From e00c7f451bf4bbc613ebd7eec332904996a66240 Mon Sep 17 00:00:00 2001
From: Matthias Hoek <matthias.hoek@uni-mainz.de>
Date: Tue, 8 Oct 2019 11:11:20 +0200
Subject: [PATCH] Beschreibung von Markdown-Zellen kombiniert und Beschreibung
 des ZDV Jupyter Hubs hinzugefuegt.

---
 Screenshot_ZDV_JupyterHub.png | Bin 0 -> 58380 bytes
 Vorbereitung_Kapitel_1.ipynb  | 332 ++++++++++++++++++++++++++++------
 2 files changed, 272 insertions(+), 60 deletions(-)
 create mode 100644 Screenshot_ZDV_JupyterHub.png

diff --git a/Screenshot_ZDV_JupyterHub.png b/Screenshot_ZDV_JupyterHub.png
new file mode 100644
index 0000000000000000000000000000000000000000..f9a95981e270a486d23f2719f6e4febd67e091b9
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HcmV?d00001

diff --git a/Vorbereitung_Kapitel_1.ipynb b/Vorbereitung_Kapitel_1.ipynb
index aabb36f..26ceba9 100644
--- a/Vorbereitung_Kapitel_1.ipynb
+++ b/Vorbereitung_Kapitel_1.ipynb
@@ -20,12 +20,19 @@
    ]
   },
   {
+   "attachments": {
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+     "image/png": 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iBCoAAAAAAACiBCoAAAAAAACi/h8RPkVMMYLLNAAAAABJRU5ErkJggg=="
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Jupyter Notebooks ausführen\n",
     "\n",
-    "Nachdem Sie das Jupyter Notebook auf Ihrem Computer installiert haben, sind Sie bereit, den Notebook-Server zu betreiben. Sie können den Notebook-Server über die Befehlszeile (mit Terminal unter Mac/Linux, Eingabeaufforderung unter Windows) starten, indem Sie ihn ausführen:\n",
+    "### Lokale Installation\n",
+    "\n",
+    "Falls Sie das Jupyter Notebook auf Ihrem Computer installiert haben, sind Sie bereit, den Notebook-Server zu betreiben. Sie können den Notebook-Server über die Befehlszeile (mit Terminal unter Mac/Linux, Eingabeaufforderung unter Windows) starten, indem Sie ihn ausführen:\n",
     "\n",
     "    jupyter notebook\n",
     "\n",
@@ -33,6 +40,15 @@
     "\n",
     "Wenn sich das Notebook in Ihrem Browser öffnet, sehen Sie das Notebook Dashboard, das eine Liste der Notebooks, Dateien und Unterverzeichnisse in dem Verzeichnis anzeigt, in dem der Notebook-Server gestartet wurde. Meistens werden Sie einen Notebook-Server im obersten Verzeichnis mit Notebooks starten wollen. Oftmals wird dies Ihr Home-Verzeichnis sein.\n",
     "\n",
+    "### ZDV Jupyter Hub\n",
+    "\n",
+    "Sie können auch den durch die ZDV angebotenen Jupyter Hub (https://jupyterhub.zdv.uni-mainz.de/hub/login) zur Bearbeitung Ihrer Notebooks verwenden. Hier müssen Sie sich zunächst mit Ihrem Uni-Account anmelden. Danach erscheint eine Auswahlseite, auf der Sie die Art der Jupyter Umgebung auswählen. Für das Praktikum ist die Standardumgebung die richtige Wahl, s. Bild unten.\n",
+    "\n",
+    "![Screenshot_ZDV_JupyterHub.png](attachment:Screenshot_ZDV_JupyterHub.png)\n",
+    "\n",
+    "Klicken Sie auf die Schaltfläche **Spawn**, dann öffnet sich, wie bei der lokalen Installation, das Notebook Dashboard.\n",
+    "\n",
+    "\n",
     "## Ein neues Notebook-Dokument anlegen\n",
     "\n",
     "Ein neues Notebook kann jederzeit erstellt werden, entweder über das Dashboard oder über die Menüoption Datei ‣ Neu aus einem aktiven Notebook heraus. Das neue Notebook wird im selben Verzeichnis erstellt und öffnet sich in einem neuen Browser-Tab. Es wird auch als neuer Eintrag in der Notebookliste auf dem Dashboard angezeigt.\n",
@@ -65,34 +81,30 @@
     "\n",
     "Eine Codezelle ermöglicht es Ihnen, neuen Code zu bearbeiten und zu schreiben, mit voller Syntaxhervorhebung und Vervollständigung der Registerkarte. Die von Ihnen verwendete Programmiersprache hängt vom Kernel ab, und der Standardkernel (IPython) führt Python-Code aus.\n",
     "\n",
-    "Wenn eine Codezelle ausgeführt wird, wird der darin enthaltene Code an den dem Notebook zugeordneten Kernel gesendet. Die Ergebnisse, die von dieser Berechnung zurückgegeben werden, werden dann im Notebook als Ausgabe der Zelle angezeigt. Die Ausgabe beschränkt sich nicht nur auf Text, sondern es sind auch viele andere mögliche Ausgabeformen möglich, darunter matplotlib-Figuren und HTML-Tabellen.\n",
-    "\n",
-    "### Markdown-Zellen\n",
-    "\n",
-    "Sie können den Berechnungsprozess versiert dokumentieren, indem Sie beschreibenden Text mit Code abwechseln und Rich Text verwenden. In IPython wird dies durch die Markierung von Text mit der Markdown-Sprache erreicht. Die entsprechenden Zellen werden als Abschriftenzellen bezeichnet. Die Markdown-Sprache bietet eine einfache Möglichkeit, dieses Textmarkup durchzuführen, d.h. festzulegen, welche Teile des Textes hervorgehoben werden sollen (Kursivschrift), Fett, Formularlisten usw."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Grundlagen zu Python bzw. Jupyter Notebooks:\n",
-    "\n",
-    "Bevor wir mit dem eigentlichen programmieren beginnen wollen müssen wir uns jedoch erst einmal mit unserem so genannten Interpreter (**Jupyter Notebook**) vertraut machen. Bei der Programmiersprache **Python** handelt es sich um eine so genannte **Interpretersprache**. Dies bedeutet, dass eingegebene Befehle, ähnlich wie bei einem Taschenrechner, direkt ausgeführt werden.\n",
-    "\n",
-    "Zum Beispiel beim berechnen von: "
+    "Wenn eine Codezelle ausgeführt wird, wird der darin enthaltene Code an den dem Notebook zugeordneten Kernel gesendet. Die Ergebnisse, die von dieser Berechnung zurückgegeben werden, werden dann im Notebook als Ausgabe der Zelle angezeigt. Zum Beispiel beim Berechnen von: "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.802747Z",
      "start_time": "2019-09-30T05:49:44.777288Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3 + 2"
    ]
@@ -101,7 +113,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Unser Interpreter, das **Jupyter Notebook**, stellt hierbei einen komfortable Interpreterumgebung dar. Diese erlaubt es uns neben **Code**-Zellen auch Texte und Formeln in so genannten **Markdown**-Zellen darzustellen. Hierbei existiert eine ganze Bandbreite an Formatierungsmöglichkeiten. Zum Beispiel:\n",
+    "Die Ausgabe beschränkt sich nicht nur auf Text, sondern es sind auch viele andere mögliche Ausgabeformen möglich, darunter matplotlib-Figuren und HTML-Tabellen.\n",
+    "\n",
+    "### Markdown-Zellen\n",
+    "\n",
+    "Sie können den Berechnungsprozess versiert dokumentieren, indem Sie beschreibenden Text mit Code abwechseln und Rich Text verwenden. In IPython wird dies durch die Markierung von Text mit der Markdown-Sprache erreicht. Die entsprechenden Zellen werden als Markdown-Zellen bezeichnet. Die Markdown-Sprache bietet eine einfache Möglichkeit, dieses Textmarkup durchzuführen, d.h. festzulegen, welche Teile des Textes hervorgehoben werden sollen (Kursivschrift), Fett, Formularlisten usw.\n",
+    "\n",
+    "Innerhalb von Markdown-Zellen können Sie mathematische Ausdrücke auch auf einfache Weise mit der Standard-LaTeX-Notation einbinden: $...$ für Inline-Mathematik und $$...$$ für angezeigte Mathematik. Wenn die Markdown-Zelle ausgeführt wird, werden die LaTeX-Abschnitte automatisch in der HTML-Ausgabe als Gleichungen mit hochwertiger Typografie dargestellt. Möglich wird dies durch MathJax, das eine große Teilmenge der LaTeX-Funktionalität unterstützt.\n",
+    "\n",
+    "Es existiert also eine ganze Bandbreite an Formatierungsmöglichkeiten. Hier einige Beispiele:\n",
     "\n",
     "**Überschriften:**\n",
     "\n",
@@ -148,6 +168,16 @@
     "Darüber hinaus bietet uns das Jupyter Notebook noch diverse weitere Optionen an welche unseren harten Alltag vereinfachen. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Grundlagen zu Python bzw. Jupyter Notebooks:\n",
+    "\n",
+    "Bevor wir mit dem eigentlichen programmieren beginnen wollen müssen wir uns jedoch erst einmal mit unserem so genannten Interpreter (**Jupyter Notebook**) vertraut machen. Bei der Programmiersprache **Python** handelt es sich um eine so genannte **Interpretersprache**. Dies bedeutet, dass eingegebene Befehle, ähnlich wie bei einem Taschenrechner, direkt ausgeführt werden.\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -259,42 +289,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.835098Z",
      "start_time": "2019-09-30T05:49:44.820977Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.3333333333333335"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "2 / 3 - 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.854156Z",
      "start_time": "2019-09-30T05:49:44.838216Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3**2 * 2 - 8  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.869444Z",
      "start_time": "2019-09-30T05:49:44.856320Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "81"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3**2**2"
    ]
@@ -308,28 +371,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.885272Z",
      "start_time": "2019-09-30T05:49:44.871522Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3**2 * 2 - 8  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.902760Z",
      "start_time": "2019-09-30T05:49:44.887994Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-54"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3**2 * (2 - 8 ) "
    ]
@@ -343,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.919994Z",
@@ -357,21 +442,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.936549Z",
      "start_time": "2019-09-30T05:49:44.922748Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.951900Z",
@@ -385,14 +481,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.968598Z",
      "start_time": "2019-09-30T05:49:44.953574Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a * variable"
    ]
@@ -406,14 +513,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:44.986417Z",
      "start_time": "2019-09-30T05:49:44.970615Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a = 4\n",
     "b = 3\n",
@@ -431,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.001728Z",
@@ -445,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.017219Z",
@@ -459,14 +577,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.034800Z",
      "start_time": "2019-09-30T05:49:45.019707Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a * b"
    ]
@@ -480,14 +609,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.050615Z",
      "start_time": "2019-09-30T05:49:45.036793Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a = 2\n",
     "b = 5\n",
@@ -497,14 +637,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.067030Z",
      "start_time": "2019-09-30T05:49:45.052096Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a = 2\n",
     "b = 5.0\n",
@@ -580,14 +731,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.100616Z",
      "start_time": "2019-09-30T05:49:45.084372Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Dies ist eine Zeichenkette'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "'Dies ist eine Zeichenkette'"
    ]
@@ -601,14 +763,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.117423Z",
      "start_time": "2019-09-30T05:49:45.102607Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0123456789 äöü *+~`´?ß-@€ python 3.7>\n"
+     ]
+    }
+   ],
    "source": [
     "s1 = '0123456789'\n",
     "s2 = 'äöü'\n",
@@ -627,14 +797,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.133467Z",
      "start_time": "2019-09-30T05:49:45.120240Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dies ist Syntaxvariante eins\n",
+      "\n",
+      "Dies ist Syntaxvariante 2\n"
+     ]
+    }
+   ],
    "source": [
     "a = 'eins'\n",
     "b = 2\n",
@@ -653,14 +833,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.149030Z",
      "start_time": "2019-09-30T05:49:45.135899Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dies ist pi auf 4 signifikante Stellen gerundet: 3.142\n",
+      "\n",
+      "Dies ist pi auf 4 signifikante Stellen gerundet: 3.142\n"
+     ]
+    }
+   ],
    "source": [
     "pi = 3.1415926535\n",
     "\n",
@@ -678,14 +868,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.164689Z",
      "start_time": "2019-09-30T05:49:45.153880Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "An einem Widerstand R wurden die folgenden Werte gemessen:\n",
+      "Spannung: 12.0+/-0.1 V\n",
+      "Strom:    0.3+/-0.01 mA\n",
+      "Hierraus resultiert ein Widerstand von 40.0+/-1.4 kOhm \n"
+     ]
+    }
+   ],
    "source": [
     "U = 12.0   #V\n",
     "dU = 0.1   #V\n",
@@ -730,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.198018Z",
@@ -752,14 +953,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.213392Z",
      "start_time": "2019-09-30T05:49:45.200009Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Leider müssen wir hier auf die Einheiten selbst achten.\n",
     "# Deshalb ist es ratsam sich die Einheiten zu den Werten zu notieren.\n",
@@ -810,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-09-30T05:49:45.228841Z",