{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples for general profile shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shape_generator import CrossSection, circle, csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example for standard egg cross section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P0</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P1</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P2</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P3</th>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P4</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P5</th>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P6</th>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P7</th>\n",
       "      <td>6a</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name   r\n",
       "label         \n",
       "P0       0  30\n",
       "P1       1  35\n",
       "P2       2  40\n",
       "P3       3  45\n",
       "P4       4  50\n",
       "P5       5  55\n",
       "P6       6  60\n",
       "P7      6a  65"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_dimensions = csv(\"\"\"\n",
    "label,name,r\n",
    "P0,0,30\n",
    "P1,1,35\n",
    "P2,2,40\n",
    "P3,3,45\n",
    "P4,4,50\n",
    "P5,5,55\n",
    "P6,6,60\n",
    "P7,6a,65\n",
    "\"\"\")\n",
    "unit = 'cm'\n",
    "profile_dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "______________________________\n",
      "Pr_P0  ->  0\n"
     ]
    }
   ],
   "source": [
    "label = 'P0'\n",
    "name, r = profile_dimensions.loc[label].values\n",
    "R = 3 * r\n",
    "roh = r / 2\n",
    "height = r * 3\n",
    "width = r * 2\n",
    "# h1 = roh - (r + roh) / (R - roh) * roh\n",
    "h1 = r/ 5\n",
    "\n",
    "cross_section = CrossSection(label=label, long_label=name, width=width, height=height, unit=unit)\n",
    "cross_section.add(circle(roh, x_m=roh))\n",
    "cross_section.add(h1)\n",
    "cross_section.add(circle(R, x_m=2 * r, y_m=-(R - r)))\n",
    "cross_section.add(2 * r)\n",
    "cross_section.add(circle(r, x_m=2 * r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 :  sqrt(-(x - 15.0)**2 + 225.0) \n",
      "1 :  (6.0, None) \n",
      "2 :  sqrt(-(x - 60.0)**2 + 8100.0) - 60.0 \n",
      "3 :  (60.0, None) \n",
      "4 :  sqrt(-(x - 60.0)**2 + 900.0) \n",
      "            x          y\n",
      "0    0.000000   0.000000\n",
      "1    1.000000   5.385165\n",
      "2    2.000000   7.483315\n",
      "3    3.000000   9.000000\n",
      "4    4.000000  10.198039\n",
      "5    5.000000  11.180340\n",
      "6    6.000000  12.000000\n",
      "7    6.947368  12.700613\n",
      "8    7.894737  13.383029\n",
      "9    8.842105  14.046330\n",
      "10   9.789474  14.691736\n",
      "11  10.736842  15.320361\n",
      "12  11.684211  15.931312\n",
      "13  12.631579  16.526287\n",
      "14  13.578947  17.104415\n",
      "15  14.526316  17.666694\n",
      "16  15.473684  18.214035\n",
      "17  16.421053  18.745608\n",
      "18  17.368421  19.262302\n",
      "19  18.315789  19.764931\n",
      "20  19.263158  20.252706\n",
      "21  20.210526  20.726919\n",
      "22  21.157895  21.186816\n",
      "23  22.105263  21.633137\n",
      "24  23.052632  22.066553\n",
      "25  24.000000  22.486363\n",
      "26  24.947368  22.893228\n",
      "27  25.894737  23.287748\n",
      "28  26.842105  23.669272\n",
      "29  27.789474  24.038393\n",
      "..        ...        ...\n",
      "66  62.812500  29.867920\n",
      "67  63.750000  29.764702\n",
      "68  64.687500  29.631447\n",
      "69  65.625000  29.467938\n",
      "70  66.562500  29.273540\n",
      "71  67.500000  29.047375\n",
      "72  68.437500  28.788889\n",
      "73  69.375000  28.497533\n",
      "74  70.312500  28.172019\n",
      "75  71.250000  27.810744\n",
      "76  72.187500  27.412637\n",
      "77  73.125000  26.976552\n",
      "78  74.062500  26.500192\n",
      "79  75.000000  25.980762\n",
      "80  75.937500  25.416140\n",
      "81  76.875000  24.803919\n",
      "82  77.812500  24.139856\n",
      "83  78.750000  23.418742\n",
      "84  79.687500  22.635871\n",
      "85  80.625000  21.785531\n",
      "86  81.562500  20.858575\n",
      "87  82.500000  19.843135\n",
      "88  83.437500  18.725922\n",
      "89  84.375000  17.488836\n",
      "90  85.312500  16.102877\n",
      "91  86.250000  14.523688\n",
      "92  87.187500  12.681193\n",
      "93  88.125000  10.439558\n",
      "94  89.062500   7.443128\n",
      "95  90.000000   0.000000\n",
      "\n",
      "[96 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "cross_section.generator(show=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.999</td>\n",
       "      <td>5.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.998</td>\n",
       "      <td>7.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.997</td>\n",
       "      <td>9.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.996</td>\n",
       "      <td>10.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.004</td>\n",
       "      <td>11.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.003</td>\n",
       "      <td>11.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.948</td>\n",
       "      <td>12.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.893</td>\n",
       "      <td>13.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.838</td>\n",
       "      <td>14.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.792</td>\n",
       "      <td>14.688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.737</td>\n",
       "      <td>15.318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.682</td>\n",
       "      <td>15.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.636</td>\n",
       "      <td>16.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.581</td>\n",
       "      <td>17.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.526</td>\n",
       "      <td>17.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15.471</td>\n",
       "      <td>18.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.425</td>\n",
       "      <td>18.747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17.370</td>\n",
       "      <td>19.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18.315</td>\n",
       "      <td>19.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19.260</td>\n",
       "      <td>20.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20.214</td>\n",
       "      <td>20.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>21.159</td>\n",
       "      <td>21.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22.104</td>\n",
       "      <td>21.636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.049</td>\n",
       "      <td>22.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>24.003</td>\n",
       "      <td>22.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>24.948</td>\n",
       "      <td>22.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25.893</td>\n",
       "      <td>23.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>26.838</td>\n",
       "      <td>23.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>27.792</td>\n",
       "      <td>24.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>62.811</td>\n",
       "      <td>29.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>63.747</td>\n",
       "      <td>29.763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>64.692</td>\n",
       "      <td>29.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>65.628</td>\n",
       "      <td>29.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>66.564</td>\n",
       "      <td>29.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>67.500</td>\n",
       "      <td>29.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>68.436</td>\n",
       "      <td>28.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>69.372</td>\n",
       "      <td>28.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>70.308</td>\n",
       "      <td>28.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>71.253</td>\n",
       "      <td>27.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>72.189</td>\n",
       "      <td>27.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>73.125</td>\n",
       "      <td>26.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>74.061</td>\n",
       "      <td>26.496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>74.997</td>\n",
       "      <td>25.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>75.942</td>\n",
       "      <td>25.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>76.878</td>\n",
       "      <td>24.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>77.814</td>\n",
       "      <td>24.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>78.750</td>\n",
       "      <td>23.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>79.686</td>\n",
       "      <td>22.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>80.622</td>\n",
       "      <td>21.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>81.558</td>\n",
       "      <td>20.862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>82.503</td>\n",
       "      <td>19.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>83.439</td>\n",
       "      <td>18.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>84.375</td>\n",
       "      <td>17.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>85.311</td>\n",
       "      <td>16.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>86.247</td>\n",
       "      <td>14.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>87.192</td>\n",
       "      <td>12.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>88.128</td>\n",
       "      <td>10.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>89.064</td>\n",
       "      <td>7.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>90.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         x       y\n",
       "0    0.000   0.000\n",
       "1    0.999   5.382\n",
       "2    1.998   7.479\n",
       "3    2.997   9.000\n",
       "4    3.996  10.197\n",
       "5    5.004  11.178\n",
       "6    6.003  11.997\n",
       "7    6.948  12.699\n",
       "8    7.893  13.383\n",
       "9    8.838  14.049\n",
       "10   9.792  14.688\n",
       "11  10.737  15.318\n",
       "12  11.682  15.930\n",
       "13  12.636  16.524\n",
       "14  13.581  17.100\n",
       "15  14.526  17.667\n",
       "16  15.471  18.216\n",
       "17  16.425  18.747\n",
       "18  17.370  19.260\n",
       "19  18.315  19.764\n",
       "20  19.260  20.250\n",
       "21  20.214  20.727\n",
       "22  21.159  21.186\n",
       "23  22.104  21.636\n",
       "24  23.049  22.068\n",
       "25  24.003  22.482\n",
       "26  24.948  22.896\n",
       "27  25.893  23.292\n",
       "28  26.838  23.670\n",
       "29  27.792  24.039\n",
       "..     ...     ...\n",
       "66  62.811  29.871\n",
       "67  63.747  29.763\n",
       "68  64.692  29.628\n",
       "69  65.628  29.466\n",
       "70  66.564  29.277\n",
       "71  67.500  29.043\n",
       "72  68.436  28.791\n",
       "73  69.372  28.494\n",
       "74  70.308  28.170\n",
       "75  71.253  27.810\n",
       "76  72.189  27.414\n",
       "77  73.125  26.973\n",
       "78  74.061  26.496\n",
       "79  74.997  25.983\n",
       "80  75.942  25.416\n",
       "81  76.878  24.804\n",
       "82  77.814  24.138\n",
       "83  78.750  23.418\n",
       "84  79.686  22.635\n",
       "85  80.622  21.789\n",
       "86  81.558  20.862\n",
       "87  82.503  19.845\n",
       "88  83.439  18.729\n",
       "89  84.375  17.487\n",
       "90  85.311  16.101\n",
       "91  86.247  14.526\n",
       "92  87.192  12.681\n",
       "93  88.128  10.440\n",
       "94  89.064   7.443\n",
       "95  90.000   0.000\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_section.df_abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = cross_section.profile_abs_figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example for custom cross section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "______________________________\n",
      "Pr_test  ->  Kreis\n",
      "-----\n",
      "sqrt(-(x - 20.0)**2 + 400.0)\n",
      "-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "no = 'test'\n",
    "name = 'Kreis'\n",
    "r = 20  # cm\n",
    "unit = 'cm'\n",
    "\n",
    "kreis = CrossSection(label=no, long_label=name, height=2*r, unit=unit)\n",
    "kreis.add_and_show(circle(r, x_m=r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "sqrt(-(x - 20.0)**2 + 400.0)\n",
      "(20.0, 20.0)\n",
      "-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kreis.add_and_show(r,r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "sqrt(-(x - 20.0)**2 + 400.0)\n",
      "(20.0, 20.0)\n",
      "(0.57735, 'slope')\n",
      "-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kreis.add_and_show(30, '°slope')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----\n",
      "sqrt(-(x - 20.0)**2 + 400.0)\n",
      "(20.0, 20.0)\n",
      "(0.57735, 'slope')\n",
      "(None, 30.0)\n",
      "-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kreis.add_and_show(None,1.5*r)"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}