anisotropy/data/analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anisotropy.database import Database, tables\n",
    "import pathlib\n",
    "import peewee as pw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = Database(pathlib.Path(\"anisotropy.db\").resolve())\n",
    "savefig = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "execution = 5\n",
    "data = \"flowRate\"\n",
    "\n",
    "if not db.getExecution(execution):\n",
    "    print(\"Execution not found\")\n",
    "\n",
    "for model in db.tables:\n",
    "    try:\n",
    "        column = getattr(model, data)\n",
    "    \n",
    "    except AttributeError:\n",
    "        pass\n",
    "\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = model.select(tables.Shape.alpha, column, tables.Shape.direction, tables.Shape.label)\n",
    "idn = db.tables.index(model)\n",
    "\n",
    "for table in reversed(db.tables[ :idn]):\n",
    "    query = query.join(table, pw.JOIN.LEFT_OUTER)\n",
    "    \n",
    "query = query.switch(tables.Shape)\n",
    "query = query.where(\n",
    "    tables.Shape.exec_id == execution,\n",
    "#    tables.Shape.label == structure,\n",
    ")\n",
    "query = query.order_by(tables.Shape.label, tables.Shape.direction, tables.Shape.alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with db:\n",
    "    if query.exists():\n",
    "        table = []\n",
    "        for row in query.dicts():\n",
    "            for k in row.keys():\n",
    "                if type(row[k]) == list:\n",
    "                    row[k] = str(row[k])\n",
    "\n",
    "            table.append(row)\n",
    "    \n",
    "    else:\n",
    "        table = None\n",
    "\n",
    "if not table:\n",
    "    print(\"Results not found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "df = DataFrame(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_33356/2997525597.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df.label == \"simple\"][df.direction == direction][\"alpha\"].reset_index(drop = True)\n",
      "/tmp/ipykernel_33356/2997525597.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df.label == \"simple\"][df.direction == direction][\"flowRate\"].reset_index(drop = True)\n"
     ]
    }
   ],
   "source": [
    "a_s = [\n",
    "    df[df.label == \"simple\"][df.direction == direction][\"alpha\"].reset_index(drop = True)\n",
    "    for direction in ['[1.0, 0.0, 0.0]', '[0.0, 0.0, 1.0]', '[1.0, 1.0, 1.0]']\n",
    "]\n",
    "k_s = [ \n",
    "    df[df.label == \"simple\"][df.direction == direction][\"flowRate\"].reset_index(drop = True)\n",
    "    for direction in ['[1.0, 0.0, 0.0]', '[0.0, 0.0, 1.0]', '[1.0, 1.0, 1.0]']\n",
    "]\n",
    "k_s2 = 2 * k_s[1] / k_s[0]\n",
    "k_s3 = 2 * k_s[2] / k_s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_33356/723496196.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df.label == \"bodyCentered\"][df.direction == direction][\"alpha\"].reset_index(drop = True)\n",
      "/tmp/ipykernel_33356/723496196.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df.label == \"bodyCentered\"][df.direction == direction][\"flowRate\"].reset_index(drop = True)\n"
     ]
    }
   ],
   "source": [
    "a_b = [\n",
    "    df[df.label == \"bodyCentered\"][df.direction == direction][\"alpha\"].reset_index(drop = True)\n",
    "    for direction in ['[1.0, 0.0, 0.0]', '[0.0, 0.0, 1.0]', '[1.0, 1.0, 1.0]']\n",
    "]\n",
    "k_b = [ \n",
    "    df[df.label == \"bodyCentered\"][df.direction == direction][\"flowRate\"].reset_index(drop = True)\n",
    "    for direction in ['[1.0, 0.0, 0.0]', '[0.0, 0.0, 1.0]', '[1.0, 1.0, 1.0]']\n",
    "]\n",
    "k_b2 = 2 * k_b[1] / k_b[0]\n",
    "k_b3 = 2 * k_b[2] / k_b[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_33356/1402688131.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df.label == \"faceCentered\"][df.direction == direction][\"alpha\"].reset_index(drop = True)\n",
      "/tmp/ipykernel_33356/1402688131.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df.label == \"faceCentered\"][df.direction == direction][\"flowRate\"].reset_index(drop = True)\n"
     ]
    }
   ],
   "source": [
    "a_f = [\n",
    "    df[df.label == \"faceCentered\"][df.direction == direction][\"alpha\"].reset_index(drop = True)\n",
    "    for direction in ['[1.0, 0.0, 0.0]', '[0.0, 0.0, 1.0]', '[1.0, 1.0, 1.0]']\n",
    "]\n",
    "k_f = [ \n",
    "    df[df.label == \"faceCentered\"][df.direction == direction][\"flowRate\"].reset_index(drop = True)\n",
    "    for direction in ['[1.0, 0.0, 0.0]', '[0.0, 0.0, 1.0]', '[1.0, 1.0, 1.0]']\n",
    "]\n",
    "k_f2 = 2 * k_f[1] / k_f[0]\n",
    "k_f3 = 2 * k_f[2] / k_f[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_f3[20] = (k_f3[18] + k_f3[21]) * 0.5\n",
    "k_f3[19] = (k_f3[20] + k_f3[18]) * 0.5\n",
    "k_f2[19] = (k_f2[20] + k_f2[18]) * 0.5\n",
    "\n",
    "k_b2[24] = (k_b2[25] + k_b2[23]) * 0.5\n",
    "k_b3[24] = (k_b3[25] + k_b3[23]) * 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 1)\n",
    "ax.plot(a_s[0], k_s2, \"s\", label = \"$k_2$ / $k_1$\")\n",
    "ax.plot(a_s[0], k_s3, \"o\", label = \"$k_3$ / $k_1$\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"Анизотропия проницаемости\")\n",
    "plt.title(\"Простая кубическая\")\n",
    "plt.show()\n",
    "\n",
    "if savefig:\n",
    "    fig.savefig(\"anisotropy-simple.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1NC8i9uxh+QH8WlIA34uIS6umvxF4omK4LR/nRGBmhVvnSP+Wm4DmVOWUqRoppWpI3c/SqXdHxGJJWwK3SnooIn5TbyGSJgOTAUaOHMmcOXN6EVJjtbe3lyqeamWPD8ofY9njA8dYj8oYujrST421p/P1ZN1FfYYpieAtkiovG02+jyAiFud/n5Z0PbA3UJkIFgPbVAyPysdVl3MpcCnA2LFjo0xPYir7k6HKHh+UP8ayxweOcR35kX0ta8XQqvnqnTdX1GeYkgj+ChxWb8GSNgLWi4gV+fv3AWdWzTYT+LSk6WSNxMvdPmBmvdWqG8/66g1vKYngHxHxWA/KHglcn10hymDgJxFxi6QTACJiGvBLsiuQHiG7fPTYHqzHzGwtja5jT93Bl/kS0a6kJIJ/70nBEbEI2L3G+GkV7wM4qSflm5k1S1/dwadK6XTuEEmbdgxI2kzSWcWFZGbWHJ1V2TSjKqeV666WckZwcER8qWMgIp6VdAhwWnFhmZkVr/JIv9kN7mU6y0hJBIMkbRARKwEkbQhsUGxYZmYGzbnXISURXA3MknR5PnwscGVD1m5mZl1qxl3NKV1MnCtpPnBAPuprEfGrhkVgZmYtlXJGAHA/MISsy4j7iwvHzMyardurhiRNBO4FjgQmAr+XdGTRgZmZWXOknBF8GdgrIp4GkLQFcBtwbZGBmZlZc6TcR7BeRxLILUtczszMeqkZ9xuknBHcIulXwE/z4Q+TdQ1hZmYFa8a9DilXDX1e0r+QPaYS4NKIuL7hkZiZWUskXTUUET8Hfl5wLGZm1gIpTyh7BWivHEXWX9wmhUVlZmZNk3JG8F/AHsAtwI+rGo7NzKyPS2kjOEXSesDBwHclPRsRk4sPzczMmiH1MtCdgPcCw8luLjMzs34ipY3gbrJ2gcuB6fm4PSNiXsGxmZlZEyQ9qpKsj6GPAEeRNxYD+xUYl5mZNUlKG8G4JsRhZmYtktLp3EhJl0m6OR/eRdJxxYdmZmbNkNJYfAXwK2DrfPjPwGcLisfMzJosJRFsHhEzgFcAImI1sKbQqMzMrGlSEsELkkaQNRAj6Z3A8kKjMjOzpkm5aug/gZnADpLuArYge0iNmZn1AylXDc2T9F7gLWSXjj4cEatSVyBpEHAfsDgiJlRNm0TWhcXifNRFEfGD1LLNzKz3Up9Z/FZgF2AosKckIuJHict+BlgIdNZJ3TUR8enEsszMrMFSLh89HfhO/hoPfBM4PKVwSaOAQwEf5ZuZlZQiousZpAeA3YH7I2J3SSPJeiE9sMsFs2WvBc4BNgZO7qRq6BxgCdllqf8REU/UKGcyMBlg5MiRY6ZPn56wac3R3t7OsGHDWh1Gp8oeH5Q/xrLHB46xEcoeH/QuxvHjx8+NiLE1J0ZEly/g3vzvXLLqHQEPJSw3Abg4fz8O+EWNeUYAG+TvPwnc3l25Y8aMiTKZPXt2q0PoUtnjiyh/jGWPL8IxNkLZ44voXYzAfdHJfjXl8tH7JG0KfD9PBvOA3yUsty9wuKRHyTqr20/Sj6uS0LKIWJkP/gAYk1CumZk1UMpVQ5/K306TdAuwSUQsSFjuVOBUAEnjyKqGjq6cR9JWEfFUPng4WaOymZk1UepVQwBExKMAkr5I9oyCn0fETfWUIelMslOUmcAUSYcDq4FngEn1lGVmZr2X8jyCv5LfVdwxChhJdknpcykriYg5wJz8/dSK8a+eNZiZWWuknBFUtzILuDEiHi8gHjMza7KUNoJl1eMkJd9ZbGZm5ZZSNfT6nixnZmZ9Q8oOfS5ZG4EqxnV9F5qZmfUZKVVD2zUjEDMza42UqqEP1hofEdc1PhwzM2u2lKqha4A/kVURdVQPBeBEYGbWD6Qkgl2BrwHDgK9ExMPFhmRmZs2U0kbwMDBR0hjg25KeBM6IiMXdLGpmZn1AShvBd3jtKqFFwHuB/wNeV2BcZmbJVq1aRVtbGy+//HKPyxg+fDgLF5a7u7OUGIcOHcqoUaMYMmRIcrkpVUP3dTNsZtZSbW1tbLzxxowePRpJ3S9Qw4oVK9h4440bHFljdRdjRLBs2TLa2trYbrv0Cz5TqoauTC7NzKwFXn755V4lgf5CEiNGjGDJkiV1LdfTTuciIravL0Qzs+IM9CTQoSefQ2qncwJuJ3tmsZmZ9SPJnc5JWl2rAzozM+vb6ul0bpCkzchvKouIZ4oMzMzMmqPeTufm5eMCcBuBmfU5Y8+6laXt/1hn/IiNhjD3K+/rVdmzZs3iiiuu4KqrrqpruRNOOIFjjjmGfffdtyHl1cudzpnZgFIrCQAse6H3j1mZP38+e+yxR93L3XPPPXz3u99tWHn1Wq+7GSQNkTRF0rX569OS0u9UMDMbIDp23CtXrmTSpEl86UtfIqLrXvsXLlzITjvtxKBBg7ot76tf/Wq35fVEStXQJcAQ4OJ8+Jh83PENj8bMrA9bsGABW265Je9///s5/vjjOfroo7td5uabb+aggw5KKu+II44o5DLZlESwV0TsXjF8u6T5DY/EzKwPW7VqFYsWLeIjH/kI3/ve99hnn3244YYbuOmmm3j++ec57rjjeN/71m2D+NWvfsXll1+eVN6KFStYtGgRZ599NsuXL+faa69tSOzdVg0BayTt0DEgaXtgTUPWbmbWTyxcuJC99tqLwYMHv1rN84EPfIDvf//7TJs2jWuuuWadZV588UWee+45tt5666TyALbffnsuu+yyhsaekgg+D8yWNEfSHWQ3ln2uoVGYmTXJ5sPWrzl+xEa9a/qcP38+73rXu5g+fTrHHnssf//731+ddtZZZ3HSSSets8zs2bMZP772fbpdlddoKVcNzZL0ZuAt+aiHI2Jl6gokDSLrqG5xREyomrYB8CNgDLAM+HBEPJpatplZve477cCa41esWNGrcufPn8/ee+/NTjvtxLnnnsvEiRO57bbbOO200zj44IPZc88911nm5ptv5sgjj0wu7/rrr+9VjJ1JuWpoKHAScAZwOnBiPi7VZ4DO+k09Dng2InYEzgfOraNcM7PSOO+885g4cSIAEyZM4I477uCSSy7htttu49prr2XatGnrLHP33Xevc+9AV+UNGTKEZcuWccIJJ3D//fdzzjnnNCT2lMbiHwErgO/kwx8FrgI+1N2CkkYBhwJnA/9ZY5YjyBIMwLXARZIURVwfZWbWZFOmTGHKlCmdTp83b16n0zozYsSImkmlN9TdPlfSnyJil+7GdbLstcA5wMbAyTWqhv4IHBQRbfnwX4B3RMTSqvkmA5MBRo4cOWb69OndbliztLe3M2zYsFaH0amyxwflj7Hs8YFjHD58ODvuuGOvylizZk3Na/nLJDXGRx55hOXLl681bvz48XMjYmyt+VPOCOZJemdE3AMg6R0kPJxG0gTg6YiYK2lcwno6FRGXApcCjB07NsaN61VxDTVnzhzKFE+1sscH5Y+x7PGBY1y4cGGvHyrTHx5M02Ho0KF13ZGckgjGAHdLejwf3hZ4WNIDZM8l2K2T5fYFDpd0CDAU2ETSjyOi8g6LxcA2QJukwcBwskZjMzNrkpREUPuWt25ExKnAqQD5GcHJVUkAYCbwMeB3wJHA7W4fMDNrrpREUHPHHBGP1xrfHUlnAvdFxEzgMuAqSY8AzwBH9aRMMzPruZRE8BDwCPlzCHIBdFYltI6ImAPMyd9PrRj/MglXH5mZWXFSEsHDEVF8P6hmZtYSKV1MuM7ezKwfS0kEm0r6YPWr8MjMzPqYWbNmccwxx9S93AknnMBdd93VsPLqlVI1dAdwWNW4AK5rfDhmZk2wYAbMOhOWt8HwUbD/VNju4F4X22+fUBYRx9Z4fbzwyMzMirBgBtw4BZY/AUT298YpDF7Y+w7d+uoTylKqhszM+o9ZZ8Kql9Yet+olNrjzG70uuvKJYgcccABf//rXu32iWOoTyg444ABOP/30lj2hzMys/1jeVnO0VjzZq2JrPVFs4cKFXHDBBSxdupT999+fE088cZ3l+soTyszM+o/ho2qOjo3XfUpYPWo9UWznnXdm2rRpzJgxo2ZjcJ95QpmkqbVeDY3CzKxZ9p8KQzZce9yQDVn5ni/2qtjOnig2c+ZMDj30UA455JB1linLE8pSzggmA+3AC8An8r8vFBaRmVmRdpsIh10Iw7cBlP097EJW7/zPvSp2/vz57Lrrrms9UWzVqlUcfvjh3HzzzVx99dXrLNNV+0Bn5RUhpY1gSUR8G0DSCcDVEfG3QqIxM2uG3SZmr0q9fFTleeed9+r7CRMmMGHCBObMmcN1113HypUra54R3H333Zx//vnJ5a1YsYJly5bx5S9/+dUnlJ166qm9ihvSEoEk/TOwCbASuF7SqXn/QWZm1olx48Z1+QyGsjyhLCURfAI4BVhD1kHc34HLyTuRMzOzvq3bRBAR/0v2rIBKRxQTjpmZNVu3iUBSrYfO09FuYGZmfVtK1dBXgMeA3t9/bWZWkIgo5K7bvqYnXVCkJIIdyB45uT9wZkTcVvdazMwKNHToUJYtW8aIESMGdDKICJYtW8bQoUPrWi6ljeAZ4POStgZOl3Qy8JW87cDMrOVGjRpFW1sbS5Ys6XEZL7/8ct070GZLiXHo0KGMGlX77unOpLQR3MhrD6cRsC1wD7BuV3lmZi0wZMgQtttuu16VMWfOnKZ0+dwbRcWYUjV0XvezmJlZX5VSNXRHMwIxM7PWSKkaWg28WDkKiIjYpLCozMysaVKqhh6IiHJXnJmZWY+l9D7ao+eiSRoq6V5J8yU9KOmrNeaZJGmJpD/kr+N7si4zM+u5lDOC10nag6xK6FUR0V1vSSuB/SKiXdIQ4LeSbo6Ie6rmuyYiPp0espmZNVJKIngKqO5OIoD9uloostvb2vPBIfmr8U9dNjOzXlFPbkdOLlwaBMwFdgS+GxGnVE2fBJwDLAH+DPxHRDxRo5zJZA/IYeTIkWOmT59eWMz1am9vZ9iwYa0Oo1Nljw/KH2PZ4wPH2Ahljw96F+P48ePnRsTYmhMjossXMJzsjOC+/PUtYHh3y1WVsSkwG9i1avwIYIP8/SeB27sra8yYMVEms2fPbnUIXSp7fBHlj7Hs8UU4xkYoe3wRvYsRuC862a+mNBb/EFgBTMxfz5M9jyBZRDyXJ4KDqsYvi4iV+eAPgDH1lGtmZr2Xkgh2iIjTI2JR/voqsH13C0naQtKm+fsNgQOBh6rm2api8HBgYXLkZmbWECmNxS9JendE/BZA0r7ASwnLbQVcmbcTrAfMiIhfSDqT7BRlJjBF0uHAauAZYFJPNsLMzHouJRGcSLZDH052CekzwMe6WygiFgDr3IgWEVMr3p9K1sW1mZm1SEpfQ38Adpe0ST78fNFBmZlZ83TbRiBphKQLyR5WP1vSBZJGFB6ZmZk1RUpj8XSy6/z/hewh9kuAa4oMyszMmieljWCriPhaxfBZkj5cVEBmZtZcKWcEv5Z0lKT18tdE4FdFB2ZmZs2Rkgg+AfyErBO5lWRVRZ+UtEKSG47NzPq4lKuGNm5GIGZm1hopVw39ohmBmJlZa6RUDW1deBRmZtYyKVcNbS9pZvXIiDi8gHjMzKzJUhLBErKup83MrB9KSQTtEXFH4ZGYmVlLpLQRnFN4FGZm1jIpZwQ3SJoC/FM+fAcwLSJWFReWmZk1S0oiuJjswfMX58PHAJcAxxcVlJmZNU9KItgrInavGL5d0vyiAjIzs+ZKaSNYI2mHjgFJ2wNrigvJzMyaKeWM4PNkzyFYRPaEsjcBxxYalZmZNU1KX0OzJL0ZeEs+6uGIWFlsWGZm1iwpfQ3tBEwle2D9+4Bp+TgzM+sHUtoIrsr/3goMAuYDlxcWkZmZNVVKIhgcEV8GXomIcyPiv4FhxYZlZmbNkpIIXsn/nlQxTgXEYmZmLZCSCD4OEBE3A0jahKzNoEuShkq6V9J8SQ9K+mqNeTaQdI2kRyT9XtLoOuM3M7Ne6jYRRMQDVcPPR8QNCWWvBPbLb0Z7O3CQpHdWzXMc8GxE7AicD5ybErSZmTVOylVDKyQ9n79WpD6rODLt+eCQ/BVVsx0BXJm/vxbYX5KrnczMmkgR1fvmqhmks4D9gLMj4qa6CpcGAXOBHYHvRsQpVdP/CBwUEW358F+Ad0TE0qr5JgOTAUaOHDlm+vTp9YRRqPb2doYNK2/bednjg/LHWPb4wDE2Qtnjg97FOH78+LkRMbbmxIjo9gVsAVwIzAL2TVmmavlNgdnArlXj/wiMqhj+C7B5V2WNGTMmymT27NmtDqFLZY8vovwxlj2+CMfYCGWPL6J3MQL3RSf71ZSqoT2BbYAryHogvbjeB9pHxHN5IjioatLivGwkDQaGA8vqKdvMzHonpa+h6sdUPgNs1N1CkrYAVkXEc5I2BA5k3cbgmcDHgN8BRwK355nLzMyaJKWvofE9LHsr4Mq8nWA9YEZE/ELSmWSnKDOBy4CrJD1ClmCO6uG6zMysh7pNBJJq3jMQEWd2tVxELAD2qDF+asX7l4EPdR+mmZkVJeWGshfy1ycq3r9QZFBmZtY8KVVD3wKQdHTHezMz6z9Szgg6uBHXzKwfSmkjuJEsCWwvaWbH+Ig4vMjAzAa0BTNg1pmwvA2Gj4L9p8JuEzud773L2+D+LuYz60LK5aPn5X9dLWTWDAtmwI1TYNVL2fDyJ7JhWHsnXzGfuprPrBspnc7dUfkie3C9/8vMemLBDDh/Vzhj0+zvghnrzjPrzNeSQIdVL2XjezJfEVK2w/qMlDMCJO0BfJTsUs+/Aj8vMiizfin1SH95W+3lq8enztdoqdthfUanZwSSdpJ0uqSHgO8Aj5N1Ujc+Ii5qWoRm/UXqEfzwUbWXrx6fOh809gi+lWciVoiuqoYeIut1dEJEvDsivkNWLWRm1VJ2tKlH8PtPhSEbrj1uyIbZ+J7M13EEv/wJIF47gu9pMmjVmYgVpqtE8EHgKWC2pO9L2h8/otJsXak72tQj+N0mwmEXwvBtAGV/D7tw3WqXivmiq/nqOYJPSWj1nInUw+0OLdNpG0FkTyG7QdJGZA+Q+SywpaRLgOsj4tdNidD6ntRLH/uLrna0ldu9/9S169ah9hE8ZMulfGb5fHfMmcO4ceNqz5N6BJ9a91/PdqRyu0NLpVw19EJE/CQiDgNGAfcDp3SzmA1Uja6G6AtSd7SpR/qNlnoEn3rmUMR2uN2hpZKuGuoQEc8Cl+avvqOeI9RGH832p6PjlG1JPTruC1Jv1ho+Kk98NcZXSz3Sb6TUI/h66v4bvR1ud2iperqY6JvqOUKtd97zd+W9cz7QeX1mEUfHqfWoKfHVW17KttTzg06NMVWdn02X81Vsr7r77lIbbVsl9Qi+4Lr/Lr/notZtSfp/IqjnlDN13tSdRKMb6VJ3xqnx1ZOoGn3pYz072oI+my7nq+e7a1WVTz12mwj/8Uc447nsb63Yikhoqd9z2ZNpP9f/E0E9R6ip86buJOptpGvUzqmIO1MbfeljD5JuUz+beqsqUna0ZdfKuv961u2rixqurjaCPqme+tvUeVN3EqnlpdarN/qO03p2dqnb0hFvd20JjUi6RX429fzf9CetrPtPWbevLipE/z8jqOeUM3Xe1OqP1PLq2TmlrLeIO1Pr+RxTjo5T192qz8ZVFY3R6Lp/X11UiP6fCOo55UydN3Un0ehGukbfcVrvzr2R1QatSro9+O66vFnLutbohOqriwrR/6uGoL7T3ZR5K6o/Ynkb6uqy0JTyUi/vS612SY0vtbx6tiVVaowFfjbdbnPKzVrWtXp+Kyn6QpVdH7xkXBF968FjY8eOjfvuu6/VYbxqTqN2EgX98zQsvgJ1G2OLf1j94jMsgYbEWN1GANmBQQPO1soeH/QuRklzI2JsrWkD44ygL2jFjUZ9hT8b61DPGV0rDiD66A2VTgRm1re06uqilMTSR9swCmsslrSNpNmS/iTpQUmfqTHPOEnLJf0hf/mSDDPrvR7czNmQXgLqvUqqJPdEFHnV0GrgcxGxC/BO4CRJu9SY786IeHv+8jVgZtZ7PbiZsyG9BNRzlVSJOmgsLBFExFMRMS9/vwJYCLyxqPWZmb2q0T2uFtHDbInuiWjKVUOSRgO/AXaNiOcrxo8je/5xG/AkcHJEPFhj+cnAZICRI0eOmT59euExp2pvb2fYsGGtDqNTZY8Pyh9j2eMDx1hty7/fwVse/i6DXln56rg1623Aw285iadHvvfVce+d84HsTKBKIO4Yd8Orw+/83fEMXblknfle3mAL7tnnBz2KMXXdlXrzGY4fP77Tq4YKTwSShgF3AGdHxHVV0zYBXomIdkmHABdExJu7Kq/fXj5akLLHB+WPsezxgWOsKaVx9/xdO7kvYZvsrvjKshp9WWjquisUdflooXcWSxpCdsR/dXUSAIiI5yOiPX//S2CIpM2LjMnMBohG9rhaRId8JerGpLDLRyUJuAxYGBHf7mSeNwB/j4iQtDdZYlpWVExmZmtpdC8BPVx36j0R3T4kqYeKvI9gX+AY4AFJf8jHfQnYFiAipgFHAidKWg28BBwVfe1WZzPr21rZlUid90QICulxtbBEEBG/hSzuLua5CLioqBjMzPq8Jtyt3P97HzUz68uacLeyE4GZWZk14XnOTgRmZmXWhKuLnAjMzMqsCQ9Jcu+jZmZlV/CVTT4jMDMb4JwIzMwGOCcCM7MBzonAzGyAcyIwMxvgmvI8gkaStAR4rNVxVNgcWNrqILpQ9vig/DGWPT5wjI1Q9vigdzG+KSK2qDWhzyWCspF0X2d9fJdB2eOD8sdY9vjAMTZC2eOD4mJ01ZCZ2QDnRGBmNsA5EfTepa0OoBtljw/KH2PZ4wPH2Ahljw8KitFtBGZmA5zPCMzMBjgnAjOzAc6JoBOSDpL0sKRHJH2xxvQNJF2TT/+9pNH5+AMlzZX0QP53v7LFWDF9W0ntkk4uW3ySdpP0O0kP5p/l0DLFKGmIpCvz2BZKOrWI+BJj/CdJ8yStlnRk1bSPSfq//PWxMsUn6e0V3/ECSR8uIr7exFgxfRNJbZIKebRuL7/jbSX9Ov8//FP17zxJRPhV9QIGAX8BtgfWB+YDu1TN8ylgWv7+KOCa/P0ewNb5+12BxWWLsWL6tcDPgJPLFB9Z9+gLgN3z4RHAoJLF+FFgev7+dcCjwOgWxTga2A34EXBkxfjXA4vyv5vl7zcrUXw7AW/O328NPAVsWqbPsGL6BcBPgIvKFh8wBzgwfz8MeF29MfiMoLa9gUciYlFE/AOYDhxRNc8RwJX5+2uB/SUpIu6PiCfz8Q8CG0raoEwxAkj6APDXPMYi9Ca+9wELImI+QEQsi4g1JYsxgI0kDQY2BP4BPN+KGCPi0YhYALxStez7gVsj4pmIeBa4FTioLPFFxJ8j4v/y908CTwM173xtVYwAksYAI4FfFxBbr+KTtAswOCJuzedrj4gX6w3AiaC2NwJPVAy35eNqzhMRq4HlZEeulf4FmBcRK8sUo6RhwCnAVwuIq9fxkR0phqRf5afDXyhhjNcCL5AdxT4OnBcRz7QoxiKWTdWQdUjam+xo+C8NiqtSj2OUtB7wLaCQ6tNcbz7DnYDnJF0n6X5J/yVpUL0B+AllBZH0NuBcsqPbsjkDOD8i2vMThLIZDLwb2At4EZglaW5EzGptWGvZG1hDVqWxGXCnpNsiYlFrw+p7JG0FXAV8LCLWOSJvsU8Bv4yIthL/Vt5DViX9OHANMAm4rJ5CfEZQ22Jgm4rhUfm4mvPk1QPDgWX58CjgeuDfIqKII5zexvgO4JuSHgU+C3xJ0qdLFF8b8JuIWJqf5v4S2LPB8fU2xo8Ct0TEqoh4GrgLKKKfmpQYi1g2Va/WIWkT4CbgyxFxT4Nj69CbGPcBPp3/Vs4D/k3SNxobXq/iawP+kFcrrQZuoCe/lUY3fPSHF1mWXQRsx2uNN2+rmuck1m5EnJG/3zSf/4NljbFqnjMoprG4N5/hZsA8skbYwcBtwKEli/EU4PL8/UbAn4DdWhFjxbxXsG5j8V/zz3Oz/P3rSxTf+sAs4LON/twaFWPVtEkU01jcm89wUD7/Fvnw5cBJdcdQ5BfQl1/AIcCfyeosv5yPOxM4PH8/lOyKm0eAe4Ht8/GnkdUd/6HitWWZYqwq4wwKSAS9jQ84mqwh+4/AN0v4PQ/Lxz9IlgQ+38IY9yI7MnyB7GzlwYplP57H/ghwbJniy7/jVVW/lbeXKcaqMiZRQCJowHd8INlVdg+QJYr1612/u5gwMxvg3EZgZjbAORGYmQ1wTgRmZgOcE4GZ2QDnRGBmNsA5EZjVQdKjkjbv7TxmZeJEYGY2wDkRmHVC0g3KninxoKTJVdNGS3pI0tV5P/DXSnpdxSz/nneY94Ckt+bL7J33v3+/pLslvaWpG2TWCScCs859PCLGkPUhNEVSde+ybwEujoidybqg/lTFtKURsSdwCa/1XPkQ8J6I2AOYCny90OjNEjkRmHVuiqT5wD1knYK9uWr6ExFxV/7+x2Q9pna4Lv87l+yhIpB1WPczSX8EzgfeVkTQZvVyIjCrQdI44ABgn4jYHbifrN+hStX9s1QOdzyDYg2vdff+NWB2ROwKHFajPLOWcCIwq2048GxEvJjX8b+zxjzbStonf/9R4LcJZXZ0LzypIVGaNYATgVlttwCDJS0EvkFWPVTtYeCkfJ7NyNoDuvJN4BxJ9+OHQlmJuPdRsx6QNBr4RV7NY9an+YzAzGyA8xmBmdkA5zMCM7MBzonAzGyAcyIwMxvgnAjMzAY4JwIzswHu/wOw0D8wsm1jIQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 1)\n",
    "ax.plot(a_b[0], k_b2, \"s\", label = \"$k_2$ / $k_1$\")\n",
    "ax.plot(a_b[0], k_b3, \"o\", label = \"$k_3$ / $k_1$\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"Анизотропия проницаемости\")\n",
    "plt.title(\"Кубическая объемноцентрированная\")\n",
    "\n",
    "if savefig:\n",
    "    fig.savefig(\"anisotropy-bodycentered.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 1)\n",
    "ax.plot(a_f[0], k_f2, \"s\", label = \"$k_2$ / $k_1$\")\n",
    "ax.plot(a_f[0], k_f3, \"o\", label = \"$k_3$ / $k_1$\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"Анизотропия проницаемости\")\n",
    "plt.title(\"Кубическая гранецентрированная\")\n",
    "\n",
    "if savefig:\n",
    "    fig.savefig(\"anisotropy-facecentered.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "19649669bd52b0be75e091dcf60d2128e4a347083ff474cfec5ff9275df3ceed"
  },
  "kernelspec": {
   "display_name": "Python 3.9.9 64-bit ('anisotropy': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}