04/matplotlib-02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlib II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kurzer Exkurs: numpy\n",
    "* einfache Handhabung von Vektoren, Matrizen oder generell großen mehrdimensionalen Arrays\n",
    "* effizient implementierte Funktionen für numerische Berechnungen\n",
    "* https://numpy.org/\n",
    "\n",
    "Wir nutzen numpy lediglich um unaufwändig Beispieldaten für die Graphen zu generieren.\n",
    "\n",
    "`numpy.linspace(start, stop, num=50, ...)`: Gibt gleichmäßig verteilte Werte über ein Intervall zurück\n",
    "\n",
    "Installieren mit conda:\n",
    "`conda install numpy`\n",
    "\n",
    "(Doku: https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0, 5, 11)\n",
    "y = x ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funktionale Graphen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "plt.plot(x, y)\n",
    "plt.xlabel('XLabel')\n",
    "plt.ylabel('YLabel')\n",
    "plt.title('Titel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mehrere Plots in Einem\n",
    "`pyplot.subplot(nrows, ncols, index)`\n",
    "\n",
    "* `nrows`: Anzahl der Zeilen\n",
    "* `ncols`: Anzahl der Spalten\n",
    "* `index`: Plot auf das sich bezogen wird"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb224894190>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y, 'r')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(y, x, 'b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objektorientierte Graphen\n",
    "Graphen werden als Objekt gespeichert und sind dadurch einfacher und flexibler in der Handhabung.\n",
    "\n",
    "(Doku figures: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.figure.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Titel')"
      ]
     },
     "execution_count": 29,
     "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": [
    "# neue Figure erschaffen\n",
    "fig = plt.figure()\n",
    "\n",
    "# neuen Graphen hinzufügen\n",
    "# pyplot.add_axes([left, bottom, width, height])\n",
    "# Zahlen in Relation zur 'Leinwand'\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# Graphen plotten\n",
    "axes.plot(x, y)\n",
    "\n",
    "# Labels setzen\n",
    "axes.set_xlabel('X Label')\n",
    "axes.set_ylabel('Y Label')\n",
    "axes.set_title('Titel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mehrere Graphen ineinander"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'kleiner plot')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# graphen (und positionen) erstellen\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3])\n",
    "\n",
    "# ersten graphen plotten\n",
    "axes1.plot(x, y)\n",
    "axes1.set_title('großer plot')\n",
    "\n",
    "# zweiten graphen plotten\n",
    "axes2.plot(y, x)\n",
    "axes2.set_title('kleiner plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots\n",
    "Mit `pyplot.subplots()` können mehrere Plots in eine Leinwand gezeichnet werden. Argumente sind u.A. \n",
    "* `nrows`: Anzahl der Zeilen\n",
    "* `ncols`: Anzahl der Spalten\n",
    "\n",
    "`subplots()` gibt ein Tuple mit der figure und den axes zurück.\n",
    "\n",
    "Doku: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.figure.Figure.html?#matplotlib.figure.Figure.subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'zweiter Graph')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "# erster Graph\n",
    "axes[0].plot(x, y)\n",
    "axes[0].set_title('erster Graph')\n",
    "\n",
    "# zweiter Graph\n",
    "axes[1].plot(y, x)\n",
    "axes[1].set_title('zweiter Graph')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leinwandgröße und dpi ändern\n",
    "`plt.figure` nimmt Argumente:\n",
    "* `figsize=(width, height)`: Breite bzw. Höhe in Inch, Standard bei `(6.4, 4.8)`\n",
    "* `dpi=100.0`: Auflösung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb223ff2ad0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(2,2), dpi=100)\n",
    "\n",
    "# Graph hinzufügen\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Überschneidungen von Graphen auflösen\n",
    "`pyplot.tight_layout()` löst Überschneidungen auf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAACLCAYAAABstOMEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAb4UlEQVR4nO3da5BceXnf8e/T98t0z10jaaTRaJeLCYvZJWJd9ibEAZtaCDFQqThAxXEqqVq/MFVQzs3xG5OkXIVTNgUvXK6sgRSu2FCOMQ5FiLlUSGEqDqxEFnNZbgFpd6WVtNJImpmevp8nL87pnu6eGWk0tzPT/ftUdZ1z/n1On7/0V2t+85ybuTsiIiIiwyQRdwdERERE9poCjoiIiAwdBRwREREZOgo4IiIiMnQUcERERGToKOCIiIjI0LlnwDGz02b2JTN7xsy+bWbvidrfZ2aXzezp6PXm/e+uiIiIyL3Zve6DY2YngBPu/nUzKwEXgLcBvwisuvvv7H83RURERLYvda8V3P0F4IVofsXMngHmd7KzmZkZX1xc3MmmIiIiIhtcuHDhhrvPDrbfM+D0MrNF4BHgq8BjwLvN7J8A54F/4e637rb94uIi58+fv59dioiIyBEQBM61lRrP3lzj0tIazy2t8ezSGr/591/JVDGzb/s1s0ubtW874JjZGPBJ4L3uvmxmvw/8B8Cj6e8C/2yT7Z4AngBYWFi4/56LiIjIoVBrtnluaY1LN8Pw0nldulnhuVtVGq2gu24yYZycyHFjtb6vAWcr9zwHB8DM0sBngM+5+wc2eX8R+Iy7P3S3zzl37pyrgiMiInI4uTs3Kw0u3VwbCDIVnl1a49pyvW/9sWyKhakCC1MFzkwXOB1NF6YKnJzIk07u/8XaZnbB3c8Ntt+zgmNmBnwEeKY33JjZiej8HIC3A9/aq86KiIjI/mi0Ai7frobB5WYlqsCEQea5pTUqjXbf+ifGc5yeKvC6l86GYWa6E2iKTBbShDHh8NnOIarHgF8CvmlmT0dtvwG808weJjxEdRH4lX3poYiIiNyXO2tNLkVVlzDIrHWDzAt3qgQ9B2+yqUS3AvPTD05zphtiipyazJNLJ+P7g+zCdq6i+gqwWTz77N53R0RERO6lHThXblfDw0gbQkyF5Vqrb/2ZsQwLUwVeuzjJwvSpbqBZmCowO5YlkTicVZjduK+rqERERORgrNZb3fNgwiBT4dmlKs/erHD5dpVme70Mk04apybDc2AePj3RdyhpYapAMTt6P+5H708sIiISs87JvJdvVblyu8rl21WevxVOL0fTO9Vm3zbj+TQLUwVeOT/Om151IjyUFAWZE+N5kkNYhdkNBRwREZE91moHXF2udcPKYIi5crtKrRn0bVPMJJmfzDM/kec1ZyaYnyhweirPmakiC1MFxgvpmP40R5MCjoiIyH2qNtphtaVbcVnjyu31QHN1uUY76L8Ny3Qxw/xknpfPlXj9y491w8z8ZJ5TEwXK+dShvSLpKFLAERER6eHu3Kk2NxwyunyrypU74fRmpdG3TTJhHC/nmJ/I8+jZqW5w6Z0e1auRjioFHBERGSlB4FxfqXP59tqGEHMlmh+8F0wunWB+Is/JiTyvPFnuCS4F5ifzzJWypA7gpnayfQo4IiIyVCr1FleXa1y9Ex4yer7nMNLl21Wu3qn1XYEEMFFIMz+R58x0kZ95cIZTA9WXqWJGh4+OGAUcERE5EoLAWVprcPVOGF6uLte4trw+35muDNwDxgzmSjnmJ/M8cnqSk6/qnPcSTk9O5Bkbwcuoh51GVEREYldvtbm+XF8PKp3Qslzj2p0aL9ypcX1lY+UlYTBbynK8nOPsTJGfeXCaufEcx8s5jo/nODVR4Ph4jkxKh49GjQKOiIjsG3dnudraEFYGqy9LAyftAuTTSY6P55grZ3nt4iTHx/McL2ejthwnxvPMjGV07otsSgFHRER2pNUOuLHa4IU71Z6wUufqnWoUYOpcvVOj2mxv2Ha6mGEuqrK8+vQEJ6KqS2/1pZzTZdOycwo4IiKywVqjteHclmsDyy+u1Bm41QvppIXBpZzjb5ws8/qfOMaJqOJyPAovx8pZsildMi37SwFHRGRE1JptbqzWubHa4MWVOi+u1Lmx2j99cbXOjZX6hsukAUq5VDesvGyuFAaWTuUlCjBThcxQPrhRjh4FHBGRI6zZDrgZBZbekNIbVjrLg1cXdUwU0syOZZkZy/LqUxPMjGWZKWW6h4o600JGPzLk6NC/VhGRQ6YdODcrdW6sNDaElMGKy6215qafUcqlwtBSyvKK42Ve99IsM2MZZktZZkthmJktZZkuZnWFkQwlBRwRkQMQBM7tarO/0rJJ1eXGaoOlysZzWwAKmWQ3nDwwM8ajZ6eYHctFbZm+4KLHAsioU8AREdkBd2e51uJWpcHSWiOcVhrcWmuwVGlyczU6RBQFl5urDVqbpJZMKsFsFEpOTRZ4ZGEyrLIMBJaZsSxF3YxOZNv0bRGRkefuVBrtvpDSCSpbBZjba5sHFoBMMsFUMQwox0o5XnlinJlSpnvIqDstZSlldSm0yH5QwBGRoVNrtlnqCyRRQFnbPLDcqjRptINNPyuZMCYLaSYLGSaLGR6YGeNvnskwVQzbpoph+1TPfDGTVGgRiZkCjogcavVWm9trzZ6QEk5vddrW+oPKUqWx6Y3lIHwm0Xg+zVQUVk5NFvjJU+PdgNI3jeZLuZQuexY5ghRwRGTfNdsBy9Umd6pNlmutnvkmy9VWz3zUXm1GQabJan3zS5shvFJoqphhspDhWCnHy+fKYWVlIKhMFjJMFtKM59O6rb/IiFDAEZF7CgJnpR4Gk+VaJ4S0uqFkOQounXAyGF62qqh0pBLGeD5Nued1dqa4aVAJDwOlmchndHmziGxJAUdkBLg7tWbQE04GgsoWFZXOuiv1Fr75+bRAeOinlE1RzodVknIuDCid+W57PkU5l14PM7mwLZ/WOSsisrcUcEQOsVY7oNJoU6m3wlc0v1pvsdZosVoPl9fq6/OrjRYr0WGg5Z7QstVJtB2FTLIbOMbzaY5Ht+MPQ0pqvbrSs04nvJSyOk9FRA4XBRyRPdRoBVHwaFGpt6k0Wt1wslpv97wXvV9vRev0Bpf1+Xrr7qGkwwyKmRTFbJJiNkUpqpKcmsz3VVUGg0knvJRyaR3uEZGhooAjI8fdabadWqtNrdmm1gjW55sB1WZP2IiqJqsDVZL14NLuVlMq9fY9qyQdCYNiNsVYNkUxm6KYCYPJ6WKhO995r5BJrq+XTUZBJny/kA3f0yEeEZF+uwo4ZvY48CEgCXzY3d+/J72SkeLu1FsB9WYYNKqNdhQ4gih0hPP1nhDSnW5oaw+8F1DvtLXW19ni/mxbSiVsPVT0BJDZUrYbOHqDSjecRAGkt7oylk2RTSUUSERE9tGOA46ZJYHfA34eeB54ysw+7e7f2avOyd7qVC6a7YBW22kGQXe+0WlrB9HLabWDbnsrCGhEbZ33B7cN14mCShQkqr3ho9UTNnrCSb0V3PUE1rtJJ41cKkk2nSSXTpDrTFNhpWO62Glbb8/1rJtNJ8ml+tfJp/tDSjGbJJNUIBEROUp2U8F5FPihu/8IwMw+AbwViCXgdG4A5u4EDoE7QRBOIVqO2t0dd9bX6y5vnAY9y32f3fu5wcC2Pftzd4IgmqfzGVt/bjsYDBudIBHQDJxmK6AVbBZEwmkrcBqtgFawvm2zG0B2mCK2yQzSycSGwJBLJ8mlkpRzKXKlLPm+MBIGjOxdQkgunSCbSpLPrK/f2TapE1tFRGQTuwk488BzPcvPAz+1u+7s3Mf+6iIf/OIP4tr9numEhEwyQSpppBIJMkkjlUyQThrpZIJ09F46mSCXTpDKpqJ2676X6VknnUyQStjAOr2f29Oe2LiPdHLjtumEDayTUNgQEZFDYzcBZ7OfZhtKBGb2BPAEwMLCwi52d3ePP3ScszNFEmbRC8wMM7rLiWjZBpY724Tznfejz6BnncT6Z8H6Z3S3TRgGffvfbNq7Td/+QZfaioiI7IHdBJzngdM9y6eAK4MrufuTwJMAZvaimV3axT7vZQa4sY+fL/emMTgcNA7x0xjET2MQv4MYgzObNZrv8OxOM0sB3wfeAFwGngLe5e7f3mkPd8vMzrv7ubj2LxqDw0LjED+NQfw0BvGLcwx2XMFx95aZvRv4HOFl4h+NM9yIiIiIdOzqPjju/lngs3vUFxEREZE9MWz3Zn8y7g6IxuCQ0DjET2MQP41B/GIbgx2fgyMiIiJyWA1bBUdERERkeAKOmT1uZt8zsx+a2a/H3Z9RY2YfNbPrZvatuPsyqszstJl9ycyeMbNvm9l74u7TKDKznJl9zcy+EY3Dv4u7T6PIzJJm9n/N7DNx92VUmdlFM/ummT1tZucPfP/DcIgqei7W9+l5LhbwTj0X6+CY2euAVeAP3f2huPsziszsBHDC3b9uZiXgAvA2fQ8OloUPLSu6+6qZpYGvAO9x9/8Tc9dGipn9GnAOKLv7W+Luzygys4vAOXeP5V5Ew1LB6T4Xy90bQOe5WHJA3P3LwFLc/Rhl7v6Cu389ml8BniF8pIocIA+tRovp6HX0f5M8QszsFPD3gA/H3ReJz7AEnM2ei6X/2GVkmdki8Ajw1Xh7MpqiwyNPA9eBL7i7xuFgfRD410AQd0dGnAOfN7ML0WObDtSwBJxtPRdLZBSY2RjwSeC97r4cd39Gkbu33f1hwkfYPGpmOmx7QMzsLcB1d78Qd1+Ex9z9NcCbgF+NTmU4MMMScLb1XCyRYRed8/FJ4I/c/c/i7s+oc/fbwP8CHo+5K6PkMeAXovM/PgG83sz+S7xdGk3ufiWaXgc+RXg6yYEZloDzFPBSMztrZhngHcCnY+6TyIGKTm79CPCMu38g7v6MKjObNbOJaD4P/Bzw3Xh7NTrc/d+6+yl3XyT8WfA/3f0fx9ytkWNmxehiB8ysCLwRONCrbIci4Lh7C+g8F+sZ4E/0XKyDZWYfB/4KeLmZPW9m/zzuPo2gx4BfIvyN9eno9ea4OzWCTgBfMrO/Jvzl6wvurkuVZdTMAV8xs28AXwP+u7v/xUF2YCguExcRERHpNRQVHBEREZFeCjgiIiIydBRwREREZOgo4IiIiMjQUcARERGRoaOAIyIiIkNHAUdERESGjgKOiIiIDB0FHBERERk6CjgiIiIydBRwREREZOgo4IiIiMjQSe1mYzO7CKwAbaDl7uf2olMiIiIiu7GrgBP5u+5+Yw8+R0RERGRP7EXA2baZmRlfXFw8yF2KiIjIAQrcCRw8mmaSCcz2b38XLly44e6zg+27DTgOfN7MHPhP7v7k3VZeXFzk/Pnzu9yliIiIbJe7U28FVBttaq12OG0GVJttatErnA/b6s12z7oBtVabWqPdXb+z7mbbNlrBhv1/8ddex0uOlfbtz2dmlzZr323Aeczdr5jZMeALZvZdd//ywI6fAJ4AWFhY2OXuREREjr5O6Kj3BI3e6WB7tdFeDylbhow21U3aas2NoWM7Ugkjn06STSfJZxLkUknymSS5dJJyPs1cOUsunSSfDtvCV4J8OlovlSSXSTJbyu3x3942+7+bjd39SjS9bmafAh4FvjywzpPAkwDnzp3z3exPRERkv7g7zbZ3Kxa1wZDRqW50KhZRlaPWaFOLwkdvVWRw+8HQ4jv4iZgwNgaKKEwUsymmx5JR6Eh0w0e2G0LWw0e2E1ZSiW5oCddNdD8/nTzaF1rvOOCYWRFIuPtKNP9G4N/vWc9ERER6tNoBa1E1o9pos9ZoU222wmkULvrnW1QbwYZ1esNHWBnpHJIJaAc7+z28N0AMho/JQnpDyMj1vPI9FZJcFFbC0NG/bj6dJJ00bD9PaBkiu6ngzAGfiv6iU8Afu/tf7EmvRETkyOkEkFoUPtYa64dX1hqtnvn2wHxri/b+bZvt+wsfCYNCJkU+E4aDQk+lYmYs1a1c5HYZPrKphELHIbTjgOPuPwJevYd9ERGRfdRqBxsCxMaKx2btbaqN1oYAUmt2gkyLWjOg0b6/cz06ASQXhY9OAClkkkwU0j3tm6+zPp+ikEn2BZl8JhldvaPgMaoO9DJxERHZmrvTaAes1dtUokBRqbe61ZAwSKxXRzbOt/raBysh9xtAzKCQTpLPpMhnEhTSYdWjkEkynk935/PROuvzyYH51KbtqnzIflLAERHZgc5VMJ0AUmm0qNTDkNGdNtqs1cNpuN6912ndxzkg6wEkCg/pFLlMkkI6yfFyujsfVjRSfdWNjfOpns9RAJGjTwFHRIaeu1NrBmFVpN4JI/3hYrPwsVrvr6L0br/WaN/XCanFTJJCNhVOMymK2SQThQzzk+HyWDasgBQ700yKQjac5jOdaSKslCiAiNyTAo6IHCpB4FSbG8NEJ2Ss1lubh5IN1ZL+YLLdS3ITRl+4KGTDADIzlmEhW6AYhZC+dXqDSU9AKWbDIJNLJUkkFEREDpICjojsmrt3w8dKLax8rNZarNabA8stVqL5Ss98WC1ZP4dku5IJ6waO3nBxvJzrVkuKnarJQPWkbxrNF7MpVUVEhoQCjsgIa7UDKvU2K/VmN4Ss9ISRznJlQ0Bpbnh/O0drsqkEpVx4OGYsmp6cyEXBJMXYYPDoCSC91ZKxbLisq2REZCsKOCJHTOfk1r6qSDTtrYqs1psbA8tAiKk2t1ctGcv2h5JSLsVcOddtK3XfSw8sr7+K2RSZ1NG+M6qIHB0KOCIHKAic1UaL5WqTO9Umy9UWd6pNVnoqIqv1zasoncCyWm9t64ZnyYR1w0WnajJVzLAwVVivomwWSAaWi5mUzh8RkSNHAUfkPtVb7W4wWa51gkr0qrW6y/3vrweZex3K2ewwzvxEnlKutKGK0ltZKUVhpdOWS+vwjYiMLgUcGTlbVVGWa+tB5U5PWBkMLPd6Mm82laCcTzOeT1POpZgdy/Lg7Fi0HLXnU93lcjQt5XQYR0RkryjgyJG0n1UUMyhlU4wX1gNJN6B0gkk3wHTmU92gkksnD+4vQkRENqWAI7Grt9rcqjRZqjS4tdZgqbL+6iyH0yZLlTp3qturovQGkdmxLC+ZHesLJp3AUu5bTlPK6pwTEZGjTgFH9lQ7cG6v9QaS/tByq9JgaW19urTaoHKX+55MFNJMFTJMFjPMT+R56GSZyWKGcm69klIeDCyqooiIjDwFHNmSu7Nabw1UU5p94aQ3rNyqNLhdbW55x9hiJslkMcNUMcNkIcMDs2NMFjJMj4XLU8V0NA0DzUQ+TSqp81FEROT+KeCMmGY74MZqnWvLda4t17i+XOPF1SikbHJYaKvLkdNJ6waVqWKGV5wodyst01FACZfT3fVUVRERkYOigDMk2oFzsye4XFupcW25zvXlWri8XOf6So2blcaGCosZTBYyTBbS3fukPHx6ohtSporrVZVOaBnLpnQJsoiIHFoKOIdcEDhLa42o2lLvhpVrK+Hy9ZUwwLy4Ut9wZZAZzIxlmStnOTGe49WnJ5grZ5kr55grZzlWyjFXzjFVzJDUSbUiIjJEFHBi4u7cXmtyfaUTWmp9853qy/WVOq1NrmmeLmY4FgWVVxwvh4GlnONYqRNgcsyMZXQOi4iIjCQFnH3k7lxbrnPxZoVLNyv8+MYal25WuHgznG721OTxfLpbZXlwdqa/4hIFl9mxrG4GJyIichcKOLsUBM61lRo/vlHh0s01Lt6scLFnvvd+LemkcXqqwOJ0kZ9+YJr5yTzHo/AyV84xW8rqRFwREZE9oICzDUHgvLBc4+KNSlSNWYsCTThfb62HmEwywcJ0gcXpAo+9ZIbFmSKL02GoOTmR17kuIiIiB0ABZ8CtSoPzl25x/tIS/+96GGieXVqj0RtiUgnOTBVYnCnyd142y5npImdnipyZLnBiXCFGREQkbiMdcNyd529VeeriEk9dvMVTF5f44fVVIKzEnJ0p8uBskTf8xDHOTEeVmJkix8s53cpfRETkEBupgNMOnO9fW1kPND9e4upyDYBSLsW5M5O8/ZF5Hj07xavmx3U+jIiIyBE11AGn2Q54+rnbfO3HS5y/uMT5S7dYqbUAOF7O8dqzU7x2cZLXLk7xsrmSDi2JiIgMiV0FHDN7HPgQkAQ+7O7v35Ne7YHvXl3mPR9/mu9dWwHgJcfGeMtPnuwGmlOTed2JV0REZEjtOOCYWRL4PeDngeeBp8zs0+7+nb3q3E4EgfOf//dFfvt/fJdyPs2H3vEwf/uls0wVM3F2S0RERA7Qbio4jwI/dPcfAZjZJ4C3ArEFnGvLNf7lf/0Gf/mDG/zcK+b47X/wKqbHsnF1R0RERGKym4AzDzzXs/w88FO7687OffE71/hXf/oNqs02v/X2h3jXows6BCUiIjKidhNwNksPGx6aZGZPAE8ALCws7GJ3d7dcazI/meeD/+gRXnJsbN/2IyIiIoffbgLO88DpnuVTwJXBldz9SeBJgHPnzm18auQeefsj8/zCq0/q4ZIiIiKCue8sc5hZCvg+8AbgMvAU8C53//ZdtnkRuLSjHW7PDHBjHz9f7k1jcDhoHOKnMYifxiB+BzEGZ9x9drBxxxUcd2+Z2buBzxFeJv7Ru4WbaJsNHdhLZnbe3c/t5z7k7jQGh4PGIX4ag/hpDOIX5xjs6j447v5Z4LN71BcRERGRPaETVkRERGToDFvAeTLuDojG4JDQOMRPYxA/jUH8YhuDHZ9kLCIiInJYDVsFR0RERGR4Ao6ZPW5m3zOzH5rZr8fdn1FkZhfN7Jtm9rSZnY+7P6PCzD5qZtfN7Fs9bVNm9gUz+0E0nYyzj8NuizF4n5ldjr4PT5vZm+Ps47Azs9Nm9iUze8bMvm1m74na9V04IHcZg1i+C0NxiCp68Of36XnwJ/DOuB/8OWrM7CJwzt1134kDZGavA1aBP3T3h6K2/wgsufv7o8A/6e7/Js5+DrMtxuB9wKq7/06cfRsVZnYCOOHuXzezEnABeBvwT9F34UDcZQx+kRi+C8NSwek++NPdG0DnwZ8iQ8/dvwwsDTS/FfhYNP8xwv9kZJ9sMQZygNz9BXf/ejS/AjxD+MxEfRcOyF3GIBbDEnA2e/BnbH+pI8yBz5vZhegZZBKfOXd/AcL/dIBjMfdnVL3bzP46OoSlQyMHxMwWgUeAr6LvQiwGxgBi+C4MS8DZ1oM/Zd895u6vAd4E/GpUthcZVb8PPAg8DLwA/G683RkNZjYGfBJ4r7svx92fUbTJGMTyXRiWgLOtB3/K/nL3K9H0OvApwkOHEo9r0fHwznHx6zH3Z+S4+zV3b7t7APwB+j7sOzNLE/5g/SN3/7OoWd+FA7TZGMT1XRiWgPMU8FIzO2tmGeAdwKdj7tNIMbNidFIZZlYE3gh86+5byT76NPDL0fwvA/8txr6MpM4P1cjb0fdhX5mZAR8BnnH3D/S8pe/CAdlqDOL6LgzFVVQA0WVnH2T9wZ+/FXOXRoqZPUBYtYHwGWd/rDE4GGb2ceBnCZ/aew34TeDPgT8BFoBngX/o7joJdp9sMQY/S1iSd+Ai8Cudc0Fk75nZ3wL+EvgmEETNv0F4Doi+CwfgLmPwTmL4LgxNwBERERHpGJZDVCIiIiJdCjgiIiIydBRwREREZOgo4IiIiMjQUcARERGRoaOAIyIiIkNHAUdERESGjgKOiIiIDJ3/D5esIUsEvbIxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8,2))\n",
    "\n",
    "axes[0].plot(x, y)\n",
    "axes[1].plot(y, x)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphen speichern\n",
    "\n",
    "`figure.savefig(filename, dpi=None)` speichert Graphen\n",
    "* `filename`: Dateiname, Format wird aus der Dateiendung generiert, Standard `png`\n",
    "* `dpi=None`: Auflösung des Graphen. Falls nicht angegeben wird die der Figure genutzt.\n",
    "\n",
    "Doku: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.figure.Figure.html?#matplotlib.figure.Figure.savefig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Leinwand und Graphen erstellen\n",
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8,2))\n",
    "\n",
    "# Graphen plotten\n",
    "axes[0].plot(x, y)\n",
    "axes[1].plot(y, x)\n",
    "\n",
    "# Layout anpassen\n",
    "plt.tight_layout()\n",
    "\n",
    "# speichern\n",
    "fig.savefig('figure.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mehrere Linien in einem Graphen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb223b64e50>]"
      ]
     },
     "execution_count": 48,
     "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": [
    "# Leinwand erstellen\n",
    "fig = plt.figure()\n",
    "\n",
    "# Graph erstellen\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "# erste Linie zeichnen\n",
    "ax.plot(x, y)\n",
    "\n",
    "# zweite Linie zeichnen\n",
    "ax.plot(x, x**3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Legende erstellen\n",
    "\n",
    "Doku: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.legend.html?#matplotlib.pyplot.legend\n",
    "(unter parameter -> loc sind alle vordefinierten Positionen aufgelistet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb223a1de10>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y+rudRpqhEayISKgpWQfv3A59JsJRl7udRr6HClZEJJTUlsPzFzu7hE//B0To13iw0i5iEZFQ4amHOVNhx0qY8hx0znI7kfwAFayISCiwFl69Fta9B6f9FQac5HYiOQjtWxARCQUL7oUvnoGJt8Lwi9xOIy2gghURCXb5j8KH9zvFOuHXbqeRFvKrYI0xKcaYF4wx3xhjVhljxhhj0owx840x3/pudUkHEZHWWv0mvH4j9D8JJv0ZjHE7kbSQvyPYvwBvWWsHAT8CVgEzgPestf2B93yPRUTkUBXmw/OXQPcfwTmPQaSmzYSSVhesMaYTMB54BMBaW2etLQVOB57wve0J4Ax/Q4qIdDgl6+CZ8yC5G/zseYhNcjuRHCJ/RrB9gCLgMWPM58aYh40xiUBXa+02AN9tlzbIKSLScVTshKfOcu5f+CIkZbqbR1rFn4KNAoYD/7TWHglUcgi7g40x04wx+caY/KKiIj9iiIiEkdoKZ+RavgN+NgfS+7qdSFrJn4ItBAqttZ/6Hr+AU7g7jDHdAXy3O5v7ZmvtLGttnrU2LzNT/zoTEcFT76zStO0LOPdx6JnndiLxQ6sL1lq7HdhsjBnoe+p44GvgVWCq77mpwFy/EoqIdATWwrzpzgL+k/4EA09xO5H4yd8pab8CnjbGxADrgUtwSnuOMeZSoAA4189tiIiEv4X3wedPwfhbIO8St9NIG/CrYK21y4Hm9mEc78/PFRHpUJY+AR/cB8MuhGNvczuNtBGt5CQi4qY1b8O866HfCfDTmVpIIoyoYEVE3LJlqTOpqdsQOPcJ5xJ0EjZUsCIibihZB0+fB4mZWkgiTKlgRUTaW2UxPH0OWC9c+BIkd3U7kQSAFrYUEWlPdZXOQhJ7tsLU1yCjn9uJJEBUsCIi7cXTAC/8ArZ+DpOfgl4j3U4kAaSCFRFpD9bC6zfAmrechSQGTXI7kQSYjsGKiLSHD++HZU/AMTfCUZe6nUbagQpWRCTQPn8KFtwLP5oCx/3W7TTSTlSwIiKB9O18ePVa6HMs/PRBLSTRgahgRUQCZcsymDMVuh4Ok/8DUTFuJ5J2pIIVEQmEXRuc03ES0uGCFyA22e1E0s5UsCIiba2yBJ46G7wNcOGLkNzN7UTiAp2mIyLSluqq4NnJsGcLXDQXMge4nUhcooIVEWkrngZ48VIozHeOuWaPdjuRuEgFKyLSFqyFN2+G1W/AqffDYT91O5G4TMdgRUTawkcPQP6jMHY6jJrmdhoJAipYERF/LX8W3v8fOOI8OP5Ot9NIkFDBioj4Y+178Oo10HsCnP53iNCvVXHoT4KISGttXQ5zLoLMw5yr42ghCWlCBSsi0hq7NzkLScSnwgXPQ1wntxNJkNEsYhGRQ1W1y1lIoqEGLnoVOnV3O5EEIRWsiMihqK+GZ8+H0gK46BXoMsjtRBKkVLAiIi3l9cCLl8Hmz+DcxyHnaLcTSRBTwYqItIS18Oav4Zt5cMofYPAZbieSIKdJTiIiLfHJTFjybzj6VzD6CrfTSAhQwYqIHMyXc+Ddu2DI2XDCPW6nkRChghUR+SHrFsArV0HuMXDGP7WQhLSY/qSIiHyfbV/C7J9DxgDfQhKxbieSEKKCFRFpTmkBPH2us4DEBc9DfIrbiSTEaBaxiMj+SgvgP2c557xe+jZ0znI7kYQgFayISFObl8BzU6ChDn72HHQ5zO1EEqK0i1hEZK8VL8LjkyAmES6br4UkxC9+F6wxJtIY87kxZp7vcW9jzKfGmG+NMbONMbq8hIgEN2th4R/ghV9A1nC47H3IHOh2KglxbTGCvQ5Y1eTxH4A/W2v7A7uBS9tgGyIigVFfAy9Ng4W/g6Hnw0VzITHd7VQSBvwqWGNMT2AS8LDvsQGOA17wveUJQOuJiUhwqiyGJ0+Hr+bAcbfDmQ/pVBxpM/5OcpoJ3AIk+x6nA6XW2gbf40JA0+9EJPjs/Ma5nmvFDjjnMRhyltuJJMy0egRrjPkJsNNau7Tp08281X7P908zxuQbY/KLiopaG0NE5NCtex8eOdE5DefiN1SuEhD+7CIeC5xmjNkIPIeza3gmkGKM2Tsy7glsbe6brbWzrLV51tq8zMxMP2KIiByCJQ/DU+dA515w+fvQc4TbiSRMtbpgrbW3Wmt7WmtzgfOB9621FwALgHN8b5sKzPU7pYiIv7weeHMGvH4j9DvBWUAipZfbqSSMBeI82F8DNxhj1uIck30kANsQEWm52nJ4dgp8+k8YfRVMeRZikw/+fSJ+aJOVnKy1C4GFvvvrgZFt8XNFRPxWuhmemQxF38CkP8FROnNQ2oeWShSR8FW4FJ49HxpqnAX7+x3vdiLpQFSwIhKeVr4ML18BSV1h6mvQZZDbiaSD0VrEIhJerIUP74fnL4buw5yZwipXcYFGsCISPhpq4dVr4cvnYOhk+OmDEB3ndirpoFSwIhIeKktg9gVQsAiO/Q2MvxlMc2vfiLQPFayIhL6i1c6yh3u2wTmPwpCz3U4kooIVkRC3bgHMmQpRMXDx69DrKLcTiQCa5CQioSz/UXjqbOic5UxmUrlKENEIVkRCj9cD79wOi/8B/U50dgvHdXI7lcg+VLAiElpqy+HFy2DNWzDqCjjpXojUrzIJPvpTKSKho6wQnjkfdn4NP/4jjLzc7UQi30sFKyKhYctSZ8H++mq4YI5zRRyRIKaCFZHgt/IV37KHmXDRXOhymNuJRA5Ks4hFJHhZCx89AM9PhW5HwGXvq1wlZGgEKyLBqaEWXpsOXzwDQ86B0/+uZQ8lpKhgRST4VJbA7Auh4L8w8TaYcIuWPZSQo4IVkeBStMa37OFWOPsROOIctxOJtIoKVkSCx/qFMOciiIiGi+dBr5FuJxJpNU1yEpHgsPRxZ9nD5B6+ZQ9VrhLaNIIVEXd5PTD/Dlj0N+fc1nMe07KHEhZUsCLintoKeOlyWP0GjJwGJ/9eyx5K2NCfZBFxR9FqePFS2LESTr0fRk1zO5FIm1LBikj7qquED/4PFv0dYhLgZ3Og/4lupxJpcypYEWkf1sI38+CtW6FsMwy7AE6421n+UCQMqWBFJPB2rYc3boG186HLYLjkLcgZ43YqkYBSwYpI4NTXwCcz4aM/QWQ0nPw7ZzJTZLTbyaSDsdaydmcFi9aXcPLgbnTtFPhlN1WwIhIY386HN26G3Rtg8Flw8r3QqYfbqaSDsNayobiSRetLWLSuhMXrd1FcUQtA5/hoTh+WFfAMKlgRaVtlhfDWDFj1GqT3h5+/An2PdTuVhDlrLQW7qli0roRF60tYvL6EHXucQu3aKZZx/dIZ0zedMX0y6JUW3y6ZVLAi0jYa6mDx350ZwtbCcb+Fo38FUbFuJ5MwtXlXFYvX+wp1XQlby2oAyEiK9ZVpOqP7pNE7IxHjwsUiVLAi4r8NH8HrN0Lxahg4CU75PaTmuJ1Kwsy2smpnhOobpRburgYgLTGG0X3SuLKPM0rtm5nkSqHuTwV7iOrr6yksLKSmpsbtKCErLi6Onj17Eh2tiS4hr3w7vHM7fPU8pGTDlNkw8BS3U0mY2LmnpnF376J1JWwsqQIgJSGaUb3TuGxcb8b0zaB/lyQiItwv1P2pYA9RYWEhycnJ5ObmBsW/kEKNtZaSkhIKCwvp3bu323GktTwNsORhWHAvNNTA+FvgmBsgun2ObUl4Kq6obSzTRetLWF9UCUByXBSjeqdx4egcxvRN57BunYKyUPengj1ENTU1Klc/GGNIT0+nqKjI7SjSWps/g9dvgO1fQd/j4Md/hPS+bqeSELS7so5PN3xXqGt2VACQFBvFUbmpnH9UL8b0yeDwHp2IDIFC3V+rC9YY0wt4EugGeIFZ1tq/GGPSgNlALrAROM9au9v/qMFD5eoffX4hqrIE3r0TPv+Pc0m5c5+Aw08H/f+UFiqrqncK1TdK/WZ7OQAJMZHk5aZxxpFZjOmTzhFZnYmKDP2rqfozgm0AbrTWLjPGJANLjTHzgYuB96y19xljZgAzgF/7H1UANm/ezPjx41m6dClpaWns3r2b4cOHs3DhQnJy9p1Ucu+99/LMM88QGRlJREQE//rXvxg1apRLySVkeb3w+ZPw7l1QW+7MDJ7wa4hNdjuZBLnymnqWbNzVOEJduXUP1kJsVAR5uancdNIAxvRNZ2jPFKLDoFD31+qCtdZuA7b57pcbY1YBWcDpwETf254AFqKCbTO9evXiyiuvZMaMGcyaNYsZM2Ywbdq0A8p10aJFzJs3j2XLlhEbG0txcTF1dXUBzebxeIiMjAzoNqSdbV3uzA7ekg85Y2HSA9DlMLdTSZDaXVnHsoLdfLZxF4vXlfDVljK8FmKiIhiencL04wcwuk8aw7JTiI0K/98VbXIM1hiTCxwJfAp09ZUv1tptxpgubbEN+c7111/PiBEjmDlzJh9//DF//etfD3jPtm3byMjIIDbWOQcxIyOj8bW33nqL6dOnk5GRwfDhw1m/fj3z5s3jrrvuIikpiZtuugmAIUOGMG/ePHJzcznjjDPYvHkzNTU1XHfddUyb5lxaLCkpiRtuuIG3336bBx54gPj4eG644QYqKirIyMjg8ccfp3v37u3wqUibqi51JjAteRgS0uHMf8HQydodLI28Xsu6ogqWbtrtfBXsbpyUFB1pGNYrhWuO7cfovukMz04lLjr8C3V/fhesMSYJeBGYbq3d09Lja8aYacA0gOzsbH9jdCjR0dHcf//9nHLKKbzzzjvExMQc8J6TTjqJe+65hwEDBnDCCScwefJkJkyYQE1NDZdffjnvv/8+/fr1Y/LkyS3a5qOPPkpaWhrV1dUcddRRnH322aSnp1NZWcmQIUO45557qK+vZ8KECcydO5fMzExmz57Nb37zGx599NG2/ggkUKyFL+c4p95UFcNRl8Gxv4H4FLeTicsqaxv4YnNpY5ku27SbPTUNAKQmRDMiJ5VzRvQkLyeNI7I6Ex/T8Qp1f34VrDEmGqdcn7bWvuR7eocxprtv9Nod2Nnc91prZwGzAPLy8qw/Odxy92sr+Xrrnjb9mYf36MSdPx180Pe9+eabdO/enRUrVnDiiQdeSzMpKYmlS5fy0UcfsWDBAiZPnsx9993HsGHD6N27N/379wfgwgsvZNasWQfd3oMPPsjLL78MOMeBv/32W9LT04mMjOTss88GYPXq1fvk8Xg8Gr2Gkp2r4PWbYNPHkDUCLngeegxzO5W4wFpL4e5qlhXsbhyhrtq2B6/vN/WArklMGtqd4dmpjMhJdW2lpGDnzyxiAzwCrLLW/qnJS68CU4H7fLdz/UooB1i+fDnz589n8eLFjBs3jvPPP7/ZIouMjGTixIlMnDiRI444gieeeIJhw4Z971+EqKgovF5v4+O9i2ksXLiQd999l0WLFpGQkMDEiRMbX4uLi2s87mqtZfDgwSxatKit/5MlkGor4IP7YPE/nYlLP/0LHHkRRITfpBNpXm2Dh5Vb97Bs03eFurPcWcc3MSaSYdnO7t7hOakcmZ1K53gtEtMS/oxgxwI/B74yxiz3PXcbTrHOMcZcChQA5/oXMXi1ZKTZ1qy1XHnllcycOZPs7GxuvvlmbrrpJp5++ul93rd69WoiIiIaR6rLly8nJyeHQYMGsWHDBtatW0ffvn159tlnG78nNzeXefPmAbBs2TI2bNgAQFlZGampqSQkJPDNN9+wePHiZrMNHDiQoqIiFi1axJgxY6ivr2fNmjUMHtz+n5O0gLXw9Vx4+zbYswWO/LlzAfTEdLeTSYAVldeyzLebd+mm3Xy5pYy6Bucf173S4jm6bzojclIZnpPKwK7JYXHKjBv8mUX8MfB9+wSOb+3PlR/273//m+zs7MbdsFdddRWPP/44H3zwARMmTGh8X0VFBb/61a8oLS0lKiqKfv36MWvWLOLi4pg1axaTJk0iIyODcePGsWLFCgDOPvtsnnzySYYNG8ZRRx3FgAEDADjllFN46KGHGDp0KAMHDmT06NHNZouJieGFF17g2muvpaysjIaGBqZPn66CDUYl6+CNm2Dd+9DtCDj3ceg10u1UEgAer2XNjnKWbvIVasFuNvmWHIyJjGBIViemjslxCjU7lS7tcJ3UjsJY6/7hz7y8PJufn+92jBZZtW6kopYAABIhSURBVGoVhx0WPqcpLFy4kD/+8Y+NI9f2Em6fY8ior3Yufv7JTIiKcyYwHXUZRGpRt3Cxp6ae5QXOZKRlBbv5vKCUilpnMlJGUgwjclIbvwb36NwhZ/e2JWPMUmttXnOv6W+VSEex+i148xYo3QRHnAcn/Q8kd3M7lfjBWsumkqp9Zvau3lGOtc4ZVYO6deKMI3s4hZqdRq+0eE1Gakcq2A5u7yQoCWOlBfDmDFj9OmQMhKmvQe/xbqeSViitquOrLWV8taWMzwtKWbZpNyWVzgIyybFRHJmTyqlDujMiJ5Uf9epMcpwmI7lJBSsSrkrWQf6jsOQRZzhzwt0w+iqIOvC8aQk+Tct0xZYyviwsa7z+KUBOegITBmYyIieVvJw0+nVJCskF8cOZClYknHjq4ZvXnWLd8AFERMHhZ8AJd0FKL7fTyfc4WJlmpyXwo54pXDAqhyOyOjMkqxMpCfqHUrBTwYqEg9LNsOwJWPYkVOyAzr3guNudU290nDWoqEw7DhWsSKjyemDtu85o9dt3nPNaB5wMeb+AfidAhGaHuk1l2rGpYEPMoVyubvv27UyfPp0lS5YQGxtLbm4uM2fObDy/tTm5ubnk5+fvc3GAQ+Hv90sLlO9wLh+39Ako2wyJXWDcDTBiKqRoXW+3qExlfyrYENPSy9VZaznzzDOZOnUqzz33HOCs5rRjx44fLFgJUtY6x1TzH3WOsXoboPcEOOl/YdAkiNRs0fakMpWWUMGGoJZcrm7BggVER0dzxRVXND43bJizcPv+i0tcc8015OXlcfHFFwNw//33s2DBAgCeeeYZ+vXrR1FREVdccQUFBQUAzJw5k7Fjx1JSUsKUKVMoKipi5MiRBMPCJWGlahcsfwaWPgYlayE+FUZdASMugYx+bqfrEFSm0loq2BDUksvVrVixghEjRrTq53fq1InPPvuMJ598kunTpzNv3jyuu+46rr/+esaNG0dBQQEnn3wyq1at4u6772bcuHHccccdvP766y26Mo8chLWw+TNntLryZfDUQq9RMP4WOPx0iNZSdoHg8Vo2llSyZns5q3eUs3p7OV9tUZlK66lg/fHmDNj+Vdv+zG5HwKn3HXzTB7lcnT+mTJnSeHv99dcD8O677/L11183vmfPnj2Ul5fz4Ycf8tJLzpUKJ02aRGpqaptm6VBq9sCXsyH/Mdi5EmKSYfjPndFqtyFupwsb1lq2ldWweke5U6a+Qv12Z0XjgvfGQI7KVPykgg1BLblc3eDBg3nhhRea/f7vuyzdXk2XUtt73+v1smjRIuLj4w/4eVp6zU/bvnBGq18+D/WV0G2oc8m4IedAbJLb6UJaaVUd32wvZ41vRLq3TMt9FwoH6NoploHdOnF033QGdE1mULdO9OuSpAuGi99UsP5owUizrbX0cnXHHXcct912G//+97+5/PLLAViyZAlVVVX06dOHr7/+mtraWmpqanjvvfcYN25c4/fOnj2bGTNmMHv2bMaMGQPASSedxN/+9jduvvlmwCn5YcOGMX78eJ5++mluv/123nzzTXbv3t1On0SIq6uClS85xbplKUTFwxFnO6fY9BjuDKGkxarrPHy7s9wp0ya7ePde0xSgU1wUA7slc/qwHgzsmszAbp0Y0DVJo1IJGBVsiGnp5eqMMbz88stMnz6d++67j7i4uMbTdHr16sV5553H0KFD6d+/P0ceeeQ+26itrWXUqFF4vd7G68U++OCDXH311QwdOpSGhgbGjx/PQw89xJ133smUKVMYPnw4EyZMIDtbp4n8oJ3fOBOWlj8LtWXO2sCn/h8MnQzxKW6nC3r1Hi8biysbC3TviLRgVxV759fFRkXQv2sSx/TPZGC3pMZRaddOsdrbIu1Kl6s7RLrMWtvoUJ9jQy2ses0ZrW76BCKinclKeb+AnKM1Wm2G12vZUlrNmh3l++ziXV9USZ3HObwRGWHITU9gULdODOiazMBuSQzs1onstAStySvtRperE3HDrg2w9HH4/CmoKobUXGfB/WEXQFKm2+mCRnFF7T67dfdOPqqs8zS+JyslngFdk5g4sEvjqLRvZpKuZSpBTQUr0pY8DbDmLWe0uu49MJEw8FRntNrnWIiIcDuhK2rqPWwqqWJDcQXriyvZWFzJhuJK1hdVNl5uDSA1IZqB3ZI5N69X46i0f9dkOumyaxKCVLAibaFsi7PQ/rInoXwrJPeAibc6i+13znI7Xbuo93jZvKuKjSVOcW4scUp0Y3EVW8uqaXo0KjM5lt7piZxwWFcGdEv2TTpKJiMpRsdJJWyoYFvBWqtfAn4IhuP+fvPUw9bPYeNHsPFjWP8BWC/0Ox4m/RH6nwyR4ffXy+u1bC2rZmOxMxrd0Hhbyebd1Xi83/2/7RQXRe/MJEb2TqN3RiK5GYn0yUgkJz1BFwKXDiH8fgMEWFxcHCUlJaSnp6tkW8FaS0lJCXFxIbYaUUOtczrNxk9g08fOSkv1Vc5rmYfB2Gth+FRI6+1uzjZgraWooraxRJvu0t1UUkVtw3fnUMdHR5KbkcjgHp35ydAe5GYk0tv3lZoQrb8j0qGpYA9Rz549KSwspKioyO0oISsuLo6ePXu6HeOH1VdDYb4z63fjx1C4BBpqAANdB8PwiyBnrDMLODE0rxxUVlXP+uIKZ1duUSUbfMdINxZXUVH73UIM0ZGGnPREctMTmTiwC7np35WoTn0R+X4q2EMUHR1N796hP0qR/dRVOqPSTZ84o9Qt+eCpAxPhLF+ZdynkjoXsMZCQ5nbaFiuvqadgV9UBu3Q3llSxq8nkoggDWanx9M5IIi8njdz0BHpnJtE7PZEeKXFERXbMyVki/lDBSsdUWwGbF/t2+X7i7P71Njizfrv/CEb9EnLGQfbooF0Awuu1FFfUsqW02vna7dxuLa2m0He/6ZKAAN06xZGbkcDJg7vROyOB3hlJ9M5IoFdaArFROuVFpC2pYKVjqNkDBYudSUmbPoGty8F6ICLKWZrw6F/5CnUUxCa7nRaAugYv28q+K869JbrV99zW0prGRRf2So6LIislnqyUeEb2TiMrJZ6eqQnkZiSQm55IYqz+you0F/1tk/BUvRs2LfruGOr2L51ZvhHR0DMPxl3v7PLtNQpiEl2JWF5Tv8/Ic/9R6M7yWvafcN0lOZas1HgGZ3Xm5MHdyEp1yrRHSjxZqfE6X1QkiKhgJTxU7fru+OnGj2HHCsBCZCz0PArG3wy545z70QdeEaittWb3bUxkBN1T4shKiWd8/8zG0uzpu+3WOU67cUVCiApWQlNFkXO6zN5jqDt916qNiodeI+HY25xZvlkj2vwC5fUeLyUVdRRX1FJU7nw17rY9xN23TUs0IymWCK2hKxI2VLAS/LxeZ3WkgsXfjVKLVzuvRSc6x02HnO2MUHsMh6hDv/yY12spra5vLMzG8mxSonsfN51925R234pIUypYcZ+1UFkMpQVQutG53b3J99j35fFd1zMm2ZnZO+xnTqF2/xFENl9c1loq6zz7FGTTUWfT8iyuqKXBe+AKU7FREWQmx5KZHEtOegJ5uamNjzOTYsnw3XbpFKvdtyKyDxWsBJ61zqSj0k37lWeTEt27KtJe8WmQmgNdD3cWy0/Jdnb3dhtKrTUUV9Q55bh61w+OOqvrPQfEiYwwZCTFNJbkoG7J35Wm77m995Nio7SQgoi0igpW2kZN2X6jzv3KtK583/fHdcZ2zqYupQ/VPY6hPD6L0phuFEV1Z4fJpKQ+hrLqeudrRz2lG+op+bicovL3KauubzZCakJ0YzEOz05pvJ+RtG95pibE6FiniAScClZaprbiwFHn7o3Y0gLs7k1E1Jbt8/b6yATK4nqwK7obO5MHsZUuFHgz2dCQwdq6NAqroqksPXB0CZW+L2ed287x0Y1fA7omMbZv+n6jzTgykmNIT4wlJkqrDYlI8AhYwRpjTgH+AkQCD1tr7wvUtqT16hsaqKrYQ11VOTXlJdQVb8SzexOmtIDo8kLiKwtJqtlKYkPpPt9XQwxbbCabvJkU2pFstpkU2szG21KSoNIcUJKdUqIZEh/N2PhoUhKi930tft/HKkwRCWUBKVhjTCTwd+BEoBBYYox51Vr7dSC2F67qPV6q6jzU1HuorvNQXVNNbVU59dXlNFSV01BTjqemAm9tBd66SmxtBdRXElFXSUR9JRENVUQ1VBHlqSLGU02Mt5pYbzVxtpp4W0M8NSSYWjo3s+1aG0WhzWS1zWCryaMkuhtlMT2oiO9BTVIWJrELnRNifGUYxeCEGI5WSYqINArUCHYksNZaux7AGPMccDoQ8IKtriynwdOA12PxWq/vvhePpwGvF7xeDx6v83qD14P1ePFaLx6vx3mf14P1Wrxej++9Fq/Hg/V68Vgv1uvB6/Xi9Xjwei3W+u5b2/iatV7n1uvF6/VgrfM+6/FgPXVQX4WprySivorI+kqiPFVEe6qJ9lQR21iCNSRQQ4KpIZEaUqkhxjS3S7V5tcRQbeKoNfHURsRTFxFPXUw8VZHplEcl4omKx0YnYKMTsbFJmJgkTFwnTGovYtJ7k5DWg84JsfSMj9LsWBGRVghUwWYBm5s8LgRGBWhb+6i5/3BS2dMem2oTXiKoMXFOAUbGUx+dQENUAp6oTnijE6mMSqQiJhFiEjExSUTEJRERm0RUXBJR8clExycTE59MbILzZWKTIDqR2MgoYt3+jxMR6cACVbDNTdHc5yRDY8w0YBpAdnZ2m214zaCrsA21GBPhzBQ1EZiICCJ8t8ZEYCIiG1+LiIgkwhjfc773+t4XERnZ5H4ExkQSGWGc74lwXjfGEBkRiYmMIDLC+XmRkRFEGOc9mL1fxvmKiIbYJIhJgphEIqLiSDCGhDb7BEREJBgEqmALgV5NHvcEtjZ9g7V2FjALIC8v78Az/Ftp1Pm3ttWPEhERabVAzUBZAvQ3xvQ2xsQA5wOvBmhbIiIiQScgI1hrbYMx5hrgbZzTdB611q4MxLZERESCUcDOg7XWvgG8EaifLyIiEsx0kqKIiEgAqGBFREQCQAUrIiISACpYERGRAFDBioiIBIAKVkREJABUsCIiIgGgghUREQkAY22bLQPc+hDGFAGb2vBHZgDFbfjzOgp9bq2nz6519Lm1nj671mvLzy7HWpvZ3AtBUbBtzRiTb63NcztHqNHn1nr67FpHn1vr6bNrvfb67LSLWEREJABUsCIiIgEQrgU7y+0AIUqfW+vps2sdfW6tp8+u9drlswvLY7AiIiJuC9cRrIiIiKvCqmCNMacYY1YbY9YaY2a4nSdUGGMeNcbsNMascDtLKDHG9DLGLDDGrDLGrDTGXOd2plBhjIkzxnxmjPnC99nd7XamUGKMiTTGfG6Mmed2llBijNlojPnKGLPcGJMf8O2Fyy5iY0wksAY4ESgElgBTrLVfuxosBBhjxgMVwJPW2iFu5wkVxpjuQHdr7TJjTDKwFDhDf+YOzhhjgERrbYUxJhr4GLjOWrvY5WghwRhzA5AHdLLW/sTtPKHCGLMRyLPWtsv5w+E0gh0JrLXWrrfW1gHPAae7nCkkWGs/BHa5nSPUWGu3WWuX+e6XA6uALHdThQbrqPA9jPZ9hce/9gPMGNMTmAQ87HYW+WHhVLBZwOYmjwvRLztpJ8aYXOBI4FN3k4QO327O5cBOYL61Vp9dy8wEbgG8bgcJQRZ4xxiz1BgzLdAbC6eCNc08p38RS8AZY5KAF4Hp1to9bucJFdZaj7V2GNATGGmM0eGJgzDG/ATYaa1d6naWEDXWWjscOBW42nd4LGDCqWALgV5NHvcEtrqURToI3/HDF4GnrbUvuZ0nFFlrS4GFwCkuRwkFY4HTfMcSnwOOM8Y85W6k0GGt3eq73Qm8jHNoMWDCqWCXAP2NMb2NMTHA+cCrLmeSMOabqPMIsMpa+ye384QSY0ymMSbFdz8eOAH4xt1Uwc9ae6u1tqe1Nhfnd9z71toLXY4VEowxib7JiBhjEoGTgICeORE2BWutbQCuAd7GmWwyx1q70t1UocEY8yywCBhojCk0xlzqdqYQMRb4Oc4oYrnv68duhwoR3YEFxpgvcf5xPN9aq1NOJJC6Ah8bY74APgNet9a+FcgNhs1pOiIiIsEkbEawIiIiwUQFKyIiEgAqWBERkQBQwYqIiASAClZERCQAVLAiIiIBoIIVEREJABWsiIhIAPw/66rIo6fEeO0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Leinwand erstellen\n",
    "fig = plt.figure()\n",
    "# Graphen erstellen\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "# beide Linien zeichnen\n",
    "ax.plot(x, x**2, label='X Square')\n",
    "ax.plot(x, x**3, label='X Cubed')\n",
    "\n",
    "# Legende erstellen\n",
    "ax.legend(loc=(0.1,0.1))\n",
    "# loc: Position, entweder absolut oder vorgefertigte Positionen: \n",
    "#   z.B. 'best','upper-right', 'center', center-left'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Erscheinungsbild\n",
    "Das Erscheinungsbild der Graphen kann natürlich auch angepasst werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb23142e0d0>]"
      ]
     },
     "execution_count": 60,
     "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": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "# color: red, blue, ... or RGB hex code\n",
    "# linewidht (lw): default1\n",
    "# linestyle (ls): '-' '--' dashed, '-.', ':','steps'\n",
    "# alpha: Transparenz\n",
    "ax.plot(x, y, \n",
    "        color='#FF8C00',\n",
    "        lw='5',\n",
    "        ls='-.'\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb223cc4b90>]"
      ]
     },
     "execution_count": 61,
     "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": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "# color: red, blue, ... or RGB hex code\n",
    "# linewidht (lw): default1\n",
    "# linestyle (ls): '-' '--' dashed, '-.', ':','steps'\n",
    "# alpha: Transparenz\n",
    "ax.plot(x, y, \n",
    "        color='#FF8C00',\n",
    "        marker='o',                # Typ der Marker (z.B: x, 1, +, o)\n",
    "        markersize=10,             # Größe der Marker\n",
    "        markerfacecolor='yellow',  # Grundfarbe der Marker\n",
    "        markeredgewidth=3,         # Breite der Umrandung der Marker \n",
    "        markeredgecolor='green'    # Farbe der Umrandung der Marker\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zoom\n",
    "Mit `set_xlim()` bzw. `set_xlim()` kann man bestimmte Bereiche aus dem Graphen anzeigen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10, 20)"
      ]
     },
     "execution_count": 68,
     "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": [
    "# Leinwand erstellen\n",
    "fig = plt.figure()\n",
    "# Graphen erstellen\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "# Graphen plotten\n",
    "ax.plot(x, y, color='purple', lw=2, ls='--')\n",
    "\n",
    "# Fenster wählen\n",
    "ax.set_xlim([2, 5])\n",
    "ax.set_ylim([-10, 20])"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}