qufu = np.array(phy_dict_qufu_std_list)\n",
    "dist3_kangla = np.array(phy_dict_kangla_std_list)\n",
    "dist3_yanshen = np.array(phy_dict_yanshen_std_list)\n",
    "\n",
    "\n",
    "ax.set_title(\"化学成分E1与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E1 %\", fontproperties=myfont)\n",
    "ax.set_ylabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "hist_qufu_E1 = ax.hist2d(dist1_E1, dist2_qufu, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37ab1938-3be1-47b1-b2bf-a111f5afcf8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E2与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E2 %\", fontproperties=myfont)\n",
    "hist_qufu_E2 = ax.hist2d(dist1_E2, dist2_qufu, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "694e2daa-015d-4e28-a4f4-b731147e8a05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E3与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E3 %\", fontproperties=myfont)\n",
    "hist_qufu_E3 = ax.hist2d(dist1_E3, dist2_qufu, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5ed884d4-e171-42bb-ab26-0068b2ec688f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E4与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E4 %\", fontproperties=myfont)\n",
    "hist_qufu_E4 = ax.hist2d(dist1_E4, dist2_qufu, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c35f7ec-05c4-4887-b9fb-eee122166ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E5与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E5 %\", fontproperties=myfont)\n",
    "hist_qufu_E5 = ax.hist2d(dist1_E5, dist2_qufu, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "704561cb-b2a6-4ebb-8c9d-0806159e18e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAkRElEQVR4nO3de5xdVX3+8c+TTBJuCSFCuBmbxBI0KiZyEUQQIgLFS1qtGgsY20qAonKrFdAfVloUURHbWmwKFKhRRIgUW0VBbqWFYAgRCAPlao1JAeWuEAj5/v5Y68jOMLfszJ41c+Z5v17zmjN777XP90xynrNm7b3XVkRgZmaDb1TpAszMRioHsJlZIQ5gM7NCHMBmZoU4gM3MCnEAj2CSts3fJ0ka3WXdaEmjumw3rut23eyzQ1JHP59f9Spffx+S3rmx+6nxvOMkbV75uUPSHl22GS1pWpdlW0raoad9Vh5vImlMD9vtUHksSa/rsn6spM0lTZQ0XdIekuZKOlrSlyTtumGv1priAB6hJI0Hrs9v8r8HpnTZ5FDgBEkTgW/nZX8JzO1j1x8GTpJ0XivAK88pSZfmsJoL/PnGvo5I51Ge1Nd2ks6QdIuk67p83SjpgrzN6MryiyW9RdKyXPPtknap7PIt+bW2jAFO6PK0mwKnVmqYC+wOfKib+rYGLqssOhKY3812Ai6StHde9HFgXpfNDgH+C1gMnE36950K/Ba4GVjddb9WhgN45PoI8PcR8QLwArAWQNLv5fXfBA4EtgRuk7QnsB9weR/7XQv8HLgH2LNLCO8LPBMRa4ErgT+uNpQ0VdITXQJyZg7ssyRdI+m/JB3Z5TlfI+nqbr5uqPQOA/hIROxX/QLeVS0BIC/fmvT++FZE/DEpzFp/EZwDbAWEpG0kXZ9fz/655q9Vfhebt14bKXjXAi9283v7AHBx5ec3A7+R9FZJb2gtzB84HwKOzB+O7wVOq+4oIi6PiFmkf79ngfMi4mvAb4CbImJVN89vBfTrT0VrL5JeD3wMmNll+T7AVyX9J7ALMBY4L69+W/7+Y0mbAf8vIn4i6aPAccCv8vrJ+fsjwDuBhcCiPHRxCrAAICLW5F7m30bEZyplLM8BWK3rGOCBiDgh99g/JqkjBznAzyLigD5edgAXSvpNN+uurDyeJek6YNvK87+D9IH1rdY2wDPAjsD/kj6YRgMXRMRhuae/Nekvhj+SdCiwPfAmUo90C0l/CKyNiAMkfR8Yn5/rJGAvUo/1FfnrEOAQSdsAlwBrch2dwO3A9/Pv918j4qK8n4nA+cCPgKsl/RXwPtb/wLHCHMAjjKQDgc8DT0ZEtSe2N/AJ0pv9TOB9EfFED/v4KDkwss9ExOV53btJ4fUD4GMRsSiPbV5I6k3+vNUoIi6Q9I+S/hb4m17Kfh/wFUk/AMYBf10JX4A3Srq6m3YXR8S5lZ//FHh9RFws6evAV0lDB9Wx1uURsV9lf68EVpGGYZ6SNBZ4CPgh8DrgKeAaUq92Wm7XAXwfuBf4DrAz6cNoNmkIYlZEnJ2HEwBebH3oSLoc+AxwVkRcmpcdkH9fjwL7twqV9O8R0VOgzge2A96fX+dlwCER8esetrcCHMAjz0RSL+ibkMY9Sb3Ww0hv0CclHQ+sk3QN8HxutwPwKmASqSe4rrLPv5H0SVLgnAO8kTS88FBe/w/AfwKH5+GDV+RtJwI/Bh4l9foe4qUeKKQe3Xmk3uObSSE2ldQL3zUingKIiG26vsj8QTO7sqh1zf180p/6rWGXWcBmpJ5kd6aRxsdvB+YA9wM35t9LB7BlROyfn3Ms6cPhlDxk80B+XfcD10TEb1Q57hjdzwMwgRScfyrpx63XmPd/JOnDqPW731VStfc+FnhH/mD9O1Lv/BDgy6Tf9+dzjZ+IiKd7eL02iBzAI0xEXAK/OyB2GHAMqce6b0Q8mTd7IofDnLxt60/wgyJiHelgTsumwF8BPyW90ZeTenAdpLAAWJD393VJHwTGR8S5kt4KHBAR5+TnmUo3QxDZOXkfD0paDswAlnb3GnPPchJQ7e2NIgXXszngfx/YE3gOOL2yXdchiJuAVwNPA8si4m5JTwNHkcL77NzrXUsaQ95F0m7AZ0m9Y0jj5nfn7SYA4yUdDPx3RJwGjK586GwdEXMktYJzQaW2hRHxT/k1vpn0l0xrH0hSJdQ/SBoa6SR96NwOXErq7Z9B+ne3whzAI1eQen7vAr7SZd0aSTdUfp4OPAz8a+7BjSId2FlECrKfVLZ9EdgGOBfYTNL2EXE//K63fQTrnz3QH4tznf+cx1ZfU6nx+S7bihQ6naShg5atSQcU/ykifiTpbODsiHioclBvc+CnEfGOLkMaj5KGDc6StB1wOLAEuCsiFkvalxRuNwPfjIh5+fVuApA/2JYAB0jaL+/r7OrvrMsQBBHxA0nPVV9YK1yVDmyeBPwF8AlJb4yIn3XpUd8MHA18kfTBcyfpA/EJ4MSX/YatCAfwCBYRCwEkreOlI/yjgasi4nfn1kr6a+DSiLiz2l7pVLY35rZXANcC/0Y67elQ4EnSgaeTlc6ZPQe4onIUvrvzgKtDEJB6kqeTwnc+6WDXsRFxB+msim5J+ix5WCH3iH+f9OGwB+nAVNW7gH8C/oj1hyJGkYL794CxeVjjqog4OffeX5+3+xRdDmgOhIi4pvVyWsskbQV8HfheRNwj6UTg25IuJv0bvZgP+s0D5ucPmNb+js5/zfy7pKMj4t6Brtk2jAN45Nqs8vg6YLGkZ4AtSEEKgKTPk4Lu693s4xDgcxFxp6TPkM6G+GRE3CXpv0ljxVdK2p50EO6CiPhW3u9HSD20k1s7i4iHSOPC3Tm0Py8qB1TrXNfW6WAnAP9O+kB4u6S35+UX5HB6Ln/w7EIaz/4CaSjll6Sx4lNJ49NHkIZaHie9d1qnra2R9DBwAy+dMw0pwFsfbJeQhh82IYX5waQPk1OBTatDEF1ey/dIvWfyOPsBwN9ExI35uZ+T9H7Sh8DVeb9rgbkRsU7SJ0g934vz9ldJ+inrDyNZIfJ8wCOTpMkR8UjDzzEW2KynsykGQ+79vhdY3MNBLyR9LCL+YQP3OxoYlc+j7mmbsaSx9e7O0KhuNyqPrXe3bnzrgJmkcRGxprvtbHhyAJuZFeIr4czMCnEAm5kV4gA2MytkWJ8FMVbjYhM273tDswqN6XaWxz7FCz0eb2tM3VrrKvEah4unefxX3V11uTGGdQBvwua8+XdnFJn1T8fk7Wu1W7tq8GdxrFtrXSVe43BxdVz687632jAegjAzK8QBbGZWSGMBLGmKpGsldUpaIenYvPyNkm6SdIek70uaUGlzsqT7JN0j6aCmajMzGwqa7AGvBU6MiNeSZp06RtJM0iQtJ0XEG0iXWX4SIK+bR5pj9WDgH9XH/cfMzIazxgI4IlZHxLL8+GnS7FQ7kianbs20dRUvTVk4lzSB9pqIeBC4jzRxiplZWxqUMeA8z+ts0qQidwLvyavez0s3g9wR+EWl2cq8rOu+FkhaKmnpC/iyeDMbvhoPYElbkG6Hclye3f/PSMMRt5Jua9Oaz7W7qQlfNlFFRCyMiN0iYrcxjOumiZnZ8NDoecBKN1C8DFgUEYsBIuJu0t1akTSDdJsZSD3e6q3RW/fiMjNrS02eBSHSHXU7I+KsyvLJ+fso0q1rvpFXXQHMkzRO0jRgJ+CWpuozMyutyR7w3qRbt9yR7+EF6bbkOyndZhzSrWb+BSAiVuRJq+8inUFxTJe79pqZtZXGAjjP2N/duC68dKeCrm1OZ/0bJJqZtS1fCWdmVsiwnozH2kPHDoM7Oc66bbeq1W5j3iye5Ma64x6wmVkhDmAzs0IcwGZmhTiAzcwKcQCbmRXiADYzK8QBbGZWiAPYzKwQB7CZWSEOYDOzQhzAZmaFOIDNzApxAJuZFeIANjMrZFhPR6kxY+iYvOFTGXpqwJ7VnRoS6v9en339y25+3T9129W06cOP1247avbMWu3W1Xy+dbfdVbNlPXVfH9SvdbCnMW2Ce8BmZoU4gM3MCnEAm5kV4gA2MyvEAWxmVogD2MysEAewmVk
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E6与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E6 %\", fontproperties=myfont)\n",
    "hist_qufu_E6 = ax.hist2d(dist1_E6, dist2_qufu, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ab4f2aba-c632-4f0c-bfc3-4d1734998125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 抗拉\n",
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"抗拉特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E1与抗拉特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E1 %\", fontproperties=myfont)\n",
    "hist_kangla_E1 = ax.hist2d(dist1_E1, dist2_kangla, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e5a9533-cdd9-432b-98e0-7098dbe72dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAiQ0lEQVR4nO3de5xdVX3+8c9DrtzDJVwEJKFcFBCCRECxFBEkBS39aVEsIlgUpbSWy08Ff7QWLba1raitYnmBggoichGkgnIHCwIJBAQDAgKKpAQiV5GEhOf3x15DTodzJvFMzlmTyfN+veY15+yz1z7fPTN5zsrae68t20RERP+tUruAiIiVVQI4IqKSBHBERCUJ4IiIShLAERGVJICjLUkblu/rShoz6LUxklYZtN6Eweu12eZYSWOX8f3VXeX/exuS9l+G9XaWtHrL87d2WG91Sau1PB/fYb0Jg7Y3VtIug9YZI2nqoGVrS3pVp222PJ4oaVyH9V7V8liSthv0+viyH5MkbSFpF0kHSDpS0r9I2rnddqM3EsDxCpLWBK4r/8j/Hdhs0CoHA8dKmgR8uyz7v8ABS9n0+4HjJZ0xEOAt7ylJ55ewOgA4fLj74eYcy+OXYdVDgfVanv+1pN3arPc+4N0tz/9D0rQ2672JZl8HjAOOHbTOqsDfDTwp+/wG4L2DNyZpfeCClkUfLjUPXk/ANyTtPrAfwEGDVtsP+G/gQuALNL/fKcDzwE+AuW32J3okARztHAb8u+0XgReBRQCSNi+vfwt4G7A2cHsJqz2B7y1lu4uAh4F7gd0GhfAewHO2FwGXA3/W2lDSFElPSbq25Wvb0pv7uqRrJN0i6R2D3vM1kq5s83W9pB0lbQWsCWwuabqkDwKzgIMlfVDShyStU7a1YOBnUfwrg4JV0qnAOoAlTZZ0Xdmft5Sav9jys1h9YN9ogncRsLjNz+3dwLktz3cFfivpzZJeN7CwfOC8F/hw+XB8J/Dp1g3Z/p7taTS/v98BZ9j+IvBb4Cbbj7Z5/+gR5UKMaCVpe5re1ra2F0s6EzgRmAqcAtwA7ACMpwkkgEnl+1PAasDf2r6qhNnRwBPl9Q3K93mAgNNsn12GLn4AHGH74VLHYcCWtk8sz6cAZ9rec1C9JwM/t32WpPVownNr2wvL61fa3rvDvq4GzAA+AHyfpmdo4KWW1caU/foSsGlZ9k3grTS9WJU248u2rgN+DGxC80F1WdnGmbbfV3qp69H8j+E4mg+7jYEjaHqhawCPAIts7y3p+zQfEADrA28EfgicU5btZ3s/SZOB81jyO9kRuLPUNgb4pu1vlP2eBHytbOcKmg+7dwFvt/14u59V9MYyjcfFykHS24DPAk/bbu2J7Q58lOa/r58D3mX7qQ7b+CBLAgPgRNvfK6+9A9iQJmz/qoTvBOAs4JyB8AWwfaakr0j6B+AzQ5R9Ok1gYXu+pGeAicDC8vqOkq5s0+5c26cDF0ragyYof00zdHBYWecE2zeVx+8qHwqLaMLvCeAu23dJ+gzwD2W9h8q2tgOeAa6m6dVOLXWMpQn7+4DvANsA+wM70QxBTLP9hZYx8MUDHzqSvkfzYfh52+eXZXuXfX8ceMvAzkm61PbbO/zMDgU2Ag6k+VC9gCbI53dYP3okARytJgFvp+m5UXqmG9CMfe5n+2lJxwAvSbqaJSH3KuDVwLo04dTag/yMpI/RBM6pND2zP6MJKoD/oOlVHyLpwzS9w/tKLT8CHqfp9T0ETJN0bWn3Tdtn2H5w4I1KQN5g+5mBZbYnD97J8kGzU3n8WuA1NGPO/0IzHno0sBst48KSNi5t1gfuohnXfXN5/Ee2/1bSrjS934U0/7bWtv2W0n488Pe2P1mGbH5R9usB4Grbv1XLcUe3/6/pWjTB+QFJP2rdz/KzexdLfvY7S7q8pe14YJ/ywfol4Jc0H6j/SvPz/myp8aO2n23z3tEDCeB4me3z4OUDYu8DjqLpse5h++my2lMlHPYq6+5D02Pc1/ZLNP+NHrAq8HHgVpp/6LNpenBjacICmmEHA1+W9B5gTdunS3ozsLftU8v7TAFmDx6CGKi3bHcjmp56R2XddYH5kjahGX54HDi9hKAYdGxE0kY0B6x+BVxme7akhTQHIscDvyk/v5slPQt8BJgGfKH0ehfRDFXsIGk68Cma3jE04+b3lPXWAtaUNAO40fangTEtHzrr295L0kBwHtFS5mm2/7PUuyvN/2QGtoEktYT6e2jG7OfQ9IbvBM6n+VD5J5rfe/RBAjjaMc1Y7tuBfxv02gJJ17c83wJ4DPhm6cGtQnNg52xgS+CqlnUXA5Nphg1Wk7Sx7Qfg5d72h/jfZw8slaR1gf8ELrH9mbJsK+AMlvTQX16dJnTmAI/a/jXwcUlfaFlnB+C0lu2vD7xg+z2lhz3gHprhgz2Bm8u6GwGHlOc/sz0wvHE+zRkG37J9UFl3IkD5YLsZ2FvSnpQhiJb3GTwEge0fSHqhdccGwlXNgc3jgb8EPippR9t3DOpR/wQ4Evhnmh7zXTQfiE/RjEtHnySAoy3bpwFIeonSIywheYXtl8+tlfT3wPm272ptr+ZUth1L20uAa4CLaU57Ohh4Gng9cIKac2ZPpQnRgaPw7c4Dbh2CgKYn+XGa8dbJkgZOXTvI9h6d9k3Sp2h6fS8vahbrcGC87V+3fJjMoOkh/3DQz+clScfRnEp2TFn2P2V/3gxsX1b9BLBtp1q6ZfvqltoH9msd4MvARbbvLfV9W9K5NL+jxZIOpjk17VDbDw0Me9g+svxv5lJJR9q+b3nXHK+UAI52Vmt5fC3NgarnaI7QXzPwgqTP0pw+9uU229gPOKkcpDqRZlz1Y7Z/JulGmrHiy8vY6lk0ZwmcU7Z7GE0P7YSBjdl+iCVnW7S6bll3qgTUwLmuXyzLTqY5CPb98v3Py+ozaXrCawP7lHHj/YEzJH2a5lxfaML11DJ08SnbP6b5d6VS9wJJjwHXs+ScaWjCfeCD7Tya4YeJwPgyBDGGJtxXbR2CGLQvF7Gk9/0xYG/gM6UGbL8g6UCaD4Ery3YXAQeUD5CP0vR8zy3rXyHpVv73MFL0UE5Di1eQtIHteT1+j/HAap3OpugXSVsAz7Y7/WpgPLj0HP8QWNf2xcuwzTGl3YtDrDOeZmy93RkareutUsbW27225sABM0kTbC9ot16MXAngiIhKciVcREQlCeCIiEoSwBERlazQZ0GM1wRPZPWlrziCaOKEpa/UA36hzvGZ4exvrZojlrdnefKJdldlrtABPJHV2bX91K0j1tgpW1Z530X33l/lfYezv7VqjljervT5D7dbniGIiIhKEsAREZUkgCMiKulZAKu5b9Utku6QdLekk8ryA8vzl8rMUK1tTpB0v6R7Je3bq9oiIkaCXh6EWwDsZfs5NfcW+7Gky2hmXnonzQxWL5O0Lc0kIdvRzC97paStB00MHhExavSsB+zGc+XpuPJl23Ns39umyQE0dylYUCbZvh/Ypc16ERGjQk/HgNXcens2zT3ArrB98xCrb0Iz4fWAR8qywds8QtJMSTNfJOeJRsSKq6cBbHuxmzuwbgrsouaGj520m//1FTMF2T7N9nTb08dR56KGiIjloS9nQZQpB6+lmdy6k0eAzVqebwrkFtkRMWr18iyIyWpuf42kVWkmi75niCaXAAdJmiBpKrAVcEuv6ouIqK2XZ0FsDJw1MDk1cJ7tSyX9H5rb0kwG/kvSbNv72r673BngZzSz9h+VMyAiYjTrWQDbvpNy6+9Byy+iuZVKuzYnAyf3qqaIiJFkhZ6MRxMndD3ZS62JXla2CWaGs79jt+l+Ip/np67TVbvVHnyy6/ccjpXt7yIauRQ5IqKSBHBERCUJ4IiIShLAERGVJIAjIipJAEdEVJIAjoioJAEcEVFJAjgiopIEcEREJQngiIhKEsAREZUkgCMiKkkAR0RUskJPR+kXFqxw0/gNZ4rFWlM7PrbnBl23Xe8/b+y6bQ3dTmM5XKux4v1drGj/9kai9IAjIipJAEdEVJIAjoioJAEcEVFJAjgiopIEcEREJQngiIhKEsAREZU
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"抗拉特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E2与抗拉特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E2 %\", fontproperties=myfont)\n",
    "hist_kangla_E2 = ax.hist2d(dist1_E2, dist2_kangla, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e31f079-9af5-4e3e-ae3f-858821e44246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"抗拉特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E3与抗拉特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E3 %\", fontproperties=myfont)\n",
    "hist_kangla_E3 = ax.hist2d(dist1_E3, dist2_kangla, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b778d6b-b7bb-46fa-bf8b-bf7a1e23abfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAh5UlEQVR4nO3deZwcVb3+8c+TnR0igcSwJJGwL0Eiu8gq3PBTFC8aBARlF6+yuICiCIrXXblXxZsfKIgsIjt4UfZNNhMMa0BWEYiELYSICZnwvX+cM9AO3ZOhOzVn0vO8X695dXVVnepT3TNPnzlVdUoRgZmZ9b4BpStgZtZfOYDNzApxAJuZFeIANjMrxAFsZlaIA9jqkrRqfhwuaWCXZQMlDeiy3tCu69XZ5iBJg3r4+mqu5v+6DUm792C9zSQtU/N8pwbrLSNp6ZrnQxqsN7TL9gZJ2rzLOgMlje0ybwVJ72y0zZrpYZIGN1jvnTXTkrRBl+VD8n6sKGmcpM0l7SHpcEnfk7RZve1aNRzA9haSlgNuzH/k/w2s3mWVfYCjJa0InJvnfR7YYxGb/gRwrKTTOwO85jUl6YIcVnsAB7a6H5HOsTy2B6vuD7yj5vl/SNqyznr7Ah+tef4TSRPqrLc1aV87DQaO7rLOUsDXOp/kfX4PsHfXjUlaGbiwZtahuc5d1xPwK0nbdO4HMLnLapOAPwIXAT8mfb5jgFeB24GZdfbHKuIAtnoOAP47IhYAC4AOAElr5uW/Bt4PrAD8OYfV9sAli9huB/BX4CFgyy4hvB0wNyI6gN8D/15bUNIYSbMl3VDzs37N8k0kzZO0fZfXXFfSNXV+bsplxgPLAWtKmijpIGAasI+kgyQdLGmlvK35ne9F9n26BKukU4GVgJA0QtKNeX92yHU+pea9WKZz30jB2wEsrPO+fRQ4r+b5FsA/JG0raaPOmfkLZ2/g0PzluCdwUu2GIuKSiJhA+vz+CZweEacA/wBui4hn6ry+VaRH/w5a/yFpQ+AzwPpd5r8X+JGkm4GNgSHA6Xnx+/LjVflf9K9GxLU5zI4Ens/LV8mPs4DdgSnA2bnr4svAIQARMV/SeZK+GRHH11RjekRs36DqXwGuqDP/7ojYucG+Lg3sBqwMrEdqGd4KvF6z2kBgaUmnAavlcisCO5FasZL0+/x+7AZMAOYCo4EnSV9MA4EzImLf3NJfmfQfw4cl7QOMAt5NapEuK+lDQEdE7CzpctIXBJKOBbYitVjfkX8mAZMkjQDOJ31JAMwA7gEuz+/vWRHxq5r6/wL4A3CNpC8CHwH+X4P31iriALY3SHo/8C3g5YiobYltA3yW9Mf+XeAjETG7wTYOIgdGdnxEXJKXfQBYFfhf4DMRcXbu2zwTOCci/tpZKCLOkPQzSd8EvrGIeu8LXAVsW2fxJpKuqTP/vIg4DbhI0nbAlcDTpK6DA/I6x0XEbXn6I5IOILVSzyF9qdwXEfdJ+gbwzbzeE3lbGwBzgOtIrdqxuR6DgMuBh4HfAOuQvow2JXVBTIiIH+fuBICFnV86ki4Bjgd+GBEX5Hk75/frOWCHmvfkiohoFKj7AyOBvYAfkbo3JkXECw3Wt4o4gK3WiqRW0K8hHSgitVr3Jf2BvizpKOB1SdcBr+Vy7wTWAIaTwqm2BfkNSV8gBc6pwCak7oUn8vKfADcD+0k6lNSqezjX5SrgOVKr7wlggqQbcrmzIuL03F+9H+nL4S0BHBEjus7LXzSb5un1gHVJfc7fI7UujwS2pKZfWNKoXGZl4D5Sv+62efp9EfFVSVsAt+T3ZRCwQkTskMsPAb4eEV/OXTaP5f16FLguIv6hmuOOUX+MgOVJwflJSVdFxJya+h1KasV2vveb5ZZ5pyHALvmL9b9IrfNJpG6Uh4Fv5Tp+NiJeqfPaVgEHsL0hIs6HNw6I7QscQWqxbhcRL+fVZudw2DGvuwupxbhrRLxOOpjTaSngi8CfSH/o00ktuEGksAA4JG/vp5I+BiwXEadJ2hbYOSJOza8zhvpdEF8DfhARC9WDEydyy3I48IKk0cAnSSF/Wg5B0eXYiKSRpO6BvwFXRsR0Sa+RDkQOAV7M798dkl4BDiN1Rfw4t3o7AAEbS5oInEBqHUPqN38wr7c8sJyk3YBbI+IkYGDNl87KEbGjpM7gPKSmmlMi4n9yfbcg/SfTuQ0kqSbUP0bqGplBag3fA1xA+lL5Nulzt17gALZ6Alia1Br+QZdl8yXdVPN8HPAscFYOwAGkAztnA2sB19asuxAYAZxG6lcdFRGPwhut7YP517MHemJbYISkj+fpkZIWAifzZgu9k0ihMwN4JiKeBr4o6cc162xM6psm12tlYF5EfCx3QXR6kNR9sD1wR153JKk1fgfwQER0dm9cQDrD4NcRMTmvOwwgf7HdAeysdABxQkTU1qdrFwQR8b+S5tXuWGe4Kh3YPBb4NPBZSZtExN1dWtS3A4cD3yG1mO8jfSHOBo7Beo0D2OqKiCkAkl4ntwhzSF4dEW+cWyvp68AFEXFfbfncNbBJLnsZcD1wKem0p32Al0kHno5TOmf2VOCymqPw9ZqztV0QACdExFY1r3kG6WDXzaSzKuqSdAKp1ffGrDRbBwJDIuLpmi+T3Ugt5D90eX9el3QMqQV+VJ7397w/2wIb5lW/RJcDmotDRFxXU/fO/VoJ+ClwcUQ8lOt3rqTzSJ/RwnzQbzKwf0Q80flfQ0Qcnv+buULS4RHx8OKus72VA9jqWbpm+gbSgaq5wLKkIAVA0rdIQffTOtuYBJyYD1IdT+pX/UJEPCDpVlJf8e9z3+qZpOA8J2/3AFIL7bjOjUXEE6R+4YYi4oDulueA6jzX9ZQ872TSQbDL8+PH8+pTSS3hFYBdcr/x7sDpkk4inesLKVxPzV0XJ0TELaS/K+U6zZf0LHATb54zDSncO7/Yzid1PwwDhuQuiIGkcF+qtguiy75czJut7y8AOwPfyHUgIuZJ2ov0JXBN3m4HsEf+AvksqeV7Xl7/akl/4l+7kaxC8njA1pWkVSJiVsWvMQRYutHZFL1F0jjglXwWQddlAgbkluN7geERcWkPtjkwl1vQzTpDSH3r9c7QqF1vQO5br7dsuc4DZpKGRsT8eutZ3+UANjMrxFfCmZkV4gA2MyvEAWxmVsgSfRbEEA2NYSyz6BWtJRpc5tckFnQseqU+pJX3aUnbV3t7XuGl5+tdlblEB/AwlmGL+kO32mI0aMTIIq/bMfPvRV63Wa28T0vavtrbc01c8Nd6890FYWZWiAPYzKwQB7CZWSGVBbDSfavulHS3pPslnZjn75Wfv55Hhqotc5ykRyQ9JGnXqupmZtYXVHkQbj6wY0TMVbq32C2SriSNvLQn8D+1KyvdXmYyaSDrd5KuXV+7y8DgZmZto7IWcCRz89PB+SciYkZEPFSnyB6kuxTMj4jHgUeAzeusZ2bWFirtA1a69fZ00j3Aro6IO7pZfTRpwOtOT+V5Xbd5iKSpkqYuwGOPmNmSq9IAjoiFke7AuhqwudINHxupN/7rW0YKiogpETExIiYOZuhiqqmZWe/rlbMg8pCDN5AGt27kKWD1muerAb5Ftpm1rSrPghihdPtrJC1FGiz6wW6KXAZMljRU0lhgPHBnVfUzMyutyrMgRgFndg5ODZwfEVdI+jDptjQjgN9Jmh4Ru0bE/fnOAA+QRu0/wmdAmFk7qyyAI+Ie8q2/u8y/mHQrlXplTibdTNHMrO0t0YPxWM8NGlVmQJ1WtFLnEoPbtPKapT4fDwJUli9FNjMrxAFsZlaIA9jMrBAHsJlZIQ5gM7NCHMBmZoU4gM3MCnEAm5kV4gA2MyvEAWxmVogD2MysEAewmVkhDmAzs0IcwGZmhXg4yiXIkjhk4cKdNmu67NAHnm66bLNaqe+84YObLrvCLU80XdZDSi653AI2MyvEAWxmVogD2MysEAewmVkhDmAzs0IcwGZmhTiAzcwKcQCbmRXiADYzK8QBbGZWiAPYzKwQB7CZWSEOYDOzQhzAZmaFeDjKXtbKcIcDn3t1Mdak5xauP7rpsrPXGtp02Tk7j2u67BrXNFf
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"抗拉特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E4与抗拉特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E4 %\", fontproperties=myfont)\n",
    "hist_kangla_E4 = ax.hist2d(dist1_E4, dist2_kangla, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d67038ca-761f-4e94-88d5-b52eb842fd74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"抗拉特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E5与抗拉特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E5 %\", fontproperties=myfont)\n",
    "hist_kangla_E5 = ax.hist2d(dist1_E5, dist2_kangla, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07811dd7-3a12-422d-9850-dec998367a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"抗拉特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E6与抗拉特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E6 %\", fontproperties=myfont)\n",
    "hist_kangla_E6 = ax.hist2d(dist1_E6, dist2_kangla, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2102c3dc-639e-4bb5-aed2-8c6509ef1410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#延伸率\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"延伸率特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E1与延伸率特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E1 %\", fontproperties=myfont)\n",
    "hist_yanshen_E1 = ax.hist2d(dist1_E1, dist2_yanshen, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5971c4af-6435-4561-bc51-832351d1d4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"延伸率特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E2与延伸率特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E2 %\", fontproperties=myfont)\n",
    "hist_yanshen_E2 = ax.hist2d(dist1_E2, dist2_yanshen, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc1433b9-c6ac-4459-b4d7-0ba806912672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"延伸率特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E3与延伸率特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E3 %\", fontproperties=myfont)\n",
    "hist_yanshen_E3 = ax.hist2d(dist1_E3, dist2_yanshen, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62e1d35f-d0c3-4ca1-9e39-249430aff9b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"延伸率特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E4与延伸率特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E4 %\", fontproperties=myfont)\n",
    "hist_yanshen_E4 = ax.hist2d(dist1_E4, dist2_yanshen, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "68a6968a-9437-40fc-af32-b58ace754fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"延伸率特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E5与延伸率特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E5 %\", fontproperties=myfont)\n",
    "hist_yanshen_E5 = ax.hist2d(dist1_E5, dist2_yanshen, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6726dcf3-7c28-44ae-92d5-1b93d93c1a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"延伸率特性均值\", fontproperties=myfont)\n",
    "ax.set_title(\"化学成分E6与延伸率特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"E6 %\", fontproperties=myfont)\n",
    "hist_yanshen_E6 = ax.hist2d(dist1_E6, dist2_yanshen, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "834620dd-0042-4011-b951-5bfa2e611c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0fc4ea1a80>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dataset = pd.DataFrame(data={'E1': dist1_E1, 'E2': dist1_E2, 'E3': dist1_E3, 'E4': dist1_E4, 'E5': dist1_E5, 'E6': dist1_E6, 'qufu': dist2_qufu})\n",
    "sns.set(rc = {'figure.figsize':(10,10)})\n",
    "sns.lmplot(x=\"E1\", y=\"qufu\", data=dataset, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb405f9f-6358-4668-933e-434f1c767dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0fc4bab8e0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"E2\", y=\"qufu\", data=dataset, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f3ecc8e0-2494-4c33-b652-be0f9c052def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0fc762ca30>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABELElEQVR4nO3de3hU1b038O++zCVXJglJCCQkgCBBKXkblR4VPQaP9hxT0Foq8mLP+/bFPq3iwQtFqs8jFEQaywG0xoNtz2nf+lJ6pD5ioR6x1bYKxwpqC2K4lUuIJuQ2uUySyVz23u8fM7MzM9mzMzPZsyYz8/s8j4bMTGbWXjPz22uv9VtrcYqiKCCEEJJwfLILQAghmYICLiGEMEIBlxBCGKGASwghjFDAJYQQRijgEkIIIyKLF+np6cG6detw6dIlmM1mVFZWYtOmTSgsLMQf//hHPPfcc/B6vZg0aRK2bt2KiooKAMCFCxewfv169Pb2wmazoaGhAVVVVTG9dnf3AGTZmMy3goJs9PQMGfJc6YLqRBvVy2iZUifFxXkR72PSwuU4DqtWrcLBgwexf/9+VFRUYNu2bejr68Pjjz+O7du3Y//+/Vi2bBk2btyo/t2GDRuwYsUKHDx4ECtWrMBTTz3ForgRiaKQ1NefiKhOtFG9jEZ1wijg2mw2LFy4UP29pqYGra2taG5uxuTJkzFjxgwAwM0334xDhw7Bbreju7sbTU1NqK+vBwDU19ejqakJdrudRZEJIcRwzPtwZVnGnj17UFdXhxkzZqCrqwvHjx8HAOzfvx8A0NbWhra2NpSWlkIQfGdFQRBQUlKCtrY21kUmhBBDMOnDDbZ582ZkZ2dj5cqV4HkeO3bswNatW+FyuXDTTTchPz8foijC4/EY8npFRbmGPE+AXv9MpqI60Ub1Mlqm1wnTgNvQ0IDm5mbs2rULPO9rXF9//fW4/vrrAQBdXV3493//d1RUVMDpdKK9vR2SJEEQBEiShI6ODpSVlcX0mkYOmhUX56Gz02HIc6ULqhNtVC+jZUqdJH3QDAB27NiBEydOoLGxEWazWb29s7MTgK+rYfv27Vi+fDmys7NRVFSE6upqHDhwAABw4MABVFdXo7CwkFWRCSHEUByL1cLOnj2L+vp6VFVVwWq1AgDKy8vR2NiIJ598Eh9//DE8Hg9uuOEGPPHEE7BYLACAc+fOYf369ejv70d+fj4aGhowc+bMmF6bWriJRXWijepltEypE70WLpOAm0wUcBOL6kQb1ctomVInE6JLgRBCMh0FXEIIYYR5WhghhETD6fLi6KkOtNuHUFqYjWvnliDLktohK7VLTwhJS2daerFz7zEoigKXR4bFxONXb5/Fw8sWYE6FLdnFixt1KWSITy/aseOVY2i6SFOjycTmdHmxc+8xDLsluDwyAMDlkTHslvy3e5NcwvhRwM0Qvzl0AZ+c78brhy4kuyiE6Dp6qgORkqcURcGRkx2MS2QcCrgZYtgthfxMNGpRk3i124fUlm04l0dGRwov8Uh9uCQhfnPoAs5+1odhtxfzqmh2IIleaWE2LCZeM+haTDxKCrKTUCpjUAuXJATrFjVJH9fOLQHHcZr3cRyH66pLGJfIOBRwCSETSpZFxMPLFsBqFmAx+UKUxcTDahb8t6fuhXnqlpwQkrbmVNiwffUNOHKyAx09QygpyMZ11SUpHWwBCriEkAnKahZx04KpyS6GoSjgkhDpOLuHkImCvklEla6zewiZKGjQjABI79k9xIdyo5OPAi4BkN6ze4gPzTZMPgq4BEB6z+6ZqFi3OCk3OvmoD5cASO/ZPRMVzcbLPNTCJQDSe3bPRDWRWpzUv8sGBVwCIL1n95CxUf8uG/QtIqp0nd1DxqbX2v70oh1vHWnB7ddVUNfHONE3iYRIx9k9eiiYjI36mo1DATfFUIAwFgWTsU2kvuZUR324KYb62ozjdHnR1TcMAOjqG4bTlXqTO2iwK7VQwE0x1NowxpmWXjzWeBi9DhcAoNfhwmONh3GmpVfz8RM1sNEJOLVQwCUZJ3gac2BunQLoTmM2KrAZHbjpBJxaqA+XZJxopjGHDxwaFdiC+4xJ5qEWLsk4yZzGTC3SzEYBl2ScwDRmLTSNmSQSBdw0NlEHepKNpjGTsThdXrx7rBV7//A3vHus1bAMFurDTWPUXzhaII/5zkUzsO+9C3D5B844AJY4pzFTbnR6SeRC/NTCTWNG9BemWys5kG3w0elObF99A2x5FgCALc+C7atviOsLRalZ6SPRC/FTwCW60i2YBJ+ErGYRkydZAQCTJ1njXjOCBsLSR6IX4qeAmwFkRcGA0wMAGHB6YuqPMiqYTNSW8tIbZ2D+zCIsvXFGsotCJoBEZ7BQH24GaO0aRCDDPzCjivXGkBN1zYJ5VYUTqjwkuRK9ED+TgNvT04N169bh0qVLMJvNqKysxKZNm1BYWIg//OEPeO6556AoCmRZxkMPPYTbbrsNAFBXVwez2QyLxdfPtnbtWixatIhFkdOC7L80Cr5CCp5RtX31DcyWXqTLbpIKrp1bgl+9fVbzPiMyWJh82ziOw6pVq7Bw4UIAQENDA7Zt24YtW7Zg3bp12L17N+bMmYNTp07h3nvvxa233gqe9/V2PP/885gzZw6LYqYkvRHyoeHIXQeRZlSR5HO6vDh6qgPt9iGUFmbj2rklyLLQxSgLgYX4w7MUOI4zZCF+Ju+izWZTgy0A1NTUYM+ePQAAnufhcDgAAA6HAyUlJWqwzVSxpBnpXap7Je2+KCD9NoYMrjMFSNk0rVhSkpwu76i+eQrM45fIhfiZvzuyLGPPnj2oq6sDx3HYuXMnHnjgAWRnZ2NwcBAvvfRSyOPXrl0LRVFQW1uLRx99FPn5+TG9XlFRrpHFR3FxnqHPp+W//vOvaLpgh6QouPnaypD7RJFXfxYX58Er+/oLvLIyqmwWkwAHPJqvYTELmDW9cMzjCX89LVq3h/9dNM8zHsF1BiDq+ouW3t9Fum+segm+rbg4D0PDHjz36+Mh3S6BvsTnfn0c/3fD7WpA/fR8N77/0z+raUq9Ay6sffG/sWHVl3DVzCLDjmGs+2LF4vtjlIppBYY/J/OAu3nzZmRnZ2PlypXwer146aWX8OKLL6K2thYfffQRHnnkEfz2t79FTk4Odu/ejbKyMrjdbmzZsgWbNm3Ctm3bYnq97u4ByLJ2mkesiovz0NnpMOS59DgG3erPzk5HSOvN6/V9Ab1eGZ2djlG/B7OYhYivwQGoLs8f83j0nh+IXCexlNMIwXUWfFvwazld3pDlGC991hN1i1Cv/Fr3RVMvwbd1djrw7rFWyLL2VYksy3jjvXO4acFUOF1ebPzJ+yGBWVGg3h6pbz7WY4jmvliw+v4km95Jhem1e0NDA5qbm7Fz507wPI+TJ0+io6MDtbW1AIDa2lpkZWXh3LlzAICysjIAgNlsxooVK/Dxxx+zLO6EEW0ubHjqFe+fvspxvgAL+H5azQLuvHEG/m3fpxMuTStRYl3/NhmiTUlKdK5ovCZq6t9Ewizg7tixAydOnEBjYyPMZjMAYMqUKbh8+TLOnz8PADh37hy6urowffp0DA0NqX27iqLgjTfeQHV1NaviTijRjvBHCszTJueMmlH10ZnOtJrQoCee9W8TVY7gPlc5LGhGu6hOMlc705Nuk2QSgUmXwtmzZ7Fr1y5UVVVh+fLlAIDy8nI0NjZi48aNWLNmjbqYyNatW2Gz2dDS0oKHHnoIkiRBlmXMmjULGzZsYFHcCS0QHLSCRKTAzHEccrNM6HG4kJtlgtUspmSaVrxrFsSz/m0wIwanAoNhLn999zpc6B1whTwm2pSkROeKBovl2FPxM8Uak4A7e/ZsnD59WvO+JUuWYMmSJaNur6iowL59+xJcstTT5++j7Avqq8wU8U6eGE+LUCtQxjJx5NOLdvzXn5tx9rM+eIL6bRX1f1BPBtGmJOVlmUKeK5iRq52N99jJaJRDkkJkRYEk+b6ckqSk5KaH4xFvC0qSFfAcoDV2qtciDO6KCAifOKIoiNgCdLq8ePngaXT0OHXLNxiULx1NStKbRy5
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"E3\", y=\"qufu\", data=dataset, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c3db89a0-d872-41cd-bb0d-1ea0ae79da65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0fc4a6ab30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"E4\", y=\"qufu\", data=dataset, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "629187a8-8215-46af-84bb-43d1c535b90f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0fc4881240>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"E5\", y=\"qufu\", data=dataset, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "472573f4-64bb-4e3e-8a64-7c9dae388904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0fc477eaa0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"E6\", y=\"qufu\", data=dataset, x_estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b1e9eb2f-bf58-4f0b-9914-3550634eafe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f0fc482a170>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"E1\", y=\"qufu\", data=dataset, kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7de49bc5-f1f7-4520-8e7e-2f8f05c868e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f32992ce-bdae-44c9-b672-03869cb81256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.049\n",
      "Model:                            OLS   Adj. R-squared:                  0.027\n",
      "Method:                 Least Squares   F-statistic:                     2.247\n",
      "Date:                Sun, 03 Jul 2022   Prob (F-statistic):             0.0393\n",
      "Time:                        14:20:56   Log-Likelihood:                -742.34\n",
      "No. Observations:                 270   AIC:                             1499.\n",
      "Df Residuals:                     263   BIC:                             1524.\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        203.3528     34.505      5.893      0.000     135.412     271.293\n",
      "x1           -39.8312     36.665     -1.086      0.278    -112.025      32.363\n",
      "x2            44.6137     25.255      1.767      0.078      -5.114      94.342\n",
      "x3             1.2704     36.149      0.035      0.972     -69.907      72.448\n",
      "x4           244.7439     83.223      2.941      0.004      80.876     408.612\n",
      "x5            57.4122     74.462      0.771      0.441     -89.206     204.030\n",
      "x6            27.2312    134.699      0.202      0.840    -237.995     292.457\n",
      "==============================================================================\n",
      "Omnibus:                       94.154   Durbin-Watson:                   1.109\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              435.794\n",
      "Skew:                          -1.358   Prob(JB):                     2.34e-95\n",
      "Kurtosis:                       8.600   Cond. No.                         916.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "x = sm.add_constant(np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose())\n",
    "# 材料与屈服特性均值回归方程\n",
    "y = np.array(dist2_qufu)\n",
    "model = sm.OLS(y, x)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641d2eb4-a8c6-4aa7-beb6-bdbb55950d53",
   "metadata": {},
   "source": [
    "$ y=203.3528-39.8312x_1+44.6137x_2+1.2704x_3+244.7439x_4+57.4122x_5+27.2312x_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "29a5fef7-b276-4729-a0bf-a34cd8db498d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.052\n",
      "Model:                            OLS   Adj. R-squared:                  0.031\n",
      "Method:                 Least Squares   F-statistic:                     2.425\n",
      "Date:                Sat, 02 Jul 2022   Prob (F-statistic):             0.0268\n",
      "Time:                        22:12:18   Log-Likelihood:                -699.25\n",
      "No. Observations:                 270   AIC:                             1412.\n",
      "Df Residuals:                     263   BIC:                             1438.\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        234.8010     29.415      7.982      0.000     176.883     292.719\n",
      "x1           -25.4511     31.256     -0.814      0.416     -86.996      36.093\n",
      "x2            33.0086     21.530      1.533      0.126      -9.384      75.401\n",
      "x3            -3.9719     30.816     -0.129      0.898     -64.650      56.706\n",
      "x4           216.7327     70.946      3.055      0.002      77.038     356.428\n",
      "x5            63.1518     63.478      0.995      0.321     -61.838     188.142\n",
      "x6            23.3812    114.829      0.204      0.839    -202.720     249.482\n",
      "==============================================================================\n",
      "Omnibus:                       91.168   Durbin-Watson:                   1.176\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              461.240\n",
      "Skew:                          -1.275   Prob(JB):                    6.97e-101\n",
      "Kurtosis:                       8.873   Cond. No.                         916.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 材料与抗拉特性均值回归方程\n",
    "y = np.array(dist2_kangla)\n",
    "model = sm.OLS(y, x)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2602996-231a-4ea9-9e31-a07dff3e54be",
   "metadata": {},
   "source": [
    "$ y=234.8010-25.4511x_1+33.0086x_2-3.9719x_3+216.7327x_4+63.1518x_5+23.3812x_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "578fe83b-c237-42bf-a814-314050684307",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.039\n",
      "Model:                            OLS   Adj. R-squared:                  0.017\n",
      "Method:                 Least Squares   F-statistic:                     1.793\n",
      "Date:                Sat, 02 Jul 2022   Prob (F-statistic):              0.101\n",
      "Time:                        22:12:18   Log-Likelihood:                -124.81\n",
      "No. Observations:                 270   AIC:                             263.6\n",
      "Df Residuals:                     263   BIC:                             288.8\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          9.9892      3.504      2.851      0.005       3.090      16.889\n",
      "x1            -7.6164      3.724     -2.045      0.042     -14.948      -0.285\n",
      "x2             3.8024      2.565      1.483      0.139      -1.248       8.853\n",
      "x3             5.2722      3.671      1.436      0.152      -1.956      12.501\n",
      "x4            -3.0590      8.452     -0.362      0.718     -19.701      13.583\n",
      "x5             1.5053      7.562      0.199      0.842     -13.385      16.395\n",
      "x6            15.4639     13.679      1.130      0.259     -11.471      42.399\n",
      "==============================================================================\n",
      "Omnibus:                        4.194   Durbin-Watson:                   0.778\n",
      "Prob(Omnibus):                  0.123   Jarque-Bera (JB):                4.281\n",
      "Skew:                          -0.291   Prob(JB):                        0.118\n",
      "Kurtosis:                       2.798   Cond. No.                         916.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 材料与延伸率特性均值回归方程\n",
    "y = np.array(dist2_yanshen)\n",
    "model = sm.OLS(y, x)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5dcb07-a23c-468f-9dde-c418288fba7a",
   "metadata": {},
   "source": [
    "$ y=9.9892-7.6164_1+3.8024_2+5.2722_3-3.0590_4+1.5053_5+15.4639x_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d61eea60-436a-4981-960a-a9bc617be061",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.022\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                     1.005\n",
      "Date:                Sat, 02 Jul 2022   Prob (F-statistic):              0.423\n",
      "Time:                        22:12:18   Log-Likelihood:                -398.92\n",
      "No. Observations:                 270   AIC:                             811.8\n",
      "Df Residuals:                     263   BIC:                             837.0\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          2.8216      9.671      0.292      0.771     -16.222      21.865\n",
      "x1            -3.6748     10.277     -0.358      0.721     -23.910      16.561\n",
      "x2             9.2533      7.079      1.307      0.192      -4.685      23.192\n",
      "x3            -6.5848     10.132     -0.650      0.516     -26.535      13.366\n",
      "x4           -21.5972     23.327     -0.926      0.355     -67.528      24.334\n",
      "x5           -11.2795     20.871     -0.540      0.589     -52.375      29.816\n",
      "x6             2.4244     37.755      0.064      0.949     -71.916      76.765\n",
      "==============================================================================\n",
      "Omnibus:                      238.636   Durbin-Watson:                   1.608\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6096.254\n",
      "Skew:                           3.424   Prob(JB):                         0.00\n",
      "Kurtosis:                      25.249   Cond. No.                         916.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 材料与屈服特性标准差回归方程\n",
    "y = np.array(dist3_qufu)\n",
    "model = sm.OLS(y, x)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e666f427-6527-4815-9781-1334569175b4",
   "metadata": {},
   "source": [
    "$ y=2.8216-3.6748x_1+9.2533x_2-6.5848x_3-21.5972x_4-11.2795x_5+2.4244x_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "13245969-b438-4985-9550-d5ba778472eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.019\n",
      "Model:                            OLS   Adj. R-squared:                 -0.003\n",
      "Method:                 Least Squares   F-statistic:                    0.8562\n",
      "Date:                Sat, 02 Jul 2022   Prob (F-statistic):              0.528\n",
      "Time:                        22:12:18   Log-Likelihood:                -343.91\n",
      "No. Observations:                 270   AIC:                             701.8\n",
      "Df Residuals:                     263   BIC:                             727.0\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.7273      7.889     -0.092      0.927     -16.260      14.806\n",
      "x1            -0.3171      8.382     -0.038      0.970     -16.822      16.188\n",
      "x2             8.1385      5.774      1.410      0.160      -3.230      19.508\n",
      "x3            -4.1692      8.264     -0.504      0.614     -20.442      12.104\n",
      "x4           -18.5395     19.027     -0.974      0.331     -56.004      18.925\n",
      "x5            -5.8194     17.024     -0.342      0.733     -39.340      27.701\n",
      "x6            16.7100     30.795      0.543      0.588     -43.927      77.347\n",
      "==============================================================================\n",
      "Omnibus:                      201.569   Durbin-Watson:                   1.642\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3303.665\n",
      "Skew:                           2.815   Prob(JB):                         0.00\n",
      "Kurtosis:                      19.185   Cond. No.                         916.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 材料与抗拉特性标准差回归方程\n",
    "y = np.array(dist3_kangla)\n",
    "model = sm.OLS(y, x)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd26db5-ef12-4d4f-8e82-460ea18b0f0b",
   "metadata": {},
   "source": [
    "$ y=-0.7273-0.3171x_1+8.1385x_2-4.1692x_3-18.5395x_4-5.8194x_5+16.7100x_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a3e50d1c-26f1-40e5-858a-145d91899fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.018\n",
      "Model:                            OLS   Adj. R-squared:                 -0.005\n",
      "Method:                 Least Squares   F-statistic:                    0.7814\n",
      "Date:                Sat, 02 Jul 2022   Prob (F-statistic):              0.585\n",
      "Time:                        22:12:18   Log-Likelihood:                 151.91\n",
      "No. Observations:                 270   AIC:                            -289.8\n",
      "Df Residuals:                     263   BIC:                            -264.6\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.5023      1.257      1.195      0.233      -0.974       3.978\n",
      "x1             0.1300      1.336      0.097      0.923      -2.501       2.761\n",
      "x2            -0.3995      0.920     -0.434      0.665      -2.212       1.413\n",
      "x3             0.3247      1.317      0.246      0.806      -2.269       2.919\n",
      "x4            -0.3529      3.033     -0.116      0.907      -6.325       5.619\n",
      "x5            -5.6869      2.714     -2.096      0.037     -11.030      -0.344\n",
      "x6             1.8671      4.909      0.380      0.704      -7.798      11.532\n",
      "==============================================================================\n",
      "Omnibus:                      223.455   Durbin-Watson:                   1.792\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6610.393\n",
      "Skew:                           3.018   Prob(JB):                         0.00\n",
      "Kurtosis:                      26.477   Cond. No.                         916.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# 材料与延伸特性标准差回归方程\n",
    "y = np.array(dist3_yanshen)\n",
    "model = sm.OLS(y, x)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790ec5d6-19c0-4e36-ae0a-b5791e9dc198",
   "metadata": {},
   "source": [
    "$ y=1.5023+0.1300x_1-0.3995x_2+0.3247x_3-0.3529x_4-5.6869x_5+1.8671x_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "88dc8bf3-4b14-4adb-b91c-2b422957b0a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_qufu_mean(x1, x2, x3, x4, x5, x6):\n",
    "    count = 203.3528 - 39.8312*x1 + 44.6137*x2 + 1.2704*x3 + 244.7439*x4 + 57.4122*x5 + 27.2312*x6\n",
    "    return count\n",
    "\n",
    "def cal_kangla_mean(x1, x2, x3, x4, x5, x6):\n",
    "    count = 234.8010 - 25.4511*x1 + 33.0086*x2 - 3.9719*x3 + 216.7327*x4 + 63.1518*x5 + 23.3812*x6\n",
    "    return count\n",
    "\n",
    "def cal_yanshen_mean(x1, x2, x3, x4, x5, x6):\n",
    "    count = 9.9892 - 7.6164*x1 + 3.8024*x2 + 5.2722*x3 - 3.0590*x4 + 1.5053*x5 + 15.4639*x6\n",
    "    return count\n",
    "\n",
    "def cal_qufu_std(x1, x2, x3, x4, x5, x6):\n",
    "    count = 2.8216 - 3.6748*x1 + 9.2533*x2 - 6.5848*x3 - 21.5972*x4 - 11.2795*x5 + 2.4244*x6\n",
    "    return count\n",
    "\n",
    "def cal_kangla_std(x1, x2, x3, x4, x5, x6):\n",
    "    count = -0.7273 + 0.3171*x1 + 8.1385*x2 - 4.1692*x3 - 18.5395*x4 - 5.8194*x5 + 16.7100*x6 \n",
    "    return count\n",
    "\n",
    "def cal_yanshen_std(x1, x2, x3, x4, x5, x6):\n",
    "    count = 1.5023 + 0.1300*x1 - 0.3995*x2 + 0.3247*x3 - 0.3529*x4 - 5.6869*x5 + 1.8671*x6\n",
    "    return count\n",
    "\n",
    "def calcutate_all(ronglianhao):\n",
    "    x1 = pd_chem_E1[ronglianhao]\n",
    "    x2 = pd_chem_E2[ronglianhao]\n",
    "    x3 = pd_chem_E3[ronglianhao]\n",
    "    x4 = pd_chem_E4[ronglianhao]\n",
    "    x5 = pd_chem_E5[ronglianhao]\n",
    "    x6 = pd_chem_E6[ronglianhao]\n",
    "    print(\"屈服均值: \" + str(cal_qufu_mean(x1, x2, x3, x4, x5, x6)) + \"\\n\"\n",
    "          \"抗拉均值: \" + str(cal_kangla_mean(x1, x2, x3, x4, x5, x6)) + \"\\n\"\n",
    "          \"延伸率均值: \" + str(cal_yanshen_mean(x1, x2, x3, x4, x5, x6)) + \"\\n\"\n",
    "          \"屈服标准差: \" + str(cal_qufu_std(x1, x2, x3, x4, x5, x6)) + \"\\n\"\n",
    "          \"抗拉标准差: \" + str(cal_kangla_std(x1, x2, x3, x4, x5, x6)) + \"\\n\"\n",
    "          \"延伸率标准差: \" + str(cal_yanshen_std(x1, x2, x3, x4, x5, x6)) + \"\\n\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5df3cbda-6b30-4ed4-a519-c8b556b1eb25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 281.0436817499999\n",
      "抗拉均值: 302.12715380000003\n",
      "延伸率均值: 11.72966825\n",
      "屈服标准差: 4.044883449999997\n",
      "抗拉标准差: 3.9242447999999994\n",
      "延伸率标准差: 0.6836438499999999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90624\n",
    "calcutate_all(90624)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6ec41f97-73a1-4118-946d-cdf53d3c4685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 280.14576220000004\n",
      "抗拉均值: 301.2504561\n",
      "延伸率均值: 11.75250665\n",
      "屈服标准差: 4.09420725\n",
      "抗拉标准差: 3.896615800000003\n",
      "延伸率标准差: 0.7077119000000001\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90626\n",
    "calcutate_all(90626)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "155043c8-3ee4-4017-acb8-303e9e7770fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 281.1477789\n",
      "抗拉均值: 302.06638649999996\n",
      "延伸率均值: 11.8371067\n",
      "屈服标准差: 4.187677149999999\n",
      "抗拉标准差: 3.9974252999999997\n",
      "延伸率标准差: 0.6685442000000003\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90627\n",
    "calcutate_all(90627)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20841591-f0bf-4db3-9dcd-02d00f66f0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 281.59236580000004\n",
      "抗拉均值: 302.4213721\n",
      "延伸率均值: 11.84703395\n",
      "屈服标准差: 4.235370449999999\n",
      "抗拉标准差: 4.034019550000003\n",
      "延伸率标准差: 0.66078115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90628\n",
    "calcutate_all(90628)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d1897150-2363-46e3-b90c-2eb483bf9a22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 280.6431412\n",
      "抗拉均值: 301.66065395000004\n",
      "延伸率均值: 11.799698150000001\n",
      "屈服标准差: 4.13804585\n",
      "抗拉标准差: 3.9504264000000022\n",
      "延伸率标准差: 0.6839600000000002\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90629\n",
    "calcutate_all(90629)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a1595b7f-0a43-4714-8f33-6eba43c635ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 282.0879321\n",
      "抗拉均值: 302.9229411999999\n",
      "延伸率均值: 11.77570985\n",
      "屈服标准差: 4.159440649999999\n",
      "抗拉标准差: 4.0227504\n",
      "延伸率标准差: 0.6695417499999997\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90630\n",
    "calcutate_all(90630)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1e16769b-eab3-4355-a438-343797e7c9b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: 281.16844065\n",
      "抗拉均值: 302.0872306499999\n",
      "延伸率均值: 11.8406746\n",
      "屈服标准差: 4.1261977000000005\n",
      "抗拉标准差: 3.898947100000001\n",
      "延伸率标准差: 0.6666137999999998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#熔炼号90631\n",
    "calcutate_all(90631)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e2b7c7d0-77fd-486a-87ef-d6677962a3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(len(dist2_qufu)):\n",
    "#     print(dist2_qufu[i])\n",
    "\n",
    "# for key in pd_chem_E1:\n",
    "#     if key in phy_dict:\n",
    "#         E1_list.append(pd_chem_E1[key])\n",
    "#         E2_list.append(pd_chem_E2[key])\n",
    "#         E3_list.append(pd_chem_E3[key])\n",
    "#         E4_list.append(pd_chem_E4[key])\n",
    "#         E5_list.append(pd_chem_E5[key])\n",
    "#         E6_list.append(pd_chem_E6[key])\n",
    "        \n",
    "#         phy_dict_qufu_mean_list.append(phy_dict_qufu_mean[key])\n",
    "#         phy_dict_qufu_std_list.append(phy_dict_qufu_std[key])\n",
    "#         phy_dict_kangla_mean_list.append(phy_dict_kangla_mean[key])\n",
    "#         phy_dict_kangla_std_list.append(phy_dict_kangla_std[key])\n",
    "#         phy_dict_yanshen_mean_list.append(phy_dict_yanshen_mean[key])\n",
    "#         phy_dict_yanshen_std_list.append(phy_dict_yanshen_std[key])\n",
    "        \n",
    "# np_E1 = np.array(E1_list)\n",
    "# np_E2 = np.array(E2_list)\n",
    "# np_E3 = np.array(E3_list)\n",
    "# np_E4 = np.array(E4_list)\n",
    "# np_E5 = np.array(E5_list)\n",
    "# np_E6 = np.array(E6_list)\n",
    "new_ronglian = []\n",
    "for key in pd_chem_E1:\n",
    "    if key in phy_dict:\n",
    "        new_ronglian.append(key)\n",
    "        \n",
    "# data = pd.DataFrame({'熔炼号': new_ronglian, '屈服均值': phy_dict_qufu_mean_list, '抗拉均值': phy_dict_kangla_mean_list, '延伸率均值': phy_dict_yanshen_mean_list,\n",
    "#                     '屈服标准差': phy_dict_qufu_std_list, '抗拉标准差': phy_dict_kangla_std_list, '延伸率标准差': phy_dict_yanshen_std_list, 'E1': np_E1, 'E2': np_E2,\n",
    "#                     'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})\n",
    "\n",
    "# data.to_excel(\"/home/bobmaster/Downloads/数学建模/data.xlsx\", sheet_name=\"Sheet1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f8eb2d39-a260-4cef-aa76-2cef3531a9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([282.11678074])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "  \n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist2_qufu)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "ronglianhao = 90623\n",
    "y_pred = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b22a9f0a-baf7-46ba-b94d-78b4d745c10b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "564a718d-a349-4118-9e6c-2e4e12ca7222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -21.09158051,   24.71549855,   -5.01065116,  226.01198632,\n",
       "         25.40913652, -139.41772546])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c655d353-db21-4c65-b157-c278042b2b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232.98837814693027"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f020a5e9-64dd-469d-9fa5-0b5c9e1eaee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -21.09158051,   24.71549855,   -5.01065116,  226.01198632,\n",
       "         25.40913652, -139.41772546])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 材料与屈服特性均值多元线性回归方程\n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist2_qufu)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "20ac260e-4ff0-42aa-a00e-f9c8052a8f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232.98837814693027"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275e20a7-9a6a-4259-b301-1c19f653caee",
   "metadata": {},
   "source": [
    "### 精确方程\n",
    "$ y=232.98837814693027-21.09158051X_1+24.71549855X_2-5.01065116X_3+226.01198632X_4+25.40913652X_5-139.41772546X_6 $\n",
    "### 精简方程\n",
    "$ y=232.9884-21.0916X_1+24.7155X_2-5.0107X_3+226.0120X_4+25.4091X_5-139.4177X_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fb34982e-7134-46e9-81b5-dab7b1cd1ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -8.33255893,   10.87035246,   -4.29381787,  197.08886361,\n",
       "         30.81475037, -133.2003945 ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 材料与抗拉特性均值多元线性回归方程\n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist2_kangla)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6a6bade7-6d61-4361-bb9b-638a75b046ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265.8372810566513"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad229fd-3c3e-4899-8f88-2777db66f815",
   "metadata": {},
   "source": [
    "### 精确方程\n",
    "$ y=265.8372810566513-8.33255893X_1+10.87035246X_2-4.29381787X_3+197.08886361X_4+30.81475037X_5-133.2003945X_6 $\n",
    "### 精简方程\n",
    "$ y=265.8373-8.3326X_1+10.8704X_2-4.2938X_3+197.0889X_4+30.8148X_5-133.2004X_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9fca5f0b-cbd1-41c1-ae2d-d65a435f4ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.07378937,  5.7620131 ,  1.5875269 , -8.09637312,  9.06779244,\n",
       "       22.52728787])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 材料与延伸特性均值多元线性回归方程\n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist2_yanshen)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d87bb9b2-32d6-484a-96ed-f13734f5359d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.172527937298794"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f877479c-85c4-40a3-9f78-774e30918740",
   "metadata": {},
   "source": [
    "### 精确方程\n",
    "$ y=7.172527937298794-5.07378937X_1+5.7620131X_2+1.5875269X_3-8.09637312X_4+9.06779244X_5+22.52728787X_6 $\n",
    "### 精简方程\n",
    "$ y=7.1725-5.0738X_1+5.7620X_2+1.5875X_3-8.0964X_4+9.0678X_5+22.5273X_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "80f5c013-be82-4e29-b989-1513daec5456",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "\n",
    "def cal_qufu_mean(ronglianhao):\n",
    "    y = np.array(dist2_qufu)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "    count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], \n",
    "    pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], \n",
    "    pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "    return count\n",
    "\n",
    "def cal_kangla_mean(ronglianhao):\n",
    "    y = np.array(dist2_kangla)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "    count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], \n",
    "    pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], \n",
    "    pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "    return count\n",
    "\n",
    "def cal_yanshen_mean(ronglianhao):\n",
    "    y = np.array(dist2_yanshen)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "    count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], \n",
    "    pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], \n",
    "    pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "    return count\n",
    "\n",
    "def cal_qufu_std(ronglianhao):\n",
    "    y = np.array(dist3_qufu)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "    count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], \n",
    "    pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], \n",
    "    pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "    return count\n",
    "\n",
    "def cal_kangla_std(ronglianhao):\n",
    "    y = np.array(dist3_kangla)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "    count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], \n",
    "    pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], \n",
    "    pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "    return count\n",
    "\n",
    "def cal_yanshen_std(ronglianhao):\n",
    "    y = np.array(dist3_yanshen)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "    model = LinearRegression().fit(X_train, y_train)\n",
    "\n",
    "    count = model.predict(np.array([pd_chem_E1[ronglianhao], pd_chem_E2[ronglianhao], \n",
    "    pd_chem_E3[ronglianhao], pd_chem_E4[ronglianhao], pd_chem_E5[ronglianhao], \n",
    "    pd_chem_E6[ronglianhao]]).reshape(-1,6))\n",
    "    return count\n",
    "\n",
    "def calcutate_all(ronglianhao):\n",
    "    print(\"屈服均值: \" + str(cal_qufu_mean(ronglianhao)) + \"\\n\"\n",
    "          \"抗拉均值: \" + str(cal_kangla_mean(ronglianhao)) + \"\\n\"\n",
    "          \"延伸率均值: \" + str(cal_yanshen_mean(ronglianhao)) + \"\\n\"\n",
    "          \"屈服标准差: \" + str(cal_qufu_std(ronglianhao)) + \"\\n\"\n",
    "          \"抗拉标准差: \" + str(cal_kangla_std(ronglianhao)) + \"\\n\"\n",
    "          \"延伸率标准差: \" + str(cal_yanshen_std(ronglianhao)) + \"\\n\"\n",
    "        )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5c631900-6f2b-4513-b5c5-11845935bf44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [281.04919773]\n",
      "抗拉均值: [302.13923671]\n",
      "延伸率均值: [11.75333675]\n",
      "屈服标准差: [4.06391763]\n",
      "抗拉标准差: [3.6079243]\n",
      "延伸率标准差: [0.68167218]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90624\n",
    "calcutate_all(90624)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c1c2cc26-d446-44a1-abd0-02848216956b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [280.5012662]\n",
      "抗拉均值: [301.6395381]\n",
      "延伸率均值: [11.70884635]\n",
      "屈服标准差: [4.0805444]\n",
      "抗拉标准差: [3.56479364]\n",
      "延伸率标准差: [0.7105368]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90626\n",
    "calcutate_all(90626)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aa442604-32e3-42d4-bfef-8f4fc16f467e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [281.1057409]\n",
      "抗拉均值: [302.02799232]\n",
      "延伸率均值: [11.85466945]\n",
      "屈服标准差: [4.19329091]\n",
      "抗拉标准差: [3.67943761]\n",
      "延伸率标准差: [0.65688161]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90627\n",
    "calcutate_all(90627)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "da4c44ad-7868-4079-a4a2-613eb065a70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [281.5443029]\n",
      "抗拉均值: [302.35074987]\n",
      "延伸率均值: [11.87558182]\n",
      "屈服标准差: [4.27624867]\n",
      "抗拉标准差: [3.74579568]\n",
      "延伸率标准差: [0.64559413]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90628\n",
    "calcutate_all(90628)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f85c3c35-b1d2-453a-a727-eb4fc5b6b8f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [280.77029025]\n",
      "抗拉均值: [301.80837018]\n",
      "延伸率均值: [11.7935581]\n",
      "屈服标准差: [4.13601804]\n",
      "抗拉标准差: [3.62497317]\n",
      "延伸率标准差: [0.67978443]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90629\n",
    "calcutate_all(90629)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2fb40911-82c1-4f1c-889c-bd0b0d0b0d93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [282.06368924]\n",
      "抗拉均值: [302.8621545]\n",
      "延伸率均值: [11.81286397]\n",
      "屈服标准差: [4.22813292]\n",
      "抗拉标准差: [3.73535752]\n",
      "延伸率标准差: [0.65672621]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90630\n",
    "calcutate_all(90630)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ac66ce07-69e7-4768-8441-18064d73ac76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "屈服均值: [281.24719713]\n",
      "抗拉均值: [302.18315812]\n",
      "延伸率均值: [11.82315402]\n",
      "屈服标准差: [4.12274657]\n",
      "抗拉标准差: [3.59635772]\n",
      "延伸率标准差: [0.66175219]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 熔炼号 90631\n",
    "calcutate_all(90631)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7f24b637-fb89-4151-bc2b-dbaf43dd7709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4.61087137,   7.89819425, -15.91215619, -26.69428153,\n",
       "        -5.1575353 , -40.74486056])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 材料与屈服特性标准差多元线性回归方程\n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist3_qufu)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bbdb09a2-f663-4326-ab6e-2fb0d2fa9b86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.062939344879167"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e090e1bf-6d3e-48b6-bf54-5bac1610324b",
   "metadata": {},
   "source": [
    "### 精确方程\n",
    "$ y=5.062939344879167+4.61087137X_1+7.89819425X_2-15.91215619X_3-26.69428153X_4-5.1575353X_5-40.74486056X_6 $\n",
    "### 精简方程\n",
    "$ y=5.0629+4.6109X_1+7.8982X_2-15.9122X_3-26.6943X_4-5.1575X_5-40.7449X_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "637da0bf-08b7-49c6-99d9-c50bf9fcf328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5.52211393,   7.22300547, -13.92408713, -20.73062805,\n",
       "        -0.08048425, -21.0089931 ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 材料与抗拉特性标准差多元线性回归方程\n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist3_kangla)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "15ccffe4-5925-4dd3-af0a-241c357bc28d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5298723495190005"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb2d548-199b-43c8-83fc-b0313325c29f",
   "metadata": {},
   "source": [
    "### 精确方程\n",
    "$ y=1.5298723495190005+5.52211393X_1+7.22300547X_2-13.92408713X_3-20.73062805X_4-0.08048425X_5-21.0089931X_6 $\n",
    "### 精简方程\n",
    "$ y=1.5299+5.5221X_1+7.2230X_2-13.9241X_3-20.7306X_4-0.0805X_5-21.0090X_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f637829b-cd1f-4c8d-978a-0a094ca8a48c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.31314118, -1.7168275 ,  0.19307053, -0.86744266, -4.87384482,\n",
       "       -4.01341483])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 材料与延伸率特性标准差多元线性回归方程\n",
    "x = np.array([dist1_E1, dist1_E2, dist1_E3, dist1_E4, dist1_E5, dist1_E6]).transpose()\n",
    "y = np.array(dist3_yanshen)\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
    "model = LinearRegression().fit(X_train, y_train)\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "782b78e7-60ac-4a61-8ee4-7e2832a7c769",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6570980963371644"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9770a1-2912-42f3-a1f8-4b73ac49cbee",
   "metadata": {},
   "source": [
    "### 精确方程\n",
    "$ y=2.6570980963371644+1.31314118X_1-1.7168275X_2+0.19307053X_3-0.86744266X_4-4.87384482X_5-4.01341483X_6 $\n",
    "### 精简方程\n",
    "$ y=2.6571+1.3131X_1-1.7168X_2+0.1931X_3-0.8674X_4-4.8738X_5-4.0134X_6 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98bf195b-4139-497c-83fc-5ae2ea1618d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dfcc7783-4618-4785-81a8-058b0d6e0e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame({'熔炼号': new_ronglian, '屈服均值': phy_dict_qufu_mean_list, '抗拉均值': phy_dict_kangla_mean_list, '延伸率均值': phy_dict_yanshen_mean_list,\n",
    "                    '屈服标准差': phy_dict_qufu_std_list, '抗拉标准差': phy_dict_kangla_std_list, '延伸率标准差': phy_dict_yanshen_std_list, 'E1': np_E1, 'E2': np_E2,\n",
    "                    'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa026eb8-24cb-454f-9ba6-c1cc2b562ddb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "28f124f1-f69c-4c6b-9d93-c9dc59b7f310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LogNorm\n",
    "fig, ax = plt.subplots(figsize=(5, 5), sharex=True, sharey=True,\n",
    "                        tight_layout=True)\n",
    "\n",
    "ax.set_ylabel(\"E1 %\", fontproperties=myfont)\n",
    "\n",
    "ax.set_title(\"化学成分E1与屈服特性的关系\", fontproperties=myfont)\n",
    "ax.set_xlabel(\"屈服特性均值\", fontproperties=myfont)\n",
    "hist_qufu_E1 = ax.hist2d(dist2_qufu, dist1_E1, bins=20, norm=LogNorm(), cmap = \"YlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "63ea2699-ad8a-4f32-ae33-53704e18073b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.step(dataset.iloc[:,7], dataset.iloc[:,1], label='pre (default)')\n",
    "plt.plot(dataset.iloc[:,7], dataset.iloc[:,1], 'o--', color='grey', alpha=0.3)\n",
    "\n",
    "plt.step(dataset.iloc[:,7], dataset.iloc[:,2], where='mid', label='mid')\n",
    "plt.plot(dataset.iloc[:,7], dataset.iloc[:,2], 'o--', color='grey', alpha=0.3)\n",
    "\n",
    "plt.grid(axis='x', color='0.95')\n",
    "plt.legend(title='Parameter where:')\n",
    "plt.title('plt.step(where=...)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a274793a-2a11-465a-a2d4-a18040ba084d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from string import ascii_letters\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('axes', unicode_minus=False)\n",
    "sns.set_theme(style=\"white\")\n",
    "sns.set(font=\"WenQuanYi Zen Hei\")\n",
    "dataset = pd.DataFrame({'屈服': phy_dict_qufu_mean_list, '抗拉': phy_dict_kangla_mean_list, '延伸率': phy_dict_yanshen_mean_list,\n",
    "                     'E1': np_E1, 'E2': np_E2, 'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})\n",
    "\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = dataset.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e42d3796-2358-4c3d-a4ba-99fe0d0f3baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font=\"WenQuanYi Zen Hei\")\n",
    "\n",
    "dataset = pd.DataFrame({'熔炼号': new_ronglian, '屈服均值': phy_dict_qufu_mean_list, '抗拉均值': phy_dict_kangla_mean_list, '延伸率均值': phy_dict_yanshen_mean_list,\n",
    "                    '屈服标准差': phy_dict_qufu_std_list, '抗拉标准差': phy_dict_kangla_std_list, '延伸率标准差': phy_dict_yanshen_std_list, 'E1': np_E1, 'E2': np_E2,\n",
    "                    'E3': np_E3, 'E4': np_E4, 'E5': np_E5, 'E6': np_E6})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2cef28a0-6382-4dd9-a737-a415f4ed5cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E1', ylabel='屈服均值'>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E1\", y=\"屈服均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0354e815-1649-4f01-b31f-6a1c4e5c19fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E1', ylabel='抗拉均值'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E1\", y=\"抗拉均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bd9df92d-4e60-4891-b731-eb2427befc4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E1', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E1\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ca257c3a-7df4-46cc-a31f-24988c850202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E2', ylabel='屈服均值'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E2\", y=\"屈服均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "06a18168-0a77-4c32-b544-24511546b829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E2', ylabel='抗拉均值'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAJPCAYAAAAnhEP5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAADHsUlEQVR4nOz9ebwtV13njX/WqqpdezjzcOch9yY3c7iBJCBTgq1iCxGbbjQgggqIoj+72wH7ETQqv0Z/aPP8nrbt0CqDQCt0sDEQBhlETKADYco83SQ3dx7OOfcMe65hreePVat21d5Ve1ft+Zy73q9Xt+Sec/apU3uoT32Hz4dwzjkUCoVCoVAoFGMFHfUBKBQKhUKhUChaUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIM0Ud9AINgdbUMxrq3f5ufn8DKSqmPR6SQqHM7ONS5HQzqvA4OdW4Hhzq3g6Hf55VSgtnZQuzXt6RIY4z3JNLkYygGgzq3g0Od28GgzuvgUOd2cKhzOxiGeV5Vu1OhUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAoFAqFYgxRIk2hUCgUCoViDFEiTaFQKBQKhWIMUSJNoVAotigr6zWsFuujPgyFQtElSqSNiHLNxrnVyqgPQ6FQbGE+9PnH8PEvPTnqw1AoFF2iRNqIuOveo3j/Jx8Y9WEoFIotzFrJwnpZVdIUis2KEmkjYmmtio2KNerDUCguGizbxbkLF1f1ulp3UKm7oz4MhULRJfqoD+BiZa1Yh2UzMM5BCRn14SgUWx7bZahZzqgPY6hU6g74qA9CoVB0jaqkjYi1kmhBWLa6y1VsHepjXK1yGYdls1EfxtBwXAbbYajWLy5hqlBsJZRIGwGOy7BRsQEAdUuJNMXWwXU5amP6mnYcBsu9eERaxRNntsPgXER/t0KxlVAibQRslBuzaDVVSVNsIRjnqI/pa9p2GFyXg/GLowEYrKBVVDVNodiUKJE2AlZLjW0rVUlTbCU457BdBj6GQshyxHExNn7HNggqtYYwqymRplBsSpRIGwFrxUYlbVyrDgpFN3AOuA6DO4ZCyHbEos44CshBEKykVdWGp0KxKVEibQSsqUqaYovCOIfDxlWkuSAA2EUynqXanQrF5keJtBEQFGnjOmStUHQDYxyMiQWCccN2GDRKxlJADoJKqJKmRJpCsRlRIm0ErJXqkM5oqt2p2Eq4jAmhNmYtRZcx3y9s3I5tUFRrSqQpFJsdJdJGwFrJwtxUFoASaYqthcs4OAB3zCwf5LIAx8Uj0iqq3alQbHqUSBsBa6U6dszlAKiZNMXWgjEOjQp3/3HCcTnAOQgI+EXS7qzWXRg69f63EmkKxWZEibQRsFasY3E2DwI1k6bYWrguoGkU9pg5+7uMA4SAkPETkIOiWncwkTOQMagSaQrFJkVldw4Z23FRrjmYnTSRyWiq3anYUrhMDOdb7ni9rl0mKmlUI2O51DAIKnUHeVMH41yJNIVik6JE2pBZKwmPtJmJDLKGEmmKrQXjgK4ROM54VatsxwUhBIQQuBeJB0e17iDnibSK8klTKDYlqt05ZNZ9kWbCNDQ1k6bYUriMQdMorLETaQyUElBCxHzakDm5VBq6OKx4Ii1n6qqSplBsUpRIGzLSI21mwoSZ0dRMmmJL4TIOnYqW4jg5+0uPNEIwdJG2VqrjDz58P77/1PJQf6+opGlKpCkUmxgl0obMqi/SMjDVTNrYUrMcnF+tjPowNh2McRBCRDzUGG1R2o4rKmmUwBny4sBG2QLn4v8Ok2rdQT5rKJGmUGxilEgbMmulOjRKMJEz1EzaGGM7LBRQrUiGEGkACMYqyNxyGCgFaIKZtNVive3X0yKr5TVreK8nzjkqNVFJy5uaEmkKxSZFibQhs1a0MDNhghCiZtLGGMflqAzxoroVYFwY2RJCAM7HppLGOYfjclDPgqPTdud6ud8iTbyOhnlDZnsh93lTRzajq4B1hWKTorY7h8xaqY6ZyQwAqJm0McayXTiOiDeihHT+AQU4537cmdiiHA+RJo5DtGEBcZyMcVA6nOdVCqRhvtdl5Sxn6nBdjrrtevYo6r5codhMqHfskFkr1TEzYQKAmkkbYyyHgTF20Xhq9QPO4S8L8DGqpInjCAgyMtxoKFlJs4b4XpcxUHlvuxOAqqYpFJsQJdKGzFrJ8kWamkkbX2zHBeMY+pD5ZoZ5rv6AV0kbk3MXJbSHOS/XmEkbvkjLhUSaat8rFJsN1e4cInXLRbXuYGbCa3camjc7otoQ44blMFAyXhuK406wOEUpYDnjcQPiMiaS1X3IUCtpUhwNc/402O6UliNKpCkUmw+lDIbIWrnhkQaIdicA1K3xqDgoBJxz3/x0XKpBmwEm+p0AAEoJbGc8BG5L1YyPppI2zKq5bG3mTR15U/P+TYk0hWKzoUTaEFnzVvtnJptEmmp5jhVy0FwIDSXSkiLm0US7k5LxOXeW4yJUqCYivmpYjGK7UwqyfFZHLisaJpUtKNLqtosnjq2O+jAUioGhRNoQWQtEQgFiJg0Yrn+SojOuywFORFD4mLTsNgPB+XyNEthjcu5kVdTH2+4cFiOZSatdHDNp337sHP7sEz9AsTJco2CFYlgokTZEZCTUbGAmDQAsezwqDgqByxhAhNCoq+cmMcEYqHGqQlp2k0gb+nbn8NudlboDAlGt38rbnZWaAw6grIynFVsUJdKGyFqpjoxO/Q9N2e5UlbTxwvFyJ7UxEhqbAdElFuKHEAKO8UgdsBwGLeB1RwiBM8TndVSLA1lTByUEeXPrtjttb2ZUfYYqtipKpA0Rab8hTTXVTNp44rgMhIhqkGW7YxUUPs5wzkN2ZAA6RjANA8cNV9IoIXCGeFyDqqS5jGFpvRppE1OtO7440zUKQ6dbst0pb6JqW7BKqFAASqQNlbVi3bffAIIzaeoDZpywHBcaJWMZFD7OuIyDNKm0UZ875pnqhkVa52iofiLFkePyvvruOS7HyaUynjyxhrViLXQzIXI7Gw5LWzVkXVZE1WeoYquiRNoQEZFQpv/fqpI2nlh2wLdOeaUlxmUMzQlaoz53YgkkfAyEEr9NNgyCAqLfqQM6JTANimPnS3j29IYvxEQlTfO/b6uKNL+Sptqdii2KEmlDJJg2ADQWB1TI+nghLBs8tcGhvNIS4rqNfExgPKKhxBJIWDmKStpwRVrBs8EYRMVH1yimCxnYjotnTq3DcRmq9XAlLW9qW3JxwHbF31RVn6GKLYoSaUOiWndQt11MB9udqpI2lohKmndhJ4CjKmmJYIyH9BAhGOqAfhRRiwuUEAxLozkug+My/+ZskO/1rKlD7m5U6g7y2YZIy2ZUJU2h2IwokTYkpP1GsJKmaxSUEDVPMUa0zjDxkQuNzQJjHDSg0uiQ24pROIy3LH4QQoaWySrf2/LmbFjv9dZK2hYXaVuwSqhQAEqkDY1mI1tAXCzMzMUbss4YH7s7YNcNX9Q1SpWhbULcpkqaNmSriygcp3VOjlLi26wMGimMpgvifd/vmbQoOOeo1t2WxYEtacGhFgcUWxwl0oZEo5KWCf27adCLdiatZjl47mxxKBeupIjh98ZVXRjajs/xjTPNiwOUElgjFmktaQMeZEiGtlI8zAyxkla3XTDOfQsOYAsvDiifNMUWR4m0IRHV7gQAM6Nf1CKgXLVx4nxpqA7w7XCatgHHyTl/3HEZQgJ3HM5ds5GtD+cYhlWaFA/TQ5hJk8gFgVw2KNI01Cx3LMyF+4mqpCm2OkqkDYm1ohWKaJFkDe2i/oDJGBrKNQfLa9V
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E2\", y=\"抗拉均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6b5819cc-f18c-4c0d-8c78-d03aacd7602a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E2', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E2\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f018dea0-b2b6-438d-ac09-06dc0041ea25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E3', ylabel='屈服均值'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E3\", y=\"屈服均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7ab1e674-e969-48fc-97a9-158c8daecfbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E3', ylabel='抗拉均值'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E3\", y=\"抗拉均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "aaf0720c-0344-4c43-99a8-e79e5c1044fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E3', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E3\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "54ad0a82-9a6f-44ad-b8d7-6a1a1fe66c4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E4', ylabel='屈服均值'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E4\", y=\"屈服均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "233a35d5-e038-4d51-98d6-8839ba1d9ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E4', ylabel='抗拉均值'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E4\", y=\"抗拉均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ffde77b5-08cd-4a12-8ecf-77192e934599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E4', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E4\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "42804580-ddf5-45f4-88fe-6418d19dade9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E5', ylabel='屈服均值'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E5\", y=\"屈服均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1a668bd2-cf05-440b-91bf-3e5f6f8ac481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E5', ylabel='抗拉均值'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E5\", y=\"抗拉均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f15683b3-e61a-4f66-883e-b99752475f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E5', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E5\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6b805e46-7ce7-4655-8279-02208706d802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E6', ylabel='屈服均值'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E6\", y=\"屈服均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e7d91915-5b74-47a4-a89a-3aafb7ae2737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E6', ylabel='抗拉均值'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E6\", y=\"抗拉均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "812d0ea4-c693-4f42-8334-1f7a84812bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E6', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E6\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3e0454af-a67c-422a-a6fd-473d08e054ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={\"figure.figsize\": (5, 5)}, font=\"WenQuanYi Zen Hei\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "49b29017-bc16-4006-93b9-7eccbdaaf8f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='E6', ylabel='延伸率均值'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"E6\", y=\"延伸率均值\", data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "543536f8-05dd-4762-a2d6-6aee54de62d2",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}