diff --git a/Aufgaben_zur_Vorbereitung_von_Kapitel_1.ipynb b/Aufgaben_zur_Vorbereitung_von_Kapitel_1.ipynb index 464b6ec..9a03e16 100644 --- a/Aufgaben_zur_Vorbereitung_von_Kapitel_1.ipynb +++ b/Aufgaben_zur_Vorbereitung_von_Kapitel_1.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Kapitel 1. Einstieg in die Welt von Python:\n", + "# Vorbereitung Kapitel 1. Einstieg in die Welt von Python:\n", "\n", "In unserer heutigen digitalen Welt sind Computer nicht mehr aus unserem Alltag wegzudenken. Ob in der Finanzwelt, Industrie aber auch in der Wissenschaft erledigen Computer in Sekundenschnelle komplizierte Rechnungen und helfen dem Anwender komplizierte Sachverhalte vereinfacht wieder zu geben. Daher empfiehlt es sich insbesondere als Physiker zumindest die Grundlagen einer beliebigen Programmiersprache zu beherrschen. \n", "\n", @@ -19,145 +19,6 @@ "Damit Sie das neu erlernte Wissen direkt vertiefen können, wird dieses Notebook an verschiedenen Stellen kleinere Aufgaben für Sie bereit halten. Die Aufgaben sind durch orangefarbene Boxen hervorgehoben. Es gilt alle Aufgaben zu bearbeiten! " ] }, - { - "attachments": { - "Screenshot_ZDV_JupyterHub.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Jupyter Notebooks ausführen\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. **Falls Sie das Jupyterhub von außerhalb des Uni-Netzwerks erreichen wollen, müssen Sie eine VPN-Verbindung zum Uni-Netzwerk aufbauen.** Eine Anleitung für die gebräuchlichsten Betriebssysteme finden Sie [hier]( https://www.zdv.uni-mainz.de/vpn-netz-zugang-von-ausserhalb-des-campus/). \n", - "\n", - "Um Zugang zum Jupyter-Hub zu erhalten, 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", - "![images/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", - "### 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", - "Dadurch werden einige Informationen über den Notebook-Server in Ihrem Terminal angezeigt, einschließlich der URL der Webanwendung (standardmäßig http://localhost:8888) und Ihr Standard-Webbrowser wird mit dieser URL geöffnet.\n", - "\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", - "## Ein neues Notebook-Dokument anlegen\n", - "\n", - "Ein neues Notebook kann jederzeit erstellt werden, entweder über das Dashboard oder über die Menüoption File ‣ 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", - "\n", - "![](https://jupyter-notebook.readthedocs.io/en/latest/_images/new-notebook.gif)\n", - "\n", - "## Notebooks öffnen\n", - "\n", - "Ein offenes Notebook hat genau eine interaktive Sitzung, die mit einem Kernel verbunden ist, der den vom Benutzer gesendeten Code ausführt und die Ergebnisse zurückgibt. Dieser Kernel bleibt aktiv, wenn das Webbrowser-Fenster geschlossen ist, und das erneute Öffnen desselben Notebooks über das Dashboard stellt die Verbindung zwischen der Webanwendung und demselben Kernel wieder her.\n", - "\n", - "## Die Notebook Benutzeroberfläche\n", - "\n", - "Wenn Sie ein neues Notebook-Dokument erstellen, werden Ihnen der Name des Notebooks, eine Menüleiste, eine Symbolleiste und eine leere Codezelle angezeigt.\n", - "\n", - "![](https://jupyter-notebook.readthedocs.io/en/latest/_images/blank-notebook-ui.png)\n", - "\n", - "**Notebook-Name**: Der Name, der oben auf der Seite neben dem Jupyter-Logo angezeigt wird, spiegelt den Namen der .ipynb-Datei wider. Wenn Sie auf den Namen des Notebooks klicken, öffnet sich ein Dialog, in dem Sie ihn umbenennen können. Wenn Sie also ein Notebook im Browser von \"Untitled0\" in \"My first notebook\" umbenennen, wird die Datei Untitled0.ipynb in My first notebook.ipynb umbenannt.\n", - "\n", - "**Menüleiste**: Die Menüleiste zeigt verschiedene Optionen, mit denen Sie die Funktionsweise des Notebooks beeinflussen können.\n", - "\n", - "**Symbolleiste**: Die Symbolleiste bietet eine schnelle Möglichkeit, die am häufigsten verwendeten Operationen innerhalb des Notebooks durchzuführen, indem Sie auf ein Symbol klicken.\n", - "\n", - "**Codezelle**: der Standardtyp der Zelle; lesen Sie weiter, um eine Erklärung der Zellen zu erhalten.\n", - "\n", - "## Aufbau eines Notebook Dokuments\n", - "\n", - "Das Notebook besteht aus einer Folge von Zellen. Eine Zelle ist ein mehrzeiliges Texteingabefeld, dessen Inhalt mit Shift-Enter oder durch Anklicken der Schaltfläche \"Play\" in der Symbolleiste oder Cell, Run in der Menüleiste ausgeführt werden kann. Das Ausführungsverhalten einer Zelle wird durch den Zellentyp bestimmt. Es gibt drei Arten von Zellen: Codezellen, Abschriftenzellen und Rohzellen. Jede Zelle beginnt damit, eine Codezelle zu sein, aber ihr Typ kann über ein Dropdown-Menü in der Symbolleiste (das zunächst \"Code\" sein wird) oder über Tastenkombinationen geändert werden.\n", - "\n", - "### Code-Zellen\n", - "\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. Zum Beispiel beim Berechnen von: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-27T12:25:06.022577Z", - "start_time": "2019-10-27T12:25:05.988009Z" - } - }, - "outputs": [], - "source": [ - "3 + 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "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", - "# Level 1.\n", - "## Level 2.\n", - "### Level 3.\n", - "\n", - "**Aufzählungen: **\n", - "\n", - "* Mit normalen\n", - "* Aufzählungspunkten\n", - " 1. oder \n", - " 2. Numerierungen\n", - " * Auf unterschiedlichen\n", - " 1. Ebenen\n", - "\n", - "**Schriftarten:**\n", - "\n", - "**Fett**\n", - "\n", - "*Italic (Kursive)*\n", - "\n", - "`True type`\n", - "\n", - "bzw. Syntax highlighting\n", - "\n", - "```python \n", - "def EasyFunc(x):\n", - " return 2 * x\n", - "```\n", - "\n", - "**Formeln mit Hilfe des Latex-Syntax:**\n", - "\n", - "$ f(x) = \\int\\limits_0^\\infty e^{-x} \\, dx $\n", - "\n", - "(Latex werdet Sie verstärkt im F-Praktikum kennen lernen)\n", - "\n", - "\n", - "**Bilder:**\n", - "\n", - "![The Python logo](https://www.python.org/static/community_logos/python-powered-w-200x80.png \"Das Python Logo\")\n", - "\n", - "\n", - "Darüber hinaus bietet uns das Jupyter Notebook noch diverse weitere Optionen an welche unseren harten Alltag vereinfachen. " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -207,8 +68,8 @@ " * Fügen Sie dem zweiten Aufzählungspunkt (2.) drei nicht nummerierte Unterpunkte hinzu.\n", " \n", "**Hinweise:**\n", - "Um die Aufgabe mit der Markdown-Zelle zu lösen, können Sie den jeweils benötigten Syntax in der Zelle oberhalb dieser Aufgabe nach gucken. Wechselen Sie hierzu in der entsprechenden Markdown-Zelle in den Editiermodus in dem Sie die entsprechende Zelle mittels einem Doppelklick anwählen. \n", - "
" + "In *Kapitel 0* wurden Ihnen bereits alle benötigten Formatierungen angezeigt. Sie können diese nachgucken in dem Sie in das Notebook *Kapitel 0* wechseln und die entsprechende Markdown-Zelle mittels Doppelklick anwählen. \n", + "
" ] }, { @@ -307,25 +168,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T13:42:34.321719Z", "start_time": "2020-02-08T13:42:34.291969Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "4**0.5" ] @@ -688,24 +538,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T13:40:14.532316Z", "start_time": "2020-02-08T13:40:14.507294Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dies ist Syntaxvariante eins\n", - "\n", - "Dies ist Syntaxvariante 2\n" - ] - } - ], + "outputs": [], "source": [ "a = 'eins'\n", "b = 2\n", @@ -724,24 +564,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T13:41:34.601805Z", "start_time": "2020-02-08T13:41:34.577256Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dies ist pi auf 4 signifikante Stellen gerundet: 3.1416\n", - "\n", - "Dies ist pi auf 4 signifikante Stellen gerundet: 3.1416\n" - ] - } - ], + "outputs": [], "source": [ "pi = 3.1415926535\n", "\n", @@ -759,25 +589,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T13:43:48.431735Z", "start_time": "2020-02-08T13:43:48.411817Z" } }, - "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+/-1.37 kOhm \n" - ] - } - ], + "outputs": [], "source": [ "U = 12.0 #V\n", "dU = 0.1 #V\n", @@ -877,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T14:00:35.030562Z", @@ -895,25 +714,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T14:00:35.310478Z", "start_time": "2020-02-08T14:00:35.290633Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.25" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "komplxe_function(1, 2, 3, 4)" ] @@ -927,26 +735,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T14:01:53.810639Z", "start_time": "2020-02-08T14:01:53.785432Z" } }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'result' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'result' is not defined" - ] - } - ], + "outputs": [], "source": [ "result" ] @@ -960,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T14:04:10.690109Z", @@ -974,25 +770,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-02-08T14:04:10.890200Z", "start_time": "2020-02-08T14:04:10.870502Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.25" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result" ] @@ -1476,7 +1261,7 @@ "\n", "Am Python-Versuchstag selbst wollen wir anhand der Messdaten von Vorversuch 1. Aufgabe 1, *Erdbeschleunigung mit der Schiefen Ebene* das fitten von Funktionen mittels $\\chi^2$ üben. Als Vorbereitung hierfür sollen Sie die Messdaten der gemessenen Zeiten und Höhen so wie ihre Fehler als Listen in Python eintippen. \n", "\n", - "Darüber hinaus definieren Sie sich eine Funktion $h(t)$ welche proportional zu $1/g$ ist. Diese Funktion soll am Ende gegen die Messdaten in einem Höhe-gegen-Zeit Diagramm gefittet werden. \n", + "Darüber hinaus definieren Sie sich eine Funktion $h(t)$ welche proportional zu $1/g$ ist. Diese Funktion soll am Ende des Versuchstages gegen die Messdaten in einem Höhe-gegen-Zeit Diagramm gefittet werden. \n", "
" ] }, diff --git a/Kapitel_0._Ihr_erstes_Notebook.ipynb b/Kapitel_0._Ihr_erstes_Notebook.ipynb index eaaab1e..0b2bc91 100644 --- a/Kapitel_0._Ihr_erstes_Notebook.ipynb +++ b/Kapitel_0._Ihr_erstes_Notebook.ipynb @@ -14,11 +14,11 @@ "\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. **Falls Sie das Jupyterhub von außerhalb des Uni-Netzwerks erreichen wollen, müssen Sie eine VPN-Verbindung zum Uni-Netzwerk aufbauen.** Eine Anleitung für die gebräuchlichsten Betriebssysteme finden Sie [hier]( https://www.zdv.uni-mainz.de/vpn-netz-zugang-von-ausserhalb-des-campus/). \n", + "Sie können auch den durch die ZDV angebotenen Jupyter Hub (https://jupyterhub.zdv.uni-mainz.de/hub/login) zur Bearbeitung Ihrer Notebooks verwenden. **Falls Sie das Jupyterhub von außerhalb des Uni-Netzwerks erreichen wollen, müssen Sie eine VPN-Verbindung zum Uni-Netzwerk aufbauen, oder über eine Remotedesktop-Verbindung arbeiten.** Wir empfehlen Ihnen letzteres. Eine Erklärung wie Sie eine Remotedesktop-Verbindung aufbauen finden Sie auf den [Seiten der ZDV](https://www.zdv.uni-mainz.de/remotedesktop-arbeiten-am-entfernten-arbeitsplatz/). Sollten Sie das Arbeiten über VPN bevorzugen so finden Sie eine Anleitung für die gebräuchlichsten Betriebssysteme [hier]( https://www.zdv.uni-mainz.de/vpn-netz-zugang-von-ausserhalb-des-campus/). \n", "\n", "Um Zugang zum Jupyter-Hub zu erhalten, 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", - "![images/Screenshot_ZDV_JupyterHub.png](attachment:Screenshot_ZDV_JupyterHub.png)\n", + "![images/Screenshot_ZDV_JupyterHub.png](images/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", @@ -63,25 +63,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-04-26T08:41:45.375773Z", "start_time": "2020-04-26T08:41:45.336524Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "1 + 1" ] @@ -159,29 +148,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-04-26T08:54:42.827020Z", "start_time": "2020-04-26T08:54:33.726074Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Checking first your IPython version...\n", - "Setting up a custom made Jupyter Style.\n", - "Searching for the Jupyter profile directory...\n", - "Your Jupyter profile directory is allocated at C:\\Users\\Daniel\\.jupyter\n", - "Checking if either the custom directory or a custom.css file already exists.\n", - "The custom directory exists, but no custom.css file was found.\n", - "Copying data now.\n", - "Done! Your custom.css file is now allocated at C:\\Users\\Daniel\\.jupyter\\custom\\custom.css\n" - ] - } - ], + "outputs": [], "source": [ "%run costum_set_up.py" ] @@ -190,10 +164,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Hier durch werden einige Styleoptionen installiert welche die von euch zu bearbeiten Aufgaben optisch hervorheben. Nachdem ihr die Optionen installiert habt müsst ihr euer Jupyter-Server neustarten geht hierfür wie folgt vor.\n", + "Hier durch werden einige Styleoptionen installiert welche die von euch zu bearbeiten Aufgaben optisch hervorheben. Nachdem ihr die Optionen installiert habt müsst ihr euer Jupyter-Server neustarten geht hierfür wie folgt vor (**lest erst die ganze Anweisung!**): \n", "\n", "1. Speichert euer Notebook ab in dem ihr auf das kleine Disketensmbol in der obigen Menüleiste klickt.\n", - "2. Anschließend müssen wir das aktuelle Notebook schließen. Geht hier für auf **File** --> **Close and Halt**." + "2. Anschließend müssen wir das aktuelle Notebook schließen. Geht hier für auf **File** --> **Close and Halt**. Jetzt müssen wir nur noch unseren Server neustarten. Klicken Sie hierfür in der oberen rechten Ecke auf die Schaltfläche **Control Panel** und anschließend auf die Fläche **Stop My Server**. \n", + "\n", + "![images/Screenshot_ZDV_JupyterHub.png](images/JupyterServerNeustarten.png)\n", + "\n", + "3. Anschließend können Sie mit der Schaltfläche **Start My Server** den Jupyter-Server neustarten. Wählen Sie wieder das **default environment** aus uns spawnen Sie den Server.\n", + "\n", + "![images/Screenshot_ZDV_JupyterHub.png](images/Screenshot_ZDV_JupyterHub.png)\n", + "4. Öfnnen Sie nun das Notebook **Aufgaben_zur_Vorbereitung_von_Kapitel_1** und bearbeiten Sie dieses als Vorbereitung für den Versuchstag. Sofern die Installation der Styleoptionen erfolgreich war sollten ihnen die Aufgaben wie folgt dargestellt werden:\n", + "\n", + "![images/Screenshot_ZDV_JupyterHub.png](images/Aufgaben_Style_Beispiel.png)" ] }, { diff --git a/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb b/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb index 865ba42..b212b3c 100644 --- a/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb +++ b/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb @@ -4,1033 +4,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Kapitel 1. Einstieg in die Welt von Python:\n", - "\n", - "In unserer heutigen digitalen Welt sind Computer nicht mehr aus unserem Alltag wegzudenken. Ob in der Finanzwelt, Industrie aber auch in der Wissenschaft erledigen Computer in Sekundenschnelle komplizierte Rechnungen und helfen dem Anwender komplizierte Sachverhalte vereinfacht wieder zu geben. Daher empfiehlt es sich insbesondere als Physiker zumindest die Grundlagen einer beliebigen Programmiersprache zu beherrschen. \n", - "\n", - "Im folgenden werden wir uns gemeinsam die Grundzüge der Programmiersprache **Python** erarbeiten. Ein besonderes Augenmerk liegt hierbei auf den verschiedenen Herausforderungen die das analysieren von Experimentdaten mit sich bringt. Um euch bestens auf die Anforderungen im **physikalische Grundpraktikum (PGP)** vorzubereiten lernen wir im Folgenden wie man:\n", - "\n", - "* einfache Rechnungen mit Python durchführt\n", - "* \"Mathematische\" Funktionen definiert\n", - "* Funktionen auf größere Zahlenmengen anwendet\n", - "* Daten in Form von Graphen richtig darstellt\n", - "* eine Ausgleichsgerade von Datenpunkten berechnen kann.\n", - "\n", - "Damit ihr das neu gelernte Wissen direkt vertiefen könnt, wird dieses Notebook an verschiedenen Stellen kleinere Aufgaben für euch bereit halten." + "# Kapitel 1. Einstieg in die Welt von Python:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Grundlagen zu Python bzw. Jupyter Notebooks:\n", + "In Ihrer Vorbereitung haben Sie bisher die folgenden Konzepte kennen gelernt:\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", + "* Aufbau eines Jupyter-Notebooks (Aufgabe 1).\n", + "* Einfache Rechenoperationen (Aufgabe 2 a.)\n", + "* Einfache Zeichenketten (engl. Strings) und formatierte Strings (Aufgabe 2 b.).\n", + "* Das definieren von Funktionen (Aufgabe 3.)\n", + "* Das definieren von Messtabellen.\n", "\n", - "Zum Beispiel beim berechnen von: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.778729Z", - "start_time": "2019-10-31T12:32:02.747485Z" - } - }, - "outputs": [], - "source": [ - "3 + 2" + "Hierauf wollen wir an unserem heutigen Versuchstag aufbauen." ] }, { "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", - "\n", - "**Überschriften:**\n", - "\n", - "# Level 1.\n", - "## Level 2.\n", - "### Level 3.\n", - "\n", - "**Aufzählungen: **\n", - "\n", - "* Mit normalen\n", - "* Aufzählungspunkten\n", - " 1. oder \n", - " 2. Numerierungen\n", - " * Auf unterschiedlichen\n", - " 1. Ebenen\n", - "\n", - "**Schriftarten:**\n", - "\n", - "**Fett**\n", - "\n", - "*Italic (Kursive)*\n", - "\n", - "`True type`\n", - "\n", - "bzw. Syntax highlighting\n", - "\n", - "```python \n", - "def EasyFunc(x):\n", - " return 2 * x\n", - "```\n", - "\n", - "**Formeln mit Hilfe des Latex-Syntax:**\n", - "\n", - "$ f(x) = \\int\\limits_0^\\infty e^{-x} \\, dx $\n", - "\n", - "(Latex werdet ihr beim F-Praktikum kennen lernen)\n", - "\n", - "\n", - "**Bilder:**\n", - "\n", - "![The Python logo](https://www.python.org/static/community_logos/python-powered-w-200x80.png \"Das Python Logo\")\n", - "\n", - "\n", - "Darüber hinaus bietet uns das Jupyter Notebook noch diverse weitere Optionen an welche unseren harten Alltag vereinfachen. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neben diesen nützlichen Befehlemm gibt es noch weitere tolle Kürzel wie zum Beispiel:\n", - "* **D + D** um eine Zelle zu **löschen** \n", - "* **Y** verwandelt eine aktuelle **Markdown**-Zelle in eine **Code**-Zelle\n", - "* **Strg** + **Shift** + **Minus** Splittet eine Zelle an der Position eures Cursors \n", - "* **F** für \"Find and Replace\" (nützlich wenn ihr zum Beispiel ein Variablennamen austauschen wollt)\n", - "* **I** + **I** Um den *\"Kernel\"* zu stoppen (wichtig falls ihr mal eine unendliche LOOP gebaut habt)\n", - "\n", - "Des weiteren könnt ihr [hier](https://www.cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/) eine Auflistung weiterer Jupyter-Befehle finden." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Python als Taschenrechner:\n", - "\n", - "Neben dem einfachen summieren zweier Zahlen ermöglicht uns Python natürlich auch das verwendet weiterer Operatoren. Hierbei haben die Operatoren ähnlich wie in der Mathematik gewisse Prioritäten (*Punkt vor Strich*). Die Operation mit dem niedrigeren Prioritätswert wird zu erst ausgeführt. \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
OperatorErgebnisPriorität
x + yDie Summe von x und y6
x - yDifferenz von x und y5
x * yProdukt von x und y4
x / yQuotient von x und y3
x % yRest von x / y2
x ** yx bei der Potenz von y1
\n", - "\n", - "Hier ein paar Beispiele:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.794349Z", - "start_time": "2019-10-31T12:32:02.778729Z" - } - }, - "outputs": [], - "source": [ - "2 / 3 - 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.809971Z", - "start_time": "2019-10-31T12:32:02.794349Z" - } - }, - "outputs": [], - "source": [ - "3**2 * 2 - 8 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.825592Z", - "start_time": "2019-10-31T12:32:02.809971Z" - } - }, - "outputs": [], - "source": [ - "3**2**2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wie in der Mathematik können wir auch bei Python Klammern verwenden um die Rechenreihenfolge zu ändern:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.841214Z", - "start_time": "2019-10-31T12:32:02.825592Z" - } - }, - "outputs": [], - "source": [ - "3**2 * 2 - 8 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.856849Z", - "start_time": "2019-10-31T12:32:02.841214Z" - } - }, - "outputs": [], - "source": [ - "3**2 * (2 - 8 ) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Um unsere Rechnungen besser zu Strukturieren können wir Zahlen auch Variablen zu ordnen. Hierzu verwenden wir das Gleichheitszeichen um einer Variablen (*links*) einem Wert (*rechts*) zu zuordnen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.872456Z", - "start_time": "2019-10-31T12:32:02.856849Z" - } - }, - "outputs": [], - "source": [ - "a = 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.888078Z", - "start_time": "2019-10-31T12:32:02.872456Z" - } - }, - "outputs": [], - "source": [ - "a" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.903701Z", - "start_time": "2019-10-31T12:32:02.888078Z" - } - }, - "outputs": [], - "source": [ - "variable = 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.919320Z", - "start_time": "2019-10-31T12:32:02.903701Z" - } - }, - "outputs": [], - "source": [ - "a * variable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Bei der Definition von Variablen ist es wichtig auf die Reihenfolge zu achten. Dies gilt nicht nur innerhalb einer Zelle..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.934941Z", - "start_time": "2019-10-31T12:32:02.919320Z" - } - }, - "outputs": [], - "source": [ - "a = 4\n", - "b = 3\n", - "a = 7\n", - "\n", - "a * b" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... sondern auch für die Reihenfolge in der die Code-Zellen ausgeführt werden (Angezeigt durch In []:). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.950562Z", - "start_time": "2019-10-31T12:32:02.934941Z" - } - }, - "outputs": [], - "source": [ - "a = 7" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.966183Z", - "start_time": "2019-10-31T12:32:02.950562Z" - } - }, - "outputs": [], - "source": [ - "a = 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.981808Z", - "start_time": "2019-10-31T12:32:02.966183Z" - } - }, - "outputs": [], - "source": [ - "a * b" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ein weiterer Vorteil (bzw. auch Nachteil) ist, dass Python eine so genannte *dynamische* Datentypenvergabe nutzt. Um besser zu verstehen was dies bedeutet gucken wir uns das Nachfolgende Beispiel an. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:02.997429Z", - "start_time": "2019-10-31T12:32:02.981808Z" - } - }, - "outputs": [], - "source": [ - "a = 2\n", - "b = 5\n", - "c = a * b\n", - "c" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.013048Z", - "start_time": "2019-10-31T12:32:02.997429Z" - } - }, - "outputs": [], - "source": [ - "a = 2\n", - "b = 5.0\n", - "c = a * b\n", - "c " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In der oberen Zelle ist **c** vom Datentyp `int` (*Integer*) was einer Ganzen Zahl entspricht. In der unteren Zelle jedoch ist **c** vom Datentype `float` (*Floating Point Number*) also eine Gleitkommazahl. Dies liegt daran das wir in der unteren Zelle **b** als Gleitkommazahl definiert haben. Um uns Arbeit abzunehmen hat Python für uns im Hintergrund dynamisch entschieden, dass somit **c** ebenfalls vom type `float` sein muss. \n", - "\n", - "Neben den primitiven Datentypen `float` und `int` gibt es noch die wichtigen Datentypen `str` (*string*) was einer Zeichenkette entspricht (z.B. Buchstaben, Wörter und Sätze), `complex` für Komplexe Zahlen und `bool` für Wahrheitswerte. Was genau Wahrheitswerte sind und für was diese verwendet werden, werdet ihr noch im **PGP2** lernen. \n", - "\n", - "Für das **PGP1** sind erstmal nur die typen `int`, `float` und `str` von Bedeutung." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Zeichenketten.\n", - "\n", - "Wie eben bereits erwähnt gibt es neben den Zahlen Datentypen `int`, `float` und `complex` auch noch den Datentyp einer Zeichenkette `str`. Zeichenketten werden in Programmiersprachen vielseitig verwendet z.B. bei einer Nutzereingabe (wie dem Passwort), Dateiname bei einer Installation, oder bei Textrückgaben von Programmen. Letzteres haben wir bereits in Aufgabe 2 mit Hilfe der `print`-Funktion gesehen.\n", - "\n", - "Für das PGP-1 wollen wir uns zunächst darauf beschränken, dass Zeichenketten in so genannten **Formatstrings** dazu genutzt werden können schönere `print` Rückgaben zu erzeugen bzw. wir mit Zeichnketten Achsenbeschriftungen an Graphen anbringen können. \n", - "\n", - "Zunächst erst aber einmal eine einfache Zeichenkette:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.028669Z", - "start_time": "2019-10-31T12:32:03.013048Z" - } - }, - "outputs": [], - "source": [ - "'Dies ist eine Zeichenkette'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hierbei kann eine Zeichenkette auch alle Symbole enthalten die euer Interpreter unterstützt. In Jupyter sind dies alle gewohnten Zeichen wie Buchstaben, Zahlen, Sonderzeichen und Leerzeichen: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.044292Z", - "start_time": "2019-10-31T12:32:03.028669Z" - } - }, - "outputs": [], - "source": [ - "s1 = '0123456789'\n", - "s2 = 'äöü'\n", - "s3 = '*+~`´?ß-@€'\n", - "s4 = 'python 3.7>'\n", - "\n", - "print(s1,s2,s3,s4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Einen **Formatstring** können wir über zwei Arten generieren (**Muss checken welche Python version im Hub genutzt wird**):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.059912Z", - "start_time": "2019-10-31T12:32:03.044292Z" - } - }, - "outputs": [], - "source": [ - "a = 'eins'\n", - "b = 2\n", - "\n", - "print('Dies ist Syntaxvariante {}'.format(a))\n", - "print()\n", - "print(f'Dies ist Syntaxvariante {b}') " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neben dem Einfügen von Strings oder Zahlen in eine Zeichenkette können wir die eingefügten Werte auch formatieren:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.075545Z", - "start_time": "2019-10-31T12:32:03.059912Z" - } - }, - "outputs": [], - "source": [ - "pi = 3.1415926535\n", - "\n", - "print(f'Dies ist pi auf 4 signifikante Stellen gerundet: {pi:.4}')\n", - "print()\n", - "print('Dies ist pi auf 4 signifikante Stellen gerundet: {:.4}'.format(pi))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... oder sofern ihr eine Rückgabe lieber über mehrere Zeilen ausgeben lassen wollt könnt ihr dieswie folgt machen:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.091155Z", - "start_time": "2019-10-31T12:32:03.075545Z" - } - }, - "outputs": [], - "source": [ - "U = 12.0 #V\n", - "dU = 0.1 #V\n", - "I = 0.30 #mA\n", - "dI = 0.01 #mA\n", - "\n", - "R = U/I #kOhm \n", - "dR = R * ((dU / U)**2 + (dI / I)**2)**0.5\n", - "\n", - "print(f'''An einem Widerstand R wurden die folgenden Werte gemessen:\n", - "Spannung: {U}+/-{dU} V\n", - "Strom: {I}+/-{dI} mA\n", - "Hierraus resultiert ein Widerstand von {R}+/-{dR:.2} kOhm ''') " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Das definieren von Funktionen:\n", - "\n", - "Anstatt Berechnungen wie bei einem Taschenrechner immer wieder manuell einzugeben, ermöglicht uns eine Programmiersprache das definieren von Funktionen. Funktionen können hierbei ähnlich wie mathematische Funktionen definiert und behandelt werden. Im folgenden wollen wir uns dies im Fall des Ohmschen Gesetzt welches durch \n", - "\n", - "$U(R, I) = R \\cdot I$ \n", - "\n", - "beschrieben wird angucken. Hierbei wird die Spannung $U$ durch die Variablen $R$ und $I$ beschrieben. Dies gilt auch analog für Funktionen in einer Programmiersprache:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.106777Z", - "start_time": "2019-10-31T12:32:03.091155Z" - } - }, - "outputs": [], - "source": [ - "def Spannung(Widerstand, Strom): # U(R,I)\n", - " return Widerstand * Strom # Wiedergabe der Funktion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Diese Funktion können wir nun auf Messdaten anwenden. Z.B. wir Messen bei einem Widerstand von $1\\,\\text{k}\\Omega$ einen Strom von $10\\,\\text{mA}$:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.122400Z", - "start_time": "2019-10-31T12:32:03.106777Z" - } - }, - "outputs": [], - "source": [ - "# Leider müssen wir hier auf die Einheiten selbst achten.\n", - "# Deshalb ist es ratsam sich die Einheiten zu den Werten zu notieren.\n", - "U = Spannung(1000, 0.01) # in V \n", - "U " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neben mathematischen Funktionen, können Funktionen in einer Programmiersprache auch viel allgemeinere Aufgaben erfüllen bzw. komplexe Algorithmen beinhalten. Hierzu lernt ihr jedoch noch mehr in anderen Programmierkursen. Wie zum Beispiel:\n", - "\n", - "* Computer in der Wissenschaft \n", - "* Programmieren für Physiker\n", - "* Einführung in die Programmierung" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tipp: \n", - "Es ist ratsam gleich von Anfang an Funktionen zu dokumentieren. Hierzu wird in Python der sogenannte `Doc-Strings`. Sie beinhalten Informationen über die Funktion selbst ihre Verwendeten Parameter und ihrer Ausgabe. Zum Beispiel für unser Beispiel des Ohmschen Gesetzt:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2019-11-04T12:09:43.420327Z", - "start_time": "2019-11-04T12:09:43.404698Z" - } - }, - "outputs": [], - "source": [ - "def Spannung(Strom, Widerstand):\n", - " '''\n", - " Diese Funktion berechnet die Spannung eines Ohmschen \n", - " Widerstands.\n", - " \n", - " Args:\n", - " Strom (float): Der gemessene Strom in mA.\n", - " Widerstand (float): Der Wert des verwendeten Widerstands\n", - " in Ohm.\n", - " \n", - " \n", - " Returns:\n", - " float: Die Berechnete Spannung in V.\n", - " '''\n", - " return Widerstand * Strom/1000" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Messtabellen in Python:\n", - "\n", - "Damit euch eine Programmiersprache wie Python Arbeit abnehmen kann, sollte es natürlich auch möglich sein größere Datenmengen z.b. die Werte einer Messtabelle in einer Variablen zu speichern. Python bietet hierfür mehrer verschiedene Konzepte alle mit unterschiedlichen Stärken und Schwächen. Die gängigsten Methoden sind listen, tuple, bzw. so genannte numpy.arrays und pandas.dataframes. Aufgrund der imitierten Zeit im PGP 1 werden wir uns hier lediglich mit zwei dieser vier Methoden auseinander setzen. \n", - "\n", - "Fangen wir zunächst mit Listen an. Eine Liste ist eine Ansammlung von Werten, welche alle den gleichen oder ganz unterschiedliche Datentypen haben können. Eine Liste kann auf zwei unterschiedliche Art und Weisen erstellt werden:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.153639Z", - "start_time": "2019-10-31T12:32:03.138020Z" - } - }, - "outputs": [], - "source": [ - "Messwerte1 = ['Wert1', 'Wert2', 'Wert3'] # Variante 1\n", - "Messwerte1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.169261Z", - "start_time": "2019-10-31T12:32:03.153639Z" - } - }, - "outputs": [], - "source": [ - "Messwerte2 = list([2, 0.9, '1']) # Variante 2\n", - "Messwerte2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sobald wir eine liste erstellt haben können wir eine ganze Reihe von unterschiedlichen Manipulationen durchführen um sie nach unserem belieben zu verändern.\n", - "\n", - "Wir können zum Beispiel die bestehende Liste um ein Wert erweitern (`append`) oder einen zusätzlichen Wert an eine beliebige Stelle in der Liste hinzufügen (`insert`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.184883Z", - "start_time": "2019-10-31T12:32:03.169261Z" - } - }, - "outputs": [], - "source": [ - "Messwerte1.append('Wert5')\n", - "Messwerte1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.200504Z", - "start_time": "2019-10-31T12:32:03.184883Z" - } - }, - "outputs": [], - "source": [ - "Messwerte1.insert(4, 'Wert4')\n", - "Messwerte1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ups, was ist denn in der letzten Zelle passiert? Wert4 wurde ja garnicht an Stelle 4 der Liste gesetzt, Python scheint nicht zählen zu können... \n", - "\n", - "Leider zählt Python doch richtig. In Python läuft der index von objekten in einer Liste oder ähnlichem immer von 0,1,2,3...n. Dies können wir auch ganz einfach überprüfen in dem wir unsere Liste in verschiedene \"Scheiben\" schneiden (so genanntes slicing). Dies geht wie folgt: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.216125Z", - "start_time": "2019-10-31T12:32:03.200504Z" - } - }, - "outputs": [], - "source": [ - "NeueWerte = ['Wert1', 'Wert2', 'Wert3', 'Wert4', 'Wert5', 'Wert6'] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Die kleinste Scheibe welche wir abschneiden können ist ein einzelner Wert:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.231747Z", - "start_time": "2019-10-31T12:32:03.216125Z" - } - }, - "outputs": [], - "source": [ - "NeueWerte[0] # Hier seht ihr, dass der erste Wert den Index 0 hat." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.247367Z", - "start_time": "2019-10-31T12:32:03.231747Z" - } - }, - "outputs": [], - "source": [ - "wert_index_2 = NeueWerte[2] \n", - "wert_index_2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wie bei einer Pizza können wir uns natürlich auch größere Stücke nehmen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.262990Z", - "start_time": "2019-10-31T12:32:03.247367Z" - } - }, - "outputs": [], - "source": [ - "NeueWerte[0:3] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.278612Z", - "start_time": "2019-10-31T12:32:03.262990Z" - } - }, - "outputs": [], - "source": [ - "NeueWerte[2:5] # Ihr seht Python behandelt den letzten Wert wie in einem offenen Intervall [2,5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.294235Z", - "start_time": "2019-10-31T12:32:03.278612Z" - } - }, - "outputs": [], - "source": [ - "NeueWerte[2:] # Hier werden alle Werte mit dem Index >= 2 zurück gegeben" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.309853Z", - "start_time": "2019-10-31T12:32:03.294235Z" - } - }, - "outputs": [], - "source": [ - "NeueWerte[-3:] # Mit negativen Zahlen fangt ihr vom Ende der Liste an" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Neben `insert`, `append` und `slicing` bietet Python noch ein paar weitere Listenmanipulationen an. Mit Hilfe des `+` Operators könnt ihr die Werte in einer Liste direkt an eine andere Liste anfügen." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.325477Z", - "start_time": "2019-10-31T12:32:03.309853Z" - } - }, - "outputs": [], - "source": [ - "Messwerte1 + NeueWerte" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Anders als `append` welches die zweite Liste als ganzes an die erste Liste anfügt:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.341096Z", - "start_time": "2019-10-31T12:32:03.325477Z" - } - }, - "outputs": [], - "source": [ - "Messwerte1.append(NeueWerte)\n", - "Messwerte1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aber aufgepasst bei `append` wird eure Liste an welche ihr die Daten anhängt (hier Messwerte1) direkt geändert (dies gilt auch für `insert`), während ihr beim `+` Operator die Variable überschreiben müsst damit die Änderung wirksam wird. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.356717Z", - "start_time": "2019-10-31T12:32:03.341096Z" - } - }, - "outputs": [], - "source": [ - "Messwerte1 = Messwerte1 + NeueWerte\n", - "# Tipp dies könnt ihr auch einfach mit Hilfe von\n", - "# Messwerte1 += NeueWerte\n", - "Messwerte1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Zwei weitere nützliche Befehle im zusammenhang von listen ist die `len`- und `range`-Funktion. \n", - "\n", - "`len` gibt euch die Länge einer Liste zurück " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.372339Z", - "start_time": "2019-10-31T12:32:03.356717Z" - } - }, - "outputs": [], - "source": [ - "print(Messwerte1)\n", - "len(Messwerte1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`range` erstellt euch ganzzahlige Werte zwischen zwei ganzen Zahlen " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.387962Z", - "start_time": "2019-10-31T12:32:03.372339Z" - } - }, - "outputs": [], - "source": [ - "range(0, # <-- Startwert\n", - " 5, # <-- Endwert (nicht mehr enthalten, offenes Ende)\n", - " 2 # <-- Schrittweite\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ihr könnt die `range` Rückgabe auch wieder in eine Liste umwandeln mit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-10-31T12:32:03.403581Z", - "start_time": "2019-10-31T12:32:03.387962Z" - } - }, - "outputs": [], - "source": [ - "list(range(0,5,2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Arbeiten mit Messreihen:\n", + "## Arbeiten mit Messreihen:\n", "\n", "Bisher hat uns das programmieren eher mehr Arbeit gemacht als uns welche abgenommen. Zeitersparnis bekommen wir sofern wir viele Rechnungen hintereinander ausführen müssen. Hierfür gibt es die **for**-Schleife. Diese Schleife führt die gleichen Zeilen eins Codes wiederholt für die Elemente in einer Liste aus:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:07:49.905202Z", "start_time": "2019-11-04T12:07:49.889579Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wert: 1\n", - "Wert: 2\n", - "Wert: 3\n", - "Wert: 4\n", - "Ergebnis: 6\n" - ] - } - ], + "outputs": [], "source": [ "liste = [1, 2, 3, 4]\n", "\n", @@ -1044,37 +54,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Bei einer Schleife ist darauf zu achten, dass der Anweisungsblock welcher wiederholt ausgeführt werden soll mit 4x Leerzeichen eingrückt wurde. Dies entspricht einmal der TAB-Taste." + "Bei einer Schleife ist darauf zu achten, dass der Anweisungsblock welcher wiederholt ausgeführt werden soll mit 4x Leerzeichen eingrückt wurde. Dies entspricht einmal \"Tab-Taste\"" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:08:53.901374Z", "start_time": "2019-11-04T12:08:53.885753Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hier läuft das Hauptprogramm\n", - "Schleife\n", - "Wert: 1\n", - "Schleife\n", - "Wert: 2\n", - "Schleife\n", - "Wert: 3\n", - "Schleife\n", - "Wert: 4\n", - "Hier läuft wieder das Hauptprogramm\n", - "Letztes Ergebnis + 5: 11\n" - ] - } - ], + "outputs": [], "source": [ "liste = [1, 2, 3, 4]\n", "print('Hier läuft das Hauptprogramm')\n", @@ -1098,25 +90,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:10:08.503300Z", "start_time": "2019-11-04T12:10:08.472059Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[10.1, 10.5, 9.8, 8.7, 11.2]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Stromwerte = [101, 105, 98, 87, 112] # mA\n", "Spannungswerte = []# Einheit? <-- Deshlab Docstrings und Help!\n", @@ -1137,25 +118,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:11:40.799393Z", "start_time": "2019-11-04T12:11:40.783772Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[10.1, 10.5, 9.8, 8.7, 11.2]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Spannungswerte = [Spannung(Strom, 100) for Strom in Stromwerte]\n", "Spannungswerte" @@ -1170,25 +140,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:12:42.522873Z", "start_time": "2019-11-04T12:12:42.507254Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "A und 0\n", - "B und 1\n", - "C und 2\n", - "D und 3\n" - ] - } - ], + "outputs": [], "source": [ "Werte1 = ['A', 'B', 'C', 'D']\n", "Werte2 = [0, 1, 2, 3]\n", @@ -1206,23 +165,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:13:30.363510Z", "start_time": "2019-11-04T12:13:30.347888Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[335.4904, 347.3442, 343.6145, 337.275, 331.6212, 342.0115, 336.2262]\n", - "[335.4904, 347.3442, 343.6145, 337.275, 331.6212, 342.0115, 336.2262]\n" - ] - } - ], + "outputs": [], "source": [ "# Gemessene Werte:\n", "frequenzen = [30.17, 30.63, 30.01, 29.98, 30.12, 29.87, 29.94] #kHz\n", @@ -1250,24 +200,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:13:49.912658Z", "start_time": "2019-11-04T12:13:49.897039Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "a und 1 und x\n", - "b und 2 und y\n", - "c und 3 und z\n" - ] - } - ], + "outputs": [], "source": [ "l1 = ['a', 'b', 'c']\n", "l2 = [1, 2, 3]\n", @@ -1290,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:46:37.221396Z", @@ -1350,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:52:38.927838Z", @@ -1371,27 +311,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:53:06.331480Z", "start_time": "2019-11-04T12:53:05.987810Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot([1,2,3,4,5], # <-- x-Daten\n", " [1,2,3,4,5] # <-- y-Daten\n", @@ -1417,27 +344,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:54:21.547247Z", "start_time": "2019-11-04T12:54:21.226301Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "xdaten = [-3, -2, -1, 0, 1, 2, 3]\n", "ydaten1 = xdaten\n", @@ -1580,27 +468,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T12:58:19.439740Z", "start_time": "2019-11-04T12:58:19.116107Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def cubic(x):\n", " '''\n", @@ -1635,27 +510,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T13:11:34.208204Z", "start_time": "2019-11-04T13:11:33.895770Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "spannung = [0.9, 2.0, 3.0, 4.1, 4.9, 6.2] # [V]\n", "strom = [105, 204, 298, 391, 506, 601] # [mA]\n", @@ -1753,27 +615,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T13:11:52.919783Z", "start_time": "2019-11-04T13:11:52.638600Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.errorbar(strom, \n", " spannung,\n", @@ -1807,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T13:13:40.357937Z", @@ -1821,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T13:13:40.844488Z", @@ -1844,27 +693,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T13:13:52.473958Z", "start_time": "2019-11-04T13:13:52.177152Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rnd_numbers2 = np.random.normal(1,2,1000)\n", "\n", @@ -1938,39 +761,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T13:15:48.283946Z", "start_time": "2019-11-04T13:15:47.327389Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.hist(rnd_numbers, \n", " bins=100, \n", @@ -2117,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T14:03:03.767521Z", @@ -2138,23 +936,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T14:04:02.759738Z", "start_time": "2019-11-04T14:04:02.714523Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10.0982598]\n", - "[[0.02490203]]\n" - ] - } - ], + "outputs": [], "source": [ "# Und jetzt fitten wir:\n", "para, pcov = curve_fit(Spannung, # <-- Funktion die an die Messdaten gefittet werden soll\n", @@ -2189,27 +978,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T14:06:00.969312Z", "start_time": "2019-11-04T14:06:00.676047Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "para2, pcov2 = curve_fit(Spannung, \n", " strom, \n", @@ -2321,22 +1084,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T14:09:37.708408Z", "start_time": "2019-11-04T14:09:37.683983Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Das chi-qudrat ist 1.26\n" - ] - } - ], + "outputs": [], "source": [ "chi_2 = [ (u - Spannung(i, para2[0]))**2/du**2 for i,u,du in zip(strom, spannung, spannung_error)]\n", "chi_2 = sum(chi_2)\n", @@ -2356,23 +1111,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-11-04T14:10:02.649772Z", "start_time": "2019-11-04T14:10:02.619695Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chi-qudrat nach URI: 1.26\n", - "Chi-qudrat nach URI-Parabel: 60.68\n" - ] - } - ], + "outputs": [], "source": [ "def Spannung2(I, R):\n", " return R * I**2\n", diff --git a/images/Aufgaben_Style_Beispiel.png b/images/Aufgaben_Style_Beispiel.png new file mode 100644 index 0000000..156e162 Binary files /dev/null and b/images/Aufgaben_Style_Beispiel.png differ diff --git a/images/Tab-Key.png b/images/Tab-Key.png new file mode 100644 index 0000000..9b23fe0 Binary files /dev/null and b/images/Tab-Key.png differ