diff --git a/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb b/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb index 6243058..d2007bc 100755 --- a/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb +++ b/Kapitel_1._Einstieg_in_die_Welt_von_Python.ipynb @@ -726,7 +726,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -743,13 +743,13 @@ "spannung_error = [0.3]*len(spannung) # Konstanter Ablesefehler [V]\n", "strom_error = [14, 9, 12, 8, 7, 11] # gemessener schwankender Fehler[mA]\n", "\n", - "plt.errorbar(spannung, strom,\n", - " yerr=strom_error, \n", - " xerr=spannung_error,\n", + "plt.errorbar(strom, spannung,\n", + " xerr=strom_error, \n", + " yerr=spannung_error,\n", " ) \n", "\n", - "plt.xlabel('Spannung [V]')\n", - "plt.ylabel('Strom [mA]')\n", + "plt.xlabel('Strom [mA]')\n", + "plt.ylabel('Spannung [V]')\n", "plt.show()" ] }, @@ -941,7 +941,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -979,7 +979,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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ZkuYCo4EDJK0DvgeMljSCQvuHNcA3s9q/mZm1LbPEHxFt3bLxrqz2Z2Zm6ZT65u6LJI3ZWr8FREQcnVlU1qNVQ9VJrSr2b+d/U+uIUmf8Z5ctCjMzK5tS39xdW85AzMysPNLcevE8Sb+V9AdJmyVtkbS5HMGZmVn3S3Nx938CX4mI1VkHY2Zm2UtTx7/BSd/MrOdI049/qaQm4BfA9ub33Y/fzKw2penHD/Ae8KUW00E7/fitZ0nTdKurJYUuSawM/7vnT7v9+M3MrGdp9+KupDrg68BRQF3z/Ii4NMO4zMwsI2ku7t4L/BdgDPCvwCBgS5ZBmZlZdtIk/sMi4rvA1oi4B/gy8PlswzIzs6ykSfw7kudNkoYD/SncgN3MzGpQmi9wzZD0CeA7wHygL/DXmUZlZt3Ot0O0Zu0m/oi4M3m5GDgk23DMzCxraXr1fCDpJklqMW95tmGZmVlW0ozxr0yWWyDpk8k8lVjezMyqWJrEvzMi/hK4E3hC0udo+wYtZmZWA9Jc3BVARDRJWgnMARoyjcrMzDKTJvFPbn4RES9JOgU4N7uQzMwsS2mqepZJOgkYnGZ5s7a4EVh18c8j39L06rkXOBRYAXyQzA5gVnZhmZlZVtKcwTcCwyLCF3TNzHqANFU9L1Fo0tYhku6WtFHSSy3mfVLSo8k9fB9NvhFsZmZllCbxHwCskvSIpPnNjxTrzQTObDVvGrAwIoYCC5NpMzMrozRDPd/vzIYjYrGkwa1mnwuMTl7fAywC/qoz2zczs85JU9Xzry2nJX0BmEihN39HDYiI9cnr3wMDii0oaQowBaChwV8bqCauCMmH7vo5p20O5yZy5ZNmqAdJIyXdLGkNcCOwuqs7Ti4WF71gHBEzIqIxIhrr6+u7ujszM0sUPeOXdDiFM/uJwFtAE6CIOLUL+9sgaWBErJc0ENjYhW2ZmVknlDrjfxk4DTg7Ir4QEf/Ah3X8nTUfuDh5fTHwyy5uz8zMOqhU4j8PWA88Lulnkk6nA105Jc0FngKOkLRO0teBm4AzJP0W+GIybWZmZVR0qCcifgH8QtK+FKpxrgQ+JeknwLyIWFBqwxExschbp3cuVDMz6w7tXtyNiK0RMScivgIMAp7DJZhmZjWrQ03XIuIdYEbyMLMcctll7UtVzmlmZj2HE7+ZWc448ZuZ5YwTv5lZzjjxm5nljBO/mVnOOPGbmeWME7+ZWc448ZuZ5YwTv5lZzjjxm5nljBO/mVnOdKhJm5lZS11p2Fbqnr5uBJctn/GbmeWME7+ZWc448ZuZ5YwTv5lZzjjxm5nljKt6eqBiFRHFqihcNWHlUqqSx8rHZ/xmZjnjxG9mljMVGeqRtAbYAnwA7IyIxkrEYWaWR5Uc4z81It6q4P7NzHLJQz1mZjlTqTP+ABZICuCnETGj9QKSpgBTABoaGsocXs/RlSoKV2CY9UyVOuP/QkQcC5wF/JmkUa0XiIgZEdEYEY319fXlj9DMrIeqSOKPiN8lzxuBecDxlYjDzCyPyp74Je0rqV/za+BLwEvljsPMLK8qMcY/AJgnqXn/cyLiXyoQh5lZLpU98UfE68Ax5d6vmZkVuJzTzCxnFBGVjqFdjY2NsXTp0kqHkYnuusVcd5VepmnqZlYpaX5HfNvGD0la1lZnBJ/xm5nljBO/mVnOOPGbmeWME7+ZWc448ZuZ5YxvvZhSOSoFqqEawZU81pNUw+9UNfIZv5lZzjjxm5nljBO/mVnOOPGbmeWME7+ZWc448ZuZ5UyuyjmzKu3q6HZ9H1yzzin2/7+7mreVs/yzkqWmPuM3M8sZJ34zs5xx4jczyxknfjOznHHiNzPLmVxV9XRUsQqCtJU13VW94+ZSZqVlUSmXpoKoVn9PfcZvZpYzTvxmZjnjxG9mljMVSfySzpT0iqTXJE2rRAxmZnlV9sQvqTfwj8BZwDBgoqRh5Y7DzCyvKnHGfzzwWkS8HhHvA/8EnFuBOMzMckkRUd4dSl8FzoyIycn0hcDnI2Jqq+WmAFOSySOAVzq5ywOAtzq5brXxsVSfnnIc4GOpVl05loMjor71zKqt44+IGcCMrm5H0tKIaOyGkCrOx1J9espxgI+lWmVxLJUY6vkdcFCL6UHJPDMzK4NKJP5/A4ZKGiLpo8CfAvMrEIeZWS6VfagnInZKmgo8AvQG7o6IlRnussvDRVXEx1J9espxgI+lWnX7sZT94q6ZmVWWv7lrZpYzTvxmZjmTi8Qv6UZJL0haIWmBpE9XOqbOknSzpJeT45kn6eOVjqkzJJ0vaaWkXZJqsuyup7QekXS3pI2SXqp0LF0h6SBJj0talfzfuqLSMXWWpDpJz0p6PjmW67t1+3kY45e0X0RsTl5fDgyLiMsqHFanSPoS8OvkIvkPASLiryocVodJ+gywC/gp8BcRsbTCIXVI0nrkVeAMYB2FarWJEbGqooF1gqRRwLvArIgYXul4OkvSQGBgRCyX1A9YBoyt0Z+JgH0j4l1JfYAlwBUR8XR3bD8XZ/zNST+xL1Czf+0iYkFE7Ewmn6bwPYiaExGrI6Kz38auBj2m9UhELAb+s9JxdFVErI+I5cnrLcBq4MDKRtU5UfBuMtkneXRb3spF4geQ9DeS/gO4APjrSsfTTS4F/rnSQeTUgcB/tJheR40mmZ5I0mBgJPBMhUPpNEm9Ja0ANgKPRkS3HUuPSfySHpP0UhuPcwEi4rqIOAiYDUwtvbXKau9YkmWuA3ZSOJ6qlOY4zLqbpL7A/cCVrT7t15SI+CAiRlD4VH+8pG4bhqvaXj0dFRFfTLnobOBh4HsZhtMl7R2LpEnA2cDpUcUXaTrwM6lFbj1ShZLx8PuB2RHxQKXj6Q4RsUnS48CZQLdcgO8xZ/ylSBraYvJc4OVKxdJVks4E/hI4JyLeq3Q8OebWI1UmuSB6F7A6Im6rdDxdIam+uWJP0scoFBF0W97KS1XP/RRaO+8C1gKXRURNnp1Jeg3YB3g7mfV0LVYoSRoH/ANQD2wCVkTEmIoG1UGS/gT4Oz5sPfI3lY2ocyTNBUZTaP+7AfheRNxV0aA6QdIXgCeAFyn8rgN8OyIerlxUnSPpaOAeCv+3egH3RcQN3bb9PCR+MzP7UC6GeszM7ENO/GZmOePEb2aWM078ZmY548RvZpYzTvzW40gal3RibfnYJemsEuucknRBXJHUTRdb7t3keXC5ullKmlTLHWWt+jjxW48TEfMiYkTzA7idQn33IyVWuwD4QbLOH8sRZxpJF9BJgBO/dRsnfuvRJB1OoSnfhcAoSQ+2eO/Hydn0ZGA8cKOk2ZL6SlooabmkF9vrLSTpqKR3+orkPglDJV2TtABH0o8k/Tp5fZqk2cnrL0l6KtnP/016zCBpjaQfSloOTAQagdntfRoxS8uJ33qspG/LHODqiPh/xZaLiDsptFu4JiIuALYB4yLiWOBU4NakHUAxlwH/K/l00UihU+cTwCnJ+41A3ySeU4DFkg4AvgN8MdnPUuCqFtt8OyKOjYj/k7x3QbV9GrHa1WOatJm14UZgZUQ0dXA9AX+b3KBkF4V2ywOA3xdZ/ingOkmDgAci4reSlgGfk7QfsB1YTuEPwCnA5cAJwDDgyeRvykeT7TTraMxmqTnxW48kaTTw34BjW8zeyZ6fcuuKrH4BhR5Cn4uIHZLWlFiWiJgj6Rngy8DDkr4ZEb+W9AaF8fnfAC9Q+PRwGIUbhBxKocf6xCKb3Vrq+My6wkM91uNI+gTwv4GLkjsxNVsLDJO0T9L58PQim+gPbEyS/qnAwe3s7xDg9Yj4e+CXwNHJW08AfwEsTl5fBjyXtNJ+GjhZ0mHJNvZNrke0ZQvQr1QMZh3hM37riS4DPgX8pNXQ/A+A+yj0NH8DeK7I+rOBX0l6kcL4envtcMcDF0raQWE46G+T+U8A1wFPRcRWSduSeUTEm8l9FeZK2idZ/jsU7uPb2kzgDkl/BE70OL91lbtzmpnljId6zMxyxonfzCxnnPjNzHLGid/MLGec+M3McsaJ38wsZ5z4zcxy5v8DeP8nmRSUohQAAAAASUVORK5CYII=\n", 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\n", 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\n", 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" ] @@ -1129,7 +1129,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1556,13 +1556,6 @@ "2. Die ausführlichen Dokumentation von Python und das Angebot etlicher nützlicher Hilfsbeiträge in verschiedenen Foren (z.B. stackoverflow) im Internet.\n", "3. Fragt bei den Assistenten nach während der Stunde nach bzw. nutzt den Emailkontakt auf der [gitlab Seite](https://gitlab.rlp.net/hoek/pgp1-python-einfuehrung/tree/master). \n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/Kapitel_2._Das_Auge_plottet_mit_und_das_Einlesen_von_Daten_Zusatz.ipynb b/Kapitel_2._Das_Auge_plottet_mit_und_das_Einlesen_von_Daten_Zusatz.ipynb index 28c5395..44dbefc 100755 --- a/Kapitel_2._Das_Auge_plottet_mit_und_das_Einlesen_von_Daten_Zusatz.ipynb +++ b/Kapitel_2._Das_Auge_plottet_mit_und_das_Einlesen_von_Daten_Zusatz.ipynb @@ -1,14 +1,35 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zusatz-Notebook\n", + "\n", + "In diesem Notebook findet ihr Zusatzinhalte, die euch beim Praktikum helfen könnten.\n", + "\n", + "Gliederung:\n", + "- Das Einlesen von Messdaten\n", + " - Mit numpy\n", + " - Mit csv\n", + "- Arbeiten mit Numpy-Arrays\n", + "- Das Auge plottet mit\n", + " - Plotgröße ändern\n", + " - Linien in allen Arten und Farben\n", + " - Mehr Text ist manchmal mehr Information\n", + "- Tabelle unter einem Plot mit matplotlib\n", + "- 3d-Plots" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Das Einlesen von Messdaten:\n", "\n", - "Im PGP-2 ist es in verschiedenen Versuchen nötig Messdaten von txt-Dateien einzulesen. Hierfür könnt ihr verschiedene Methoden verwenden. Dies ist oft in Python der Fall, das mehrere Wege ans Ziel führen. Im folgen möchten wir euch zwei Methoden etwas näher bringen.\n", + "Im PGP-2 ist es in verschiedenen Versuchen nötig, Messdaten von txt-Dateien einzulesen. Hierfür könnt ihr verschiedene Methoden verwenden. Dies ist oft in Python der Fall, dass mehrere Wege ans Ziel führen. Im Folgenden möchten wir euch zwei Methoden etwas näher bringen.\n", "\n", - "### Mit Hilfe von numpy:\n", + "## Mit Hilfe von numpy:\n", "\n", "Eine weitere nützliche `numpy`-Funktion verwenden. Gucken wir uns jedoch zunächst einmal einen typischen Beispieldatensatz (*BeispielDatenPGP2.txt*) an: \n", "\n", @@ -29,17 +50,17 @@ "11\t6,3\t2,55\t50\t24,3\t-0,006860\n", "```\n", "\n", - "Wie ihr sehen könnt besteht diese Datei aus mehreren bei Leerzeichen getrennten Spalten und durch Umbrüche getrennte Zeilen. Allgemein unterscheidet man bei solchen Datensätzen zwischen zwei Informationskategorien: \n", + "Wie ihr sehen könnt, besteht diese Datei aus mehreren bei Leerzeichen getrennten Spalten und durch Umbrüche getrennte Zeilen. Allgemein unterscheidet man bei solchen Datensätzen zwischen zwei Informationskategorien: \n", "\n", "1. Der Kopfzeile (*Header*) \n", "2. Den Messwerten selbst\n", " \n", - "Der Header enthält allegemeine Informationen über unsere Daten. In unserem obigen Beispiel ist dies z.B. der Name der einzelnen Messwerte wie *Hallspannung UH* und dessen Einheiten *mV*. Gefolgt ist der Header von unseren tatsächlichen Messwerten, welche wir für unsere Analyse brauchen. Was für uns einfach lesbar und verständlich ist, bedeutet im allgemeinen nicht, dass das gleiche auch für unseren Computer gilt. Probieren wir doch erstmal diesen Datensatz mit Hilfe von `np.genfromtxt` einzulesen." + "Der Header enthält allegemeine Informationen über unsere Daten. In unserem obigen Beispiel ist dies z.B. der Name der einzelnen Messwerte wie *Hallspannung UH* und deren Einheiten *mV*. Gefolgt ist der Header von unseren tatsächlichen Messwerten, welche wir für unsere Analyse brauchen. Was für uns einfach lesbar und verständlich ist, bedeutet im Allgemeinen nicht, dass das gleiche auch für unseren Computer gilt. Probieren wir doch erstmal diesen Datensatz mit Hilfe von `np.genfromtxt` einzulesen." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:40:49.463314Z", @@ -53,17 +74,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:40:49.677736Z", "start_time": "2020-08-26T06:40:49.578999Z" } }, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "Some errors were detected !\n Line #2 (got 1 columns instead of 11)\n Line #4 (got 6 columns instead of 11)\n Line #5 (got 6 columns instead of 11)\n Line #6 (got 6 columns instead of 11)\n Line #7 (got 6 columns instead of 11)\n Line #8 (got 6 columns instead of 11)\n Line #9 (got 6 columns instead of 11)\n Line #10 (got 6 columns instead of 11)\n Line #11 (got 6 columns instead of 11)\n Line #12 (got 6 columns instead of 11)\n Line #13 (got 6 columns instead of 11)\n Line #14 (got 6 columns instead of 11)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# Notebook befindet, reicht es den Dateinamen anzuegebn. Ansonsten\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# müsst ihr den gesamten Pfad angeben.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenfromtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpfad\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# <-- Einlesen der txt-Datei\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/lib64/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mgenfromtxt\u001b[0;34m(fname, dtype, comments, delimiter, skip_header, skip_footer, converters, missing_values, filling_values, usecols, names, excludelist, deletechars, replace_space, autostrip, case_sensitive, defaultfmt, unpack, usemask, loose, invalid_raise, max_rows, encoding, like)\u001b[0m\n\u001b[1;32m 2120\u001b[0m \u001b[0;31m# Raise an exception ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2121\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minvalid_raise\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2122\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2123\u001b[0m \u001b[0;31m# Issue a warning ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2124\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Some errors were detected !\n Line #2 (got 1 columns instead of 11)\n Line #4 (got 6 columns instead of 11)\n Line #5 (got 6 columns instead of 11)\n Line #6 (got 6 columns instead of 11)\n Line #7 (got 6 columns instead of 11)\n Line #8 (got 6 columns instead of 11)\n Line #9 (got 6 columns instead of 11)\n Line #10 (got 6 columns instead of 11)\n Line #11 (got 6 columns instead of 11)\n Line #12 (got 6 columns instead of 11)\n Line #13 (got 6 columns instead of 11)\n Line #14 (got 6 columns instead of 11)" + ] + } + ], "source": [ "pfad = 'BeispielDatenPGP2.txt' # <-- Pfad zur Datei, sofern sich die Datei am selben Ort wie euer\n", - " # Notebook befindet reicht es den Dateinamen anzuegebn. Ansonsten\n", + " # Notebook befindet, reicht es den Dateinamen anzuegebn. Ansonsten\n", " # müsst ihr den gesamten Pfad angeben. \n", "np.genfromtxt(pfad) # <-- Einlesen der txt-Datei" ] @@ -72,19 +106,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Wie ihr seht bekommen wir eine etwas längliche Fehlermeldung, welche so viel sagt wie: *Hey du ich verstehe deine Datei nicht, mal hast du mehr Spalten mal weniger*. Das Problem hier ist, dass unser *Header* scheinbar eine andere Spaltenanzahl als unser Messdaten aufweist. Dies Problem können wir relative einfach mit dem Paramter `skip_header` umgehen: " + "Wie ihr seht, bekommen wir eine etwas längliche Fehlermeldung, welche so viel sagt wie: *Hey du, ich verstehe deine Datei nicht, mal hast du mehr Spalten mal weniger*. Das Problem hier ist, dass unser *Header* scheinbar eine andere Spaltenanzahl als unsere Messdaten aufweist. Dieses Problem können wir relativ einfach mit dem Paramter `skip_header` umgehen: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:40:51.998152Z", "start_time": "2020-08-26T06:40:51.983189Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., nan, nan, -53., nan, nan],\n", + " [ 2., nan, nan, -40., nan, nan],\n", + " [ 3., nan, nan, -29., nan, nan],\n", + " [ 4., nan, nan, -21., nan, nan],\n", + " [ 5., nan, nan, -11., nan, nan],\n", + " [ 6., nan, nan, -1., nan, nan],\n", + " [ 7., nan, nan, 10., nan, nan],\n", + " [ 8., nan, nan, 22., nan, nan],\n", + " [ 9., nan, nan, 32., nan, nan],\n", + " [ 10., nan, nan, 42., nan, nan],\n", + " [ 11., nan, nan, 50., nan, nan]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = np.genfromtxt(pfad, \n", " skip_header=3 # <-- Überspringt die ersten 3 Zeilen unserer Files\n", @@ -96,7 +151,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Wie ihr sehen könnt ist uns hier schonmal ein Teilerfolg gelungen. Unsere Messwerte wurden eingelesen und wurden als ein `numpy.array` gespeichert. Ein `numpy.array` ist eine etwas bessere Form einer Liste wie ihr sie bereits kennt (denkt euch einfach das array() weg, dann sieht es genauso aus wie eine Liste. Guckt euch auch gerne mal in eurer Freizeit an wie numpy arrays funktionieren (z.B. auf youtube) arrays nehmen euch viel Arbeit ab). Des Weiteren seht ihr aber auch, dass Python nicht all unsere Messwerte richtig einglesen hat. Lasst uns nochmal die Messdaten der ersten Zeile mit unseren eingelesenen Daten vergleichen:\n", + "Wie ihr sehen könnt, ist uns hier schonmal ein Teilerfolg gelungen. Unsere Messwerte wurden eingelesen und wurden als ein `numpy.array` gespeichert. Ein `numpy.array` ist eine etwas bessere Form einer Liste wie ihr sie bereits kennt (denkt euch einfach das array() weg, dann sieht es genauso aus wie eine Liste. Guckt euch auch gerne mal in eurer Freizeit an, wie numpy-Arrays funktionieren (z.B. auf YouTube). Arrays nehmen euch viel Arbeit ab. Des Weiteren seht ihr aber auch, dass Python nicht all unsere Messwerte richtig einglesen hat. Lasst uns nochmal die Messdaten der ersten Zeile mit unseren eingelesenen Daten vergleichen:\n", "\n", "Messdaten:\n", "\n", @@ -110,33 +165,54 @@ "[ 1., nan, nan, -53., nan, nan]\n", "``` \n", "\n", - "Tja dumm gelaufen \"leider\" studiert ihr in Deutschland. In Deutschland (aber auch in anderen Ländern) ist es üblich ein Dezimalkomma **\",\"** zu verwenden, wohingegen in den USA der Punkt **\".\"** das übliche Trennungszeichen ist. Sprich Python versteht nicht wie er die Zahl -9,9 in eine Float-Zahl umwandeln soll. Aus diesem Grund bekommt ihr den Wert **nan** (not a number) zurück. Aber keine Sorge auch dieses Problem lässt sich relativ einfach mit Hilfe des `converters` parameter lösen. \n", + "Tja, dumm gelaufen. \"Leider\" studiert ihr in Deutschland. In Deutschland (aber auch in anderen Ländern) ist es üblich, ein Dezimalkomma **\",\"** zu verwenden, wohingegen in den USA der Punkt **\".\"** das übliche Trennungszeichen ist. Sprich Python versteht nicht, wie er die Zahl -9,9 in eine Float-Zahl umwandeln soll. Aus diesem Grund bekommt ihr den Wert **nan** (not a number) zurück. Aber keine Sorge, auch dieses Problem lässt sich relativ einfach mit Hilfe des `converters` Parameters lösen. \n", "\n", - "Der Paramter `converts` benötigt ein sogenanntes python dictionary als Eingabe. Keine Sorge es klingt kompliziert ist aber relativ einfach. Bei einem dictionary wird einem Schlüsselbegriff (key) ein Wert (value) zu gewiesen. Dies sieht wie folgt aus:\n", + "Der Paramter `converts` benötigt ein sogenanntes python dictionary als Eingabe. Keine Sorge, es klingt kompliziert, ist aber relativ einfach. Bei einem dictionary wird einem Schlüsselbegriff (key) ein Wert (value) zugewiesen. Dies sieht wie folgt aus:\n", "\n", "```python\n", "{key1: value1, key2: value2, key3: value3}\n", "```\n", "\n", - "Für unseren Parameter `converts` müssen wir ein dictionary erstellen welches einer gewissen Spalte eine Funktion zuordnet. In unserem Fall muss diese Funktion das Komma durch einen Punkt ersetzen. Dies ist relativ einfach und sieht dann so aus:\n", + "Für unseren Parameter `converts` müssen wir ein dictionary erstellen, welches einer gewissen Spalte eine Funktion zuordnet. In unserem Fall muss diese Funktion das Komma durch einen Punkt ersetzen. Dies ist relativ einfach und sieht dann so aus:\n", "\n", "```python\n", "{1: lambda v1: float(v1.replace(b',', b'.'))}\n", "```\n", "\n", - "Hierbei steht der **key** 1 für die 1. Spalte (nicht vergessen in Python starten wir bei der 0 mit dem Zählen). Als **value** wurde hier eine sogenannte `lambda`-Funktion verwendet. Eine `lambda`-Funktion ist lediglich eine mini-Funktion welche in den meisten Fällen nicht gespeichter werden soll. Unsere `lambda`-Funktion nimmt einen paramter genannt v1, was unserem Messwert entspricht und ersetzt bei diesem Wert das Komma durch ein Punkt. (Nebenbemerkung: Das kleine b vor dem String bedeutet das wir den String als bit-codiertes Wort lesen.) Es sieht sehr kompliziert aus, ist aber in der Anwendung sehr einfach. Gucken wir uns dieses Beispiel doch einfach einmal an:" + "Hierbei steht der **key** 1 für die 1. Spalte (nicht vergessen, in Python starten wir bei der 0 mit dem Zählen). Als **value** wurde hier eine sogenannte `lambda`-Funktion verwendet. Eine `lambda`-Funktion ist lediglich eine Minifunktion, welche in den meisten Fällen nicht gespeicht werden soll. Unsere `lambda`-Funktion nimmt einen Paramter genannt v1, was unserem Messwert entspricht, und ersetzt bei diesem Wert das Komma durch einen Punkt. (Nebenbemerkung: Das kleine b vor dem String bedeutet, dass wir den String als bit-codiertes Wort lesen.) Es sieht sehr kompliziert aus, ist aber in der Anwendung sehr einfach. Gucken wir uns dieses Beispiel doch einfach einmal an:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:40:53.808043Z", "start_time": "2020-08-26T06:40:53.799066Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1. , -9.9, nan, -53. , nan, nan],\n", + " [ 2. , -6.6, nan, -40. , nan, nan],\n", + " [ 3. , -4.7, nan, -29. , nan, nan],\n", + " [ 4. , -3.4, nan, -21. , nan, nan],\n", + " [ 5. , -1.8, nan, -11. , nan, nan],\n", + " [ 6. , -0.2, nan, -1. , nan, nan],\n", + " [ 7. , 0.9, nan, 10. , nan, nan],\n", + " [ 8. , 2.2, nan, 22. , nan, nan],\n", + " [ 9. , 3.3, nan, 32. , nan, nan],\n", + " [ 10. , 4.3, nan, 42. , nan, nan],\n", + " [ 11. , 6.3, nan, 50. , nan, nan]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.genfromtxt(pfad, \n", " skip_header=3, \n", @@ -148,19 +224,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Wie ihr seht haben wir für Spalte 1. erfolgreich die Messwerte einlesen können. Um dies nun auch mit unseren anderen Spalten machen zu können müssen wir nur unser dictionary entsprechend erweitern:" + "Wie ihr seht, haben wir für Spalte 1. erfolgreich die Messwerte einlesen können. Um dies nun auch mit unseren anderen Spalten machen zu können, müssen wir nur unser dictionary entsprechend erweitern:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:40:55.635140Z", "start_time": "2020-08-26T06:40:55.624172Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.000e+00, -9.900e+00, -2.730e+00, -5.300e+01, 2.400e+01,\n", + " -6.864e-03],\n", + " [ 2.000e+00, -6.600e+00, -2.030e+00, -4.000e+01, 2.400e+01,\n", + " -6.927e-03],\n", + " [ 3.000e+00, -4.700e+00, -1.510e+00, -2.900e+01, 2.410e+01,\n", + " -6.867e-03],\n", + " [ 4.000e+00, -3.400e+00, -1.090e+00, -2.100e+01, 2.410e+01,\n", + " -6.842e-03],\n", + " [ 5.000e+00, -1.800e+00, -5.700e-01, -1.100e+01, 2.380e+01,\n", + " -6.892e-03],\n", + " [ 6.000e+00, -2.000e-01, -4.000e-02, -1.000e+00, 2.410e+01,\n", + " -6.860e-03],\n", + " [ 7.000e+00, 9.000e-01, 5.200e-01, 1.000e+01, 2.400e+01,\n", + " -6.849e-03],\n", + " [ 8.000e+00, 2.200e+00, 1.090e+00, 2.200e+01, 2.400e+01,\n", + " -6.892e-03],\n", + " [ 9.000e+00, 3.300e+00, 1.620e+00, 3.200e+01, 2.410e+01,\n", + " -6.869e-03],\n", + " [ 1.000e+01, 4.300e+00, 2.150e+00, 4.200e+01, 2.420e+01,\n", + " -6.851e-03],\n", + " [ 1.100e+01, 6.300e+00, 2.550e+00, 5.000e+01, 2.430e+01,\n", + " -6.860e-03]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = np.genfromtxt(pfad,\n", " skip_header=3, \n", @@ -176,14 +284,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ihr könnt sehen wir haben alle Messwerte erfolgreich eingelesen auch wenn diese hier etwas gewöhnungsbedürftig in einer scientific-Schreibweise angegeben sind. Bei dieser Schreibweise steht z.B. 3.2e+01 für $3.2 \\cdot 10^1$. \n", + "Ihr könnt sehen, wir haben alle Messwerte erfolgreich eingelesen, auch wenn diese hier etwas gewöhnungsbedürftig in einer scientific-Schreibweise angegeben sind. Bei dieser Schreibweise steht z.B. 3.2e+01 für $3.2 \\cdot 10^1$. \n", "\n", - "Außerdem sind unsere Daten nicht in Form einer Liste sondern in sogennanten nump arrays abgespeichert. Mit diesen Arrays können wir wie gewohnt Arbeiten und noch mehr:" + "Außerdem sind unsere Daten nicht in Form einer Liste, sondern in sogennanten nump arrays abgespeichert. Mit diesen Arrays können wir wie gewohnt arbeiten und noch mehr:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:21.653723Z", @@ -213,27 +321,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Mit Hilfe des CSV-package:\n", + "## Mit Hilfe des CSV-package:\n", "\n", - "Eine weitere Methode welche zum Einlesen von Datein verwendet werden kann kommt mit dem CSV-Package. txt- oder csv-Dateien nutzen zur Trennung von Spalten Trennzeichen (separator), wie z.B. ‘,‘ ,‘;‘ oder auch ein Leerzeichen. Aus diesem Grund werden Dateien dieser Variante als CSV-Datei, das für comma separated variable steht, genannt. Wie im obigen Beispiel bereit gezeigt ist im deutschsprachigen Raum zu beachten, dass Kommas als Dezimaltrennzeichen benutzt werden, und daher nicht als Spaltentrennzeichen verwendet werden sollte.\n", + "Eine weitere Methode, welche zum Einlesen von Datein verwendet werden kann, kommt mit dem CSV-Package. txt- oder csv-Dateien nutzen zur Trennung von Spalten Trennzeichen (separator), wie z.B. ‘,‘ ,‘;‘ oder auch ein Leerzeichen. Aus diesem Grund werden Dateien dieser Variante als CSV-Datei, das für comma separated variable steht, genannt. Wie im obigen Beispiel bereit gezeigt, ist im deutschsprachigen Raum zu beachten, dass Kommas als Dezimaltrennzeichen benutzt werden und daher nicht als Spaltentrennzeichen verwendet werden sollten.\n", "\n", - "Für das Einlesen solcher CSV-Dateien hält Python das csv-Modul bereit. Hierbei ist zu beachten, dass die eingelesenen Werte zunächst vom Typ str sind und vor der Weiterverwendung in Rechnungen entsprechend umgewandelt werden müssen. Folgendes Beispiel demonstriert die notwendigen Schritte:\n" + "Für das Einlesen solcher CSV-Dateien hält Python das csv-Modul bereit. Hierbei ist zu beachten, dass die eingelesenen Werte zunächst vom Typ str sind und vor der Weiterverwendung in Rechnungen entsprechend umgewandelt werden müssen. Folgendes Beispiel demonstriert die notwendigen Schritte:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:23.905481Z", "start_time": "2020-08-26T06:41:23.888526Z" } }, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "could not convert string to float: 'Messwertnummer'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreadCSV\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Erste Spalte enthält die Messwertnummber (Die Werte werden in Fließkommazahlen umgewandelt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mmesswertnummer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;31m# Zweite Spalte enthält die Hallspannung\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mhallspannung\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# <-- Hier müssen wir wieder das Komma erstzen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Messwertnummer'" + ] + } + ], "source": [ "import csv\n", - "Messwertnummer = [] # Liste zur Speicherung der Spannungswerte\n", - "Hallspannung = [] # Liste zur Speicherung der Stromwerte\n", + "Messwertnummer = [] # Liste zur Speicherung der Spannungswerte\n", + "Hallspannung = [] # Liste zur Speicherung der Stromwerte\n", "\n", "pfad = 'BeispielDatenPGP2.txt'\n", "\n", @@ -242,29 +362,29 @@ " # optionale Parameter müssen explizit benannt werden\n", " readCSV = csv.reader(csvfile, delimiter='\\t')\n", " \n", - " # alle Zeilen der Textdatei nacheinander einlesen\n", + " # Alle Zeilen der Textdatei nacheinander einlesen\n", " for row in readCSV: \n", " # Erste Spalte enthält die Messwertnummber (Die Werte werden in Fließkommazahlen umgewandelt)\n", " messwertnummer = float(row[0])\n", " # Zweite Spalte enthält die Hallspannung\n", " hallspannung = float(row[1].replace(',', '.')) # <-- Hier müssen wir wieder das Komma erstzen\n", - " # die Werte werden in die entsprechenden Listen eingefügt\n", + " # Die Werte werden in die entsprechenden Listen eingefügt\n", " Messwertnummer.append(messwertnummer)\n", - " Hallspannung.append(hallspannung)\n" + " Hallspannung.append(hallspannung)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Leider gibt uns auch dieser Ansatz erstmal eine Fehlermeldung, da wir wieder unseren Header überspringen müssen. Dazu wird eine weitere for-Schleife eingesetzt, die nur die Header-Zeilen liest ohne sie weiterzuverarbeiten.\n", + "Leider gibt uns auch dieser Ansatz erstmal eine Fehlermeldung, da wir wieder unseren Header überspringen müssen. Dazu wird eine weitere for-Schleife eingesetzt, die nur die Header-Zeilen liest, ohne sie weiterzuverarbeiten.\n", "\n", - "Um hieraus nutzen ziehen zu können müssen wir den obigen Code wie folgt modifizieren:" + "Um hieraus nutzen ziehen zu können, müssen wir den obigen Code wie folgt modifizieren:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:25.941118Z", @@ -274,8 +394,8 @@ "outputs": [], "source": [ "import csv\n", - "Messwertnummer = [] # Liste zur Speicherung der Spannungswerte\n", - "Hallspannung = [] # Liste zur Speicherung der Stromwerte\n", + "Messwertnummer = [] # Liste zur Speicherung der Spannungswerte\n", + "Hallspannung = [] # Liste zur Speicherung der Stromwerte\n", "\n", "pfad = 'BeispielDatenPGP2.txt'\n", "\n", @@ -286,7 +406,7 @@ " for i in range(n):\n", " next(readCSV) # Zeilen auslesen, aber icht weiterverarbeiten\n", " \n", - " for row in readCSV: # restliche Zeilen mit Daten auslesen und in Liste speichern \n", + " for row in readCSV: # Restliche Zeilen mit Daten auslesen und in Liste speichern \n", " messwertnummer = float(row[0])\n", " hallspannung = float(row[1].replace(',', '.')) \n", " Messwertnummer.append(messwertnummer)\n", @@ -297,14 +417,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Zusatz: Arbeiten mit Numpy-Arrays: \n", + "# Arbeiten mit Numpy-Arrays: \n", "\n", - "In unseren Aufgaben haben oftmals viele Zeilen Code definieren müssen bevor wir etwas berechnen konnten. Viele der von uns definierten Funktionen existieren bereits und sind in anderen Packages enthalten. Ein sehr nützliches Package, ist dass das **Numpy**-Package. Hierbei sind insbesondere die **Numpy-arrays** eine tolle Sache, welche viele Prozesse vereinfacht. Im nachfolgenden möchte ich euch anhand der Beispielaufgabe noch zeigen, wie Numpy-arrays funktionieren. Sofern ihr mehr lernen möchtet gibt es viele Turotials zu Numpy im Internet bzw. auf Youtube." + "In unseren Aufgaben haben wir oftmals viele Zeilen Code definieren müssen, bevor wir etwas berechnen konnten. Viele der von uns definierten Funktionen existieren bereits und sind in anderen Packages enthalten. Ein sehr nützliches Package ist das **Numpy**-Package. Hierbei sind insbesondere die **Numpy-arrays** eine tolle Sache, welche viele Prozesse vereinfacht. Im Nachfolgenden möchte ich euch anhand der Beispielaufgabe noch zeigen, wie Numpy-arrays funktionieren. Sofern ihr mehr lernen möchtet, gibt es viele Turotials zu Numpy im Internet bzw. auf YouTube." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:39.587951Z", @@ -318,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:39.968931Z", @@ -330,9 +450,9 @@ "# Dies sind die gleichen Messdaten wie in dem Beispiel zum fitten der Schiefenebene in \n", "# Erweiterete_Musterloesung_Fitten_mit_der_Schiefenebene.ipynb\n", "h = np.array([0.095, 0.112, 0.134, 0.148, 0.17, 0.188, 0.21, 0.235, 0.25, 0.276]) # [m]\n", - "t = np.array([[2.65, 2.4, 2.17, 2.06, 1.91, 1.8, 1.68, 1.6, 1.52, 1.46], # Messwerte 1 [s]\n", - " [2710, 2360, 2190, 2060, 1900, 1780, 1690, 1690, 1530, 1440], # Messwerte 2 [ms]\n", - " [2.66, 2.36, 2.19, 2.06, 1.9, 1.8, 1.68, 1.59, 1.52, 1.44]]) # Messwerte 3 [s]\n", + "t = np.array([[2.65, 2.4, 2.17, 2.06, 1.91, 1.8, 1.68, 1.6, 1.52, 1.46], # Messwerte 1 [s]\n", + " [2710, 2360, 2190, 2060, 1900, 1780, 1690, 1690, 1530, 1440], # Messwerte 2 [ms]\n", + " [2.66, 2.36, 2.19, 2.06, 1.9, 1.8, 1.68, 1.59, 1.52, 1.44]]) # Messwerte 3 [s]\n", "delta_t = np.array([0.1]*len(h)) # [s]\n", "delta_h = np.array([5]*len(h)) # [mm]" ] @@ -341,19 +461,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Das Arbeiten mit Numpy-arrays funktioniert ähnlich wie mit Listen, d.h. das Slicing ist das Gleich:" + "Das Arbeiten mit Numpy-arrays funktioniert ähnlich wie mit Listen, d.h. das Slicing ist da gleich:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:40.362339Z", "start_time": "2020-08-26T06:41:40.351368Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 2\n", + "[3, 4, 5] [3 4 5]\n" + ] + } + ], "source": [ "l = [1,2,3,4,5,6]\n", "a = np.array([1,2,3,4,5,6])\n", @@ -371,28 +500,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:40.677497Z", "start_time": "2020-08-26T06:41:40.670515Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.65e+00, 2.40e+00, 2.17e+00, 2.06e+00, 1.91e+00, 1.80e+00,\n", + " 1.68e+00, 1.60e+00, 1.52e+00, 1.46e+00],\n", + " [2.71e+03, 2.36e+03, 2.19e+03, 2.06e+03, 1.90e+03, 1.78e+03,\n", + " 1.69e+03, 1.69e+03, 1.53e+03, 1.44e+03],\n", + " [2.66e+00, 2.36e+00, 2.19e+00, 2.06e+00, 1.90e+00, 1.80e+00,\n", + " 1.68e+00, 1.59e+00, 1.52e+00, 1.44e+00]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:40.937805Z", "start_time": "2020-08-26T06:41:40.930833Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.17, 2.06])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t[0, 2:4] # <-- Erste spalte, die Werte 2 bis 4" ] @@ -401,21 +557,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Jedoch ist die große Stärke von Numpy-arrays, dass diese sich ähnlich wie Vektoren bzw. Matrizen verhalten. Zum Beispiel beim multiplizieren von einem \"Vektor\" mit einem Skala:" + "Jedoch ist die große Stärke von Numpy-arrays, dass diese sich ähnlich wie Vektoren bzw. Matrizen verhalten. Zum Beispiel beim multiplizieren von einem \"Vektor\" mit einem Skalar:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:41.338608Z", "start_time": "2020-08-26T06:41:41.330629Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005,\n", + " 0.005])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Mit listen hätte das ganze so ausgesehen:\n", + "# Mit listen hätte das Ganze so ausgesehen:\n", "delta_h_liste = [dh * 10**(-3) for dh in delta_h]\n", "\n", "# Jetzt können wir einfach schreiben:\n", @@ -427,33 +595,62 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Das gleiche gilt für Matrizen:" + "Das Gleiche gilt für Matrizen:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:41.739986Z", "start_time": "2020-08-26T06:41:41.734001Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.65e+00, 2.40e+00, 2.17e+00, 2.06e+00, 1.91e+00, 1.80e+00,\n", + " 1.68e+00, 1.60e+00, 1.52e+00, 1.46e+00],\n", + " [2.71e+03, 2.36e+03, 2.19e+03, 2.06e+03, 1.90e+03, 1.78e+03,\n", + " 1.69e+03, 1.69e+03, 1.53e+03, 1.44e+03],\n", + " [2.66e+00, 2.36e+00, 2.19e+00, 2.06e+00, 1.90e+00, 1.80e+00,\n", + " 1.68e+00, 1.59e+00, 1.52e+00, 1.44e+00]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t # <-- Spalte 2 hatte ich in ms definiert ..." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:41.924492Z", "start_time": "2020-08-26T06:41:41.918507Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2.65, 2.4 , 2.17, 2.06, 1.91, 1.8 , 1.68, 1.6 , 1.52, 1.46],\n", + " [2.71, 2.36, 2.19, 2.06, 1.9 , 1.78, 1.69, 1.69, 1.53, 1.44],\n", + " [2.66, 2.36, 2.19, 2.06, 1.9 , 1.8 , 1.68, 1.59, 1.52, 1.44]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t[1] = t[1] * 10**(-3) #... wir wollen aber s\n", "t" @@ -469,14 +666,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:42.322427Z", "start_time": "2020-08-26T06:41:42.313451Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 6, 9])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.array([1,2,3]) * np.array([3,3,3]) # Etwas anders als in der Mathematik, \n", " # hier bekommt ihr kein Skalar sondern \n", @@ -485,14 +693,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:42.517430Z", "start_time": "2020-08-26T06:41:42.511446Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 5, 6])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.array([1,2,3]) + np.array([3,3,3]) " ] @@ -506,14 +725,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:42.925352Z", "start_time": "2020-08-26T06:41:42.917362Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 4, 9])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.array([1, 2, 3])**2" ] @@ -527,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:43.321027Z", @@ -542,14 +772,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:43.534966Z", "start_time": "2020-08-26T06:41:43.527983Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[34.4453625,\n", + " 28.2528,\n", + " 23.097154500000002,\n", + " 20.814858,\n", + " 17.8939305,\n", + " 15.892200000000003,\n", + " 13.843872,\n", + " 12.556800000000003,\n", + " 11.332512000000001,\n", + " 10.455497999999999]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "result = []\n", "t_spalte1 = t[0]\n", @@ -567,14 +817,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:43.908270Z", "start_time": "2020-08-26T06:41:43.902286Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([34.4453625, 28.2528 , 23.0971545, 20.814858 , 17.8939305,\n", + " 15.8922 , 13.843872 , 12.5568 , 11.332512 , 10.455498 ])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "fallhoehe(t[0], 9.81)\n", "# Dies liegt daran, das wir innerhalb unsrer Funktion ein Produkt zwischen einem \"Skalar\" und einem \"Vetor\" bilden." @@ -584,19 +846,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Es gibt auch andere tolle Funktionen die mit dem numpy-Package kommen. Zum Beispiel eine Funktion für den Mittelwert und die Standardabweichung: " + "Es gibt auch andere tolle Funktionen, die mit dem numpy-Package kommen. Zum Beispiel eine Funktion für den Mittelwert und die Standardabweichung: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:44.276300Z", "start_time": "2020-08-26T06:41:44.269317Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5 1.118033988749895\n" + ] + } + ], "source": [ "a = np.array([1,2,3,4])\n", "print(np.mean(a), np.std(a))" @@ -606,21 +876,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Die Zeitwerte hatten wir als eine Art n x m Matrix gespeichert. Viele der Funktionen von numpy erlauben es uns diese nur über bestimmte Spalten oder Zeilen anzuwenden. Im folgenden möchten wir das ganze anhand von `np.mean` und `np.std` uns anschauen. \n", + "Die Zeitwerte hatten wir als eine Art n x m Matrix gespeichert. Viele der Funktionen von numpy erlauben es uns, diese nur über bestimmte Spalten oder Zeilen anzuwenden. Im Folgenden möchten wir das Ganze anhand von `np.mean` und `np.std` uns anschauen. \n", "\n", "Gucken wir uns zunächst nochmal die genaue n x m Form von t an: " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:44.636346Z", "start_time": "2020-08-26T06:41:44.632347Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 10)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "t.shape" ] @@ -634,42 +915,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:45.061303Z", "start_time": "2020-08-26T06:41:45.053323Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.9266666666666665" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "np.mean(t) # <-- gibt den Mittelwert über alle Werte" + "np.mean(t) # <-- Gibt den Mittelwert über alle Werte" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:45.259770Z", "start_time": "2020-08-26T06:41:45.252788Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.67333333, 2.37333333, 2.18333333, 2.06 , 1.90333333,\n", + " 1.79333333, 1.68333333, 1.62666667, 1.52333333, 1.44666667])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(t, axis=0) # <-- Mittelwert entlang der ersten n-achse -> 10-Werte entlang der m-Achse " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:45.660701Z", "start_time": "2020-08-26T06:41:45.653716Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.925, 1.935, 1.92 ])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(t, axis=1) # <-- Mittelwert entlang der m-achse -> 3 Werte entlang der n-Achse" ] @@ -683,14 +998,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:46.031706Z", "start_time": "2020-08-26T06:41:46.022730Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.02624669, 0.01885618, 0.00942809, 0. , 0.00471405,\n", + " 0.00942809, 0.00471405, 0.04496913, 0.00471405, 0.00942809])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.std(t, axis=0)" ] @@ -699,19 +1026,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Beim plotten selbst können wir noch die Funktion `np.arange` nutzen um uns das Leben etwas zu erleichtern. Bisher mussten wir uns Gleitkommazahlen immer sehr aufwendig erstellen. " + "Beim Plotten selbst können wir noch die Funktion `np.arange` nutzen, um uns das Leben etwas zu erleichtern. Bisher mussten wir uns Gleitkommazahlen immer sehr aufwendig erstellen. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:46.763991Z", "start_time": "2020-08-26T06:41:46.421664Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -729,31 +1079,44 @@ "source": [ "# Das Auge plottet mit:\n", "\n", - "In diesem Abschnitt möchte ich noch ein paar weitere nützlichen Befehle rund ums Thema **Plotten** zeigen. Hierbei handelt es sich nur um verschiedene Beispiele wie ihr eure Plots noch etwas weiter ausschmücken könnt. Nehmen wir hierfür wieder unseren Spannungsplot aus Kapitel 1.:" + "In diesem Abschnitt möchte ich noch ein paar weitere nützlichen Befehle rund ums Thema **Plotten** zeigen. Hierbei handelt es sich nur um verschiedene Beispiele, wie ihr eure Plots noch etwas weiter ausschmücken könnt. Nehmen wir hierfür wieder unseren Spannungsplot aus Kapitel 1:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:47.033616Z", "start_time": "2020-08-26T06:41:46.808219Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "# ------------\n", "# \"Messwerte\":\n", "# ------------\n", "spannung = [0.9, 2.0, 3.0, 4.1, 4.9, 6.2] # [V]\n", - "strom = [105, 204, 298, 391, 506, 601] # [mA]\n", + "strom = [105, 204, 298, 391, 506, 601] # [mA]\n", "spannung_error = [0.3]*len(spannung) \n", "strom_error = [14, 9, 12, 8, 7, 11]\n", "\n", "# Spannungsfunktion:\n", - "Spannung = lambda I,R: I*R # <--- Eine lambda-Funktion ist eine andere Variante um kleine \n", + "Spannung = lambda I,R: I*R # <--- Eine lambda-Funktion ist eine andere Variante, um kleine \n", " # Funktionen zu definieren.\n", "\n", "plt.errorbar(strom, \n", @@ -780,21 +1143,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Plotgröße ändern\n", + "## Plotgröße ändern:\n", "\n", - "Als erstes kann es nützlich sein die Größe unseres Plottes zu erhöhen. Die können wir über `plt.figure()` erreichen. Je nach dem solltet ihr auch die fontsize eurer Beschriftung entsprechend anpassen." + "Als Erstes kann es nützlich sein, die Größe unseres Plots zu erhöhen. Die können wir über `plt.figure()` erreichen. Je nach dem solltet ihr auch die fontsize eurer Beschriftung entsprechend anpassen." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:47.464824Z", "start_time": "2020-08-26T06:41:47.197543Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(7,5), # <-- figsize gibt die Seitenlänge des Plots in\n", " # Zoll (1 Zoll = 2.54 cm) wieder. (7,5) ist ein gutes Format für A4\n", @@ -828,21 +1204,44 @@ } }, "source": [ - "### Linien in allen Arten und Farben:\n", + "## Linien in allen Arten und Farben:\n", "\n", - "Das zweite nützliche Feature sind verschiedene Arten von Hilfslinien welche wir zu unserem Plot hinzufügen können. Fangen wir mit einem einfachen Gitternetz an dies könnt über `plt.grid` zu eurem Plot hinzufügen:" + "Das zweite nützliche Feature sind verschiedene Arten von Hilfslinien, welche wir zu unserem Plot hinzufügen können. Fangen wir mit einem einfachen Gitternetz an. Dies könnt ihr über `plt.grid` zu eurem Plot hinzufügen:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:47.885189Z", "start_time": "2020-08-26T06:41:47.654772Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(strom, \n", " spannung,\n", @@ -862,7 +1261,7 @@ "plt.ylabel('Spannung [V]')\n", "plt.xlabel('Strom [mA]')\n", "\n", - "plt.grid() # <-- Dieser Befehl fügt das Gitternet hinzu.\n", + "plt.grid() # <-- Dieser Befehl fügt das Gitternetz hinzu.\n", "\n", "plt.show" ] @@ -871,19 +1270,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Natürlich können wir auch für das Gitternet verschiedene Optionen spezifizieren:" + "Natürlich können wir auch für das Gitternetz verschiedene Optionen spezifizieren:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:48.309117Z", "start_time": "2020-08-26T06:41:48.095687Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(strom, \n", " spannung,\n", @@ -905,7 +1317,7 @@ "\n", "plt.grid(color='k', # Farbe \n", " ls='dashed', # Linientyp\n", - " axis='x') # Axe\n", + " axis='x') # Achse\n", "\n", "\n", "plt.show()" @@ -920,14 +1332,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:49.048140Z", "start_time": "2020-08-26T06:41:48.534527Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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NGiRmk2xqasLo0aPzxyZpyh6INpfg+DYXm4vHR4pP7NnKJduxOXzkiovGwsLCwkIfoks0RLQZwF4ABwC0Kb+m9Jo4//zzg0grkPjcXKT5krEl+VbTYGCSRmGtk8s3aez6oTvW4C9QSjlBBOrVSzZdTnxuLtJ8ydiSfKtpMDBJo7DWyeWbNHb9EKolGs6NGen43Fyk+ZKxJflW02Bgkka6fCICER1yrqg32re2TPW8SRqZNHb9ILonKxF9BKAeyZsnlUqpBSk4swDMAoDS0tKJ8+bN843nOA6i0aj2+0vybS7B8G0u+ZtLRUUFAOCBBx5ANBrFd26rwOBSoO9xJ+Mb37qj/fnKykrxXLrCNyWXrO3JCuAz7r/HAlgP4LzO+OlcNKtXr9a6q+yBe9eaE5+biyRfsk5pvtU0NUzSVCo2XNdMZWWlWr58uRrYH6rhMaiTBkVUVVVVSleNSRqZMnaRLReNUupj999dAP4A4IxM4p1++ulBpBVIfG4u0nzJ2JJ8q2kwMEkjLn///v24ffZMPDEL6BsB5l8Xw+2zZ2Yll7COXT+ITfBEVExEfbyfAUwBUJ1JzCVLlgSRWiDxublI8yVjS/KtpsHAJI24/G/ffitO6rsTl45NPi4fBwwt2YGeKWYnkzQyaez6QfIT/HEAXiei9QD+DmC5Umql4PtZWFiEEAU9gblXHXpu7heS5y0yg5jPRyn1IYCxQcbs27dvkOEyis/NRZovGVuSbzUNBiZpxOVPKZ+Kh1atxjkjY+3nHnohgtYDsSO4Jmlk0tj1hd/ifDYOu+FH15AvdSqVP7XmQ51wb6I++uij6qQTj1dV34NSS6CWfw9qeNlArdYFYUI2NvwIVbOx+vp67N27F4BMs7G6ujp861vf0mpiNX/+fAwYMEC72djq1avbG3bpNBvzGh5JNBtbuXIlBgwYoN1s7Be/+AUikQgAvWZjHJ3q6uowbdo0sWZjHJ169uyJSZMmaTcbq6urw5133qndbKyurg6jRo0SaTZWUlKCkpIS7WZjLS0tuPXWW7Waja1evRoDBgzQbjb28MMPo6SkBIBes7EePXrgsiuuxszH5qPmQeD2JRF86Ws34Gc/+xmAQ5uN1dXV4eqrr9ZuNrZ9+3YMGDBAu9nYY489hgEDBmg1G+vduzfGjx8v1mysrq4O48aNs83GssG3uQTDt7nkby7oYJNUSqlIAdSIgVDTp0055PnuyKUrfFNygW02ZmFhYRq8SchDrBXY5gA/n1+Z8nmLLsBv5s/Gke4TfFNTk9ZvNA/c35ic+NxcJPmSdUrzraapYZKm0teF0y7YJI1MGbvIlU/wa9asMSY+NxdpvmRsSb7VNBiYpFFY6+TyTRq7fgjVBO/dDDMhPjcXab5kbEm+1TQYmKRRWOvk8k0au34wwkVjYWGRf+jYKdJrLHb4eQB2HT4DGDHB69okL7jggvbnJWyS8XgcDQ0NWva7eDyOBQsWaNskR4wY0Z67jk3yvPPOw0svvSRik/Ry17VJlpSUtOeuY5Pk6BSPx1FdXS1mk+TodPLJJ2Pjxo3aNsl4PI7W1lZtm2Q8HtfWiWuTnDx5srb9DgD69euHRCKhZZP0rqGuTbKgoKA996D3ZI3H4/jggw+0bZJe7ro2SY+vY5McO3Ys1q1bJ2aTjMfjWLlyZf7YJN977z2tmw4euDc1OPG5uUjyJeuU5ltNU8MkTaWvC6dWkzQyZewiV26yer+xTYjPzUWaLxlbkm81DQYmaRTWOrl8k8auH3yXaIjoEY3XNyqlfphxFhYWFhYWgaOzNfgrAcxN8/q7AXTbBH/KKacYE5+bizRfMrYk32oaDEzSKKx1cvkmjV0/dDbB/1wp9ZvOXkxER2ecAQNjxowxJj43F2m+ZGxJvtU0GJikUVjr5PJNGrt+6GwN/q/pXqyU+kXGGTDg3SU3IT43F2m+ZGxJfr5p6m0kne7gwiSN7NgNBkHE7+wT/AIiKgHwNICnlFLvZvxuPtC1SSqlRG2SjuNo2yQ9u56uTbKlpYVlk0wkEmI2SS93XZtkQ0MDyybJ0clxHFGbJEeneDzOskk6jsOySXo2OR1wbZKAvv0OABobG7Vtkt411LVJ1tfXa9skR4wYgQMHDrTz0+nkOA7LJunlrmuT9Pg6NsnW1lZRm6TjOLI2SQAjAdwP4F0kN82+G0BZZ6/J5Ehnk1y6dKmWbcgD15bEic/NRZIvWac0P981hU//FZM0lb4unFrt2D0S6MQmqT35Irk7038C+ADAX3Vfxznshh9dQ77UqVTu1RrUBB9m5Eut2djwQ8sHT0Q9AByL5D6rxQA0/jYIHosXLzYmPjcXab5kbEm+1TQYmKRRWOvk8k0au37otFUBEZ0L4FoA0wFsQHI9/jtKqYaM37kL8NanTYjPzUWaLxlbkm81DQYmaRTWOrl8k8auH3w/wRPRNiSXZN4FME4pdYlS6n+zNblbWIQdFRUVKR0yRb2BzZs3tz8mokOab1lYdBWdfYKfpJTa0h1J6LpobrjhBlEXjVJK20WjXKeIrovmnHPOYblovvrVr4q5aLzcdV00xx9/PMtFw9FJKSXqouHodN5557FcNEoploumIzydIgXA4FLga9d+EV+78dBJneOimTFjBstFM3jwYG0XjXcNdV00/fv3F3PRKKVYLhovd10XjcfXcdFMmTJF1EWjlJJz0QD4kd9zHA7nSHeT9cUXX2TdfODe1ODE5+YiyZesU5qfT5risBuqy5cvVwP7QzU8BnXSoIiqqqpKyZPIxaTrwtHUjt0jgU5usnb2Cf5mImrs5HkC8BUAP0r/ayQYeL99TYjPzUWaLxlbkp9PmnZES0sLbp89E0/MAvpGgPnXxXD77JnYUNO1mCZpZMduMAgifmcT/GMA+qR5/WMZZ2BhkWcgIvTsAUw+Bbh0bPJc+Thg6IodKCkuympuFrkF3wleKfXjIN6AiHoC+AeAj5VS0zKJdckllwSRUiDxublI8yVjS/LzSdOOKOgJzL3q0HNzvwC8vhFoTvDjmaSRHbvBIIj43dEP/tsAaoIItHfv3iDCBBKfm4s0XzK2JD+fNPWglMI9P5iLh1ZFDjn/0AsR3PvD+7sU0ySN7NgNBkHEF53giWgwgKkAHg8innfHWwqc+NxcpPmSsSX5+aRpR3zv+/fgnR19sWJd8nHVOqD6036Yc9fdXYpnkkZ27AaDIOJT8iasDIhoGZJe+j4A5qRaoiGiWQBmAUBpaenEefPm+cZzHAfRaFT7/SX5Npdg+PmUi+dtr6ysBABUV1dj2W/no+ZBYOwPCnDFNRUYM2bMEbxs555PGoUxl4qKirVKqdNSPulnr/EOAI+kOH4K4Mo0r5sG4Ffuz/8B4Pl075XOJvnmm29q2YY8cG1JnPjcXCT5knVK8/NJU6SwP0YKoEYMhJo+bUqnvKBzMem6cDS1Y/dIIMNeNIUAxgH4t3t8DsBgADOJ6BedvO4cAFcQ0WYkWxxcSEQZNVcYPnx4Ji8PND43F2m+ZGxJfj5p6jiO9+GnHbFWYJsD/Hz+wU/rSinWp/eu5GLSdZGMbdJ14SKI+DoT/OcAXKCUmq+Umg/gIgCjAFwFYIrfi5RS9yilBiulypD0y7+ilLo+k2QP/zZg0ODE5+YizZeMLcm3mgLN+4GysjJWLKlcujs2F3bs8qAzwR8NoKTD42IAA5RSBwDEM87AwsLCwkIEnXaTdPHfANYR0atIfnv1PADziKgYwEs6b6KUehXAq11L8SCOPfbYTEMEFp+bizRfMrYk32oaDEzSKKx1cvkmjV1f+C3Oq0NvmA4EcKV7DNJ5TVcOu+FH15AvdSoV/lrh3kBNd4S9Tg7ypdZsbPih8wkeSC7l1CL5iX84EQ1XSr2W+a+XJHS7Se7YsQP79+8HINNNcvfu3Zg9e7ZWl8JHHnkEpaWl2t0kX3jhBfTqlbzcOt0ke/XqhSFDhoh0k1yxYgVKS0u1u0k+9NBD6NMn2bVCp5skR6fdu3fj8ssvF+smydHpwIEDuPDCC7W7Se7evRvf/e53tbtJ7t692/f/wOHg7snaq1cvRKNR7W6STU1NuOOOO7S6Sb7yyisoLS3V7ib54IMPol+/fgCC7ya5e/dufPnLX9buJrlt2zaUlpZqd5NcsGABSktLtbpJKqVw5plninWT3L17N8aPHy+3J2vylwN+BmAzgOUAnnOPP6V7XVeOdJ/gub8BJfk2l2D4Nhebi80lMz4y/AQ/HcBIpZS9oWphYWERJvjN/N4BYAWAknS8II50n+APHDig9RvNA/c3Jic+NxdJvmSd0nyraWqYpKn0deHUapJGpoxdZPhFpxiSLppKInrEOyR/6fhh1apVxsTn5iLNl4wtybeaBgOTNAprnVy+SWPXDzpLNH9yj6zDu7lhQnxuLtJ8ydiSfKtpMDBJo7DWyeWbNHb9kHaCV0r9JuN3SQNdF41SSnRPVsdxtPdk9dwcui6alpYW1p6siURCbE9WL3ddF01DQwNrT1aOTo7jiO7JytEpHo+z9mR1HIe1JytHJ66LBtB3ZwBAY2Oj9p6s3jXUddHU19eL7cnqOA5rT1Yvd10XjcfXcdG0traK7snqOI7cnqzeAeAjAB8efqR7XVeOdGvw27dv11qT8sBd8+LE5+YiyZesU5pvNU0NkzSVvi6cWk3SyJSxiwzX4E8DcLp7nItkN8mMmoZ1FbW1tcbE5+YizZeMLcm3mgYDkzQKa51cvklj1w9pJ3il1O4Ox8dKqV8guYlHt8P7U8iE+NxcpPmSsSX5VtNgYJJGYa2Tyzdp7Poh7Ro8EU3o8LAHkp/odb8Ba2FhYWGRJehM1A91+LkNyW+1flkkmzSYOHGiMfG5uUjzJWNL8q2mwcAkjcJaJ5dv0tj1g46L5oKM3yUgDB482Jj43Fyk+ZKxJflW02BgkkZhrZPLN2ns+kFnieYoAF8EUNaRr5T6ScbvfvA9tGySW7ZsAREBkLNJ3nLLLVr2u4ULFyIajWrbJJcvX47CwkIA+jbJ4cOHi9gkq6qqEI1GtW2STz75ZHvzKB2bJEcnx3FwxRVXiNkkOTrF43FMmTKFZZOcM2cOyyY5evRoEZtkIpHAoEGDWDbJO++8U8sm+fLLLyMajWrbJBctWoSjjz4agIxN8pprrtG2SW7duhXRaFTbJumNF12b5KRJk0RtkhMmTBC3Sa4E8DsAdwH4rneke11XDttsLPy5cPk2F5uLzSUzPjJsNjZYKXWpBk8cgwYNMiY+NxdpvmRsSb4f1/sLIBUqKiraf06Ob9lcssWXjG3SdZGMbdJ14SKQ+H4zv3cAWADg1HS8IA674UfXkIt1QnNjjFxFLmrqh3ypNRsbfuh80WkSgLVEtJGI3iGiDUT0Tua/Wvjw1qFMiM/NRZovGVuS78c9fKB6qKysTHleMpds8SVjm3RdJGObdF24CCK+zhJNecbvEhASiYQx8bm5SPMlY0vyrabBwCSNwlonl2/S2PWDjk1yCxH1BHCcDl8SXlMlE+Jzc5HmS8aW5FtNg4FJGoW1Ti7fpLHrBx2b5G0A7gfwKQDvV4oC8LmM3/3ge2jZJG+66SbRbpIAtLtJJhIJVjfJc889l9VNcsaMGWLdJL3cdW2Sxx9/PKubJFcnnW6SHrg2Sa5OnG6SAFjdJAGIdZO8+eabWd0khwwZot1N0ruGujbJ/v37i3WTBMDqJunlrmuT9Pg6NsmpU6eKdpME0C3dJN8HUJqOF8SR7ibrc889x7r5wL2pwYnPzUWSL1mnNF+XC/emqtW0+3ORvi6cWk3SyJT5CBneZN0GoEGDJw7vt54J8bm5SPMlY0vyO3KJqFN7ZFFvtH8K6ojOXmc1zZyfL3Vy+SbNR37QmeA/BPAqEd1DRHd6R7oXEVEhEf2diNYT0b+I6McZZ2uRt4gUACdEgT8szUqnaguLcMLvo713ILn+fsSh8TqCu1k3gN4A3gJwVmevSbdEs3PnTq0/WTxw/yTixOfmIsmXrFOa35ELH2/78uXL1cD+UA2PQZ14bIGqqqo65Hm/12WSS7b5JmkqfV04tZqkkSnzETJZolFK/TjVofE6pZRqch/2do/0xuVOsH379kxeHmh8bi7SfMnYkvx03JaWFtw+eyaemAX0jQC//norbp89Ey0tLd2eS3fzJWObdF0kY5t0XbgIIn7aCZ6IjiGiB4moiohe8Q6d4ETUk4jWAdgF4EWl1FuZJOvdaZYCJz43F2m+ZGxJfiqut6ZORCgpLsJJfXfi0rHJ58rHAUNLdqCkuCjtmn0QuWSTLxnbpOsiGduk68JFEPFJpfk2IBGtQrLZ2BwA3wTwdQC1Sqnva78JUX8AfwBwm1Kq+rDnZgGYBQClpaUT582b5xvHcRxEo1HdtxXl21yC4Xfkduwx46GoN/DiPcA5Iw+ee30jMOU/geb9h3IrKysDyyXbfJuLzUWXX1FRsVYpdVrKJ/3WbtTBtfS17r/vdDi3Jt3rUsSZC2BOZ5x0a/Bvv/221pqUB+6aFyc+NxdJvmSd0vyOXKRYS//Jj+aqq86KKLUE7cf0MyPqpz++v9PXZZpLtvkmaSp9XTi1mqSRKfMRMrRJep+TdhDRVCIaD2BAuhe5Szv93Z+LAFwM4D2N9/PFMccck8nLA43PzUWaLxlbkp+O+73v34N3dvTFinXJx1XrgOpP+2HOXXd3ey7dzZeMbdJ1kYxt0nXhIoj4OhP8/yOifkj2gZ8D4HEA39F43UAAq93GZGuQXIN/vsuZAu3f7pICJz43F2m+ZGxJfjpuYWEh5v/6Ccx8DGiIAbN/U4BHfrWwffOU7sylu/mSsU26LpKxTbouXAQRX6cXjTcpNwDQ3r5PKfUOgPFdzMvCoh3l5eVoiAFnzAWOGXQSysuN6X9nYWE0dFw0w4joOSJyiGgXEf2RiIZ1R3KHY8iQIcbE5+YizZeMLcnvyFUH79ccgVgrsM0Brrr6+iOe6+x1VtPM+flSJ5dv0nzkBx0XzZsA/gfAU+6pryDphjkz43c/+B5es7Fv3HfffQBSN0e66KKL8MQTTwDQbzY2ffp0VrMx3SZWS5YsAaDXHGny5MlobW3FX/7yFwB6TaxuuOEGvP7664Hv9VlbW6vdHKmrTay4Ouk0sfra174GAHjggQcQjUZFdLrggguQSCRYzcZ0m1h5zcZ0m1gtXbq0vcmajk433XQTqqqqWDqla2LVFZ2uv/56LF26FPX19QD0/j/V1NSgZ8+e2jpNnjxZu9mYpE7l5eXYvXs3q9nYvffey/7/lE6nTF0076Q4tz7d67py2D1Zw58Ll8/Yd7JLzcbCeh1tLjaX7tqTdQUR3Q3gafc/2TUAqohogPsLok4jhoWFhYVFN0Nngv+y++/h30L5CpITfretxxcUFBgTn5uLNF8ytiTfj+v3DdWKigrWpttW08z5+VInl2/SfOQLv4/22TjspttdQy7WCbvpdrZT6DbkS61GbbpNRKcT0fEdHt/gOmge8ZZnuhvPPvusMfG5uUjzJWNL8v24fgOWu+m21TRzfr7UyeWbNB/5oTObZCWAVgAgovMA/BeAJ5H0wy/I+J27AK0tqropPjcXab5kbEm+1TQYmKRRWOvk8k0au37obA2+pzp4A/UaAAuUUs8AeMbtEBkYdPdkVUqJ7snqOI72nqze3qC6tq6WlhbWnqyJREJsT1Yvd12bZENDA2tPVo5OjuNo7cnq6cTdk5WjUzweZ+3J6jgOa09Wjk7cPVkB3l6fjY2N2nuyetdQ1yZZX18vtier4zisPVm93HVtkh5fxybZ2toquier4zhye7ICqAbQy/35PQDndXzO73WZHOnW4B3HYa1Ncde8OPG5uUjyJeuU5ltNU8MkTaWvC6dWkzQyZeyii83GngLwZyL6I4BmAH8BACIajizt0fr+++8bE5+bizRfMrYk32oaDEzSKKx1cvkmjV0/+E7wSqkHkGwwtgjAJPc3hfea2zJ+5y7AW3oxIT43F2m+ZGxJvtU0GJikUVjr5PJNGrt+6NQHr5R6M8W5TRm/q4WFhYWFOHTaBRuDs88+25j43Fyk+ZKxJflW02BgkkZhrZPLN2ns+iFUE3yfPn2Mic/NRZovGVuSbzUNBiZpFNY6uXyTxq4fdFoViEPXJrlly5b2r7BL2SRvueUW7S6F0WhU2ya5fPny9k0qdG2Sw4cPF7FJVlVVIRqNatskf//736Nfv34A9GySHJ0cx8EVV1whZpPk6BSPxzFlyhSWTXLOnDksm6Ru10+uTTKRSGDQoEEsm+Sdd96pZZN8+eWXEY1GtW2STz/9NI4++mgAMjbJa665RtsmuXXrVkSjUW2bpDdedG2SkyZNErVJTpgwQcYmmY3DdpMMfy5cvs3F5mJzyYyPDLtJGoNhw2T7mnHic3OR4HdsyNWxAdfhUId9pd+E3Lsam4uwadpVmKRRWOvk8k0au77wm/mzcaT7BB+Px7V+o3ng/sbkxOfmIsFHFxtymZB7V2PnuqYeJOvk8qWvC6dWkzQyZeyii190Mg7eWp8J8bm5SPA9Eb0GXIef947uyKWrfKtpMDBJo7DWyeWbNHb9EKoJ3sLCwsJCH0asweu6aIqKikSbjdXV1Wk3G6urq2M1G1NKsZqNFRYWspuNefDex89F4+Wu66LZt28fq9kYR6e6ujrRZmMcnQCwmo3V1dWxmo3V1dWJNRuLRCKsZmOxWEy72Zh3DXVdNHv37hVrNlZXV8dqNublruui8fg6LpoePXqINhurq6uTazaWjcNu+NE1eHUixzfBUCr/NM0H5EutRm34YSKWLVtmTHxuLkHziSjltnZFvYHNmzd3yst27pnE5iJMmmYCkzQKa51cvklj1w+hmuDr6mT39+bE5+YizQeASAFwQhT4zm3+lsnuyEXyOnIRdk2lYpt0XSRjm3RduAgivtgET0QnENFqInqXiP5FRN+Wei8LoKqqCv0iwJqfABv++TpWrFiR7ZQsLCyyDb+1m0wPAAMBTHB/7gNgE4DRnb0m3Rr8nj17WGtT3DUvTnxuLkHz0WG9/dFHH1UnnXi8WnEXlFoCVfU9qOFlA1Vzc3PKdfls555J7FzWtCMk6+Typa8Lp1aTNDJl7CIba/BKqR1KqX+6P+8FUAPgM5nErK6uDiK1QOJzc5HiExG+ffutOKnvTlw6NnmufBwwtGQHSoqLujWXrvCtpsHAJI3CWieXb9LY9QOpFF+ECRpEVAbgNQBjlFKNhz03C8AsACgtLZ04b9483ziO4yAajWq/ryQ/27l0bE1Q1Bt48R7gnJEHn399IzDlP4Hm/cnHlZWVxuTeXbFtLjaXfMiloqJirVLqtJRP+n20D+oAUAJgLYAvpOPaZmP6fHRYernyiqnqqrMiSi1B+zH9zIj66Y/vT7lEk+3cbS42F5tLcHxkyyZJRL0BPANgiVLq/zKNd/7552eeVEDxublI8i+eUo53dvTFinXJx1XrgOpP+2HOXXd3ey5cvtU0GJikUVjr5PJNGrt+kHTREICFAGqUUg8HEbNXL9kv3nLic3OR5Pfu3Rvzf/0EZj4GNMSA25dE8MivFrb3n+/OXLh8q2kwMEmjsNbJ5Zs0dv0g+Qn+HABfA3AhEa1zj8syCeh9vVkKnPjcXKT55eXlaIgBZ8wFTp0wCeXl5VnLRfI6chFmTSVjm3RdJGObdF24CCK+2K8gpdTrAI78qqVFIFApbo7HWoFtDvDC/MpOeRYWFvmBUH2TdcSIEcbE5+YizffQvB8oKyvLai6S15GLXNBUIrZJ10UytknXhYsg4oeqm+Q555wj2k0ykUhg/PjxWl0K33vvPWzatEm7m+TRRx/N6iZ59dVXi3WT9HLX7Sa5Z88eVjdJjk6JRALRaFSsmyRHp3POOYfVTTKRSODss8/W7iaZSCSwZ88ekW6S1157Laub5KBBg7S7SXrXULeb5M6dO8W6SSYSCZxwwgna3SSbmpqwadMm7W6SXq063SQvvPBC0W6SiUQCLS0t+dNN0hRbkqm5QLObpIm5m8C3udhcwpgLcmVPVotD4bcn6+HdI5Vdh7ewyEuEag2+b9++xsTn5iLNl4wtybeaBgOTNAprnVy+SWPXF34f7bNx2A0/uoZ8qVOp/Kk1X+pUKn9qtRt+pMHTTz9tTHxuLtJ8ydiSfKtpMDBJo7DWyeWbNHb9YMQavK6LpqGhQdRF4ziO9p6sH374IWtP1l27drFcNIlEgu2i0XFn1NbWtueu66LZtm0by0XD0clxHNE9WTk6xeNxlovGcRzWnqwcnbgumkQiwXLRNDY2artovGuo66LZsmWLmIvGcRzWnqxbt25l7cnq1arjomltbRV10TiOk197sppy19rmEhzf5mJzsblkxkcnSzRZn9Q7Hukm+KamJq2CPXAvKCc+NxdJvmSd0nyraWqYpKn0deHUapJGpozdzib4UK3Br1mzxpj43Fyk+ZKxJflW02BgkkZhrZPLN2ns+iFUE7y3VmpCfG4u0nzJ2JJ8q2kwMEmjsNbJ5Zs0dv0QqgnewsLCwkIfoZrgJ0+ebEx8bi7SfMnYknyraTAwSaOw1snlmzR2/RAqm+SJJ54oapNsaWnB17/+dS373fLly1FYWKhtk9yyZUt7DB2b5Nlnny1mk3zttddQWFiobZN8880323PXsUlydGppacGFF14oZpPk6DRixAht+10sFkNLSwu++c1vatskW1paMGTIEBGb5LnnnsuySfbp0wdDhw7Vskm+9dZbKCws1LZJvvbaa+06BG2TbGlpwdSpU7V1qqurQ2FhobZN0hsvOjbJMWPGYO/evWI2yZaWFowYMcLaJLPBt7kEw7e52FxsLpnxkSsuGgsLCwsLfYRqgj/llFOMic/NRZovGVuSbzUNBiZpFNY6uXyTxq4fQjXBjxkzxpj43Fyk+ZKxJflW02BgkkZhrZPLN2ns+iFUE7x3E8WE+NxcpPmSsSX5VtNgYJJGYa2Tyzdp7PrBCBdN0PDbCONwJO9PWFhYWOQmjJjgdW2SHfc17czWpYvFixcfYpOsr6/X7iZZX1/P6ibZq1cvVjfJfv36idkkvdx1bZItLS2sbpK6Oq1fvx719fWi3SQ5OhUUFLC6SdbX17O6SdbX14t1kxwwYADLJhmPx7W7SXrXUNcmuW/fPrFukvX19axukl7uujZJj69jkywsLBTtJllfX59f3SS54O5VGlbky4YJSuVPrflSp1L5U6vd8CMNFi9ebEx8bi7SfMnYknyraTAwSaOw1snlmzR2/SA2wRPRE0S0i4iqg4rpLV9IgROfm4s0XzK2JN9qGgxM0iisdXL5Jo1dP0h+gl8E4FLB+BYWFhYWncFv7SaIA0AZgGpdfro1+Hg87rcGlXKNveMafFFvqI8++qjT1/vF5+SSDT53bc+k3LmxJWs16bqYpKn0deHUapJGpoxd5Moa/Guvvdal10UKgBOiwHdu87dMcuNzc5HmS8aW5EvWyY1v0nXhwiSNwlonl2/S2PUDKUEvOBGVAXheKeX7lSwimgVgFgCUlpZOnDdvnm88x3EQjUaPOO953SsrK4/g79ixA88sfhTvPQh87gcFuOKaiiO+Iea9/oEHHkgZn5NLNvgm5cLl21xsLjaXzPgVFRVrlVKnpXzS76N9EAcCXqLx+xMHPks0jz76qDrpxOPVirug1BKoqu9BDS8bqJqbm1O+Poyd5EzLhcu3udhcbC6Z8dHJEo0RX3TSxSWXXNLp8x2/wQoAPXsAk08BLh2bfFw+Dhi6YgdKiotwIMGP31Vud/AlY0vyJevkxjfpunBhkkZhrZPLN2ns+kHSJvkUgDcAjCSi7UQ0M9OYe/fuZfELegJzrzr03NwvJM9nGp+bizRfMrYkX7JObnyTrgsXJmkU1jq5fJPGrh/EJnil1LVKqYFKqd5KqcFKqYWZxvS+VtzJex5yTCmfiodWRQ7hPPRCBPf+8P7Dl5K04nNy6W6+ZGxJvmSd3PgmXRcuTNIorHVy+SaNXT+EykXDxcVTyvHOjr5YsS75uGodUP1pP8y56+5spmVhYWHRLRB10WgncbDZ2Dfuu+8+AKmbWPXr1w8bNmwAcGjTnauuSq7DOI5zxJ6s/fr1w73fuRE1DwLjfngUfrng99i5cyeAg82RvLX7hx9+GDfddJNWE6sFCxaguLhYu9nYu+++ix07dgDQazZ2yimnoLm5WbvZ2OjRo7Wbja1evRrFxcXazcaeeOIJtLW1AdBrNuanU6pmY/v27cPFF1+s3Wxs06ZNiEaj2s3GODp95jOfwfDhw7Wbje3btw+33HKLdrOxffv2YejQoVo6LV26FJFI8q9PHZ1OPfVU1NbWajcbKygowA033KDVbOxvf/sbiouLtZuNVVZWtv+f0tGppqYGPXsm103T6bRv3z5cccUV2s3GamtrUVxcrN1s7Mknn0RxcbFWs7ETTzwRxx13HKvZ2L333qvdbGzfvn04+eST0zYby5qLhnukc9E4juN3F7nTLzpFCqBGDISaPm1Kp6/3i8/JJRt87t15k3Lnxpas1aTrYpKm0teFU6tJGpkydpErX3R65plnUp73ivFDrBXY5gA/n1+Z8nnv9X7xOblkiy8ZW5IvWSc3vknXhQuTNAprnVy+SWPXD6Ga4DNB836grKws22lYWFhYdBtCNcEfe+yxxsTn5iLNl4wtybeaBgOTNAprnVy+SWPXF35rN9k47IYfXUO+bJigVP7Umi91KpU/tdoNP9LAu1ufDkQEIkJFRcUh3271zntHV+Nzud3Bl4wtyZeskxvfpOvChUkahbVOLt+ksesHI1oV6O7JGo/HRfdkdRxHe0/WTz75hLUna2NjI2tP1kQiIbYnq5e7rk2ytraWtSerrk7r16+H4ziie7JydIrH46w9WR3HYe3JytGJuydrIpFg7cna2NiovSerdw11bZKffvqp2J6sjuOw9mT1cte1SXp8HZtka2ur6J6sjuPk156spjT3sbkEx7e52FxsLpnx0ckSTdYn9Y5Hugn+wIEDWgV74F5QTnxuLpJ8yTql+VbT1DBJU+nrwqnVJI1MGbudTfChWoNftWqVMfG5uUjzJWNL8q2mwcAkjcJaJ5dv0tj1Q6gmeG/ty4T43Fyk+ZKxJflW02BgkkZhrZPLN2ns+iFUE7yFhYWFhT5CNcFPnTrVmPjcXKT5krEl+VbTYGCSRmGtk8s3aez6IVQ2yWOPPfaQbnid2e+AZDfJLVu2aNnv6urqEIvFcOONN2rZ77xuf7o2yU2bNrXnrmOTnDBhAmpqakRskq+++ioikYi2TfKll15CPB4HoGeT5OgUi8Vw0UUXidkkOTqVlZWhqalJ2yYZi8Uwe/ZsbZtkLBZDWVmZiE3ytNNOw9tvv61tkywqKsJ1112nZZN84403EIlEtG2SK1asQCKR3DItaJtkLBbD5Zdfrm2TdBwHkUhE2ybpjRcdm+RnP/tZ1NbWitkkY7EYRo0aZW2S2eDbXILh21xsLjaXzPjIFReNhYWFhYU+jNjwwwMR1QLY0gmlH4AGRsgoAIfB58Tn5iLJl6xTmm81TQ2TNJW+LpxaTdLIlLF7olLqmJTP+H20N/EAsIDJ9/3TJdP4XchFjC9ZZzfkbjU1XNNuuC7atRqmkTFj1+8I2xLNcwbF5+YizZeMLcm3mgYDkzQKa51cvkljNyWMWqIJGkT0D+W3V2EOIV/qBPKn1nypE8ifWrNRZ9g+wXOxINsJdBPypU4gf2rNlzqB/Km12+vM6U/wFhYWFvmMXP8Eb2FhYZG3sBO8hYWFRY4i1BM8ET1BRLuIqLrDuQFE9CIR/dv992j3PBHRI0T0PhG9Q0QTspc5D0R0AhGtJqJ3iehfRPRt93xO1UpEhUT0dyJa79b5Y/f8UCJ6y63nd0RU4J4/yn38vvt8WVYLYIKIehLR20T0vPs4V+vcTEQbiGgdEf3DPZdTYxcAiKg/ES0joveIqIaIPp/tOkM9wQNYBODSw87dDeBlpdRnAbzsPgaAcgCfdY9ZAH7dTTkGgTYA31VKjQZwFoBbiGg0cq/WOIALlVJjAYwDcCkRnQXgZwB+rpQaDqAewEyXPxNAvXv+5y4vTPg2gJoOj3O1TgC4QCk1roOLJNfGLgD8EsBKpdQoAGOR1Da7dWZqpM/2AaAMQHWHxxsBDHR/Hghgo/tzJYBrU/HCdgD4I4CLc7lWABEA/wRwJpLf/uvlnv88gBfcn18A8Hn3514uj7Kdu2Z9g5H8D38hgOcBUC7W6ea8GUD0sHM5NXaR/NbpR4frku06w/4JPhWOU0rtcH/eCeA49+fPANjWgbfdPRcquH+ejwfwFnKwVnfZYh2AXQBeBPABgD1KqTaX0rGW9jrd5xsAlHZrwl3HLwDcBSDhPi5FbtYJAArAKiJaS0Sz3HO5NnaHAqgF8L/ustvjRFSMLNeZixN8O1TyV2PO+ECJqATAMwDuUEo1dnwuV2pVSh1QSo1D8hPuGQBGZTej4EFE0wDsUkqtzXYu3YRJSqkJSC5L3EJE53V8MkfGbi8AEwD8Wik1HsA+HFyOAZCdOnNxgv+UiAYCgPuv1zT5YwAndOANds+FAkTUG8nJfYlS6v/c0zlZKwAopfYAWI3kUkV/IvL2LuhYS3ud7vP9AOzu3ky7hHMAXEFEmwE8jeQyzS+Re3UCAJRSH7v/7gLwByR/cefa2N0OYLtS6i338TIkJ/ys1pmLE/yfAHzd/fnrSK5Xe+dvcO9enwWgocOfTkaDiAjAQgA1SqmHOzyVU7US0TFE1N/9uQjJ+ww1SE70X3Jph9fp1f8lAK+4n5KMhlLqHqXUYKVUGYCvIJn3dcixOgGAiIqJqI/3M4ApAKqRY2NXKbUTwDYiGumemgzgXWS7zmzfnMjwxsZTAHYA2I/kb9CZSK5Nvgzg3wBeAjDA5RKA/0FyTXcDgNOynT+jzklI/mn3DoB17nFZrtUK4HMA3nbrrAYw1z0/DMDfAbwPYCmAo9zzhe7j993nh2W7hi7U/B8Ans/VOt2a1rvHvwD8wD2fU2PXzX0cgH+44/dZAEdnu07bqsDCwsIiR5GLSzQWFhYWFrATvIWFhUXOwk7wFhYWFjkKO8FbWFhY5CjsBG9hYWGRo7ATvIWFhUWOwk7wFqEEEf3AbSn8jtuG9kz3/B1EFOmG959BRLVE9HgXXtvLfe1/HXZ+CRHVEdGX/F5rYcGBneAtQgci+jyAaQAmKKU+B+AiHGzcdAeSnShTva5nwKn8Til1cxdedzGATQCudr+lDABQyW+z/imo5Cws7ARvEUYMBOAopeIAoJRylFKfENHtAAYBWE1EqwGAiJqI6CEiWg/g80R0JxFVu8cdLqfM3aRhERFtcj9JX0REf3U3ajgjXULuJ/pn3U0dNhPRre57vU1EbxLRgA70a5HsPbMVyV47FhYisBO8RRixCsAJ7mT8KyI6HwCUUo8A+ATJzSUucLnFAN5SyU1EmgHciGSP+bMAfIOIxru84QAeQrJ75SgAX0WyRcQcAPdq5jUGwBcAnA7gAQAxlews+AaAG4DkrlVI/sXxHJKtNq7t0hWwsNCAneAtQgelVBOAiUjuhFML4HdENMOHfgDJLpxAcsL+g1Jqnxvj/wCc6z73kVJqg1IqgWTPlJdVso/HBiQ3ldHBaqXUXqVULZI9259zz3eMMc3lNbt5TRdYOrKwAJDsYWxhEToopQ4AeBXAq0S0AclOfYtSUFtcbjrEO/yc6PA4Af3/JzoxrgUwyW0VDCSbUV2I5OYmFhaBwn6CtwgdiGgkEX22w6lxALa4P+8F0MfnpX9B8hNzxG1de5V7rltARH2R/IthiFKqTCXbBd8Cu0xjIQT7Cd4ijCgBMN/tHd+GZBtdbyu4BQBWEtEnHdbhAQBKqX8S0SIkW+4CwONKqbfdbRC7A1ch2cu94yf9PwL4byI66rDzFhYZw7YLtrDoAtw1/9OUUrcGHHcRkv3hlwUZ1yI/YZdoLCy6hmYA5V35opMfiGgJgPMBtAQV0yK/YT/BW1hYWOQo7Cd4CwsLixyFneAtLCwschR2grewsLDIUdgJ3sLCwiJH8f8B7AFxNse1wpoAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(strom, \n", " spannung,\n", @@ -965,12 +1390,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Manchmal ist es ebenfalls sinnvoll besondere Linien einzuführen um Dinge hervorzuheben. Hierzu könnt ihr die Funktionen `plt.hlines` and `plt.vlines` für horizontale und vertikale Linien verwenden." + "Manchmal ist es ebenfalls sinnvoll, besondere Linien einzuführen, um Dinge hervorzuheben. Hierzu könnt ihr die Funktionen `plt.hlines` und `plt.vlines` für horizontale und vertikale Linien verwenden." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:49.469050Z", @@ -978,7 +1403,20 @@ }, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(strom, \n", " spannung,\n", @@ -1033,23 +1471,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Mehr Text ist manchmal mehr Information:\n", + "## Mehr Text ist manchmal mehr Information:\n", "\n", - "Als letztes möchte ich euch noch zeigen wir ihr auf zwei unterschiedliche Arten und weisen Texte zu eurem Plot hinzufügen könnt. Dies kann nützlich sein um besondere Messwerte hervorzuheben oder einfach zusätzliche Informationen unter zu bringen.\n", + "Als Letztes möchte ich euch noch zeigen, wie ihr auf zwei unterschiedliche Arten und Weisen Texte zu eurem Plot hinzufügen könnt. Dies kann nützlich sein, um besondere Messwerte hervorzuheben oder einfach zusätzliche Informationen unterzubringen.\n", "\n", "Fangen wir mit `plt.annotate` an. Mit dieser Funktion können wir einen beschrifteten Pfeil in unseren Plot einfügen. Dies könnte zum Beispiel so aussehen:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:50.595682Z", "start_time": "2020-08-26T06:41:50.205694Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.errorbar(strom, \n", " spannung,\n", @@ -1080,10 +1531,10 @@ " color='gray',\n", " ls='solid')\n", "\n", - "plt.annotate(f\"Messwertnummer {3 + 1}\", # Beschriftung des Pfeils\n", - " xy = (strom[3], spannung[3]), # Position der Pfeilspitze\n", + "plt.annotate(f\"Messwertnummer {3 + 1}\", # Beschriftung des Pfeils\n", + " xy = (strom[3], spannung[3]), # Position der Pfeilspitze\n", " xytext = (strom[3]-300, spannung[3] + 1), # Position des Textes (und des Pfeilendes)\n", - " arrowprops = {'arrowstyle': '->'}, # Style des Pfeils\n", + " arrowprops = {'arrowstyle': '->'}, # Style des Pfeils\n", " fontsize=14)\n", "plt.show()" ] @@ -1092,12 +1543,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Eine andere nützliche Art Beschriftungen in einen Plot hinzuzufügen stellt die Methode `plt.text` dar. Diese Methode ist insbesondere nützlich um Fitdaten in einen Plot zu zeigen. Führen wir zunächst wie in Kapitel 1. gezeigt einen Fit unsere Messdate durch:" + "Eine andere nützliche Art, Beschriftungen in einen Plot hinzuzufügen, stellt die Methode `plt.text` dar. Diese Methode ist insbesondere nützlich, um Fitdaten in einen Plot zu zeigen. Führen wir zunächst wie in Kapitel 1. gezeigt einen Fit unserer Messdate durch:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:50.907823Z", @@ -1111,7 +1562,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:51.302076Z", @@ -1123,8 +1574,8 @@ "para, pcov = curve_fit(Spannung, \n", " strom, \n", " spannung,\n", - " sigma=spannung_error, # <-- Diesesmal mit Fehler\n", - " absolute_sigma=True # <-- Diesen Option müssen wir auf Wahr setzen, da \n", + " sigma=spannung_error, # <-- Dieses Mal mit Fehler\n", + " absolute_sigma=True # <-- Diese Option müssen wir auf Wahr setzen, da \n", " # wir in der Regel absolute und keine relativen \n", " # Unsicherheiten messen.\n", " )\n", @@ -1135,14 +1586,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2020-08-26T06:41:52.290775Z", "start_time": "2020-08-26T06:41:51.613585Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(7,5))\n", "plt.errorbar(strom, \n", @@ -1181,11 +1645,11 @@ " ls='solid')\n", "\n", "\n", - "R = para[0] * 1000 # Widerstand in Ohm\n", + "R = para[0] * 1000 # Widerstand in Ohm\n", "deltaR = pcov[0][0]**(1/2)* 1000 # Fehler des Widerstands in hm\n", "\n", "plt.text(100, # <-- X-Position des Textes\n", - " 4.5, # <-- Y-Position des Textes\n", + " 4.5, # <-- Y-Position des Textes\n", " f'R: ({R:.2f} +/- {deltaR:.2f}) Ohm\\n$\\chi^2$/ndof: {chi2:.2f}/{ndof}', \n", " fontsize=14\n", " )\n", @@ -1197,7 +1661,156 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ich hoffe der Zusatz hilft euch. Viel Spaß im PGP!" + "# Tabelle unter einem Plot mit matplotlib:\n", + "\n", + "Es ist manchmal nützlich, eine Tabelle mit den Messdaten unter einem Plot darzustellen. Dies spart Arbeit, um die Messwerte aufzuschreiben oder zum Beispiel in LaTeX zu übertragen.\n", + "\n", + "Im Folgenden ein einfaches Beispiel zur Erstellung eines Plots mit einer Tabelle:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Imports\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\"\"\"\n", + "plt.subplots erstellt ein Plot mit mehreren Unterplots\n", + "Die Unterplots werden in einem Gitter geordnet\n", + "\"\"\"\n", + "fig, ax = plt.subplots(nrows=2, # nrows: Die Anzahl der Zeilen des Gitters\n", + " ncols=1, # ncols: Die Anzahl der Spalten des Gitters\n", + " figsize=(7,5), # figsize: Die Größe der Abbildung (Breite, Höhe) in Zoll\n", + " dpi=300) # dpi: Die Auflösung der Abbildung\n", + "\n", + "\"\"\"\n", + "Hinweis: Es kommt bei der Erstellung von einem Plot mit einer Tabelle darunter oft dazu,\n", + "dass man die Höhe bei figsize (im Beispiel auf 5 eingestellt) erhöhen muss, damit eine lange\n", + "Tabelle noch hineinpasst.\n", + "\"\"\"\n", + "\n", + "# Beispieldaten\n", + "x = [1, 2, 3]\n", + "dx = [0.1, 0.2, 0.2]\n", + "y = [1, 3, 5]\n", + "dy = [0.5, 0.5, 0.6]\n", + "\n", + "\"\"\"\n", + "Statt plt.errorbar benutzt man ax[n], wenn man subplots benutzt,\n", + "wobei n die Nummer des Plots ist (Die Nummerierung beginnt mit 0).\n", + "Dies ist nötig, um zu bestimmen, wo das Plot im Gitter hinkommt.\n", + "\"\"\"\n", + "# Beispielplot\n", + "ax[0].errorbar(x,\n", + " y,\n", + " xerr=dx,\n", + " yerr=dy,\n", + " ls=\"\",\n", + " marker=\"d\",\n", + " capsize=2\n", + " )\n", + "\n", + "ax[0].set_xlabel(r\"$x$\") # set_xlabel statt xlabel bei subplots\n", + "ax[0].set_ylabel(r\"$y$\") # set_ylabel statt ylabel bei subplots\n", + "\n", + "# Tabelle\n", + "table = ax[1].table(np.transpose([x, dx, # np.transpose transponiert wie bei einer Matrix\n", + " y, dy]),\n", + " cellLoc=\"center\", # Zentriert die Einträge der Tabelle\n", + " colLabels=[r\"$x$\", r\"$\\Delta x$\", # Labels der Spalten (Reihenfolge beachten!)\n", + " r\"$y$\", r\"$\\Delta y$\"],\n", + " loc=\"center\" # Zentriert die Tabelle selbst\n", + " )\n", + "\n", + "table.set_fontsize(8) # Damit kann man die Schriftgröße für die Tabelle ändern\n", + "ax[1].axis(\"off\") # Dies ist nötig, damit bei der Tabelle keine Achsen abgebildet werden\n", + "\n", + "# Abbildung mit plt.savefig speichern:\n", + "# plt.savefig(\"Plot_mit_Tabelle.pdf\")\n", + "# oder mit plt.show nur anzeigen lassen:\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3d-Plots:\n", + "\n", + "Beim Praktikum kann es bei einem Versuch vorkommen, dass man 3d-Plots erstellen muss.\n", + "Wie dies geht, wird im Folgenden anhand eines einfachen Beispiels demonstriert." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Imports\n", + "from mpl_toolkits import mplot3d\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# X- und Y-Achse (siehe np.linspace)\n", + "x = np.linspace(-20, 20, 10)\n", + "y = np.linspace(-20, 20, 10)\n", + "\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "# Die Funktion für die Z-Werte\n", + "def f(x, y):\n", + " return x**2 - y**2\n", + "\n", + "Z = f(X, Y)\n", + "\n", + "fig = plt.figure(dpi=150) # dpi: Die Auflösung der Abbildung\n", + "ax = plt.axes(projection=\"3d\")\n", + "ax.contour3D(X, Y, Z, 50) # Wenn man einen anderen Wert für 50 eingibt, ändert sich die Dichte der Linien des Plots\n", + "\n", + "# Achsenbeschriftung\n", + "ax.set_xlabel(\"$x$\")\n", + "ax.set_ylabel(\"$y$\")\n", + "ax.set_zlabel(\"$z$\")\n", + "\n", + "plt.show() # Oder plt.savefig(\"DATEINAME.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wir hoffen, der Zusatz hilft euch. Viel Spaß im PGP!" ] } ], @@ -1217,9 +1830,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }