{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "f536eab6-2776-4535-972a-3ece6be99b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some configs that should not be shown!\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6597a28-a4fc-46bb-a9b2-3cafbc944ef5",
   "metadata": {},
   "source": [
    "# Start here\n",
    "# First headline\n",
    "Some nice text!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c9dbddc-f5f5-4f58-a2e2-dfff0faf517f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.3 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n",
      "\n",
      "[notice] A new release of pip available: 22.3 -> 22.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas==1.5.2 -q\n",
    "!pip install matplotlib==3.6.2 -q"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f41454c-0bf0-4e53-992b-34fb47bf7dd1",
   "metadata": {},
   "source": [
    "Import the classes necessary to run the software."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c4c7d02-c06b-49c3-a6e6-9819e9eae44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5173f8-7c64-40f4-8fbe-e3bf28ad96a3",
   "metadata": {},
   "source": [
    "A bit of sample code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "66ada60d-8a49-473b-bc38-a40999ba761a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "362880"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "for i in range(1, 10):\n",
    "    a *= i\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbc2840-9793-4a92-802b-78d0b333e632",
   "metadata": {},
   "source": [
    "Acces a document by key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e5fd8c8-200f-4857-b63d-1172b842f11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "{'test': 1, 'more content': 'some_more1', 'more content1': 'some_more2'}"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a document by key\n",
    "some_dict = {\"test\": 1, \"more content\": \"some_more1\", \"more content1\": \"some_more2\"}\n",
    "some_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e818c095-e2d9-40c7-9234-ceddcb27cddb",
   "metadata": {},
   "source": [
    "Pandas Example!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8eb6d0be-9fc0-4c89-ad18-801c0349d5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrll}\n",
      "\\toprule\n",
      "{} &  test & more content & more content1 \\\\\n",
      "\\midrule\n",
      "0 &     1 &   some\\_more1 &    some\\_more2 \\\\\n",
      "1 &     1 &   some\\_more1 &    some\\_more2 \\\\\n",
      "2 &     1 &   some\\_more1 &    some\\_more2 \\\\\n",
      "3 &     1 &   some\\_more1 &    some\\_more2 \\\\\n",
      "4 &     1 &   some\\_more1 &    some\\_more2 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame([some_dict] * 10)\n",
    "print(df.head().to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1237c23-2ab4-4fa0-9895-0691c20827b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "N = 400\n",
    "t = np.linspace(0, 2 * np.pi, N)\n",
    "r = 0.5 + np.cos(t)\n",
    "x, y = r * np.cos(t), r * np.sin(t)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, \"k\")\n",
    "ax.set(aspect=1)\n",
    "plt.show()\n",
    "plt.savefig(\"diagramm.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# End here\n",
    "\n",
    "This should not be shown!"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2007221166.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\phhor\\AppData\\Local\\Temp\\ipykernel_2708\\2007221166.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    This should not be shownm\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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