{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16fb2c72-c190-46af-a4b7-6c81825f1b1c",
   "metadata": {},
   "source": [
    "# Analýza dat z tahového diagramu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "435f734d-e2f4-4665-b3e2-40f272f86127",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from lmfit import Model\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d0fd4e-1b7e-4524-a6a9-c5a4550ceb97",
   "metadata": {},
   "source": [
    "Dva datové soubory .xls obsahující data z tahové zkoušky jsme získali z věřejně přístupných [zdrojů]( \n",
    "https://professorkazarinoff.github.io/Engineering-Materials-Programming/07-Mechanical-Properties/plotting-stress-strain-curves.html). Popisují naměřená data při zkoušce oceli 1045 slitiny hliníku 6061. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaac0b63-0a94-41b7-b9b5-3d9b7c52c794",
   "metadata": {},
   "source": [
    "## Načtení dat z Excelu\n",
    "\n",
    "Nejdříve si pomocí funkce ```%ls``` ověříme, že nás adresář obsahuje data z měření. Tato funkce nám vypíše obsah adresáře.  \n",
    "\n",
    "Pomocí funkce ```pd.read_excel()``` z balíčku pandas je možné přímo načíst data. Data ze dvou excelových souborů budou uložena ve dvou datových objektech nazvaných ```steel_df``` a ```al_df```. Datový objekt pandas je tabulkový datový typ.\n",
    "\n",
    "K zobrazení prvních pěti řádků každého datového rámce můžeme použít metodu ```.head()```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0c12f4cb-72b4-4336-b17b-1d8e78540eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Al60601_raw_data.xls  Steel1018_raw_data.xls\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    },
    {
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       "      <th>3</th>\n",
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       "      <td>4</td>\n",
       "      <td>11.031</td>\n",
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       "      <td>365.380981</td>\n",
       "      <td>0.009881</td>\n",
       "      <td>0.022061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>542</td>\n",
       "      <td>5</td>\n",
       "      <td>11.928</td>\n",
       "      <td>0.03616</td>\n",
       "      <td>447.813965</td>\n",
       "      <td>0.014085</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   TESTNUM  POINTNUM    TIME    POSIT       FORCE       EXT       CH5  CH6  \\\n",
       "0      542         1   8.470  0.02256  201.146011 -0.001444  0.007552  NaN   \n",
       "1      542         2   8.632  0.02330  206.599442  0.000302  0.007552  NaN   \n",
       "2      542         3  10.027  0.02846  287.512573  0.003044  0.018898  NaN   \n",
       "3      542         4  11.031  0.03232  365.380981  0.009881  0.022061  NaN   \n",
       "4      542         5  11.928  0.03616  447.813965  0.014085  0.033652  NaN   \n",
       "\n",
       "   CH7  CH8  \n",
       "0  NaN  NaN  \n",
       "1  NaN  NaN  \n",
       "2  NaN  NaN  \n",
       "3  NaN  NaN  \n",
       "4  NaN  NaN  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%ls ../data/ \n",
    "df_al = pd.read_excel('../data/Al60601_raw_data.xls')\n",
    "df_al.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c91fb519-ab2b-42d1-9775-bcdbde6e2f6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    },
    {
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       "      <th>POSIT</th>\n",
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      "text/plain": [
       "   TESTNUM  POINTNUM    TIME    POSIT       FORCE       EXT       CH5  CH6  \\\n",
       "0      523         1   6.189  0.07302  202.924728  0.000402 -0.028272  NaN   \n",
       "1      523         2   6.549  0.07396  205.714890 -0.000238 -0.034549  NaN   \n",
       "2      523         3   7.148  0.07624  217.763336 -0.000713 -0.030140  NaN   \n",
       "3      523         4   9.146  0.08438  316.306122  0.002377 -0.025968  NaN   \n",
       "4      523         5  10.041  0.08822  417.003357  0.003089 -0.024100  NaN   \n",
       "\n",
       "   CH7  CH8  \n",
       "0  NaN  NaN  \n",
       "1  NaN  NaN  \n",
       "2  NaN  NaN  \n",
       "3  NaN  NaN  \n",
       "4  NaN  NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_steel = pd.read_excel('../data/Steel1018_raw_data.xls')\n",
    "df_steel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5197d1fa-5936-4e44-b637-971d9ba1abfd",
   "metadata": {},
   "source": [
    "Vidíme, že naměřená data obashují různé hodnoty. Pro nás je nejdůležitejší sloupec síla (**FORCE**). Kromě těchto hodnot máme k dispozici ještě hodnotu **EXT** hodnoty prodloužení z mechanického extenzometeru v \\% a **CH5**, který obsahuje hodnoty prodloužení z laserového extensometru v \\%. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bb46c3-8131-48db-b267-055ca2b7a072",
   "metadata": {},
   "source": [
    "## Úprava dat\n",
    "\n",
    "Protože tato data jsme získali z americké laboratoře, máme sílu určenou v librách (pounds) a průmer zkušební tyče je 0.506 palců (inch). Nejdříve musíme tyto hodnoty převést na jednotky SI.\n",
    "\n",
    "* Pro převod síly nebo hmotnosti z liber (lb) na newtony (N) použijeme následující vztah: 1 lb $\\approx$  4,4822162 N.\n",
    "* Pro převod délky z palců (inch) na milimetry (mm) použijeme následující vztah:\n",
    "1 inch = 25.4 mm.\n",
    "\n",
    ":::{note} Síla v **N**, rozměry v **mm**, napětí v **MPa**\n",
    " $$1 \\frac{\\mathrm{N}}{\\mathrm{mm}^2} =   1\\,10^6 \\frac{\\mathrm{N}}{\\mathrm{m}^2} = 1 \\mathrm{MPa}$$\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cc10ea58-42bb-4943-b55e-36ac192af529",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = 0.506 * 25.4 # mm\n",
    "force_al = df_al['FORCE'].to_numpy() *  4.4822162#N\n",
    "force_steel = df_steel['FORCE'].to_numpy() * 4.4822162 #N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ce46d8-d94b-4fa7-bd24-33d7b04a1eaa",
   "metadata": {},
   "source": [
    "Následně si určíme smluvné napětí vydělením síly počátečným průřezem a převedem prodloužení z procent. Protože laserový exntezometr je přesnější, budeme počítat s těmito daty. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "adcdd1fd-9e8e-4be0-a4e5-e253ade6e30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.pi*d**2/4\n",
    "stress_al = force_al / A\n",
    "stress_steel = force_steel/A\n",
    "\n",
    "strain_al = df_al['CH5'].to_numpy()*0.01\n",
    "strain_steel = df_steel['CH5'].to_numpy()*0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc410579-371e-4088-9e02-da764a346fc1",
   "metadata": {},
   "source": [
    "## Vykreslení závislosti"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a61c8d-d5f4-4d83-a489-94d6b06af3fd",
   "metadata": {},
   "source": [
    "Nyní, když máme data z tahového testu připravena, můžeme vytvořit rychlý graf pomocí metody ```ax.plot()``` knihovny Matplotlib. První pár hodnot (x, y), který předáme do ```ax.plot()```, je ( strain_al}, stress_al), a druhý pár (x, y), který předáme, je (strain_steel, stress_steel). Příkaz `plt.show()` zobrazí graf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "15396027-7813-4c5f-baa1-a543f2031f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(strain_al, stress_al, strain_steel, stress_steel)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba8df4d-3576-475d-8d88-054e9cd5c54a",
   "metadata": {},
   "source": [
    "Náš graf vylepšíme přidáním popisek jednotlivých křivek a os.\n",
    "\n",
    "| Matplotlib metoda | Popis |\tPříklad |\n",
    "| --- | --- | --- |\n",
    "| ax.set_xlabel() | popisek osy x |\tplt.xlabel('strain (in/in)') |\n",
    "| ax.set_ylabel() | popisek osy y |\tplt.ylabel('stress (ksi)') |\n",
    "| ax.set_title() | \tpoisek grafu |\tplt.title('Stress Strain Curve') |\n",
    "| ax.legend() |\tlegenda |\tplt.plt(x,y, label='steel') |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0cdf343c-f0a7-4fc8-a673-fa74a8eb1c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(strain_al, stress_al)\n",
    "ax.plot(strain_steel, stress_steel)\n",
    "\n",
    "ax.set_xlabel('Poměrná deformace (m/m)')\n",
    "ax.set_ylabel('Napětí (MPa)')\n",
    "ax.set_title('Smluvní tahový diagram  slitiny hliníku 6061 a oceli 1018')\n",
    "ax.legend(['Al6061','Steel1018'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb810eee-9248-47b1-b803-f260641dcb0d",
   "metadata": {},
   "source": [
    "Nyní můžeme uložit graf jako obrázek ve formátu .png pomocí metody ```plt.savefig()``` knihovny Matplotlib. Kód níže vytvoří graf a uloží obrázek s názvem `tahovy_diagram.png`. Argument `dpi=300` uvnitř metody `plt.savefig()` specifikuje rozlišení uloženého obrázku. Obrázek bude uložen ve složce ```static``` stejného adresáře jako náš kód.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c4d7a566-a7b0-4f96-abd6-d3ce434f085b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(strain_al, stress_al, label = 'Al6061')\n",
    "ax.plot(strain_steel, stress_steel, label = 'Steel1018')\n",
    "\n",
    "ax.set_xlabel('Poměrná deformace (m/m)')\n",
    "ax.set_ylabel('Napětí (MPa)')\n",
    "ax.set_title('Smluvní tahový diagram  slitiny hliníku 6061 a oceli 1018')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('static/tahovy_diagram.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c900da48-3bb8-4d75-a4fd-5d2cb4a4673a",
   "metadata": {},
   "source": [
    "# Nalezení meze pevnosti\n",
    "\n",
    "Mez pevnosti je bod na grafu s největší hodnotou napětí. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9c9c956-2e9b-4ba2-a16f-60f786a40c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "uts_steel = np.max(stress_steel)\n",
    "uts_al = np.max(stress_al)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a0c4e2-1b6c-4f71-8936-27da05a76df2",
   "metadata": {},
   "source": [
    "Pro vykreslení dodáme ještě deformace, ve kterých nastává mez pevnosti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5940b66a-8b38-4e5b-bd14-a72aecdb90e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mez pevnosti pro hliník 6061 je 328.87169171951257 MPa a nastává při poměrném prodloužení 0.10797487258911133.\n",
      "Mez pevnosti pro ocel 1018 je 663.4758721964481 MPa a nastává při poměrném prodloužení 0.12940356254577637.\n"
     ]
    }
   ],
   "source": [
    "uts_strain_steel = strain_steel[stress_steel==uts_steel][0]\n",
    "uts_strain_al = strain_al[stress_al==uts_al][0]\n",
    "\n",
    "\n",
    "\n",
    "print(f'Mez pevnosti pro hliník 6061 je {uts_al} MPa a nastává při poměrném prodloužení {uts_strain_al}.')\n",
    "print(f'Mez pevnosti pro ocel 1018 je {uts_steel} MPa a nastává při poměrném prodloužení {uts_strain_steel}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7223d87e-ee00-4043-b6c8-7a1683833237",
   "metadata": {},
   "source": [
    "Body si můžeme přidat do grafu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c27efb51-e74e-43bf-8eb2-a03927a13008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rGw5knsq5hKhPLsCXvf2wlER17gw3zvPLkXgvX/U5FJebqWl8ec7Gb7hq7qc4zwXM2tYfaJCMTvACR5ILHpWGk90rIR2SOkNiBiR675HRDTv2JuQsWJjw4iuCPRshZz3krPPev4c9GyBvJ4d14xwZ405ISV2hy1BI7ATtUioHg3YdoV0H9x6X3OxXxRk33VSpzgJA4uLIuPlmSO7WrGmpkyqUHISiA1CUC4W57r1iuIbxhbmwP+vQPCX51a8/PvVQ4Ejq7I5XUhdo3/XQK7GT5VzCkAULExqqcGA7bF8G2ctg+7ewfTns2wxadmi+hAxI6wf9ToP23Q6dWMrf41PDvsy+vBJ75yOP4svOJqpLFzJuvCE8K7dFICbevZI6NXw9vmLI3+lyggd2uPe8He6Y5+2EvO3uQiBvh8vZVEpDpAsY7btC+y7uuCd3h85D3AVBu46N20fTIC2+P4tRo0apNSQY5srKXM5g+7deYPACRIFfV+YpfdzJIP1ISO3rvY5wOQHTeqlCQY4rQszd5hUrVvMqPnBomQ49XdDoPAzS+7vfSkofiG4Xuv1ogURksaqOCnR+y1mYpldSCFlfwcbPYNPnkP3toWKJiGjIOBL6Tzp0pdhpsCvzNm2PCCSkuVeXoTXPl7/bu8j49tBrVZVnVdpnuguM1L6QPgB6/QgyBoZ9zrOlsGBhGs9XDNuWuOCw8RPY8pWrOJUI6DIcRlx6KDCkH+Xu1DGmPhLS4IhT3Ktc0QGXYy2v1yp/Xz7H3Q0Hrhizz4nQ5yTofSJ06B6S5LcGFixMwxQdgFVvw4pXXe6hpAAQ6DwYRl8NvU+AnuOsGMkET2wSdBnmXv5UXWX7xk9hw3z3+u5fblrKEV7gOB66jnBFWpbzCIjVWZjA+Yph/Vz4bjaseRd8hdChhytS6nW8y/bHp4Q6lcZUpgq7Vh8KHJs+d7cRg7uY6TzUBZzOQ10uJKlzKFPbbKzOwjStsjLYshCWzYaVr7sH2OJTXdHSkB9D9zF2ZWbCmwhkHOVeY691z5SU32SR/a0b/urvrug0IgqOOhtGXQU9j7VbeP1YsDDV2/cDfPOsy77v3wLR8XDkmS5AHHGyPVxlWq7IaOh2tHuVK/XBzpWw7BX47z9hxWsQkwSZo6DHWOh+jHtvw3dcWTGUOUTVVU4vfPLQnSZHnAJDfwwDzoDYxNCmz5jmUFwAa/4Dm7+ALYtgxwpAIa6Dy1GPvsrdqtvC1bcYyoKFcUVNK1+DL5+ArYtdOe7RU2HMNZCcGerUGRNaB/e5oLH0JXcRpaXQaYh3l9XJ7kaOmIRQp7LeLFiY+tm11jVm98OX7k6RsdfCsCmWizCmOrnb4NuX4fuPXAApLXbPDh15Boz7lavDayEsWJjA7FwNn/0Zlv/b3YI48X4XJCKsPyxjAlJc4C6y1s+FpS+6Zzu6jXLFVAPPc02mhDELFqZ2ezbA/Afc3U3R8e6HfeyvXcNuxpiGKcqDb2fBor+5hwNjk2HIRTDoPOhxLESG371EYRUsRKQD8A9gMK650CuBNcArQC9gE/BjVd3rzX8HcBVQClyvqu/XtQ0LFgHKzYZP/xeWPO+yzcdcA8dOg4TUUKfMmNZDFTYvgMUzYdWb7lmkdinuBpGB50Df08Im9x5uwWIm8Jmq/kNEYoB44LfAHlV9QERuBzqq6m0iMhCYBYwBugJzgf6qWlrbNixYBOD7j+FfU92DSEdPheNvca15GmOCpzgf1s9zleJr33f9rBwxHs75a1g0TR82wUJE2gPfAn3UbyMisgY4SVWzRaQLMF9VB3i5ClT1fm++94HpqvplbduxYFGL/Vvh84fd8xLpA+CSF1xDa8aY5uUrhiUz4YPfQZnP5TSOnurupgpRTiOcnuDuA+wCnhWRYcBiYBrQSVWzAbyAUV5Y3g1Y6Ld8ljfuMCJyDXANQI8ePYKT+pZu8XPwn1vdbX4jL4cJf3IV2caY5hcVA2N+Dn1PhW+e9m7DfdO1TXX0FTD80sb1H9IMghnSooCRwFOqOgLIB26vZf7q2oyoNtujqjNUdZSqjkpPT298SlsTVZj/ILw1zbXV9OvFcPajFiiMCQcpvWHCPXDTKrjwade22ry74ZGB8MplrtiqrKzu9YRAMHMWWUCWqi7yPs/BBYsdItLFrxhqp9/8/u0HZwLbgpi+1idvJ7xzkysjHToZzn3cmuUwJhxFxbq7pYZcBLvXw5LnDuU2OvaGM/8P+o4PdSorCVrOQlW3A1tEZIA3ajywEngTuMIbdwXwhjf8JjBZRGJFpDfQD/gqWOlrVXxF8PH98NhwWP0fmHAvnP83CxTGtARpfQ/lNi56xvUr/8IF8O5trtHDMBHsm39/Dbzo3Qm1AfgZLkDNFpGrgB+AiwFUdYWIzMYFFB9wXV13Qhlg72aYfTlkL3UPAp3ye/fjM8a0LFGxMPhCV/k9d7p7ZmPLV+45qCPPCnmnYfZQXku2ZyM8d6a7Jfa8p1yrsMaY1mH5v+HD6bD/B9ctwOifwwm3NFmJQX3vhgqPp0NM/a37EP4x3vVQN/UdCxTGtDaDL4RpS+HSf0OPcfDJA/DP811/5CFgwaIl2rkaXvoxJHWBKz9w/VsbY1qfiEh3u+3kF+H8/wdZX8P/OxFWvwOFuc2blGbdmmm83G3w5q8gMhYufwPS+4c6RcaY5jBsMlz5nuv57+WfwIM9XU9/zST8WrcyNVv7Prz2C3f303lPQEJaqFNkjGlOXUfAr752Fd+bv4D0o5pt0xYsWgJV+OR/Yf59rtOVi5+FtH6hTpUxJhSi23kdL53YrJu1YBHuysrg/TvcbXTDpsBZj0J0XKhTZYxpYyxYhLt3bnTtPI39pXvYLkyaNzbGtC0WLMLZhvkuUIz7lXvCU6prPssYY4LPLlPD1fbl7snslCPglN9ZoDDGhJQFi3C0e717+CYmES57zVVoGWNMCFmwCDf7tsDz54KWuecoOvYMdYqMMcbqLMJKSaHLURQdgKlv2+2xxpiwYcEinCx5HnLWubZgugwNdWqMMaaCFUOFkxWvQuehri0YY4wJIxYswsm2pa4rVGOMCTNWDBUO9myEz/4MvoMQkxDq1BhjzGEsWIRaqQ9mnuM6OAGIsENijAk/VgwVSqquOY/yQAGu/XpjjAkzFixC6dOH3B1Qx98CR53jxokdEmNM+LEzU6hs+hw+vg+GXuKa8zjiFDc+e1lo02WMMdWwYBEKZWXw/p3QoQec+bBr96m8D+1+p4U2bcYYU42g16aKyCbgAFAK+FR1lIikAK8AvYBNwI9Vda83/x3AVd7816vq+8FOY7NShf/cAtlL4dwnITbRjU/MgD/stSbIjTFhqbnOTCer6nBVHeV9vh2Yp6r9gHneZ0RkIDAZGARMAp4UkdZV47vmP/DN067Z8eE/qTzNAoUxJkyF6ux0LjDTG54JnOc3/mVVLVLVjcB6YEzzJy9Idq2FN6+H1H5w6h+t2XFjTIvRHMFCgQ9EZLGIXOON66Sq2QDee4Y3vhuwxW/ZLG9cJSJyjYh8IyLf7Nq1K4hJb2Lv3AQoTHkZIu15CmNMy9EcZ6zjVHWbiGQAH4rI6lrmre5SWw8boToDmAEwatSow6aHpe/mwKbPXI93aX1DnRpjjKmXoOcsVHWb974TeA1XrLRDRLoAeO87vdmzgO5+i2cC24KdxqDLz4G3b4LuY+GYX4Q6NcYYU29BDRYikiAiSeXDwARgOfAmcIU32xXAG97wm8BkEYkVkd5AP+CrYKaxWXx0NxQfgLMfhcjoUKfGGGPqLdjFUJ2A18RV5EYBL6nqeyLyNTBbRK4CfgAuBlDVFSIyG1gJ+IDrVLU0yGkMrk2fw+Ln3N1PGUeFOjXGGNMgotoyivxrMmrUKP3mm29CnYzqqcJzZ0LO9zBtqfWlbYwJGyKy2O9xhjrZjf3BtPRF2LwATrzVAoUxpkWzYBEsviKY/wBkjoajfxbq1BhjTKPYzf7BMu9u2L8FzvmrPZltjGnx7CwWDHs2wMKnYOQVcMTJoU6NMcY0mgWLYPj0z+4W2ZN/G+qUGGNMk7Bg0dRyvodvZ8GoqyCpc6hTY4wxTcKCRVMqLYE3roOodnDctFCnxhhjmoxVcDelFa/DD1/C+TMgqVOoU2OMMU3GchZNpawMvpoBSV1gyMWhTo0xxjQpy1k0ldVvQdZXcN5TdqusMabVsbNaU1CFL5+A5O4w9JJQp8YYY5qcBYumsO4D2LIIfnQjRLSuXmCNMQYsWDReWRnM+xN07A0jLw91aowxJiiszqKxVrwKO76DC/5hfVUYY1qteuUsvM6MrJylnKprLLDTYBh8YahTY4wxQVNrsBCRCBH5iYi8IyI7gdVAtoisEJGHRKRf8yQziD74HTwzqWHLbv8OctbB6KvtDihjTKtWVzHUx8Bc4A5guaqWAYhICnAy8ICIvKaqLwQ3mUH0xV8bvuzCJyE6Ho46p+nSY4wxYaiuYHGqqpZUHamqe4B/A/8WkbZZUL/vB/juXzD655CQGurUGGNMUNUaLKoGChHJAOL8pv9QXTBpExbPBC2DcdeFOiXGGBN0ARW0i8g5IrIO2Ah8AmwC3g1iusJb0QFY/Cz0PRU6dA91aowxJugCrZX9EzAWWKuqvYHxwIKgpSrcrXgdCnLghN+EOiXGGNMsAg0WJaqaA0SISISqfgwMD2RBEYkUkf+KyNve5xQR+VBE1nnvHf3mvUNE1ovIGhGZWN+daTY7V7qK7W6jQp0SY4xpFoEGi30ikgh8CrwoIo8BvgCXnQas8vt8OzBPVfsB87zPiMhAYDIwCJgEPBm2z3Ts+wE69LTbZY0xbUagZ7tzgALgRuA94Hvg7LoWEpFM4EzgH36jzwVmesMzgfP8xr+sqkWquhFYD4wJMH2NV3QgsPkKc2H3OkjODG56jDEmjNR6N5SIHAPMAI4AvgOuUtWZtS1TxaPArUCS37hOqpoNoKrZ3h1WAN2AhX7zZXnjmsf9mRCX7FqOTc6E9t3ce/krMgaWvgTLXoHiPBh5WbMlzRhjQq2u5yyeAG7BFT+dAzwCBFSXICJnATtVdbGInBTIItWM0xrWfQ1wDUCPHj0CSU7NEju5yupTfgf7s2D/Vvf+w0Io3Fd53shYGHyBe7Yi8+jGbdcYY1qQuoJFhKp+6A3/S0TuqMe6jwPOEZEzcM9mtBeRF4AdItLFy1V0AXZ682cB/vehZgLbqluxqs7A5XgYNWpUtQElYEmdoctw17x4VUV5kLsV9m+Bg/ugz8n2AJ4xpk2qK1h0EJELavqsqq/WtKCq3oFrJgQvZ3GLql4qIg8BVwAPeO9veIu8CbwkIg8DXYF+wFf12puGKquhrj42EdIHuJcxxrRhdQWLT6hcke3/WYEag0UtHgBmi8hVwA/AxQCqukJEZgMrcXdaXaeqpQ1Yf/3kbIDuY4O+GWOMacnqau7jZ02xEVWdD8z3hnNwD/VVN9+9wL1Nsc2ASQRIddUlxhhjytV1N9RNtU1X1YebNjnGGGPCUV3FUH8GluLagSqi+juWWjZfIUTF1T2fMca0YXUFi5G4p6rPBBYDs3BPXzfuDqRwUuZzRVHGGGNqVOtZUlWXqurtqjoceBr3lPVKEWk9vf2063D48xTGGGMqCbSJ8nRgBDAE9zzEztqXaEGKC1yjgMYYY2pUVwX3z4BLcA/VzQF+rKqtJ1AAlBZZnYUxxtShrjqLp3FtQv2Aa+ZjgvjdZqqqLb84SiKhGR7nMMaYlqyuYHFys6QilESgFdXXG2NMMNT1UN4nzZWQkCgrhdJiiG4X6pQYY0xYq7WCW0TeEpGzRSS6mml9RORuEbkyeMkLspKD7t2ChTHG1KquYqifAzcBj4rIHmAXrrK7F64DpMdV9Y2aFw9zFcHC7oYyxpja1FUMtR3XedGtItIL6AIcBNaqakHwkxdk5ZX1ZVbBbYwxtakrZ1FBVTcBm4KWklCIT3V3Q+W3rruBjTGmqbXtdi60zN02Gxkb6pSYEHlnwztMmDOBoTOHMmHOBN7Z8E6ok2RMWAo4Z9EqlXglaTFWZ9EWvbPhHaZ/MZ3C0kIAsvOzmf7FdADO7HNmCFNmTPipMViIyC+A71R1QTOmp3kVe8HC7oYKC2VlSqGvlBKfUlRaSkmpUuIro7i0jGJfGSWlZZSUasVw5fFlFPtNq1iutIwSn7rpVZZbXPYQxVJYKQ2FpYXc9dlDvL+oK9GREcRERRATKcRERRAdGeE3LoLoSCEmKtJ7Lx8XUTFvxbgoqTTNjTu0DrH+VEwLUFvOYhbwFxHpgOvdzv/JNQFUVYcGMW3BV5Lv3qMTQpuOVqKwpJS9BcXsyS9mf0EJB4p8HCj0caCwhLxCX+XP3nCe9/lAkY+8Il+TPx8ZExVBrHdyjvY76cdERlDcIafaZYo0h++27qfYCzglVYJVU4uOFJLbRZOSEENKQgypCbGHhhMPfU5NjCEjKZbkdtEWYEyzqzFYqOp+4AoRyQDOar4kNSN7zqJWJaVl7DpQxM4DRezMLWRXXhE5eS4Y7C0oZm9BCXvzD30uKK79rrLYqAiS4qJJiouqeKUlxpMUF01ibBTt46KIj43yu/Ku6Yq+/OpdDr+a95sWGVH7VfuEOV3Izs8+bHyXxC58MPWkapcpK1NKyqrkcPyCSolPKS4tpdinXq6mai5IKfa5XFOx37L7D5awJ6+YnPwiVm3PZU9+MfsKSqpNQ2JsFJkd23mv+IrhnqkJ9EpNoF1MZK3HwZiGqLPOQlV3isiDqnqb/3gReRC4rYbFWobYJPfexpooLyj2sTPXCwIHCisN7zpQVBEg9uQXV7t8UlwUKQkxdIyPIS0xhn6dEkmJj6GjdzXcMT6G5HYuKLSPiyYxLorE2ChiosLrfoppI6dVqrMAiIuMY9rIaTUuExEhxEZEEhsFBPm+CF9pGXsLStiTX0xOXhE5+cXsyC0ka+9B71XAog17OFDkq7Rcl+Q4eqUm0CstgT5p7r13WgI9UuLD7hiYliPQCu7TODwwnF7NuJYlroN7LzoQ0mQ0JVVlV14RW/a4k0nW3oNs2VPAlr0FbNvngkFelZMLuKKQ9MRY0tvH0T0lnqN7diQjKY6M9rFkJMWSnhRLRlIcKQkxreaEU16J/diSx9iev53OCZ2ZNnJa2FRuR0VGkO5995BU43z7D5awZU8Bm3Ly2bgrn405+Wzcnc97y7PZ65c7iRDo1rEdR6QnMqRbMkMzOzAsM5mM9tbqsqlbXU2UXwv8EugjIsv8JiUBXwQzYc2izDtpRhzWmknIlJUpa3YcYP/BEvK9cvz8olLyikrIKyolv8hHfpEr/y8fzvOm5xeVklfoo7i0rNI60xJjyezYjoFd25PhnfQzkmK9QOCGO8S3zXLwM/ucGTbBoaGS20WT3C2Zwd2SD5u2r6CYjbtd8Ni0O58Nu/NZvzOPT9fuosyrfumSHMfQzPLg0YEhmckktwuf/4QJD3XlLF7C9b99P3C73/gDqronaKlqLqXeVVdk+NxB/OJXP/D715fXOD0+JpLEWFesk+C9d+sQQ2JsovscF0XX5HZ0T2lH947xZHaMtzLsNqxDfAwjesQwokfHSuMLin2s2JbLt1v2sSxrP8uy9vH+ih0V03unJVQEkOHdkxnYJdl+R21cXc197Af2A1NE5Higt6o+LyJpItJbVTfWtKyIxAGf4kp2o4A5qnqXiKQAr+Dal9qE61Bpr7fMHcBVQClwvaq+39gdrFUIcxYlpWVk7T3I5px8fthTwOacAuJjIilTJULgn1cd4wWDSBJjo0mIjSQhJoqIiLZ39W+aXnxMFKN7pTC6V0rFuP0FJSzb6oLH0i37WLghhzeWbgMgMkLo3ymJYZnJDO/egeP7p9Otg90Y0pbU9pzFSGCdqh4QkbuAMbi2oZ7HBYAXgONqWXcRcIqq5nmt1n4uIu8CFwDzVPUBEbkdl2O5TUQGApOBQUBXYK6I9FcNYs9EFcGi6XMWZWXKzgNFbMpx2f9NOQVs2p3Ptv0HyckrZntuIaVlh27DjImMoLi0jN5pCcTHRHFc37QmT5MxtUmOj+b4fukc3y+9YtyO3MKK3Me3Wft4d/l2Xv56CwB9MxL5Ud80hnVPZki3ZHqnJRJpFzOtVm1nyX3AsyLyBHA+MBKYB6CqW0Wk5ho3N48Ced7HaO+lwLnASd74mcB8XEX5ucDLqloEbBSR9bgA9WV9dypgTRAs9heUsH7XAb7fmc/3u/PYtDufzTmusrGw5FDdQUxkBN1T3K2OR6QnktmxHT1S4umVlkDPlHg6JsQw6dFP+X5XvlehaUzodWofx4RBnZkwqDPgbqD4flce89fs4pO1u3jl6y0898UmABJiIhma2YGTj0xn/FGd6JOW0CbrwVqr2s6SUUAkrkioSFXLREQBRCSgp9hEJBJYDPQFnlDVRSLSSVWzAVQ123uOA6AbsNBv8SxvXHXrvQa4BqBHjx6BJKV6FcEisLLYnQcKWbbFXWEty9rP6u257MgtqpgeExlBj9R4eqXG86O+afRMS6B3agI9U+Pp2qFdnVddv5l4JL94YTEJVjZswpSI0Dcjib4ZSVx9fB98pWV8vyuf77bu57usfSzauIf7/rOa+/6zml6p8ZxyZCfGH5XB0T07Ehdtv+uWrLZgEQ9cqqr5IvIvEZkBpHsn6p8Bf69r5V4R0nDvKfDXRGRwLbNXdyat9nFZVZ0BzAAYNWpUwx+pLX9cWKq/FbTYV8aHK3fw1rfb+DZrH9n73f34kRFCv4xEjuubRv9OSfTLSKRvRiKZHeMblQ2fOKgTo3p2JMKuxkwLERUZwYDOSQzonMRFR2cCsHXfQT5avZOPVu3ghUWbeWbBRqIjhUFdkxnZoyMje3ZgbJ9U0hItB92S1PYE91K/4T+LyGm4yu7+wB9U9cNAN6Kq+0RkPjAJ2CEiXbxcRRegvH3wLKC732KZwLZAt9HU1mw/wHUvLWH9zjw6t49jTO8UhnqVe4O6BufOEBHhuSvH4Kty66sxLUm3Du24bGxPLhvbk4JiH19+n8PXm/ay5Ie9vPSVCx4icHSPjpw6sBOnHtWJvhmJoU62qUN9Cuu/A9rhrva/q2tmEUkHSrxA0Q44FXgQeBO4Atfe1BVAeU97bwIvicjDuArufsBX9Uhf/ZV5t85WqbP4eM1Orn1hMYmx0fzt0qM5bWCnZqu4S4wNn9t4jWms+Jgoxh/VifFHdQLcXYArt+Xy8ZqdzF21gwfeXc0D766mT1oCpw7sxGkDOzGyR0erKA9DAZ2ZRORq4A/AR7jior+KyN2q+kwti3UBZnr1FhHAbFV9W0S+BGaLyFXAD8DFAKq6QkRmAysBH3BdUO+Egmq7Vc0tLOHGV5ZyRHoiz/5sNBlJ9nSrMU0lOjKCYd07MKx7B244tT/b9h1k7qodfLhyB88u2MiMTzeQkhDDKUdmMHFQZ47vl2Z1HWEi0MvY3wAjVDUHQERScU9w1xgsVHUZMKKa8TnA+BqWuRe4N8A0NZ7PaxMo6lDZ6auLs9hXUMLzV46xQGFMkHXt0I7Lx/Xi8nG9yC0s4dO1u/hw5Q7eX7GdOYuziI+J5IR+6Zw0IJ0T+qfT1Z7tCJlAg0UW4N+A0gFgS9MnJ0T8KpT/8912BnRKYmhmh9Clx5g2qH1cNGcN7cpZQ7tS7Ctj4YYc3luxnY9X7+S9FdsB6N8pkRP6pXPigHSO6Z3aatopawkCDRZbgUUi8gaHnpX4SkRuAlDVh4OUvuAqvwtKD1Uor9qeywUjqr1j1xjTTGKiIjihv8tNqCrrdubxifdsx/NfbuYfn2+kT3oCfzhrICf0S7eWDZpBoMHie+9VrrxSutYH88JelWBRUlrGgUIfHRNiQpgoY4w/EdfUSP9OSfz8hD4UFPuYv2YXD763mqnPfk1aYiynDezEhEGdOPaIVGKjrI4jGAIKFqr6x2AnJCSqBIvyzmZSLVgYE7biY6I4Y0gXTjkyg/dXbOeDlTt4c+lWZn31A4mxUZw4IJ2fjunBsdZkTpMK9G6odOBWXLtNFbW+qnpKkNLVPKoEi70FrrMfy1kYE/7ioiM5d3g3zh3ejSJfKV98n8MHK7bz4codvLMsm4mDOnHnGQPpkRpf98pMnQIthnoR11LsWcAvcM9H7ApWoppNRbBwT3KX9wyXEm/BwpiWJDYqkpMHZHDygAzuOruUpz/fyBMfr+fUhz9h3BGpHNWlPTec2s9uw22EQG8lSFXVp3EP2X2iqlcCY4OYruZR3tyH974333IWxrR0cdGRXHdyXz6+5SQmj+nOzgNFzFm8hVi7c6pRAs1ZlPfNmC0iZ+Ka4cgMTpKa0X7v7t/2XQHY4xVDpViwMKbF69Q+jrvPdc3RlZaptYDbSIEGi3tEJBm4Gfgr0B64MWipai7lt8xGuuBQnrPoEG9dShrTmljzIY0X6N1Qb3uD+4GTg5ecZqaVG6zdk19CYmyU3XpnjDFV1BosROQPtUxWVf1TE6eneRV7fTPFuO459hYU0zHBchXGGFNVXTmL/GrGJeD6yU4FWnawiPQCQ6nrwGhPfrHdCWWMMdWoNVio6v+VD3vdqE7DdXz0MvB/NS3XYuTtcO+JrsvIvQXFVrltjDHVqPNeMhFJEZF7gGW44DJSVW9T1Z11LBr+YrwOV7ymyi1nYYwx1aurzuIh4AJcF6ZDVDWvWVLV7A49Z9HBgoUxxhymrpzFzbhe634HbBORXO91QERyg5+8ICv0diEumSJfKfnFpXS022aNMeYwddVZtO5HHss7PfIVsq/IBYkOVmdhTKOUlJSQlZVFYWFhqJNigLi4ODIzM4mObtyFcNvu8Lm8bajSEvYWuKIoy1kY0zhZWVkkJSXRq1cve2o6xFSVnJwcsrKy6N27d6PW1bpzDnXJ3er6327Xkd0H3NPb6YmxdSxkjKlNYWEhqampFijCgIiQmpraJLm8th0sACQSRMjJd89apFqwMKbRLFCEj6Y6Fm07WERGQ5lrIzH3oHtv365tl8wZY0x1ghYsRKS7iHwsIqtEZIWITPPGp4jIhyKyznvv6LfMHSKyXkTWiMjEYKWtgpZV1FvkFvoA12m8Mable+211xARVq9eDcCmTZsYPHhwxfRly5Yxbtw4Bg0axJAhQyqKahYvXsyQIUPo27cv119/PerXhtzs2bMZOHAggwYN4ic/+UnF+EmTJtGhQwfOOuusZtq75hfMnIUPuFlVj8L1fXGdiAwEbgfmqWo/YJ73GW/aZFxvfJOAJ0UkuC36qVYEiwOFPmIiI6zNe2NaiVmzZvGjH/2Il19++bBpPp+PSy+9lL/97W+sWLGC+fPnV9wtdO211zJjxgzWrVvHunXreO+99wBYt24d999/PwsWLGDFihU8+uijFev7zW9+wz//+c9m2a9QCdqZUVWzVXWJN3wAWAV0A84FZnqzzQTO84bPBV5W1SJV3QisB8YEK30ukf45ixKS4qKsrNWYViAvL48FCxbw9NNPVxssPvjgA4YOHcqwYcMASE1NJTIykuzsbHJzcxk3bhwiwuWXX87rr78OwN///neuu+46OnZ0hSEZGRkV6xs/fjxJSUnB37EQapYCehHpBYwAFgGdVDUbXEARkfJvvBuw0G+xLG9cdeu7BrgGoEePHg1PmJaBFxwOFPpIirP6CmOa0h/fWsHKbU37/O7Aru256+xBtc7z+uuvM2nSJPr3709KSgpLliwhJSWlYvratWsRESZOnMiuXbuYPHkyt956K1u3biUz81C/bpmZmWzdurViGYDjjjuO0tJSpk+fzqRJk5p038JZ0M+OIpII/Bu4QVVza7lyr26CVjMOVZ2Ba4KEUaNGVTtPQPxyFnmFJSRasDCmVZg1axY33HADAJMnT2bWrFlcd911FdN9Ph+ff/45X3/9NfHx8YwfP56jjz6a9u3bH7au8nOWz+dj3bp1zJ8/n6ysLI4//niWL19Ohw4dmmOXQi6oZ0cRicYFihdV9VVv9A4R6eLlKroA5Q0SZgHd/RbPxHXfGjx+waLIV0acdXpkTJOqKwcQDDk5OXz00UcsX74cEaG0tBQR4Ze//GXFPJmZmZx44omkpaUBcMYZZ7BkyRIuvfRSsrKyKubLysqia9euFcuMHTuW6OhoevfuzYABA1i3bh2jR49u3h0MkWDeDSXA08AqVX3Yb9KbwBXe8BXAG37jJ4tIrIj0BvoBXwUrfYDXraq7aigsKSU22iq3jWnp5syZw+WXX87mzZvZtGkTW7ZsoXfv3pWCwMSJE1m2bBkFBQX4fD4++eQTBg4cSJcuXUhKSmLhwoWoKs8//zznnnsuAOeddx4ff/wxALt372bt2rX06dMnJPsYCsHMWRwHXAZ8JyJLvXG/BR4AZovIVcAPwMUAqrpCRGYDK3F3Ul2nqqVBTF+lnMX+gyV0To4L6uaMMcE3a9Ysbr/99krjLrzwQu67776Kzx07duSmm25i9OjRiAhnnHEGZ555JgBPPfUUU6dO5eDBg5x++umcfvrpgAswH3zwAQMHDiQyMpKHHnqI1NRUAI4//nhWr15NXl4emZmZPP3000ycGPy7/5uTqDa8yD8cjBo1Sr/55puGLfzm9bD2fbhlDaPumctpAztx/wVDmjaBxrQxq1at4qijjgp1Moyf6o6JiCxW1VGBrqNtl7t4OQtVJfdgCcnt7IE8Y4ypThsPFu6hvCJfGcWlZXbrrDHG1KBtB4viAxDdjvwi19RHYqwFC2OMqU7bDhb5uyGxE/lFrh49wYKFMcZUq20HC1UQIa8iZ2HPWRhjTHXadrDwHhDPLXTNkydZi7PGGFOtth0sSoshMoZdB1zHR2nW8ZExrca9997LoEGDGDp0KMOHD2fRokU8+uijFBQUNHidU6dOZc6cOQA8/vjj9O3bFxFh9+7dFfOoKtdffz19+/Zl6NChLFmypGLalVdeSUZGRqWm0gGWLl3K2LFjGT58OKNGjeKrr4L7PHJDtO1g4SuC6HYU+8oAiLMnuI1pFb788kvefvttlixZwrJly5g7dy7du3dvdLDwd9xxxzF37lx69uxZafy7775b0bz5jBkzuPbaayumTZ06taLJc3+33nord911F0uXLuXuu+/m1ltvbZI0NqW2fXb0FUJkNAUlroK7XbTVWRjTGmRnZ5OWlkZsrCstSEtLY86cOWzbto2TTz6Zk08+GXBNlY8bN46RI0dy8cUXk5eXB7gOkE488USOPvpoJk6cSHZ29mHbGDFiBL169Tps/BtvvMHll1+OiDB27Fj27dtXsfwJJ5xQqfXbciJCbq5rnXf//v0V7VGFk7Z9+0+7FCjIYU9eMQAdE2JCnCBjWpl3b4ft3zXtOjsPgdMfqHWWCRMmcPfdd9O/f39OPfVULrnkEq6//noefvhhPv74Y9LS0ti9ezf33HMPc+fOJSEhgQcffJCHH36YO+64g1//+te88cYbpKen88orr3DnnXfyzDPPBJS8rVu30r37oTZRy5s579KlS43LPProo0ycOJFbbrmFsrIyvvjii8C+i2bUxoNFB8jbwZ78ItrHRREd2bYzWsa0FomJiSxevJjPPvuMjz/+mEsuuYQHHqgcYBYuXMjKlSs57rjjACguLmbcuHGsWbOG5cuXc9pppwFQWlpa64m+quqaUKqrU7WnnnqKRx55hAsvvJDZs2dz1VVXMXfu3IC32RzadrAoLoDoBHLyi0m1ym1jml4dOYBgioyM5KSTTuKkk05iyJAhzJw5s9J0VeW0005j1qxZlcZ/9913DBo0iC+//LJB283MzGTLli0Vn/2bOa/JzJkzeeyxxwC4+OKLufrqqxu07WBq25fSJQUQE8++ghI6xttts8a0FmvWrGHdunUVn5cuXUrPnj1JSkriwIEDAIwdO5YFCxawfv16AAoKCli7di0DBgxg165dFcGipKSEFStWBLztc845h+effx5VZeHChSQnJ9eZM+natSuffPIJAB999BH9+vWr1/42BwsW0fHkFfns6W1jWpG8vDyuuOIKBg4cyNChQ1m5ciXTp0/nmmuu4fTTT+fkk08mPT2d5557jilTpjB06FDGjh3L6tWriYmJYc6cOdx2220MGzaM4cOHV1uH8Je//IXMzEyysrIYOnRoRW7gjDPOoE+fPvTt25ef//znPPnkkxXLTJkypaKoq7wpc3D9e998880MGzaM3/72t8yYMaN5vqh6aNtNlD8yBHodx4RNUzgiPZGnLj26aRNnTBtkTZSHH2uivLFKCiAqjvyiUuJjLGdhjDE1advBIioOfEXkF/usXShjjKlF2w4Wce2hKJf8Ih/xVmdhjDE1atvBIrY9ZYW5lJQqCTGWszDGmJq07WAR3Y6yYtdOjN0NZYwxNWvbwQKlVN2TlQlWwW2MMTUKarAQkWdEZKeILPcblyIiH4rIOu+9o9+0O0RkvYisEZGJwUwbAFp2KFhYzsIYY2oU7JzFc8CkKuNuB+apaj9gnvcZERkITAYGecs8KSLBrUhQxVfmnjPpYE9wG2NMjYIaLFT1U2BPldHnAuWNtMwEzvMb/7KqFqnqRmA9MCaY6UMVn5ezSG5nwcIY47z22muICKtXr64Yl5iYWDG8b98+LrroIo488kiOOuqoSu1IvffeewwYMIC+fftWarywpmVq6hAp3ISizqKTqmYDeO8Z3vhuwBa/+bK8cYcRkWtE5BsR+WbXrl0NT4mW4fV7ZMHCmBB5Z8M7TJgzgaEzhzJhzgTe2fBOqJPErFmz+NGPfsTLL79c7fRp06YxadIkVq9ezbffflvxdHRpaSnXXXcd7777LitXrmTWrFmsXLmy1mVq6hAp3IRTBXd1bfhW2xaJqs5Q1VGqOio9Pb3hW9SyipxFotVZGNPs3tnwDtO/mE52fjaKkp2fzfQvpjdJwPj222854YQTGDhwIBEREYgId911V53L5eXlsWDBAp5++ulqg0Vubi6ffvopV111FQAxMTF06NABgK+++oq+ffvSp08fYmJimDx5Mm+88Uaty9TUIVK4CUWw2CEiXQC8953e+Cygu998mcC2oKbEL2dhFdzGNL/HljxGYWlhpXGFpYU8tuSxRq23sLCQSy65hD//+c+sXLmSO++8k1tuuYXp06fXuezrr7/OpEmT6N+/PykpKZX60AbYsGED6enp/OxnP2PEiBFcffXV5OfnAzV3fFTbMi1FKILFm8AV3vAVwBt+4yeLSKyI9Ab6AcHttdzLWcRERRATFU6ZLGPahu352+s1PlBz585l5MiRjBnjqj2HDh3Knj17EBFGjx7NtddeyymnnFLRlam/WbNmMXnyZAAmT558WH8XPp+PJUuWcO211/Lf//6XhISEirqJmjo+qm2ZliLYt87OAr4EBohIlohcBTwAnCYi64DTvM+o6gpgNrASeA+4TlVLg5k+UHxlEG9PbxsTEp0TOtdrfKCWL1/OkCFDKj4vWbKEkSNHsmXLFk444QSeeuopevfuzd69eystl5OTw0cffcTVV19Nr169eOihh3jllVcqBYHMzEwyMzM55phjALjooosqch81dXxU2zItRbDvhpqiql1UNVpVM1X1aVXNUdXxqtrPe9/jN/+9qnqEqg5Q1XeDmTa3wTLKEKIiLFdhTChMGzmNuMi4SuPiIuOYNnJao9abmprKsmXLAFi7di2vvvoqkydPZvHixaxevZobb7yRY489lp49e1Zabs6cOVx++eVs3ryZTZs2sWXLFnr37s3nn39eMU/nzp3p3r07a9asAWDevHkMHDgQgNGjR7Nu3To2btxIcXExL7/8Muecc06ty7QUbbugXstQVaIja+8f1xgTHGf2ORNwdRfb87fTOaEz00ZOqxjfUFOmTOHNN99k8ODBpKWlMWvWLFJTU1m8eDEPP/wwAwYMqHa5WbNmcfvtt1cad+GFF/LSSy9VGvfXv/6Vn/70pxQXF9OnTx+effZZAKKionj88ceZOHEipaWlXHnllQwaNKjWZaZMmcL8+fPZvXs3mZmZ/PGPf6yoCA8nbbvzo5nnsGnbDq6MeZCPbj6pSdNlTFsVzp0fXXjhhcyePZvIyLZV9GydHzXW3k3sjkwnLqpt/XCMaav+/e9/t7lA0VTadrAoOsCBqBTKWnjuyhhjgq1tBwvck4ClZRYsjDGmNm07WIhQUlpGYlzbruc3xpi6tO1gAZSVlRFrD+QZY0yt2vZZUiLwlZaSGGuNCBpjTG3afLAoKvGRnhQb6pQYY0xYa7vBouQg5O9me2kS7a3OwhhjatV2g0VxAWgpu0oTrXlyY4ypQ9sNFhHuwZwIykiynIUxxtSq7QaLA9kA7NKO9EiND3FijDGtiX8XrP5q60K1pu5Ya1rmkUceYdCgQQwePJgpU6ZQWFhYdZVNqu0Gi5zvAdikneiXkRTixBjTdu1/6y3WnTKeVUcNZN0p49n/1luhTlLQ1NSFam3dsVa3zNatW/nLX/7CN998w/LlyyktLa2xC9im0naDhZez2E4aXZLj6pjZGBMM+996i+zf/wHftm2gim/bNrJ//4cmCRgN7Vb14YcfZvDgwQwePJhHH320Yvzzzz/P0KFDGTZsGJdddhkAL7zwAmPGjGH48OH8z//8D6WltXfBU1MXqjV1x1rbMj6fj4MHD+Lz+SgoKKBr16517ltjtN3C+jJ3UDsmxRMV2XZjpjGhtPORR9EqxSdaWMjORx4l+eyzG7ze8m5Vn3/+ecaMGcPvf/97CgsL6+xWdfHixTz77LMsWrQIVeWYY47hxBNPJCYmhnvvvZcFCxaQlpbGnj17WLVqFa+88goLFiwgOjqaX/7yl7z44otcfvnl9U5vdd2xLlq0qMb5u3Xrxi233EKPHj1o164dEyZMYMKECfXebn203bOk7yAASTWULRpjgs+XnV2v8YFqaLeqn3/+Oeeffz4JCQkkJiZywQUX8Nlnn/HRRx9x0UUXkZaWBkBKSgrz5s1j8eLFjB49muHDhzNv3jw2bNjQoPTW1B1rTfbu3csbb7zBxo0b2bZtG/n5+bzwwgsN2nag2m6wKHFXM+3aJYQ4Ica0XVFdutRrfKAa2q1qTf37qOphJ29V5YorrmDp0qUsXbqUNWvW1JlzqUlN3bHWZO7cufTu3Zv09HSio6O54IIL+OKLLxq07UC13WDhO0gx0STFx4Q6Jca0WRk33oDEVa4zlLg4Mm68oVHrbWi3qieccAKvv/46BQUF5Ofn89prr3H88cczfvx4Zs+eTU5ODgB79uxh/PjxzJkzh507d1aM27x5c4PSW1N3rDXp0aMHCxcupKCgAFVl3rx5Qe9wqu3WWZQc5CCxJLezYGFMqJTXS+x85FF82dlEdelCxo03NKq+AhrererIkSOZOnVqRfHV1VdfzYgRIwC48847OfHEE4mMjGTEiBE899xz3HPPPUyYMIGysjKio6N54oknDgtAVdNVXReqtXXHWtMyF110ESNHjiQqKooRI0ZwzTXXNOo7q0ub7VbV99p17F76Dv8+aS7Xndw3CCkzpm2yblXDT6vsVlVEJonIGhFZLyK3171EwxwsOEBn2Uu3ZMtZGNNWWLeqDRdWxVAiEgk8AZwGZAFfi8ibqrqyqbd1cMAFLN4p9Mlo39SrNsaYViesggUwBlivqhsARORl4FygyYNFxqjzyBh1XlOv1hhjWqVwK4bqBmzx+5zljatERK4RkW9E5Jtdu3Y1W+KMMaatCrdgUd1TKIfVwKvqDFUdpaqj0tPTmyFZxpj6aOk3zrQmTXUswi1YZAHd/T5nAttClBZjTAPExcWRk5NjASMMqCo5OTnExTW+/btwq7P4GugnIr2BrcBk4CehTZIxpj4yMzPJysrCiojDQ1xcHJmZmY1eT1gFC1X1icivgPeBSOAZVV0R4mQZY+ohOjqa3r17hzoZpomFVbAAUNX/AP8JdTqMMcYcEm51FsYYY8KQBQtjjDF1avFtQ4nILqBhTT1CGrC7CZPTErS1fW5r+wttb59tfxump6oG/OxBiw8WjSEi39SnIa3WoK3tc1vbX2h7+2z72zysGMoYY0ydLFgYY4ypU1sPFjNCnYAQaGv73Nb2F9rePtv+NoM2XWdhjDEmMG09Z2GMMSYAFiyMMcbUqdUGi7q6ZxXnL970ZSIyMtBlw1Ej93eTiHwnIktFpP4dmodIAPt8pIh8KSJFInJLfZYNR43c3xZ3jAPY3596v+VlIvKFiAwLdNlw1ch9Du4xVtVW98I1Qvg90AeIAb4FBlaZ5wzgXVwfGmOBRYEuG26vxuyvN20TkBbq/QjCPmcAo4F7gVvqs2y4vRqzvy3xGAe4v8cCHb3h01vyf7ix+9wcx7i15iwqumdV1WKgvHtWf+cCz6uzEOggIl0CXDbcNGZ/W6o691lVd6rq10BJfZcNQ43Z35YokP39QlX3eh8X4vq/CWjZMNWYfQ661hosAumetaZ5AuraNcw0Zn/B9Ub4gYgsFpFrgpbKptWY49Raj3FtWtoxru/+XoXLOTdk2XDRmH2GIB/jsGuivIkE0j1rTfME1LVrmGnM/gIcp6rbRCQD+FBEVqvqp02awqbXmOPUWo9xbVraMQ54f0XkZNyJ80f1XTbMNGafIcjHuLXmLALpnrWmeVpi166N2V9Utfx9J/AaLjsc7hpznFrrMa5RCzzGAe2viAwF/gGcq6o59Vk2DDVmn4N/jENdqROMFy7HtAHozaGKokFV5jmTyhW+XwW6bLi9Grm/CUCS3/AXwKRQ71NT7LPfvNOpXMHdKo9xLfvb4o5xgL/pHsB64NiGflfh9GrkPgf9GIf8CwriF38GsBZ3d8Gd3rhfAL/whgV4wpv+HTCqtmXD/dXQ/cXdefGt91rRUvY3wH3ujLtaywX2ecPtW/ExrnZ/W+oxDmB//wHsBZZ6r29qW7YlvBq6z81xjK25D2OMMXVqrXUWxhhjmpAFC2OMMXWyYGGMMaZOFixMiyAi0SJyodfG1SWNXNe1ItK+qdJmTFtgwcI0OREp9RozWy4i/xKR+MauU1VLgHG4O7oak7aLgK6qmhvAvNOrNshXzTzpIrJIRP4rIsc3Jm3NRUTOE5E/NGC5O0Tkp/WYf66IdKzvdkx4smBhguGgqg5X1cFAMe7Wv6bwJ1zbOa9UN1FEAmmRoB1wVxOlB2A8sFpVR6jqZ4EsICKRTbj9hrgVeLIBy00APqjH/P8EftmA7ZgwZMHCBNtnQF8RSRGR172mlRd6T6GWX73PFJEPvCaWLxCR//WaWn5PRKK9+Y4G3gQuFZH3yxtBFJH5InKfiHwCTPM+PygiX4nI2vKrfRHpJSKfATcC34jIsdUlVkTu9JqIngsM8Bt/hJeexSLymdcc+HDgf4EzvJxUOxGZ4qV9uYg86Ld8nojcLSKLgHHe5we99c0VkTFe2jeIyDn+aRaRJd7rWL/13ept51sReaCmNFazf/2BIlXd7X1+TkSeEpGPvW2fKCLPiMgqEXnOb7n2QIyq7gp0Ge94TQngN2JaglA/hGKv1vcC8rz3KOAN4Frgr8Bd3vhTgKXe8HTgcyAaGAYUAKd7014DzvOmfQlkeOOnAM96w/OBJ/22PR/4P2/4DGCuNxwPxHnDRwKLq0n30bgHFuNxD7Otx3sSGpgH9POGjwE+8oanAo97w12BH4B0b98/As7zpinwY79taZX9/MDvO1haTZr7cegBrNNxT+jGe59TaktjlX38Wfn3431+Dte6qeBaOM0FhuAuJBcDw735LgDurs8y3rzrgNRQ/ybt1fhXa21I0IRWOxFZ6g1/BjwNLAIuBFDVj0QkVUSSvXneVdUSEfkO16b/e97474BeuCv8/sBsEQF3It7nt72qxVKveu+LveXLl3nEu9ouwQWMqo4HXlPVAgARedN7T8T1I/Avb/sAsdUsPxqYr6q7vOVeBE4AXgdKgX/7zVtcZT+L/L6D8jRHA497OZhS7zsAOBUXLAsAVHVPPdLYBdhVZdxbqqretneo6nde+ld4aVkKTAKerecyADtxQTQH06JZsDDBcFBVh/uPEL8zmJ/y5gOKAFS1TERKVLV8fBnuNyrAOlU9qYbt5Vf5XOS9l3LoN34jsEtVr/LqNgprWFd1TRpEAPuq7lM1qtvHcoWqWur3uep++n8H/mnegcttRPilWapJZ6BpPAgkVxlX/n2V+Q2Xfy5PyxhcDrE+ywDEeds0LZzVWZjm8inwUwAROQnYrQHckeRZA6SLyDhv+WgRGVLP7Xfk0BX1ZbgcTHVpPN+re0gCzgbw0rlRRC72ti/i152ln0XAiSKS5lViTwE+qWc6/SUD2apaViXNHwBXineXmYik1CONq4C+9UmEiAzCVeKX1jlz5eUE117VpvosZ8KTBQvTXKYDo0RkGfAAcEWgC6rrNexC4EER+RZXxDG2ntt/CpgqIgtxxTlVcyOo6hJckdZSXJGR/91NPwWu8ra/gmp6XlPVbOAO4GNcg25LVPWNeqbT35PAFVXTrKrv4SqPv/GK+8pv760zjbiAOKKGnF5NTudQkVl9HA0sVFVfA5Y1YcYaEjSmjRGRx3B1DnMDnP9D4HIvGNZ3O2+q6rwGJNOEGctZGNP23Ie70yogqnpafQOFZ7kFitbDchbGGGPqZDkLY4wxdbJgYYwxpk4WLIwxxtTJgoUxxpg6WbAwxhhTJwsWxhhj6vT/AWOhy2kPSpZJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(strain_al, stress_al, label = 'Al6061')\n",
    "ax.plot(strain_steel, stress_steel, label = 'Steel1018')\n",
    "\n",
    "ax.plot(uts_strain_al, uts_al, \"o\", label = '$\\sigma_P$ Al6061')\n",
    "ax.plot(uts_strain_steel, uts_steel, \"o\", label = '$\\sigma_P$ ocel 1018')\n",
    "\n",
    "\n",
    "ax.set_xlabel('Poměrná deformace (m/m)')\n",
    "ax.set_ylabel('Napětí (MPa)')\n",
    "ax.set_title('Smluvní tahový diagram  slitiny hliníku 6061 a oceli 1018')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('static/tahovy_diagram.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d128eb-36f5-4241-b12c-982c3394ea5f",
   "metadata": {},
   "source": [
    "# Určení Youngova modulu pružnosti\n",
    "\n",
    "Zvolíme si část křivky, pro kterou je průběh lineárrní. Pro slitinu hliníku to bude přibližně oblast mezi 0 a 250 MPa a pro ocel mezi 0 a 350 MPa.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a0f3e837-1d64-4d8c-9903-f6b967954828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x74f053aa0c40>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_stress_steel = stress_steel[stress_steel<350]\n",
    "lin_strain_steel = strain_steel[stress_steel<350]\n",
    "\n",
    "lin_stress_al = stress_al[stress_al<250]\n",
    "lin_strain_al = strain_al[stress_al<250]\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(lin_strain_al, lin_stress_al, label = 'Al6061')\n",
    "ax.plot(lin_strain_steel, lin_stress_steel, label = 'Steel1018')\n",
    "\n",
    "ax.set_xlabel('Poměrná deformace (m/m)')\n",
    "ax.set_ylabel('Napětí (MPa)')\n",
    "ax.set_title('Smluvní tahový diagram  slitiny hliníku 6061 a oceli 1018')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e78726b-ef3f-4b38-a763-1ddce099ec60",
   "metadata": {},
   "source": [
    "V jednoduchém přístupu použijeme funkci aproximace bodů polynomem prvního stupně. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ed730d76-8034-41b2-b67f-3e6ae2c66a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ocel 1018: Napětí = 30.353246542441244 + 267660.33488346846 * deformace\n",
      "Al6061: Napětí = -13.32484752659266 + 68493.0090973414 * deformace\n"
     ]
    }
   ],
   "source": [
    "param_steel = np.polyfit(lin_strain_steel, lin_stress_steel, 1)\n",
    "param_al = np.polyfit(lin_strain_al, lin_stress_al, 1)\n",
    "\n",
    "print(f'Ocel 1018: Napětí = {param_steel[1]} + {param_steel[0]} * deformace')\n",
    "print(f'Al6061: Napětí = {param_al[1]} + {param_al[0]} * deformace')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48e7d53-347e-43c8-a32c-51c5f97dddba",
   "metadata": {},
   "source": [
    "A zpětné získáme fitovaná data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "781c2a66-0340-4500-81e0-2c9eb43d7b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x74f053933e80>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit_stress_steel = np.polyval(param_steel, lin_strain_steel)\n",
    "fit_stress_al = np.polyval(param_al, lin_strain_al)\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "ax.plot(lin_strain_al, lin_stress_al, label = 'Al6061')\n",
    "ax.plot(lin_strain_steel, lin_stress_steel, label = 'Steel1018')\n",
    "ax.plot(lin_strain_al, fit_stress_al,\"--\" ,label = 'fit Al6061')\n",
    "ax.plot(lin_strain_steel, fit_stress_steel,\"--\", label = 'fit Steel1018')\n",
    "\n",
    "ax.set_xlabel('Poměrná deformace (m/m)')\n",
    "ax.set_ylabel('Napětí (MPa)')\n",
    "ax.set_title('Smluvní tahový diagram  slitiny hliníku 6061 a oceli 1018')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ffffed-ac49-49df-9daf-4e1ed6a54dba",
   "metadata": {},
   "source": [
    "Sklon přímky nám udáva hodnotu Youngova modulu pružnosti."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "978819aa-5659-449a-8e0d-b7fded441c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E(ocel 1018) = 267.66033488346847 GPa\n",
      "E(Al6061) = 68.4930090973414 GPa\n"
     ]
    }
   ],
   "source": [
    "print(f'E(ocel 1018) = {param_steel[0]/1000} GPa')\n",
    "print(f'E(Al6061) = {param_al[0]/1000} GPa')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ead23d-03ec-486f-ae16-6383f5ccec5a",
   "metadata": {},
   "source": [
    "Výsledky můžeme srovnat s výsledky v tabulkách, např. [matweb.com](https://www.matweb.com/search/datasheet_print.aspx?matguid=3a9cc570fbb24d119f08db22a53e2421) nebo [asm.matweb.com](https://asm.matweb.com/search/specificmaterial.asp?bassnum=ma6061t6). Na základě našich měření a hodnot v tabulkách se můžeme pokusit odhadnout o jaký typ zpracování oceli 1018 se jedná. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b39ded4-098d-41dd-9675-644a4a34a14d",
   "metadata": {},
   "source": [
    "Alternativou je využití složitějších modelů k fitování křivek, například knihovna [lmfit](https://lmfit.github.io/lmfit-py/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "91520c50-97ea-4a5c-a8a2-37e776aa182c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[Model]]\n",
      "    Model(model_hooke)\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = leastsq\n",
      "    # function evals   = 13\n",
      "    # data points      = 91\n",
      "    # variables        = 1\n",
      "    chi-square         = 139374.949\n",
      "    reduced chi-square = 1548.61055\n",
      "    Akaike info crit   = 669.399783\n",
      "    Bayesian info crit = 671.910643\n",
      "    R-squared          = 0.84966458\n",
      "[[Variables]]\n",
      "    E:  307574.304 +/- 6556.48153 (2.13%) (init = 0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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aQOBYrWbyO6X8eqja6Zx2qoeedqwrpULC1nRVXlGFwdJ05Wm7OG+1gc0Vmy0JxMGB9dM4uGOo35MEh/XNonjCJTxzQw5ZmWm6xW0YaU1EKRUS7pquDJYRUY7Dd11rA461l3SXnQaP7h3AkZ+H2o8D7c/Q9bHCT5OIUiokPH3AGyy1AHcd5rbay5HELaT3+ofTfQfWF9R7lvZnRB9NIkopt/wZmuvIUx9IVmYaK/IHu71nxpINJJ0yzumD6NDWP5BRdxppyXVeh/yq6KB9Ikqpetz1b4z/b0m9fcwdedvT3J0lW5awv8PdTmUH1hdQe7A7+6qqmXZtH+3PiAFaE1FK1RPI0Fwbb3M9XGs1rsnj4Jax1FWdaD/umJmm/RkxQpOIUqoef/f6cOXug99xwmGztkvZ3+4j+7mUhOYc/H4yddpsFbO0OUspVU8ga1n5YqnVHCGjVz4pDgkkfedUim76UputYpzWRJRS9XRt476T/IKe7QJ+1t4W/yKjwzf245rK7lRt+wOV1mNttoptmkSUUk4WFpdTuGmv23OBLF64/+h+8t7KI6nlsbID300BkwzocN14oUlEKeVkxpINHjdz8me5khlLNlDRdgIJyfvs5XW/DuLgzmOz0LXfI35oElFKOfGWKLzVHhYWlzN+8SckdX3CqbP10T4fICIBzTlRsUOTiFJxKtDJgjaeJg0KeK09PLLuUpK6Hjuu2n49NfvO4skd37Mif7AmjTilo7OUikPBTBa0cTdpUICR53RxmwiKdhbV22nwwPoCavadBej+HfFOayJKxSF/Jwt6q634U4txTR6HtoyhtuokpzLtQI9vmkSUikP+TBZcWFzOuPlfU11r6UYvr6hi3PyvAd/DbhdtXMTfVvzNqWxK9oeM31hCFTpxsCnRJKJUHPLUr+FYK5j8Tqk9gdhU1xomv1PqMYEYY8h+Pdup7P1r36dzRmf7sXagNy2aRJSKQ+62iHWtFbju+uer/JYPb6Ho5yKnspY7nmX1xgQ697Uc68TBpkeTiFJxKJB+DV8O1xzm7NlnO5VVfv83TG06B7B02Du+UzUtDU4iItIZeB3Lbsh1wExjzLMi0hqYC3QFtgC/Ncb8ar1nPPAHoBa4yxizpKFxKKWc+aoVZKYlU1HlvtaRV7CMcUN61NumlroUDmyY7FTka3VfFd9CMcS3BvirMaYXcA7wFxE5DcgHlhpjugNLrcdYz40AegOXAi+ISKLbJyulArKwuJy8gmV0y3+PvIJlXof0Thram+QE97ugl++rqJdA1oxaQ6VLArHRYbxNV4OTiDFmhzFmjfX7A8B6IAu4GphlvWwWMMz6/dXAHGPMEWPMj8BGoF9D41CqqQt0bsiwvlnMuP4MslyG4KacsJiMnhPtx9d1v46S0SUkJyaHdHVfFR9COtlQRLoCfYEvgROMMTvAkmiA462XZQHbHG4rs5a5e94YESkSkaLdu/1f+E2ppsjT3JC/zvvaayJZkT8YASRpPxm98mnWuhAAU5dI5foCJg2YZL8+0N0LVfwLWce6iKQDbwP3GGP2i7ivJmOZ/OrK7XpvxpiZwEyA3NxcT2vCKdUk+FrGxFOTUq0xPju/W3WbRW3qevvxwR/voO5wp3q1lFB22Kv4EJIkIiLJWBLIbGPMf63FP4tIB2PMDhHpAOyylpcBnR1u7wRsD0UcSsUrx90B4VhTFRz7YPc0NwQ8d35vqtjEsEXDINVyXFuVxaEtdwKeaxg6jFc5anBzlliqHC8D640xTzmcWgyMtn4/GljkUD5CRFJEpBvQHfiqoXEoFY9sHeX3zF3rcRkTG3dNTY5cayoXz7/YkkCs7uv5GsdVPKA7DKqAhKImkgfcBJSIyFpr2UNAATBPRP4AbAWuBzDGlIrIPOBbLCO7/mKMqa33VKWaONfahzuOicH2gf/XeV9Ta+q3/to6v1ftXMWtS261l19x0hUU/KYAgFv6hyR01YQ0OIkYYz7HfT8HwIUe7pkKTG3ou5WKZ+46yl1lNk92OrYlEnez1e+/5NR6CyZ+PuJzWqW0ClHEqinSGetKRSl/5l64qXC47fwe0m87E0ous18z9oyx/DnnzyGLVTVdmkSUilLeOsptPM04t3V+V9dWc+YbZ/Ifh0H1RaOKSElMCWWoqgnTJKJUlHK3iKKrRIeh9K5DgAec+Q0f7viX/fyjAx7lmu7XhDVm1fRoElEqSjk2S3mqkdQaw8LiciYtLj1WK0k4zP4O+Xy449h1X9/8NQmiG5mq0NO/VUpFMduMctdJfzbHNU9m/H9L7AmkWbslZPSYZD/ffM+fKRldoglEhY3WRJQKM18zzf3haX8QYyzzRSRpH+ndp9nPmZrmVP4wgcqQ/SmUck+TiFIN4CtB+DPT3B+elhu5d+5a0k58iaTmW+zXHtx8F3VHOgK6MKIKP00iSgXJnwThaVHEYPbfcF1uZMPeDaT3yrcf1xzqStVPt9uPdWFE1Rg0iSgVJH8ShKe5Hr6G7vqS91Ye+4/utx9X/jAeU3Ns0uBxzZOZeFVvXbZEhZ32tikVJE8JwrHcU3OSgNcNozwp3F5In1l97Ankuu7XMSX7Qzqmt7evefXMDTkUT7hEE4hqFFoTUSpIniYDZjZPJq9gGdsrqmiVluzmTsveB4E0adWZOs54/QynssIbC8lolgHo/uYqcrQmolSQ3K2am5gg/Hqo2r67oKcZ5eD/lrJPrX7KKYHcfebdlIwusScQpSJJayJKBcl1xFRm82R+PeQ5abjyNXJq35F9DJwz0Klszag1JCe6r90oFQmaRJTywdswXscRU3kFy/xOIr5GTrmutntbn9u4+8y7g/wTKBU+2pyllBe2Yby25inbMF53neLemqcy05LJykzzueFT6S+l9RJIxo6nOVGGN/SPolRYaE1EKS8CmefhqaNdgElDfQ+3dU0eNQdPoWrrbRzgSFATFJVqDFoTUcoLf4bxgqXGcvBITb3rBBh5ThevH/5zvptTL4EcWF9A1dbb7MeuW+EqFS20JqKUF55qF46d4p62sfVnwp9r8riz751Me9P99f6O5lKqMWlNRCkv3A3jFeCCnu3sx562sW3eLMljArns7cvqJZCS0SWMyR7jcdSWroOlopEmEdUkLCwuJ69gGd3y3yOvYJnfs8WH9c3iurOyEIcyA7y9utz+DH+bvACMMfSZ1YeyyjJ72fTzplMyusR+7C5x6TpYKlppc5aKew1dSfeT73bjupW5Y+e6P01eUL/pCnBKHjaeVuzVTnUVjTSJqLgX6Eq6rvNCPC2WaKtpeNrrw1ZzOFR9iP5v9ne6d8HQBZxy3CkeY3ZdsVepaKVJRMW9QJqb3NVaBOrVROBYTcNbzcHf2odSsUqTiIp7nmoTBsssc8emIne1FgP1EolrH4VrzWHbgW31EsiKG1fQslnLhv5xlIoqmkRU3HPX3GTj2D9iO3bHYJlp7k8fhdY+VFOiSUTFPcdahrskUVVdy+R3SjlcXefxGVmZaazIH+z1PZ9u+5Q7lt3hVLbu5nWIiIc7lIp9mkRUk2BrbuqW/57b/g1vCyf6M7zWtfaRUJ3F5LNe1gSi4p4mEdWkeBtt5YmnxRIBlm5dyj2f3ONUdmB9AQDjf9L1rlT80ySimhRPw3FTkhLcbiCVlZnmd9/H4Z1XUf1rnv3Y2zBiG2/LzCsVCzSJqCbF03BcwOtcD0fPrXmOf5X8y6nMVvtw5W29q4ZOglQqGmgSUU2Ot4l83moFxhiyX892ur5m6z1UHWzv8V3e1rsKdBKkUtFIk4hSVt6Sy+B5g9ldtduprOWOZyk/6Lmm4atD3tdMeKVigSYRpbw4XHOYs2ef7VT26Q2f0jq1Nd3y3/N4X5aP/o2FxeU+Z8IrFQs0iSjlga9Jg55Gevkzp2TGkg1uE4iAU+1FO95VtAvJUvAi8oqI7BKRbxzKWovIRyLyg/XrcQ7nxovIRhHZICJDQhGDUqHibsmSNaPW1Jt13pAl2z01WRmOdaoHsr+7UpESqv1EXgMudSnLB5YaY7oDS63HiMhpwAigt/WeF0QkEaWiQJ9Zfbj8v5fbjy/sciElo0tITkyud+2wvllMu7YPWZlpCJYaiLc5JTYLi8tJ8DAJMcuhKctbx7tS0SIkzVnGmM9EpKtL8dXAIOv3s4DlwIPW8jnGmCPAjyKyEegHrAxFLEoF4/Pyzxn78VinMn/Wuwp0yXZb7aLW1G/Mcq3FeKqtBDpZUqlwCufOhicYY3YAWL8eby3PArY5XFdmLatHRMaISJGIFO3evdvdJUo1WJ9ZfZwSyPBTh4dtwURPW+kmitSrxXjqYBfQJi0VNSKxPa67ery7PkaMMTONMbnGmNx27dq5u0SpoL3x7Rtu9zmfeO7EsL3TU+2i1ph6NZpxQ3p4/GXRJi0VLcKZRH4WkQ4A1q+7rOVlQGeH6zoB28MYh1L19JnVhydWPWE/npI3pVGWaw+kdjGsb5b7f12hc0lU9AhnElkMjLZ+PxpY5FA+QkRSRKQb0B34KoxxKGW3aucqt7WPYacMC+l7FhaXk1ewjG7575FXsMyeIAKtXWR5SDo6l0RFi5B0rIvIW1g60duKSBkwESgA5onIH4CtwPUAxphSEZkHfAvUAH8xxtRvJFYqhIwx3LLkFlb/vNpe9vmIz2mV0irk7/K1JtY9c9e6vc9d7cLX/u1KRVqoRmfd6OHUhR6unwpMDcW7lfJlR+UOHv3iUXsC+fMZf2ZszlgfdwXP15pYxzVPdrt/SWZz98OIbc/UCYcqGumMdRW36kwdczfM5ZnVz2Aw5PfLZ0SPESQmhG9a0sLicp9rYrkZ3eu1PNBhxEo1Jk0iKi5t3reZSYWTKN5VzICOA5hw7gSy0sP3QbywuJxJi0vd7kliY+vH2OfhGk/lSkUzTSIqrlTXVfPqN6/y0tcvkZaUxmN5jzH05KFh3abWtQ/EHcd+DE9rbmlnuYpFmkRU3Cj9pZQJhRP4/tfvueTESxjffzxt09qG/b2eJhA6cpxIGEhnuS7AqKKdJhEV86pqqnhx7YvM+nYWbVPb8uwFzzK4i+dVdEP9wexrzobrFrv+dpbrzocqFmgSUTHtqx1fMWnlJLYd2MZ13a/jvtz7aNmspcfrw/HB7Kl5CjzXMPzpLNedD1Us0CSiYtL+o/t5qugp3v7hbTpndOblS16mX4d+Pu8L5QezrUZTXlHldoOp45onM/Gq3kF/4Huq4ehsdRVNNImomHP+3PPZe3gvCZLALb1vYWzOWNKS/OuUDtUHs2uNxoA9kfja1dBf2gGvYkEkFmBUKiiF2wvpM6sPew/vBWD25bO5L/c+vxMIeP4AThCpt0SJN+5qNLYEsiJ/cEiamxqy6ZVSjUVrIirq1Zk6znj9DKeywhsLyWiWEfCz3I2MAuz7e/jbR9IYTU06W13FAk0iKqot3LiQR1Y8Yj+++8y7ua3PbUE/z/YBPPmdUrdLj4B/fSSN1dSks9VVtNMkoqLS0dqjnPXGWU5la0atcbtNbTAOV9d5Pe9Yo3A3JFgXRlTKQvtEVNR5Ye0LTgnkid884XGf82D4MznQVqOwdaCXV1RhcG7uCmZ/daXijdZEVNTYd2QfA+cMdCpbd/O6kC9Z4qvfQoALelp20vQ2JDhUHehKxTJNIioqjP/feN7d/K79+JUhr3B2+7PD8i5vkwPBMsrq7dXl5J7YWudqKOWDJhHVqFz7F267oBVPffd7+/kTmp/Ax9d/HNYYPI3QcmSrbehcDaW80ySiGo3rBL1fM6fz1HfH5mQsvHohJ2ee7PMZDR3y6jp01ts+5k/fkKMd6Ep5oUlENRpb/0JCajktuv3dXp54uCdr//Qfn/eHct0rx6GzeQXLPNY2dK6GUt5pElGNZntFFUkZJaR1mm0vq/z+Yaj1b9JguBYk9DVcV+dqKOWZJhHlt4Y0Je0+tJuME2djmpdQV5PBkV1DqNmXC1gWKvRHuDq5tbahVPA0iSi/BNuUZIxhwcYFPLnqSUxqFUd+voyjewcCiQ7X+BdDODu5tbahVHB0smGUW1hcTl7BsoAWBwwHb01Jnmzbv40//r8/MrFwIj1a9+Dg5ns4uvd8HBMI+L+3uC5IqFT00ZpIFFtYXM64+V9TXXtsccBx878GGn9nu0Cakmrqapi9fjbPFz9PUkISj5zzCMNPHc5v1i5vUE1Cm52Uij6aRKLY5HdK7QnEprrWMPmd0kb/4PS3KWnD3g1MLJxI6Z5SBnUaxMPnPEz7Fu2BwPYW90SbnZSKLppEopinVWY9lYeTrwRwtPYo/1z3T14peYWWKS2Zcd4MhnQd4rRkidYklIo/mkSiVKT6PjzxlgDW7lrLxMKJbN63maEnD2Vc7jgyUzM9PkeThlLxQ5NIlHHct9uTzLTQrGYbKNcEcKj6ENO+nMZb371F+xbtefGiFxmYNdDLE5RS8UaTSIQ5zr1olZbMwaM19fpBXE0a2ruRovPs8/LPeXTlo+w8uJMbe97IXWfeRYvkFpEOSynVyDSJRJDr3IsKP4a6ZqYlR7Q5qOJwBdNXTeedze/QrVU3Xr/sdXKOz4lYPEqpyNIkEoRQLAII/m2O5CgtOTFitRBjDEu2LGHaV9PYf2Q/f8r+E2Oyx9AssVlE4lFKRQdNIn7w1uTUkEUAA1muIyuCI5l2HtzJ1C+msrxsOae3OZ2ZF8+kR2ud4KeU0iTikz9NTsEuAuhrcySw1D4ite1qnalj/vfzeXr109TU1XB/7v2M6jWKxIRE3zcrpZqEuE4i/jQ7uV5zQc92fPLdbvvxoaM1fjU5BbMIoLu5F8kJQnpqEhWHqiM6j2LLvi1MWjmJ1T+vpn/7/kw8dyKdW3Zu9DiUUtEtbpOIPwsGurvmjS+22p/hq5bgKJhFAKNx8l1NXQ2zSmfxwtoXSElMYfKAyVxzyjUh3+dcKRUf4jaJ+LP3RKAd2540ZBHAaJp8t37PeiYWTmT93vVc1OUiHur/EO2at4t0WEqpKBaxJCIilwLPYlnS9d/GmIJQPt+fBQOD3YciWpqcQuVwzWFe+volXit9jcyUTJ4a9BQXn3hxpMNSSsWAiCQREUkE/gFcDJQBq0RksTHm21C9w58FA/3p2AbL3IwWKUlR0+QUSkU7i5i8cjJb9m/hmlOu4a+5f6VVSqtIh6WUihGRqon0AzYaYzYDiMgc4GogZEnE1mk9kyn8JvEbvqjrRRs5QPfDZTAJ6Hgmi1ok0vbwKgC+qOtFB/ZwYsIu+zFAggh1/f7COZeNgl9+gHfugbVY/rM57344+QLYsQ4+HF8/mAsnQJf+sPVLWPpo/fOXToMO2bDpE/jsyfrnr3oG2naHDR9A4fP1z1/7T2jVCb55G1a9Uv/8b1+HFm2geDasfROASup4WiqYJ5VktejIzItncm7ZN/Dm7+rff8t7lq8rnoPvlzifS06FUW9bvv90Omz+1Pl88+Pghjcs3388Cbatgp8+d75m0j7rVw/Jy9P5EwdC72HQ749w9BDMvr7+vTm/g74j4eAemHGS++fHsxMHwp4foPJn9+eP6wqHfoUj+xo1rKh14xzL7/lHjwT/jJ5XQlVF/b/nJw6ENidbflc3f+r+fPs+lt8px9+TkwbBzYuCjyfMIpVEsoBtDsdlQH/Xi0RkDDAGoEuXLgG9wFZTSH0nEeqgWWICJ6SnwoFj17RNT4E9x46TEgUcVhxplphAl9bNaXtSm4DeHe0+pYopspfd1HKTyeCOy9+kefM2UPZNpENTSsUYMf7uTRrKl4pcDwwxxtxmPb4J6GeMudPTPbm5uaaoqKixQoxLew/vpeCrAj748QNOyTyFyQMmk90uO9JhKaXCSERWG2Nyw/X8SNVEygDHSQedgO0RiiXuGWN4d/O7TF81ncrqSv6c82duO/02khMjsxqwUip+RCqJrAK6i0g3oBwYAbhpjFcNtaNyB49+8Sifl39OdrtsJp87mVOOOyXSYSml4kREkogxpkZE7gCWYBni+4oxpjQSscSrOlPH3A1zeWb1MxgM+f3yGdFjhC5ZopQKqYjNEzHGvA+8H6n3x7PN+zYzqXASxbuKGdBxABPOnUBWenwMSVZKRZe4nbHeFFXXVfPqN6/y0tcvkZaUxmN5jzH05KG6ZIlSKmw0icSJ0l9KmVA4ge9//Z5LTryE8f3H0zatbaTDUkrFOU0iMa6qpooX1r7A69++TtvUtjx7wbMM7jI40mEppZoITSIx7KsdXzFp5SS2HdjGdd2v477c+2jZrGWkw1JKNSGaRGLQ/qP7earoKd7+4W06Z3Tm5Utepl+HfpEOSynVBGkSiTFLty5l6hdT2XN4D7f0voWxOWNJSwp8LxOllAoFTSIx4peqX3j8y8f56KePOPW4U/n74L/Tu23vSIellGriNIlEOWMM2a8fW9/qzr53csvpt5CcoEuWKKUiT5NIFCuvLOf+5ffbjxcMXaBLliilooomkShUW1fLnA1zeHbNswjC6NNGc/eZd+uCiUqpqKNJJMpsqtjEhMIJrNu9jt9k/YZHznmEDukdIh2WUkq5pUkkSlTXVvPvkn8zs2Qm6cnpTPvNNK7odoUuWaKUimqaRKJAye4SJhROYGPFRi7vdjkP9nuQ1qmtIx2WUkr5pEkkgg5VH+L5tc/zxrdv0K55O54f/Dzndz4/0mEppZTfNIlEyMrtK5m8cjLlleX89tTfcu9Z95LeLD3SYSmlVEA0iTSyfUf28WTRkyzcuJCuLbvy6pBXyW0ftu2PlVIqrDSJNKKPfvqIqV9MpeJIBbf1uY3bz7idlMSUSIellFJB0yTSCHYf2s3UL6eydOtSerXuxYsXvUivNr0iHZZSSjWYJpEwMsawYOMCnlz1JEfrjnLPmfcwuvdokhL0x66Uig/6aRYm2/ZvY/LKyXy580tyT8hl0oBJnNjyxEiHpZRSIaVJJMRq6mqYvX42zxc/T1JCEo+c8wjDTx1OgiREOjSllAo5TSIhtGHvBiYWTqR0TymDOg3i4XMepn2L9pEOSymlwkaTSAgcrT3KP9f9k1dKXqFlSktmnDeDIV2H6JIlSqm4p0mkgdbuWsvEwols3reZq066igfOfoDM1MxIh6WUUo1Ck0iQDlYf5Nk1zzLnuzm0b9GeFy96kYFZAyMdllJKNSpNIkH4vPxzHl35KDsP7uTGnjdy15l30SK5RaTDUkqpRqdJJAAVhyuYvmo672x+h5NancTrl71OzvE5kQ5LKaUiRpOIH4wxLNmyhGlfTWP/kf38KftPjMkeQ7PEZpEOTSmlIkqTiA87D+5k6hdTWV62nNPbnM7Mi2fSo3WPSIellFJRQZOIB3Wmjvnfz+fp1U9TU1fD/bn3M7LXSF2yRCmlHOgnohuflX3GlC+msPPgTvq378/EcyfSuWXnSIellFJRR5OIgyO1R7jwPxey78g+ACYPmMw1p1yjkwaVUsoDTSJWC35YwITCCfbjuVfO5bQ2p0UwIqWUin5NPonsP7qfvLfy7MeXdbuM6edNj2BESikVOxq0tKyIXC8ipSJSJyK5LufGi8hGEdkgIkMcys8SkRLrueckgm1FM9fNdEog71/7viYQpZQKQENrIt8A1wL/dCwUkdOAEUBvoCPwsYicaoypBV4ExgBfAO8DlwIfNDCOgOw8uJOL519sP7719Fu596x7GzMEpZSKCw1KIsaY9YC7juergTnGmCPAjyKyEegnIluAlsaYldb7XgeG0YhJZMrKKcz7fp79ePlvl9MmrU1jvV4ppeJKuPpEsrDUNGzKrGXV1u9dy90SkTFYai106dKlQQFtqtjEsEXD7Mf5/fIZ2Wtkg56plFJNnc8kIiIfA+52VnrYGLPI021uyoyXcreMMTOBmQC5ubker/PGGMPYpWNZUb4CgERJpPDGQponNw/mcUoppRz4TCLGmIuCeG4Z4Dg7rxOw3VreyU152Fz3znX88OsPADx5/pMM6TrExx1KKaX8Fa6NvxcDI0QkRUS6Ad2Br4wxO4ADInKOdVTWzYCn2kxI3Hr6reR1zGPNTWs0gSilVIg1qE9ERK4B/g60A94TkbXGmCHGmFIRmQd8C9QAf7GOzAIYC7wGpGHpUA9rp/qVJ13JlSddGc5XKKVUkyXGBNXV0Ohyc3NNUVFRpMNQSqmYIiKrjTG5vq8MTrias5RSSjUBmkSUUkoFTZOIUkqpoGkSUUopFTRNIkoppYKmSUQppVTQNIkopZQKWszMExGR3cBPDkVtgV8iFI6/NMbQiYU4NcbQiYU4YyXGFsaYduF6QcwkEVciUhTOCTShoDGGTizEqTGGTizEqTFaaHOWUkqpoGkSUUopFbRYTiIzIx2AHzTG0ImFODXG0ImFODVGYrhPRCmlVOTFck1EKaVUhGkSUUopFbSIJxERaS0iH4nID9avx3m47lIR2SAiG0Uk35/7RWS89foNIlJvW0MRWSwi30RbjCLSXETeE5HvRKRURAq8xOb2nQ7nRUSes55fJyJnhjpefzRmnCJysYisFpES69fB0Rajw/kuIlIpIvdHY4wiki0iK61/D0tEJDXa4hSRZBGZZY1vvYiMj2CM11t/VnUikuvyvIB/dxozxmB/bzDGRPQ/YDqQb/0+H3jCzTWJwCbgJKAZ8DVwmrf7gdOs16UA3az3Jzo881rgTeCbaIsRaA5cYL2mGfA/4LJA3ulwzeVYdo8U4Bzgy3D9TL38/Bo7zr5AR+v3pwPl0RajwzPfBv4D3B9tMWLZ+XQdcIb1uE2U/v/+HTDH+n1zYAvQNUIx9gJ6AMuBXIdnBfy7E4EYA/69McZEviYCXA3Msn4/Cxjm5pp+wEZjzGZjzFFgjvU+b/dfjeUv1hFjzI/ARutzEJF04D7gsWiM0RhzyBjzCYD1WWuATgG+0zH2143FF0CmiHQIZbxu4oponMaYYmPMdmt5KZAqIinRFCOAiAwDNltj9Edjx3gJsM4Y8zWAMWaPObbNdTTFaYAWIpKEZdvto8D+SMRojFlvjNng5n3B/O40aoxB/t5ERRI5wRizA8D69Xg312QB2xyOy6xl3u73ds8U4P8Ah6I4RgBEJBO4Clga4Dt9XROWeD1o7DgdXQcUG2OORFOMItICeBCY7COuiMUInAoYEVkiImtE5IEojXM+cBDYAWwFnjTG7I1QjA15X6RjdOTv7w1JATw0aCLyMdDezamH/X2EmzJfY5Pd3iMiOcApxph7RaRrNMboEFMS8BbwnDFmc5Dv9HRNyONt4H2hjNPyQJHewBNY/kXt83I/3hPKGCcDTxtjKkXc3e5WY8eYBAwEzsbyD66lYtmv290/aCIZZz+gFugIHAf8T0Q+9vA7E6kYw3VPpH9vGieJGGMu8nRORH4WkQ7GmB3WatguN5eVAZ0djjsBtmqXp/s93XMucJaIbMHy5z9eRJYbYwZFUYw2M4EfjDHPeAjN1/3ermkWhng9aew4EZFOwALgZmPMpiiMsT8wXESmA5lAnYgcNsY8H0UxlgGfGmN+ARCR94EzcV8rjmScvwM+NMZUA7tEZAWQi6WpsLFjbMj7Ih1jML83UdGxPgPnzrLpbq5JwvIXohvHOol6e7sf6I1zR9ZmXDqygK7417He6DFi6a95G0jwEpfHdzpccwXOHW9fhfNnGiVxZlqvuy6Av4eNGqPLcyfhX8d6Y/8cj8PSH9fcev/HwBVRGOeDwKvWZ7UAvgWyIxGjw73Lce60Dvh3JwIxZhLg740xJiqSSBss/7L5wfq1tbW8I/C+w3WXA99jGXHwsK/7recetl6/Afejm7riXxJp1Bix/KvBAOuBtdb/bvMQW713ArcDt1u/F+Af1vMlLn9pQv4z9fIzbLQ4gb9haSNf6/Df8dEUo8t7J+FHEonQ/+9RWDpZv8FNAoyGOIF0LCPcSrEkkHERjPEaLLWDI8DPwJKG/O40ZowE+Xujy54opZQKWjSMzlJKKRWjNIkopZQKmiYRpZRSQdMkopRSKmiaRJRSSgVNk4hSSqmgaRJRSikVtP8P33e1NQgbYw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E(ocel 1018) = 307.57430351056655 GPa\n"
     ]
    }
   ],
   "source": [
    "def model_hooke(epsilon, E):\n",
    "    return epsilon * E\n",
    "\n",
    "emodel = Model(model_hooke)\n",
    "\n",
    "result = emodel.fit(lin_stress_steel, epsilon=lin_strain_steel, E=0*1e3)\n",
    "\n",
    "print(result.fit_report())\n",
    "\n",
    "plt.plot(lin_strain_steel, lin_stress_steel, 'o')\n",
    "plt.plot(lin_strain_steel, result.init_fit, '--', label='initial fit')\n",
    "plt.plot(lin_strain_steel, result.best_fit, '-', label='best fit')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(f'E(ocel 1018) = {result.params[\"E\"]/1000} GPa')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}