diff --git a/general-introduction.ipynb b/general-introduction.ipynb
index 6ef2bf7..01ed7f0 100644
--- a/general-introduction.ipynb
+++ b/general-introduction.ipynb
@@ -6,15 +6,18 @@
    "source": [
     "# Kleine Einführung in Python\n",
     "\n",
-    "Liebe Studenten, dieses Notebook soll eine kurz Einführung für Python und die Jupyternotebooks im allgemeinen darstellen.\n",
-    "Wir beginnen mit absoluten Basics und wollen uns im Verlauf als Beispiel zur Nutzung der Notebooks anschauen wie man eine Funktion berechnen und darstellen kann.\n",
+    "Liebe Studierende, dieses Notebook soll eine kurze Einführung in Python und die Jupyter-Notebooks zur Mechanik Vorlesung bieten. Wir beginnen mit absoluten Python Basics und wollen uns im Verlauf als Beispiel zur Nutzung der Notebooks anschauen, wie man eine Funktion berechnen und darstellen kann.\n",
     "\n",
-    "## Hello World."
+    "Übrigens, dies ist eine Zelle vom Type 'Markdown'. In diesem Zellentyp kann Latex-Syntax verwendet werden, insbesondere zur Darstellung von Formeln, z.B. \n",
+    "\n",
+    "$$\\vec{F}=-\\vec{\\nabla}V(\\vec{r})$$\n",
+    "\n",
+    "## Hello World: Einfache Ein- und Ausgaben auf dem Bildschirm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -31,21 +34,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Who are you?laura\n",
-      "Hello laura , I am your Notebook!\n"
+      "Who are you? Steffen\n",
+      "Hello  Steffen , I am your Notebook!\n"
      ]
     }
    ],
    "source": [
-    "name=input('Who are you?') \n",
-    "print('Hello',name,', I am your Notebook!') #ein string kann einfach mit Variablen kombiniert werden"
+    "name=input('Who are you? ') \n",
+    "print('Hello ',name,', I am your Notebook!') #ein string kann einfach mit Variablen kombiniert werden"
    ]
   },
   {
@@ -57,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -81,15 +84,13 @@
    "source": [
     "# Typen und elementare Operationen\n",
     "\n",
-    "- Alle Werte sind Objekte und haben einen bestimmten Typ zugewiesen, der bestimmt welche Operationen ausgführt werden dürfen\n",
-    "- Der Typ kann mit `type()` bestimmt werden\n",
-    "\n",
-    "\n"
+    "- Alle Werte in Python sind Objekte und werden einem bestimmten Typ zugewiesen, dieser legt fest, welche Operationen ausgeführt werden dürfen\n",
+    "- Der Typ einer Größe kann mit `type()` bestimmt werden\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +99,7 @@
        "int"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -109,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -118,7 +119,7 @@
        "float"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -129,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +139,7 @@
        "7"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -151,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -160,7 +161,7 @@
        "16"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -180,7 +181,7 @@
        "16"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -198,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +208,7 @@
        "1.3333333333333333"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -225,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +235,7 @@
        "3"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +262,7 @@
        "6"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -290,7 +291,7 @@
        "bool"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -301,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +311,7 @@
        "True"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -330,7 +331,7 @@
        "False"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -345,12 +346,12 @@
    "source": [
     "# Listen\n",
     "\n",
-    "Listen können beliebige Objekte enthalten, die durch Kommata getrennte werden und in eckigen Klammern eingeschlassen sind."
+    "Listen können beliebige Objekte enthalten, die durch Kommata getrennt werden und in eckigen Klammern eingeschlossen sind."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -368,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -377,7 +378,7 @@
        "4"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -397,7 +398,7 @@
        "2"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -408,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -417,7 +418,7 @@
        "'a'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -430,14 +431,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Listen können mittels *Slicing* bequeem geteilt werden:\n",
+    "Listen können mittels *Slicing* bequem geteilt werden:\n",
     "- liste[start : stop]\n",
     "- liste[start : stop : schrittweite]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +454,7 @@
        "[4, False, 'x', 3.0]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -467,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -476,7 +477,7 @@
        "[2, 4, 'a', False, 2, 'x', 4, 3.0, 5]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,20 +491,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`a.+TAB` zeigt alle möglichen Methoden and die bei diesem Opjekt verwendet werden können."
+    "`a.+TAB` zeigt alle möglichen Methoden an, die bei diesem Opjekt verwendet werden können."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-25-847a753940c9>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-30-847a753940c9>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-25-847a753940c9>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    liste2.\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-30-847a753940c9>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    liste2.\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -517,7 +518,7 @@
    "source": [
     "# Funktionen\n",
     "\n",
-    "Sollen Code-Teile mehrfach genutzt werden, ist es üblich Funktionen zu erstellen, um das Programm übersichtlicher zu gestalten und Wiederholungen zu vermeiden.\n",
+    "Sollen Code-Teile mehrfach genutzt werden, empfiehlt es sich, Funktionen zu erstellen, was das Programm übersichtlicher gestaltet und Wiederholungen vermeidet.\n",
     "\n",
     "Funktionen werden mit dem Befehl `def` eingeleitet:\n",
     "\n",
@@ -538,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -569,13 +570,13 @@
     "## if/else\n",
     "\n",
     "```python\n",
-    "if <Bedingung>:\n",
+    "if <Bedingung1>:\n",
     "    <Befehle>\n",
     "    ...\n",
-    "elif <Bedingung>:\n",
+    "elif <Bedingung2>:\n",
     "    <Befehle>\n",
     "    ...\n",
-    "elif <Bedingung>\n",
+    "elif <Bedingung3>\n",
     "    <Befehle>\n",
     "    ...\n",
     "else:\n",
@@ -607,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -666,17 +667,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "55\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ende=10\n",
     "i=sum=0\n",
@@ -694,13 +687,13 @@
    "source": [
     "# Numpy\n",
     "\n",
-    "*Numpy* ist eine Bibliothek die unter anderem effiziente Array-Typen zur Verfügung stellt um Python auch sinnvoll für wissenschaftliches Rechnen nutzen zu können.\n",
-    "Will man Bibliotheken nutzen müssen diese zuerst mit `import` eingebunden werden dabei kann man gantze Bibliotheken oder nur Teile einbinden und diese auch benennen. (So wird numpy zumeist als np bezeichnet um den schreibaufwand zu reduzieren).\n"
+    "*Numpy* ist eine Bibliothek die unter anderem effiziente Array-Typen zur Verfügung stellt, womit Python auch sinnvoll für wissenschaftliches Rechnen genutzt werden kann.\n",
+    "Will man Bibliotheken nutzen, so müssen diese zuerst mit `import` eingebunden werden. Dabei kann man ganze Bibliotheken oder nur Teile einbinden und diese auch benennen. (So wird numpy zumeist als np bezeichnet um den Schreibaufwand zu reduzieren).\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -710,7 +703,7 @@
        "       [4, 5, 6]])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -726,12 +719,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "a ist ein 2 Dimensionales Array. Mehrdimensionale Arrays können mit Numpy einfach durch verschachtelte listen erzeuge werden."
+    "a ist ein 2-dimensionales Array. Mehrdimensionale Arrays können mit Numpy einfach durch verschachtelte Listen erzeugt werden."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -740,7 +733,7 @@
        "2"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -751,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -760,7 +753,7 @@
        "(2, 3)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -771,17 +764,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.,  0.,  0.],\n",
-       "       [ 0.,  0.,  0.]])"
+       "array([[0., 0., 0.],\n",
+       "       [0., 0., 0.]])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,19 +785,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.,  1.,  1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.,  1.,  1.],\n",
-       "       [ 1.,  1.,  1.,  1.,  1.]])"
+       "array([[1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1., 1.]])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -826,17 +819,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-5. , -4.5, -4. , -3.5, -3. , -2.5, -2. , -1.5, -1. , -0.5,  0. ,\n",
+       "        0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#print(np.linspace.__docstring__)\n",
-    "np.linspace(-5,5,20)"
+    "np.linspace(-5,5,21)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -845,19 +850,19 @@
        "array([ 0,  2,  4,  6,  8, 10])"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "array=np.arange(12) # ähnlich wie range gibt aber array zurück\n",
+    "array=np.arange(12) # ähnlich wie range, gibt aber array zurück\n",
     "array[::2] # slicing funktioniert wie bei Listen  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +881,7 @@
        "       [ 8,  9, 10, 11]])"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -892,24 +897,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Vorsicht! Zuweisungen von array erzeugen keine Kopie des Array sondern lediglich eine Referenz. Will man eine Kopie kann dies durch `array.copy`erzielt werden."
+    "Vorsicht! Zuweisungen von array erzeugen keine Kopie des Array sondern lediglich eine Referenz. Will man eine Kopie, kann dies durch `array.copy`erzielt werden."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0 1 2 3 4]\n",
-      "[ 0  1 -1  3  4]\n",
-      "[0 1 2 3 4]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "a = np.arange(5)\n",
     "print(a)\n",
@@ -926,32 +921,21 @@
    "source": [
     "## Elementare Operationen\n",
     "\n",
-    "Die meisten operationen wie `+,-,*` werden elementweise durchgführt. Dafür ist es meist erforderlich, dass die beiden array die gleich `.shape` haben"
+    "Die meisten operationen wie `+,-,*` werden elementweise durchgeführt. Dafür ist es meist erforderlich, dass die beiden array die gleiche `.shape` haben"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 6,  8, 10])"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.array([2,3,4])+np.array([4,5,6]) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -960,7 +944,7 @@
        "array([ 0.        ,  0.84147098,  0.90929743,  0.14112001, -0.7568025 ])"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,19 +962,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0  1  2  3]\n",
-      " [ 4  5  6  7]\n",
-      " [ 8  9 10 11]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "brray=np.arange(12).reshape(3,4)\n",
     "print(brray)"
@@ -998,22 +972,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "66\n",
-      "[ 6 22 38]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# summieren von Elementen\n",
     "\n",
-    "print(np.sum(brray)) # summe über alle Elemente\n",
+    "print(np.sum(brray)) # Summe über alle Elemente\n",
     "\n",
     "print(np.sum(brray,axis=1)) #axis=1 enspricht der Summation der Zeilenvektoren (axis=0 Spaltenvektoren resp.)\n",
     "\n"
@@ -1030,21 +995,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0 1 2]\n",
-      " [3 4 5]]\n",
-      "[0 1 2]\n",
-      "[[0 2 4]\n",
-      " [3 5 7]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "a=np.arange(6).reshape(2,3)\n",
     "print(a)\n",
@@ -1060,43 +1013,19 @@
    "source": [
     "## Matrix-Operationen\n",
     "\n",
-    "Numpy und scipy bringen viele Funktionalitäten bezüglich Matritzen mit. Es lohnt sich hier ein weiterführendes Tutorial anzuschauen!\n"
+    "Numpy und scipy bringen viele Funktionalitäten bezüglich Matrizen mit. Es lohnt sich hier ein weiterführendes Tutorial anzuschauen!\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0 1 2]\n",
-      " [3 4 5]]\n",
-      "[[10 11]\n",
-      " [12 13]\n",
-      " [14 15]]\n",
-      "[[ 40  43]\n",
-      " [148 160]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Dot-Produkt. Seit python 3.5 kann man auch @ als Operator verwenden\n",
     "\n",
     "v=np.array([1,2,3])\n",
-    "v.dot(v) #für Vejktoren wird das Skalarprodukt berechnet\n",
+    "v.dot(v) #für Vektoren wird das Skalarprodukt berechnet\n",
     "\n",
     "a=np.arange(6).reshape(2,3)\n",
     "b=np.arange(10,16).reshape(3,2)\n",
@@ -1117,18 +1046,18 @@
    "source": [
     "# Plots\n",
     "\n",
-    "Es gibt viele verschiedene Module mit unterschieldichen Funktionen, Stärken und Schwächen. Ein vielseitiges und häufig verwendetes Modul ist matplotlib. Auch hier empfehle ich einschlägige Tutorials anzuschauen um einen Überblick über die Funktionalität zu erlangen.\n",
+    "Es gibt viele verschiedene Module mit unterschieldichen Funktionen, Stärken und Schwächen. Ein vielseitiges und häufig verwendetes Modul ist matplotlib. Auch hier empfiehlt es sich, einschlägige Tutorials anzuschauen um einen Überblick über die Funktionalität zu erlangen.\n",
     "Im folgenden ein simples Beispiel:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1138,7 +1067,7 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt #matplotlib wir meistens plt benannt\n",
+    "import matplotlib.pyplot as plt #matplotlib wird meistens plt benannt\n",
     "import numpy as np\n",
     "from numpy import linspace, pi\n",
     "\n",
@@ -1160,7 +1089,7 @@
     "plt.xlim(-4, 4)\n",
     "plt.xlabel('x')\n",
     "plt.ylabel('y')\n",
-    "plt.ylim(-4, 4)\n",
+    "plt.ylim(-5, 5)\n",
     "plt.title(\"Bahnkurve \" +r\"$e^{-\\frac{\\phi}{\\alpha}}$\"+\"\\n\", fontsize=20)\n",
     "plt.legend()\n",
     "plt.show()\n",
@@ -1192,7 +1121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,