{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n",
    "Die Bilbiothek [matplotlib](https://matplotlib.org) kann für die Erzeugung verschiedener Diagramme genutzt werden. \n",
    "\n",
    "Sie kann mit Hilfe von `pip` leicht installiert werden:\n",
    "\n",
    "    $ pip install matplotlib\n",
    "    \n",
    "Wenn alles geklappt hat, können wir die Bilbiothek importieren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir erstellen eine Liste mit X- und Y-Werten, die in einem Koordinatensystem gezeichnet werden soll."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs = [-3,-2,-1,0,1,2,3]\n",
    "ys = [9,4,1,0,1,4,9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit Hilfe der Funktion `plot` können einfache Plots erstellt werden. Der Plot wird anschließend mit der Funktion `savefig` als PNG- und SVG-Bild abgespeichert, um ihn weiterverwenden zu können."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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(xs, ys)\n",
    "plt.savefig(\"plot.png\")\n",
    "plt.savefig(\"plot.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es wurde eine PNG-Datei im Bitmap- und eine SVG-Datei im Vektorformat gespeichert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plot.png: PNG image data, 432 x 288, 8-bit/color RGBA, non-interlaced\r\n",
      "plot.svg: SVG Scalable Vector Graphics image\r\n"
     ]
    }
   ],
   "source": [
    "! file plot.png plot.svg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das PNG-Bild\n",
    "\n",
    "![Bild PNG](plot.png) \n",
    "\n",
    "Das SVG-Bild\n",
    "\n",
    "![Bild SVG](plot.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bevor ein neuer Plot erstellt werden kann, muss mit `plt.clf()` der bisherige Plot zurückgesetzt werden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für viele weitere Plots und zahlreiche Konfigurationsmöglichkeiten hilft ein Blick in die [Beispiele](https://matplotlib.org/tutorials/introductory/sample_plots.html) und die Beschreibung der Methode [plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot)."
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}