{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06e817b5",
   "metadata": {},
   "source": [
    "# Python Einführung\n",
    "\n",
    "In diesem Jupyter Notebook lernen wir die Grundlegenden Funktionen von Python kennen. Hierbei werden wir die folgenden Kapitel behandeln:\n",
    "<ol>\n",
    "    <li>Datentypen</li>\n",
    "    <li>Packages</li>\n",
    "    <li>Funktionen</li>\n",
    "    <li>Schleifen und Bedingungen</li>\n",
    "    <li>Zufallszahlen</li>\n",
    "    <li>Plotten</li>\n",
    "</ol>\n",
    "\n",
    "Am besten öffnest du ebenfalls ein Python Skript und wandelst die aufgeführten Beispiele nach Lust und Laune ab. \n",
    "\n",
    "Da es laut [Wikipedia](https://de.wikipedia.org/wiki/Hallo-Welt-Programm#Geschichte) Tradition ist, starten wir auch diese Einführung mit einem \"Hello World!\" Programm.\n",
    "\n",
    "Mit dem Befeh <code> print( )</code> wird eine Bildschirmausgabe erzeugt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e3c66fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5706bd",
   "metadata": {},
   "source": [
    "# 1. Datentypen\n",
    "\n",
    "In Python gibt es die üblichen Datentypen:\n",
    "\n",
    "    - Integer (int)\n",
    "    - Float   (float)\n",
    "    - Complex (complex)\n",
    "    - String  (str)\n",
    "    - Boolean (bool)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bb60ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Achtung: Python Besonderheit\n",
    "\n",
    "Bevor wir starten, hier eine kleine Besonderhiet von Python (zumindest im Vergleich zu MATLAB, Java, R,...):\n",
    "\n",
    "In Python wird der Code durch Einrückungen struckturiert, bei den oben genannten Sprachen geschieht das durch Klammern. Du kannst also nicht beiebig zwischen verscheidenen Einrückungen wechseln.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e7201c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uneingerückt läuft ohne Probleme\n"
     ]
    }
   ],
   "source": [
    "print(\"Uneingerückt läuft ohne Probleme\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7aa53b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eingerückt wirft keinen Fehler\n"
     ]
    }
   ],
   "source": [
    "    print(\"Eingerückt wirft keinen Fehler\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d94f7350",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "unexpected indent (Temp/ipykernel_25964/440451899.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"C:\\Users\\rieder\\AppData\\Local\\Temp/ipykernel_25964/440451899.py\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    print(\"Und eingerückt macht Probleme\")\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
     ]
    }
   ],
   "source": [
    "print(\"Aber uneinegerückt\")\n",
    "    print(\"Und eingerückt macht Probleme\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3266bc",
   "metadata": {},
   "source": [
    "Am besten achtest du, dass dein ganzer Code linksbündig ist, außer du bist gerade in Funktionen oder Schleifen."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167ed5c4",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Grundlegendes\n",
    "\n",
    "Mit <code>type()</code> kann der Datentyp eines Objektes ausgegeben werden.\n",
    "\"Zahlen\" werden immer als \"naheliegenden\" Datentype initialisiert, können jedoch mit z.B. <code>int( )</code> als Integer umgewandelt werden. \n",
    "\n",
    "Strings werden mit <code>\" \"</code> oder <code>' '</code> erstellt. \n",
    "Außerdem wird der Imagiänarteil einer komplexen Zahl mit <code>j</code> gekennzeichnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51d4d26f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'complex'>\n",
      "<class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "print(type(42))\n",
    "print(type(42.0))\n",
    "print(type(float(42)))\n",
    "print(type('Zweiundvierzig'))\n",
    "print(type(\"42\"))\n",
    "print(type(4+2j))\n",
    "print(type(True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93648c78",
   "metadata": {},
   "source": [
    "Mit <code>#</code> wird ein Kommentar markiert, d.h. der Inteprter ignoeriert was nach # kommt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a1a84a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wichtige Inforamtion zum Verstehen des Codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c81d456",
   "metadata": {},
   "source": [
    "Selbstverständlich können Werte auch in Variablen gespeichert werden. Dies geschieht mit <code>=</code>.\n",
    "    \n",
    "So wird zum Beispiel durch <code>x=3</code> wird eine Variable x mit dem Wert 3 erstellt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8f7de1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "x=3\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3759d6c",
   "metadata": {},
   "source": [
    "Für numerische Datentypen funktioneren die Grundrechenarten wie zu erwarten. Eine Potenz $a^b$ erhält man durch <code> a**b </code>.\n",
    "\n",
    "Der Modulo-Operator wird in Python mit <code>%%</code> aufgerufen und mit <code>//</code> wird ganzzahlig geteilt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d17f7804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "12\n",
      "63.699999999999996\n",
      "2.0\n",
      "9\n",
      "1\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(3+3)\n",
    "print(16-4)\n",
    "print(7*9.1)\n",
    "print(4/2)\n",
    "print(3**2)\n",
    "\n",
    "print(5%2)\n",
    "print(3//2)\n",
    "print(3//4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72002ad6",
   "metadata": {},
   "source": [
    "Booleans nehmen die Werte <code>True</code> und <code>False</code> an.\n",
    "Python verfügt über die üblichen logischen Operatoren: \"und\", \"oder\", \"nicht\" und \"xor\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8093ef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "True\n",
      "True\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "x=True\n",
    "y=False\n",
    "print(x&y)       #und\n",
    "print( x and y)  #alterntaives und\n",
    "\n",
    "print(x|y)       #oder\n",
    "print(x or y)    #alternatives oder\n",
    "\n",
    "print(not x)     #nicht\n",
    "print(x^y)       #xor, exclusives oder\n",
    "\n",
    "\n",
    "print(x is True) #Werteabfrage für True oder False\n",
    "#print(x is 1)   #klappt nur mit Booleans....1 und 0 wirft einen Fehler\n",
    "print(x==1)      #Vergleich mit \"numerischen True\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5694efb3",
   "metadata": {},
   "source": [
    "Hier wurde schon einer der in Python implementierten Vergleichsoperatoren benutzt.\n",
    "Vergleichsoperatoren vergleichen zwei Objekte und liefern einen Boolen zurück. Für skalare Zahlen sind in Python die folgenden Operatoren definiert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24ff5cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "True\n",
      "False\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "print(1==1)      #gleich\n",
    "print(1!=1)      #ungleich\n",
    "print(1<2)       #kleiner als\n",
    "print(1>2)       #größer als\n",
    "print(1<=2)      #kleiner oder gleich\n",
    "print(1>=2)      #größer oder gleich"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fc2449",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Listen\n",
    "\n",
    "In python lassen sich Listen mit <code> [ ] </code> initialisieren und diverse Operationen darauf ausführen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65f17ad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 0]\n",
      "[0, 1, 2, 3, 4, 5, 6]\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "l=[1,2,3,4,5,6]\n",
    "l.append(0)\n",
    "print(l)\n",
    "l.sort()\n",
    "print(l)\n",
    "print(len(l))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406a3705",
   "metadata": {},
   "source": [
    "# 2. Packages\n",
    "\n",
    "Bis jetzt haben wir nur mit der Basisfunktion von Python gearbeitet, jedoch gibt es unzählige Packages, die Funktionen und Datensätze enthalten.\n",
    "Diese müssen einmalig mit dem Befehl <code>import</code> im Skript geladen werden. \n",
    "Um nicht immer den kompletten Namen des Packages tippen zu müssen ist es übliche ein Package unter einer Abkürzung zu importieren.\n",
    "Das wohl wichtigste Package in Python ist numpy, meist mit np abgekürzt. Die Python Basisversion kann zum Beipsiel nicht mit Matrizen oder Vektoren umgehen, deshalb beginnt in der Regel jedes Skript mit  <code> import numpy as np </code> um mathematische Grundoperationen auszuführen.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aba8fb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f95f182",
   "metadata": {},
   "source": [
    "## Vektoren und Matrizen\n",
    "\n",
    "### Vektoren\n",
    "\n",
    "Wie gerade schon angedeuted lassen sich Werte auch in Arrays speichern. Arrays sind ein, zwei oder n- dimensionale Objekte in denen sich mehrere Werte speichern lassen.\n",
    "Aufgrund des mathematischen Schwerpunkts benutzen wir hier in dieser Einfühung die Worte Array und Vektor, bzw. 2D Array und Matrix synonym.\n",
    "Mit der Funktion <code>np.array( )</code> können wir einen Vektor erstellen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "faea4ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 7]\n",
      "[1 2 7 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "x=np.array([1,2,7])\n",
    "y=np.array([4,5,6])\n",
    "\n",
    "print(x)\n",
    "print(np.append(x,y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0410b6a4",
   "metadata": {},
   "source": [
    "Die oben beschriebenen Grundrechenarten können auch Vektoren angewendet werden und werden dabei elementweise verstanden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0534878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  7 13]\n",
      "[ 4  5 10]\n"
     ]
    }
   ],
   "source": [
    "print(x+y)\n",
    "print(x+3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9fbdb5e",
   "metadata": {},
   "source": [
    "Die grundlegenden Funktionen wie <code>log(),exp(),min(),sin(),..</code> sind in numpy implementiert und lassen sich einfach auf Vektoren anwenden.\n",
    "Besonders nützlich ist die Funktion <code>len()</code>, welche die Länge des Vektors ausgibt.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a308e84c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.69314718 1.94591015]\n",
      "[0.84147098 0.90929743 0.6569866 ]\n",
      "7\n",
      "7\n",
      "3\n",
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "print(np.log(x))\n",
    "print(np.sin(x))\n",
    "print(x.max())\n",
    "print(max(x))\n",
    "\n",
    "print(len(x))\n",
    "print(np.shape(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6420741",
   "metadata": {},
   "source": [
    "Informationen zu Funktionnen können mit <code>help()</code> ausgegeben werden.\n",
    "Übrigens: Durch drücken von \"Tab\" öffnet sich ein drop-down-Menü, welches Funktionen auflistet, die auf dem Object (z.B. auf dem Vektor) ausgeführt werden können, oder gibt mögliche Vervollständigungen einer bereits begonnenen Funktion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f679e25b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on ufunc:\n",
      "\n",
      "sqrt = <ufunc 'sqrt'>\n",
      "    sqrt(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n",
      "    \n",
      "    Return the non-negative square-root of an array, element-wise.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x : array_like\n",
      "        The values whose square-roots are required.\n",
      "    out : ndarray, None, or tuple of ndarray and None, optional\n",
      "        A location into which the result is stored. If provided, it must have\n",
      "        a shape that the inputs broadcast to. If not provided or None,\n",
      "        a freshly-allocated array is returned. A tuple (possible only as a\n",
      "        keyword argument) must have length equal to the number of outputs.\n",
      "    where : array_like, optional\n",
      "        This condition is broadcast over the input. At locations where the\n",
      "        condition is True, the `out` array will be set to the ufunc result.\n",
      "        Elsewhere, the `out` array will retain its original value.\n",
      "        Note that if an uninitialized `out` array is created via the default\n",
      "        ``out=None``, locations within it where the condition is False will\n",
      "        remain uninitialized.\n",
      "    **kwargs\n",
      "        For other keyword-only arguments, see the\n",
      "        :ref:`ufunc docs <ufuncs.kwargs>`.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    y : ndarray\n",
      "        An array of the same shape as `x`, containing the positive\n",
      "        square-root of each element in `x`.  If any element in `x` is\n",
      "        complex, a complex array is returned (and the square-roots of\n",
      "        negative reals are calculated).  If all of the elements in `x`\n",
      "        are real, so is `y`, with negative elements returning ``nan``.\n",
      "        If `out` was provided, `y` is a reference to it.\n",
      "        This is a scalar if `x` is a scalar.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    lib.scimath.sqrt\n",
      "        A version which returns complex numbers when given negative reals.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    *sqrt* has--consistent with common convention--as its branch cut the\n",
      "    real \"interval\" [`-inf`, 0), and is continuous from above on it.\n",
      "    A branch cut is a curve in the complex plane across which a given\n",
      "    complex function fails to be continuous.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> np.sqrt([1,4,9])\n",
      "    array([ 1.,  2.,  3.])\n",
      "    \n",
      "    >>> np.sqrt([4, -1, -3+4J])\n",
      "    array([ 2.+0.j,  0.+1.j,  1.+2.j])\n",
      "    \n",
      "    >>> np.sqrt([4, -1, np.inf])\n",
      "    array([ 2., nan, inf])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.sqrt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5dd395",
   "metadata": {},
   "source": [
    "Vektoren mit Nullen oder bestimmten Zahlenfolgen können wie folgt erzeugt werden\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2045760",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0.]\n",
      "[0.         0.74444444 1.48888889 2.23333333 2.97777778 3.72222222\n",
      " 4.46666667 5.21111111 5.95555556 6.7       ]\n",
      "[1.  3.5 6.  8.5]\n",
      "[3 3 3 6 6 6]\n",
      "[3 6 6 6]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros(3))            #Nullvektor der Länge 3\n",
    "print(np.linspace(0,6.7,10))  #Vektor mit 10 äquidistanten Einträgen zwischen 0 und 6.7\n",
    "print(np.arange(1,11,2.5))    #Vektor mit Werten zwischen 1 und 11 mit dem Abstand 2.5\n",
    "\n",
    "x=[3,6]\n",
    "print(np.repeat(x,3))         #Jedes Element des Vektors x wird 3 mal wiederholt\n",
    "print(np.repeat(x,[1,3]))     #Das erste Element in x wird einmal wiederholt, das zweite 3 mal\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9733846e",
   "metadata": {},
   "source": [
    "Es gibt verschiedene Arten auf die Einträge von Vektoren zuzugreifen, z.B. über den Index, bzw. über Vektoren mit Indizes\n",
    "\n",
    "(Anders als z.B. R oder MATLAB indiziert Python ab 0, d.h. auf das erste Element eines Vektors wird mit 0 zugegriffen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f7e6024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10.]\n",
      "0.0\n",
      "[1. 3.]\n",
      "10.0\n",
      "9.0\n"
     ]
    }
   ],
   "source": [
    "x=np.linspace(0,10,11)\n",
    "print(x)                     \n",
    "\n",
    "print(x[0])                     #Zugriff auf das erste Element (Index 0)\n",
    "\n",
    "inds=[1,3]\n",
    "print(x[inds])                  #Zugriff auf das zweite und vierte Element\n",
    "\n",
    "print(x[-1])                    #Negative Indizierung: Zugriff auf letztes\n",
    "print(x[-2])                    #    bzw. vorletztes Element\n",
    "\n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb48d1df",
   "metadata": {},
   "source": [
    "Gewöhnungsbedürftig an Python ist, dass wenn auf die Indizes 0 bis 5 zugegriffen wird, dass nur 5 Einträge, also nur bis 4 zurückgegeben wird"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7ee065cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 2. 3. 4.]\n"
     ]
    }
   ],
   "source": [
    "print(x[0:5])    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2565cefa",
   "metadata": {},
   "source": [
    "Auch mithilfe von logischen Vektoren lässt sich auf Elemente eines Vektors zugreifen. Dies vereinfacht den Zugriff auf Einträge mit gewissen Eigenschaften, z.B. alle durch zwei teilbaren Einträge. Hierbei wird nur auf Elemente zugegriffen bei denen der Indexvektor True ist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "12ef6fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10.]\n",
      "[ True False  True False  True False  True False  True False  True]\n",
      "[ 0.  2.  4.  6.  8. 10.]\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "\n",
    "teilbar=x%2==0      #Boolenvektor mit True an den Stellen mit Rest 0\n",
    "print(teilbar)\n",
    "\n",
    "print(x[teilbar])   # Ausgabe an allen Trues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd1224a",
   "metadata": {},
   "source": [
    "### Matrizen\n",
    "\n",
    "Wie bereits erwähnt sind Matrizen zweidimensionale Arrays und werden deshalb auch so indiziert.\n",
    "Wir werden hier Matrizen als <code>np.array(...)</code> initialisieren. Eigentlich gibt es in numpy die Subklasse <code>np.matrix(...)</code> welche strikt zwei-dimensional ist und deshalb sehr selten verwendet wird. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ec251fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "[[2. 2. 2.]\n",
      " [2. 2. 2.]\n",
      " [2. 2. 2.]]\n"
     ]
    }
   ],
   "source": [
    "Telefon=np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "Eins=np.ones([3,3])\n",
    "\n",
    "print(Telefon)\n",
    "print(Eins+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5738babc",
   "metadata": {},
   "source": [
    "Und der Zugriff erfolgt entweder elementweise, oder spalten/zeilenweise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "44027b68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "[1 2 3]\n",
      "[2 5 8]\n"
     ]
    }
   ],
   "source": [
    "print(Telefon[2,1])           # Eintrag an Stelle 3,2\n",
    "print(Telefon[0,:])           # 1. Zeile\n",
    "print(Telefon[:,1])           # 2. Spalte\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbea23fe",
   "metadata": {},
   "source": [
    "Auch für Matrizen gibt es unzählige Rechenoperationen. Hier eine kleine Auswahl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0bcc46a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  4  9]\n",
      " [16 25 36]\n",
      " [49 64 81]]\n",
      "[[ 1  4  9]\n",
      " [16 25 36]\n",
      " [49 64 81]]\n",
      "[[ 30  36  42]\n",
      " [ 66  81  96]\n",
      " [102 126 150]]\n",
      "[[ 2.  3.  4.]\n",
      " [ 5.  6.  7.]\n",
      " [ 8.  9. 10.]]\n",
      "[[5. 5. 5.]\n",
      " [5. 5. 5.]\n",
      " [5. 5. 5.]]\n",
      "[[6. 6. 6.]\n",
      " [6. 6. 6.]\n",
      " [6. 6. 6.]]\n",
      "[[1 4 7]\n",
      " [2 5 8]\n",
      " [3 6 9]]\n",
      "[-0.33333333  0.66666667  0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(Telefon*Telefon)               #elementweise Multiplikation\n",
    "print(np.multiply(Telefon,Telefon))  #Alternative elementweise Mulitplikation\n",
    "print(Telefon@Telefon)               #Matrixmultiplikation\n",
    "\n",
    "print(Telefon+Eins)                  #Matrixaddition\n",
    "print(5*Eins)                        #Mulitplikation mit einer Konstanten\n",
    "print(5+Eins)                        #Addition mit einer Konstanten\n",
    "\n",
    "print(Telefon.T)                     #Transponieren\n",
    "\n",
    "b=np.array([1,2,3])           \n",
    "x=np.linalg.solve(Telefon,b)        # Löse Ax=b\n",
    "\n",
    "print(x)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a43e413",
   "metadata": {},
   "source": [
    "# 3. Funktionen\n",
    "\n",
    "Durch Funktionen lassen sich gewisse Operationen leichter an verscheidenen Stellen ausführen und der Code wird dadurch übersichtlicher. Definiert werden Funktionen in einer Funktionsumgebung und können dann an einer beliebeigen Stelle im Skript aufgerufen werden.\n",
    "\n",
    "Innerhalb einer Funktion können Variablen definiert werden, diese überschreiben keine gleichnamigen Variablen außerhalb der Funktion.\n",
    "\n",
    "Falls es lokal, innerhalb einer Funktion eine Variable nicht gibt, wird auf die globale Variable mit dem gleichen Namen zugegriffen.\n",
    "\n",
    "Mit <code>return</code> wird ein Wert zurück gegeben. Ohne das Stichwort return wird die letzte Zeile inerhalb der Funktion zurückgegeben.\n",
    "\n",
    "Achtung: Anders als in R, MATLAB oder Java werden in Python Funktionen(und Schleifen, usw.) nicht durch Klammern oder ähnliches definiert, sondern durch <code>def</code> und Einrückungen. Eine Funktion endet in der letzten Zeile die noch einerückt ist.\n",
    "Beachte den Doppelpunkt am Ende der def-Zeile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c6c78b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "8\n",
      "1\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "def addition(a,b,c):\n",
    "    a=a+b+c              #hier wird a lokal in der Funktion überschrieben\n",
    "    print(a)\n",
    "    return a\n",
    "\n",
    "def globalerZugriff():   #Funktion ohne Argument\n",
    "    return d\n",
    "a=1\n",
    "b=2\n",
    "c=5\n",
    "d=19\n",
    "\n",
    "print(addition(a,b,c))  #ausführen der Funktion\n",
    "print(a)                #das globale a wurde nicht überschrieben\n",
    "\n",
    "print(globalerZugriff())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b5d334",
   "metadata": {},
   "source": [
    "Bei der Übergabe können default-Parameter festgelegt werden, die benutzt werden, sofern nichts anderes angegeben wird. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df77366f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8660254037844386\n",
      "0.8660254037844386\n",
      "1.7320508075688772\n"
     ]
    }
   ],
   "source": [
    "def sinus(x,ampl=1,shift=0):\n",
    "    return ampl*np.sin(x-shift)\n",
    "\n",
    "x=np.pi/3\n",
    "\n",
    "print(sinus(x))\n",
    "print(sinus(x,1,0))\n",
    "print(sinus(x,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7470a04f",
   "metadata": {},
   "source": [
    "Außerdem kann die Reihenfolge der Inputparamter vertauscht werden, wenn diese mit <code> = </code> übergeben werden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9b699fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2622064772118446\n"
     ]
    }
   ],
   "source": [
    "print(sinus(ampl=1.5,x=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81aff41b",
   "metadata": {},
   "source": [
    "# 4. Schleifen und Bedingungen\n",
    "\n",
    "## 4.1 If - Bedingungen\n",
    "Eine If Bedigung hat eine Boolean als Input und führt je nach Zustand eine Operation aus.\n",
    "\n",
    "Die einfachste If- Bedingung hat die Gestalt\n",
    "\n",
    "<code> if Bedingung:\n",
    "    Anweisung \n",
    "</code>\n",
    "\n",
    "Dies lässt sich zu einem \"Entweder, oder\" erweitern\n",
    "\n",
    "<code> if Bedingung:\n",
    "    Anweisung \n",
    "else:\n",
    "    Anweisung alternativ\n",
    "</code>\n",
    "\n",
    "Mit <code> else if </code> lassen sich mehrere Fälle abfragen, wobei immer der erste Fall der zutrifft eintritt\n",
    "\n",
    "<code> if Bedingung:\n",
    "    Anweisung \n",
    "elif Bedinung 2:\n",
    "    Anweisung alternativ\n",
    "else:\n",
    "    Anweisung alternativ 2\n",
    "</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0177829",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kleine Zahl\n",
      "Null oder positiv\n",
      "Bleibt nur noch Null\n"
     ]
    }
   ],
   "source": [
    "x=0\n",
    "\n",
    "if  x<10:\n",
    "    print(\"Kleine Zahl\")\n",
    "    \n",
    "if x<0:\n",
    "    print(\"neativ\")\n",
    "else:\n",
    "    print(\"Null oder positiv\")\n",
    "    \n",
    "if x>0:\n",
    "    print(\"positiv\")\n",
    "elif x<0:\n",
    "    print(\"negativ\")\n",
    "else:\n",
    "    print(\"Bleibt nur noch Null\")\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0205a38",
   "metadata": {},
   "source": [
    "Diese Art von If-Bedinung ist jedoch nur skalarwertig. Wollen wir Vektoren abfragen, können wir dies mit der Hilfe einer Schleife oder folgendermaßen machen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cbe8b790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['negativ' 'positiv oder Null' 'positiv oder Null' 'positiv oder Null'\n",
      " 'negativ']\n"
     ]
    }
   ],
   "source": [
    "vector=np.array([-1,3,6,3,-6])\n",
    "\n",
    "if_output=np.where(vector<0,\"negativ\",\"positiv oder Null\")\n",
    "\n",
    "print(if_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495845e5",
   "metadata": {},
   "source": [
    "## 4.2 For Schleife\n",
    "\n",
    "In einer for-Schleife läuft ein Zähler über jedes Element eines Vektors. In der Regel sind dies Integer von 0 bis n-1, da man so auf andere Vektoren und Matrizen zugreifen kann, doch ist es auch möglich über z.B. Floats, Booleans oder andere Objekte zu iterieren.\n",
    "\n",
    "Der Vektor von 0 bis n-1 kann mit <code>range(n)</code> erzeugt werden. Alternativ kann auch ein Vektor von n1 bis n2-1 mit <code>range(n1,n2)</code> erzeugt werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6828ff26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apfel\n",
      "Banane\n",
      "Kiwi\n",
      "Birne\n",
      "Mango\n",
      "Start bei 3\n",
      "3\n",
      "4\n",
      "5\n",
      "Treffer\n",
      "Daneben\n",
      "Treffer\n",
      "Treffer\n",
      "Daneben\n",
      "Treffer\n",
      "0.0\n",
      "0.3333333333333333\n",
      "0.6666666666666666\n",
      "1.0\n",
      "1.3333333333333333\n",
      "1.6666666666666665\n",
      "2.0\n",
      "2.333333333333333\n",
      "2.6666666666666665\n",
      "3.0\n"
     ]
    }
   ],
   "source": [
    "frucht=['Apfel','Banane','Kiwi','Birne','Mango']\n",
    "\n",
    "for zähler in range(len(frucht)):\n",
    "    print(frucht[zähler])\n",
    "\n",
    "    \n",
    "print(\"Start bei 3\")\n",
    "    \n",
    "for zähler in range(3,6):\n",
    "    print(zähler)\n",
    "    \n",
    "for bool in [True,False,True,True,False,True,]:\n",
    "    if bool:\n",
    "        print(\"Treffer\")\n",
    "    else:\n",
    "        print(\"Daneben\")\n",
    "        \n",
    "for float in np.linspace(0,3,10):\n",
    "    print(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5d3450",
   "metadata": {},
   "source": [
    "## 4.3 While-Schleife\n",
    "\n",
    "Prinzipiell kann jede for-Schleife durch eine while-Schleife ersetzt werden und umgekehrt. Jedoch gibt es durchaus Anwendungen bei dennen das eine sinnvoller ist als das andere.\n",
    "\n",
    "Eine while-Schleife wiederholt so lange einen Prozess,  bis eine Bedingung erfüllt ist.\n",
    "\n",
    "Insbesondere beim hochzählen eines Zählers ist der Operator <code>+=</code> nützlich. Er addiert zum Wert davor den Wert danach.\n",
    "(Analog gibt es auch <code>-=,*=</code> und <code>/=</code> )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "027dce26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apfel\n",
      "birne\n",
      "banane\n",
      "nutella\n",
      "yap\n",
      "yap\n",
      "yap\n",
      "yap\n",
      "100\n",
      "90\n",
      "900\n",
      "90.0\n"
     ]
    }
   ],
   "source": [
    "zähler=0\n",
    "\n",
    "output=['apfel','birne','banane','nutella']\n",
    "\n",
    "while zähler<len(output):\n",
    "    print(output[zähler])\n",
    "    zähler+=1\n",
    "    \n",
    "zähler=0\n",
    "bedingung=True\n",
    "\n",
    "while bedingung:\n",
    "    print(\"yap\")\n",
    "    if zähler==3:\n",
    "        bedingung=False\n",
    "    zähler+=1\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89f7fdd",
   "metadata": {},
   "source": [
    "## 5  Zufallszahlen\n",
    "\n",
    "\n",
    "\n",
    "Um Plots wie Histogramme oder Boxplots zu erklären, führen wir noch kurz die Generierung von Zufallszahlen ein.\n",
    "\n",
    "Einer der wichtigsten Befehle ist hierbei <code> np.random.rand(n) </code>, welcher einen Vektor mit n Zufallszahlen auf [0,1] erzeugt. Flexibler ist der Befehl <code> np.random.choice(x,n,replace,prob) </code>, welcher n Elemente aus x zieht. Mit dem Boolean replace kann \"Zurücklegen\" aktiviert, bzw. deaktiviert werden. Mit dem Vektor prob kann die Wahrscheinlichkeit, dass ein Element gezogen wird gewichtet werden.\n",
    "\n",
    "Alle am PC generierten Zufallszahlen sind nicht echt zufällig, siehe [Wikipedia](https://de.wikipedia.org/wiki/Pseudozufall). Sie werden mit einem Algorithmus berechnet und können nicht vom echten Zufall unterscheiden werden, jedoch können sie reproduziert werden, indem der selbe Startwert, ein sog. Seed übergeben wird. Wird kein Seed übergeben wird bei Python die Systemzeit als Seed benutzt.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0f9a7652",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.653995701646616\n",
      "[0.01816794 0.68804102 0.06483648]\n",
      "[[0.40824512 0.71706737 0.87340484]\n",
      " [0.22356456 0.09396803 0.62469893]\n",
      " [0.48677466 0.50133861 0.04151941]]\n",
      "[7 4 7]\n",
      "[2 2 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy.random as nr\n",
    "\n",
    "nr.seed(None)                            #Wird normal nicht gebraucht, nur hier da der seed der weiter unten angewählt wird bei mehrmaliger Ausführung nicht gelöscht wird\n",
    "\n",
    "print(nr.rand())                         #Zufällige Zahl zwischen 0 und 1\n",
    "print(nr.rand(3))                        #Vetor mit zufällligen 3 Zahlen \n",
    "print(nr.rand(3,3))                      #Zufällige 3x3 Matrix\n",
    "\n",
    "nr.seed(440)                            #Alle Werte über dieser Zeile sind bei jeder Ausführung\n",
    "                                        #des Skripts anders, alle unterhalb immer gleich\n",
    "\n",
    "print(nr.randint(1,10,3))               #3 zufällige Integer zwischen 1 und 9\n",
    "\n",
    "print(nr.choice(range(10),3,True))      #3 Zufällige zahlen aus range(10), mit zurücklegen\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117a2fd3",
   "metadata": {},
   "source": [
    "Es können auch Zufallszahlen nach bestimmten Verteilungen erzeugt werden. Mehr Verteilungen findest du [hier](https://numpy.org/doc/1.16/reference/routines.random.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "b2042903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02090791080071198\n",
      "-1.2084068982801401\n",
      "4.330125065193528\n",
      "7\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "## Stetige Verteilungen\n",
    "print(nr.beta(1,2))\n",
    "print(nr.normal(0,1))\n",
    "print(nr.exponential(3))\n",
    "#[...]\n",
    "\n",
    "## Diskrete Verteilungen\n",
    "print(nr.binomial(10,0.5))\n",
    "print(nr.poisson(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b243eda",
   "metadata": {},
   "source": [
    "# 6. Plotten\n",
    "In Python gibt es mehrere Methoden zu plotten. Die wahrscheinlich üblichste ist mit Hilfe des packages matplotlib und der Funktion subplots.\n",
    "\n",
    "Die Funktion <code>subplots()</code> gibt zwei Objekte zurück, eine \"Figure\" und \"Achsen\". Diese Bezeichnungen sind nicht unbedingt intuitiv, jedoch kann man es sich so merken:\n",
    "\n",
    "Figure bzeichnet das \"Bild\", welches später z.B. abgespeichert werden kann und Achsen, den Plot an sich, welchem dann Elemente wie Kurven oder Punkten hinzugefügt werden können.\n",
    "\n",
    "Dieses Konzept wird gleich etwas klarer.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0769d4",
   "metadata": {},
   "source": [
    "## 6.1 Lineplots, Scatterplots \n",
    "\n",
    "Einfach nur eine Kurve zu plotten ist sehr einfach. Dies wird mit der Methode .plot() auf dem Achsenobjekt gemacht."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "021d0808",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x=np.linspace(0,3*np.pi,1000)\n",
    "y=np.sin(x)\n",
    "\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "ax.plot(x,y)\n",
    "\n",
    "plt.show() # Sonst werden eigenartige Ausgaben erzeugt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095a7464",
   "metadata": {},
   "source": [
    "Der Plot lässt sich jedoch auch beliebig hübscher machen mit z.B. Legenden und Achsenbeschriftungen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "240286df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pi=np.pi\n",
    "x=np.linspace(0,3*pi,1000)\n",
    "y=np.sin(x)\n",
    "y2=np.cos(x)\n",
    "x_line=np.arange(0,3.1,0.5)*pi\n",
    "y_line=np.sin(x_line)\n",
    "\n",
    "\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,5))   # optinonaler Parameter `figsize` ändert die Größe des Plots\n",
    "ax.plot(x,y,label='Sinus',color='r')  # 'mit color lassen sich die Farben des Plots äbdern, der Wert der `label' übergeben wird erscheint in der Legende\n",
    "ax.plot(x,y2,label='Cosinus',color='green')\n",
    "ax.plot(x_line,y_line,label='Approx. Sinus',linestyle='dashed',marker='o')  # ``linestyle` und `marker` ändern das Aussehen der Kurven\n",
    "\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "\n",
    "ax.set_title('Sinus & Cosinus')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c05ad7",
   "metadata": {},
   "source": [
    "Scatterplots lassen sich mit dem Befehl <code>plt.scatter</code> erzeugen. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9d0b7b1c",
   "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": [
    "n=200\n",
    "\n",
    "störterm=(np.random.rand(n)-0.5)*0.5\n",
    "x=np.random.rand(n)*np.pi*3\n",
    "y=np.sin(x)+störterm\n",
    "y2=1/5*x+störterm-1\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "ax.scatter(x,y, alpha=0.1)      #`alpha` lässt die Punkte transparent werden. Je kleiner der Wert, desto weniger gut sichtbar \n",
    "ax.scatter(x,y2,marker='+',color='r')\n",
    "ax.plot(np.array([0,3*np.pi]),1/5*np.array([0,3*np.pi])-1,color='g',linewidth=4)   #Lineplot hinzufügen, `linewidth` steuert die Linienstärke\n",
    "ax.grid()   #Gitter \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dfa1b10-96cb-45ef-bc75-7e1a7291e477",
   "metadata": {},
   "source": [
    "Statt sämtliche Einstellungen händisch durchzuführen, kann auch ein von matplotlib vorgefertigter Style benutzt werden ([hier die Styles](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "8f8777be-44c7-4cf0-8d87-c9afaa109cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(-1,1,1000)\n",
    "y1=x**2\n",
    "y2=x**3\n",
    "\n",
    "\n",
    "#hier eine kleine Auswahl an verschiedenen Styles\n",
    "plt.style.use('seaborn') \n",
    "#plt.style.use('classic') \n",
    "#plt.style.use('dark_background') \n",
    "#plt.style.use('default') \n",
    " \n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,5))\n",
    "ax.plot(x,y1,linewidth=4,label='$x^2$') # In $ eingefasste Zeichen werden im `Mathemodus` kompiliert -> hier Potenzschreibweise\n",
    "ax.plot(x,y2,linewidth=4,label='$x^3$')\n",
    "ax.set_xlabel(\"x-Werte\")\n",
    "ax.set_ylabel(\"f(x)\")\n",
    "ax.set_title(\"Hübsche Plots und ein griechischer Buchstabe $\\Omega$\")\n",
    "ax.legend(loc='lower right')   # mit `loc`lässt sich die Position der legende ändern\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee31a77",
   "metadata": {},
   "source": [
    "Es lassen sich auch zweidimensionale Plots, wie z.B Contourplots erstellen.\n",
    "\n",
    "Hier werden zwei Möglichkeiten präsentiert, welche das Selbe ausdrücken, jedoch die Daten unterscheidlich visualisieren.\n",
    "\n",
    "Diese Gelenheit nutzen wir gleich um mehrere Plots in eine Figure zu packen. Hierfür übergibt man einfach subplot() optionale Argumente der Form <code>plt.subplot(zeilen,spalten)</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "8807b3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('default') \n",
    "\n",
    "delta=0.1\n",
    "x = np.arange(-5, 5, delta)                        # range in x\n",
    "y = np.arange(-5, 5, delta)                        # range in y\n",
    "X, Y = np.meshgrid(x, y)                           # Erzeugt die nötigen \"Gittermatrizen\"\n",
    "Z = np.sqrt(X**2 + Y**2)                           # Funktion die geplottet werden soll. Ausgewertet an jedem Gitterpunkt\n",
    "\n",
    "num=7\n",
    "\n",
    "fig, ax = plt.subplots(2,1)\n",
    "CS = ax[0].contour(X, Y, Z,num)                        # Contourplot mit 'num' Höhenlinien   \n",
    "ax[0].clabel(CS, inline=True, fontsize=10)             # Beschriftung der Höhenlinien \n",
    "ax[0].set_title('Contourplot')\n",
    "\n",
    "ax[1].contourf(X,Y,Z,num)                              # contour-surface-plot\n",
    "ax[1].set_title('Contourfplot')\n",
    "\n",
    "\n",
    "fig.suptitle('Verschiedene Konturplots')               #Titel der ganzen Figure\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2e6672",
   "metadata": {},
   "source": [
    "## 6.2 \"Statistische\" plots\n",
    "\n",
    "Histogramme eignen sich gut, um die Häufigkeit Werten zu visualisieren. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "224fcea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v=np.append(np.random.normal(1,1.5,1000),np.random.normal(7,1,750))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(v,20)                #20 bins\n",
    "ax.set_xlabel('Kotostand in[€]')\n",
    "ax.set_ylabel('Häufigkeit')\n",
    "ax.set_title('Kontostand von Studenten ;)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b142c851-76c6-4c87-8759-6b627ec694db",
   "metadata": {},
   "source": [
    "Hierfür eignen sich auch sogenannte Boxplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "987e6389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expo=np.random.exponential(1,1000)\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(expo,vert=False)\n",
    "ax.set_title(\"Aussagekräftiger Boxplot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca31bcb5-8d35-42e5-b004-8c493de993e6",
   "metadata": {},
   "source": [
    "Hier noch die Möglichkeit durch Balken- oder Tortendiagramme nominale Daten zu visualisieren.\n",
    "\n",
    "Schlussendlich wollen wir noch unsere Plots speichern. Dies kann mit dem Befehl <code>plt.savefig()</code> geamcht werden. Hierzu muss ein Name, ggfs. auch ein Pfad übergeben werden und als optinalen Paramter bietet es sich an, die Auflösung 'dpi - dots per inch' zu übergeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "99b92001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Apfel', 'Banane', 'Birne', 'Erdbeere'\n",
    "sizes = [15, 30, 45, 10]\n",
    "explode = (0, 0.1, 0, 0)  # Nur das zweite Kuchenstück explodieren lassen\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(11,5))\n",
    "ax[0].barh(labels,sizes)\n",
    "ax[0].set_title(\"Horizontale Balken\")\n",
    "ax[1].bar(labels,sizes)\n",
    "ax[1].set_title(\"Vertikale Balken\")\n",
    "ax[2].pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',shadow=True, startangle=90)\n",
    "ax[2].axis('equal')  # Sonst könnte der Kreis eine Ellispe werden\n",
    "ax[2].set_title(\"Mhhhhh.....Torte!\")\n",
    "\n",
    "\n",
    "plt.savefig(\"diagramme.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4715e650-6280-42ab-a202-3c9bdc83d8ff",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}