commit 33029d1389c7828c84469d4c3dd623827fcbf57e
Author: timoschneider <timoschneider04@gmail.com>
Date:   Thu Jun 6 16:30:11 2024 +0200

    added jupyter nb

diff --git a/Evaluation.ipynb b/Evaluation.ipynb
new file mode 100644
index 0000000..1239d8b
--- /dev/null
+++ b/Evaluation.ipynb
@@ -0,0 +1,635 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4a89aebf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ce41f624",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = []\n",
+    "values = []\n",
+    "\n",
+    "files = os.listdir('out')\n",
+    "files.sort()\n",
+    "\n",
+    "for filename in files:\n",
+    "    if filename.endswith(\".json\"):\n",
+    "        with open('out/'+filename) as output:\n",
+    "            output_json = json.load(output)\n",
+    "            samples += output_json['x']\n",
+    "            values += output_json['y']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e2edd6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data={'samples': samples, 'avg': 0, 'std': 0, 'values': values})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "67cd6668",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['avg'] = df.apply(lambda row: np.array(row['values']).sum() / len(row['values']), axis=1)\n",
+    "df['std'] = df.apply(lambda row: np.sqrt(np.array(row['values']).var()), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3080eb16",
+   "metadata": {
+    "scrolled": false
+   },
+   "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>samples</th>\n",
+       "      <th>avg</th>\n",
+       "      <th>std</th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.599000</td>\n",
+       "      <td>2.799410</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 6.500000000000005, 0.0, 6.5000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.631500</td>\n",
+       "      <td>2.070829</td>\n",
+       "      <td>[0.0, 0.0, 3.2500000000000027, 6.5000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1.664000</td>\n",
+       "      <td>1.272142</td>\n",
+       "      <td>[2.6000000000000023, 3.900000000000003, 0.0, 2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.654900</td>\n",
+       "      <td>0.908579</td>\n",
+       "      <td>[0.6500000000000006, 3.2500000000000027, 1.950...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20</td>\n",
+       "      <td>1.133600</td>\n",
+       "      <td>0.673453</td>\n",
+       "      <td>[0.0, 1.3000000000000012, 0.6500000000000006, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>50</td>\n",
+       "      <td>1.220960</td>\n",
+       "      <td>0.431168</td>\n",
+       "      <td>[1.1700000000000008, 1.1700000000000008, 1.040...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>100</td>\n",
+       "      <td>1.106820</td>\n",
+       "      <td>0.310923</td>\n",
+       "      <td>[1.1700000000000008, 1.235000000000001, 1.4950...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>200</td>\n",
+       "      <td>1.097460</td>\n",
+       "      <td>0.212839</td>\n",
+       "      <td>[0.9100000000000008, 1.4625000000000012, 0.975...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>500</td>\n",
+       "      <td>1.102712</td>\n",
+       "      <td>0.136280</td>\n",
+       "      <td>[0.9880000000000008, 1.0790000000000008, 1.001...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>1.106060</td>\n",
+       "      <td>0.100732</td>\n",
+       "      <td>[1.0075000000000007, 1.209000000000001, 0.9555...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2000</td>\n",
+       "      <td>1.112072</td>\n",
+       "      <td>0.070502</td>\n",
+       "      <td>[1.277250000000001, 1.098500000000001, 1.08875...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.108662</td>\n",
+       "      <td>0.043625</td>\n",
+       "      <td>[1.090700000000001, 1.1089000000000009, 1.1232...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.105114</td>\n",
+       "      <td>0.032244</td>\n",
+       "      <td>[1.1180000000000008, 1.0796500000000009, 1.122...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20000</td>\n",
+       "      <td>1.108188</td>\n",
+       "      <td>0.022374</td>\n",
+       "      <td>[1.0741250000000009, 1.123525000000001, 1.1371...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>50000</td>\n",
+       "      <td>1.106677</td>\n",
+       "      <td>0.014118</td>\n",
+       "      <td>[1.1052600000000008, 1.1278800000000009, 1.115...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>100000</td>\n",
+       "      <td>1.107418</td>\n",
+       "      <td>0.010126</td>\n",
+       "      <td>[1.1035700000000008, 1.0917400000000008, 1.111...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>200000</td>\n",
+       "      <td>1.106957</td>\n",
+       "      <td>0.007035</td>\n",
+       "      <td>[1.108770000000001, 1.1150425000000008, 1.1036...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>500000</td>\n",
+       "      <td>1.106960</td>\n",
+       "      <td>0.004477</td>\n",
+       "      <td>[1.102660000000001, 1.099852000000001, 1.11181...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1000000</td>\n",
+       "      <td>1.107122</td>\n",
+       "      <td>0.003119</td>\n",
+       "      <td>[1.1097190000000008, 1.106612000000001, 1.1135...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2000000</td>\n",
+       "      <td>1.107152</td>\n",
+       "      <td>0.002248</td>\n",
+       "      <td>[1.1059262500000009, 1.1094655000000009, 1.107...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>5000000</td>\n",
+       "      <td>1.107034</td>\n",
+       "      <td>0.001477</td>\n",
+       "      <td>[1.105048100000001, 1.106229800000001, 1.11045...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10000000</td>\n",
+       "      <td>1.107038</td>\n",
+       "      <td>0.000980</td>\n",
+       "      <td>[1.107393300000001, 1.107971150000001, 1.10760...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>20000000</td>\n",
+       "      <td>1.107093</td>\n",
+       "      <td>0.000682</td>\n",
+       "      <td>[1.107080650000001, 1.1058697000000008, 1.1065...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>50000000</td>\n",
+       "      <td>1.107103</td>\n",
+       "      <td>0.000438</td>\n",
+       "      <td>[1.106985230000001, 1.106864330000001, 1.10673...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>100000000</td>\n",
+       "      <td>1.107097</td>\n",
+       "      <td>0.000300</td>\n",
+       "      <td>[1.107135120000001, 1.106441765000001, 1.10693...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>200000000</td>\n",
+       "      <td>1.107100</td>\n",
+       "      <td>0.000229</td>\n",
+       "      <td>[1.107198267500001, 1.1066414125000008, 1.1074...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>500000000</td>\n",
+       "      <td>1.107083</td>\n",
+       "      <td>0.000136</td>\n",
+       "      <td>[1.107150577000001, 1.1068575310000008, 1.1071...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1000000000</td>\n",
+       "      <td>1.107091</td>\n",
+       "      <td>0.000095</td>\n",
+       "      <td>[1.107090458500001, 1.107013505000001, 1.10705...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>2000000000</td>\n",
+       "      <td>1.107100</td>\n",
+       "      <td>0.000067</td>\n",
+       "      <td>[1.106987911250001, 1.1070893340000008, 1.1071...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5000000000</td>\n",
+       "      <td>1.107091</td>\n",
+       "      <td>0.000044</td>\n",
+       "      <td>[1.107101422700001, 1.107028651300001, 1.10703...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>10000000000</td>\n",
+       "      <td>1.107071</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>[1.1070528943500009, 1.107091172850001, 1.1070...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>20000000000</td>\n",
+       "      <td>1.107083</td>\n",
+       "      <td>0.000028</td>\n",
+       "      <td>[1.107116142600001, 1.107052413675001, 1.10708...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>50000000000</td>\n",
+       "      <td>1.107088</td>\n",
+       "      <td>0.000010</td>\n",
+       "      <td>[1.107083811470001, 1.107081873690001, 1.10711...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>100000000000</td>\n",
+       "      <td>1.107081</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>[1.107080884000001]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>200000000000</td>\n",
+       "      <td>1.107081</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>[1.107080584805001]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>500000000000</td>\n",
+       "      <td>1.107090</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>[1.107089721517001]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         samples       avg       std  \\\n",
+       "0              1  1.599000  2.799410   \n",
+       "1              2  1.631500  2.070829   \n",
+       "2              5  1.664000  1.272142   \n",
+       "3             10  1.654900  0.908579   \n",
+       "4             20  1.133600  0.673453   \n",
+       "5             50  1.220960  0.431168   \n",
+       "6            100  1.106820  0.310923   \n",
+       "7            200  1.097460  0.212839   \n",
+       "8            500  1.102712  0.136280   \n",
+       "9           1000  1.106060  0.100732   \n",
+       "10          2000  1.112072  0.070502   \n",
+       "11          5000  1.108662  0.043625   \n",
+       "12         10000  1.105114  0.032244   \n",
+       "13         20000  1.108188  0.022374   \n",
+       "14         50000  1.106677  0.014118   \n",
+       "15        100000  1.107418  0.010126   \n",
+       "16        200000  1.106957  0.007035   \n",
+       "17        500000  1.106960  0.004477   \n",
+       "18       1000000  1.107122  0.003119   \n",
+       "19       2000000  1.107152  0.002248   \n",
+       "20       5000000  1.107034  0.001477   \n",
+       "21      10000000  1.107038  0.000980   \n",
+       "22      20000000  1.107093  0.000682   \n",
+       "23      50000000  1.107103  0.000438   \n",
+       "24     100000000  1.107097  0.000300   \n",
+       "25     200000000  1.107100  0.000229   \n",
+       "26     500000000  1.107083  0.000136   \n",
+       "27    1000000000  1.107091  0.000095   \n",
+       "28    2000000000  1.107100  0.000067   \n",
+       "29    5000000000  1.107091  0.000044   \n",
+       "30   10000000000  1.107071  0.000040   \n",
+       "31   20000000000  1.107083  0.000028   \n",
+       "32   50000000000  1.107088  0.000010   \n",
+       "33  100000000000  1.107081  0.000000   \n",
+       "34  200000000000  1.107081  0.000000   \n",
+       "35  500000000000  1.107090  0.000000   \n",
+       "\n",
+       "                                               values  \n",
+       "0   [0.0, 0.0, 0.0, 6.500000000000005, 0.0, 6.5000...  \n",
+       "1   [0.0, 0.0, 3.2500000000000027, 6.5000000000000...  \n",
+       "2   [2.6000000000000023, 3.900000000000003, 0.0, 2...  \n",
+       "3   [0.6500000000000006, 3.2500000000000027, 1.950...  \n",
+       "4   [0.0, 1.3000000000000012, 0.6500000000000006, ...  \n",
+       "5   [1.1700000000000008, 1.1700000000000008, 1.040...  \n",
+       "6   [1.1700000000000008, 1.235000000000001, 1.4950...  \n",
+       "7   [0.9100000000000008, 1.4625000000000012, 0.975...  \n",
+       "8   [0.9880000000000008, 1.0790000000000008, 1.001...  \n",
+       "9   [1.0075000000000007, 1.209000000000001, 0.9555...  \n",
+       "10  [1.277250000000001, 1.098500000000001, 1.08875...  \n",
+       "11  [1.090700000000001, 1.1089000000000009, 1.1232...  \n",
+       "12  [1.1180000000000008, 1.0796500000000009, 1.122...  \n",
+       "13  [1.0741250000000009, 1.123525000000001, 1.1371...  \n",
+       "14  [1.1052600000000008, 1.1278800000000009, 1.115...  \n",
+       "15  [1.1035700000000008, 1.0917400000000008, 1.111...  \n",
+       "16  [1.108770000000001, 1.1150425000000008, 1.1036...  \n",
+       "17  [1.102660000000001, 1.099852000000001, 1.11181...  \n",
+       "18  [1.1097190000000008, 1.106612000000001, 1.1135...  \n",
+       "19  [1.1059262500000009, 1.1094655000000009, 1.107...  \n",
+       "20  [1.105048100000001, 1.106229800000001, 1.11045...  \n",
+       "21  [1.107393300000001, 1.107971150000001, 1.10760...  \n",
+       "22  [1.107080650000001, 1.1058697000000008, 1.1065...  \n",
+       "23  [1.106985230000001, 1.106864330000001, 1.10673...  \n",
+       "24  [1.107135120000001, 1.106441765000001, 1.10693...  \n",
+       "25  [1.107198267500001, 1.1066414125000008, 1.1074...  \n",
+       "26  [1.107150577000001, 1.1068575310000008, 1.1071...  \n",
+       "27  [1.107090458500001, 1.107013505000001, 1.10705...  \n",
+       "28  [1.106987911250001, 1.1070893340000008, 1.1071...  \n",
+       "29  [1.107101422700001, 1.107028651300001, 1.10703...  \n",
+       "30  [1.1070528943500009, 1.107091172850001, 1.1070...  \n",
+       "31  [1.107116142600001, 1.107052413675001, 1.10708...  \n",
+       "32  [1.107083811470001, 1.107081873690001, 1.10711...  \n",
+       "33                                [1.107080884000001]  \n",
+       "34                                [1.107080584805001]  \n",
+       "35                                [1.107089721517001]  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "91bacd93",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xffff7989b290>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['log-linear']], layout='constrained')\n",
+    "ax = axs['log-linear']\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_xlabel('samples')\n",
+    "ax.set_ylabel('intagral')\n",
+    "ax.plot(df['samples'], df['avg'], color=\"red\", linewidth=1.5)\n",
+    "ax.plot(df['samples'], df['avg'] + df['std'], color=\"blue\", linewidth=1)\n",
+    "ax.plot(df['samples'], df['avg'] - df['std'], color=\"blue\", linewidth=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e4a992a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['log-linear']], layout='constrained')\n",
+    "ax = axs['log-linear']\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_xlabel('samples')\n",
+    "ax.set_ylabel('intagral')\n",
+    "\n",
+    "ax.plot(df['samples'][18:36], df['avg'][18:36], color=\"red\", linewidth=1.5)\n",
+    "ax.plot(df['samples'][18:33], df['avg'][18:33] + df['std'][18:33], color=\"blue\", linewidth=1)\n",
+    "ax.plot(df['samples'][18:33], df['avg'][18:33] - df['std'][18:33], color=\"blue\", linewidth=1)\n",
+    "\n",
+    "for i in range(18, 33):\n",
+    "    ax.plot(df['samples'][i], np.array(df['values'][i]).max(), \"go\", linewidth=1)\n",
+    "    ax.plot(df['samples'][i], np.array(df['values'][i]).min(), \"go\", linewidth=1)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3ed7523e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['log-linear']], layout='constrained')\n",
+    "ax = axs['log-linear']\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_xlabel('samples')\n",
+    "ax.set_ylabel('intagral')\n",
+    "\n",
+    "ax.plot(df['samples'][24:36], df['avg'][24:36], color=\"red\", linewidth=1.5)\n",
+    "ax.plot(df['samples'][24:33], df['avg'][24:33] + df['std'][24:33], color=\"blue\", linewidth=1)\n",
+    "ax.plot(df['samples'][24:33], df['avg'][24:33] - df['std'][24:33], color=\"blue\", linewidth=1)\n",
+    "\n",
+    "for i in range(24, 33):\n",
+    "    ax.plot(df['samples'][i], np.array(df['values'][i]).max(), \"go\", linewidth=1)\n",
+    "    ax.plot(df['samples'][i], np.array(df['values'][i]).min(), \"go\", linewidth=1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4b52e2ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xffff774ecd90>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['log-log']], layout='constrained')\n",
+    "ax = axs['log-log']\n",
+    "ax.set_xscale('log')\n",
+    "ax.set_yscale('log')\n",
+    "ax.set_xlabel('samples')\n",
+    "ax.set_ylabel('standart deviation')\n",
+    "ax.plot(df['samples'], np.abs(df['std']), color=\"orange\", linewidth=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cf83bc8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.plot(10**np.linspace(0, 12, 12), np.pi/np.sqrt(10**np.linspace(0, 12, 12)), color=\"blue\", linewidth=1)\n",
+    "\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d95ac22",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MonteCarlo Function.ipynb b/MonteCarlo Function.ipynb
new file mode 100644
index 0000000..311c3c9
--- /dev/null
+++ b/MonteCarlo Function.ipynb	
@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5867788",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "from scipy.integrate import quad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ba84224d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Point:\n",
+    "    x: float\n",
+    "    y: float\n",
+    "\n",
+    "    def __init__(self, point: (float, float)):\n",
+    "        self.x, self.y = point\n",
+    "\n",
+    "\n",
+    "class LinearBounds:\n",
+    "    lower: float\n",
+    "    higher: float\n",
+    "\n",
+    "    def __init__(self, lower: float, higher: float):\n",
+    "        self.lower = lower\n",
+    "        self.higher = higher\n",
+    "\n",
+    "    def length(self) -> float:\n",
+    "        return self.higher - self.lower\n",
+    "\n",
+    "    def get_random_value(self) -> float:\n",
+    "        return random.uniform(self.lower, self.higher)\n",
+    "\n",
+    "\n",
+    "class Bounds:\n",
+    "    x: LinearBounds\n",
+    "    y: LinearBounds\n",
+    "\n",
+    "    def __init__(self, x: LinearBounds, y: LinearBounds):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "\n",
+    "    def area(self) -> float:\n",
+    "        return self.x.length() * self.y.length()\n",
+    "\n",
+    "    def get_random_point(self) -> Point:\n",
+    "        return Point((self.x.get_random_value(), self.y.get_random_value()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2120dbad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getRealIntegral(f, bounds: LinearBounds) -> float:\n",
+    "    return quad(f, bounds.lower, bounds.higher)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bdc2240a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getIsInside(f, p: Point) -> bool:\n",
+    "    y_0: float = f(p.x)\n",
+    "    if abs(y_0) > abs(p.y) and y_0*p.y >= 0:\n",
+    "        return True\n",
+    "\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d3f58a1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def function(x: float) -> float:\n",
+    "    return x**2-3\n",
+    "\n",
+    "bounds = Bounds(LinearBounds(-1, 4), LinearBounds(-4, 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "93672263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The approximated Integral of the function is: 6.67280\n",
+      "The real Integral of the function is: 6.66667\n",
+      "That's an error of 0.00613 or 0.09200% \n",
+      "And the whole thing took 1.37645 Seconds for 1000000 samples\n"
+     ]
+    }
+   ],
+   "source": [
+    "startTime = time.time()\n",
+    "pointsInside: int = 0\n",
+    "samples: int = 10**6\n",
+    "\n",
+    "for i in range(samples):\n",
+    "    toTestPoint = bounds.get_random_point()\n",
+    "    if getIsInside(function, toTestPoint):\n",
+    "        if toTestPoint.y >= 0:\n",
+    "            pointsInside += 1\n",
+    "        else:\n",
+    "            pointsInside -= 1\n",
+    "\n",
+    "integral = (pointsInside / samples) * bounds.area()\n",
+    "print(f\"The approximated Integral of the function is: {integral:.5f}\")\n",
+    "\n",
+    "real_value: float = getRealIntegral(function, bounds.x)\n",
+    "print(f\"The real Integral of the function is: {real_value:.5f}\")\n",
+    "\n",
+    "error: float = abs(real_value-integral)\n",
+    "print(f\"That's an error of {error:.5f} or {(error/real_value)*100:.5f}% \")\n",
+    "\n",
+    "print(f\"And the whole thing took {time.time()-startTime:.5f} Seconds for {samples} samples\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce2f249e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MonteCarlo visual Circle.ipynb b/MonteCarlo visual Circle.ipynb
new file mode 100644
index 0000000..4756020
--- /dev/null
+++ b/MonteCarlo visual Circle.ipynb	
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5867788",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ba84224d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Point:\n",
+    "    x: float\n",
+    "    y: float\n",
+    "\n",
+    "    def __init__(self, point: (float, float)):\n",
+    "        self.x, self.y = point\n",
+    "\n",
+    "\n",
+    "class LinearBounds:\n",
+    "    lower: float\n",
+    "    higher: float\n",
+    "\n",
+    "    def __init__(self, lower: float, higher: float):\n",
+    "        self.lower = lower\n",
+    "        self.higher = higher\n",
+    "\n",
+    "    def length(self) -> float:\n",
+    "        return self.higher - self.lower\n",
+    "\n",
+    "    def get_random_value(self) -> float:\n",
+    "        return random.uniform(self.lower, self.higher)\n",
+    "\n",
+    "\n",
+    "class Bounds:\n",
+    "    x: LinearBounds\n",
+    "    y: LinearBounds\n",
+    "\n",
+    "    def __init__(self, x: LinearBounds, y: LinearBounds):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "\n",
+    "    def area(self) -> float:\n",
+    "        return self.x.length() * self.y.length()\n",
+    "\n",
+    "    def get_random_point(self) -> Point:\n",
+    "        return Point((self.x.get_random_value(), self.y.get_random_value()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2120dbad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getRealIntegral(f, bounds: LinearBounds) -> float:\n",
+    "    return np.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bdc2240a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getIsInside(p: Point) -> bool:\n",
+    "    if p.x**2 + p.y**2 <= 1:\n",
+    "        return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d3f58a1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def function(x: float) -> float:\n",
+    "    return np.sqrt(1 - x**2)\n",
+    "\n",
+    "bounds = Bounds(LinearBounds(-1.25, 1.25), LinearBounds(-1.25, 1.25))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e00e9ced",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2674/2110725246.py:2: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  return np.sqrt(1 - x**2)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xffff69a5c310>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['linear-linear']], layout='constrained')\n",
+    "ax = axs['linear-linear']\n",
+    "ax.set_xscale('linear')\n",
+    "ax.set_yscale('linear')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('f')\n",
+    "ax.set_box_aspect(1)\n",
+    "samples = np.linspace(bounds.x.lower, bounds.x.higher, 5000)\n",
+    "\n",
+    "ax.plot(samples, function(samples), color=\"blue\", linewidth=3)\n",
+    "ax.plot(samples, -function(samples), color=\"blue\", linewidth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "93672263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The approximated Integral of the function is: 3.15625\n",
+      "The real Integral of the function is: 3.14159\n",
+      "That's an error of 0.01 or 0.46656% \n",
+      "And the whole thing took 0.50002 Seconds for 1000 samples\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "startTime = time.time()\n",
+    "pointsInside: int = 0\n",
+    "samples: int = 10**3\n",
+    "\n",
+    "for i in range(samples):\n",
+    "    toTestPoint = bounds.get_random_point()\n",
+    "    if getIsInside(toTestPoint):\n",
+    "        pointsInside += 1\n",
+    "        ax.plot(toTestPoint.x, toTestPoint.y, \"o\", color=\"limegreen\")\n",
+    "    else:\n",
+    "        ax.plot(toTestPoint.x, toTestPoint.y, \"ro\")\n",
+    "\n",
+    "integral = (pointsInside / samples) * bounds.area()\n",
+    "print(f\"The approximated Integral of the function is: {integral:.5f}\")\n",
+    "\n",
+    "real_value: float = getRealIntegral(function, bounds.x)\n",
+    "print(f\"The real Integral of the function is: {real_value:.5f}\")\n",
+    "\n",
+    "error: float = abs(real_value-integral)\n",
+    "print(f\"That's an error of {error:.2f} or {(error/real_value)*100:.5f}% \")\n",
+    "\n",
+    "print(f\"And the whole thing took {time.time()-startTime:.5f} Seconds for {samples} samples\")\n",
+    "\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce2f249e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MonteCarlo visual Function.ipynb b/MonteCarlo visual Function.ipynb
new file mode 100644
index 0000000..ff25d8f
--- /dev/null
+++ b/MonteCarlo visual Function.ipynb	
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5867788",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from scipy.integrate import quad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ba84224d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Point:\n",
+    "    x: float\n",
+    "    y: float\n",
+    "\n",
+    "    def __init__(self, point: (float, float)):\n",
+    "        self.x, self.y = point\n",
+    "\n",
+    "\n",
+    "class LinearBounds:\n",
+    "    lower: float\n",
+    "    higher: float\n",
+    "\n",
+    "    def __init__(self, lower: float, higher: float):\n",
+    "        self.lower = lower\n",
+    "        self.higher = higher\n",
+    "\n",
+    "    def length(self) -> float:\n",
+    "        return self.higher - self.lower\n",
+    "\n",
+    "    def get_random_value(self) -> float:\n",
+    "        return random.uniform(self.lower, self.higher)\n",
+    "\n",
+    "\n",
+    "class Bounds:\n",
+    "    x: LinearBounds\n",
+    "    y: LinearBounds\n",
+    "\n",
+    "    def __init__(self, x: LinearBounds, y: LinearBounds):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "\n",
+    "    def area(self) -> float:\n",
+    "        return self.x.length() * self.y.length()\n",
+    "\n",
+    "    def get_random_point(self) -> Point:\n",
+    "        return Point((self.x.get_random_value(), self.y.get_random_value()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2120dbad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getRealIntegral(f, bounds: LinearBounds) -> float:\n",
+    "    return quad(f, bounds.lower, bounds.higher)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bdc2240a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getIsInside(f, p: Point) -> bool:\n",
+    "    y_0: float = f(p.x)\n",
+    "    if abs(y_0) > abs(p.y) and y_0*p.y >= 0:\n",
+    "        return True\n",
+    "\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d3f58a1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def function(x: float) -> float:\n",
+    "    return x**2-3\n",
+    "\n",
+    "bounds = Bounds(LinearBounds(-1, 4), LinearBounds(-4, 15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e00e9ced",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0xffff78e10310>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['linear-linear']], layout='constrained')\n",
+    "ax = axs['linear-linear']\n",
+    "ax.set_xscale('linear')\n",
+    "ax.set_yscale('linear')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('f')\n",
+    "samples = np.linspace(bounds.x.lower, bounds.x.higher, 10000)\n",
+    "ax.plot([bounds.x.lower, bounds.x.higher], [0, 0], \"k\", linewidth=2)\n",
+    "ax.plot([0, 0], [bounds.y.lower - 1, bounds.y.higher + 1], \"k\", linewidth=2)\n",
+    "\n",
+    "ax.plot(samples, function(samples), color=\"blue\", linewidth=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "93672263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The approximated Integral of the function is: 6.74500\n",
+      "The real Integral of the function is: 6.66667\n",
+      "That's an error of 0.08 or 1.17500% \n",
+      "And the whole thing took 0.52917 Seconds for 1000 samples\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "startTime = time.time()\n",
+    "pointsInside: int = 0\n",
+    "samples: int = 10**3\n",
+    "\n",
+    "for i in range(samples):\n",
+    "    toTestPoint = bounds.get_random_point()\n",
+    "    if getIsInside(function, toTestPoint):\n",
+    "        if toTestPoint.y >= 0:\n",
+    "            pointsInside += 1\n",
+    "            ax.plot(toTestPoint.x, toTestPoint.y, \"o\", color=\"limegreen\")\n",
+    "        else:\n",
+    "            pointsInside -= 1\n",
+    "            ax.plot(toTestPoint.x, toTestPoint.y, \"o\", color=\"darkgreen\")\n",
+    "    else:\n",
+    "        ax.plot(toTestPoint.x, toTestPoint.y, \"ro\")\n",
+    "\n",
+    "integral = (pointsInside / samples) * bounds.area()\n",
+    "print(f\"The approximated Integral of the function is: {integral:.5f}\")\n",
+    "\n",
+    "real_value: float = getRealIntegral(function, bounds.x)\n",
+    "print(f\"The real Integral of the function is: {real_value:.5f}\")\n",
+    "\n",
+    "error: float = abs(real_value-integral)\n",
+    "print(f\"That's an error of {error:.2f} or {(error/real_value)*100:.5f}% \")\n",
+    "\n",
+    "print(f\"And the whole thing took {time.time()-startTime:.5f} Seconds for {samples} samples\")\n",
+    "\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce2f249e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/MonteCarlo visual Heart.ipynb b/MonteCarlo visual Heart.ipynb
new file mode 100644
index 0000000..310097a
--- /dev/null
+++ b/MonteCarlo visual Heart.ipynb	
@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b5867788",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ba84224d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Point:\n",
+    "    x: float\n",
+    "    y: float\n",
+    "\n",
+    "    def __init__(self, point: (float, float)):\n",
+    "        self.x, self.y = point\n",
+    "\n",
+    "\n",
+    "class LinearBounds:\n",
+    "    lower: float\n",
+    "    higher: float\n",
+    "\n",
+    "    def __init__(self, lower: float, higher: float):\n",
+    "        self.lower = lower\n",
+    "        self.higher = higher\n",
+    "\n",
+    "    def length(self) -> float:\n",
+    "        return self.higher - self.lower\n",
+    "\n",
+    "    def get_random_value(self) -> float:\n",
+    "        return random.uniform(self.lower, self.higher)\n",
+    "\n",
+    "\n",
+    "class Bounds:\n",
+    "    x: LinearBounds\n",
+    "    y: LinearBounds\n",
+    "\n",
+    "    def __init__(self, x: LinearBounds, y: LinearBounds):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "\n",
+    "    def area(self) -> float:\n",
+    "        return self.x.length() * self.y.length()\n",
+    "\n",
+    "    def get_random_point(self) -> Point:\n",
+    "        return Point((self.x.get_random_value(), self.y.get_random_value()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c2bd4e3",
+   "metadata": {},
+   "source": [
+    "## Heart function\n",
+    "\n",
+    "$\\left(x^{2}+y^{2}-1\\right)^{3}=x^{2}y^{3}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bdc2240a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getIsInside(p: Point) -> bool:\n",
+    "    if (p.x**2 + p.y**2 - 1)**3 <= p.x**2 * p.y**3:\n",
+    "        return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d3f58a1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bounds = Bounds(LinearBounds(-1.25, 1.25), LinearBounds(-1.25, 1.25))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "93672263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The approximated Integral of the function is: 3.64688\n",
+      "And the whole thing took 0.58856 Seconds for 2000 samples\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplot_mosaic([['linear-linear']], layout='constrained')\n",
+    "ax = axs['linear-linear']\n",
+    "ax.set_xscale('linear')\n",
+    "ax.set_yscale('linear')\n",
+    "ax.set_xlabel('x')\n",
+    "ax.set_ylabel('f')\n",
+    "ax.set_box_aspect(1)\n",
+    "\n",
+    "startTime = time.time()\n",
+    "pointsInside: int = 0\n",
+    "samples: int = 2 * 10**3\n",
+    "\n",
+    "for i in range(samples):\n",
+    "    toTestPoint = bounds.get_random_point()\n",
+    "    if getIsInside(toTestPoint):\n",
+    "        pointsInside += 1\n",
+    "        ax.plot(toTestPoint.x, toTestPoint.y, \"ro\")\n",
+    "\n",
+    "integral = (pointsInside / samples) * bounds.area()\n",
+    "print(f\"The approximated Integral of the function is: {integral:.5f}\")\n",
+    "\n",
+    "print(f\"And the whole thing took {time.time()-startTime:.5f} Seconds for {samples} samples\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce2f249e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/out/output01.json b/out/output01.json
new file mode 100644
index 0000000..d3a25f9
--- /dev/null
+++ b/out/output01.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/out/output02.json b/out/output02.json
new file mode 100644
index 0000000..fb37695
--- /dev/null
+++ b/out/output02.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/out/output03.json b/out/output03.json
new file mode 100644
index 0000000..c7c75ea
--- /dev/null
+++ b/out/output03.json
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\ No newline at end of file
diff --git a/out/output04.json b/out/output04.json
new file mode 100644
index 0000000..471afc5
--- /dev/null
+++ b/out/output04.json
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\ No newline at end of file
diff --git a/out/output05.json b/out/output05.json
new file mode 100644
index 0000000..7241b92
--- /dev/null
+++ b/out/output05.json
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\ No newline at end of file
diff --git a/out/output06.json b/out/output06.json
new file mode 100644
index 0000000..dd03b33
--- /dev/null
+++ b/out/output06.json
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\ No newline at end of file
diff --git a/out/output07.json b/out/output07.json
new file mode 100644
index 0000000..0054cc5
--- /dev/null
+++ b/out/output07.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/out/output08.json b/out/output08.json
new file mode 100644
index 0000000..1e247b6
--- /dev/null
+++ b/out/output08.json
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\ No newline at end of file
diff --git a/out/output09.json b/out/output09.json
new file mode 100644
index 0000000..40ed3bc
--- /dev/null
+++ b/out/output09.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/out/output10.json b/out/output10.json
new file mode 100644
index 0000000..55023a7
--- /dev/null
+++ b/out/output10.json
@@ -0,0 +1 @@
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diff --git a/out/output11.json b/out/output11.json
new file mode 100644
index 0000000..3cb3a8f
--- /dev/null
+++ b/out/output11.json
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diff --git a/out/output12.json b/out/output12.json
new file mode 100644
index 0000000..f18f733
--- /dev/null
+++ b/out/output12.json
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\ No newline at end of file