Electricity and magnetism/Lab3 | Internal resistance and matching in sources/Lab3 | Internal resistance and matching in sources.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.linalg\n",
    "pd.options.mode.chained_assignment = None\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>U0</th>\n",
       "      <th>I</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.75</td>\n",
       "      <td>12.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.00</td>\n",
       "      <td>12.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.75</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>12.12</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      U0    I     U\n",
       "0  12.12 0.25 12.12\n",
       "1  12.12 0.50 12.11\n",
       "2  12.12 0.75 12.09\n",
       "3  12.12 1.00 12.08\n",
       "4  12.12 1.25 12.07\n",
       "5  12.12 1.50 12.05\n",
       "6  12.12 1.75 12.04\n",
       "7  12.12 2.00 12.03\n",
       "8   4.50 0.10  4.30\n",
       "9   4.50 0.20  3.90\n",
       "10  4.50 0.30  3.70\n",
       "11  4.50 0.40  3.30\n",
       "12  4.50 0.50  2.90\n",
       "13  4.50 0.60  2.70\n",
       "14  4.50 0.75  2.25"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data3.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "expData = df[df.U0 == 12.12]\n",
    "batteryData = df[df.U0 == 4.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>U0</th>\n",
       "      <th>I</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.50</td>\n",
       "      <td>12.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.75</td>\n",
       "      <td>12.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.00</td>\n",
       "      <td>12.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>12.12</td>\n",
       "      <td>1.75</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>12.12</td>\n",
       "      <td>2.00</td>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     U0    I     U\n",
       "0 12.12 0.25 12.12\n",
       "1 12.12 0.50 12.11\n",
       "2 12.12 0.75 12.09\n",
       "3 12.12 1.00 12.08\n",
       "4 12.12 1.25 12.07\n",
       "5 12.12 1.50 12.05\n",
       "6 12.12 1.75 12.04\n",
       "7 12.12 2.00 12.03"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.05190476 12.13264286]\n"
     ]
    }
   ],
   "source": [
    "basis = np.vstack((expData.I,np.ones(len(expData)))).T\n",
    "koef = scipy.linalg.lstsq(basis, expData.U)[0]\n",
    "print(koef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "233.74816513762258\n"
     ]
    }
   ],
   "source": [
    "x0 = -koef[1]/koef[0]\n",
    "print(x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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9p969e+v999/XvHnzNGrUqBQ9nivb3E2nT59WTExMvO3npkWLFsnT01NBQUFJLudu21xitm3bJh8fn2QddpKc9ceV1+Fu209KlpeSnElJ7fXHlffb5CzvpitXrujdd9+VlPAhX2l5JiMOMQKQJVy5ckU//fSTunTpotDQ0Hj/Ro8erZMnT+r3339P9nJPnTqlP//8M+7nQ4cOad26dapXr548PT1Vu3ZtzZ49O959Zs2aJQ8Pj3ifXjdq1EgtWrRQlSpVNGzYMA0aNEinT5+WpLhTst78Y+BuoqOj9cYbb2jYsGEJTjq+Va9evTRlyhR16NAhwYmKycmfI0cO1alTR3Xr1tWTTz6pTp066YcffpAkXb9+XbGxsfEOKbh06VK84/9vX86t/249/jaxZbmy9+B2165d099//63Lly+7nNGyLC1ZsiTec7/VzePCd+7cecfzqFOnTryCcOXKFQ0cOFCvvvqqypcvr65du6p06dLq2bOnihQpotdee03Zs2fXiBEjkvUaJZTJ1fUnKbeOc+fOndW5c+c7zgJ2t/sllDk564er7rZd3uqxxx5TtmzZ1LlzZ8XGxsabC+Gq5Gxz0j+fRPv4+GjdunXxLj979qw+/vhjdejQQQULFkxyOXfb5hLz66+/KjAw0OXDeyTX1x9XXoektp/kLi8lOV2RFutPUu+3yV2eJI0dO1anT5/WzJkz7/i91qBBAw4xAoCkLFiwQFevXtWAAQPu+HbRxo0b67333tP06dPVokWLZC23QIEC6tq1q9599924P+YKFSqkHj16SPrn1JqtWrVSz5491blzZ23evFlvvvmmevfunegu89dff12zZ8/WgAED4iaufvDBB2revLkeeugh9enTRzlz5lRERITq1Kmjtm3bxt03IiJCRYsWVf/+/ZPM/uCDD+q3335L8EwqN7ma/8qVK1q5cmXcp5kzZsxQrVq1JP3z6VndunX13//+V7lz55aHh4c++OAD+fv76+LF5E10v9uybj+N6O06deqkRx99VEWKFNHWrVs1YsQINWzYMO6Pj6Qy7tq1SyNGjNDq1avv+i2lH330kZo1ayYPDw917NhRuXLl0qFDh/Tzzz/rvffe0/333x/32np5eWn48OEJLsfHx0fjxo1Tu3bt1KNHD1WrVi1Zr9WtXF1/khIZGakdO3bEjfPcuXPjxjmlUnP9uCmp7fImX19fPf3005owYYK6dOmiPHnyJPuxkrPNSf9MCu7Vq5cmTZokPz8/1a9fX4cOHdLYsWMVFRV11z0At7rbNne7ixcvaurUqfrtt980YsQIrVy5Mu666OhoHTlyRHv37lW5cuUSvL8r609Sr4Or289NyX1dXc3pirRYf1x5v03O8s6dO6eQkBD17NlTTz755B3X169fX1988YViY2Pl4ZH6n/ezBwFAljB9+nRVqFDhjnIgSd7e3nryySc1b948Xb9+PVnLLV26tEaOHKm33npLnTt3Vq5cubR48WL5+vpKUtxZktasWaN27dppzJgxGjx4sMaPH5/oMn18fDRp0iTNmDEj7pPxwMBALV26VFevXlXXrl3VqVMnhYWF3VEyYmNj9dZbb7k0CdEYo2bNmql48eKJ3sbV/Dt27FDDhg3VuHFjvfLKKwoKCtKkSZPirv/+++9VtmxZde/eXQMGDNDjjz+e4DHYrkhoWa588hsbG6tXXnlFrVq10gcffKAOHTrEO3ViUhnnz5+v/fv364cfflCrVq0SfZwmTZooPDxcp0+fVrdu3dSuXTt99NFHKlmyZNyx31u3btWYMWM0duzYu45V69at1b59e7344ov39Kmoq+tPUrZu3apKlSqpSpUq6tmzpxo1aqTPPvssxbluSs31Q0p6u7xVhw4dJP3zCW9KJGebu2n06NEaPny45s+fr65du2r06NFq0aKF1qxZo1KlSrm0jKS2uVutW7cu7g/tt99+Ww0bNoz7d+HCBU2ePFnvvPNOoo/lyvqT1Ovg6vbj6vJSmtMVabH+uPJ+m5zlffjhh4qOjo47xOh2VapUUWRkpPbs2XPX5aSUSctzqKaFgIAAa+fOnXbHgE2cTuc9ny8cd9q+fXum+EbOS5cuxTs1aFrr0aOHtmzZojVr1qTbYyJh6T32yFhuHf/kbpevvvqqZs2apX379qXJJ60ZgdPpVI8ePXTgwIEEr3/rrbd04MCBJL+ELKNKze3f7vUnvdfHu/1+N8astSwrwW9H5BAjAACQ5ezcuVPbtm3TZ599phEjRmTZciBJuXPnvuuZkUqUKJHsEzS4u9RefzLb+khBAAAAWc7zzz+vVatWqX379sk6zj0zqlWr1l0nMD/33HPpmCZrSO31J7OtjxQEAEhEZt0dD2Rlrm6XN7+QCriVXetPZlsfM/b+DQAAAADpioIAAAAAIA4FAQAAAEAcCgIASbrnb6YEAAAZx738XqcgAJC3t7euXbtmdwwAAJBKrl27Jm9v7xTdl4IAQIUKFdLRo0d19epV9iQAAJCJWZalq1ev6ujRoypUqFCKlsFpTgEod+7ckqRjx44pKirK5jSJi4yMlK+vr90xYAPG3r0x/u6N8U8+b29vFS5cOO73e3JREABI+qckpPSNJL04nc67flsosi7G3r0x/u6N8U9/HGIEAAAAIA4FAQAAAEAcCgIAAACAOBQEAAAAAHEoCAAAAADiUBAAAAAAxKEgAAAAAIhDQQAAAAAQJ9MVhHORli5FZtxvegUAAAAys0xXEC7csOQIceqbiAOKiom1Ow4AAACQpaRLQTDGTDHGnDLGbLnlshBjzA5jzCZjzA/GmDyuLKtoTg9VKOynNxdsVauPw7V46wlZlpV24QEAAAA3kl57EL6W1Pq2y5ZKqmpZVnVJuyQNd2VBPp7S9N4N9GX3OjJGev6bter0xUqtP3QudRMDAAAAbihdCoJlWeGSzt522RLLsqL//XGlpBKuLs8Yo+aVC2vxwEC992hV7TtzRY9++qde/n6dDv19NRWTAwAAAO7FpNfhOcaYMpIWWpZVNYHrfpI007KsbxO5bx9JfSSpYMGCtWfNmhXv+mvRln7ZH6VfD0QpJlZqXspL7cplk182k9pPAza7fPmy/Pz87I4BmzD+7ouxd2+Mv3tj/NNGcHDwWsuy6iR0ne0FwRjzf5LqSHrMciFMQECAtXPnzgSvO3kxUqOX7NLstYfl5+Olfg9WUPdGpeXj5XnvTwAZgtPplMPhsDsGbML4uy/G3r0x/u6N8U8bxphEC4KtZzEyxvSQ1FbS066Ug6QUzu2rDztW16IBTVWzVF69t2i7mo0K04INRxUby0RmAAAAICm2FQRjTGtJr0pqb1lWqk4cqFgkt6b2qqdvn62v3L7eGjBjgx799A+t2vd3aj4MAAAAkOWk12lOp0uKkBRgjDlijHlW0nhJuSQtNcZsMMZ8ntqP26RCAS3s10SjnqihU5euq9PElXpu6hrtOXU5tR8KAAAAyBK80uNBLMvqksDFk9PjsT08jB6vXUIPVy+qySv26zPnXrUaE64u9UpqYPP7VcDPJz1iAAAAAJlCpvsm5ZTy9fbUS8HlFTbUoa71S2nG6sMK+ihU43/frWs3YuyOBwAAAGQIblMQbsrv56O3H6mqJa8EqkmFAhq5ZJeCRzo1a81hxTCRGQAAAG7O7QrCTWUL+umLbnU0+4WGKuLvq1fnbNLDnyxX+K7TdkcDAAAAbOO2BeGmumXy6Ye+jTT+qZq6eiNG3aesVrfJq7T9+EW7owEAAADpzu0LgiQZY9S2ejEtHRSoN9tW1qYjF9Tmk+UaMnujjl+4Znc8AAAAIN1QEG7h4+WpZ5vcp/ChwerdtKx+3HBMwSOdGrl4py5FRtkdDwAAAEhzFIQE+Ofw1uttKmnZ4CC1qlJE40P3yBHi1DcRBxQVE2t3PAAAACDNUBDuomS+HBrbuaZ+fLmxyhfy05sLtqrVmHAt2XpClsUZjwAAAJD1UBBcUL1EHs3o00Bfdq8jI6nPN2vV6YuV2nD4vN3RAAAAgFRFQXCRMUbNKxfW4oGBerdDVe07c1kdJvyhftPX6/DZq3bHAwAAAFIFBSGZvDw91LVBaTmHBqv/g+W1dNsJNRsVpvd+3qYLV5nIDAAAgMyNgpBCfj5eGtQyQM4hwepQs5i+XLFfgSGh+nL5Pl2PjrE7HgAAAJAiFIR7VMTfVx91rKFfBjTVAyXz6N2ft6v56DD9tPEYE5kBAACQ6VAQUknFIrk1tVc9ffNsPfn5eKvf9PXq8OmfWr3/rN3RAAAAAJdREFJZ0woFtbBfE418ooZOXojUk19EqPe0Ndp7+rLd0QAAAIAkURDSgKeHUcfaJRQ6xKGhrQIUsfdvtfw4XG/O36Izl6/bHQ8AAABIFAUhDWXP5qmXgsvLOdShp+uX0verD8kR4tT433fr2g0mMgMAACDjoSCkgwJ+PvrvI1W15JVANSqXXyOX7FLwSKdmrzmsmFgmMgMAACDjoCCko3IF/TSxex3Ner6hCvv7auicTXr4k+UK33Xa7mgAAACAJAqCLerdl0/z+zbSuC41deVGtLpPWa1uk1dp+/GLdkcDAACAm6Mg2MQYo3Y1ium3QUF64+FK2nTkgtp8slxDZ2/UiQuRdscDAACAm6Ig2MzHy1PPNS2r8KHB6t20rBZsOCbHyFCNWrJTl69H2x0PAAAAboaCkEH45/DW620qadngILWsXETjft8jR0iovll5UNExsXbHAwAAgJugIGQwJfPl0CddamrBS41VtqCf3py/Ra3GhGvptpOyLM54BAAAgLRFQcigapTMo5l9GmhS9zqyJPWetkadJq7UxsPn7Y4GAACALIyCkIEZY9SicmEtHhiodzpU1b7Tl/XIhD/Uf/p6HT571e54AAAAyIIoCJmAt6eHujUoLefQYPV7sLyWbDuhZqPC9N7P23ThapTd8QAAAJCFUBAyET8fLw1uGSDnkGA98kAxfblivwJDQvXl8n26Hh1jdzwAAABkARSETKiIv69CnqihRf2bqkbJPHr35+1qPjpMP208xkRmAAAA3BMKQiZWqWhuTetVT9N61VPObF7qN329Onz6p1bvP2t3NAAAAGRSFIQsIPD+gvq5f1OFdKyukxci9eQXEeozbY32nr5sdzQAAABkMhSELMLTw+iJOiUVOsShoa0C9Ofev9Xy43D9Z8EW/X35ut3xAAAAkElQELKY7Nk89VJweTmHOvRUvVL6btUhBYU4NSF0j67dYCIzAAAA7o6CkEUV8PPROx2qavHAQDUsl18hi3fqwVFOzVl7RDGxTGQGAABAwigIWVz5Qn6a1L2OZvZpoEK5fDRk9ka1HbdCy3eftjsaAAAAMiAKgpuoXza/fujbWJ90qalLkVHqNnm1npmyWjtOXLQ7GgAAADIQCoIb8fAwal+jmJYNDtIbD1fShsPn1Wbscr06Z6NOXIi0Ox4AAAAyAAqCG/Lx8tRzTcsqbKhDvRrfp/nrj8kxMlSjluzU5evRdscDAACAjSgIbixPjmx6o21lLRscpBaVi2jc73vkCAnVtysPKjom1u54AAAAsAEFASqZL4fGdamp+S81VtkCfnpj/ha1GhOupdtOyrI44xEAAIA7oSAgzgMl82jm8w00sVttWZJ6T1ujzhNXatOR83ZHAwAAQDqhICAeY4xaVimixQMD9U6Hqtpz6rLaj/9D/aev1+GzV+2OBwAAgDRGQUCCvD091K1BaTmHOvRycHkt2XZCzUaF6X+LtuvC1Si74wEAACCNUBBwV7l8vTWkVYBChzjU/oFimrR8n4JGhmryiv26Ec1EZgAAgKyGggCXFPXPrpFP1NDP/ZqqWnF/vbNwm5qPDtPCTceYyAwAAJCFpEtBMMZMMcacMsZsueWyfMaYpcaY3f/+N296ZMG9qVwst755tr6m9qqnHNk89fL36/Xop3/qrwNn7Y4GAACAVJBeexC+ltT6tsuGSVpmWVYFScv+/RmZRND9BfVz/6b6qGN1Hb9wTU98HqHnv1mjfacv2x0NAAAA9yBdCoJlWeGSbv+I+RFJU//9/6mSOqRHFqQeTw+jJ+uUlHNIsIa0vF8rdp9Ry4/D9Z8FW/T35et2xwMAAEAKmPQ6ftwYU0bSQsuyqv778/aPcuIAACAASURBVHnLsvL8+/9G0rmbPydw3z6S+khSwYIFa8+aNStdMiN5Lly3tGDPDTmPRCubh9S2rLdalvFWNk+Tao9x+fJl+fn5pdrykLkw/u6LsXdvjL97Y/zTRnBw8FrLsuokdF2GKAj//nzOsqwk5yEEBARYO3fuTLOcuHd7Tl3Wh7/u0NJtJ1XU31eDWwbosZrF5eFx70XB6XTK4XDce0hkSoy/+2Ls3Rvj794Y/7RhjEm0INh5FqOTxpiikvTvf0/ZmAWpqHwhP03qXkcz+zRQoVw+GjJ7o9qOW6EVu8/YHQ0AAABJsLMg/CjpmX///xlJC2zMgjRQv2x+/dC3sT7pUlMXI6PUdfIqPTNltXacuGh3NAAAACQivU5zOl1ShKQAY8wRY8yzkj6Q1MIYs1tS839/Rhbj4WHUvkYxLRscpP9rU0nrD51Tm7HL9dqcTTp5MdLueAAAALiNV3o8iGVZXRK5qll6PD7s5+Plqd6BZfVEnRIa//seTY04oB83HlPvwLJ6PrCscvqky6oIAACAJPBNykhXeXJk0xttK2vZIIeaVSqkT5btVlCIU9+tOqjomFi74wEAALg9CgJsUSp/Do1/qpZ+6NtI9xXIof/7YYtaj12uZdtPKr3OrAUAAIA7URBgq5ql8mrW8w31Rbfaio219OzUNeoyaaU2H7lgdzQAAAC3REGA7YwxalWliBa/Eqh3Hqmi3Scvq934FRowY70On71qdzwAAAC3QkFAhuHt6aFuDcvIOdShl4LL6dctJ9RsdJjeX7RdF65F2R0PAADALXDqGGQ4uXy9NbRVRXVtUFqjluzSxOX7NHPNYfV7sIJKxzI/AQAAIC1REJBhFfXPrpFP1FCvxvfp/V+2652F21Qwu9H1AsfVploRGWPsjggAAJDlcIgRMrzKxXLrm2fra2qvevLxlF76fp0e++xPrTlw1u5oAAAAWQ4FAZlG0P0F9d/G2fXR49V17Pw1dfw8Qs9/s0b7Tl+2OxoAAECWQUFApuJhjJ6sW1KhQxwa3OJ+rdh9Ri0/DteIBVv09+XrdscDAADI9CgIyJRyZPNSv2YV5BwarE51S+rbVYfkCHHqU+ceRUbF2B0PAAAg06IgIFMrmMtH7z1aTYsHNlX9svn00a879eBIp+auPaJYzngEAACQbBQEZAnlC+XSl8/U1Yw+DVQgl48Gz96oduNX6I89Z+yOBgAAkKlQEJClNCibX/P7NtbYzg/o/NUoPf3lKvX4arV2nrhkdzQAAIBMgYKALMfDw+iRB4pr2eAgvd6motYdPKeHxoZr2NxNOnUx0u54AAAAGRoFAVmWr7en+gSWU9jQYPVsfJ/mrjuioBCnRi/dpSvXo+2OBwAAkCFREJDl5c2ZTW+2razfBgXpwUqF9Mmy3QoKcer7VYcUHRNrdzwAAIAMhYIAt1E6f05NeKqWfujbSPcVyKHXf9is1mOXa9n2k7IszngEAAAgURDghmqWyqtZzzfUF91qKybW0rNT16jLpJXafOSC3dEAAABsR0GAWzLGqFWVIlrySqD++0gV7Tp5We3Gr9DAGet15NxVu+MBAADYhoIAt+bt6aHuDcvIOdShvo5y+mXLCT04Kkzv/7JdF65F2R0PAAAg3VEQAEm5fb31auuKCh3iULvqxTQxfJ8cIaGasmK/bkQzkRkAALgPCgJwi2J5smvUkzW0sF8TVSnmr/8u3KYWH4dp0ebjTGQGAABugYIAJKBKMX9982w9fd2zrny9PNX3u3V6/LM/tfbgWbujAQAApCkKApAIY4wcAYW0aEBTffh4NR05d02PfxahF79dq/1nrtgdDwAAIE142R0AyOg8PYw61S2ldjWK6cvl+/V52F4t3XZSXRuUVv9mFZQvZza7IwIAAKQa9iAALsqRzUv9m1WQc6hDT9YtqW9WHlTQR6H6zLlXkVExdscDAABIFRQEIJkK5fLV/x6tpsUDm6p+2Xz68NcdenCkU/PWHVFsLBOZAQBA5kZBAFKofKFc+vKZupreu4Hy+/lo0KyNajd+hf7Yc8buaAAAAClGQQDuUcNy+bXgpcYa2/kBnb8apae/XKWeX63WrpOX7I4GAACQbBQEIBV4eBg98kBxLRscpOEPVdSag+fUeky4hs/bpFMXI+2OBwAA4DIKApCKfL099XxQOYUPDVaPRvdpztojcox06uOlu3TlerTd8QAAAJJEQQDSQN6c2fSfdpX126AgBQcU0thlu+UY6dT01YcUHRNrdzwAAIBEURCANFQ6f05NeLqW5vVtpNL5cmj4vM16aOxy/b7jpCyLMx4BAICMh4IApINapfJq9gsN9XnXWoqOtdTr6zV6atIqbTl6we5oAAAA8VAQgHRijFHrqkW15JVAvd2+inaevKS241bolZkbdPT8NbvjAQAASKIgAOnO29NDzzQqI+dQh150lNOizccVPNKp93/ZrgvXouyOBwAA3BwFAbBJbl9vvda6okKHONS2elFNDN8nR0iovvpjv25EM5EZAADYg4IA2KxYnuwa/eQD+unlJqpcLLfe/mmbWn4cpkWbjzORGQAApDsKApBBVC3ur2+fra+vetaVj5en+n63To9/9qfWHjxrdzQAAOBGKAhABmKMUXBAIS0a0FQfPl5NR85d0+OfRejFb9fqwJkrdscDAABuwMvuAADu5Olh1KluKbWrUUyTwvfri/C9WrrtpLo2KK3+zSooX85sdkcEAABZFHsQgAwsRzYvDWheQc6hDj1Rp6SmRRxQUEioPg/bq8ioGLvjAQCALIiCAGQChXL56v3HqmnxwEDVK5NPH/yyQ81GhemH9UcUG8tEZgAAkHooCEAmUqFwLk3uUVff966vvDm99crMjWo/YYX+3HPG7mgAACCLsL0gGGNeMcZsNcZsMcZMN8b42p0JyOgalSugH19qojGdHtC5K1F66stV6vX1X9p98pLd0QAAQCZna0EwxhSX1F9SHcuyqkrylNTZzkxAZuHhYdShZnEtGxyk4Q9V1F8HzqrVmHANn7dJpy5G2h0PAABkUrbvQdA/Z1LKbozxkpRD0jGb8wCZiq+3p54PKqfwocF6plEZzVl7RI6RTo35bZeuXI+2Ox4AAMhkjN3f1GqMGSDpPUnXJC2xLOvpBG7TR1IfSSpYsGDtWbNmpW9IZBiXL1+Wn5+f3TEytFNXYzV71w39dSJG/j5Gj5b3VtPiXvL0MHZHu2eMv/ti7N0b4+/eGP+0ERwcvNayrDoJXWdrQTDG5JU0V1InSeclzZY0x7KsbxO7T0BAgLVz5850SoiMxul0yuFw2B0jU1h78Jz+t2i71h48p/sL+2n4Q5XkCCgoYzJvUWD83Rdj794Yf/fG+KcNY0yiBcHuQ4yaS9pvWdZpy7KiJM2T1MjmTECWULt0Xs15oaE+71pLN6Jj1fPrv/T0l6u05egFu6MBAIAMzO6CcEhSA2NMDvPPx5rNJG23OROQZRhj1LpqUS15JUhvtaus7ccvqu24FXpl5gYdPX/N7ngAACADsrUgWJa1StIcSeskbf43z0Q7MwFZUTYvD/VofJ/CXg3WC0Hl9PPm4woe6dQHv+zQxcgou+MBAIAMxO49CLIsa4RlWRUty6pqWVY3y7Ku250JyKpy+3pr2EMVFTrEobbViurzsL1yhDj19R/7dSM61u54AAAgA7C9IABIf8XzZNfoTg9oYb8mqlgkl976aZtafhymXzYfl91nNgMAAPaiIABurGpxf333XH191aOusnl56MXv1qnj5xFae/Cc3dEAAIBNKAiAmzPGKLhiIS3q31QfPFZNh85e1eOf/am+363VgTNX7I4HAADSmZfdAQBkDF6eHupcr5Ta1SimScv3aWL4Pi3ddlJdG5RW/wcrKG/ObHZHBAAA6YA9CADiyenjpYHN75dziEMda5fU1D8PKDAkVJ+H7VVkVIzd8QAAQBqjIABIUKHcvnr/sWr6dWCg6pbJpw9+2aFmo8I0f/1RxcYykRkAgKyKggDgru4vnEtTetTV98/VV54c3ho4c4PaT1ihP/eesTsaAABIAxQEAC5pVL6Afnq5iT7uVEPnrkTpqUmr1Ovrv7T75CW7owEAgFREQQDgMg8Po0drltCywUF6rXVF/bX/rFqNCdfweZt16lKk3fEAAEAqoCAASDZfb0+96CinsFeD1b1hGc1ec1iOEKfG/rZbV29E2x0PAADcAwoCgBTLlzOb3mpfRb8NCpIjoKA+/m2XHCFOzVh9SDFMZAYAIFOiIAC4Z2UK5NSnT9fW3BcbqkTe7Bo2b7PajF2u0J2nZFkUBQAAMpNkFwRjTE5jjGdahAGQudUunU9zX2ykz56upevRMer51V/qOnmVthy9YHc0AADgoiQLgjHGwxjzlDHmZ2PMKUk7JB03xmwzxoQYY8qnfUwAmYUxRg9VK6olrwRpRLvK2nbsotqNX6FBszbo2PlrdscDAABJcGUPQqikcpKGSypiWVZJy7IKSWoiaaWkD40xXdMwI4BMKJuXh3o2vk/OocF6PrCcFm46ruCRTn346w5djIyyOx4AAEiElwu3aW5Z1h2/zS3LOitprqS5xhjvVE8GIEvwz+6tYQ9VVNcGpTRqyS595tyrmX8d1oBmFfRU/VLy9mQqFAAAGYkrv5ktY0wtY4xfojdIoEAAwK1K5M2hjzs9oIX9miigcC6N+HGrWn4crl+3HGciMwAAGYgrBWGWpJmS1htjmhhjlhhj1v87/8A3jfMByGKqFvfX973ra0qPOvLyMHrh23V64vMIrTt0zu5oAABArhWEapLul/SwpIWSpkl65t/7jky7aACyKmOMHqxYWL8MaKr3H6umA39f1WOf/qmXvlung39fsTseAABuzZU5CJesf/b/7zLGHLMs61tJMsYMkbQ6TdMByNK8PD3UpV4pta9RTBPD92li+D4t2XZC3RqUUb8Hyytvzmx2RwQAwO24sgehiDGmuzGmuqQbNy/8tzQwuxDAPcvp46VXWtyvsKEOdaxdQl//uV+BIaH6ImyvIqNi7I4HAIBbceUP/Lck1ZU0TlIJY8xWY8xsY8x/JRVMy3AA3Euh3L56/7Hq+nVgoOqUzqv3f9mhZqPCtGDDUcXGMpEZAID0kOQhRpZlTbz1Z2NMCf0zL6G6pPA0ygXAjd1fOJe+6llPf+45o/cWbdeAGRv05fL9alMsRg67wwEAkMW58k3K5tafLcs6YlnWL5ZlfWhZVteEbgMAqaFR+QL66eUmGv1kDf19+bo+/CtSz379l/acumR3NAAAsiyXvknZGNPPGFPq1guNMdmMMQ8aY6bqn7MaAUCq8/AweqxWCf0+xKEn7vfW6v1n1WrMcr3+w2aduhRpdzwAALIcVwpCa0kxkqYbY44bY7YZY/ZL2i2pi6QxlmV9nYYZAUC+3p56uGw2hb0arG4NSmvWX4flCHFq7G+7dfVGtN3xAADIMlw5zamxLOtTSZ8aY7wlFZB0zbKs82kbDQDulC9nNr3VvoqeaVRGH/26Qx//tkvfrTqowS3vV8faJeXpwRGPAADcC1f2IBwzxuw0xsyWNExSPUn8BgZgq/sK5NRnXWtr7osNVTxvdr02d7PajF2u0J2n9M9ZmAEAQEokWRAsy8qrfw4z+vbfi7pK2mqMmW6M8U/LcACQlNql82nei4306dO1FBkdo55f/aVuk1dr67ELdkcDACBTcumLzizL2m9Z1gLLst6xLOsJScUlbZU0Jk3TAYALjDFqU62olr4SpP+0rawtxy6o7bgVGjRrg46dv2Z3PAAAMpUUfROy9Y93JTVI5TwAkGLZvDzUq8l9ChsarD6BZbVw03EFj3Tqo1936FJklN3xAADIFFz5HoRBxpjmxphCt13uI8k3zZIBQAr5Z/fW8Icq6ffBQXqoahF96tyroBCnpkUcUFRMrN3xAADI0FzZg1BY0iBJa/89zekSY8xnkv6QNDdN0wHAPSiRN4fGdK6pn15uooDCufSfBVvV6uNw/brlBBOZAQBIRJKnObUs67Wb/2+MySOpmqQASfMsy1qahtkAIFVUK+Gv73vXV+jOU/rfoh164du1qlsmr15vU0k1S+W1Ox4AABmKK9+DEOff7z5Y/u8/AMg0jDF6sGJhBVYoqFlrjmj00l169NM/9XD1onqtVUWVyp/D7ogAAGQIKZqkDACZlZenh56qX0phQx0a0KyCft9+Ss1GO/Xfn7bp3JUbdscDAMB2FAQAbimnj5deaXG/nEMderxWCX39534FhYRqYvheRUbF2B0PAADbUBAAuLXCuX31wePV9cuAQNUqnVf/W7RDzUaFacGGo4qNZSIzAMD9UBAAQFJAkVz6umc9ffdcffln99aAGRvU4dM/tHLf33ZHAwAgXVEQAOAWjcsX0MJ+TTTqiRo6fem6Ok9cqeem/qU9py7ZHQ0AgHRBQQCA23h4GD1eu4RChzj0ausArdp3Vq3GLNf//bBZpy9dtzseAABpioIAAInw9fZUX0d5OYc61LV+Kc3867AcIaEat2y3rt1gIjMAIGuiIABAEvL7+ejtR6pqySuBalqhoEYt3SXHyFDN+uuwYpjIDADIYigIAOCisgX99Hm32przQkMVy5Ndr87dpIc/Wa6wXaftjgYAQKqhIABAMtUpk0/zXmykCU/V0tUbMXpmymp1m7xK245dtDsaAAD3jIIAAClgjNHD1Yvqt0FB+k/bytp89IIeHrdcg2dt1PEL1+yOBwBAitleEIwxeYwxc4wxO4wx240xDe3OBACuyubloV5N7lPY0GD1aVpWP206JkeIUyGLd+hSZJTd8QAASDbbC4KksZJ+tSyroqQakrbbnAcAks0/u7eGt6mk3wcH6aGqRTQhdK8cIU5NizigqJhYu+MBAOAyWwuCMcZfUqCkyZJkWdYNy7LO25kJAO5Fibw5NKZzTf34cmNVKOyn/yzYqlYfh2vx1hOyLM54BADI+Iydv7CMMQ9Imihpm/7Ze7BW0gDLsq7cdrs+kvpIUsGCBWvPmjUrvaMig7h8+bL8/PzsjgGbZLbxtyxLG07HaNbOGzp+xdL9eT3UKSCbyuXxtDtappPZxh6pi/F3b4x/2ggODl5rWVadhK6zuyDUkbRSUmPLslYZY8ZKumhZ1puJ3ScgIMDauXNnumVExuJ0OuVwOOyOAZtk1vGPjonVzDWH9fHS3Tpz+boerl5Ur7WqqFL5c9gdLdPIrGOP1MH4uzfGP20YYxItCHbPQTgi6YhlWav+/XmOpFo25gGAVOfl6aGn65eWc6hD/R8sr9+3n1Kz0U69s3Cbzl+9YXc8AADisbUgWJZ1QtJhY0zAvxc10z+HGwFAluPn46VBLQPkHOrQYzVL6Ks/9ivwo1BNCt+n69ExdscDAECS/XsQJKmfpO+MMZskPSDpfzbnAYA0VTi3rz7sWF2LBjRVzVJ59d6i7Wo2KkwLNhxVbCwTmQEA9rK9IFiWtcGyrDqWZVW3LKuDZVnn7M4EAOmhYpHcmtqrnr59tr5y+3prwIwNevTTP7Rq3992RwMAuDHbCwIAuLsmFQpoYb8mGvVEDZ26dF2dJq7Uc1PXaM+py3ZHAwC4IQoCAGQAHh5Gj9cuodAhDg1tFaCV+/5WqzHhemP+Zp2+dN3ueAAAN0JBAIAMxNfbUy8Fl1fYUIe61i+lGasPyxESqnHLduvaDSYyAwDSHgUBADKg/H4+evuRqlrySqCaVCigUUt3yTEyVLPWHFYME5kBAGmIggAAGVjZgn76olsdzX6hoYr6Z9erczbp4U+WK2zXabujAQCyKAoCAGQCdcvk0w99G2n8UzV19UaMnpmyWt0mr9K2YxftjgYAyGIoCACQSRhj1LZ6MS0dFKg321bWpiMX9PC45Roye6OOX7hmdzwAQBZBQQCATMbHy1PPNrlP4UOD1btpWf244ZiCRzo1cvFOXYqMsjseACCToyAAQCbln8Nbr7eppGWDg9SqShGND90jR4hT30QcUFRMrN3xAACZFAUBADK5kvlyaGznmvrx5cYqX8hPby7YqlZjwrVk6wlZFmc8AgAkDwUBALKI6iXyaEafBvqyex0ZSX2+WatOX6zUhsPn7Y4GAMhEKAgAkIUYY9S8cmEtHhiodztU1b4zl9Vhwh/qN329Dp+9anc8AEAmQEEAgCzIy9NDXRuUlnNosPo/WF5Lt51Qs1FhenfhNp2/esPueACADIyCAABZmJ+Plwa1DJBzSLA61CymyX/sV1CIU5PC9+l6dIzd8QAAGRAFAQDcQBF/X33UsYYW9W+qGiXz6L1F29V8dJh+3HiMicwAgHgoCADgRioVza1pverpm2fryc/HW/2nr1eHCX9o1b6/7Y4GAMggKAgA4IaaViiohf2aaOQTNXTy4nV1mrhSvaet0d7Tl+2OBgCwGQUBANyUp4dRx9olFDrEoaGtAhSx92+1/Dhcb8zfrDOXr9sdDwBgEwoCALi57Nk89VJweTmHOvR0/VKavvqwHCFOjf99t67dYCIzALgbCgIAQJJUwM9H/32kqpa8EqhG5fJr5JJdCh7p1Ow1hxUTy0RmAHAXFAQAQDzlCvppYvc6mvV8QxX299XQOZv08CfLFb7rtN3RAADpgIIAAEhQvfvyaX7fRhrXpaau3IhW9ymr1W3yKm0/ftHuaACANERBAAAkyhijdjWK6bdBQXrj4UradOSC2nyyXENnb9SJC5F2xwMApAEKAgAgST5ennquaVmFDw3Wc03u04INx+QYGaqRi3fq8vVou+MBAFIRBQEA4DL/HN76v4cra9ngILWoXETjQ/fIERKqb1YeVFRMrN3xAACpgIIAAEi2kvlyaFyXmlrwUmOVLeinN+dvUasx4Vqy9YQsizMeAUBmRkEAAKRYjZJ5NLNPA03qXkeS1Oebteo0caU2Hj5vczIAQEpREAAA98QYoxaVC2vxwEC906Gq9p2+rEcm/KF+09fr8NmrdscDACQTBQEAkCq8PT3UrUFpOYcGq9+D5bV02wk1GxWm937epgtXo+yOBwBwEQUBAJCq/Hy8NLhlgJxDgvXIA8X05Yr9CgwJ1ZfL9+l6dIzd8QAASaAgAADSRBF/X4U8UUOL+jdVjZJ59O7P29V8dJh+2niMicwAkIFREAAAaapS0dya1quepvWqp5zZvNRv+np1+PRPrd5/1u5oAIAEUBAAAOki8P6C+rl/U4V0rK6TFyL15BcR6jNtjfaevmx3NADALSgIAIB04+lh9ESdkgod4tDQVgH6Y88Ztfw4XG/O36Izl6/bHQ8AIAoCAMAG2bN56qXg8gp7NVhP1Sul71cfkiPEqQmhe3TtBhOZAcBOFAQAgG0K+PnonQ5VtXhgoBqWy6+QxTsVPNKp2WsOKyaWicwAYAcKAgDAduUL+WlS9zqa2aeBCuf20dA5m9R23Aot333a7mgA4HYoCACADKN+2fz6oW9jfdKlpi5FRqnb5NXqPmW1Dl+KtTsaALgNL7sDAABwKw8Po/Y1iqlVlcL6JuKgxv2+R8t3RWlT5EYNbhmgIv6+dkcEgCyNPQgAgAzJx8tTzzUtq7ChDrUs46UFG47JMTJUo5bs1OXr0XbHA4Asi4IAAMjQ8uTIpi4VfbRscJBaVC6icb/vkSMkVN+uPKjoGA49AoDURkEAAGQKJfPl0LguNTX/pcYqW8BPb8zfolZjwrV020lZFmc8AoDUQkEAAGQqD5TMo5nPN9DEbrVlSeo9bY06T1ypTUfO2x0NALIECgIAINMxxqhllSJaPDBQ73Soqj2nLqv9+D/Uf/p6HT571e54AJCpURAAAJmWt6eHujUoLedQh14OLq8l206o2agw/W/Rdl24GmV3PADIlDJEQTDGeBpj1htjFtqdBQCQ+eTy9daQVgEKHeJQ+weKadLyfQoMCdWXy/fpenSM3fEAIFPJEAVB0gBJ2+0OAQDI3Ir6Z9fIJ2ro535NVb2Ev979ebuajw7Twk3HmMgMAC6yvSAYY0pIeljSl3ZnAQBkDZWL5dY3z9bX1F71lDObl17+fr0e/fRP/XXgrN3RACDDM3Z/omKMmSPpfUm5JA2xLKttArfpI6mPJBUsWLD2rFmz0jckMozLly/Lz8/P7hiwCePvvu5l7GMtS38cjdbc3VE6f91SrUKeejIgm4rktP0zMriIbd+9Mf5pIzg4eK1lWXUSus7WgmCMaSupjWVZfY0xDiVSEG4VEBBg7dy5M13yIeNxOp1yOBx2x4BNGH/3lRpjf+1GjCav2KfPnHt1PTpWT9UvpQHNKii/n0/qhESaYdt3b4x/2jDGJFoQ7P74pLGk9saYA5JmSHrQGPOtvZEAAFlR9myeevnBCnIODVbneiX13apDCgpxakLoHkVGMZEZAG6ytSBYljXcsqwSlmWVkdRZ0u+WZXW1MxMAIGsrmMtH73aopsUDA9WwXH6FLN6p4JFOzVl7RLGxTGQGALv3IAAAYIvyhfw0qXsdzezTQIVy+WjI7I1qO26FVuw+Y3c0ALBVhikIlmU5k5p/AABAaqtfNr9+6NtYn3SpqYuRUeo6eZWembJaO05ctDsaANgiwxQEAADs4uFh1L5GMS0bHKT/a1NJ6w+dU5uxy/XanE06eTHS7ngAkK4oCAAA/MvHy1O9A8sq/NVg9Wx8n+atPyJHyP+3d+fRVdX3+sefTybCLINQ5kkEwQiCjAI5AatgGa5QFUWqOKCIZZSqvevW2ntvtUYmFVBUwFYo0gKOiArmJCCKDFLCPIMgQhFEZJAM3/tHDvz4oVGGJN9zct6vtbJyzt4n8Kz14Wx42Pu7T1BjPtio777P9h0PAIoEBQEAgLNcUipB/9W9iRaOCKjLFVX07EdbFEgNavrSncrOyfUdDwAKFQUBAIB81K5USs/f3kJzH2yvepVL6T/nrlHX8Yu0YN0++f6gUQAoLBQEAAB+xtW1K2jW/e30Yv+Wys11uvevy9V38qdavfsb39EAngFwigAAFfVJREFUoMBREAAAOAdmphua/kLvD++k/+7VVFv2f6eez3+soTM/1xcHj/mOBwAFhoIAAMB5iI+NUf92dRUcFdDglAaav+YrdRmdrifnrdfh41m+4wHARaMgAABwAcomxmvUDY0VHBVQz+bVNXnRNiWnpumVxdt1MpuFzAAiFwUBAICLUK18ST1zczO9+9uOSqpRXv/9zjpdNyZd767ey0JmABGJggAAQAFoUr2c/nZPG716d2uVSojV4Bkr1XvSEi3fcdB3NAA4LxQEAAAKUPLll+rdIR31dJ+r9OU3x/XrFz7R/X9brm3//s53NAA4JxQEAAAKWGyM6ZZWtZT2cEAjfnm5Fm8+oOvHZujxN9fo6+++9x0PAH4SBQEAgEJSKiFOQ7o0VHBUim5tVUuvLd2lQGpQE9K26ERWju94APCjKAgAABSyS8uW0P/elKT3h3VUm/oVlfr+RnV+JqjZK3YrN5eFzADCCwUBAIAiclmVsnr5zlaaObCtKpctoZH/+Je6P7dYizcf8B0NAE6jIAAAUMTa1q+kNx68VuP7Ntfh41m645WlumvqZ9r41RHf0QCAggAAgA8xMaZezWto4chk/f7Gxlq585C6jc/QI/9crX3fnvAdD0AUoyAAAOBRYnysBnZqoPRRKRpwbT3N+Xy3AqlBjflwk45+n+07HoAoREEAACAMVCidoP/q3kQLRiSr8xVV9OzCzUpODWrG0l3Kzsn1HQ9AFKEgAAAQRupUKq0Jt7fQ3Afbq17lUvr93Ex1Hb9IC9fvk3Pc8QhA4aMgAAAQhq6uXUGz7m+nF/u3VE6u0z2vLtdtL32qzN2HfUcDUMxREAAACFNmphua/kIfDO+kP/Vqqk37vlOP5xdr2MzPtfvQMd/xABRTFAQAAMJcfGyMftOuroKjAnow0EDvrflKnUen68l563X4eJbveACKGQoCAAARolxivH7XtbHSHg6ox1XVNXnRNiWnpmnK4u06mc1CZgAFg4IAAECEqX5JSY2+pZne+W0HXVm9vP70zjr9cmy63l29l4XMAC4aBQEAgAjVtHp5/e2e1po2oJUS42I1eMZK9Zm0RCt2HvQdDUAEoyAAABDBzEyBRlU0b2hH/aVPknYfOq4+kz7RA39boe0HjvqOByACxfkOAAAALl5sjOnWVrXVo1l1vbxou15I36oF6/epX5vaGtKloSqVKeE7IoAIwRkEAACKkVIJcRrSpaGCowK6pVUtvbZ0lwKpQU0MbtGJrBzf8QBEAAoCAADFUJWyifrzTUl6f1hHtalfUU/P36jOzwQ1Z+Vu5eaykBlA/igIAAAUY5dVKauX72ylv9/XVpXKlNCIWf9Sj+cX6+MtB3xHAxCmKAgAAESBdg0q6c3B12p83+b65liW+r28VAOmfqZN+474jgYgzFAQAACIEjExpl7Na2jhyGQ91q2xlu88pK7jMvTo7NXa/+0J3/EAhAkKAgAAUSYxPlb3JzdQxqgU3dW+nmav3K3k1KDGfrhJR7/P9h0PgGcUBAAAolSF0gn6Q48mWjAiWZ0bV9H4hZsVeCaoGUt3KTsn13c8AJ5QEAAAiHJ1KpXWhH4tNOfB9qpTsZR+PzdT3cYv0kcb9sk57ngERBsKAgAAkCS1qF1B/3ignV64o4Wyc53unrZct7+0VGv2HPYdDUARoiAAAIDTzExdr6ymD4Z30hM9m2rjviPq/txiDZv5uXYfOuY7HoAiQEEAAAA/EB8bozvb11VwVECDAg303pqv1Hl0up58b70OH8/yHQ9AIaIgAACAfJVLjNcjXRsr7eGAul9VTZMztimQmqapH2/XyWwWMgPFEQUBAAD8rOqXlNSYW5rr7Yc6qEn1cnri7XW6fmy65mXuZSEzUMxQEAAAwDm7skZ5vXZPG00d0EoJcTF6cPpK9Zm0RCt2HvQdDUABoSAAAIDzYmZKaVRF84Z01FO9k7T70HH1mfSJBr22QjsOHPUdD8BFivMdAAAARKa42Bj1bV1bPZtX10sZ2/VixlZ9uG6f7mhbR0O6NFTF0gm+IwK4AJxBAAAAF6VUQpyGXtdQwVEB3XxNLf31kx1KfjpNk4JbdSIrx3c8AOfJa0Ews1pmlmZm68xsrZkN9ZkHAABcuCplE/Vk7yS9P6yTWterqL/M36DOzwQ19/Pdys1lITMQKXyfQciWNNI510RSW0mDzayJ50wAAOAiNKxaVq/c1Uoz7mujimUSNPz1f6nnhMVasuWA72gAzoHXguCc2+ucWxl6fETSekk1fGYCAAAFo32DynprcAeNu7W5Dh3N0u0vL9WAqZ9p074jvqMB+AkWLvcuNrO6kjIkXemc+/asfQMlDZSkSy+9tOWsWbOKPB/Cw3fffacyZcr4jgFPmH/0YvaR72SO04JdWXp7a5ZOZEudasbppsvidUniz/9fJfOPbsy/cKSkpKxwzl3zY/vCoiCYWRlJ6ZL+1zk356de26hRI7dx48aiCYawEwwGFQgEfMeAJ8w/ejH74uPQ0ZN69qPNeu3TnYqPjdHATvV1X8f6Kl0i/xsrMv/oxvwLh5nlWxB8r0GQmcVLmi1p+s+VAwAAENkqlE7Q4z2aasGIZKU0qqJxCzYr8ExQf/9sl7Jzcn3HAyD/dzEySa9IWu+cG+MzCwAAKDp1KpXWhH4tNHtQe9WuWEqPzcnUjc8uUtqG/QqHqxuAaOb7DMK1kvpL6mxmq0JfN3rOBAAAikjLOhX0zwfaaVK/FjqZnasB05ap38tLtWbPYd/RgKjl9ZOUnXOLJZnPDAAAwC8zU7ekaupyRVXNWLpT4xduVvfnFuumq2vo4Rsa+Y4HRB2vBQEAAOCUhLgY3XVtPfVuWVMT07Zqysfb9W7mXl1XK1Yt2mapXGK874hAVPB9iREAAMD/p1xivB7t1lhpDwfUPama5m3PUvLTaZr68XadzGYhM1DYKAgAACAs1bikpMbc2lx/bJeoK6qV0xNvr9P1Y9P1XuZeFjIDhYiCAAAAwlrd8rGafm8bTb2rlRLiYjRo+kr9+oVPtGLnId/RgGKJggAAAMKemSmlcRXNG9JRT/VO0q6Dx9Rn0hI9OH2Fdhw46jseUKywSBkAAESMuNgY9W1dWz2aVddLi7ZpcsY2fbhun/q1qaMhXRqqYukE3xGBiMcZBAAAEHFKl4jTsOsuV/DhgH7dspb++skOJaem6YX0rTqRleM7HhDRKAgAACBiVSmXqCd7J2n+sE5qVbeinnpvg7qMTtcbn+9Rbi4LmYELQUEAAAAR7/KqZTXlrlaacW8bXVIqXsNeX6WeExZrydYDvqMBEYeCAAAAio32l1XW2w910Nhbm+nQ0Szd/tJS3T1tmTbvO+I7GhAxKAgAAKBYiYkx3XR1TS0cmaxHujbWsu0HdcO4DD02J1P7j5zwHQ8IexQEAABQLCXGx2pQoIHSf5ei37Srq38s/0KB1KDGLdikYyezfccDwhYFAQAAFGsVSyfojz2basGIZAUaXapxCzYrkBrUzM92KYeFzMAPUBAAAEBUqFu5tCb2a6nZg9qpZoWSenROprqNz1Dahv1yjqIAnEJBAAAAUaVlnYqaPai9JvVroZPZuRowbZnueGWp1uw57DsaEBYoCAAAIOqYmbolVdMHw5P1eI8mWvflt+rx/GKNeH2V9nxz3Hc8wCsKAgAAiFoJcTEacG09BUel6P5ODfRO5l6lPBPUX+Zv0LcnsnzHA7ygIAAAgKhXvmS8Hu3WWB+NTNavkqppUnCrAqlBvbpkh7Jycn3HA4oUBQEAACCkZoVSGntrc73z2w5qVLWsHn9rra4fm6H5a/aykBlRg4IAAABwlitrlNeM+9poyl3XKC7G9MBrK3XzC59o5a5DvqMBhY6CAAAA8CPMTJ0bV9V7Qzvqyd5J2vH1MfWeuESDp6/Uzq+P+o4HFJo43wEAAADCWVxsjG5rXVs9m1XX5IxtmpyxTR+s+0p3tK2jIZ0bqkLpBN8RgQLFGQQAAIBzULpEnIb/8nKljwro1y1r6tUlO9QpNU0vpm/Viawc3/GAAkNBAAAAOA9VyiXqyd5Xaf6wTrqmTgU9+d4GdRmdrjc+36PcXBYyI/JREAAAAC7A5VXLauqA1ppxbxtdUipew15fpV4TPtaSrQd8RwMuCgUBAADgIrS/rLLefqiDxtzSTF9/971uf2mp7pm2TJv3HfEdDbggFAQAAICLFBNj6t2ipj56OKBHujbWZ9sP6oZxGXpsTqb2HznhOx5wXigIAAAABSQxPlaDAg2U/rsU/aZdXf1j+RcKpAY1fsFmHTuZ7TsecE4oCAAAAAWsYukE/bFnU304IlnJl1+qsQs2KZAa1OvLdimHhcwIcxQEAACAQlKvcmlNuqOl/vlAO9WoUFKPzM7UjeMXKW3jfjlHUUB4oiAAAAAUsmvqVtScQe01sV8LncjO0YCpy9T/lc+09svDvqMBP0BBAAAAKAJmphuTqunD4cn6Q/cmWvPlYXV/brFGzFqlL7857jsecBoFAQAAoAglxMXo7g71lD4qRQM71dc7q/cq5Zmgnp6/Qd+eyPIdD6AgAAAA+FC+ZLwe63aFPhqZrG5X/kITg1sVSA3q1SU7lJWT6zseohgFAQAAwKOaFUppXN+r9fZDHdSoalk9/tZaXT82Q/PXfMVCZnhBQQAAAAgDSTXLa8Z9bTTlrmsUG2N64LUVuvmFT7Ry1yHf0RBlKAgAAABhwszUuXFVzR/aUX++KUk7vj6m3hOXaPD0ldr59VHf8RAlKAgAAABhJi42Rre3qa30UQEN7dJQH23Yr+vGpOtPb6/ToaMnfcdDMUdBAAAACFOlS8Rp+C8vV3BUQH1a1NS0JdvVKTVNkzO26kRWju94KKYoCAAAAGGuarlEPdXnKr03tJNa1qmgP8/boC6j0/Xmqj3KzWUhMwoWBQEAACBCNPpFWU0b0FrT722j8iXjNXTmKv3HxI/16bavfUdDMUJBAAAAiDDXXlZZ7/y2g0bf3Ez/PvK9+k7+VPe+ukxb9h/xHQ3FAAUBAAAgAsXEmPq0rKm0hwP6XddGWrrtoG4Yt0j/OTdT/z7yve94iGAUBAAAgAiWGB+rBwOXKTgqoDva1Nbry75QIDVNzy7crGMns33HQwSiIAAAABQDlcqU0BO9rtQHwzupY8NLNebDTUp5JqjXl+1SDguZcR4oCAAAAMVI/UvL6IX+LfXPB9qp+iUl9cjsTP3q2UUKbtwv5ygK+HneC4KZdTWzjWa2xcwe9Z0HAACgOLimbkXNGdReE25voWMnc3TX1GXq/8pnWvvlYd/REOa8FgQzi5U0QVI3SU0k3WZmTXxmAgAAKC7MTL+6qpoWjEjWH7o30ZovD6v7c4s1cta/tPfwcd/xEKZ8n0FoLWmLc26bc+6kpJmSennOBAAAUKwkxMXo7g71lD4qRQM71tfbq79UIDWop+dv0JETWb7jIczEef79a0j64oznuyW1OftFZjZQ0sDQ0+/NbE0RZEN4qizpgO8Q8Ib5Ry9mH92YfyF5JPQV5ph/4aiT3w7fBeGcOOcmS5osSWa23Dl3jedI8IT5RzfmH72YfXRj/tGN+Rc935cY7ZFU64znNUPbAAAAAHjguyAsk9TQzOqZWYKkvpLe8pwJAAAAiFpeLzFyzmWb2UOS3pcUK2mKc27tz/zY5MJPhjDG/KMb849ezD66Mf/oxvyLmPGBGQAAAABO8X2JEQAAAIAwQkEAAAAAcFrEFAQz62pmG81si5k96jsPCp+Z7TCzTDNbZWbLQ9sqmtmHZrY59L2C75woGGY2xcz2n/k5J/nN2/I8GzoerDazFv6SoyDkM/8/mtme0DFglZndeMa+x0Lz32hmN/hJjYJiZrXMLM3M1pnZWjMbGtrOMaCY+4nZ8/73KCIKgpnFSpogqZukJpJuM7MmflOhiKQ455qfcf/jRyUtdM41lLQw9BzFwzRJXc/alt+8u0lqGPoaKGlSEWVE4ZmmH85fksaGjgHNnXPzJCl0/O8rqWnoZyaG/p5A5MqWNNI510RSW0mDQ3PmGFD85Td7ife/NxFRECS1lrTFObfNOXdS0kxJvTxngh+9JL0aevyqpP/wmAUFyDmXIengWZvzm3cvSX91eT6VdImZVSuapCgM+cw/P70kzXTOfe+c2y5pi/L+nkCEcs7tdc6tDD0+Imm9pBriGFDs/cTs88P7vwhESkGoIemLM57v1k//4UHx4CR9YGYrzGxgaFtV59ze0OOvJFX1Ew1FJL95c0yIHg+FLiGZcsYlhcy/GDOzupKulrRUHAOiylmzl3j/exMpBQHRqYNzroXyTiUPNrNOZ+50effo5T69UYJ5R6VJkhpIai5pr6TRfuOgsJlZGUmzJQ1zzn175j6OAcXbj8ye979HkVIQ9kiqdcbzmqFtKMacc3tC3/dLmqu8U4j7Tp1GDn3f7y8hikB+8+aYEAWcc/uccznOuVxJL+n/XUbA/IshM4tX3j8Qpzvn5oQ2cwyIAj82e97/fkVKQVgmqaGZ1TOzBOUtTnnLcyYUIjMrbWZlTz2WdL2kNcqb+52hl90p6U0/CVFE8pv3W5J+E7qTSVtJh8+4DAHFxFnXlN+kvGOAlDf/vmZWwszqKW+h6mdFnQ8Fx8xM0iuS1jvnxpyxi2NAMZff7Hn/+xXnO8C5cM5lm9lDkt6XFCtpinNuredYKFxVJc3NO24oTtIM59x8M1smaZaZ3SNpp6RbPGZEATKzv0sKSKpsZrslPS7pKf34vOdJulF5i9OOSRpQ5IFRoPKZf8DMmivvspIdku6XJOfcWjObJWmd8u6AMtg5l+MjNwrMtZL6S8o0s1Whbb8Xx4BokN/sb+P974/lXdIHAAAAAJFziREAAACAIkBBAAAAAHAaBQEAAADAaRQEAAAAAKdREAAAAACcRkEAAAAAcBoFAQAAAMBpFAQAwHkzs/vN7EUzK2lm6WYWe9b+58xs5xnPE8wsw8wi4gM6ASCaURAAABciSdJqSXdLmnPmJ5maWV1JKZISzKysJDnnTkpaKOnWIk8KADgvFAQAwIW4SlKmpH6S3jxr3xOS/kfSOklNz9j+Ruj1AIAwRkEAAFyIKyVtkFTfObfj1EYzaxra97qk9aHHp6yR1KoIMwIALgAFAQBwXsyslqSjkuIkfXPW7v+R9AfnnFNeQTh9BiF0GdLJU5cdAQDCE4vFAADn69T6g+OSEk9tNLM2krpKutrMJoT2ZZ71syUknSiinACAC8AZBADA+bpKUqZz7pCkWDM7VRL+LKmHc66uc66upGY64wyCmVWSdMA5l1XUgQEA544zCACA85Uk6d3Q4w8kdTAzSUpwzi049SLn3D4zK2NmFZ1zB5V3Z6N3f/CrAQDCiuVdJgoAwPkzsxaShjvn+p/Da+dIetQ5t6nwkwEALhSXGAEALphzbqWktLM/KO1sZpYg6Q3KAQCEP84gAAAAADiNMwgAAAAATqMgAAAAADiNggAAAADgNAoCAAAAgNMoCAAAAABOoyAAAAAAOO3/ALFc6liGv5VoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,7))\n",
    "plt.ylabel(\"$U(В)$\")\n",
    "plt.rc('font', size=15)\n",
    "plt.xlabel(\"$I(A)$\")\n",
    "plt.grid();\n",
    "\n",
    "\n",
    "x = np.arange(0, x0 + 1,0.01)\n",
    "y = koef[0] * x + koef[1]\n",
    "\n",
    "plt.xlim(0,x0 + 50)\n",
    "plt.ylim(0, 14)\n",
    "\n",
    "plt.plot(x, y, '-', label = r\"Апроксимована залежність напруги $U$ від сили струму $А$\")\n",
    "plt.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,7))\n",
    "plt.ylabel(\"$U(В)$\")\n",
    "plt.rc('font', size=15)\n",
    "plt.xlabel(\"$I(A)$\")\n",
    "plt.grid();\n",
    "\n",
    "\n",
    "x = np.arange(0, max(expData.I) + 1,0.01)\n",
    "y = koef[0] * x + koef[1]\n",
    "\n",
    "plt.xlim(0,3)\n",
    "\n",
    "plt.plot(expData.I, expData.U, 'o', label = r\"Виміряні значення $U$\")\n",
    "plt.plot(x, y, '-', label = r\"Апроксимована залежність напруги $U$ від сили струму $А$\")\n",
    "plt.legend(loc=\"best\");\n",
    "plt.savefig(\"exp.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>U0</th>\n",
       "      <th>I</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     U0    I    U\n",
       "8  4.50 0.10 4.30\n",
       "9  4.50 0.20 3.90\n",
       "10 4.50 0.30 3.70\n",
       "11 4.50 0.40 3.30\n",
       "12 4.50 0.50 2.90\n",
       "13 4.50 0.60 2.70\n",
       "14 4.50 0.75 2.25"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batteryData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>U0</th>\n",
       "      <th>I</th>\n",
       "      <th>U</th>\n",
       "      <th>Re</th>\n",
       "      <th>Ri</th>\n",
       "      <th>Pi</th>\n",
       "      <th>Pe</th>\n",
       "      <th>P0</th>\n",
       "      <th>Iz</th>\n",
       "      <th>Pe0</th>\n",
       "      <th>Pi0</th>\n",
       "      <th>PP0</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4.30</td>\n",
       "      <td>43.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.12</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>3.90</td>\n",
       "      <td>19.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.78</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.13</td>\n",
       "      <td>13.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.70</td>\n",
       "      <td>12.33</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.11</td>\n",
       "      <td>7.59</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.18</td>\n",
       "      <td>17.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>8.25</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.32</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.27</td>\n",
       "      <td>26.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.90</td>\n",
       "      <td>5.80</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.45</td>\n",
       "      <td>6.33</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.36</td>\n",
       "      <td>35.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.70</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.62</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.40</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.69</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     U0    I    U    Re   Ri   Pi   Pe    P0   Iz  Pe0  Pi0  PP0     n\n",
       "8  4.50 0.10 4.30 43.00 2.00 0.02 0.43 10.12 2.25 0.04 0.00 0.04  4.44\n",
       "9  4.50 0.20 3.90 19.50 3.00 0.12 0.78  6.75 1.50 0.12 0.02 0.13 13.33\n",
       "10 4.50 0.30 3.70 12.33 2.67 0.24 1.11  7.59 1.69 0.15 0.03 0.18 17.78\n",
       "11 4.50 0.40 3.30  8.25 3.00 0.48 1.32  6.75 1.50 0.20 0.07 0.27 26.67\n",
       "12 4.50 0.50 2.90  5.80 3.20 0.80 1.45  6.33 1.41 0.23 0.13 0.36 35.56\n",
       "13 4.50 0.60 2.70  4.50 3.00 1.08 1.62  6.75 1.50 0.24 0.16 0.40 40.00\n",
       "14 4.50 0.75 2.25  3.00 3.00 1.69 1.69  6.75 1.50 0.25 0.25 0.50 50.00"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batteryData['Re'] = batteryData.apply(lambda row: row['U'] / row['I'], axis=1)\n",
    "batteryData['Ri'] = batteryData.apply(lambda row: (row['U0'] - row['I']*row['Re']) / row['I'], axis=1)\n",
    "batteryData['Pi'] = batteryData.apply(lambda row: row['I']**2 * row['Ri'], axis=1)\n",
    "batteryData['Pe'] = batteryData.apply(lambda row: row['I']**2 * row['Re'], axis = 1)\n",
    "batteryData['P0'] = batteryData.apply(lambda row: row['U0']**2 / row['Ri'], axis = 1)\n",
    "batteryData['Iz'] = batteryData.apply(lambda row: row['U0'] / row['Ri'], axis = 1)\n",
    "batteryData['Pe0'] = batteryData.apply(lambda row: row['Pe'] / row['P0'], axis = 1)\n",
    "batteryData['Pi0'] = batteryData.apply(lambda row: row['Pi'] / row['P0'] , axis = 1)\n",
    "batteryData['PP0'] = batteryData.apply(lambda row: (row['Pi'] + row['Pe']) / row['P0'] , axis = 1)\n",
    "batteryData['n'] = batteryData.apply(lambda row: (row['Pi'] + row['Pe']) / row['P0'] * 100, axis = 1)\n",
    "batteryData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.1624714   4.58043478]\n"
     ]
    }
   ],
   "source": [
    "basis = np.vstack((batteryData.I,np.ones(len(batteryData)))).T\n",
    "koef = scipy.linalg.lstsq(basis, batteryData.U)[0]\n",
    "print(koef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4483719247467446\n"
     ]
    }
   ],
   "source": [
    "x0 = -koef[1]/koef[0]\n",
    "print(x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,7))\n",
    "plt.ylabel(\"$U(В)$\")\n",
    "plt.rc('font', size=15)\n",
    "plt.xlabel(\"$I(A)$\")\n",
    "plt.grid();\n",
    "\n",
    "\n",
    "x = np.arange(0, x0 + 1,0.01)\n",
    "y = koef[0] * x + koef[1]\n",
    "\n",
    "#plt.axis('scaled')\n",
    "plt.xlim(0,x0 + 0.5)\n",
    "plt.ylim(0, np.max(y) + 1)\n",
    "\n",
    "plt.plot(batteryData.I, batteryData.U, 'o', label = r\"Вихідні значення $U$\")\n",
    "plt.plot(x, y, '-', label = r\"Апроксимована залежність напруги $U$ від сили струму $А$\")\n",
    "plt.legend(loc=\"best\");\n",
    "plt.savefig('battery.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "plt.ylabel(r\"$\\frac{P}{P_0}$\")\n",
    "plt.rc('font', size=20)\n",
    "plt.xlabel(r\"$\\frac{R_i}{R_e}$\")\n",
    "plt.grid();\n",
    "\n",
    "\n",
    "#plt.axis('scaled')\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(0, np.max(batteryData['PP0'])+ 0.5)\n",
    "\n",
    "from scipy.interpolate import make_interp_spline, BSpline\n",
    "\n",
    "plt.yticks([0, 0.2, 0.25, 0.4, 0.5, 0.6, 0.8, 1])\n",
    "\n",
    "x = [0]\n",
    "y = [0]\n",
    "z = [0]\n",
    "u = [0]\n",
    "x = np.append(x, batteryData.apply(lambda row: row['Ri'] / row['Re'], axis = 1))\n",
    "y = np.append(y, batteryData['Pe0'])\n",
    "z = np.append(z, batteryData['Pi0'])\n",
    "u = np.append(u, batteryData['PP0'])\n",
    "\n",
    "#plt.plot(batteryData.I, batteryData.U, 'o', label = r\"Вихідні значення $U$\")\n",
    "plt.plot(x, y, '-', label = r\"$\\frac{P_e}{P_0}$\")\n",
    "plt.plot(x, z, '-', label = r\"$\\frac{P_i}{P_0}$\")\n",
    "plt.plot(x, u, '-', label = r\"$\\frac{P_e+P_i}{P_0}$\")\n",
    "plt.plot(x, y, 'o')\n",
    "plt.plot(x, z, 'o')\n",
    "plt.plot(x, u, 'o')\n",
    "plt.legend(loc=\"best\");\n",
    "plt.savefig('p.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.04651163 0.15384615 0.21621622 0.36363636 0.55172414\n",
      " 0.66666667 1.        ]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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