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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Управление потоком жидкости"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"assets/fis.png\" width=\"600\" style=\"background-color: white\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Создание лингвистических переменных\n",
+    "\n",
+    "Входные X: level (уровень жидкости) и flow (расход жидкости) \\\n",
+    "Выходные Y: influx (приток жидкости)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from skfuzzy import control as ctrl\n",
+    "\n",
+    "level = ctrl.Antecedent(np.arange(1.5, 9.0, 0.1), \"level\")\n",
+    "flow = ctrl.Antecedent(np.arange(0, 0.6, 0.01), \"flow\")\n",
+    "influx = ctrl.Consequent(np.arange(0, 0.6, 0.01), \"influx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Формирование нечетких переменных для лингвистических переменных и их визуализация"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/user/Projects/python/ckmai/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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JJ58MwPXXX8+qVat48cUX+fe//82FF17IWWedxdatW4ee093dzU9+8hOeeuopNm7cSHZ2Np2dnVxxxRW8//77VFZWUlxczNlnn01nZyfgGeFZtGgRiYmJVFVV8eSTT3LHHXcMi72/v58zzzyTlJQU3nvvPT744AOSk5M566yz6Ovr89s9i/bbmUXENLML04iJsrD2070cl5829hPYqyA2BbJn+D44Gbv8EoiK83xdCsZeEFzdXE1CdALHZBzj+9hCWV83tH4S+OtmHQ2xo+/r9ZWvfGXY+08//TQTJ05k06ZNXHTRRdx44428//77Q8nMCy+8wCWXXILFYqGuro5nnnmGuro68vM99XM333wzb775Js888wwPPPAA4ElCfvOb3zBnzv6dq7/4xS8Ou+6TTz5Jeno67777Lueeey5vvfUWtbW1rFy5ktzcXAB+/OMfc8YZ+xuyvvTSS7jdbp566qmhjSOfeeYZ0tPTWblyJV/60pdGfR/GQsmNSBiKj4liZkEaa3buZfH8yWM/QV0VFJaBNcrnsYkXouMgf66nqLji2jE/vbq5mtlZs4m26lf+MK2fwJOnBv6617zrSVhHaevWrdx9991UVVXR2to6VA9TV1fHzJkz+dKXvsTzzz/PySefzI4dO1i1ahVPPPEEABs2bMDlcnH00UcPO2dvby8TJkwYej82NpbZs2cPO2bXrl3ceeedrFy5kubmZlwuF93d3dTV1QGwZcsWbDbbUGIDMG/evGHnWL9+Pdu2bSMlJWXY4z09PUNTa/6g73SRMFU2KcO7nYrdbs8IgRd/RMWPisrh3y97mmiOoXWCYRisb17PRcdc5MfgQlTW0Z5Ew4zrjsF5553HpEmTWLZsGfn5+bjdbmbOnDk0rXPppZfy3e9+l0ceeYQXXniBWbNmMWvWLACcTidRUVGsXbuWqKjhL1aSk5OH/p+QkHBQS44rrriC3bt38z//8z9MmjSJuLg45s+fP6bpJKfTSWlpKc8///xBH5s4ceKozzNWSm5EwlTppEyWvbeDxvZ95KUljP6JrZ9AT5uKiYONrQI++B9ot0N60aif9mnHp+zt3auVUiOJTRzTCIoZdu/ezZYtW1i2bNnQtNP7778/7JgLLriAa665hjfffJMXXniBxYsXD31s7ty5uFwumpubh54/Wh988AG/+c1vOPvsswFPYXBra+vQx4855hjsdju7du0iJycHgI8+Gr4f0/HHH89LL71EdnY2qampY7r+eKigWCRMlU7KABj7knB7JVisnmkpCR62z4b768a2JLy6uRoLFmZPnH3kgyXoZGRkMGHCBJ588km2bdvGP//5T5YsWTLsmKSkJBYtWsRdd93F5s2bueSSS4Y+dvTRR3PppZeyePFiXnnlFXbs2MHq1atZunQpr7/++mGvXVxczO9+9zs2b95MVVUVl156KQkJ+18onXHGGUydOpUrrriCf//733zwwQfceeedAEOjQJdeeilZWVlccMEFvPfee+zYsYOVK1fy3e9+l/r6el/dpoMouREJUxNT4pg0IXHsm/nZV0POcRCXcuRjJXCSsmDCNE/yOQY1LTVMy5hGSqy+nqHIarXy4osvsnbtWmbOnMn3vvc9fvaznx103KWXXsr69es5+eSTKSoaPrL3zDPPsHjxYm666SaOOeYYFi1axEcffXTQcZ/329/+lr1793L88cdz+eWX893vfpfs7Oyhj0dFRbF8+XKcTicnnHAC3/zmN4dWS8XHxwOQmJjIv/71L4qKivg//+f/cOyxx3LVVVfR09Pj15Eci3Hg+rEI0NHRQVpaGu3t7QEdIhMxw5KXa9i6y8lfvnPS6J/0q+Nh6hfhnJ/7LzDxzvJve/Yfuvb9Ix/7mfOXn88JOSdw1/y7/BhYcOvv76elpYWJEycSExNjdjhh7YMPPuCkk05i27ZtTJ06dczP99XXSiM3ImGsbFImmxo76BptE01nC+yp1f42waqoHJo3Qs/o9i9q62ljR/sO7W8jfvPqq6/y1ltvsXPnTt5++22uueYaTjzxRK8SG19SciMSxsomZ+ByG6y3t43uCWqWGdxsFWC4wbFmVIevb1kPoGJi8ZvOzk6+/e1vM336dL7+9a9zwgkn8Oc//9nssLRaSiScTZuYTGp8NGs+3cuCaVlHfoK9ElILIN3m/+Bk7CZMg4QMT1Hx1C8e8fDq5mqyErIoSC4IQHASiRYvXjxsdVaw0MiNSBizWi0cP5YmmnVV+1flSPCxWj2jaqMsKq5urmZu9tyD9i8RCXdKbkTCXNmkDNbV7cV9pCaa/T3QWOOZ+pDgZSuH+jXgOnwdVb+r39Msc2JJYOISCSJKbkTCXOmkTDp7BvikufPwBzbWgKvPU7QqwauoAvqc0LzpsIdt2rOJXlev6m0kIim5EQlzJbZ0oqyWI+93U1cJMUmQMyswgYl38ueCNWZ/8fch1DTXEB8Vz/QJ0wMUmEjwUHIjEuYSYqM4Lj/1yHU39iooLIUorTMIajEJkDfHk4weRk1zDcdlHUeMVfu6SORRciMSAUonZbDm0z2HPsAwPMmNloCHhqKKw47cGIYxVEwsEomU3IhEgLJJmdj37KO5o2fkA3bXQvduFROHClu5p4Fmu2PED9d31rO7Z7eSmzBw2mmnceONNx7y4xaLheXLl4/6fCtXrsRisdDW1jbu2IKZkhuRCFA2+QhNNO2VgAVsJwQuKPHe4A7Sh1gSXt1SDcCciXMCFZGYpLGxkf/6r/8yO4ygo+RGJALkpMZTmJHAmkMlN3WVkD0D4tMCG5h4JzkbMo7yNDkdQU1zDVPTppIWp69nuMvNzSUuLs7sMIKOkhuRCOGpuznUyE2VloCHGlv5IYuKq5ur1U8qjLjdbm655RYyMzPJzc3lhz/84dDHPj8t9eGHH1JSUkJ8fDxlZWUsX74ci8VCTU3NsHOuXbuWsrIyEhMTWbBgAVu2bAnMJxMgSm5EIkTZpAw2OtrZ1+ca/oHuPdD6ieptQk1ROTRtgF7nsIc7+jqobatVchNGnnvuOZKSkqiqquKnP/0p9913H2+99dZBx3V0dHDeeecxa9Ys1q1bx/3338+tt9464jnvuOMOfvGLX7BmzRqio6P5xje+4e9PI6C05lMkQpROymTAbbC+vo2KKRP2f2BwakNtF0KLrQIMFzjWwpRThx5e37weA0PFxKOwb2AfO9p3BPy6R6UdRUJ0wqiPnz17Nvfccw8AxcXF/PrXv2bFihWcccYZw4574YUXsFgsLFu2jPj4eGbMmIHD4eDqq68+6Jw//vGPOfVUz/fNbbfdxjnnnENPTw/x8fHj+MyCh5IbkQhxTG4KKXHRrP107+eSm0pIzoGMyabFJl6YON1TI2WvGpbcVDdXkxmfSVFKkYnBhYYd7Tu4+K8XB/y6L537EjMmzBj18bNnzx72fl5eHs3NzQcdt2XLFmbPnj0sQZk3b+QXLQeeMy8vD4Dm5maKisLj+0bJjUiEiLJaKClKP3jFVN1n+9uouWJosVqhcN5B+92sb1lPycQSNcschaPSjuKlc18y5bpjERMzfCNGi8WC2+0eVwwHnnPwe2W85wwmSm5EIkjppAye+WAnbreB1WqBgT5oWAen3212aOKNonL44BFwu8Fqpd/dz4bWDVw751qzIwsJCdEJYxpBCXbHHHMMv//97+nt7R1aQfXRRx+ZHJU5VFAsEkHKJmXSvq+f2pbPilAb18NAj4qJQ5WtHHrboWUzAJ/s+YR9A/tUbxOhvva1r+F2u7nmmmvYvHkzf//73/n5z38OEHEjeUpuRCJISVE6Vgv7l4TbKyE6HnLVLDMkFZSCJWpoSXh1czWx1tiwGo2Q0UtNTeUvf/kLNTU1lJSUcMcdd3D33Z5R2XApFB4tTUuJRJDkuGiOzUtlzc69XDKvyFOvUVAK0bFmhybeiE2CvNmer+MJV1HdXM1xWccRG6WvZ7hYuXLlQY8duK+NYRjDPrZgwQLWr18/9P7zzz9PTEzMUKHwaaeddtBzSkpKDnos1Cm5EYkwZZMy+NfWVk+zzLoqmHuZ2SHJeNgq4JO/YRgGNc01nDP1HLMjEhP97//+L1OmTKGgoID169dz6623ctFFF5GQMPql5+FA01IiEaZ0ciY7WrvY49gCXc37+xRJaCoqh707aWzeQPO+ZuZOVL1NJGtqauKyyy7j2GOP5Xvf+x4XXnghTz75pNlhBZxGbkQiTOkkTxPNhn+vJBOgUM0yQ5rN0zZj/Sd/BmBOtpplRrJbbrmFW265xewwTKeRG5EIk58WT1pCDBZ7lWcjuMRMs0OS8UjNh7Qitu6qJjsxm8x4fT1FlNyIRBiLxUJxdjIZe6rVciFcFJWzvfNTpqZNNTuSoBZuRbPhyFdfIyU3IhFoVpZBbu9O7W8TLmzl1Lq6mKqWCyOKiooCoK+vz+RI5EhcLk9jX6t1fOmJ6TU3jz76KD/72c9oampizpw5PPLII4fshQHw8MMP89hjj1FXV0dWVhZf/epXWbp0acSt4RcZj/LoWqwYuArLiTI7GBm3vsJS7JuimWKY/is9KFmtVhITE+no6AAgNjY24ja1CwWGYdDR0UFsbGxoJzcvvfQSS5Ys4fHHH6e8vJyHH36YM888ky1btpCdnX3Q8S+88AK33XYbTz/9NAsWLOCTTz7h61//OhaLhYceesiEz0AkNB07sJlWI5VuSx56rR/6Po1LwmWxMLVrj9mhBK20tDSAoQRHgpPFYmHChAnjTj5NTW4eeughrr76aq688koAHn/8cV5//XWefvppbrvttoOO//DDDznxxBP52te+BsDkyZO55JJLqKqqOuhYETm03PYaVrqPJrrFSVFWktnhyDjVdu4AYMqubSZHErwsFgvp6emkpqYOTX1I8ImOjvbJqJppyU1fXx9r167l9ttvH3rMarWycOFCVq1aNeJzFixYwO9//3tWr17NvHnz2L59O2+88QaXX375Ia/T29tLb2/v0PvK2iXiufqJ3VXDBsuXSW52cvqxOWZHJOO0vW07E6zxpNevHWqiKSOzWq3jnvKQ4GdactPa2orL5SInZ/gv1pycHD7++OMRn/O1r32N1tZWTjrpJAzDYGBggG9961v84Ac/OOR1li5dyr333uvT2EVCWtMGLP3dtGTOpXGX0+xoxAdq22qZmmKDnk+g9RPInm52SCKmCqn0deXKlTzwwAP85je/Yd26dbzyyiu8/vrr3H///Yd8zu233057e/vQm91uD2DEIkHIXgVRcZBXwrbmTrOjER/Y3r6dKdmzwWL1fH1FIpxpIzdZWVlERUWxa9euYY/v2rWL3NzcEZ9z1113cfnll/PNb34TgFmzZtHV1cU111zDHXfcMeJQY1xcHHFxcb7/BERCVV0l5M9lSm4mf9m4FcMwtHIkhPW7+9nZsZOLj7kYcmZ6kpvSK8wOS8RUpo3cxMbGUlpayooVK4Yec7vdrFixgvnz54/4nO7u7oMSmMH9C7Q5k8goGIbnj19ROcU5yXT3uWho7zE7KhkHe6edAfcAU9Onelox1FWaHZKI6UydllqyZAnLli3jueeeY/PmzVx77bV0dXUNrZ5avHjxsILj8847j8cee4wXX3yRHTt28NZbb3HXXXdx3nnnDSU5InIY7XbobARbBcXZKQBs3aWpqVC2vW07AFPSpniaoO6pBWeLyVGJmMvUpeAXX3wxLS0t3H333TQ1NVFSUsKbb745VGRcV1c3bKTmzjvvxGKxcOedd+JwOJg4cSLnnXceP/7xj836FERCS91n9Ri2eRQkJBAfY2Vbs5PTjjl4XykJDbVttaTHpXt6Sn3WRBN7FRx7rrmBiZjIYkTYfE5HRwdpaWm0t7eTmppqdjgigfX6TbB9JXxnLQDnPvIex+Wl8ZOvzjY3LvHaLf+6hV1du3juv57zPPDQDJj5FfjSoRdaiIS7kFotJSLjVFc1rJ9UcXYKW7ViKqRtb9vuqbcZZCvXiimJeEpuRCJFTwc0b4Si8qGHpmUns7XZqYL8EOVyu9jRvuPg5KahGvpVKC6RS8mNSKRwrAHD/bmRm2Q6ewZo7uw9zBMlWDmcDvrcfZ5i4kFF5eDqg8Ya0+ISMZuSG5FIUVcFCRkwYdrQQ8U5gyumtFNxKKptqwUYPnKTMwtikrQkXCKakhuRSGGv9ExZHLAC0ZaRQGy0VXU3Iaq2vZaUmBQmJkzc/2BUNBSWgn21eYGJmEzJjUgkcA1A/Zr9S4U/Ex1lZUpWElubNXITira3bWdK+pSDd5i2VXiKilVLJRFKyY1IJGjeCH1OzyZvn1Ock8I2TUuFpNr22uFTUoNs5dDdCrtrAx+USBBQciMSCeqqwBoD+XMP+lBxdjKfNHdqxVSIcRtudrTvGF5MPMh2AmDxTEWKRCAlNyKRwF4FeXMgJuGgDxVnJ9PW3c/urj4TAhNvNXY1sm9g38gjN/FpkD1DRcUSsZTciEQCe9WIU1IAxTnJgFZMhZqhlVJpIyQ34FkSrs38JEIpuREJd+0OT8PMzxUTD5o0IYloq4VtWjEVUra3bScxOpHcpNyRD7BVQOsn0L0nsIGJBAElNyLhbrDu4hAjNzFRVo7SiqmQU9tey5S0EVZKDbLN8/yrJeESgZTciIS7uirIOAqSD935uzgnWdNSIWZwGfghZUyG5BwVFUtEUnIjEu4OU28zaFp2ikZuQohhGIdeBj7IYvFMRdap7kYij5IbkXDW64SmDfunKA6hODuZVmcve7ViKiTs6t5FV3/XoYuJBxVVQMM6GNDXVSKLkhuRcOZYC4ZrWLPMkQyumNrWotGbULC9bTvA4aelwPN1H+iBpn8HICqR4KHkRiSc2as8e55MnH7Yw47KSsJq0XLwUFHbXkt8VDz5SfmHPzBvNkQnaL8biThKbkTCWV0lFM4b1ixzJHHRUUyekKQGmiGitq2Wo9KOIsoadfgDo2Kg4HgVFUvEUXIjEq7cbqj/yLOZ2yhMy05mm4qKQ8L29iOslDrQYFGx2mtIBFFyIxKuWjZDb8chN+/7PC0HDw2GYVDbVnvkYuJBRRXQ1Qx7d/g3MJEgouRGJFzVVYIlCgpKR3V4cXYKTR09dPT0+zkwGY/dPbvp6OsY/chN4Qmef7WZn0QQJTci4cpe5SkojU0a1eHTsj9bMaWpqaB2xJ5Sn5eY6SkoV1GxRBAlNyLhqq7yiEvADzR1YjIWC2zT1FRQq22rJcYaQ2FK4eifZJunJpoSUZTciISjziZo+3TUxcQACbFR2DIStWIqyG1v387ktMlEW6NH/yRbBTRvhn1tfotLJJgouREJR4Ov0scwcgOenYrVhiG4jamYeFBRBWB4Vs+JRAAlNyLhqK4K0oogNW9MT5umFVNBr7atdvTFxIMyp0BilupuJGIouREJR/bKMU1JDSrOTsHRto+u3gE/BCXjtadnD3t794595MZi8YzeqO5GIoSSG5Fw09cNjetHvb/NgYo/WzFVqx5TQWlopdThuoEfim2ep9eYS0v9JfwpuREJNw3V4B74rM5ibKZ+ltxoaio4bW/bTrQlmqKUorE/2VYB/d2eLvEiYU7JjUi4sVdCbApkzxjzU5PjoilIT1BRcZCqba+lKLWImKiYsT85vwSi4jQ1JRFByY1IuKmrgsIyOFJTxUPw9JjScvBgtL1tu3dTUgDRcZA/V0XFEhGU3IiEE7fb88rciympQVoOHrxq22uZkjbGlVIHKir3fH+oiaaEOSU3IuGk9RPoafOqmHhQcU4ydXu66el3+S4uGbf23nZa97V6P3IDnrqbzkZot/suMJEgpORGJJzYK8Fi9UxLeWladgqGoRVTwWZ7+3aA8Y3c2OZ5/q1T3Y2ENyU3IuHEvhpyZkJcitenUAPN4FTbVovVYmVy2mTvT5KUBROmeZJgkTCm5EYknNRVjmtKCiAtIYac1DgtBw8ytW212FJsxEXFje9ERRUauZGwp+RGJFw4W2BP7biKiQcVZ6eogWaQ2d6+naPSjhr/iWwV0LwRejrGfy6RIKXkRiRcDDXLHN/IDcDkrEQ+3d097vOI79g77UxOnTz+E9nKwXCDY834zyUSpJTciIQLeyWkFkC6bdynsmUkUr93H4aWDAcFl9tFY1cjBckF4z/ZhGmQkKGpKQlrSm5EwoV9tU9GbQAKMxJx9g7Q1q0+RMGgubuZAfcA+cn54z+Z1er5PlFRsYQxJTci4aC/x9NTygf1NgC2zAQA6vfu88n5ZHwcTgcAhcmFvjmhrRzq14BL3d8lPCm5EQkHjTXg6tu/j8k42TISAbDvVd1NMBhMbnwycgNQNB/6nNC8yTfnEwkySm5EwkFdJcQkQc4sn5wuPTGGpNgo7HuU3AQDh9PBhPgJxEfH++aE+XPBGqMmmhK2lNyIhAN7FRSWQlS0T05nsViwZSZqWipIOJwOClJ8UEw8KCYe8uaoiaaELSU3IqHOMDzJjc039TaDCjMSNS0VJBxOh29WSh2oqEIjNxK2lNyIhLrdtdC929Px2YcKMxI0LRUk/JLc2Mo9DTTbHb49r0gQUHIjEurslYAFCk/w6WkHp6W01425+l39NHc3+2fkBjR6I2FJyY1IqKurhOwZEJ/m09PaMhLoHXDT4uz16XllbJq6mnAbbt8nN8nZkDlFyY2EJSU3IqHOXuXzKSnw1NwA2PeoqNhM9c56AN8nN+CZmlJRsYQhJTcioax7D7R+4vNiYjhwIz/V3ZipwdmABQt5SXm+P7ltHjRtgF51gJfwouRGJJTZV3v+9cPITUp8DOmJMVoObjKH00FOUg4xUTG+P7mtAgwXONb6/twiJlJyIxLK7JWQnAvpk/xyeq2YMl+9s94/U1IAE6d7arUGk2SRMKHkRiSU1VV5phYsFr+c3qa9bkzX4GzwX3KjJpoSppTciISqgT5oWOezZpkj0S7F5vPLHjcHss0D+0fgdvvvGiIBpuRGJFQ1roeBHr8UEw8qzEigoW0fLrf2ujFDz0APrfta/ZzclENvO7Rs9t81RAJMyY1IqLJXQnQC5M322yVsGYn0uwyaOnr8dg05tIauBsCH3cBHUlAKligtCZewouRGJFTZqzx/mPyxiuYzQ8vBVVRsCkenpzVCYXKh/y4Sm+RJkFVULGFEyY1IKDKM/cXEflSQ/tlGfqq7MYXD6SDaEk12YrZ/L2SrUFGxhBUlNyKhaO8O6Gr2azExQEJsFFnJcVoObhKH00FuUi5R1ij/XqioHPbuhM5d/r2OSIAouREJRXWf9QPycbPMkdgyE7RiyiQOp4OCFD8WEw8aLErX6I2ECSU3IqHIXunZgC0x0++X0l435nE4Hf6ttxmUmgdpRfuTZpEQp+RGJBTZV3uW8AZAYUaCCopN4nA6/LtS6kBF5eoQLmHD9OTm0UcfZfLkycTHx1NeXs7q1Yev2G9ra+Pb3/42eXl5xMXFcfTRR/PGG28EKFqRILCvDZo3+73eZpAtM5Gmjh76BrTJWyA5+5y097b7d4+bA9nKPXsn9WsKUkKf18nNihUrOPfcc5k6dSpTp07l3HPP5e233x7TOV566SWWLFnCPffcw7p165gzZw5nnnkmzc3NIx7f19fHGWecwc6dO/nTn/7Eli1bWLZsGQUFAfrhFwkG9R8BRsBGbmwZibgNaGzXH71Acjg9y8ADltwUVYC7HxzrAnM9ET/yKrn5zW9+w1lnnUVKSgo33HADN9xwA6mpqZx99tk8+uijoz7PQw89xNVXX82VV17JjBkzePzxx0lMTOTpp58e8finn36aPXv2sHz5ck488UQmT57Mqaeeypw5c7z5NERCU10lJGZB5pSAXK4ww7PXjX2PkptACnhykz0D4lJVVCxhwavk5oEHHuCXv/wlf/jDH/jud7/Ld7/7XV544QV++ctf8sADD4zqHH19faxdu5aFCxfuD8ZqZeHChaxatWrE57z22mvMnz+fb3/72+Tk5DBz5kweeOABXC7XIa/T29tLR0fHsDeRkGav8rzK9lOzzM/LT0/AYoF6FRUHVIOzgbioOLISsgJzQWsUFJapqFjCglfJTVtbG2edddZBj3/pS1+ivb19VOdobW3F5XKRk5Mz7PGcnByamppGfM727dv505/+hMvl4o033uCuu+7iF7/4BT/60Y8OeZ2lS5eSlpY29Gaz2UYVn0hQcvWDY23ApqQAYqOt5KXGa8VUgA0WE1sClMQCn3UIr1ITTQl5XiU3559/Pq+++upBj//5z3/m3HPPHXdQh+J2u8nOzubJJ5+ktLSUiy++mDvuuIPHH3/8kM+5/fbbaW9vH3qz2+1+i0/E75o2QH93wIqJBxVmJGpaKsDqnfWBWyk1yFYOPW2we2tgryviY9HePGnGjBn8+Mc/ZuXKlcyfPx+AyspKPvjgA2666SZ+9atfDR373e9+d8RzZGVlERUVxa5dw3fE3LVrF7m5uSM+Jy8vj5iYGKKi9u/Weeyxx9LU1ERfXx+xsbEHPScuLo64uLgxf44iQcleBVFxkBfYOrPCzAR2tnYF9JqRrsHZwNzsuYG9aGEZWKyeuq6JxwT22iI+5FVy89vf/paMjAw2bdrEpk2bhh5PT0/nt7/97dD7FovlkMlNbGwspaWlrFixgkWLFgGekZkVK1Zw/fXXj/icE088kRdeeAG3243V6hl0+uSTT8jLyxsxsREJO3WVkD8XogObsNsyEnlva2tArxnJDMPA4XRw7hT/jYSPKC4FcmZ6kujSKwJ7bREf8iq52bFjh08uvmTJEq644grKysqYN28eDz/8MF1dXVx55ZUALF68mIKCApYuXQrAtddey69//WtuuOEGvvOd77B161YeeOCBQyZQImHFMDx/dGZfFPBLF2Yk0NLZS0+/i/gYP/c5Etp72+nq7wr8tBR4pjy3rQj8dUV8yKvkxlcuvvhiWlpauPvuu2lqaqKkpIQ333xzqMi4rq5uaIQGwGaz8fe//53vfe97zJ49m4KCAm644QZuvfVWsz4FkcBpq4POxv19gALIlunpDl6/dx/TspMDfv1I4+jyLAMPSOuFz7OVw+onwdkCyRMDf30RHxh1crNkyRLuv/9+kpKSWLJkyWGPfeihh0YdwPXXX3/IaaiVK1ce9Nj8+fOprNQ+DBKB7J/t3h3AlVKDBpMb+95uJTcB4OgM8B43Bxr8/qpfDdPPCfz1RXxg1MlNdXU1/f39Q/8/lIAuWxSJJPZKmFAMSRMCfunc1HiirRb1mAoQh9NBYnQiaXFpgb94ug1SCzz1XUpuJESNOrl55513Rvy/iARIXZUpozYAUVYL+ekJ1O/VcvBAcDgdFKQUmPdi0aYmmhLaTG+cKSKj0NMBzRs9nZtNYstM0EZ+AeJwOsyZkhpUVAEN1dDfY14MIuPgVUFxV1cXDz74ICtWrKC5uRn353az3L59u0+CE5HP1H8EhtuUYuJBhemJbGpU+5JAcDgdnJh/onkB2MrB1QeNNQHfMFLEF7xKbr75zW/y7rvvcvnll5OXl6c6GxF/s6+GhEzIKjYtBFtmAv/YNHJrFPEdwzBocDaYswx8UM5MiEna38dMJMR4ldz87W9/4/XXX+fEE018ZSESSeyVnlfTJr6QsGUmsre7H2fvAMlxpu4iEdZ29+ym19Vr7rRUVDQUlnrqvPRrXkKQVzU3GRkZZGZm+joWERmJawDq15habwOejfwA7Fox5Vf1nfWAScvAD2Sr8IzcGIa5cYh4wavk5v777+fuu++mu1u/5ET8rnkj9DlNWyk1yJbx2V43Sm78qsHZAARBclNUDt2tsLvW3DhEvDDqseW5c+cOq63Ztm0bOTk5TJ48mZiYmGHHrlu3zncRikS6uiqwxnh6SploYkoccdFWLQf3M4fTQVpcGsmxJm+WWHgCYPFMiWZNMzcWkTEadXIz2NxSRALMXgX5JRCTYGoYFouFggwtB/c305eBD4pPg+wZns385l5mdjQiYzLq5Oaee+7xZxwicij2KphxgdlRAJ6pKfsejdz4U9AkN+CZmtr5gdlRiIyZVzU3drud+vr6ofdXr17NjTfeyJNPPumzwEQEaHdAuz1oluPaMhOo18iNXwVVcmOrgNYt0L3H7EhExsSr5OZrX/vaUAuGpqYmFi5cyOrVq7njjju47777fBqgSESzf9Yk1uRi4kGFGYnU792HoRU0fuFyu2jsagye5GZwhd5g01aREOFVcvOf//yHefPmAfDyyy8za9YsPvzwQ55//nmeffZZX8YnEtnqqiDjKEjONjsSwDMt5ewdoK273+xQwlLLvhYG3APmbuB3oPRJkJy7P8kWCRFeJTf9/f3ExcUB8Pbbb3P++ecDMH36dBobG30XnUikC7IdYm2ZnqJmrZjyj8E9bgqTC02O5DMWC9jmeZJskRDiVXJz3HHH8fjjj/Pee+/x1ltvcdZZZwHQ0NDAhAkTfBqgSMTqdULThqCZkgLPtBSgFVN+4nA6AIJn5AY+a6K5Dgb6zI5EZNS8Sm5+8pOf8MQTT3DaaadxySWXMGfOHABee+21oekqERknx1owXEE1cpORGENSbJQ28vOTBmcDE+InEB8db3Yo+9kqYKAHmv5tdiQiozbmBjGGYTBlyhTq6uoYGBggIyNj6GPXXHMNiYmJPg1QJGLZqzx7jWQdY3YkQywWC7bMRE1L+Um9s56ClCApJh6UNxuiEzz73RSWmR2NyKiMeeTGMAymTZtGU1PTsMQGYPLkyWRnB0fho0jIq6uEwnlg9WqA1W8KtZGf3wTVMvBBUTFQUKqiYgkpY/6tabVaKS4uZvfu3f6IR0QA3G6o/8j0ZpkjKcxI1LSUnzQ4G4IvuYH9RcXaAkBChFcvCR988EG+//3v85///MfX8YgIQMtm6O3w1DsEmcFpKe1141v97n52de8KzuSmqAK6mmHvDrMjERmVMdfcACxevJju7m7mzJlDbGwsCQnDe97s2aPdLEXGpa4SrNGe6YAgU5iRQO+AmxZnL9kpQVT4GuKanE24DXdwJjeFJ3j+rauCzCnmxiIyCl4lNw8//LCPwxCRYexVkDsbYoOvQN82uBx8zz4lNz7k6PIsAw/K5CYxEyZO93xfllxidjQiR+RVcnPFFVf4Og4ROVBdJRxzttlRjKhwaCO/bkonZRzhaBktR6cDCxbykvLMDmVktnJPciMSArxehlFbW8udd97JJZdcQnNzMwB/+9vf2Lhxo8+CE4lInU3Q9mlQFhMDpMbHkJYQo+XgPuZwOshJyiEmKsbsUEZmK4fmzbCvzexIRI7Iq+Tm3XffZdasWVRVVfHKK6/gdDoBWL9+Pffcc49PAxSJOIOvjoOwmHiQLTNBK6Z8zOF0kJ8URDsTf15RBWB4VvGJBDmvkpvbbruNH/3oR7z11lvExsYOPf7FL36RykrthSAyLnVVkF4EqUE6PYGn7kYjN77lcDooTAmSnlIjyZwCiVmeKVORIOdVcrNhwwa+/OUvH/R4dnY2ra2t4w5KJKLZK4N61Aa0kZ8/BOUGfgeyWDyjN6q7kRDgVXKTnp4+Yvfv6upqCgqC+IdTJNj1dUPj+qCttxlky0ykoW0fLrf2uvGFnoEeWve1BlfDzJHYyj09z1z9ZkciclheJTf//d//za233kpTUxMWiwW3280HH3zAzTffzOLFi30do0jkaFgH7oGg6gQ+EltGIv0ug10dPWaHEhYauhqAIF0GfqCiCujv9nSrFwliXiU3DzzwANOnT8dms+F0OpkxYwannHIKCxYs4M477/R1jCKRw14FsSmQPcPsSA6rMMOzHFxFxb7h6PTscVOYHMQ1NwB5cyAqTlNTEvS8Sm5iY2NZtmwZtbW1/PWvf+X3v/89H3/8Mb/73e+IiorydYwikaOuCmwngDW4f44KBzfyU1GxTzicDqIt0WQnBnnj4eg4yJ+romIJel5t4jeoqKgIm80GgMVi8UlAIhHL7fa8Iq64zuxIjighNoqs5DiN3PhIg7OB3KRcooI8qQU89WD/ftnTRFO/9yVIeb2J329/+1tmzpxJfHw88fHxzJw5k6eeesqXsYlEltZPoKct6IuJBxVmJGg5uI/UO+spSAnyeptBtgrobIR2u9mRiBySVyM3d999Nw899BDf+c53mD9/PgCrVq3ie9/7HnV1ddx3330+DVIkItgrwWINymaZI7FlJmo5uI84nA6OzTzW7DBGZ7DYfXA/JpEg5FVy89hjj7Fs2TIuuWR/A7Xzzz+f2bNn853vfEfJjYg36qogZybEpZgdyajYMhJYu3OP2WGEhQZnA6cXnW52GKOTNAEmTPMk47MvNDsakRF5NS3V399PWVnZQY+XlpYyMDAw7qBEIpK96rMt7kODLTORpo4e+gbcZocS0rr6u2jrbQv+ZeAHslV4knGRIOVVcnP55Zfz2GOPHfT4k08+yaWXXjruoEQijrMF9tQG/f42ByrMSMBtQGO76m7Go76zHgiBPW4OVFQOzRuhp8PsSERGNOppqSVLlgz932Kx8NRTT/GPf/yDigrPK82qqirq6uq0iZ+INwb3DQmlkZvB5eB79jFpQpLJ0YSuBmeIbOB3IFsFGG5wrIGpXzQ7GpGDjDq5qa6uHvZ+aamn6LG2thaArKwssrKy2Lhxow/DE4kQ9kpILYC0IN/E7QD56QlYLFCvouJxcTgdxEXFkZWQZXYoo5dVDAkZnqkpJTcShEad3Lzzzjv+jEMkstVVhdSUFEBstJXc1HitmBonh9NBfnJ+aO0VZrF4vl/t2sxPgpPX+9yIiI/090BjTUhNSQ2yZSRi36Oam/EYTG5Cjq0c6teAS4tIJPh4tRS8p6eHRx55hHfeeYfm5mbc7uGrJdatW+eT4EQiQmMNuPpCbuQGoDAzgZ2tXWaHEdIcTgdzs+eaHcbYFVVAn9NTWJw3x+xoRIbxKrm56qqr+Mc//sFXv/pV5s2bF1rDqSLBpq4SYpI8e9yEmMKMRN7b2mp2GCHLMAwcTgfnTjnX7FDGLn8uWGM8U6pKbiTIeJXc/PWvf+WNN97gxBNP9HU8IpHHXgWFpRA1rlZvprBlJNDS2UtPv4v4mBDoixRkOvo66OrvCs1pqZgEyC/xfP+WX2N2NCLDeFVzU1BQQEpKaOyiKhLUDMPzx8EWevU24NnID1CPKS/VOz173BQmh84quWFs5fu3MRAJIl4lN7/4xS+49dZb+fTTT30dj0hk2b0NuneHTLPMzyvMSADQiikvOTodAKE5cgOe5KbdDu0OsyMRGcarcfCysjJ6enqYMmUKiYmJxMTEDPv4nj3qNyMyKvYqwAKFJ5gdiVfy0hKItlqo36PkxhsNzgYSoxNJj0s3OxTvDK7ws1dC2lfMjUXkAF4lN5dccgkOh4MHHniAnJwcFRSLeKuuErJnQHya2ZF4JcpqIT89QdNSXqp31lOQUhC6v0OTsyHjKE9R8UwlNxI8vEpuPvzwQ1atWsWcOaqQFxkXexVMPsnsKMalMCNB01JecjgdFCSFUNuFkRRVqO5Ggo5XNTfTp09n3z69UhMZl+490PpJyBYTD9JGft5rcDZQkBLiyY2tHJo2QK/T7EhEhniV3Dz44IPcdNNNrFy5kt27d9PR0THsTURGYahZZmgWEw+yZSaov5QXDMPwJDeh1DBzJLZyMFzgWGt2JCJDvJqWOuusswA4/fTThz1uGAYWiwWXyzX+yETCnb0KknMhfZLZkYxLYUYie7v7cfYOkBwXenv1mGV3z256XD2hu1Jq0MTpnpoxexVMOdXsaEQAL5MbNdEU8YG6KrDN8zQhDGG2zM+Wg+/p5ti8VJOjCR0Op2f5dMjucTPIaoXCeZ7ieJEg4VVyc+qpys5FxmWgDxrWwel3mx3JuNky9m/kp+Rm9EJ+j5sDFZXDB78Ct9uT7IiYzOvvwvfee4/LLruMBQsW4HB4fkh/97vf8f777/ssOJGw1bgeBnpCvpgYICs5jthoK3btdTMmDqeD1NhUUmLDYLd3WwX0dkDLZrMjEQG8TG7+3//7f5x55pkkJCSwbt06ent7AWhvb+eBBx7waYAiYcleCdEJkDfb7EjGzWq1aDm4FxxOR+gXEw8qOB4sUZqakqDhVXLzox/9iMcff5xly5YN2534xBNPZN26dT4LTiRs1VV6/iBExRz52BBgy0jURn5j5HA6KEwJ8XqbQbFJnkRd+91IkPAqudmyZQunnHLKQY+npaXR1tY23phEwpthgH21ZwltmCjMSNC01Bg5nA7yk8Kg3maQrUIjNxI0vEpucnNz2bZt20GPv//++0yZMmXcQYmEtb07oKt5f1+eMGDL9IzcGIZhdighweV20djVGPob+B2oqBzaPoXOJrMjEfEuubn66qu54YYbqKqqwmKx0NDQwPPPP8/NN9/Mtdde6+sYRcJL3WdD9yHaLHMktoxEnL0DtO/rNzuUkNCyr4UB90D41NzA/uJ4TU1JEPBqKfhtt92G2+3m9NNPp7u7m1NOOYW4uDhuvvlmvvOd7/g6RpHwYq/0bHyWmGl2JD5TmDG4180+0hNjTY4m+NV31gOEV3KTmgfpRZ7kfcYFZkcjEc6r5MZisXDHHXfw/e9/n23btuF0OpkxYwbJycm+jk8k/Axu3hdGbJmevW7se7uZVRiaHc4DqaGrAQiTPW4OZCv3JO8iJhtTcvONb3xjVMc9/fTTYwri0Ucf5Wc/+xlNTU3MmTOHRx55hHnzjvzL/8UXX+SSSy7hggsuYPny5WO6pogp9u317AWyILxGODMSY0iKjVJR8Sg5Oh1MiJ9AQnSC2aH4lq0cNr4Kfd0Qm2h2NBLBxpTcPPvss0yaNIm5c+f6rHDwpZdeYsmSJTz++OOUl5fz8MMPc+aZZ7Jlyxays7MP+bydO3dy8803c/LJJ/skDpGAqF/j+TeMionBM5o7WFQsR1bvrA+vKalBRRXgHoCGaph8otnRSAQbU3Jz7bXX8oc//IEdO3Zw5ZVXctlll5GZOb66gYceeoirr76aK6+8EoDHH3+c119/naeffprbbrttxOe4XC4uvfRS7r33Xt57773DLj/v7e0d2mQQUNdyMVddJSRmQWb4rSrURn6jFxbdwEeSPQPiUj1TU0puxERjWi316KOP0tjYyC233MJf/vIXbDYbF110EX//+9+9Gsnp6+tj7dq1LFy4cH9AVisLFy5k1apVh3zefffdR3Z2NlddddURr7F06VLS0tKG3mw225jjFPEZe5Xn1W2IN8scSWFGoqalRsnhdITXMvBB1igoLNu/IlDEJGNeCh4XF8cll1zCW2+9xaZNmzjuuOO47rrrmDx5Mk6nc0znam1txeVykZOTM+zxnJwcmppG3ivh/fff57e//S3Lli0b1TVuv/122tvbh97sdvuYYhTxGVe/Z1oqzIqJB2mvm9Hpd/ezq3tX+BUTD7KVe5J4t9vsSCSCjat9q9VqxWKxYBgGLpfLVzEdUmdnJ5dffjnLli0jKytrVM+Ji4sjNTV12JuIKZr+DQP7wqJZ5kgKMxLoHXDT4uw98sERrKmrCbfhDs9pKfAkNz1t0PqJ2ZFIBBtzctPb28sf/vAHzjjjDI4++mg2bNjAr3/9a+rq6sa8FDwrK4uoqCh27do17PFdu3aRm5t70PG1tbXs3LmT8847j+joaKKjo/nf//1fXnvtNaKjo6mtrR3rpyMSOPbVEBUH+SVmR+IXtozPloPvUVHx4TicDgAKk8Okr9TnFZaBxaol4WKqMSU31113HXl5eTz44IOce+652O12/vjHP3L22WdjtY59ECg2NpbS0lJWrFgx9Jjb7WbFihXMnz//oOOnT5/Ohg0bqKmpGXo7//zz+cIXvkBNTY3qaSS41VVC/lyIjjM7Er8ozPQsa65XUfFhOTodWLCQm3TwC7iwEJcCOTM9ybyISca0Wurxxx+nqKiIKVOm8O677/Luu++OeNwrr7wy6nMuWbKEK664grKyMubNm8fDDz9MV1fX0OqpxYsXU1BQwNKlS4mPj2fmzJnDnp+eng5w0OMiQcUwPHUIsy8yOxK/SY2PIS0hRsvBj8DhdJCdmE1sVBjv5FxUAdtWHPk4ET8ZU3KzePFiLD5e5XHxxRfT0tLC3XffTVNTEyUlJbz55ptDRcZ1dXVejQqJBJW2OuhsDNt6m0G2THUHPxKH0xG+9TaDbOWw+klwtkDyRLOjkQg05k38/OH666/n+uuvH/FjK1euPOxz/RWTiE8NNhMM05VSg2wZ2sjvSBxOB0UpRWaH4V+2cs+/9io49lxzY5GIpCERkUCwV8GEaZA0ulV+oUob+R1Zg7MhPPe4OVC6DVILVFQsplFyIxIIdVVhPyUFnr1uGtr24XJrr5uR9Az00LKvJfynpeCz/W5UVCzmUHIj4m89HdC8EYrKzY7E72wZifS7DHZ19JgdSlAa7AYeEclNUYWnx1S/vhck8JTciPhb/UdguCNi5KYww7McXEXFI2twRlByYysHVx801pgdiUQgJTci/mavgoQMT81NmCsc3MhPRcUjcnQ6iLZEk52YbXYo/pczE2KSPPs7iQSYkhsRf7NXeV7FRsCWBgmxUWQlx2kjv0NwOB3kJOUQbR3TQtXQFBUNhaX7VwqKBFD4/7YVMZNr4LNmmeFfbzPIs9eNRm5G4nA6wrftwkhsFZ7kRs1UJcCU3Ij4U/NG6HN6iisjRGFGopaDH4LD6Qj/ZeAHKiqH7t2wW33/JLCU3Ij4U10VWGM8PaUihC0jAYdqbkbkcDrIT8o3O4zAKTwBsGi/Gwk4JTci/mSv9HQBj0kwO5KAsWUm0ti+j36X2+xQgkpXfxdtvW2RNXITnwY5x6moWAJOyY2IP9lXR1S9DXiWg7sNaGjT6M2BHE4HQGTV3ICn5YiKiiXAlNyI+Eu7A9rtEZfc2D5bDq4eU8M5Oj3JTX5yBE1LgaeouPUT6N5jdiQSQZTciPjLYJ1BBBUTA+SnJ2CxaCO/z2voaiDWGktWQnj3FzvI4M7casUgAaTkRsRf6qog4yhIjoAN2w4QG20lNzVeK6Y+p76znvzkfKyWCPu1mz4JknNVVCwBFWE/ZSIBZK+MuFGbQbaMRE1LfY7D6YiMtgufZ7F4Rm/qVHcjgaPkRsQfep3Q9J+Iq7cZVJiZoGmpz2lwNkRmcgOen4OGdTDQZ3YkEiGU3Ij4g2MtGK7ITW4yEtVf6gCGYUTeBn4HslXAQA80rjc7EokQSm5E/MFe5dnjY+J0syMxhS0jgZbOXnr6XWaHEhQ6+jpw9jsjb6XUoLzZEJ2gJeESMEpuRPyhrhIK50VEs8yR2DK1HPxAEbvHzaCoGCgoVVGxBExk/uYV8Se3C+o/2r8ENgINJjdaMeUxmNxEbM0N7C8qVhNNCQAlNyK+1vIx9HZ46gwiVG5qPNFWC/UqKgY8G/glRCeQHpdudijmsVVAVzPs3WF2JBIBlNyI+FpdJViioOB4syMxTZTVQn56AnVKbgCod9ZTkFyAxWIxOxTzFJZ5/tWScAkAJTcivmav8hRQxiaZHYmppk5Moraly+wwgsKO9h0clXaU2WGYKzHTU2CvomIJACU3Ir5WVxnRU1KDinNS2NrcaXYYQaG2rZap6VPNDsN8tnIlNxIQSm5EfKmzCdo+jehi4kHTspOp37uP7r4Bs0MxVVtPG7t7djM1TckNRRXQvBn2tZkdiYQ5JTcivjT4qlQjNxRnJ2MYsD3Cp6a2t28HYEr6FJMjCQK2csDwrCYU8SMlNyK+VFcF6UWQmmd2JKablp0MEPFTU7XttVgtVianTjY7FPNlToHELM/UrYgfKbkR8SV7ZcS2XPi8lPgY8tLi2brLaXYoptretp2ilCJio2LNDsV8Fotnakp1N+JnSm5EfKWv29M7R8nNkGnZyWxtjuzkpratlilpmpIaYiv39F5z9ZsdiYQxJTcivtKwDtwDnlemAkBxdgrbIj25addKqWGKKqC/G5o2mB2JhDElNyK+Yq+C2BTInmF2JEGjOCeZT3d3RWwDzc6+Tpq7m1VMfKC8ORAVp6kp8SslNyK+UlcFthPAGmV2JEGjODsZtwE7WiNzxdTgSiktAz9AdBzkz1VRsfiVkhsRX3C7Pa9EtQR8mP0rpiJzamp723YsWJicNtnsUIJL0Web+amJpviJkhsRX2j9BHrawDbP7EiCSnpiLBNT4ti2KzKXg9e21VKQXEBCdILZoQQXWwV0NkK73exIJEwpuRHxBXslWKz7mwPKkOIIXjGlYuJDGFxRqCaa4idKbkR8wb4acmZCXIrZkQSdSE5utrdtVzHxSJImwIRiz4sCET9QciPiC3WVWgJ+CNNyUtjZ2kXfgNvsUAKqu7+bhq4GFRMfSlG5Rm7Eb5TciIyXswX21GrzvkMozk5mwG3w6e7IWjG1o30HgKalDsVWDs0boafD7EgkDCm5ERmvoWaZSm5GUhyhK6Zq22sBOCrtKJMjCVK2CjDcaqIpfqHkRmS87JWQWgDpNrMjCUoTkuPITIqNuB5TtW215CXlkRSTZHYowSmrGBIyPfVqIj6m5EZkvOyrNWpzBJ4eU5G1HFzFxEdgsXh+blRULH6g5EZkPPp7oKFaxcRHUJydHHE9pmrba1VMfCRF5VC/BlwDZkciYUbJjch4NNaAq08jN0dQnJ3M9pYuBlyRsWKqZ6CH+s56FRMfia0c+pyewmIRH1JyIzIedZUQk+TZ40YOqTgnhT6Xm7o93WaHEhA7O3ZiYDAlTdNSh5U/F6wxWhIuPqfkRmQ87FVQWApR0WZHEtQibcVUbZtnpZRGbo4gJgHyS9QhXHxOyY2ItwxDzTJHaWJKHKnx0RFTd1PbVkt2YjYpsdqx+ohs5UpuxOeU3Ih4a3ctdO/2FEXKYVksFopzUtgaIQ00t7dvVzHxaBVVeBpotjvMjkTCiJIbEW/ZKwELFJ5gdiQhoTg7mW0tkTNyoympURosxteScPEhJTci3qqrhJzjID7N7EhCwrTPloO73YbZofhVn6sPe6dde9yMVnI2ZBylomLxKSU3It6yV4FtntlRhIzinBR6+t042vaZHYpffdrxKS7DpWmpsSiqUN2N+JSSGxFvdO+B1k9UTDwG+1dMhXfdzWBPKS0DHwNbOTRtgN7ImLYU/1NyI+KNwX44KiYetby0eJJio8K+x9T2tu1MiJ9Aeny62aGEjqIKMFzgWGt2JBImlNyIeMNeCcm5kD7J7EhChsViYVpOStjvdaNiYi9kHeOpXdPUlPiIkhsRb9RVeUZtLBazIwkpxdnJYZ/cbG/frimpsbJaoXCep0hfxAeU3IiM1UAfNKxTvY0XirOT2barE8MIzxVT/e5+dnbs1MiNN4rKof4jcLvMjkTCgJIbkbFq+jcM9KhZpheKc5Lp6nPR2N5jdih+Ye+0M+AeUHLjDVsF9HZAy8dmRyJhQMmNyFjVVUJ0AuTNNjuSkFOc7WlHEK5TU9vbtgNaKeWVglKwRGlqSnxCyY3IWNkrPb+Io2LMjiTkFKQnEB9jDds2DLVttaTHpZMZn2l2KKEnNtHzgkFFxeIDSm5ExsIw9hcTy5hZrZahnYrDUW17LVPSpmBRobl3bBUauRGfUHIjMhZ7d0BXs4qJx6E4O3yXg29v2656m/EoKoe2T6GzyexIJMQpuREZi8HN+wrLzI0jhE3LTmZrGK6Ycrld7GjfoeRmPAZfNGhqSsZJyY3IWNRVwsTpkKiaCm8VZyfT0TNAS2ev2aH4lMPpoM/dp2Li8UjNg/QiNdGUcQuK5ObRRx9l8uTJxMfHU15ezurVqw957LJlyzj55JPJyMggIyODhQsXHvZ4EZ+yV2kJ+DgV54TniqnaNk9PKY3cjJOt3FO0LzIOpic3L730EkuWLOGee+5h3bp1zJkzhzPPPJPm5uYRj1+5ciWXXHIJ77zzDqtWrcJms/GlL30Jh8MR4Mgl4uxrg+bNnj444jVbRgKx0eG3Yqq2vZaUmBQmJkw0O5TQZiuHxvXQ1212JBLCTE9uHnroIa6++mquvPJKZsyYweOPP05iYiJPP/30iMc///zzXHfddZSUlDB9+nSeeuop3G43K1asCHDkEnHqPwIMjdyMU3SUlSlZSWE3crO9bTtT0rVSatyKKsA94NkFXMRLpiY3fX19rF27loULFw49ZrVaWbhwIatWrRrVObq7u+nv7yczc+QaiN7eXjo6Ooa9iXjFXgWJWZCpmorxKg7DBpq17WqY6RPZMyAuVUvCZVxMTW5aW1txuVzk5OQMezwnJ4emptEtBbz11lvJz88fliAdaOnSpaSlpQ292Wy2ccctEaqu0vOqUq/Mx604zPa6cRtudrTvUDGxL1ijPKsR7aqlFO+ZPi01Hg8++CAvvvgir776KvHx8SMec/vtt9Pe3j70ZrfbAxylhAVXPzjWakrKR4qzk9nT1cduZ3ismGrsamTfwD6N3PiKrcIzUup2mx2JhChTk5usrCyioqLYtWvXsMd37dpFbm7uYZ/785//nAcffJB//OMfzJ596B4/cXFxpKamDnsTGbOmDdDfrWJiHynOSQbCZ8XU0EqpNCU3PmGbBz1t0PqJ2ZFIiDI1uYmNjaW0tHRYMfBgcfD8+fMP+byf/vSn3H///bz55puUlWkzNQkAexVExUHeHLMjCQuTJiQRbbWETXKzvW07idGJ5CYd/kWZjFJhGVisWhIuXjN9WmrJkiUsW7aM5557js2bN3PttdfS1dXFlVdeCcDixYu5/fbbh47/yU9+wl133cXTTz/N5MmTaWpqoqmpCaczPH5JSpCqq4SC4yE6zuxIwkJMlJWjspLYFibLwdVTysfiUiBnpjbzE69Fmx3AxRdfTEtLC3fffTdNTU2UlJTw5ptvDhUZ19XVYbXuz8Eee+wx+vr6+OpXvzrsPPfccw8//OEPAxm6RArD8IzczL7I7EjCSnFOcliN3ExJVzGxTxVVwDZt8SHeMT25Abj++uu5/vrrR/zYypUrh72/c+dO/wckcqB2O3Q2qlmmj03LTuGFqk8xDCOkRzzchpva9lq+WPRFs0MJL7ZyWP0kOFsgWRsjytiYPi0lEvQGh8a1UsqnZuan0ursw9G2z+xQxmVH+w66+ruYMWGG2aGEl8GfNzXRFC8ouRE5EnslTCiGpAlmRxJWSidlALD2070mRzI+1c3VWC1WZk889KpN8UK6DVILVFQsXlFyI3IkdVVQpFEbX5uQHMeUrKSQT25qmms4JuMYkmKSzA4l/NjKVVQsXlFyI3I4PR3QvFFTUn5SOimDNTtDPLlpqaEku8TsMMJTUQU01kB/j9mRSIhRciNyOI41YLhVTOwnZZMz+LipA2fvgNmheGX3vt182vEpJRNLzA4lPNnKwdXnSXBExkDJjcjh1FVBQiZkFZsdSVgqnZSB24DqutAcvalpqQFgbvZccwMJVzkzISZJTTRlzJTciByOvdLz6jGElyoHsylZyaQnxoTs1FRNcw05iTnkJeeZHUp4ioqGwlKtmJIxU3IjciiuAahfo2JiP7JaLZQWZYRsUXF1c7VGbfxtsImmYZgdiYQQJTcih9K8EfqcKib2s9LJGVTX7cXlDq0/Xr2uXjbt3qRiYn8rKofu3bB7m9mRSAhRciNyKPbVYI2BfL0y96eySZl09bn4uKnD7FDGZNPuTfS7+5Xc+FvhCYBFU1MyJkpuRA6lrhLySyAmwexIwtrswjRioiwhNzVV3VxNQnQCx2QcY3Yo4S0+DbJnqKhYxkTJjcih2Ks0JRUA8TFRHJefFnJFxdXN1czOmk20NSha9IW3onKN3MiYKLkRGUm7w9Mws0j72wRC2aTQKio2DIP1zes1JRUotgpo/QS695gdiYQIJTciIxnsZ6ORm4Aom5yBo20fje2h0UTz045P2du7VyulAqVITTRlbJTciIykrgoyjoLkbLMjiQilkzKB0GmiWd1cjQWLmmUGSvokSM5VciOjpuRGZCT2Kk1JBdDElDgmTUgMmbqbmpYapmVMIyU2xexQIoPFArZ5aqIpo6bkRuTzep3QtEFTUgEWSpv5VTdXM3eipqQCqqgCGtbBQJ/ZkUgIUHIj8nmOtWC4NHITYKWTM9jU2EFXkDfRbOtpY0f7DhUTB5qtAgZ6oHG92ZFICFByI/J59irP3hpZ2r8kkMomZeJyG6yvbzM7lMNa3+L546pi4gDLmw3RCfuL/UUOQ8mNyOfVVULhPLDqxyOQirOTSY2PZm2Q191UN1eTlZBFQXKB2aFElqgYKFATTRkd/fYWOZDbDfUfqVmmCaxWC8dPymBNkNfdDDbLtKhTfOAVlXuKitVEU45AyY3IgVo2Q2+HZ35fAq5sUgbr6vbiDtImmv2ufjbu3kjJxBKzQ4lMtnLoaoa9O8yORIKckhuRA9VVgjXaM/wtAXf8pAw6ewb4pLnT7FBGtGnPJnpdvaq3MUvhCZ5/tSRcjkDJjciB7FWQOxtiE82OJCKV2NKJslqCdr+bmuYa4qPimT5hutmhRKbETJg4XUXFckRKbkQOVFep/W1MlBgbzXH5qawL0rqbmuYajss6jhhrjNmhRC5buUZu5IiU3IgM6myCtk9VTGyy0iAtKjYMY6iYWExUVOGpjdvXZnYkEsSU3IgMGlxiqmJiU5VNyqRuTzfNnT1mhzJMfWc9u3t2K7kx2+DIav1H5sYhQU3JjciguipIL4LUPLMjiWhlkzMAgm6/m+qWagDmTJxjciQRLnMKJGZ5ppBFDkHJjcgge6VGbYJATmo8BekJQTc1Vd1czdS0qaTFpZkdSmSzWDxTU9rMTw5DyY0IQF+3p2eNbZ7ZkQie0Ztga6JZ01yjflLBwlYO9WvA1W92JBKklNyIgKfbsHtAzTKDRNmkDDY2tNPT7zI7FAA6+jqobatVchMsiipgYB80bTA7EglSSm5EwDPEHZcK2TPMjkSA0kmZ9LsM1tvbzA4FgPXN6zEwVEwcLPLmQFScpqbkkJTciICnmLiwDKxRZkciwDG5KaTERQdN3U11czWZ8ZkUpRSZHYoARMdB/lwVFcshKbkRcbs9rwBVTBw0oqwWSorSg6bupqalhpKJJWqWGUyKyj0/t2qiKSNQciPS+gn0tGnzviBTGiRNNPvd/fyn9T+akgo2tgrobIS2OrMjkSCk5EbEXgkWq5plBpmySZm0dfezvdVpahyf7PmEfQP7VEwcbAY387OvNjcOCUpKbkTsqyFnJsSlmB2JHKCkKJ2YKAvvftJqahwfNnxIfFQ8Myao2DyoJE2ACdPURFNGpORGpK5SS8CDUHJcNKdPz+GPa+wYJtVVGIbB8m3LOWPSGcRGxZoSgxyGrUJNNGVESm4ksjlbYE+tOoEHqYtOKOTjpk42NnSYcv11zeuo66zjy8VfNuX6cgRF5dC8EXrM+f6Q4KXkRiLb4D4ZGrkJSqcUTyQ7JY6X19hNuf6rW1+lMLmQ0hzVYwUlWwUYbjXRlIMouZHIZq+E1AJIKzQ7EhlBdJSVr5QWsrzaEfDdirv6u/jHp/9g0bRFWC36VRmUsoohIUOb+clB9BMrkc2+WlNSQe7C0kI6egb4x6ZdAb3u33f+nZ6BHi6YdkFArytjYLF4fn6V3MjnKLmRyNXfAw3VmpIKclMmJnPC5Az+GOCpqVe3vsqC/AXkJuUG9LoyRkNNNAfMjkSCiJIbiVyNNeDq08hNCLiwzMb721qp39sdkOttb99OTUsNi4oXBeR6Mg5FFdDn9BQWi3xGyY1ErrpKiEny7HEjQe2cWXkkxETx/9Y6AnK95duWkxaXxhdtXwzI9WQc8ueCNUZLwmUYJTcSuexVUFgKUdFmRyJHkBQXzbmz8/jjWrvf2zH0u/t5bdtrnHPUOdrbJhTEJEB+iTbzk2GU3EhkMgw1ywwxF5XZqN+7j8rtu/16nQ8cH7C7Z7f2tgkltnK1YZBhlNxIZNpdC9271SwzhJROymBKVpLf97x5deurHJt5LNMzp/v1OuJDtnJot0N7YKYtJfgpuZHIZK8ELFB4gtmRyChZLBYuLLPxt/800b6v3y/XaN3Xyr/q/8WiaYv8cn7xk8EVj5qaks8ouZHIVFcJOcdBfJrZkcgYfOX4AgbcBn9Z3+CX87++/XUsFgvnTDnHL+cXP0nOhoyjVFQsQ5TcSGSyV2kJeAjKTo3ntKMn+mXPG8MweHXrq5xedDppcUp6Q05RhUZuZIiSG4k83Xug9RMlNyHqwjIb6+vb+bjJt80SN7RuoLa9li9PUyFxSLKVQ9N/oNdpdiQSBJTcSOQZXFWhYuKQdPqx2WQlx/LHNfU+Pe+r214lLymP8jx9X4QkWzkYLnCsNTsSCQJKbiTy2CshORfSJ5kdiXghJsrKl+cW8Gq1g74Bt0/OuW9gH3/b8TcumHYBUdYon5xTAmzidE8NnfpMCUpuJBLVVXlGbSwWsyMRL11YZmNPVx///Ng3zTTf/vRtuvq7uGCqmmSGLKsVCud5FgtIxFNyI5FloA8a1mnzvhB3dE4KJbZ0XvbR1NSr216lPLecwpRCn5xPTFJUDvUfgdtldiRiMiU3Elma/g0DPSomDgMXldlYuaWZXR094zqPvcPOR00fqUlmOLBVQG8HtHxsdiRiMiU3ElnqKiE6AfJmmx2JjNO5c/KIjbbyp7XjG71ZXruclJgUFhYt9FFkYpqC48ESpakpUXIjEcZeCQWlEBVjdiQyTqnxMZw/J59H39nmde3NW5++xXMbn+PcqecSHx3v4wgl4GKTPC9cVFQc8ZTcSOQwjP3FxBIWfnj+cZw4LYtvPreG31V+OurnGYbBcxuf46aVN/EF2xe4qewmP0YpAWWr0MiNKLmRCLJ3B3Q1q5g4jCTGRvP4ZaUsnj+Zu5b/h6VvbMbtNg77HJfbxdLVS/n5mp9z5cwr+ckpPyEuKi5AEYvfFZVD26fQ2WR2JGKiaLMDEAmYwc37bGqWGU6irBZ+eP5x2DIT+dHrm6hv28cvLpxDfMzB+9V093dz63u38q/6f3FXxV1cdMxFJkQsfjX44sVeBTO0tD9SBcXIzaOPPsrkyZOJj4+nvLyc1atXH/b4P/7xj0yfPp34+HhmzZrFG2+8EaBIJaTVVXo2+krIMDsS8YOrTjqKxy49nrc37eKyp6rY29U37OOt+1r5xt+/QVVjFY988RElNuEqNQ/SitREM8KZnty89NJLLFmyhHvuuYd169YxZ84czjzzTJqbm0c8/sMPP+SSSy7hqquuorq6mkWLFrFo0SL+85//BDhyCTlqlhn2zpqZxx+uqWB7axf/57EP+XR3FwDb27Zz2RuX0dzdzLNnPcsphaeYHKn4VVG5mmhGOIthGIefoPaz8vJyTjjhBH79618D4Ha7sdlsfOc73+G222476PiLL76Yrq4u/vrXvw49VlFRQUlJCY8//vgRr9fR0UFaWhrt7e2kpqb67hOR4LavDX4yGRb9Bkq+ZnY04mef7u7i6898RPu+fm4+P5pfb7qDnMQcfnP6b8hLzjM7PPG31cvgzdvgNjvEJpodjZjA1Jqbvr4+1q5dy+233z70mNVqZeHChaxatWrE56xatYolS5YMe+zMM89k+fLlIx7f29tLb2/v0Pvt7e0ALHiqjKgE9ZCJKDl5sPpBWP0TsyORQEiFgSSDH60awN01hV27vsJJ731odlQSAMU4eTmmn767CwC1WQknsT/YSUpKCpYjtM8xNblpbW3F5XKRk5Mz7PGcnBw+/njkHSabmppGPL6paeTK+KVLl3Lvvfce9PjGm7Z6GbWIhJ7NwOtmByEBsh1IMzsI8Y+fjG7mJexXS91+++3DRnra2tqYNGkSdXV1pKXp23+8Ojo6sNls2O12TfP5gO6nb+l++p7uqW/pfo5dSkrKEY8xNbnJysoiKiqKXbuG7y66a9cucnNzR3xObm7umI6Pi4sjLu7gPSzS0tL0jeRDqampup8+pPvpW7qfvqd76lu6n75l6mqp2NhYSktLWbFixdBjbrebFStWMH/+/BGfM3/+/GHHA7z11luHPF5EREQii+nTUkuWLOGKK66grKyMefPm8fDDD9PV1cWVV14JwOLFiykoKGDp0qUA3HDDDZx66qn84he/4JxzzuHFF19kzZo1PPnkk2Z+GiIiIhIkTE9uLr74YlpaWrj77rtpamqipKSEN998c6houK6uDqt1/wDTggULeOGFF7jzzjv5wQ9+QHFxMcuXL2fmzJmjul5cXBz33HPPiFNVMna6n76l++lbup++p3vqW7qf/mH6PjciIiIivmT6DsUiIiIivqTkRkRERMKKkhsREREJK0puREREJKyEZXLz6KOPMnnyZOLj4ykvL2f16tWHPf6Pf/wj06dPJz4+nlmzZvHGG28EKNLQMJb7uXHjRr7yla8wefJkLBYLDz/8cOACDRFjuZ/Lli3j5JNPJiMjg4yMDBYuXHjE7+dIM5b7+corr1BWVkZ6ejpJSUmUlJTwu9/9LoDRBr+x/v4c9OKLL2KxWFi0aJF/AwxBY7mnzz77LBaLZdhbfHx8AKMNE0aYefHFF43Y2Fjj6aefNjZu3GhcffXVRnp6urFr164Rj//ggw+MqKgo46c//amxadMm48477zRiYmKMDRs2BDjy4DTW+7l69Wrj5ptvNv7whz8Yubm5xi9/+cvABhzkxno/v/a1rxmPPvqoUV1dbWzevNn4+te/bqSlpRn19fUBjjw4jfV+vvPOO8Yrr7xibNq0ydi2bZvx8MMPG1FRUcabb74Z4MiD01jv56AdO3YYBQUFxsknn2xccMEFgQk2RIz1nj7zzDNGamqq0djYOPTW1NQU4KhDX9glN/PmzTO+/e1vD73vcrmM/Px8Y+nSpSMef9FFFxnnnHPOsMfKy8uN//t//69f4wwVY72fB5o0aZKSm88Zz/00DMMYGBgwUlJSjOeee85fIYaU8d5PwzCMuXPnGnfeeac/wgs53tzPgYEBY8GCBcZTTz1lXHHFFUpuPmes9/SZZ54x0tLSAhRd+Aqraam+vj7Wrl3LwoULhx6zWq0sXLiQVatWjficVatWDTse4Mwzzzzk8ZHEm/sph+aL+9nd3U1/fz+ZmZn+CjNkjPd+GobBihUr2LJlC6eccoo/Qw0J3t7P++67j+zsbK666qpAhBlSvL2nTqeTSZMmYbPZuOCCC9i4cWMgwg0rYZXctLa24nK5hnY3HpSTk0NTU9OIz2lqahrT8ZHEm/sph+aL+3nrrbeSn59/UEIeiby9n+3t7SQnJxMbG8s555zDI488whlnnOHvcIOeN/fz/fff57e//S3Lli0LRIghx5t7eswxx/D000/z5z//md///ve43W4WLFhAfX19IEIOG6a3XxCR0XnwwQd58cUXWblypQoMxyElJYWamhqcTicrVqxgyZIlTJkyhdNOO83s0EJKZ2cnl19+OcuWLSMrK8vscMLG/PnzhzWCXrBgAcceeyxPPPEE999/v4mRhZawSm6ysrKIiopi165dwx7ftWsXubm5Iz4nNzd3TMdHEm/upxzaeO7nz3/+cx588EHefvttZs+e7c8wQ4a399NqtTJt2jQASkpK2Lx5M0uXLo345Gas97O2tpadO3dy3nnnDT3mdrsBiI6OZsuWLUydOtW/QQc5X/wOjYmJYe7cuWzbts0fIYatsJqWio2NpbS0lBUrVgw95na7WbFixbBM+EDz588fdjzAW2+9dcjjI4k391MOzdv7+dOf/pT777+fN998k7KyskCEGhJ89f3pdrvp7e31R4ghZaz3c/r06WzYsIGampqht/PPP58vfOEL1NTUYLPZAhl+UPLF96jL5WLDhg3k5eX5K8zwZHZFs6+9+OKLRlxcnPHss88amzZtMq655hojPT19aCnd5Zdfbtx2221Dx3/wwQdGdHS08fOf/9zYvHmzcc8992gp+AHGej97e3uN6upqo7q62sjLyzNuvvlmo7q62ti6datZn0JQGev9fPDBB43Y2FjjT3/607CloZ2dnWZ9CkFlrPfzgQceMP7xj38YtbW1xqZNm4yf//znRnR0tLFs2TKzPoWgMtb7+XlaLXWwsd7Te++91/j73/9u1NbWGmvXrjX++7//24iPjzc2btxo1qcQksIuuTEMw3jkkUeMoqIiIzY21pg3b55RWVk59LFTTz3VuOKKK4Yd//LLLxtHH320ERsbaxx33HHG66+/HuCIg9tY7ueOHTsM4KC3U089NfCBB6mx3M9JkyaNeD/vueeewAcepMZyP++44w5j2rRpRnx8vJGRkWHMnz/fePHFF02IOniN9ffngZTcjGws9/TGG28cOjYnJ8c4++yzjXXr1pkQdWizGIZhmDVqJCIiIuJrYVVzIyIiIqLkRkRERMKKkhsREREJK0puREREJKwouREREZGwouRGREREwoqSGxEREQkrSm5EREQkrCi5EZGgcNppp3HjjTeO+viPP/6YiooK4uPjKSkpYefOnVgsFmpqavwWo4iEhrDqCi4ioeuVV14hJiZm1Mffc889JCUlsWXLFpKTk+ns7PRjdCISSpTciEhQyMzMHNPxtbW1nHPOOUyaNAlAyY2IDNG0lIgEhQOnpSZPnswDDzzAN77xDVJSUigqKuLJJ58cOtZisbB27Vruu+8+LBYLP/zhDw8637PPPkt6evqwx5YvX47FYgHAMAwWLlzImWeeyWCLvT179lBYWMjdd9/tl89RRAJDyY2IBKVf/OIXlJWVUV1dzXXXXce1117Lli1bAGhsbOS4447jpptuorGxkZtvvnnM57dYLDz33HN89NFH/OpXvwLgW9/6FgUFBUpuREKcpqVEJCidffbZXHfddQDceuut/PKXv+Sdd97hmGOOITc3l+joaJKTk8nNzQWgtbV1zNcoKCjgiSeeYPHixTQ1NfHGG29QXV1NdLR+NYqEMv0Ei0hQmj179tD/LRYLubm5NDc3+/w6F154Ia+++ioPPvggjz32GMXFxT6/hogElqalRCQofX7llMViwe12j/r5Vqt1qJZmUH9//0HHdXd3s3btWqKioti6dat3wYpIUFFyIyJhaeLEiXR2dtLV1TX02Eh74Nx0001YrVb+9re/8atf/Yp//vOfAYxSRPxByY2IhKXy8nISExP5wQ9+QG1tLS+88ALPPvvssGNef/11nn76aZ5//nnOOOMMvv/973PFFVewd+9ec4IWEZ9QciMiYSkzM5Pf//73vPHGG8yaNYs//OEPw5aMt7S0cNVVV/HDH/6Q448/HoB7772XnJwcvvWtb5kUtYj4gsX4/KS0iIiISAjTyI2IiIiEFSU3IiIiElaU3IiIiEhYUXIjIiIiYUXJjYiIiIQVJTciIiISVpTciIiISFhRciMiIiJhRcmNiIiIhBUlNyIiIhJWlNyIiIhIWPn/XpDKyTMjSOcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import skfuzzy as fuzz\n",
+    "\n",
+    "level[\"low\"] = fuzz.zmf(level.universe, 2, 4)\n",
+    "level[\"average\"] = fuzz.trapmf(level.universe, [2, 4, 6, 8])\n",
+    "level[\"high\"] = fuzz.smf(level.universe, 6, 8)\n",
+    "level.view()\n",
+    "\n",
+    "flow[\"low\"] = fuzz.zmf(flow.universe, 0.2, 0.3)\n",
+    "flow[\"average\"] = fuzz.trapmf(flow.universe, [0.15, 0.25, 0.35, 0.45])\n",
+    "flow[\"high\"] = fuzz.smf(flow.universe, 0.3, 0.4)\n",
+    "flow.view()\n",
+    "\n",
+    "influx[\"low\"] = fuzz.zmf(influx.universe, 0.2, 0.3)\n",
+    "influx[\"average\"] = fuzz.trapmf(influx.universe, [0.15, 0.25, 0.35, 0.45])\n",
+    "influx[\"high\"] = fuzz.smf(influx.universe, 0.3, 0.4)\n",
+    "influx.view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Формирование и визуализация базы нечетких правил\n",
+    "\n",
+    "В случае ошибки необходимо в файле\n",
+    "```\n",
+    ".venv/lib/python3.13/site-packages/skfuzzy/control/visualization.py\n",
+    "```\n",
+    "удалить лишний отступ на 182 строке, должно быть:\n",
+    "```python\n",
+    "        if not matplotlib_present:\n",
+    "            raise ImportError(\"`ControlSystemVisualizer` can only be used \"\n",
+    "                              \"with `matplotlib` present in the system.\")\n",
+    "\n",
+    "        self.ctrl = control_system\n",
+    "\n",
+    "        self.fig, self.ax = plt.subplots()\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qwjBAapCbm4sRI0ZAEASlSyEvYRggUhGLxYLIyEh06NBB6VJIpw4fPoxDhw5xvoDOMAwQqQhXEpDSzGYz/P39MWTIEKVLIS9iGCBSEYYBUprZbEZGRgZCQkKULoW8iGGASEUYBkhJDQ0N2LhxIx8R6BDDAJFKuN1u2Gw2hgFSzPr169HU1MQlhTrEMECkEpWVlWhqauK+BKQYs9mMhIQEJCQkKF0KeRnDAJFKcFkhKUkUReTm5vIRgU4xDBCphMViAcAwQMooKirCDz/8wDCgUwwDRCphtVoRFhaGsLAwpUshHTKbzQgODkZ6errSpZACGAaIVIIrCUhJZrMZ2dnZCAgIULoUUgDDAJFKMAyQUmpqarBjxw4+ItAxhgEilbBarVxJQIrIy8uD2+3mkkIdYxggUgFRFDkyQIoxm8247bbbEB0drXQppBCGASIVOHnyJOrr6xkGyOtcLhfWrl3LRwQ6xzBApAJcVkhK2bVrF2pqahgGdI5hgEgF2HCIlJKbm4vw8HDceeedSpdCCmIYIFIBq9WKDh06oEuXLkqXQjpjNpsxfPhwGI1GpUshBTEMEKlA8+RBQRCULoV05OjRo/jmm2/4iIAYBojUgCsJSAm5ubkwGAwYNmyY0qWQwhgGiFSAYYCUkJubiwEDBqBz585Kl0IKYxggUgGGAfI2h8OBdevWsdEQAWAYIFJcfX09ampqGAbIqzZv3oyGhgbOFyAADANEiuOyQlKC2WxGt27dkJKSonQppAIMA0QKaw4D3JeAvCk3Nxf33nsvV7AQAIYBIsVZrVaYTCZ07dpV6VJIJ0pLS1FaWsr5AuTBMECkMKvVipiYGDZ9Ia8xm80ICAjAkCFDlC6FVIJhgEhhXElA3mY2mzFo0CAEBwcrXQqpBMMAkcIYBsib6uvrsWnTJq4ioIuYlC5ASg0OJyw1DWhyuuFvMiAuPBjBAT71I5IPslgsyM7OVroM0on8/HycO3eO8wXoIpq/U5ZW1mHJLhsKDlXBVmuHeMFrAoDYzkHITIrE2LRYJESFKlUm0RU5HA4cP36cKwnIa8xmM5KSktCzZ0+lSyEV0WwYqKi1Y+bKImwpq4bRIMDlFi/7PSIAa60di3dZ8dEOC9LjIzBvZApiOgd5v2CZcVREmyoqKgCwxwB5hyiKyM3NxejRo5UuhVRGk3eL5YU2zFldDOdPAeBKQeBCza9vP1KDrNc3YW5OMkanxspep9w4KqJ9bDhE3vTtt9/i2LFjnC9Al9FcGFhQUIpX8kra9L0utwiXW8SMFUWorndgcmaCxNV5B0dFfIfVaoUgCIiJiVG6FNIBs9mM0NBQ3H333UqXQiqjqdUEywttbQ4Cl3olrwSfFdokOZY3LS+0Iev1Tdh+pAZA60dFlmvwZ/ZlVqsVXbt2hb+/v9KlkA6YzWZkZ2fz3xtdRjMjAxW1dsxZXSzpMWevLsaAnhGa+bTMURHfw2WFJJdL5xGF4ix27tyJRYsWKV0aqZBmwsDMlUWeOQJScbpFzFxZhMVPpEl6XDlIPSrSJSQAD/vAvAktuvAiXVrdiJib4pUuiXzEteYRASK6PvUuvg+6BaWVdZxHRBcRRFGU9g4rg9LKOmS/sVm24+dPG4j4SPW+MSpq7ch6fRMcTrdkxwwwGZA/LUMzoyJad+2LNNCdkz2pHVoyj6hZ8+ucR0QX0kQYeGF1MRbvsl73+XhbGA0CfpPWHS/kJEt+bKn85v1d2H6kRtKf32gQMKBHuCZGRbSMF2mS24Wrq1pzjTAaBJgMgs+srqL20cQEwoJDVbIEAeD8s/SCkqrLvn7s2DH85S9/wcSJE2U5b0uVVtZhS1m15D+/yy1iS1k1yqrqJD0u/YyTPUluCwpKMWNFERxOd6uvES63CIfTjRkrirCgoFSmCkkrVB8G6h1O2Grtsp7DVmNHg8MJACgqKsL48ePRvXt3/P3vf8fSpUtlPff1LNllg9Egz37jRoOAT3fyhiMHXqRJblxdRVJSfRiw1jRc9nxVaiKAdxb/L+68807ceuutWLJkCZzO8+EgMDAQSj5JUWJUhNqHF2mSm1yrqypk/uBF6qX61QRNEk6au5bnZ89B0/HzF3CXy+X5em1tLfz8/BAUFCTrfx06dLhsP3tvjoqwdbE0uASWvEHvq6tIeqq/A/ibvDN48d7/vIN3XpqJr7/+GgaDAW73+RBy4403Yvbs2bDb7WhsbITdbr/sv/r6elRVVXl+3dDQcNHvbenIQkBAwEUBwT+qJ8S7J8v5Y0MEYKlpQHJ0mKzn0QtepEluzfOIpHbhPCI1r64ieag+DMSFB0MAZH1UIAD41bAMjM/ZBbPZjKlTp+Lw4cMAgODgYDz99NNtPrYoinA4HFcMERf+d6WgcfSsH7ZK9DNei7dGX3wdL9LkDc3ziORaXfXpTpuqV1eRPFQfBoIDTIjtHASrjMPloYIDfW9LQV1dHY4fP+4ZFQCAhoaGdh1bEAQEBgYiMDAQnTt3btX3Fh87jXv/KX8c8Nboi6/jRZq8wRvziF4A/53pjSbuAplJkbLOqPerLsHBgwdx9OjRi4IAAMyaNUuW87ZE86iInISfzkPtx8meJDdvr64i/dBEGBibFivrRXbZi8/gySefvOLrBw4cwNdff63IioLmURE5xYYHcfKgBHiRJm/w1uoqS037RkRJezQRBhKiQpEeHyH56IDRICA9PgKJN3bEe++9hylTpnheEwQBkZGR+Oyzz5CWlobY2FhMnToVmzdvvmi1gdzkHhXJTIyU5dh6w4s0eYO35vdwHpH+aCIMAMC8kSkwSXxTFEQ3RsefX0ooCALefPNN/OEPfwBwfuLfzJkzcfz4cRQUFGDkyJH4/PPPkZGRgejoaPz2t79FXl4ezp07J2lNl5J7VGRcP7YhlQIv0uQN3prfw3lE+qOZv/GYzkGYK/HkqarcBbhvUD+EhIRg4MCBmDFjBvr374+nn34aQUFBGDVqFEwmEwYNGoS33noLNpsNO3bswPjx45Gfn49hw4YhMjIS48ePx6pVq9DY2ChpfYD8oyKcnS4NXqTJG37R0Z/ziEgWmtio6EILCkol6e72x6FJ2L5ozkXthv38/Dyf9FetWoWcnJyrfr8oiti5ex+WrM5D/oaNOFJWAv+m0xiRPQQPPvggRowYgdBQaW60FbV2ZL66UdL169y1UFoNDidueeEr2ZfAfvfCMM7x0BFRFFFcXIz8/Hzk5+dj48aNiHz8HbiDw2U7Z/fwIGyaninb8UmdNHdVmZyZgIiQgHbt0vViTjIeTo3FfT3mYfny5Z4VBOfOnYPBYEBUVBT69+9/xWNcvhVtCjAwBdEDAUDEbnst8hdtgGPa/0NWajJGjRqFnJwcdOrUqc0/86pvj0reyObFnGQGAQl5YwksJ3vqQ0VFBdavX+8JAJWVlQgICEB6ejpmzZqF49E98eXB07ItYeU8In3S3MhAM6m2hh03bhw+++wzz14EgiCgoKAAGRkZbT+fALhEIKD2CI589hLQUIPBgwdj1KhReOCBBxAVFdXin3N5oQ0zVhS1+Pe3xB+HJuHZzHhJj0ncapva5tSpU9i4caPn5n/o0CEIgoA77rgDWVlZyMrKwoABA9ChQwcA5z+QZL+xWbZ68qcN5ONDHdJsGGjm+aReUgVbjf2iYVoB5z9NZSZGYly/2Cv+Ay8qKsKtt97q+XVgYCBuuukmrFy5EklJSQDat1+4UQAyO1aj7KuPsWnTJrjdbtx999148MEHMWrUKMTExFz1+ytq7ch6fRMcEk4aMxkEFDw3iKMCMuBFmlrC4XBgx44dnpt/YWEh3G434uPjPTf/zMzMazYp+837u7D9SI2kwdNoEDCgRzjbXuuU5sPAhRocTlhqGtDkdMPfZEBceHCLhlVHjBiBNWvW4Omnn8bUqVMxcuRIHD16FIsXL4YttJckcxSmD03E6JROWL16NVasWIF169ahqakJqampePDBB/Hggw8iPv7iT+t8w2sP/87oUm63G/v37/fc/Ddv3ozGxkZERERgyJAhngAQFxfX4mPK8UGB84j0zafCQFt9//33+PDDD/HXv/4V/v7+qKurw4QJE5BXVo/wEb+T7Dz/GJWCh1PPL+U7ffo0zGYzVqxYgTVr1sButyMlJcUTDPwjYjH0zS2SnftS/JQpD16kCQCsVqvn5r9+/XqcOHECHTp0wMCBAz03/1tvvRUGQ9tXh0j9CPHC6xPpD8PAVdhqGjD41QI4RekW8lztom632/HVV1/h888/x5dffokzZ85gwJTX8WNoIp8/axAv0vpTW1uLgoICTwAoKyuDwWBAamqq5+bfv39/BAQESHpeKVdXcR6RvjEMXIVSw70OhwPr16/HC3sEVNnlazDD5UPyar5Ii6IIQWh7oORFWp3Onj2Lbdu2eW7+e/bsgSiKSEpK8tz8Bw0ahBtuuEH2Wto6pwmiG6LLiVkjkvBk5s3yFUiawDBwBUpPBKt3OJHCNeua1yXtfnRIHw/BYIRgbPmf86VLYEl5LpcL33zzjefmv3XrVpw9exaRkZGem/+QIUMQG6vM31dbVlfdGROKgvlPoX9KIlatWtWu0EraxzvBFSi9Fa03+9wnR4fJfCZ9crvdqCn8EsZDu9B5+GR0uKkPBNENUbj6M+Lmf3MDeoRftgSWvO/w4cOem/+GDRtQW1uL4OBgZGRkYN68ecjKysItt9yiiptoTOcgLH4irdWrq76MmY+cnBz861//wjPPPKNU+aQCHBm4goz5BbI2j7neEP0+20mM/Nd22c7fbOXTA9An9uJmSA6HA/7+/qq4wF2qratFlKjn5MmTnqVhMTExmPf2hygTb0RBSRWs1Q3ABX++LVkCS/Krrq7Ghg0bPAGgvLwcRqMRaWlpnk//aWlp8Pf3V7rUFmnp+2Xy5Ml4//33UVhYiFtuuUWBSkkNODJwCW9uRXu1G5k3+9w7nU4UFhYiPz8fa9euxa5du/DCCy/g+eef90oN13N5x8efCQBiOwchMykSY9NikRAl/020pfXcf0tnmEwm5OTk4JNPPkFw8Ple7887k9Ah9AY8/483kfPAKFWEGr2y2+3YunWr5+a/b98+AMDNN9+MX/7yl8jKykJGRgY6duyocKVtExxgatHI3/z587Fp0yY88sgj+Prrrz3NjUhfeAW6hBqG6OPCgyH89PvkrOKR+7JQdrAYjY2NMBqNnq2Zw8KUf3TQkmegIgBrrR2Ld1nx0Q7LFTtMKlnPw/+zFX8bmYLg4J/rOXr0KJxnG9AvqdtlozIkL5fLhT179nhu/tu2bUNTUxO6du2K7OxsTJs2DUOGDEF0dLTSpXpVhw4dsGzZMqSmpuKPf/wjFixYoHRJpACGgUuoYStab/S5NzTUomjfbs+vm4MAAISHh8PlcsFoNMp2/mu5cHY0gOvO3Wh+ffuRGmS9vglzc5IxWsKJd22tZ8cV6ikvLwcA3HTTTZLVR1cmiiJKS0s9N/+CggKcOnUKoaGhGDRoEObPn4+srCz06tVLlY/FvOmWW27Bq6++imeffRZDhw695iZt5Js4Z+ASxcdO495/bpX9POYpd19zCE/uPvdj74zBmYL38eabb17x94SEhOD2229H3759kZqaitTUVPTo0UP2i6ZU66anD03E5MwE1dXz4Ycf4vHHH0djYyMCAwPbfVy6WGVl5UWb/FRUVMBkMqF///6e5/6pqanw8/NTulTVEUURDzzwALZt24b9+/frboRE7xgGLqGWrWi9tbzx3//+N8aPH49z587B5XKhV69eePvtt7F7924UFhaisLAQFosFANCpUyf07dvXExD69u2Lbt26SRYQ1NasR456ir9chPfffx9Hjx6V7Lh6Vl9fjy1btnhu/vv37wcApKSkeG7+AwcOREhIiMKVakN1dTV69+6NXr16IS8vr10dEklbGAauQOnVBM281fiouLgYOTk5OHLkCJ588kksXLjwou+prq7G7t27PQFh9+7dOHbsGAAgKirqonCQmpqKyMjWb4Gqtja+ctVz6w+rcby0CFu3yj/65IsunPCan5+PHTt24Ny5c+jWrRuys7ORlZWFwYMH48Ybb1S6VM1av349srOz8fe//x1/+tOflC6HvIRh4ArUshWtN2+Qp0+fxpw5czBmzBjceeed1z3OsWPHPMGgOSTU1NQAOL+U7sJwcMcdd6BTp2tPllPbBj9y1WOqOYLU+p349NNPJTuuLxNFEQcPHvTc/Ddu3IgzZ84gLCwMmZmZnk//iYmJun/uL6UZM2bg1Vdfxfbt25Gamqp0OeQFDANXoHQHwgupbej8akRRhNVq9QSEwsJC7NmzB2fOnAEAxMfHXzSCcPvtt3uGbtX05+2NenKM3+Ctv/4/2Y6vdceOHbvouf+xY8fg5+eHu+66y3Pzv+OOO2Aycf6zXM6dO4e77roLtbW12LdvH0JD2fvC1/HddAUJUaFIj4+Q7ZNqa25Mo1NjUV3vkGwzErna2wqCgLi4OMTFxeGhhx4CcL4LX2lp6UWPF1atWoXGxkYIgoBevXohNTUVnbN/6/WOjyUlJfjggw/wxBNPICHh4omGcnagFN0u/BiWKPlxtezMmTPYtGmT5+Z/4MABAMBtt92GMWPGICsrC3fffbenVwPJz8/PD0uXLkWfPn0wZcoUfPTRR0qXRDJjGLiKeSNTkPX6JklvCCaDgHkjU1r9fZMzExAREtCmzUiU7HNvMBiQlJSEpKQkjB07FsD5Z77ff/+9JxwUFRXhQM+Tstx4gfPL/ApKqvACzoeBoqIivPTSS/j3v/8NURQRHR19WRgoOFQlWz2CwQhLk75vaufOncOuXbs8N/+dO3fC5XKhe/fuyM7OxuzZszF48GB06dJF6VJ1LT4+Hm+//TbGjx+PYcOG4ZFHHlG6JJIRHxNcg9qG6NuyGYmcjXik4K1NmT65vwte+dtLWL16NUwmE5xOJwRBwD//+U88++yzXq0HAIp1tEmUKIooLi723Pw3bdqE+vp6dOrUCYMHD0ZWVhays7O9snSVWkcURYwbNw7/93//h2+++Yb9MXwYw8B1qHG/8NZuRqJm3urrcOyDKThXVX7Z18PDw3HDDTfAZDLBaDTCP7IHTvabJHs91+szoXU//PCD5+afn5+PyspKBAQE4O677/Y89+/Tp49ija2o5U6fPo3bbrsNXbt2xebNmzlXw0fxb/U61DhEnxAVihdykvECklW3eU9reavj4009E1BSVQ5BEHBh/k1LS0NKSgqcTiecTidOmzqhwAv1eOvn9pZTp05h48aNnpv/oUOHIAgCbr/9dkyYMAFZWVm466672Pdeg8LCwrB06VKkp6dj7ty5+O///m+lSyIZcGSghXxxiF4NvNnx8bSlGDNnzsSmTZs8ezF88MEHeOyxxxSpR8sjAw6HAzt27PDc/AsLC+F2u9GzZ0/PJ//MzEyEh4crXSpJ5K9//Stmz56NgoICZGRkKF0OSUw7HyEV1tb9wunavLEpk/DTeYKjB2Djxo3YsGEDZsyYgcLCwss2ZfJmPVridrtRVFTkuflv3rwZdrsd4eHhGDJkCJ544glkZWXxmbIP+8tf/oJ169Zh3Lhx+Pbbbz1bdJNv4MhAO2h9iF4t5O74eGOIEX8bEICqqipUVVWhsrISlZWVaGxsxEcffXTZM1C1dKBUmtVq9dz8169fjxMnTiAwMBADBw70fPrv3bs3W9bqSEVFBXr37o3MzEz87//+Lyd8+hDeudqhpfuF07VlJkXK1vFREN0o2bgag59/D8D55Y5GoxFOpxNhYWFXvJHJWY8BIjITW9+u2Rtqa2tRUFDgCQBlZWUwGAzo27cvJk6ciKysLPTv358bLOlYTEwMFi1ahAcffBCLFi3CxIkTlS6JJMIwQIobmxaLj3ZYZDm2KBgQYPva82u32w232w2j0YgJEyZcMQzIWY8bAk41NqG0sg4JUco+Rjp79iy2bdvmufnv2bMHoigiMTERQ4cOxcsvv4xBgwZdt5U06cuoUaPw1FNPYerUqUhPT8d//dd/KV0SSYCPCUgV5Nyb4G/DfoHU1FRUVVXB7f55Fv/AgQPx3HPPYcSIEZc9KpCjngvrUmKCqdvtxr59+zw3/61bt+Ls2bOIjIzEkCFDMGTIEGRnZyM21rvNqUh7Ghoa0LdvXwQGBmLnzp0ICAhQuiRqJ4YBUgW5N2U6cOAA+vfvj/r6egBAXFwcOnfujN27dyM6OhqPPfYYnnzyScTFxclWz6Wal57OzUnGaJm6Qx45cuSi5/61tbUICgpCRkaG57l/SkoKn/1Sq33zzTdIS0vDM888g9dff13pcqidGAZINeTu+Lh161YMGTIETU1N+Pjjj/Hoo49i3759WLhwIZYsWYK6ujpkZ2dj4sSJyMnJwYpvf5S0nmuZPjQRkzMTrviaKIo4fPgw4uOv37SquroaGzZs8ASA8vJyGI1G3HnnnZ6bf79+/eDv7y/1j0A69MYbb2DatGnIzc3FPffco3Q51A4MA6Qqcnd8XL16NRYsWIAvvvgCQUE/D883NDTgP//5D9577z3s2LEDkZGRmDBhAjr0HYmP9ta0u56WuFK7aqfTiWeeeQYLFy7E5s2bkZ6eftHrdrsdW7du9dz89+3bBwDo1auX5+afkZFx2RJKIimIooh7770Xe/bswf79+xEVFaV0SdRGDAOkOssLbYp2fPzuu++waNEifPLJJzh58iTueGgKTsUPhSgYZNvACLj4sQZwPqD8+te/xpo1ayAIAmbMmIEXX3wRe/fuRX5+PtatW4dt27ahqakJXbt29dz8hwwZgl/84hey1Ul0ocrKSvTu3Rt9+vSB2WzmUlONYhggVVJDx8ezZ8/i888/x8KFC7Htm4OIuu/3MMWkwCAAcmSC5gmPi59IQ1VVFYYPH479+/fD5XIBAG644QYA51v/hoSEYNCgQZ4AcPPNN/O5Pynmq6++wvDhw/Haa69h2rRpSpdDbcAwQKqmlo6Phw4dwqJFi/DxF3kQew1FcPIg2c715tAIPPHQvaitrcWlb88///nP+OUvf4k777wTfn5+stVA1FrPPfcc/vnPf2LXrl3o06eP0uVQKzEMkGaooeNjU1MTfr94G7460ijbskPHd+twdPUbV3x9xYoVGDlypOTnJWovh8OB/v37w263Y8+ePQgO1lbLbb3jwx3SjOaOj31iOyE5OkyR1s/+/v4oroVscwdcbhExacPx6quvYtKkSbj11lsv6oGwbds2Wc5L1F4BAQFYtmwZKioq8Pvf/17pcqiV2IGQqBXqHU7YZNy3AAB+rHPit8/9zhN2zp49i/3792PPnj0YOHCgrOcmao+kpCS8+eabmDhxIoYNG4Zf/epXSpdELcTHBEStwC2Oia5NFEX8+te/Rn5+Pr799lt2tNQIPiYgaoUmGTsSKnEeIqkJgoD33nsPoaGhGDdunGc1DKkbwwBRK/ibvPOW8dZ5iOTQqVMnLFmyBNu2bcO8efOULodagFccolaICw+G3Kv5hZ/OQ6Rl6enpeP755zF37lxs375d6XLoOhgGiFohOMCEWJl3GYwND1JkpQSR1GbNmoW0tDSMGTMGp0+fVrocugaGAaJWykyKhNEgz/iA0SAgMzFSlmMTeZvJZMKSJUtw8uRJTJo06bImWqQeDANErTQ2LVbWPgPj+nH2NfmOuLg4vPvuu1i+fDk++eQTpcuhq2AYIGqlhKhQpMdHSD46YDQISI+PkLWtMpESRo8ejQkTJuDZZ59FaWmp0uXQFbDPAFEbVNTakfX6JjgkXAJ46a6FRL6kvr4et99+Ozp27Ijt27fD399f6ZLoAhwZIGqDmM5BmJuTLOkxX8xJZhAgnxUSEoKlS5di//79mDVrltLl0CUYBojaaHRqLKYPTZTkWH8cmoSHUzlXgHxb37598dJLL+Hll19Gfn6+0uXQBfiYgKidlhfaMGd1MZxusVUTC40GASaDgBdzkhkESDfcbjeGDRuG4uJifPvtt+jSpYvSJREYBogkUVFrx8yVRdhSVg2jQbhmKGh+PT0+AvNGpvDRAOnO8ePHceutt6J///5YtWoVBEHuVl50PQwDRBIqrazDkl02FJRUwVpz8e6GAs43FMpMjMS4frFcNUC69uWXXyInJwcLFizAs88+q3Q5uscwQCST9z/+FE//aTY2bt6KsNBgxIUHs7Mg0QWmTJmChQsXorCwECkpKUqXo2ucQEgkk2O2coS56zAgKRrJ0WEMAkSXmD9/PhISEvDII4+gsbFR6XJ0jWGASCbl5eW46aablC6DSLUCAwOxfPlyHD58GNOnT1e6HF1jGCCSicViYRgguo7k5GS89tpreOedd7Bq1Sqly9EthgEimZSXlyMuLk7pMohUb9KkSbj//vvx+OOP4+jRo0qXo0sMA0QycDqdqKio4MgAUQsIgoBFixYhMDAQjz76KFwul9Il6Q7DAJEMfvjhB7hcLoYBohaKiIjA4sWLUVBQgPnz5ytdju4wDBDJoLy8HAD4mICoFQYPHow///nPmDVrFr7++muly9EVhgEiGTSHge7duytcCZG2vPjii+jTpw/GjBmDuro6pcvRDYYBIhlYLBZER0cjMDBQ6VKINMXPzw/Lli1DZWUlJk+erHQ5usEwQCQDriQgaruePXvinXfewSeffIKlS5cqXY4uMAwQyYANh4jaZ9y4cRgzZgwmTZqEI0eOKF2Oz2MYIJIBwwBR+wiCgHfeeQcREREYO3Yszp07p3RJPo1hgEhiDocDx44d42MConYKCwvD0qVLUVhYiLlz5ypdjk9jGCCSmNVqBQCODBBJoF+/fpg7dy7mzZuHTZs2KV2Oz2IYIJJY87JChgEiacyYMQMDBw7EuHHjUFtbq3Q5PolhgEhiFosFBoMB3bp1U7oUIp9gNBqxePFiNDQ0YOLEiRBFUemSfA7DAJHEysvLERMTAz8/P6VLIfIZMTExWLRoEVasWIGFCxcqXY7PYRggkhhXEhDJY9SoUXjqqafw+9//HgcOHFC6HJ/CMEAkMYvFwpUERDJ5/fXX0b17d4wZMwZnz55VuhyfwTBAJDGODBDJJygoCMuXL8f333+PGTNmKF2Oz2AYIJJQfX09Tpw4wTBAJKPevXvj5Zdfxptvvonc3Fyly/EJDANEErJYLAC4dTGR3H73u9/hnnvuwYQJE/Djjz8qXY7mMQwQSag5DHBkgEhegiDgo48+gsFgwPjx4+F2u5UuSdMYBogkVF5eDn9/f0RHRytdCpHPi4yMxMcff4y8vDy88cYbSpejaQwDRBIqLy9H9+7dYTDwrUXkDcOGDcMf/vAHzJgxA3v37lW6HM0SRLZyIpLMqFGjUF9fj7y8PKVLIdINh8OB/v37o6GhAXv37kVwcLDSJWkOP74QSYjLCom8LyAgAMuWLcMPP/yAqVOnKl2OJjEMEEmIYYBIGUlJSXjrrbfw/vvv4z//+Y/S5WgOwwCRRE6dOoXTp09zWSGRQh5//HE89NBDeOqpp2Cz2ZQuR1MYBogkwq2LiZQlCALeffdddOzYEWPHjoXL5VK6JM1gGCCSCMMAkfI6deqETz/9FNu3b8dLL72kdDmawTBAJBGLxYKgoCB06dJF6VKIdC09PR3PP/885s6di23btildjiYwDBBJpLy8HHFxcRAEQelSiHRv1qxZ6NevH8aOHYtTp04pXY7qMQwQSYQrCYjUw2QyYcmSJTh16hQmTZoEttS5NoYBIolYLBauJCBSkbi4OLz77rv47LPP8PHHHytdjqoxDBBJQBRFjgwQqdDDDz+Mxx57DJMnT0ZJSYnS5agWwwCRBE6cOAG73c4wQKRCb731FqKjozFmzBg0NTUpXY4qMQwQSYBbFxOpV0hICJYtW4b9+/fj+eefV7ocVWIYIJJAc48BzhkgUqc77rgDL730EubPn49169YpXY7qMAwQSaC8vBxhYWHo1KmT0qUQ0VU899xzyMrKwqOPPooTJ04oXY6qMAwQScBisfARAZHKGQwGfPLJJ3A6nXjssce43PACDANEEmhuOERE6ta1a1d8+OGHMJvNePvtt5UuRzUYBogkwGWFRNpx3333YcqUKZg+fTr279+vdDmqwDBA1E5utxtWq5VhgEhDXn75ZSQmJuKRRx6B3W5XuhzFMQwQtdPx48fR1NTExwREGhIYGIhly5bhyJEjmD59utLlKI5hgKiduHUxkTYlJyfjtddew7/+9S988cUXKC8vx8CBAzFlyhSlS/M6QeR0SqJ2Wbx4MR599FHU1dUhJCRE6XKIqBVEUcTIkSORn58PURRht9sRFxfnCfl6YVK6ACKts1gsiIiIYBAg0qD6+noEBASgoaHB8zWbzQaHw4GAgAAFK/MuhgGiduJKAiJtstvtuO2223DkyJGLvu52u1FSUoKUlBSFKvM+zhkgaieGASJtMhqNSEhI8Pz/hQ4cOKBESYphGCBqJ3YfJNKmgIAArF27FgUFBbj99tsveu2bb7654vc0OJwoPnYa+2wnUXzsNBocTi9UKj9OICRqB6fTicDAQCxYsACTJk1SuhwiaiNRFJGbm4tp06ahtLQUN998M4qLiwEApZV1WLLLhoJDVbDV2nHhTVMAENs5CJlJkRibFouEqFBF6m8vhgGidigvL0ePHj2wdu1aDBs2TOlyiKid3G435s+fjy5duiD7gdGYubIIW8qqYTQIcLmvfrtsfj09PgLzRqYgpnOQF6tuP4YBonYoKCjA4MGDcejQISQmJipdDhFJZHmhDXNWF8PpFq8ZAi5lNAgwGQTMzUnG6NRYGSuUFlcTELVD81rk2FjtvOmJ6NoWFJTilbySNn2v66fwMGNFEarrHZicmSBxdfLgBEKidigvL0d0dDQCAwOVLoWIJLC80NbmIHCpV/JK8FmhTZJjyY1hgKgduJKAyHdU1NoxZ3WxpMecvboYFbXq3wiJYYCoHcrLy7lBEZGPmLmyCM5WzA9oCadbxMyVRZIeUw4MA0TtwIZDRL6htLIOW8qqWzVZsCVcbhFbyqpRVlUn6XGlxjBA1EYOhwPHjh1jGCDyAUt22WA0CLIc22gQ8OlOdc8dYBggaiOr1QqAWxcT+YKCQ1WSjwo0c7lFFJRUyXJsqTAMELVR87JCzhkg0rZ6hxM2mSf52Wrsqm5dzDBA1Ebl5eUwGo2IiYlRuhQiagdrTQPk7r4nArDUNFz39ymFYYCojSwWC2JiYmAysXcXkZY1Od0+dZ62YBggaiMuKyTyDf4m79wKvXWetlBvZUQqx2WFRL4hLjwY8qwj+Jnw03nUimGAqI3YfZDINwQHmBAr8y6DseFBCA5Q7yNFhgGiNqivr8eJEyf4mIDIR2QmRcraZyAzMVKWY0uFYYCoDSwWCwD2GCDyFWPTYmXtMzCun7p3NmUYIGoDhgEi35IQFYr0+AjJRweMBgHp8RGIjwyV9LhSYxggaoPy8nL4+/uja9euSpdCRBKZNzIFJonDgMkgYN7IFEmPKQeGAaI2KC8vR/fu3WEw8C1E5CtiOgdhbk6ypMd8MScZMTJPTpQCr2REbcCVBES+aXRqLKYPTZTkWH8cmoSHU9U9V6AZwwBRG7DHAJHvmpyZgL+PSkGAydDqOQRGg4AAkwH/GJWCZzPjZapQegwDRG3A7oNEvm10aizyp2VgQI9wALhuKGh+fUCPcORPy9DMiEAz9XZAIFKZs2fPwmAwwG634/Tp0xwZIPJxMZ2DsPiJNJRW1mHJLhsKSqpgq7FftKmRgPMNhTITIzGuX6zqVw1cjSCKotybNRH5hLi4OFRUVKBTp06oqanBiBEjkJqaiuzsbNx1111Kl0dEXtDgcMJS04Ampxv+JgPiwoNV3VmwpRgGiFooOzsb69evR/Nbxmg0wuVyIS0tDTt37lS4OiKituOcAaIW+s1vfoMLs7PL5QIA/OMf/1CqJCIiSTAMELXQAw88AH9/f8+vDQYDpkyZgoyMDAWrIiJqP4YBohbq2LEj7rvvPgjC+VnDsbGx+Nvf/qZwVURE7ccwQNQKY8eO9TwqWLx4MYKD1bs/ORFRS3ECIVErnD17FiEhIcjKysLatWuVLoeISBIMA0QtcOFyorMN9bglLgqhHfyv/41ERBqg/cWRRDLxNBo5VAVb7RUajXQOQmZSJMamxSIhSpuNRoiIAI4MEF2motaOmSuLsKWsGkaDAJf76m+R5tfT4yMwb2SKJnYnIyK6FMMA0QWWF9owZ3UxnG7xmiHgUkaDAJNBwNycZIzWWE9yIiKGAaKfLCgoxSt5Je0+zvShiZicmSBBRURE3sGlhUQ4PyIgRRAAgFfySvBZoU2SYxEReQPDAOleRa0dc1YXS3rM2auLUVFrl/SYRERyYRgg3Zu5sgjOVswPaAmnW8TMlUWSHpOISC4MA6RrpZV12FJW3arJgi3hcovYUlaNsqo6SY9LRCQHhgHStSW7bDAaBFmObTQI+HQn5w4QkfoxDJCuFRyqknxUoJnLLaKgpEqWYxMRSYlhgHSr3uGETeZJfrYaOxocTlnPQUTUXgwDpFvWmgbI3WRDBGCpaZD5LERE7cMwQLrV5HT71HmIiNqKYYB0y9/knX/+3joPEVFb8SpFuhUXHgx51hH8TPjpPEREasYwQLoVHGBCrMy7DMaGByE4gDuFE5G6MQyQrmUmRcraZyAzMVKWYxMRSYlhgHRtbFqsrH0GxvXjdsZEpH4MA6RrCVGhSI+PkHx0wGgQkB4fgfjIUEmPS0QkB4YB0r15I1NgkjgMmAwC5o1MkfSYRERyYRgg3YvpHIS5OcmSHvPFnGTEyDw5kYhIKgwDRABGp8Zi+tBESY71x6FJeDiVcwWISDsEURTl7shKpBnLC22Ys7oYTrfYqomFRoMAk0HAiznJDAJEpDkMA0SXqKi1Y+bKImwpq4bRIFwzFDS/nh4fgXkjU/hogIg0iWGA6CpKK+uwZJcNBSVVsNXYL9rUSMD5hkKZiZEY1y+WqwaISNMYBohaoMHhhKWmAU1ON/xNBsSFB7OzIBH5DIYBIiIineNqAiIiIp1jGCAiItI5hgEiIiKdYxggIiLSOYYBIiIinWMYICIi0jmGASIiIp1jGCAiItI5hgEiIiKdYxggIiLSOYYBIiIinWMYICIi0jmGASIiIp1jGCAiItI5hgEiIiKdYxggIiLSOYYBIiIinWMYICIi0jmGASIiIp1jGCAiItI5hgEiIiKdYxggIiLSOYYBIiIinWMYICIi0jmGASIiIp1jGCAiItI5hgEiIiKd+/9o0ehHYljteAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rule1 = ctrl.Rule(level[\"low\"] & flow[\"high\"], influx[\"high\"])\n",
+    "rule2 = ctrl.Rule(level[\"low\"] & flow[\"average\"], influx[\"high\"])\n",
+    "rule3 = ctrl.Rule(level[\"low\"] & flow[\"low\"], influx[\"average\"])\n",
+    "rule4 = ctrl.Rule(level[\"average\"] & flow[\"high\"], influx[\"high\"])\n",
+    "rule5 = ctrl.Rule(level[\"average\"] & flow[\"average\"], influx[\"average\"])\n",
+    "rule6 = ctrl.Rule(level[\"average\"] & flow[\"low\"], influx[\"average\"])\n",
+    "rule7 = ctrl.Rule(level[\"high\"] & flow[\"high\"], influx[\"average\"])\n",
+    "rule8 = ctrl.Rule(level[\"high\"] & flow[\"average\"], influx[\"low\"])\n",
+    "rule9 = ctrl.Rule(level[\"high\"] & flow[\"low\"], influx[\"low\"])\n",
+    "\n",
+    "rule1.view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Создание нечеткой системы и добавление нечетких правил в базу знаний нечеткой системы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "influx_ctrl = ctrl.ControlSystem(\n",
+    "    [\n",
+    "        rule1,\n",
+    "        rule2,\n",
+    "        rule3,\n",
+    "        rule4,\n",
+    "        rule5,\n",
+    "        rule6,\n",
+    "        rule7,\n",
+    "        rule8,\n",
+    "        rule9,\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "influxes = ctrl.ControlSystemSimulation(influx_ctrl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Пример расчета выходной переменной influx на основе входных переменных level и flow\n",
+    "\n",
+    "Система также формирует подробный журнал выполнения процесса нечеткого логического вывода"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=============\n",
+      " Antecedents \n",
+      "=============\n",
+      "Antecedent: level                   = 2.5\n",
+      "  - low                             : 0.875\n",
+      "  - average                         : 0.25\n",
+      "  - high                            : 0.0\n",
+      "Antecedent: flow                    = 0.4\n",
+      "  - low                             : 0.0\n",
+      "  - average                         : 0.4999999999999997\n",
+      "  - high                            : 1.0\n",
+      "\n",
+      "=======\n",
+      " Rules \n",
+      "=======\n",
+      "RULE #0:\n",
+      "  IF level[low] AND flow[high] THEN influx[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[low]                                             : 0.875\n",
+      "  - flow[high]                                             : 1.0\n",
+      "                                 level[low] AND flow[high] = 0.875\n",
+      "  Activation (THEN-clause):\n",
+      "                                              influx[high] : 0.875\n",
+      "\n",
+      "RULE #1:\n",
+      "  IF level[low] AND flow[average] THEN influx[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[low]                                             : 0.875\n",
+      "  - flow[average]                                          : 0.4999999999999997\n",
+      "                              level[low] AND flow[average] = 0.4999999999999997\n",
+      "  Activation (THEN-clause):\n",
+      "                                              influx[high] : 0.4999999999999997\n",
+      "\n",
+      "RULE #2:\n",
+      "  IF level[low] AND flow[low] THEN influx[average]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[low]                                             : 0.875\n",
+      "  - flow[low]                                              : 0.0\n",
+      "                                  level[low] AND flow[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                           influx[average] : 0.0\n",
+      "\n",
+      "RULE #3:\n",
+      "  IF level[average] AND flow[high] THEN influx[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[average]                                         : 0.25\n",
+      "  - flow[high]                                             : 1.0\n",
+      "                             level[average] AND flow[high] = 0.25\n",
+      "  Activation (THEN-clause):\n",
+      "                                              influx[high] : 0.25\n",
+      "\n",
+      "RULE #4:\n",
+      "  IF level[average] AND flow[average] THEN influx[average]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[average]                                         : 0.25\n",
+      "  - flow[average]                                          : 0.4999999999999997\n",
+      "                          level[average] AND flow[average] = 0.25\n",
+      "  Activation (THEN-clause):\n",
+      "                                           influx[average] : 0.25\n",
+      "\n",
+      "RULE #5:\n",
+      "  IF level[average] AND flow[low] THEN influx[average]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[average]                                         : 0.25\n",
+      "  - flow[low]                                              : 0.0\n",
+      "                              level[average] AND flow[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                           influx[average] : 0.0\n",
+      "\n",
+      "RULE #6:\n",
+      "  IF level[high] AND flow[high] THEN influx[average]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[high]                                            : 0.0\n",
+      "  - flow[high]                                             : 1.0\n",
+      "                                level[high] AND flow[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                           influx[average] : 0.0\n",
+      "\n",
+      "RULE #7:\n",
+      "  IF level[high] AND flow[average] THEN influx[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[high]                                            : 0.0\n",
+      "  - flow[average]                                          : 0.4999999999999997\n",
+      "                             level[high] AND flow[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                               influx[low] : 0.0\n",
+      "\n",
+      "RULE #8:\n",
+      "  IF level[high] AND flow[low] THEN influx[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - level[high]                                            : 0.0\n",
+      "  - flow[low]                                              : 0.0\n",
+      "                                 level[high] AND flow[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                               influx[low] : 0.0\n",
+      "\n",
+      "\n",
+      "==============================\n",
+      " Intermediaries and Conquests \n",
+      "==============================\n",
+      "Consequent: influx                   = 0.4315251220586045\n",
+      "  low:\n",
+      "    Accumulate using accumulation_max : 0.0\n",
+      "  average:\n",
+      "    Accumulate using accumulation_max : 0.25\n",
+      "  high:\n",
+      "    Accumulate using accumulation_max : 0.875\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.4315251220586045)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "influxes.input[\"level\"] = 2.5\n",
+    "influxes.input[\"flow\"] = 0.4\n",
+    "influxes.compute()\n",
+    "influxes.print_state()\n",
+    "influxes.output[\"influx\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Визуализация функции принадлежности для выходной переменной influx\n",
+    "\n",
+    "Функция получена в процессе аккумуляции и используется для дефаззификации значения выходной переменной influx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "influx.view(sim=influxes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Пример решения задачи регрессии на основе нечеткого логического вывода"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Загрузка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>Al2O3</th>\n",
+       "      <th>TiO2</th>\n",
+       "      <th>Density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.06250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05404</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    T  Al2O3  TiO2  Density\n",
+       "0  20    0.0   0.0  1.06250\n",
+       "1  25    0.0   0.0  1.05979\n",
+       "2  35    0.0   0.0  1.05404"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>Al2O3</th>\n",
+       "      <th>TiO2</th>\n",
+       "      <th>Density</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>55</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.04158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.08438</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    T  Al2O3  TiO2  Density\n",
+       "0  30   0.00   0.0  1.05696\n",
+       "1  55   0.00   0.0  1.04158\n",
+       "2  25   0.05   0.0  1.08438"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "density_train = pd.read_csv(\"data/density/density_train.csv\", sep=\";\", decimal=\",\")\n",
+    "density_test = pd.read_csv(\"data/density/density_test.csv\", sep=\";\", decimal=\",\")\n",
+    "\n",
+    "display(density_train.head(3))\n",
+    "display(density_test.head(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Инициализация лингвистических переменных и автоматическое формирование нечетких переменных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/user/Projects/python/ckmai/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp = ctrl.Antecedent(density_train[\"T\"].sort_values().unique(), \"temp\")\n",
+    "al = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"al\")\n",
+    "ti = ctrl.Antecedent(np.arange(0, 0.3, 0.005), \"ti\")\n",
+    "density = ctrl.Consequent(np.arange(1.03, 1.22, 0.00001), \"density\")\n",
+    "\n",
+    "temp.automf(3, variable_type=\"quant\")\n",
+    "temp.view()\n",
+    "al.automf(3, variable_type=\"quant\")\n",
+    "al.view()\n",
+    "ti.automf(3, variable_type=\"quant\")\n",
+    "ti.view()\n",
+    "density.automf(5, variable_type=\"quant\")\n",
+    "density.view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Нечеткие правила"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rule11 = ctrl.Rule(\n",
+    "    temp[\"low\"] & al[\"low\"] & ti[\"low\"],\n",
+    "    density[\"low\"],\n",
+    ")\n",
+    "rule12 = ctrl.Rule(\n",
+    "    temp[\"average\"] & al[\"low\"] & ti[\"low\"],\n",
+    "    density[\"lower\"],\n",
+    ")\n",
+    "rule13 = ctrl.Rule(\n",
+    "    temp[\"high\"] & al[\"low\"] & ti[\"low\"],\n",
+    "    density[\"lower\"],\n",
+    ")\n",
+    "\n",
+    "rule21 = ctrl.Rule(\n",
+    "    temp[\"low\"] & al[\"average\"] & ti[\"low\"],\n",
+    "    density[\"low\"],\n",
+    ")\n",
+    "rule22 = ctrl.Rule(\n",
+    "    temp[\"average\"] & al[\"average\"] & ti[\"low\"],\n",
+    "    density[\"low\"],\n",
+    ")\n",
+    "rule23 = ctrl.Rule(\n",
+    "    temp[\"high\"] & al[\"average\"] & ti[\"low\"],\n",
+    "    density[\"lower\"],\n",
+    ")\n",
+    "\n",
+    "rule31 = ctrl.Rule(\n",
+    "    temp[\"low\"] & al[\"high\"] & ti[\"low\"],\n",
+    "    density[\"high\"],\n",
+    ")\n",
+    "rule32 = ctrl.Rule(\n",
+    "    temp[\"low\"] & al[\"high\"] & ti[\"low\"],\n",
+    "    density[\"high\"],\n",
+    ")\n",
+    "rule33 = ctrl.Rule(\n",
+    "    temp[\"high\"] & al[\"high\"] & ti[\"low\"],\n",
+    "    density[\"average\"],\n",
+    ")\n",
+    "\n",
+    "rule41 = ctrl.Rule(\n",
+    "    temp[\"low\"] & al[\"low\"] & ti[\"average\"],\n",
+    "    density[\"low\"],\n",
+    ")\n",
+    "rule42 = ctrl.Rule(\n",
+    "    temp[\"average\"] & al[\"low\"] & ti[\"average\"],\n",
+    "    density[\"low\"],\n",
+    ")\n",
+    "rule43 = ctrl.Rule(\n",
+    "    temp[\"high\"] & al[\"low\"] & ti[\"average\"],\n",
+    "    density[\"lower\"],\n",
+    ")\n",
+    "\n",
+    "rule51 = ctrl.Rule(\n",
+    "    temp[\"low\"] & al[\"low\"] & ti[\"high\"],\n",
+    "    density[\"higher\"],\n",
+    ")\n",
+    "rule52 = ctrl.Rule(\n",
+    "    temp[\"average\"] & al[\"low\"] & ti[\"high\"],\n",
+    "    density[\"high\"],\n",
+    ")\n",
+    "rule53 = ctrl.Rule(\n",
+    "    temp[\"high\"] & al[\"low\"] & ti[\"high\"],\n",
+    "    density[\"high\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Создание нечеткой системы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[IF (temp[low] AND al[low]) AND ti[low] THEN density[low]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[average] AND al[low]) AND ti[low] THEN density[lower]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[high] AND al[low]) AND ti[low] THEN density[lower]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[low] AND al[average]) AND ti[low] THEN density[low]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[average] AND al[average]) AND ti[low] THEN density[low]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[high] AND al[average]) AND ti[low] THEN density[lower]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[high] AND al[high]) AND ti[low] THEN density[average]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[low] AND al[low]) AND ti[average] THEN density[low]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[average] AND al[low]) AND ti[average] THEN density[low]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[high] AND al[low]) AND ti[average] THEN density[lower]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[low] AND al[low]) AND ti[high] THEN density[higher]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[average] AND al[low]) AND ti[high] THEN density[high]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax,\n",
+       " IF (temp[high] AND al[low]) AND ti[high] THEN density[high]\n",
+       " \tAND aggregation function : fmin\n",
+       " \tOR aggregation function  : fmax]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fuzzy_rules = [\n",
+    "    rule11,\n",
+    "    rule12,\n",
+    "    rule13,\n",
+    "    rule21,\n",
+    "    rule22,\n",
+    "    rule23,\n",
+    "    rule31,\n",
+    "    rule32,\n",
+    "    rule33,\n",
+    "    rule41,\n",
+    "    rule42,\n",
+    "    rule43,\n",
+    "    rule51,\n",
+    "    rule52,\n",
+    "    rule53,\n",
+    "]\n",
+    "\n",
+    "density_cntrl = ctrl.ControlSystem(fuzzy_rules)\n",
+    "\n",
+    "sim = ctrl.ControlSystemSimulation(density_cntrl)\n",
+    "\n",
+    "fuzzy_rules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Пример использования полученной нечеткой системы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=============\n",
+      " Antecedents \n",
+      "=============\n",
+      "Antecedent: temp                    = 20\n",
+      "  - low                             : 1.0\n",
+      "  - average                         : 0.0\n",
+      "  - high                            : 0.0\n",
+      "Antecedent: al                      = 0.295\n",
+      "  - low                             : 0.0\n",
+      "  - average                         : 0.0\n",
+      "  - high                            : 1.0\n",
+      "Antecedent: ti                      = 0.0\n",
+      "  - low                             : 1.0\n",
+      "  - average                         : 0.0\n",
+      "  - high                            : 0.0\n",
+      "\n",
+      "=======\n",
+      " Rules \n",
+      "=======\n",
+      "RULE #0:\n",
+      "  IF (temp[low] AND al[low]) AND ti[low] THEN density[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[low]                                              : 1.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                       (temp[low] AND al[low]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                              density[low] : 0.0\n",
+      "\n",
+      "RULE #1:\n",
+      "  IF (temp[average] AND al[low]) AND ti[low] THEN density[lower]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[average]                                          : 0.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                   (temp[average] AND al[low]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            density[lower] : 0.0\n",
+      "\n",
+      "RULE #2:\n",
+      "  IF (temp[high] AND al[low]) AND ti[low] THEN density[lower]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[high]                                             : 0.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                      (temp[high] AND al[low]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            density[lower] : 0.0\n",
+      "\n",
+      "RULE #3:\n",
+      "  IF (temp[low] AND al[average]) AND ti[low] THEN density[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[low]                                              : 1.0\n",
+      "  - al[average]                                            : 0.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                   (temp[low] AND al[average]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                              density[low] : 0.0\n",
+      "\n",
+      "RULE #4:\n",
+      "  IF (temp[average] AND al[average]) AND ti[low] THEN density[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[average]                                          : 0.0\n",
+      "  - al[average]                                            : 0.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "               (temp[average] AND al[average]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                              density[low] : 0.0\n",
+      "\n",
+      "RULE #5:\n",
+      "  IF (temp[high] AND al[average]) AND ti[low] THEN density[lower]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[high]                                             : 0.0\n",
+      "  - al[average]                                            : 0.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                  (temp[high] AND al[average]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            density[lower] : 0.0\n",
+      "\n",
+      "RULE #6:\n",
+      "  IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[low]                                              : 1.0\n",
+      "  - al[high]                                               : 1.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                      (temp[low] AND al[high]) AND ti[low] = 1.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                             density[high] : 1.0\n",
+      "\n",
+      "RULE #7:\n",
+      "  IF (temp[low] AND al[high]) AND ti[low] THEN density[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[low]                                              : 1.0\n",
+      "  - al[high]                                               : 1.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                      (temp[low] AND al[high]) AND ti[low] = 1.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                             density[high] : 1.0\n",
+      "\n",
+      "RULE #8:\n",
+      "  IF (temp[high] AND al[high]) AND ti[low] THEN density[average]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[high]                                             : 0.0\n",
+      "  - al[high]                                               : 1.0\n",
+      "  - ti[low]                                                : 1.0\n",
+      "                     (temp[high] AND al[high]) AND ti[low] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                          density[average] : 0.0\n",
+      "\n",
+      "RULE #9:\n",
+      "  IF (temp[low] AND al[low]) AND ti[average] THEN density[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[low]                                              : 1.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[average]                                            : 0.0\n",
+      "                   (temp[low] AND al[low]) AND ti[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                              density[low] : 0.0\n",
+      "\n",
+      "RULE #10:\n",
+      "  IF (temp[average] AND al[low]) AND ti[average] THEN density[low]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[average]                                          : 0.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[average]                                            : 0.0\n",
+      "               (temp[average] AND al[low]) AND ti[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                              density[low] : 0.0\n",
+      "\n",
+      "RULE #11:\n",
+      "  IF (temp[high] AND al[low]) AND ti[average] THEN density[lower]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[high]                                             : 0.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[average]                                            : 0.0\n",
+      "                  (temp[high] AND al[low]) AND ti[average] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                            density[lower] : 0.0\n",
+      "\n",
+      "RULE #12:\n",
+      "  IF (temp[low] AND al[low]) AND ti[high] THEN density[higher]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[low]                                              : 1.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[high]                                               : 0.0\n",
+      "                      (temp[low] AND al[low]) AND ti[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                           density[higher] : 0.0\n",
+      "\n",
+      "RULE #13:\n",
+      "  IF (temp[average] AND al[low]) AND ti[high] THEN density[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[average]                                          : 0.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[high]                                               : 0.0\n",
+      "                  (temp[average] AND al[low]) AND ti[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                             density[high] : 0.0\n",
+      "\n",
+      "RULE #14:\n",
+      "  IF (temp[high] AND al[low]) AND ti[high] THEN density[high]\n",
+      "\tAND aggregation function : fmin\n",
+      "\tOR aggregation function  : fmax\n",
+      "\n",
+      "  Aggregation (IF-clause):\n",
+      "  - temp[high]                                             : 0.0\n",
+      "  - al[low]                                                : 0.0\n",
+      "  - ti[high]                                               : 0.0\n",
+      "                     (temp[high] AND al[low]) AND ti[high] = 0.0\n",
+      "  Activation (THEN-clause):\n",
+      "                                             density[high] : 0.0\n",
+      "\n",
+      "\n",
+      "==============================\n",
+      " Intermediaries and Conquests \n",
+      "==============================\n",
+      "Consequent: density                  = 1.1724924997377486\n",
+      "  lower:\n",
+      "    Accumulate using accumulation_max : 0.0\n",
+      "  low:\n",
+      "    Accumulate using accumulation_max : 0.0\n",
+      "  average:\n",
+      "    Accumulate using accumulation_max : 0.0\n",
+      "  high:\n",
+      "    Accumulate using accumulation_max : 1.0\n",
+      "  higher:\n",
+      "    Accumulate using accumulation_max : 0.0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.1724924997377486)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sim.input[\"temp\"] = 20\n",
+    "sim.input[\"al\"] = 0.3\n",
+    "sim.input[\"ti\"] = 0.0\n",
+    "sim.compute()\n",
+    "sim.print_state()\n",
+    "display(sim.output[\"density\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Функция для автоматизации вычисления целевой переменной Y на основе вектора признаков X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fuzzy_pred(row):\n",
+    "    sim.input[\"temp\"] = row[\"T\"]\n",
+    "    sim.input[\"al\"] = row[\"Al2O3\"]\n",
+    "    sim.input[\"ti\"] = row[\"TiO2\"]\n",
+    "    sim.compute()\n",
+    "    return sim.output[\"density\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Тестирование нечеткой системы на обучающей выборке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>Al2O3</th>\n",
+       "      <th>TiO2</th>\n",
+       "      <th>Density</th>\n",
+       "      <th>DensityPred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.06250</td>\n",
+       "      <td>1.077498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05979</td>\n",
+       "      <td>1.076593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05404</td>\n",
+       "      <td>1.069156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05103</td>\n",
+       "      <td>1.061106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.04794</td>\n",
+       "      <td>1.045833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.04477</td>\n",
+       "      <td>1.046360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.03826</td>\n",
+       "      <td>1.047642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>65</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.03484</td>\n",
+       "      <td>1.046360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.03182</td>\n",
+       "      <td>1.045833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>20</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.08755</td>\n",
+       "      <td>1.077498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.07105</td>\n",
+       "      <td>1.067145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.06760</td>\n",
+       "      <td>1.067145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>55</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.06409</td>\n",
+       "      <td>1.067988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>65</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05691</td>\n",
+       "      <td>1.062538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>70</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.05291</td>\n",
+       "      <td>1.047191</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     T  Al2O3  TiO2  Density  DensityPred\n",
+       "0   20   0.00   0.0  1.06250     1.077498\n",
+       "1   25   0.00   0.0  1.05979     1.076593\n",
+       "2   35   0.00   0.0  1.05404     1.069156\n",
+       "3   40   0.00   0.0  1.05103     1.061106\n",
+       "4   45   0.00   0.0  1.04794     1.045833\n",
+       "5   50   0.00   0.0  1.04477     1.046360\n",
+       "6   60   0.00   0.0  1.03826     1.047642\n",
+       "7   65   0.00   0.0  1.03484     1.046360\n",
+       "8   70   0.00   0.0  1.03182     1.045833\n",
+       "9   20   0.05   0.0  1.08755     1.077498\n",
+       "10  45   0.05   0.0  1.07105     1.067145\n",
+       "11  50   0.05   0.0  1.06760     1.067145\n",
+       "12  55   0.05   0.0  1.06409     1.067988\n",
+       "13  65   0.05   0.0  1.05691     1.062538\n",
+       "14  70   0.05   0.0  1.05291     1.047191"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_train = density_train.copy()\n",
+    "\n",
+    "result_train[\"DensityPred\"] = result_train.apply(fuzzy_pred, axis=1)\n",
+    "\n",
+    "result_train.head(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Тестирование нечеткой системы на тестовой выборке"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>Al2O3</th>\n",
+       "      <th>TiO2</th>\n",
+       "      <th>Density</th>\n",
+       "      <th>DensityPred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.05696</td>\n",
+       "      <td>1.073918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>55</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.04158</td>\n",
+       "      <td>1.047642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.08438</td>\n",
+       "      <td>1.076518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.08112</td>\n",
+       "      <td>1.073918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.07781</td>\n",
+       "      <td>1.069156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>40</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.07446</td>\n",
+       "      <td>1.067145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>60</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.06053</td>\n",
+       "      <td>1.067988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>35</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.17459</td>\n",
+       "      <td>1.172492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>65</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.14812</td>\n",
+       "      <td>1.124995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>1.07424</td>\n",
+       "      <td>1.067145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>50</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>1.07075</td>\n",
+       "      <td>1.067145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>55</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>1.06721</td>\n",
+       "      <td>1.067988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.22417</td>\n",
+       "      <td>1.204157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>30</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.21310</td>\n",
+       "      <td>1.180834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.20265</td>\n",
+       "      <td>1.173397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>60</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.18265</td>\n",
+       "      <td>1.172492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.17261</td>\n",
+       "      <td>1.172492</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     T  Al2O3  TiO2  Density  DensityPred\n",
+       "0   30   0.00  0.00  1.05696     1.073918\n",
+       "1   55   0.00  0.00  1.04158     1.047642\n",
+       "2   25   0.05  0.00  1.08438     1.076518\n",
+       "3   30   0.05  0.00  1.08112     1.073918\n",
+       "4   35   0.05  0.00  1.07781     1.069156\n",
+       "5   40   0.05  0.00  1.07446     1.067145\n",
+       "6   60   0.05  0.00  1.06053     1.067988\n",
+       "7   35   0.30  0.00  1.17459     1.172492\n",
+       "8   65   0.30  0.00  1.14812     1.124995\n",
+       "9   45   0.00  0.05  1.07424     1.067145\n",
+       "10  50   0.00  0.05  1.07075     1.067145\n",
+       "11  55   0.00  0.05  1.06721     1.067988\n",
+       "12  20   0.00  0.30  1.22417     1.204157\n",
+       "13  30   0.00  0.30  1.21310     1.180834\n",
+       "14  40   0.00  0.30  1.20265     1.173397\n",
+       "15  60   0.00  0.30  1.18265     1.172492\n",
+       "16  70   0.00  0.30  1.17261     1.172492"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_test = density_test.copy()\n",
+    "\n",
+    "result_test[\"DensityPred\"] = result_test.apply(fuzzy_pred, axis=1)\n",
+    "\n",
+    "result_test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Оценка результатов на основе метрик для задачи регрессии"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'RMSE_train': 0.0156766544822719,\n",
+       " 'RMSE_test': 0.014603864614526584,\n",
+       " 'RMAE_test': 0.1057231829134674,\n",
+       " 'R2_test': 0.9436578668511394}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "\n",
+    "rmetrics = {}\n",
+    "rmetrics[\"RMSE_train\"] = math.sqrt(\n",
+    "    metrics.mean_squared_error(result_train[\"Density\"], result_train[\"DensityPred\"])\n",
+    ")\n",
+    "rmetrics[\"RMSE_test\"] = math.sqrt(\n",
+    "    metrics.mean_squared_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
+    ")\n",
+    "rmetrics[\"RMAE_test\"] = math.sqrt(\n",
+    "    metrics.mean_absolute_error(result_test[\"Density\"], result_test[\"DensityPred\"])\n",
+    ")\n",
+    "rmetrics[\"R2_test\"] = metrics.r2_score(\n",
+    "    result_test[\"Density\"], result_test[\"DensityPred\"]\n",
+    ")\n",
+    "\n",
+    "rmetrics"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index 5af1466..d398e87 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "anyio"
@@ -6,6 +6,7 @@ version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
     {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
@@ -26,6 +27,8 @@ version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
+markers = "platform_system == \"Darwin\""
 files = [
     {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
@@ -37,6 +40,7 @@ version = "23.1.0"
 description = "Argon2 for Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
     {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
@@ -57,6 +61,7 @@ version = "21.2.0"
 description = "Low-level CFFI bindings for Argon2"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
     {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
@@ -94,6 +99,7 @@ version = "1.3.0"
 description = "Better dates & times for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
     {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
@@ -113,6 +119,7 @@ version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
     {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
@@ -128,6 +135,7 @@ version = "2.0.4"
 description = "Simple LRU cache for asyncio"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
     {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
@@ -139,6 +147,7 @@ version = "24.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
     {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
@@ -158,6 +167,7 @@ version = "2.16.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
     {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
@@ -172,6 +182,7 @@ version = "4.12.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
+groups = ["main"]
 files = [
     {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
     {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
@@ -193,6 +204,7 @@ version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
     {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
@@ -210,6 +222,7 @@ version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
     {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
@@ -221,6 +234,7 @@ version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
     {file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
@@ -300,6 +314,7 @@ version = "3.4.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main"]
 files = [
     {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4f9fc98dad6c2eaa32fc3af1417d95b5e3d08aff968df0cd320066def971f9a6"},
     {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0de7b687289d3c1b3e8660d0741874abe7888100efe14bd0f9fd7141bcbda92b"},
@@ -414,6 +429,7 @@ version = "3.1.0"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cloudpickle-3.1.0-py3-none-any.whl", hash = "sha256:fe11acda67f61aaaec473e3afe030feb131d78a43461b718185363384f1ba12e"},
     {file = "cloudpickle-3.1.0.tar.gz", hash = "sha256:81a929b6e3c7335c863c771d673d105f02efdb89dfaba0c90495d1c64796601b"},
@@ -425,6 +441,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main"]
+markers = "sys_platform == \"win32\" or platform_system == \"Windows\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -436,6 +454,7 @@ version = "0.2.2"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"},
     {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
@@ -453,6 +472,7 @@ version = "1.3.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "contourpy-1.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a045f341a77b77e1c5de31e74e966537bba9f3c4099b35bf4c2e3939dd54cdab"},
     {file = "contourpy-1.3.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:500360b77259914f7805af7462e41f9cb7ca92ad38e9f94d6c8641b089338124"},
@@ -526,6 +546,7 @@ version = "0.12.1"
 description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
     {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
@@ -541,6 +562,7 @@ version = "1.8.9"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "debugpy-1.8.9-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:cfe1e6c6ad7178265f74981edf1154ffce97b69005212fbc90ca22ddfe3d017e"},
     {file = "debugpy-1.8.9-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ada7fb65102a4d2c9ab62e8908e9e9f12aed9d76ef44880367bc9308ebe49a0f"},
@@ -576,6 +598,7 @@ version = "5.1.1"
 description = "Decorators for Humans"
 optional = false
 python-versions = ">=3.5"
+groups = ["main"]
 files = [
     {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
@@ -587,6 +610,7 @@ version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["main"]
 files = [
     {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
     {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
@@ -598,6 +622,7 @@ version = "2.1.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf"},
     {file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"},
@@ -612,6 +637,7 @@ version = "0.0.4"
 description = "Notifications for all Farama Foundation maintained libraries."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
     {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
@@ -623,6 +649,7 @@ version = "2.21.1"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "fastjsonschema-2.21.1-py3-none-any.whl", hash = "sha256:c9e5b7e908310918cf494a434eeb31384dd84a98b57a30bcb1f535015b554667"},
     {file = "fastjsonschema-2.21.1.tar.gz", hash = "sha256:794d4f0a58f848961ba16af7b9c85a3e88cd360df008c59aac6fc5ae9323b5d4"},
@@ -637,6 +664,7 @@ version = "1.31.0"
 description = "a framework for automated feature engineering"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "featuretools-1.31.0-py3-none-any.whl", hash = "sha256:87c94e9ae959c89acd83da96bd2583f3ef0f6daaa9639cbb6e46dbde2c742a18"},
     {file = "featuretools-1.31.0.tar.gz", hash = "sha256:01bfb17fcc1715b4c3623c7bc94a8982122c4a0fa03350ed478601bb81f90155"},
@@ -672,6 +700,7 @@ version = "4.55.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "fonttools-4.55.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:c17a6f9814f83772cd6d9c9009928e1afa4ab66210a31ced721556651075a9a0"},
     {file = "fonttools-4.55.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c4d14eecc814826a01db87a40af3407c892ba49996bc6e49961e386cd78b537c"},
@@ -745,6 +774,7 @@ version = "1.5.1"
 description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
 optional = false
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+groups = ["main"]
 files = [
     {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
     {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
@@ -756,6 +786,7 @@ version = "1.0.0"
 description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "gymnasium-1.0.0-py3-none-any.whl", hash = "sha256:b6f40e1e24c5bd419361e1a5b86a9117d2499baecc3a660d44dfff4c465393ad"},
     {file = "gymnasium-1.0.0.tar.gz", hash = "sha256:9d2b66f30c1b34fe3c2ce7fae65ecf365d0e9982d2b3d860235e773328a3b403"},
@@ -786,6 +817,7 @@ version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
@@ -797,6 +829,7 @@ version = "0.62"
 description = "Open World Holidays Framework"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "holidays-0.62-py3-none-any.whl", hash = "sha256:4db5019092279716276a9fdaa65d4edd066257a6b8caecbfde7e4af520b349f2"},
     {file = "holidays-0.62.tar.gz", hash = "sha256:85020562b176f19bb83779d0aa9926ea1dd7fe00568ec119d6e8c907afbdc22c"},
@@ -811,6 +844,7 @@ version = "1.0.7"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "httpcore-1.0.7-py3-none-any.whl", hash = "sha256:a3fff8f43dc260d5bd363d9f9cf1830fa3a458b332856f34282de498ed420edd"},
     {file = "httpcore-1.0.7.tar.gz", hash = "sha256:8551cb62a169ec7162ac7be8d4817d561f60e08eaa485234898414bb5a8a0b4c"},
@@ -832,6 +866,7 @@ version = "0.28.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "httpx-0.28.0-py3-none-any.whl", hash = "sha256:dc0b419a0cfeb6e8b34e85167c0da2671206f5095f1baa9663d23bcfd6b535fc"},
     {file = "httpx-0.28.0.tar.gz", hash = "sha256:0858d3bab51ba7e386637f22a61d8ccddaeec5f3fe4209da3a6168dbb91573e0"},
@@ -856,6 +891,7 @@ version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
     {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -870,6 +906,7 @@ version = "0.12.4"
 description = "Toolbox for imbalanced dataset in machine learning."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "imbalanced-learn-0.12.4.tar.gz", hash = "sha256:8153ba385d296b07d97e0901a2624a86c06b48c94c2f92da3a5354827697b7a3"},
     {file = "imbalanced_learn-0.12.4-py3-none-any.whl", hash = "sha256:d47fc599160d3ea882e712a3a6b02bdd353c1a6436d8d68d41b1922e6ee4a703"},
@@ -894,6 +931,7 @@ version = "6.4.5"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717"},
     {file = "importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065"},
@@ -913,6 +951,7 @@ version = "6.29.5"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
     {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
@@ -946,6 +985,7 @@ version = "8.30.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "ipython-8.30.0-py3-none-any.whl", hash = "sha256:85ec56a7e20f6c38fce7727dcca699ae4ffc85985aa7b23635a8008f918ae321"},
     {file = "ipython-8.30.0.tar.gz", hash = "sha256:cb0a405a306d2995a5cbb9901894d240784a9f341394c6ba3f4fe8c6eb89ff6e"},
@@ -982,6 +1022,7 @@ version = "8.1.5"
 description = "Jupyter interactive widgets"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245"},
     {file = "ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17"},
@@ -1003,6 +1044,7 @@ version = "20.11.0"
 description = "Operations with ISO 8601 durations"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
     {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
@@ -1017,6 +1059,7 @@ version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
     {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
@@ -1036,6 +1079,7 @@ version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
     {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
@@ -1053,6 +1097,7 @@ version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
     {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
@@ -1064,6 +1109,7 @@ version = "0.10.0"
 description = "A Python implementation of the JSON5 data format."
 optional = false
 python-versions = ">=3.8.0"
+groups = ["main"]
 files = [
     {file = "json5-0.10.0-py3-none-any.whl", hash = "sha256:19b23410220a7271e8377f81ba8aacba2fdd56947fbb137ee5977cbe1f5e8dfa"},
     {file = "json5-0.10.0.tar.gz", hash = "sha256:e66941c8f0a02026943c52c2eb34ebeb2a6f819a0be05920a6f5243cd30fd559"},
@@ -1078,6 +1124,7 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -1089,6 +1136,7 @@ version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
     {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
@@ -1118,6 +1166,7 @@ version = "2024.10.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
     {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
@@ -1132,6 +1181,7 @@ version = "1.1.1"
 description = "Jupyter metapackage. Install all the Jupyter components in one go."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"},
     {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"},
@@ -1151,6 +1201,7 @@ version = "8.6.3"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"},
     {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"},
@@ -1173,6 +1224,7 @@ version = "6.6.3"
 description = "Jupyter terminal console"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
     {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
@@ -1197,6 +1249,7 @@ version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
     {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
@@ -1217,6 +1270,7 @@ version = "0.10.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
     {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
@@ -1242,6 +1296,7 @@ version = "2.2.5"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
     {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
@@ -1256,6 +1311,7 @@ version = "2.14.2"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyter_server-2.14.2-py3-none-any.whl", hash = "sha256:47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd"},
     {file = "jupyter_server-2.14.2.tar.gz", hash = "sha256:66095021aa9638ced276c248b1d81862e4c50f292d575920bbe960de1c56b12b"},
@@ -1292,6 +1348,7 @@ version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
     {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
@@ -1311,6 +1368,7 @@ version = "4.3.1"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyterlab-4.3.1-py3-none-any.whl", hash = "sha256:2d9a1c305bc748e277819a17a5d5e22452e533e835f4237b2f30f3b0e491e01f"},
     {file = "jupyterlab-4.3.1.tar.gz", hash = "sha256:a4a338327556443521731d82f2a6ccf926df478914ca029616621704d47c3c65"},
@@ -1344,6 +1402,7 @@ version = "0.3.0"
 description = "Pygments theme using JupyterLab CSS variables"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"},
     {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"},
@@ -1355,6 +1414,7 @@ version = "2.27.3"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"},
     {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
@@ -1380,6 +1440,7 @@ version = "3.0.13"
 description = "Jupyter interactive widgets for JupyterLab"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54"},
     {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
@@ -1391,6 +1452,7 @@ version = "1.4.7"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "kiwisolver-1.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8a9c83f75223d5e48b0bc9cb1bf2776cf01563e00ade8775ffe13b0b6e1af3a6"},
     {file = "kiwisolver-1.4.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:58370b1ffbd35407444d57057b57da5d6549d2d854fa30249771775c63b5fe17"},
@@ -1514,6 +1576,7 @@ version = "3.0.2"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"},
     {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"},
@@ -1584,6 +1647,7 @@ version = "3.9.3"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "matplotlib-3.9.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:41b016e3be4e740b66c79a031a0a6e145728dbc248142e751e8dab4f3188ca1d"},
     {file = "matplotlib-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e0143975fc2a6d7136c97e19c637321288371e8f09cff2564ecd73e865ea0b9"},
@@ -1648,6 +1712,7 @@ version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
     {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
@@ -1662,6 +1727,7 @@ version = "3.0.2"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "mistune-3.0.2-py3-none-any.whl", hash = "sha256:71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205"},
     {file = "mistune-3.0.2.tar.gz", hash = "sha256:fc7f93ded930c92394ef2cb6f04a8aabab4117a91449e72dcc8dfa646a508be8"},
@@ -1673,6 +1739,7 @@ version = "0.10.1"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
 python-versions = ">=3.8.0"
+groups = ["main"]
 files = [
     {file = "nbclient-0.10.1-py3-none-any.whl", hash = "sha256:949019b9240d66897e442888cfb618f69ef23dc71c01cb5fced8499c2cfc084d"},
     {file = "nbclient-0.10.1.tar.gz", hash = "sha256:3e93e348ab27e712acd46fccd809139e356eb9a31aab641d1a7991a6eb4e6f68"},
@@ -1695,6 +1762,7 @@ version = "7.16.4"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
     {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
@@ -1732,6 +1800,7 @@ version = "5.10.4"
 description = "The Jupyter Notebook format"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
     {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
@@ -1753,17 +1822,39 @@ version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
+groups = ["main"]
 files = [
     {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
     {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
 ]
 
+[[package]]
+name = "networkx"
+version = "3.4.2"
+description = "Python package for creating and manipulating graphs and networks"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"},
+    {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"},
+]
+
+[package.extras]
+default = ["matplotlib (>=3.7)", "numpy (>=1.24)", "pandas (>=2.0)", "scipy (>=1.10,!=1.11.0,!=1.11.1)"]
+developer = ["changelist (==0.5)", "mypy (>=1.1)", "pre-commit (>=3.2)", "rtoml"]
+doc = ["intersphinx-registry", "myst-nb (>=1.1)", "numpydoc (>=1.8.0)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.15)", "sphinx (>=7.3)", "sphinx-gallery (>=0.16)", "texext (>=0.6.7)"]
+example = ["cairocffi (>=1.7)", "contextily (>=1.6)", "igraph (>=0.11)", "momepy (>=0.7.2)", "osmnx (>=1.9)", "scikit-learn (>=1.5)", "seaborn (>=0.13)"]
+extra = ["lxml (>=4.6)", "pydot (>=3.0.1)", "pygraphviz (>=1.14)", "sympy (>=1.10)"]
+test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+
 [[package]]
 name = "notebook"
 version = "7.0.7"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "notebook-7.0.7-py3-none-any.whl", hash = "sha256:289b606d7e173f75a18beb1406ef411b43f97f7a9c55ba03efa3622905a62346"},
     {file = "notebook-7.0.7.tar.gz", hash = "sha256:3bcff00c17b3ac142ef5f436d50637d936b274cfa0b41f6ac0175363de9b4e09"},
@@ -1787,6 +1878,7 @@ version = "0.2.4"
 description = "A shim layer for notebook traits and config"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
     {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"},
@@ -1804,6 +1896,7 @@ version = "2.1.3"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c894b4305373b9c5576d7a12b473702afdf48ce5369c074ba304cc5ad8730dff"},
     {file = "numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b47fbb433d3260adcd51eb54f92a2ffbc90a4595f8970ee00e064c644ac788f5"},
@@ -1868,6 +1961,7 @@ version = "7.7.0"
 description = "A decorator to automatically detect mismatch when overriding a method."
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"},
     {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"},
@@ -1879,6 +1973,7 @@ version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
     {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
@@ -1890,6 +1985,7 @@ version = "2.2.3"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"},
     {file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"},
@@ -1972,6 +2068,7 @@ version = "1.5.1"
 description = "Utilities for writing pandoc filters in python"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
 files = [
     {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"},
     {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"},
@@ -1983,6 +2080,7 @@ version = "0.8.4"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"},
     {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"},
@@ -1998,6 +2096,8 @@ version = "4.9.0"
 description = "Pexpect allows easy control of interactive console applications."
 optional = false
 python-versions = "*"
+groups = ["main"]
+markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
     {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
@@ -2012,6 +2112,7 @@ version = "11.0.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "pillow-11.0.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:6619654954dc4936fcff82db8eb6401d3159ec6be81e33c6000dfd76ae189947"},
     {file = "pillow-11.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b3c5ac4bed7519088103d9450a1107f76308ecf91d6dabc8a33a2fcfb18d0fba"},
@@ -2104,6 +2205,7 @@ version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb"},
     {file = "platformdirs-4.3.6.tar.gz", hash = "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907"},
@@ -2120,6 +2222,7 @@ version = "0.21.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "prometheus_client-0.21.0-py3-none-any.whl", hash = "sha256:4fa6b4dd0ac16d58bb587c04b1caae65b8c5043e85f778f42f5f632f6af2e166"},
     {file = "prometheus_client-0.21.0.tar.gz", hash = "sha256:96c83c606b71ff2b0a433c98889d275f51ffec6c5e267de37c7a2b5c9aa9233e"},
@@ -2134,6 +2237,7 @@ version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main"]
 files = [
     {file = "prompt_toolkit-3.0.48-py3-none-any.whl", hash = "sha256:f49a827f90062e411f1ce1f854f2aedb3c23353244f8108b89283587397ac10e"},
     {file = "prompt_toolkit-3.0.48.tar.gz", hash = "sha256:d6623ab0477a80df74e646bdbc93621143f5caf104206aa29294d53de1a03d90"},
@@ -2148,6 +2252,7 @@ version = "6.1.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["main"]
 files = [
     {file = "psutil-6.1.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:ff34df86226c0227c52f38b919213157588a678d049688eded74c76c8ba4a5d0"},
     {file = "psutil-6.1.0-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:c0e0c00aa18ca2d3b2b991643b799a15fc8f0563d2ebb6040f64ce8dc027b942"},
@@ -2178,6 +2283,8 @@ version = "0.7.0"
 description = "Run a subprocess in a pseudo terminal"
 optional = false
 python-versions = "*"
+groups = ["main"]
+markers = "os_name != \"nt\" or sys_platform != \"win32\" and sys_platform != \"emscripten\""
 files = [
     {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
     {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
@@ -2189,6 +2296,7 @@ version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"},
     {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"},
@@ -2203,6 +2311,7 @@ version = "2.22"
 description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
     {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
@@ -2214,6 +2323,7 @@ version = "2.18.0"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a"},
     {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"},
@@ -2228,6 +2338,7 @@ version = "3.2.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "pyparsing-3.2.0-py3-none-any.whl", hash = "sha256:93d9577b88da0bbea8cc8334ee8b918ed014968fd2ec383e868fb8afb1ccef84"},
     {file = "pyparsing-3.2.0.tar.gz", hash = "sha256:cbf74e27246d595d9a74b186b810f6fbb86726dbf3b9532efb343f6d7294fe9c"},
@@ -2242,6 +2353,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -2256,6 +2368,7 @@ version = "2.0.7"
 description = "A python library adding a json log formatter"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"},
     {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
@@ -2267,6 +2380,7 @@ version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
     {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
@@ -2278,6 +2392,8 @@ version = "308"
 description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
+groups = ["main"]
+markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""
 files = [
     {file = "pywin32-308-cp310-cp310-win32.whl", hash = "sha256:796ff4426437896550d2981b9c2ac0ffd75238ad9ea2d3bfa67a1abd546d262e"},
     {file = "pywin32-308-cp310-cp310-win_amd64.whl", hash = "sha256:4fc888c59b3c0bef905ce7eb7e2106a07712015ea1c8234b703a088d46110e8e"},
@@ -2305,6 +2421,8 @@ version = "2.0.14"
 description = "Pseudo terminal support for Windows from Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "os_name == \"nt\""
 files = [
     {file = "pywinpty-2.0.14-cp310-none-win_amd64.whl", hash = "sha256:0b149c2918c7974f575ba79f5a4aad58bd859a52fa9eb1296cc22aa412aa411f"},
     {file = "pywinpty-2.0.14-cp311-none-win_amd64.whl", hash = "sha256:cf2a43ac7065b3e0dc8510f8c1f13a75fb8fde805efa3b8cff7599a1ef497bc7"},
@@ -2320,6 +2438,7 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -2382,6 +2501,7 @@ version = "26.2.0"
 description = "Python bindings for 0MQ"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "pyzmq-26.2.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:ddf33d97d2f52d89f6e6e7ae66ee35a4d9ca6f36eda89c24591b0c40205a3629"},
     {file = "pyzmq-26.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dacd995031a01d16eec825bf30802fceb2c3791ef24bcce48fa98ce40918c27b"},
@@ -2503,6 +2623,7 @@ version = "0.35.1"
 description = "JSON Referencing + Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
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 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
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 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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 description = "Pure python rfc3986 validator"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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+optional = false
+python-versions = "*"
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 description = "A set of python modules for machine learning and data mining"
 optional = false
 python-versions = ">=3.9"
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 files = [
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@@ -2719,6 +2860,7 @@ version = "1.14.1"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
 python-versions = ">=3.10"
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 files = [
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@@ -2769,6 +2911,7 @@ version = "1.8.3"
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 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
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 optional = false
 python-versions = ">=3.9"
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 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
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 optional = false
 python-versions = ">=3.7"
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 optional = false
 python-versions = ">=3.8"
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 description = "Extract data from python stack frames and tracebacks for informative displays"
 optional = false
 python-versions = "*"
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 optional = false
 python-versions = ">=3.8"
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 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
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 description = "A tiny CSS parser"
 optional = false
 python-versions = ">=3.8"
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 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">=3.8"
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 files = [
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 optional = false
 python-versions = ">=3.7"
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 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
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 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
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 optional = false
 python-versions = ">=3.8"
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 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["main"]
 files = [
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 description = "RFC 6570 URI Template Processor"
 optional = false
 python-versions = ">=3.7"
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 files = [
     {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
     {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
@@ -3010,6 +3168,7 @@ version = "2.2.3"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
     {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
@@ -3027,6 +3186,7 @@ version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
     {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
@@ -3038,6 +3198,7 @@ version = "24.11.1"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
     {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
@@ -3049,6 +3210,7 @@ version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
@@ -3060,6 +3222,7 @@ version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
     {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
@@ -3076,6 +3239,7 @@ version = "4.0.13"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"},
     {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
@@ -3087,6 +3251,7 @@ version = "0.31.0"
 description = "a data typing library for machine learning"
 optional = false
 python-versions = "<4,>=3.9"
+groups = ["main"]
 files = [
     {file = "woodwork-0.31.0-py3-none-any.whl", hash = "sha256:5cb3370553b5f466f8c8599b1bf559584dc0b798cc1f2da26bbd7029d256c6f9"},
     {file = "woodwork-0.31.0.tar.gz", hash = "sha256:6ef82af1d5b6525b02efe6417c574c810cfdcc606cb266bd0d7fb17a1d066b67"},
@@ -3108,6 +3273,6 @@ test = ["boto3 (>=1.34.32)", "moto[all] (>=5.0.0)", "pyarrow (>=14.0.1)", "pytes
 updater = ["alteryx-open-src-update-checker (>=3.1.0)"]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = "^3.12"
-content-hash = "299cd82afa9f00a090d3ef039f4dabc019809a8a0f8b4111a3086133dec02d69"
+content-hash = "a197d25a1a6a326bbd7d2b02971962ce1f940062d9e78fe5b9a2e0864e76b100"
diff --git a/pyproject.toml b/pyproject.toml
index 4ef6233..ec42c77 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,12 +9,15 @@ package-mode = false
 [tool.poetry.dependencies]
 python = "^3.12"
 jupyter = "^1.1.1"
+rpds-py = "^0.18.0"
 numpy = "^2.1.0"
 pandas = "^2.2.2"
 matplotlib = "^3.9.2"
 imbalanced-learn = "^0.12.3"
 featuretools = "^1.31.0"
 gymnasium = "^1.0.0"
+scikit-fuzzy = "^0.5.0"
+networkx = "^3.4.2"
 
 
 [build-system]