Jupiter_EMC_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "capital-soccer",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import basic librarys \n",
    "import numpy as np #is used for mathematical formulas\n",
    "import matplotlib.pyplot as plt # is used for plot grafics\n",
    "\n",
    "\n",
    "#Time [ms] simulation    \n",
    "T = 20 \n",
    "#Number of samples\n",
    "N = 100\n",
    "#Time [ms] for Plot    \n",
    "t=np.linspace(0,T,N*T)\n",
    "\n",
    "#Define sinus funktion amplidude[x], frequency[Hz], angle in [degree], order[1,2,3,...,n]\n",
    "def sin(a,f,angle_deg,k):\n",
    "    phi=angle_deg*(np.pi/180)\n",
    "    x=a*np.sin(2*np.pi*f*k*t/1000+phi*k)\n",
    "    return x\n",
    "\n",
    "\n",
    "#Define Plot \n",
    "def graph(x,legend):\n",
    "    plt.plot(t,x,linewidth=2, label=legend)\n",
    "\n",
    "#Define Grid for PLots  \n",
    "def grid():\n",
    "    plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0)\n",
    "    plt.grid(b=True, color='black', linestyle='-',alpha=0.1)\n",
    "    plt.xlim(0, T)\n",
    "    plt.ylabel(r'$\\hat x$ [Amplitude]', fontsize='x-large')\n",
    "    axes1 = plt.gca()\n",
    "    axes1.set_xlabel(\"t [Time in ms]\", fontsize='x-large')  \n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "generic-track",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "####dictionary####\n",
    "\n",
    "#Sinus like this\n",
    "y=sin(230,50,45,1)\n",
    "\n",
    "#Basic polt\n",
    "graph('funktion','legend')\n",
    "\n",
    "#Basic plot title\n",
    "plt.title('Sinus Funkion',fontsize='x-large')\n",
    "\n",
    "#print plot\n",
    "plt.show()\n",
    "'''\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "designed-bibliography",
   "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": [
    "#example Sinus\n",
    "fx=sin(1,50,0,1) #use funktion (x=1, f=50Hz,phi=0°,1st Order)\n",
    "graph(fx,\"Sinus\")#use funktion for generating a graph\n",
    "plt.show()       #show the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "helpful-onion",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "floral-elevation",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "responsible-ozone",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "recent-colorado",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "pandas           : 1.2.0\n",
      "numpy            : 1.19.2\n",
      "matplotlib.pyplot: unknown\n",
      "watermark        : 2.2.0\n",
      "\n",
      "Compiler    : MSC v.1916 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 10\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel\n",
      "CPU cores   : 4\n",
      "Architecture: 64bit\n",
      "\n",
      " \n",
      "Last updated: Wed Feb 24 2021 12:07:52Mitteleuropäische Zeit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "\n",
    "# python, ipython, packages, and machine characteristics\n",
    "%watermark -v -m -p pandas,numpy,matplotlib,watermark \n",
    "\n",
    "# date\n",
    "print (\" \")\n",
    "%watermark -u -n -t -z "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beneficial-singer",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}