{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "remove_cell" ] }, "source": [ "
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Ingenieurinformatik – Übung

\n", " Lehrstuhl Computational Civil Engineering
\n", " Kontakt: Email senden | Individuelle Kontakte siehe Webseite des Lehrstuhls
\n", " Links: \n", " Vorlesungsskript | \n", " Webseite des Lehrstuhls\n", "
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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotten von Funktionen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualisierung von Daten ist wichtig in vielen Berufen und besonders in allen wissenschaftlichen. Visualisierung wird benötigt, um Informationen aus Datensätzen zu komprimieren, um sie verstehen und präsentieren zu können. In dieser Aufgabe beschäftigen Sie sich mit den grundlegenden Funktionen des Pythonmoduls matplotlib." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aufgabenstellung" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotten Sie die Quadratfunktion $\\sf f(x) = x^2$ und ihre analytische Ableitung für Werte im Bereich $\\sf x \\in [-9,9]$. Dabei sollte $\\sf f(x)$ mit Punkten und durchgezogener Linie in blauer Farbe und die Ableitung als gestrichelte Linie in roter Farbe dargestellt sein. Die x-Achse des gezeichneten Koordinatensystems soll Werte von -10 bis 10 und die y-Achse von -20 bis 100 aufgetragen haben. Weiterhin sind beide Funktionen mittels Legende zu beschriften und Achsenbeschriftung, ein Graphiktitel und ein Gitternetz einzufügen. Der finale Plot soll als Bilddatei abgespeichert werden." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lösungshinweis\n", "\n", "Ihre Darstellung der Funktionen könnte wie folgt aussehen:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "tags": [ "remove_input" ] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "xmin = -9\n", "xmax = 9\n", "dx = 1.0\n", "\n", "x = []\n", "y = []\n", "y_abl = []\n", "cx = xmin\n", "\n", "while cx <= xmax:\n", " x.append(cx)\n", " y.append(cx**2)\n", " y_abl.append(2*cx)\n", " cx += dx\n", "\n", "plt.figure(figsize=(9,6))\n", " \n", "plt.plot(x,y, 'bo-', label='x2')\n", "plt.plot(x,y_abl, 'r--', label='Ableitung')\n", "\n", "plt.xlabel('Argument')\n", "plt.ylabel('Funktionswert')\n", "plt.title('Quadratfunktion und ihre Ableitung')\n", "\n", "plt.legend()\n", "plt.xlim(-10,10)\n", "plt.ylim(-20,100)\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "loesung", "hide-cell" ] }, "source": [ "### Lösungsvorschlag" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "tags": [ "loesung", "hide-cell" ] }, "outputs": [ { "data": { "image/png": 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w4oknMmvWrGKdV1lSkiMiIpKEnn76aU455RQATjnlFJ5++mkADjzwQJo1a0Z6ejqnnnoqEyZM2Ohx7777Lp9//jkHHHAA7dq149133+Xnn38uVSw9e/YEYL/99mPGjBkATJgw4c/4unXrRu3atUt1jO2htatERERKa/z4Ld+Wmbn12+vV2/rtRVi0aBHvvfce33zzDWbGhg0bMDO6d++OmW10302vhxDo27cvt912W7GPV6lSpY26vVavXr3R7VWrVgV8Ha3169f/eZyoqSVHREQkyTz33HP06dOHmTNnMmPGDGbPnk3Tpk2ZMGECn3zyCb/88gt5eXk8++yzdOrUaaPHdu3aleeee47ff/8dgMWLFzNz5syN7lO9enWWL1/+5/WsrCy+/fZb1qxZwx9//MG77767zRg7derEmDFjAHjrrbdYsmRJaU+7xJTkiIiIJJmnn36aE088caNtvXr14qmnnuKggw5iwIAB7LXXXjRt2nSz+7Vu3ZpbbrmFI488krZt23LEEUfw22+/bXSfunXr8pe//IW99tqLK6+8kt12242TTjqJtm3bkp2dzb777rvNGG+44Qbeeust2rdvz+uvv07Dhg2pXr166U++BNRdJSIikmTGF9G9demll3LppZdu8TH5tTIAJ598MieffPJW7/PUU09tdNvQoUMZOnToVh+z//77/xlbzZo1efPNN6lUqRIff/wx48aN+7Nbq7woyREREZEyN2vWLE466STy8vKoUqUKjzzySLnHoCRHREREylyLFi348ssvI41BNTkiIiKSkpTkiIiIbIdEGCKdCuL5PCrJERERKaGMjAwWLVqkRKeUQggsWrSIjIyMuOxfNTkiIiIl1KhRI+bMmcOCBQviepzVq1fHLQFIFBkZGTRq1Cgu+1aSIyIiUkKVK1emadOmcT/O+PHjizUnjRRN3VUiIiKSkpTkiIiISEpSkiMiIiIpSUmOiIiIpCQlOSIiIpKSlOSIiIhISlKSIyIiIilJSY6IiIikJCU525CTA02aQFqa/8zJiToiERGR8peMn4ea8XgrcnKgXz/IzfXrM2f6dYDs7OjiEhERKU/J+nmolpytGDSo4A+aLzfXt4uIiFQUyfp5GGmSY2aXmdlUM/vGzJ42swwzq2Nmb5vZD7GftaOKb9askm0XERFJRcn6eRhZkmNmuwKXAvuHEPYC0oFTgAHAuyGEFsC7seuRaNy4ZNtFRERSUbJ+HkbdXVUJ2MHMKgGZwFzgeGBU7PZRwAkRxcbgwZCZufG2ypV9u4iISEUxeDCkp2+8LTMz8T8PLYQQ3cHN/g8YDKwC3gohZJvZ0hBCrUL3WRJC2KzLysz6Af0A6tevv9+YMWPiEuM77zTg0Ueb8fvvValSJY+8PHj22YnUrr0uLscrqRUrVlCtWrWow4iEzr3inXtFPW/QuevcozV9ejUuuGB/MjPXs2pVOg0arOHcc3/m8MN/j9sxu3Tp8nkIYf9S7SSEEMkFqA28B9QHKgMvAKcDSze535Jt7atly5ahPEyfHkJ6eggXXVQuhyuWcePGRR1CZHTuFU9FPe8QdO4VVSKce15eCF27hlC3bghLl5bfcYHPQilzjSi7qw4HfgkhLAghrAOeBw4G5ptZQ4DYz/iliSXUsqUPmXv4Yfjhh6ijERERib+33oJ334XrroOaNaOOpmSiTHJmAR3NLNPMDOgKTANeAvrG7tMXeDGi+Ip0ww1QtWriD5sTEREprbw8uPpqaNoULrgg6mhKLrIkJ4QwCXgO+AKYEotlBDAEOMLMfgCOiF1PGDvtBFdcAf/5D0yaFHU0IiIi8ZOTA1995QXGVatGHU3JRTq6KoRwQwhhjxDCXiGEM0IIa0IIi0IIXUMILWI/F0cZY1EuvxwaNICrroII67ZFRETiZvVquPZa2G8/OPnkqKPZPlEPIU9K1at7t9UHH8Crr0YdjYiISNm77z6f7G/oUF+vKhkladjRO+88aNECBgyADRuijkZERKTsLFkCt94K3brBYYdFHc32U5KznSpXhttug6lTYdSobd9fREQkWdx2GyxdCrffHnUkpaMkpxR69oSOHeH66zdfuExERCQZzZoF99wDZ5wBbdtGHU3pKMkpBTPvq/z1V39BiIiIJLvrr/efN98cbRxlQUlOKR1yCBx7rDftLVwYdTQiIiLb7+uvYfRouOSSxF98sziU5JSBIUNgxYrEX6hMRERkawYM8FmNBw6MOpKyoSSnDLRuDWedBfffD7/8EnU0IiIiJffee/D663DNNVCnTtTRlA0lOWXkppugUiWfOElERCSZ5OX5BLeNG3tXVapQklNGdt0V+veHp56CL76IOhoREZHiGzMGPv/ci40zMqKOpuwoySlDV18Ndev6TxERkWSwdq0vOt22LWRnRx1N2VKSU4Zq1vSl6N95x5emFxERSXQPPQQ//+wT/6WnRx1N2VKSU8YuuMCXpL/qKu/jFBERSVTLlnkX1WGHwVFHRR1N2VOSU8aqVvWh5F995UvUi4iIJKqhQ32Ot6FDfYLbVKMkJw5OPtmXpr/2Wl+qXkREJNHMnQt33QWnnOKfWalISU4cpKV53+asWT53joiISKK58UZYvz61J7JVkhMnXbt6/+bgwb5kvYiISKKYNg0eewwuvBCaNYs6mvhRkhNHt9/uS9UPGRJ1JCIiIgUGDIBq1VJ/AlslOXG0zz5w+ukwfDjMnh11NCIiIjBhArz0ks/pVq9e1NHEl5KcOLv5ZgihYOl6ERGRqIQAV14Ju+zis/SnOiU5cZaVBZdeCqNGwZQpUUcjIiIV2X//CxMn+nqLmZlRRxN/SnLKwcCBPhuylnsQEZGorFvnn0d77glnnhl1NOVDSU45qFPHl65//XUYNy7qaEREpCJ67DH4/nsfFFOpUtTRlA8lOeXkkktgt9203IOIiJS/FSt8XpxDDoEePaKOpvwoySknGRlehPzZZ/Cf/0QdjYiIVCR33QXz56fu8g1boiSnHJ1+Ouy9t3ddrV0bdTQiIlIRzJ8Pw4ZBr17QsWPU0ZQvJTnlKD3d+0J//hkefjjqaEREpCL45z9h1Sq49daoIyl/SnLKWbdu0KWLv+iWLYs6GhERSWU//AAjRkC/ftCyZdTRlD8lOeXMbOOl7UVEROLlmmugalW44YaoI4mGkpwI7L+/L21/112+1L2IiEhZmzQJnnsOrrgCdtop6miioSQnIoMH+xL3N94YdSQiIpJqQvApS3baCS6/POpooqMkJyLNmsHf/+6TM02bFnU0IiKSSl59FT74wLupqlePOproKMmJ0LXXwo47+jTbIiIiZWHDBhgwwAuNzz036miipSQnQvXr+3pWL74IH34YdTQiIpIKRo2CqVN9yHjlylFHEy0lORHr3x8aNvTF0rKyIC0NmjSBnJyoIxMRkWSRk+OfHWlpPly8eXPo2TPqqKKnJCdiO+4I3bvDjz/CrFleLDZzpr9IleiIiMi25OT4Z8bMmf4ZsmEDzJkDTz0VdWTRU5KTAN5+e/NtubkwaFD5xyIiIsll0CD/zChszRp9hoCSnIQwe3bR22fNKt84REQk+Wzps0KfIUpyEkLjxiXbLiIikk+fIVumJCcBDB4MmZkbb8vM9O0iIiJbc/PNvmRQYfoMcUpyEkB2ti+glpVVsO2ii3y7iIjI1ixe7AXH9ep5spOV5Z8p+gxRkpMwsrNhxgxYvRpatYKxY2HVqqijEhGRRPbrr3DddXD00fD775CX558lSnCckpwEU7UqPPAA/PyzT+QkIiKyJZddBuvWwX33bd5lJUpyEtJhh8Hpp8Ptt8N330UdjYiIJKI33oD//MeXCGrWLOpoEpOSnAR1xx0+UeCFF3pfq4iISL5Vq7x2c4894Ioroo4mcUWa5JhZLTN7zsy+M7NpZnaQmdUxs7fN7IfYz9pRxhiVnXaCIUNg3DjNfCwiIhu79VYva3jgAS9zkKJF3ZIzHHgjhLAHsA8wDRgAvBtCaAG8G7teIZ13HnToAP/4ByxZEnU0IiKSCL77zssZzjgDunSJOprEFlmSY2Y1gL8CjwGEENaGEJYCxwOjYncbBZwQTYTRS0uDhx6CRYvgmmuijkZERKIWgpcx7LijlzXI1lmIqODDzNoBI4Bv8Vacz4H/A34NIdQqdL8lIYTNuqzMrB/QD6B+/fr7jRkzplzijsL99zdn7NhG3Hffl7RuvWyj21asWEG1atUiiixaOveKd+4V9bxB565zd2+/vRO33ronl102neOO+y3CyOKvS5cun4cQ9i/NPqJMcvYHJgJ/CSFMMrPhwDLgkuIkOYW1atUqTJ8+Pb4BR2j5cthzT5/o6bPPoFKlgtvGjx9P586dI4stSjr3zlGHUe4q6nmDzl3n7mULrVpB8+bw4Yfe2p/KzKzUSU6UT9EcYE4IYVLs+nNAe2C+mTUEiP38PaL4Ekb16jB8OHz1lc+FICIiFc811/jsxg89lPoJTlmJ7GkKIcwDZptZq9imrnjX1UtA39i2vsCLEYSXcHr2hO7dfWbLOXOijkZERMrTxInw8MNw6aWwzz5RR5M8os4FLwFyzOxroB1wKzAEOMLMfgCOiF2v8Mzg3nth/Xro3z/qaEREpLysXw8XXAC77AI33RR1NMml0rbvEj8hhMlAUf1tXcs7lmTQrJm35AwaBK+95i07IiKS2u67z8sVxo718gUpvqhbcqSErrjCi5Avvhhyc6OORkRE4mnBgqpcd51/qT3xxKijST5KcpJMlSo+w+Uvv8DgwVFHIyIi8XTffbuzfr0W4NxeSnKSUOfO0KcPDBsGM2dmRh2OiIjEwWuvwQcf1Of666Fp06ijSU5KcpLUsGFQrRrcfXdLLeApIpJicnO9LCErayWXXx51NMlLSU6SatDAF/D86qtaPPFE1NGIiEhZGjzYyxL69/+eKlWijiZ5KclJYueeC61b/8EVV/gEUSIikvymTfPW+r59oV27P6IOJ6kpyUliaWlw2WXfs3gxDBwYdTQiIlJaIcDf/+7lCMOGRR1N8lOSk+R2330l/fvDiBHw8cdRRyMiIqXxxBPw/vtw++1Qv37U0SQ/JTkp4MYboVEjnxFz/fqooxERke2xeLHPhXbQQXDOOVFHkxqU5KSAatXgnnvg66/9p4iIJJ+BA7UAZ1nT05giTjgBevSA66+H2bOjjkZEREri44+97KB/f2jbNupoUoeSnBSRv4BnXp4W8BQRSSb5C3A2auTlB1J2lOSkkCZNvCXn+efhlVeijkZERIqjcLlBtWpRR5NalOSkmH/8A1q3hksu0QKeIiKJbvZs/3Lao4eXHUjZUpKTYqpUgQcfhBkz4JZboo5GRES2pn9/LzO4914twBkPxUpyzGyzpcGK2iaJ4a9/hTPP9Imkpk6NOhoRESnKK694ecH113u5gZS94rbkjC1i23NlGYiUraFDoUYNuPBCtICniEiCyc31soLWrb3MQOKj0tZuNLM9gDZATTPrWeimGkBGPAOT0qlf32fMPO88GD3a10AREZHEcMstXlbw/vtoAc442lZLTiugB1ALOLbQpT1wXnxDk9I6+2w4+GCfQXPRoqijERER8DKCYcO8rOCvf406mtS21ZacEMKLZvYKcHUI4dZyiknKSFqaz5y5774wYAA88kjUEYmIVGwheBlBjRpeViDxtc2anBDCBuCIcohF4mDvveGyy+DRR+Gjj6KORkSkYhs9Gj74wBMcLcAZf8UtPP7IzO4zs0PMrH3+Ja6RSZm54QbYbTc4+WTIyvIWniZNICcn6shERFJfTo6/56aleRlBixZw1llRR1UxbLW7qpCDYz//WWhbAA4r23AkHqpVg7/9De6+u2DbzJnQr5//np0dTVwiIqkuJ8ffa/MnZw3BJwB8+mm995aHYrXkhBC6FHFRgpNEnn9+8225uTBoUPnHIiJSUQwatPns86tX6723vBR3MsCdzOwxM3s9dr21mZ0T39CkLM2aVbLtIiJSenrvjVZxa3JGAm8Cu8Sufw9oresk0rhxybaLiEjp6b03WsVNcuqFEMYAeQAhhPXAhrhFJWVu8GDIzNx4W0aGbxcRkfg4/vjNt2Vm6r23vBQ3yVlpZnXxYmPMrCPwR9yikjKXnQ0jRiRxlnUAACAASURBVPjoKjNIT4dateDEE6OOTEQkNc2ZA088AU2besuNmb8HjxihouPyUtwk53LgJaC5mX0IjAYuiVtUEhfZ2T6NeF4evPUWzJ8Pl18edVQiIqknL8+X01mzBt5800e05uX5e7ASnPJTrCHkIYTPzexQfJkHA6aHENbFNTKJq8MO8+Uehg2Do4+G446LOiIRkdRx113w3ns+EWuLFlFHU3EVd3TV/4CbgN2AmUpwUsPNN/uSD+ecA/PmRR2NiEhqmDwZrrkGevb0yf8kOsXtruoLTAd64bMff2Zmd2/jMZLgqlb1iapWrvSF4vLyoo5IRCS55ebCaaf5kg0jRngdjkSnuJMB/gy8DbwLfABkAnvGMS4pJ3vu6c2qb74J990XdTQiIsntyith2jRfo6pu3aijkeJ2V/0EvADsBDwG7BVC6BbPwKT8nH8+HHssXHUVTJkSdTQiIsnplVfggQd8QEfXrlFHI1D87qp7gFnAqcClQF8zax63qKRcmXlxXK1a3sy6enXUEYmIJJd587z+Zp99NAdOIilud9XwEEJv4HDgc+BGfNZjSRENGsDIkfDNNzBgQNTRiIgkjxB8VfHly+Gpp7zeURJDcbur7jSzScAkYB/gekCD4lJMt25w6aUwfDi88UbU0YiIJIf77vP3zDvvhNato45GCivWPDnARGBoCGF+PIOR6N1+u8/tcOaZXp9Tv37UEYmIJK5vvvFi42OOgb//PepoZFPFrcmZC6wAMLPTzewuM8uKX1gSlYwMb25dutTnzwkh6ohERBLT6tVex1izJvz73xounoiKm+Q8COSa2T7AVcBMfGkHSUF77+0tOi+/DA8/HHU0IiKJaeBAb/EeOdLrGiXxFDfJWR9CCMDxwPAQwnCgevzCkqhdcgkcdRT84x8+54OIiBR4803417/8vfLoo6OORrakuEnOcjMbCJwOvGpm6UDl+IUlUUtLg8cfhx139ObYNWuijkhEJDEsWOB1i23aeKu3JK7iJjknA2uAc0II84BdgWFxi0oSQsOG8Nhjvg7LdddFHY2ISPRCgHPPhcWLvX5xhx2ijki2prjz5MwLIdwVQvhf7PqsEIJqciqA446DCy6AO+7wUVciIhXZiBHw0kvegtO2bdTRyLYUd56cnmb2g5n9YWbLzGy5mS2Ld3CSGO68E1q2hD59/NuLiEhF9N13cNllcOSRPqeYJL7idlcNBY4LIdQMIdQIIVQPIdQoiwDMLN3MvjSzV2LX65jZ27Gk6m0zq10Wx5Htl5npzbK//w79+mlYuYhUPGvXQna2vx+OHOl1i5L4ivtnmh9CiNcYm/8DCu97APBuCKEFvuq5FhlIAO3bwy23wNix/g8uIlKRXH89fPGFr/PXsGHU0UhxFTfJ+czMnjWzU2NdVz3NrGdpD25mjYBjgEcLbT4eGBX7fRRwQmmPI2XjiiugSxcfMvnjj1FHIyJSPsaNg6FDvSX7BH0iJZXiJjk1gFzgSODY2KVHGRz/X/jkgnmFtu0UQvgNIPZTUywliLQ0GDUKqlTxZtt166KOSEQkvhYvhjPOgBYt4K67oo5GSspCRAUWZtYD6B5CuNDMOgNXhBB6mNnSEEKtQvdbEkLYrC7HzPoB/QDq16+/35gxY8or9ISyYsUKqlWrVq7HHD++Pjfd1IYzzpjB2WfPKNdjFxbFuSeKinruFfW8QecexbmHADfd1JoJE+px//1f0KrVinKPoSL/3bt06fJ5CGH/Uu0khLDNC9AI+C/wOzAfGAs0Ks5jt7LP24A5wAxgHt5S9CQwHWgYu09DYPq29tWyZctQUY0bNy6S4551VghpaSF88EEkhw8hRHfuiaCinntFPe8QdO5RePzxECCEIUMiOXwIoWL/3YHPQinyjBBCsburHgdeAnbBJwJ8ObatNMnVwBBCoxBCE+AU4L0Qwumx4/SN3a0v8GJpjiPxMXw4NG0Kp5/ui3mKiKSSH3/0+sPOnb0eUZJTcZOc+iGEx0MI62OXkUD9OMU0BDjCzH4AjohdlwRTvTrk5MCvv8JFF0UdjYhI2Vm3zr/AVaoEo0dDenrUEcn2Km6Ss9DMTo/NaZNuZqcDi8oqiBDC+BBCj9jvi0IIXUMILWI/Nf1cgurQAW680efQycmJOhoRkbJx880waZLPbrzbblFHI6VR3CTnbOAkvHbmN+BvsW1SwQ0cCJ06wYUXwi+/RB2NiEjpTJgAgwf7Apy9e0cdjZRWcdeumhVCOC6EUD+E0CCEcEIIYWa8g5PEl54OTzzhv59xBqxfH208IiLb648/vJuqSRO4556oo5GyUGlrN5rZVSGEoWZ2L7DZWPMQglbvEJo0gQcf9LlzbrtNK5aLSHK66CKYM8dbc6pXjzoaKQvbasnJX27hM+DzIi4iAJx2ml9uuMGnPE9L8+RHtToikshycvy9ysx/P/546Ngx6qikrGw1yQkhvBz7NTeEMKrwBZ/XRuRPnTv7z3nzfBKtmTN9GnQlOiKSiHJy/D1qZqHiizfe0HtWKilu4fHAYm6TCmzw4M1XKM/NhUGDoolHRGRrBg3y96jC9J6VWrZVk3M00B3Y1cwKl2HVAFRiKhuZNatk20VEoqT3rNS3rZacuXg9zmo2rsV5CTgqvqFJsmncuGTbRUSiVKNG0dv1npU6ttqSE0L4CvjKzL4NIXxa+DYzOxZf3kEE8O6qfv02b/4988xIwhER2aJnnvEh4+npsGFDwfbMTH8vk9RQ3Jqch81s7/wrZnYqcG18QpJklZ3tM4RmZflIhUaNoEEDH14+U7MqiUiCmDTJv3wdcgg89ljBe1ZWlr+HZWdHHaGUla225BTyN+A5M8sGOgF9gCPjFpUkrezsjd8gvvvOh2Meeyx8+KHmnhCRaM2a5cPEd90Vnn8e6tWDvn23/ThJTsWd8fhnfKXwsXjCc2QI4Y94BiapYY894D//gW+/9Xl0CjcLi4iUpxUr4LjjYNUqePllT3AktW01yTGzKWb2tZl9DTwH1AGaAJNi20S26YgjfIr0V16Bq6+OOhoRqYg2bPBW5ilTYMwYaN066oikPGyru6pHuUQhKe/CC2HaNLjzTthzTzjnnKgjEpGKZOBAeOkluPdeOEpjgyuMbY2u+rNc1MzSgZ229RiRLbn7bvjhB7jgAmjevGCGZBGReHr8cRg2zL9sXXxx1NFIeSpWTY6ZXQLMB94GXo1dXoljXJKCKlWCZ5+FFi2gVy/48ceoIxKRVPf++3D++d5tPnx41NFIeSvuEPL/A1qFENqEEPaOXdrGMzBJTTVresGfGfToAUuWRB2RiKSqn36Cnj295XjMGP+iJRVLcZOc2YBGU0mZaN4c/vtf+PlnOOkkWLcu6ohEJNUsXepfpMC/WNWqFW08Eo3i5rU/A+PN7FVgTf7GEMJdcYlKUt4hh/ikW2edBf/3f3D//d66IyJSWuvXw8kne5f4O+/A7rtHHZFEpbhJzqzYpUrsIlJqZ57p8+cMG+Yjri65JOqIRCQV9O8Pb70Fjz4Khx4adTQSpWIlOSGEm+IdiFRMt90G06f7m1KLFtCtW9QRiUgyu/9+v1x+uaaqkGImOWY2Dgibbg8hHFbmEUmFkp4OOTnQqZM3L3/8sSbpEpHt89Zb3v3dowfcfnvU0UgiKG531RWFfs8AegHryz4cqYiqVfNJug480N+cJk2C+vWjjkpEksm0adC7N7RpA0895V+gRIrbXfX5Jps+NLP34xCPVFCNG8OLL3r/ec+eXixYtWrUUYlIMli40L8gZWT4FyYtBCz5ijsZYJ1Cl3pmdhSwc5xjkwqmQwcYORImTPDJu8JmHaQiIhtbu9YnF/31V/+ilJUVdUSSSIrbXVW4JWc98Augki4pc6ecAt99Bzfd5COutKCniGxJCL5MzAcfeG1fx45RRySJZqtJjpk1DiHMCiE0La+ARG64wROdgQOhVSs44YSoIxKRRHTHHb4u1XXXwWmnRR2NJKJtdVe9kP+LmY2NcywigE8K+PjjcMABkJ0NX34ZdUQikmheeslbenv3hhtvjDoaSVTbSnIKz0HbLJ6BiBS2ww7wwgtQpw4cdxz89lvUEYlIopg82Vtu9tvP6/jSirtAkVQ423pphC38LhJ3DRv6mjNLlsDxx8OqVVFHJCJRmzfPv/jUquWtOZmZUUckiWxbSc4+ZrbMzJYDbWO/LzOz5Wa2rDwClIqtXTsvKPzsM18GIi8v6ohEJCqrVvkXnkWL/AtQw4ZRRySJbqtJTgghPYRQI4RQPYRQKfZ7/vUa5RWkVGzHHw9DhsCYMT7qSkQqnhDg7LPhk0/gySdh332jjkiSQXGHkItE6sorfcTVP//pE3+9+irMmnUojRvD4MFeoCwiqSUnBwYN8v/1GjXgjz98vbsTT4w6MkkWSnIkKZjBQw/BRx/BAw/8uZWZM6FfP7+mREckdeTk+P92bi6A8ccfvlTDbrtFHZkkE9WkS9KoUgVWrtx8e26uf9sTkdQxaFB+glNgwwb9r0vJKMmRpPLrr0VvnzWrfOMQkfja0v+0/telJJTkSFJp3Lhk20UkOe20U9Hb9b8uJaEkR5LK4MGbz4thBv/3f9HEIyJl76OPYOlS/98uLDPT3wNEiktJjiSV7GwYMcJXGjYLNGzob3x33gnTp0cdnYiU1gcfwJFHeoHx8OEF/+tZWf6/rwEGUhJKciTpZGfDjBnw3nvvM3cufPwxrF0Lhx4K334bdXQisr3efRe6dfMuqfffh0suKfhfnzFDCY6UnJIcSXp77w3jx3vTdufO8PXXUUckIiX1xhvQowfsvrv/P2s2YykLSnIkJbRu7d/8qlSBLl3giy+ijkhEiuvll31m8z32gPfegwYNoo5IUoWSHEkZLVt6f3716tC1q0//LiKJ7fnnoWdP2GcfT3Dq1Ys6IkklSnIkpTRr5i06derA4YfDhx9GHZGIbMkzz8BJJ8GBB8Lbb0Pt2lFHJKlGSY6knKwsb9Fp2BCOOsr790UksYwe7YXEf/mL1+PUrBl1RJKKlORIStp1V09usrKge3d4552oIxKRfI89Bmee6fVzr73mXcwi8RBZkmNmu5nZODObZmZTzez/YtvrmNnbZvZD7KcaMGW7NGwI48b5aI0ePeD116OOSEQefBDOPddbWV9+GXbcMeqIJJVF2ZKzHrg8hLAn0BG4yMxaAwOAd0MILYB3Y9dFtkuDBp7otGkDJ5wAL70UdUQiFdfw4XDhhXDssfDCC7DDDlFHJKkusiQnhPBbCOGL2O/LgWnArsDxwKjY3UYBJ0QToaSKunV9krF27aBXL3juuagjEql4hg6F/v0L/gerVo06IqkILIQQdQyYWRPgA2AvYFYIoVah25aEEDbrsjKzfkA/gPr16+83ZsyY8gk2waxYsYJq1apFHUYkSnruK1emc/XVbZk2rQYDB07j8MN/j2N08VVR/+4V9bwhuc999OgsHn+8KYcdNp9rrvmO9PSSfe4k87mXVkU+9y5dunweQti/VDsJIUR6AaoBnwM9Y9eXbnL7km3to2XLlqGiGjduXNQhRGZ7zn358hAOPTSEtLQQRo4s85DKTUX9u1fU8w4hOc89Ly+Ea68NAULo0yeE9eu3bz/JeO5lpSKfO/BZKGWOEenoKjOrDIwFckIIz8c2zzezhrHbGwLJ+3VbEk61aj6a47DD4Kyz4NFHo45IJDWFAAMGwC23eKHx449DenrUUUlFE+XoKgMeA6aFEO4qdNNLQN/Y732BF8s7NkltmZk+qqNbNzjvPLj//qgjEkktIcBll3kdzoUXwsMPQ5omLJEIRPmy+wtwBnCYmU2OXboDQ4AjzOwH4IjYdZEylZEB//0vHHccXHwx3H131BGJpIa8PLjoIh9J1b8/3HefEpyUsnQpLFsWdRTFVimqA4cQJgC2hZu7lmcsUjFVrQr/+Q+cdhr84x+wZo03r4vI9tmwAc4/3yf7u/pquO02sC29y0viW78epkyBlSuhUyf/A++2G9x+uzfRJYHIkhyRRFCliq+f06cPDBwIa9fC9ddHHZVI8tmwwevcnnjC/4duvFEJTlJ64w2fc2PSJPj8c8jNhQMO8BWP09O9aW6//aKOstiU5EiFV6mSvzFXqQI33OCJzs036w1apLjWrfMvCs8844XGgwZFHZFs08qVnsRMnAg//eSFUwCPPAKvvAL77utFix06QMeOBY/r27fo/SUoJTki+BeUf//bE53Bg+HLL+Gbb2D2bGjc2LdlZ0cdpUjiyMnxZGbWLK9xW7UKhg2DK66IOjLZTF6ef2szg6ef9orwKVO8+Q2gWTNYscKHnz7wADz1VMrM1qgkRyQmLQ0eeghmzPBh5vlmzoR+/fx3JToinuD06+c9GeAJTuXKvl6cJIAFC7y7Kf/yyScwYQLstZd/o6tf3/vnO3TwS/36BY/daafo4o4DJTkihaSlwfTpm2/PzfVvrUpyRPx/IT/Bybdunf5HIrFmDXz1lWeYu+0G77wDRxzht6WlQdu2cMop3kwNcNJJfqkglOSIbGL27KK3z5pVvnGIJKot/S/of6QcrF7t819MmuT1NF9+6YWEt93mw0Pbt/fRTx07eoFwBV/mXUmOyCYaN/Yuqk3VrVv+sYgkkhDgX//yn0Vp3Lh840l5y5ZR6/PP4cMPvaXm7LO9deass/zn/vvDpZd6l1OnTv6YOnXgqquijTuBKMkR2cTgwRvXG4C/nyxc6O8nw4alTE2eSLEtXeqfrS+84A0E337rtTj5MjP9f0e2UwgFQzqvusoLA7/9lnb5GeWpp3qSU6WKd081a+aFULJVmodSZBPZ2TBiBGRl+XtOVpaPvOrfH+69Fw45xIuTRSqKzz/3XpBXXvHZwT/91EcaF/4fGTFC9TglMneudzsNGABdunirTL7ff/cn9aab+GroUFiyxEc85WvVSglOMaklR6QI2dmbv2H37esJzlln+RQSo0b5shAiqSoEePBBX4dqp53gf/8rmDKlqP8R2YJVq+CLL+Cgg7xZ+PLL4a7Yko2VK0O7dvCXv/iQ7vR0GDnyz4cuGT8eatWKJOxUoCRHpAR69oR99oHeveH44+HKK72JXl+qJNUsX+7dts88A927w+jRqksrtnnz4K23CoqDv/7al0j47jtvheneHRo18oxx3319oiGJCyU5IiXUvDl89JGvdzVsmP/+zDP+niWSCqZMgb/9DX780QftXHWVFtncosWLfR6aiRP9SdtrL/j4Y2/6rV7dl0S48kpPaHbd1R/TtatfJO6U5Ihsh4wMnxj0kEN85vN99/UJ0o48MurIRErn8cd97cXateG99+DQQ6OOKAHNn++Jy6RJ8P33vi0tzYeX7bWXJzBTpsCee3r3k0RGublIKZx6Knz2Gey8M3Tr5gsT5s+ULpJMVq6EM8/0ATx/+YtPv1KhE5wQfOKfMWO8hqZTJ191FKBGDRg/3pOY227zbHDpUn/y8m/Pn11YIqWWHJFS2mMP/0J30UW+sOeECT4QYuedo45MpHimTfM6s2+/9UVqr7uuAn4+L1/uI55atfLre+8NU6f67xkZPrwsf8mDHXbQzIdJQkmOSBnIzPRm/r/+1ZOdfff1dfA6d446MpGte+opLzDOzIQ33yxYESDlff+9DxfLLw6eOtVbZr75xm/v29eflI4dfWkEjS5ISkpyRMrQWWf5dBe9e3u3/M03+zQYKtqURLN6tc/99PDD3hPzzDMFdbEpZ/58T2YmT/ZmKjO45RZ44gkvPjrwQDjxRDj44ILHXHlldPFKmVGSI1LG9t7bJ0vr188XLPzf//y9tF69qCMTcT/+6In45Mlw9dX+eV8p1T4NPvjARwdMnFiwTkulSnDOOZ7NDRrklxYt9C0khekvKxIH1at7N8CDD3pN4r77+lBzkaiNHevLMsycCS+/DEOGJHGCE4JnbE8+CZdc4i0yn33mt82b5/90Bx4Id9zhxXLLlhU0V7Vq5RclOCktWV/aIgnPDC64wN9je/f2kSpDhvj8OvlL1IiUl7Vrfb6b4cP9NTlmjK8ckFSWLvVJ9erV8wn2DjsMFi3y23bc0fuK16zx6717w0knRRerJASlsCJx1r69r/1z7LFwxRXe9b9kSdRRSUUyc6bP6TR8uNfh/O9/SZDg5OX5OPaHHmKPIUO8KLh2bT8J8AUqjz/ei4q++soToPHjffw76JuEAGrJESkXtWp5N8Hw4V7P2L69T6nx2GM+ErVxY18eQmsBSVnIyfFyk1mzvNFj5UrvknruOejVK+rotmDOHC8ONvP1U0Lw4YorVlCndm2vjj79dDj6aL9/tWr+DySyFUpyRMqJmX+L7tgRjjnGJw7MN3OmFyqDEh0pnZwcfy3l5vr1BQv8tXfnnQmY4Dz2GLz2mhcHz53r2w44wJOc9HR44QVo1oyPZsygc5cu0cYqSUndVSLlrGNHn35jU7m5/u1bpDQGDSpIcPKFUNDLU+7y8ny2wccf9yK1o44quO2dd3yIV+fOHuCkSd6Xlq9rV2jaVF1Pst3UkiMSgV9/LXq7JlGV0vjxx4LR0psqt9fWwoVeO5OeDvfdB9deC3/84bfVqOFVzytXeqHw6NGaZE/iSi05IhFo3HjLt910E6xYUX6xSPL7/Xe4+GKvzd1So8fWXnPbbe1aX4H73nu9n3X33aF+fV+cEqB5czjlFG/F+fZbr7h/+21PcEAJjsSdkhyRCAwevHmXVUaGj4C98Ub/bHjgAVi3LpLwJEmsWOFJcfPm8NBDcO65nm9s+trKzPTXXKmEAD//7OuVTJvm28aPhw4d4NJL/fd99oGhQz3RAS8SfughX/lzzz01J42UO73iRCKQnQ0jRvgwXjP/+eij/qX44499jrKLLoI2bXxETAhRRyyJZN06T4KbN/ek+KijfOmlBx/0182mr60RI7azoH3lSs+Ojj3WF6ds3hxOO81flOAFZs89B7Nnex/s2LE+fDBl14eQZKOaHJGIZGcX/cHTsSO8/z68+qqve9W7t5cxDB3qEwpKxRWC5xTXXOP1N4ccAi++6K+Zwrb02tqiDRs8S5o40Yt/W7XymQOrVoXbboPddoPu3f1AHTrAXnv542rUSMAhWyIFlOSIJCAz6NHDW/tHj/bh5p07+9Dz226LOjqJwuTJtbj6am/ta9PGl2Q45pjtHHi0fLmvPQJw6qm+s5Ur/Xrdut69BD65zvz5BTU0IklGSY5IAktP95XNTznFay1uvdXLHo46qhXNmsWpmFQSypQp3qL32mvtaNQI/v1v6NPHXxvFkpsLX3zhLTT5LTWVKnl9DfgQ7bPP9haajh19JuHCmZMSHEliSnJEksAOO3jvwbnnekvO8OE70bKl13sOHOgjdiW1zJrlLXijR3uvUL9+P/GvfzVnhx228qC8PPjhB2/uOe00z4T+8Q9f+gCgSRM4+GBPZvLyvBD41lvL43REIqEkRySJ1KkDw4bBfvtN4vXXD+KOO+CRR7xG4+KL2foHoCSFxYs9kb33Xr9++eWeyH799Wx22KH55g+YPt1HPE2c6MlN/sJo7dt7v9Z553k9TYcOXjwsUoFodJVIEtp55zWMGuWTxR50kLfytGoFI0d6Dakkn1WrvLi8eXNfguHkkz1/GTbMk1tbv95Xer3/fu+vmjTJH/jjj3Dzzb4swt/+5sP0pkyBPfbw2/fbD447TgmOVEhqyRFJYm3b+tI/48bB1Vd7/c6dd8KQIf7lXbPhJ74NGwqKy+fM8WLzIbcF2u65DqpU8Y0nn0ynzz7zyfcAdt7ZV+Du0AEOP9xX4M4vJBaRP6klRyQFdOniX+zHjIHVq31kVpcuPlFckyZeetGkiS/eKNHKySn4mzRo4L9fevZyeuw4jh/PHcJrVU6gbbddvI8KfGK9SpWYe/zx8Oyzvm7D3LkFQ7erVlWCI7IFaskRSRFmPqfOCSd4nc6AAT7fTj6tdB69nBy44LwNZK2axiEs4oMFh2IElmS2pOb0eTAdaNECjjgCOnXyB1WtCu+/z0/jx7Nb585Rhi+SdNSSI5JiKleGCy+EWrU2vy031+t3pHyFceP57exBNOrblV9X1eYb9uYRzvPbMK7f4Q7vd1y4EL7/3vuvTjwx4qhFkp9ackRS1Jw5RW+fO9cnrO3Vyy97763anTKzejV8+SVMmkSY/BUfnftvxj5vdBwxmhNXPsGv7MMTnMEkOjCJDn8+7N7F2Qw/OsK4RVKUkhyRFNW4sXdRbap2bahXD265Bf75T184Oj/h2X9/JTzFFoJf0tLgpZfg5psJX32FxVZVnZu2G71GzWdJlZ35/dAhrPnb/Vxz8w5FJp+a1FEkPtRdJZKiilrpPDPT518ZP95bdB56yCe8veMOXx+raVOfO+7DD32uOClkyRJ4803PDLt394Lgjz9m7Vr4bHIlvvu1GvdW+gcn8F+aZczl0hNmcVfOzixYAE++1YAz+u3AkCFxWiFcRIqklhyRFJVfXDxokM+e27ixf5jmb99pJzj/fL8sXuyNEWPH+jQsd98NDRt6WUivXvDXv/pKABXG+vU+10ytWp75ffqpZ4EAZuTt2ZrZ7Y5nxJDqPDABli7tTvXq3elxApzRC7p1K3o1hG39TUSkbFWkty2RCqe4q1HXqeNrMp55Jixb5iugjx0Ljz8ODzzg3VvHH+9zzR12mE/fklLWr4cXXvBx+JMmwWef+ex8Awb49MNt2rDm+luYGDoycuoBPPdWDVZ8611/J5zgieDhh0NGxrYPVeIVwkVkuynJEZGN1KjhC1OfeqovTP3GG57wjBkDjz0GNWv6BLq9esGRR8LzzydZy8TKlT5z8MSJPr/M3//udTX9+rFh+Uq+SmvP/9b246d6HehY+xDynoSxYzN5441BrF7tc9ucdpqff5cuPppNRBKTkhwR2aIddywoSl69Gt55xxOeF1+EJ57wFp0NGwqWkki4uXhCKKik/uc/PSP75puC+6f0kAAAFCVJREFUgI8++s8k56WBH9P3hiYsXVXVb1sI917tv+66qy8B1auXT19T7BXARSRSCZvkmFk3YDiQDjwaQhgScUgiFVpGhs+k3KMHrFvnxcs9e8KKFRvfLzfXE4KPPoJGjfyy664FP4uqVdmWnJz81qJDt95atGBBQZfTxInevPTdd57o/P476+s0YMm5A5m9a0e+q3EgPy2rz5zzfbj922+3IjYwaiM77+y7SdMwDZGkk5BJjpmlA/cDRwBzgE/N7KUQwrfRRiYi4F00RxzhPT9FWbXKF8bOXxC7sFq1CpKfwglQ4eu1ahU0wOTkeOtQbi6A/dlalLZuDae2/gratSNUrkLuwJvZ8fbrAcizNOY1aMv0ml24o+tqfv5tB+bMuc8TsnEbx1O/vh+3qAQHYP58JTgiySohkxzgQODHEMLPAGb2DHA8oCRHJIFsaS6erCyYMcMTk19/9ZaSOXM2/n3OHF9Fff5871UqLDOzIOmZNMn3U4dFHMlbdGASHXMnsu9ZXwJrOWGXT3hj0QHst+YwDiaDSXTg87AfqxfsyC6VodEqaNMGjjpq86Rql1181QTwNaSKOhfNYSOSvCxs+u6SAMzsb0C3EMK5setnAB1CCBcXuk8/oB9A/fr19xszZkwksUZtxYoVVKtWLeowIqFzj/7c33mnAXfc0Yo1awqKVKpW3cAVV0zn8MN/L9Y+1q0zFi2qwoIFVVm4sCoLFlRl5dw11PvlW7Lmfc0Tvx/LZxxIZ8YxjsPIZQc+Y38m0oF5WXvzU1ZHMnfJoF69NdSv75d69dZQp8460tOL//5WFucST4nyN4+Czr1innuXLl0+DyHsX6qdhBAS7gL0xutw8q+fAdy7pfu3bNkyVFTjxo2LOoTI6NwTw5NPhpCVFYKZ/3zyye3c0dKlIZx1VgitW/vOYnMK31T77gAhZJAb2vFFSGddAD9WWSuzc4mDRPqblzede8UEfBZKmU8kanfVHGC3QtcbAXMjikVEtqLE877MnbtxcfD++/uUy9WqeTVz69ZwyinQoQMceCDNX61FZj/Izd2ByewLxG+WYM1hI5JaEjXJ+RRoYWZNgV+BU4DTog1JREps1SovdNljD7/eqZOvGQFevbzvvj58CXxc9s8/b7aLjWcJDjRubIk/F4+IJISETHJCCOvN7GLgTXwI+b9DCFMjDktEtuWXX+B///MWmkmT4Ouvff2I/FUpTzrJp03u2BHatSveFMEUtLCMH/8+nTt3jl/8IpJSEjLJAQghvAa8FnUcIrIFixbBJ5/4ZdAgX9zqzjt98avq1eGAA+Cqq7zbKX9SvksvjTpqEalAEjbJEZEE9OmncM893krzww++LS3NW2j23BP694cLLvDfNS2wiERMSY6IbCwEn+I3vzB40iS45RZfqGnxYl/boWNHOPtsb6XZf39vuQHYffdoYxcRKURJjkhFt3y5Fwg3aOA1NQcfDPPm+W0ZGbDffr5KN/g0x3PnFkxHLCKSwJTkiFQkIcDUqRu30kydChdfDMOH+zTA3bp560zHjtC27cbLbGt9AxFJIkpyRFLZvHmeyKxa5XPPmPn6BnPnQu3a3t3Us6dvA09oHn882phFRMqIkhyRVPP00/Dii95Sk78YU4sWnuQAPPmkL+DUooW6nUQkpSnJEUlGIcCPPxZ0O02ZAu+9591J48fDxx97K80ll/jP9u0LHtulS2Rhi4iUJyU5Islg6VJfy+D/27v3IKnKM4/j34eBgSggiyiySkSFgAiL3IcghqhhDRqVRAysulbpJlplEqm4VWqRoK4hFbJZi9VNjDdK3XUXd8vLUt7ipXDVpBhuAiOLchFWEVQURbxkAHn2j+dMuunMpRl65kyf/n2quubMec9p33feY/fDe97zPtXV8MADTPzhD+Hjj6Ps8MNjTZqdO6FPH7j99jhORKTCKcgR6Wj27YuRmYaJwbW18NprsHgxTJ4MAwbw/sSJ9Js2LUZpTjnlwDVpFOCIiAAKckTSt3VrBDSDBsGIEbB8OUyYEGVHHx2BzCWXwPHHx77TT+f1/fvpp/QGIiLNUpAj0t727IH583PzabZti/3XXhtBzsiRMXm4piYCG00OFhFpFQU5Im1l//64zdRwy+mYY+Cmm+Ix7V/9Cnr2jNtP48fHyM2IEXFe1665J6FERKTVFOSIlMru3bn0BldeCQsX5iYHH3EETJ8e22bwxhvQvXs69RQRqRAKckRao74eVq06cOXgXbtgx44IYo49FmbOjFtO48fD4MEHrhasAEdEpM0pyBFpiTts2RLBzLRpkc/pxhth3rwoP/bYCGTGj4/5Nl27wpw5qVZZREQU5Ig0bssWePDB3EjNjh2x//e/jwSWF18ca9OMHx/5nkREpMNRkCOVbd++XMLK2tp4VPvrX4e33oKf/ASGDIFzzolgpqYGhg2L84YPj5eIiHRYCnKksuzZE4vl7dwZiSmXL4dPP42yI4+ESZNiu6YGPvwQevVKr64iInJIFORIdn32GaxceeDKwVOmwD33RPDSqRNcfnlulObEE3Nr0nTpogBHRKTMKciRbNi/HzZsgLffhjPOiH2jR8c6NQADBsRcmobklJ06RUJLERHJLAU5UrZ6rl0b+ZwaRmk++gj69oXt22NE5uab4UtfgnHjYr+IiFQUBTnS8e3ZA2vWxG2nZcvg7ruhupqjXngBHnkkElROn5677dTgootSq7KIiKRPQY50LO7x6tQJnn8+1ptZsSIW34NIjfDmmzBwIG9efDH9FyzIrTIsIiKSR0GOpGv37njCqWFy8JIlsGABTJ0ak3/N4OqrcysH9+//p8nBe3v1UoAjIiJNUpAj7eeLL2DdulgReNAgWL8eTj45Jg0DfOUr8fRTnz7x++mnw8svp1dfEREpawpypO24w6JFuYnBy5bFyM1VV8Edd8BJJ0VW7rFjY3Jw795p11hERDJEQY6Uxh//CK+8ErebzGDWrPj5ox/Btm0wYgRcemncdjrttDinqgp++tN06y0iIpmlIEcOza23wsKFkZF7797YV1MTQQ7AM8/Al78cj3KLiIi0IwU50rKPPoKlS3OTg199FTZtgs6d4b334PDD4dprc5m4+/XLnTt4cHr1FhGRiqYgRw60bx/U1UVwcthhcNttcM01UWYGQ4fCN74Bn3wSaQ9+8Yt06ysiItIEBTmVbtcuePbZ3OPbK1bA55/HvrPOgokTYe7cGKEZOxZ69ky7xiIiIkVRkFNJPv001qSprYUJEyLj9qZNsVpw164wciRceWUENKeeGueMHh0vERGRMqMgJ+vq6+N205IlcRuqYU2aG2+MIGf48JhvM2IEVFenW1cREZESUpCTFTt25NajWbIEBg6MtWiqq+HFF+G442D27Nzk4IYF97p0idtQIiIiGaMgpxzV18PmzTBkSPx+7rnwxBOxXVUVozMNa9GYwdq1f0qFICIiUikU5JSDrVvhpZdyj3C/8gp06wYffhjl3/oWfO1rMUIzenQ80p1PAY6IiFQgBTkdza5dkf6gtjbm0nTvHredfv7zeKR7zJjYP3585IKCmCwsIiIiB1CQ0xHU1cH8+TFSs25d5HwCOPPMWD34e9+LJ6CGDYsF+ERERKRF+sZsT9u25SYG19bCj38M550Xj3YvWhSjMzNmRGAzdmwstgcwYECq1RYRESlHCnLaymefRcbtvn3jyadRo2JuDcQTTSNH5h7nHjcu0iNo7oyIiEjJKMgplfXrY4Sm4bVmDVxyCdx3XzyuPWVKPPVUUxML7XXrlju3U6fUqi0iIpJVCnJa44MPYgG999+HSy+NfRdcEPNpevSIkZnrrou0CBAjNPfem159RUREKpCCnGI99hg8/HDMpdmwIfYddVSM1pjBnXdC796xdk1VVbp1FREREQU5RfvDH+C55+J20xVXxCThMWNy82gmTUq3fiIiInIABTnF+tnPYN48TQ4WEREpE6nMeDWzfzSz18xsjZk9ama98spuMLONZva6mf11GvVrVHW1AhwREZEyktZjPc8Cw9z9r4D1wA0AZjYUmAGcApwN/MbMNMFFREREDloqQY67P+Pu+5JflwDHJdvnAwvdvd7dNwMbgXFp1FFERETKW0dYoOVy4Klk+1jgrbyyrck+ERERkYPSZhOPzew54JhGima7+38nx8wG9gEPNpzWyPHexPt/H/h+8mu9mb16aDUuW32A99OuRErU9spTqe0GtV1trzyDD/UN2izIcfezmis3s8uAc4Ez3RsyUrIV6J932HHAtibe/y7gruS9lrv7mEOudBlS29X2SlKp7Qa1XW2vPGa2/FDfI62nq84GrgPOc/fP8ooWATPMrKuZnQAMApamUUcREREpb2mtk/MvQFfgWYvHspe4+1XuvtbM/hP4X+I21tXu/kVKdRQREZEylkqQ4+4DmymbC8w9yLe869BqVNbU9spUqW2v1HaD2l6p1PZDYLnpMCIiIiLZ0REeIRcREREpubIJcsxsupmtNbP9ZjamoKzFVBBm1tvMnjWzDcnPv2ifmpeWmT1kZquS1xYzW9XEcVvMrC457pBnqHcEZnaTmb2d1/6pTRx3dnItbDSz69u7nm2huVQoBcdlot9b6kMLtyXla8xsVBr1LDUz629mi81sXfJ5d00jx0w2s115/x/MSaOubaGl6zfD/T44rz9XmdnHZjar4JhM9LuZLTCz9/KXfSn2+7lVn+3uXhYv4GTimfkXgDF5+4cCq4mJzCcAm4CqRs7/JXB9sn09MC/tNpXgb/JPwJwmyrYAfdKuY4nbexPw9y0cU5VcAycC1cm1MTTtupeg7VOAzsn2vKau3yz0ezF9CEwlFhE1oAaoTbveJWp7P2BUst2DSHtT2PbJwONp17WN2t/s9ZvVfi9oYxXwDnB8FvsdOB0YBbyat6/F7+fWfraXzUiOu69z99cbKSo2FcT5wP3J9v3ABW1T0/Zh8VjaRcB/pF2XDmYcsNHd33D3PcBCou/LmjedCiWLiunD84EHPCwBeplZv/auaKm5+3Z3X5ls7wbWoVXf82Wy3wucCWxy9/9LuyJtwd1fBHYW7C7m+7lVn+1lE+Q0o9hUEH3dfTvEBwlwdDvUrS1NAt519w1NlDvwjJmtSFaHzoofJMPUC5oY0qyE1CD5qVAKZaHfi+nDzPezmQ0ARgK1jRRPMLPVZvaUmZ3SrhVrWy1dv5nvdyJJdVP/eM1qvxfz/dyqvk9rnZxGWRGpIBo7rZF9Zf3IWJF/h5k0P4oz0d23mdnRxHpEryURdIfWXNuBO4BbiP69hbhdd3nhWzRybllcD8X0u/15KpRCZdnvBYrpw7Lt52KYWXfgYWCWu39cULySuJXxSTIv7TFi4dQsaOn6zXq/VwPnATc0Upzlfi9Gq/q+QwU53kIqiCYUmwriXTPr5+7bk+HN91pTx/bQ0t/BzDoD3wZGN/Me25Kf75nZo8RQX4f/siv2GjCzu4HHGykqOjVIR1NEvzeWCqXwPcqy3wsU04dl288tMbMuRIDzoLs/UlieH/S4+5Nm9hsz6+PuZZ/fqIjrN7P9nvgmsNLd3y0syHK/U9z3c6v6Pgu3q4pNBbEIuCzZvgxoamSoHJwFvObuWxsrNLPDzaxHwzYxabXsE5gW3HufRuNtWgYMMrMTkn8VzSD6vqxZ06lQ8o/JSr8X04eLgL9NnrapAXY1DHeXs2Su3b3AOne/tYljjkmOw8zGEZ/jH7RfLdtGkddvJvs9T5Mj9Fnt90Qx38+t+2xPe6Z1sS/iS20rUA+8C/wur2w2Mev6deCbefvvIXkSCzgSeB7YkPzsnXabDuFvcR9wVcG+vwSeTLZPJGaerwbWErc7Uq93Cdr9r0AdsCa5uPsVtj35fSrxVMqmDLV9I3E/elXy+m2W+72xPgSuarjuiaHrXyfldeQ9cVnOL+A0Ygh+TV5fTy1o+w+S/l1NTEL/atr1LlHbG71+K6Hfk7YdRgQtR+Tty1y/E0HcdmBv8p1+RVPfz6X4bNeKxyIiIpJJWbhdJSIiIvJnFOSIiIhIJinIERERkUxSkCMiIiKZpCBHREREMklBjoiUnJlNMzM3syFp16UlZnaqNZHRXkTKm4IcEWkLM4GXiQW7DmBmVe1fnWadSqy/ISIZoyBHREoqybs0kVjka0ayb7KZLTazfwfqzKxTsiz9WjN73MyeNLMLk2O3mFmfZHuMmb2QbN9kZveb2TPJMd82s1+aWZ2ZPZ2kQ8DMRpvZ/yRJHn/XsFK2mb1gZvPMbKmZrTezScnKqf8AfNfMVpnZd9v5zyUibUhBjoiU2gXA0+6+HthpZqOS/eOIVUqHErnXBgDDgb8DJhT53icB5wDnA/8GLHb34cDnwDlJoHM7cKG7jwYWAHPzzu/s7uOAWcCN7r4HmAM85O6nuvtDrW20iHQ8HSpBp4hkwkxgfrK9MPn9CWCpu29O9p8G/Je77wfeMbPFRb73U+6+18zqgCrg6WR/HRE0DQaGERmsSY7Jz23UkPByRXK8iGSYghwRKRkzOxI4AxhmZk4EGQ48CXyaf2gzb7OP3Chzt4KyegB3329mez2Xl2Y/8XlmwFp3b2pkqD75+QX6/BPJPN2uEpFSuhB4wN2Pd/cB7t4f2EyM3OR7GfhOMjenLzA5r2wLMDrZ/s5B/vdfB44yswkAZtbFzE5p4ZzdQI+D/O+ISBlQkCMipTQTeLRg38PA3zSybyvwKnAnUAvsSspuBv7ZzF4iRlyKlsyxuRCYZ2ariSzeX23htMXAUE08FskeZSEXkVSYWXd3/yS5xbUUmOju76RdLxHJDt2TFpG0PG5mvYBq4BYFOCJSahrJERERkUzSnBwRERHJJAU5IiIikkkKckRERCSTFOSIiIhIJinIERERkUxSkCMiIiKZ9P8MliKz2uVXigAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "xmin = -9\n", "xmax = 9\n", "dx = 1.0\n", "\n", "x = []\n", "y = []\n", "y_abl = []\n", "cx = xmin\n", "\n", "while cx <= xmax:\n", " x.append(cx)\n", " y.append(cx**2)\n", " y_abl.append(2*cx)\n", " cx += dx\n", "\n", "plt.figure(figsize=(9,6))\n", " \n", "plt.plot(x,y, 'bo-', label='x2')\n", "plt.plot(x,y_abl, 'r--', label='Ableitung')\n", "\n", "plt.xlabel('Argument')\n", "plt.ylabel('Funktionswert')\n", "plt.title('Quadratfunktion und ihre Ableitung')\n", "\n", "plt.legend()\n", "plt.xlim(-10,10)\n", "plt.ylim(-20,100)\n", "\n", "plt.grid()\n", "\n", "plt.savefig('a2_12_plot.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }