From 6e6d574a6e4efcf199d574fc5966d853f053f93f Mon Sep 17 00:00:00 2001 From: DerGrumpf Date: Fri, 13 Dec 2024 13:06:49 +0100 Subject: [PATCH] added v8 --- .obsidian/app.json | 2 +- .obsidian/appearance.json | 2 +- .obsidian/workspace.json | 26 +- .../7.Lösungen_Pandas_Seaborn.slides.html | 8099 +++++++++++++++++ .../7.Lösungen_Pandas_Seaborn.ipynb | 704 ++ .../wise_24_25/lernmaterial/8.Folium.ipynb | 6260 +++++++++++++ .../lernmaterial/people_in_germany.csv | 1998 ++-- Material/wise_24_25/v8.ipynb | 775 ++ Timetable.pdf | Bin 26797 -> 26756 bytes 9 files changed, 16853 insertions(+), 1013 deletions(-) create mode 100644 Material/wise_24_25/Folien/7.Lösungen_Pandas_Seaborn.slides.html create mode 100644 Material/wise_24_25/lernmaterial/7.Lösungen_Pandas_Seaborn.ipynb create mode 100644 Material/wise_24_25/lernmaterial/8.Folium.ipynb create mode 100644 Material/wise_24_25/v8.ipynb diff --git a/.obsidian/app.json b/.obsidian/app.json index 40f0567..4453943 100644 --- a/.obsidian/app.json +++ b/.obsidian/app.json @@ -2,7 +2,7 @@ "alwaysUpdateLinks": true, "pdfExportSettings": { "includeName": true, - "pageSize": "Letter", + "pageSize": "A4", "landscape": false, "margin": "0", "downscalePercent": 75 diff --git a/.obsidian/appearance.json b/.obsidian/appearance.json index c356393..6d3b1e1 100644 --- a/.obsidian/appearance.json +++ b/.obsidian/appearance.json @@ -1,4 +1,4 @@ { "cssTheme": "Tokyo Night", - "theme": "system" + "theme": "obsidian" } \ No newline at end of file diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index ae00620..7bfacc6 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -11,12 +11,14 @@ "id": "85d70f5e9df52245", "type": "leaf", "state": { - "type": "pdf", + "type": "markdown", "state": { - "file": "KC_Deutsch_HS_Anhrung.pdf" + "file": "Timetable.md", + "mode": "source", + "source": false }, - "icon": "lucide-file-text", - "title": "KC_Deutsch_HS_Anhrung" + "icon": "lucide-file", + "title": "Timetable" } }, { @@ -225,19 +227,19 @@ }, "active": "85d70f5e9df52245", "lastOpenFiles": [ + "Material/wise_24_25/v8.ipynb", + "Material/wise_24_25/Untitled.ipynb", + "Material/wise_24_25/lernmaterial/7.Lösungen_Pandas_Seaborn.ipynb", + "Material/wise_24_25/Folien/7.Lösungen_Pandas_Seaborn.slides.html", + "Material/wise_24_25/7.Lösungen_Pandas_Seaborn.ipynb", + "Material/wise_24_25/lernmaterial/8.Folium.ipynb", + "Material/wise_24_25/lernmaterial/Einführung Folium.ipynb", + "KC_Deutsch_HS_Anhrung.pdf", "Material/wise_24_25/Folien/6.Lösungen_Monte_Carlo.html", "Material/wise_24_25/6.Lösungen_Monte_Carlo.ipynb", - "Material/wise_24_25/Untitled.ipynb", "Material/wise_24_25/lernmaterial/6.Monte_Carlo.ipynb", "README.md", "Timetable.md", - "KC_Deutsch_HS_Anhrung.pdf", - "Material/wise_24_25/lernmaterial/people_in_germany.csv", - "Material/wise_24_25/lernmaterial/Bees.csv", - "Material/env/lib/python3.12/site-packages/seaborn-0.13.2.dist-info/WHEEL", - "Material/env/lib/python3.12/site-packages/seaborn-0.13.2.dist-info/REQUESTED", - "Material/env/lib/python3.12/site-packages/seaborn-0.13.2.dist-info/RECORD", - "Material/env/lib/python3.12/site-packages/seaborn-0.13.2.dist-info/METADATA", "Material/env/lib/python3.12/site-packages/seaborn-0.13.2.dist-info/LICENSE.md", "Lectures/17 18.02.2025.md", "Lectures/16 17.02.2025.md", diff --git a/Material/wise_24_25/Folien/7.Lösungen_Pandas_Seaborn.slides.html b/Material/wise_24_25/Folien/7.Lösungen_Pandas_Seaborn.slides.html new file mode 100644 index 0000000..bac78b1 --- /dev/null +++ b/Material/wise_24_25/Folien/7.Lösungen_Pandas_Seaborn.slides.html @@ -0,0 +1,8099 @@ + + + + + + + +7.Lösungen_Pandas_Seaborn slides + + + + + + + + + + + + + + + + + +
+
+
+
+
+
+
+
+
+
+
+
+
+
+ + + diff --git a/Material/wise_24_25/lernmaterial/7.Lösungen_Pandas_Seaborn.ipynb b/Material/wise_24_25/lernmaterial/7.Lösungen_Pandas_Seaborn.ipynb new file mode 100644 index 0000000..0c89778 --- /dev/null +++ b/Material/wise_24_25/lernmaterial/7.Lösungen_Pandas_Seaborn.ipynb @@ -0,0 +1,704 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "19f8cec2-622f-43cd-95e1-baa89283fb88", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "# Lösungen Pandas & Seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "82d381b3-a37f-4739-9760-7902e155fb37", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "skip" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "13695d29-0bcd-4902-af52-df3b0f353d33", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Aufgabe - Erstellen eines Dataframes\n", + "\n", + "*5 Punkte*\n", + "\n", + "Erstellen Sie einen Pandas Data Frame mit dem namen `uni_addr`, nachdem Schema folgender Tabelle:\n", + "\n", + "| Address | plz |\n", + "|--------------------------|----------------------------|\n", + "| Johannes-Selenka-Platz 1 | 38118 Braunschweig |\n", + "| Universitätspl. 2 | 38106 Braunschweig |\n", + "| Harburger Str. 6 | 21614 Buxtehude |\n", + "| Adolph-Roemer-Straße 2A | 38678 Clausthal-Zellerfeld |\n", + "| Constantiapl. 4 | 26723 Emden |\n", + "| Weender Landstraße 3-7 | 37073 Göttingen |\n", + "| Wilhelmsplatz 1 | 37073 Göttingen |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7a142742-a3fa-4719-82f0-b56e4082ced7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "uni_addr = pd.DataFrame({\n", + " \"Address\": [\n", + " \"Johannes-Selenka-Platz 1\", \"Universitätspl. 2\",\n", + " \"Harburger Str. 6\", \"Adolph-Roemer-Straße 2A\",\n", + " \"Constantiapl. 4\", \"Weender Landstraße 3-7\",\n", + " \"Wilhelmsplatz 1\"\n", + " ],\n", + " \"plz\": [\n", + " \"38118 Braunschweig\", \"38106 Braunschweig\",\n", + " \"21614 Buxtehude\", \"38678 Clausthal-Zellerfeld\",\n", + " \"26723 Emden\", \"37073 Göttingen\", \"37073 Göttingen\",\n", + " ]\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b14422b3-7306-4158-b335-3531f5c56f62", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Addressplz
0Johannes-Selenka-Platz 138118 Braunschweig
1Universitätspl. 238106 Braunschweig
2Harburger Str. 621614 Buxtehude
3Adolph-Roemer-Straße 2A38678 Clausthal-Zellerfeld
4Constantiapl. 426723 Emden
5Weender Landstraße 3-737073 Göttingen
6Wilhelmsplatz 137073 Göttingen
\n", + "
" + ], + "text/plain": [ + " Address plz\n", + "0 Johannes-Selenka-Platz 1 38118 Braunschweig\n", + "1 Universitätspl. 2 38106 Braunschweig\n", + "2 Harburger Str. 6 21614 Buxtehude\n", + "3 Adolph-Roemer-Straße 2A 38678 Clausthal-Zellerfeld\n", + "4 Constantiapl. 4 26723 Emden\n", + "5 Weender Landstraße 3-7 37073 Göttingen\n", + "6 Wilhelmsplatz 1 37073 Göttingen" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uni_addr" + ] + }, + { + "cell_type": "markdown", + "id": "4c861e0c-ddae-4249-94d6-903e605a2b82", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Aufgabe - Extrahieren einer Series\n", + "\n", + "*1 Punkte*\n", + "\n", + "Exthahieren Sie die Series `plz` aus dem zuvor erstelltem Data Frame `uni_addr` und speichern Sie ihr Ergebnis in `uni_plz`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1e197cdc-e6d4-4eef-a384-c026b84ec115", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 38118 Braunschweig\n", + "1 38106 Braunschweig\n", + "2 21614 Buxtehude\n", + "3 38678 Clausthal-Zellerfeld\n", + "4 26723 Emden\n", + "5 37073 Göttingen\n", + "6 37073 Göttingen\n", + "Name: plz, dtype: object" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uni_plz = uni_addr[\"plz\"]\n", + "uni_plz" + ] + }, + { + "cell_type": "markdown", + "id": "b28feb13-64d9-44f3-bff3-2295dc98ea7f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Aufgabe - Read CSV\n", + "\n", + "*1 Punkt*\n", + "\n", + "Nutzen sie die Funktion `pd.read_csv` um das Datenset `unis_nd.csv` in die Variable `unis_nd` einzulesen.\n", + "\n", + "Falls Sie hilfe benötigen lesen Sie gerne die Dokumentation im [Getting Started](https://pandas.pydata.org/docs/getting_started/intro_tutorials/02_read_write.html) Guide.\n", + "\n", + "_Hinweis: Die Datei liegt in keinem Ordner, sondern im selben wie dieses Notebook!_" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6c613dad-1541-4be4-8661-b515ac4eb357", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + "
University nameType of universitySponsorshipRight of promotionFounding yearNumber of studentsAddresslatlonplzpic
0Hochschule für Bildende Künste BraunschweigArtistic universitypublicyes1963976.0Johannes-Selenka-Platz 152.25773810.50231538118 Braunschweighttps://www.hbk-bs.de/fileadmin/_processed_/5/...
\n", + "
" + ], + "text/plain": [ + " University name Type of university \\\n", + "0 Hochschule für Bildende Künste Braunschweig Artistic university \n", + "\n", + " Sponsorship Right of promotion Founding year Number of students \\\n", + "0 public yes 1963 976.0 \n", + "\n", + " Address lat lon plz \\\n", + "0 Johannes-Selenka-Platz 1 52.257738 10.502315 38118 Braunschweig \n", + "\n", + " pic \n", + "0 https://www.hbk-bs.de/fileadmin/_processed_/5/... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unis_nd = pd.read_csv(\"unis_nd.csv\")\n", + "unis_nd.head(1)" + ] + }, + { + "cell_type": "markdown", + "id": "3f6fee4f-eb87-4233-a706-41a8cc6ce96c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Aufgabe\n", + "\n", + "*2 Punkte*\n", + "\n", + "Selektieren Sie die Spalten _University name_, _Founding year_ & _Number of students_, speichern sie ihr Ergebnis in der Variablen `select`.\n", + "\n", + "Geben Sie danach die ersten 5 Werte von Oben der Selektion aus." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "07d925f9-2b82-4e3c-82bc-043db2853df8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + "
University nameFounding yearNumber of students
0Hochschule für Bildende Künste Braunschweig1963976.0
1Technische Universität Carolo-Wilhelmina zu Br...174517709.0
2Hochschule 2120051084.0
3Technische Universität Clausthal17753446.0
4Hochschule Emden/Leer20094481.0
\n", + "
" + ], + "text/plain": [ + " University name Founding year \\\n", + "0 Hochschule für Bildende Künste Braunschweig 1963 \n", + "1 Technische Universität Carolo-Wilhelmina zu Br... 1745 \n", + "2 Hochschule 21 2005 \n", + "3 Technische Universität Clausthal 1775 \n", + "4 Hochschule Emden/Leer 2009 \n", + "\n", + " Number of students \n", + "0 976.0 \n", + "1 17709.0 \n", + "2 1084.0 \n", + "3 3446.0 \n", + "4 4481.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "select = unis_nd[\n", + " [\"University name\", \"Founding year\", \"Number of students\"]\n", + "]\n", + "select.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "d74bab02-9618-45a4-b6a9-db529c1de9a2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Aufgabe\n", + "\n", + "*2 Punkte*\n", + "\n", + "Nutzen Sie die Funktion `value_counts` und erstellen Sie ein Balkendiagramm, mittels matplotlib, über die Anzahl an Staatlichen und Privaten Hochschulen Niedersachsen.\n", + "\n", + "Die dazugehörige Spalte im Datenset ist _Sponsorship_. Finden Sie eine geeignete Darstellung." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8061a97e-a26f-49ef-924e-12894926732e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "c = unis_nd[\"Sponsorship\"].value_counts()\n", + "plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-dark.mplstyle')\n", + "plt.bar(c.keys(), c, color=[\"black\", \"white\"])\n", + "plt.title(\"Universitäten nach Finanzierungstatus\")\n", + "plt.ylabel(\"Anzahl\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c995d6b7-5136-4fbf-b12a-80b9121bad48", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Aufgabe\n", + "\n", + "*8 Punkte*\n", + "\n", + "Erstelle das Dataset `people_in_germany`.\n", + "\n", + "Folgendes Szenario:\n", + "\n", + "Du bist Part einer Massenhaft angelegten Studie um die bereits bekannten Zahlen des Statistischen Bundesamtes zu überprüfen.\n", + "\n", + "Dazu sind 4 Größen bekannt aus den Angaben des Statistischen Bundesamt für die [männliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-maenner.html) & die [weibliche Population](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Gesundheit/Gesundheitszustand-Relevantes-Verhalten/Tabellen/koerpermasse-frauen.html):\n", + "\n", + "||Körpergröße (in cm)|Gewicht (in Kg)|\n", + "|-|-|-|\n", + "|Männlich|178.9|85.8|\n", + "|Weiblich|165.8|69.2|" + ] + }, + { + "cell_type": "markdown", + "id": "795b3a33-af97-4671-a131-58a9eb8bcbfb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "Gehe dabei wie folgt vor:\n", + "- Treffe annahmen über die Verteilung und finde geeignete Werte für $\\mu$ & $\\sigma$. **Erkläre** deine Annahmen mit einem kurzen Text.\n", + "- Die Samplegröße beträgt 1000. Sample dementsprechend aus den gegebenen Werten.\n", + "- Speichere die gesampleten Personen nach dem Schema: |gender|height|weight| in der File `people_in_germany.csv` als csv.\n", + " -> Nutze für das `gender` Attribut den Datentyp `bool` mit der Kodierung: `True = female` & `False = male`.\n", + "- Stelle dein Ergebnis angemessen dar. Das theme `darkgrid` darf nicht verwendet werden.\n", + "- **Beschreibe** & **Interpretiere** den Plot. Gehe dabei von der Hypothese aus das es **keine** Unterschiede zwischen den Geschlechtern beim samplen geben sollte.\n", + "\n", + "*Tipp: Dataclasses erleichtern die Aufgabe ungemein!*" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fd039a25-70cf-4245-a2e0-db38bbaf5d0a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# define dataclass\n", + "import numpy as np\n", + "from dataclasses import dataclass\n", + "@dataclass\n", + "class Individual:\n", + " gender: bool\n", + " height: np.float64\n", + " weight: np.float64" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "eb36da58-8c37-4bee-82ae-eac31f1ebacc", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# fixed values\n", + "samples = 1000\n", + "rng = np.random.default_rng()\n", + "individuals = list()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d2f1e60a-e670-43b0-91e1-9fae768da4cd", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Simulation part\n", + "for i in range(samples):\n", + " if i % 2 == 0:\n", + " gender = False\n", + " height = rng.normal(178.9, 10)\n", + " weight = rng.normal(85.8, 26)\n", + " \n", + " else:\n", + " gender = True\n", + " height = rng.normal(165.8, 11)\n", + " weight = rng.normal(69.2, 19)\n", + "\n", + " height = np.round(height, decimals=1)\n", + " weight = np.round(weight, decimals=1)\n", + " \n", + " individual = Individual(gender=gender,height=height,weight=weight)\n", + " individuals.append(individual)\n", + "\n", + "individuals = pd.DataFrame(individuals)\n", + "individuals.to_csv('people_in_germany.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6bde202f-3900-46d7-a6df-fdedd14cd367", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style('white')\n", + "sns.jointplot(data=individuals, x=\"height\", y=\"weight\", hue=\"gender\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Material/wise_24_25/lernmaterial/8.Folium.ipynb b/Material/wise_24_25/lernmaterial/8.Folium.ipynb new file mode 100644 index 0000000..881b65d --- /dev/null +++ b/Material/wise_24_25/lernmaterial/8.Folium.ipynb @@ -0,0 +1,6260 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "11ce8688-2dd2-4a18-aa6c-bce96a801782", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-1720c646ec279d2e", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# 8. Programmierübung: Folium\n", + "\n", + "
\n", + "
\n", + " Willkommen zur achten Programmierübung Einführung in Python 3.\n", + "
\n", + " \n", + "
\n", + "\n", + "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", + "\n", + "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", + "\n", + "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ebd295cc-8e6d-435e-a91a-881bf3798b5b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-275178a1d9bade57", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "# Geospatial Data\n", + "\n", + "In the following we will look at how maps can be displayed using Python & the Folium API.\n", + "\n", + "[Folium](https://python-visualization.github.io/folium/) is a Python wrapper that binds the open source library [Leaflet.js](https://leafletjs.com/).\n", + "\n", + "In this way, the developers aim to combine the advantages of data processing with Python, as well as the visualization advantages of the Web.\n", + "\n", + "Objectives of this exercise:\n", + "1. create simple maps with different terrain options\n", + "2. set and adjust markers\n", + " 1. creating a marker\n", + " 2. popup & tooltip\n", + " 3. icons & colors\n", + " 4. circle marker\n", + " 5. setting own markers\n", + "3. layer groups\n", + "4. plugins\n", + "5. handling GeoJSON data\n", + "\n", + "\n", + "__For the entire exercise, all parameters are basically given for illustrative purposes. Unless explicitly requested, you do not have to do this.__" + ] + }, + { + "cell_type": "markdown", + "id": "22adfbb3-e71d-4228-ac02-b8c7135a8943", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d928ae513b1e6ce0", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Import Folium\n", + "\n", + "First we need to Import Folium as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2725fd60-dbe5-4739-8cbf-3ef9a585032f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-a22fce114edbd7bf", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "# Execute this cell everytime you restart the notebook!!!\n", + "import folium" + ] + }, + { + "cell_type": "markdown", + "id": "4a676854-ebc9-439f-8414-5fe39e65dc13", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d18e7620cb44ddf4", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "## Creating maps\n", + "\n", + "The default object of every Folium App is the [Map](https://python-visualization.github.io/folium/modules.html#module-folium.map).\n", + "\n", + "### Location\n", + "To create it, you only need the _location_ parameter which takes a tuple (or list) of two floats.\n", + "The first value is the latitude (lat) and the second the longitude (lon). (For Braunschweig this would be lat: 52.264150 & lon: 10.526420)\n", + "\n", + "### Tiles\n", + "Folium supports the display of different types of maps. These are specified as a string for the tiles parameter when creating the map.\n", + "\n", + "Possible maps are:\n", + "\n", + "1. _OpenStreetMap_ (default)\n", + "2. _Stamen Terrain_\n", + "3. _Stamen Toner_\n", + "\n", + "### Zoom\n", + "The default _zoom_ setting of the map can be adjusted by the following 3 parameters:\n", + "\n", + "- _zoom_start_ (default: 10) creates the map with a zoom setting between _min_zoom_ & _max_zoom_.\n", + "- _min_zoom_ (default: 0) & _max_zoom_ (default: 18) limits the possible zoom radius. Generally not necessary.\n", + "\n", + "### Optimization\n", + "If one of the following cells has excessive computation times, the parameter _prefer_canvas=True_ should be specified for the map object. This will force the web browser to use the web graphics library ([WebGL](https://github.com/KhronosGroup/WebGL)) and will result in a speed bonus, e.g. thousands of markers. By default _prefer_canvas_ is set to _False_ and should only be used if you really want to display lots of data!\n", + "\n", + "The following example illustrates the creation of the map object:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ecd035c9-c1bc-4393-8681-2c058caca527", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-1a3da7284c8a4450", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = folium.Map(\n", + " location=(52.264150, 10.526420),\n", + " tiles='OpenStreetMap',\n", + " #iles='Stamen Toner',\n", + " zoom_start=13,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "94976e7f-1354-428e-ba9d-32ad696e27f4", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-bbb8367d84f43da2", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "## Exercise 1: Create a map\n", + "\n", + "Create a map with the coordinates of your hometown, or your favorite city (Braunschweig does not count as a solution). Save your Solution in the variable `my_map`.\n", + "\n", + "Adjust the _zoom_start_ parameter so that your place is visible in the center. To find out which coordinates your place has you can use the online tool [latlong.net](https://www.latlong.net/).\n", + "\n", + "Use _CartoDB Positron_ as tileset.\n", + "\n", + "Set the _prefer_canvas_ parameter to _True_.\n", + "\n", + "Also please add your city as a comment.\n", + "\n", + "In case of problems, please check the Folium API [Map](https://python-visualization.github.io/folium/modules.html#module-folium.map) object.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a05ad147-1990-4d96-983d-c128b60125b5", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-6099ccde55688a94", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your City: \n", + "### BEGIN SOLUTION\n", + "# Your City: Braunschweig\n", + "my_map = folium.Map(\n", + " location=(52.264150, 10.526420),\n", + " tiles='cartodb positron',\n", + " zoom_start=13,\n", + " prefer_canvas=True\n", + " )\n", + "my_map\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b554a2ec-d32e-4ea6-a91f-a1fe554804cd", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-1b94f6587760b8e8", + "locked": true, + "points": 3, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "# Your Solutions are tested here...\n", + "assert isinstance(my_map, folium.Map)\n", + "assert isinstance(my_map.location, list)\n", + "assert len(my_map.location) == 2" + ] + }, + { + "cell_type": "markdown", + "id": "d057d2a5-9699-4a88-bea7-46adaa096f54", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-ad368f2ba205c714", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "## Marker \n", + "\n", + "The quintessence of any GeoSpatial Data project is the visualization of data.\n", + "\n", + "The [Marker](https://python-visualization.github.io/folium/modules.html#folium.map.Marker) object expects the following parameters:\n", + "- _location_ (Mandatory) Tuple of Floats (Like Map Object) to set a marker.\n", + "- _popup_ (String or folium.Popup object) small info box that can be customized using HTML\n", + "- _tooltip_ (String) hover text with instruction\n", + "- _icon_ (folium.Icon) to customize the marker\n", + "- _dragable_ (bool, default False) allows the user to move the marker\n", + "\n", + "To set a simple marker it is first created with a location.\n", + "Then the marker has to be added to the map with the function _add_to()_. \n", + "\n", + "In the following the HBK BS should be marked on the map." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "59a91300-1dc7-4d27-826c-14a94fa13a45", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-3b289b02455e2512", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = folium.Map(\n", + " location=(52.264150, 10.526420),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=14,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "my_marker = folium.Marker(\n", + " location=(52.25802230834961, 10.503097534179688)\n", + " )\n", + "\n", + "my_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "179b1901-ac37-4724-aa59-c76345c61805", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d9ba7c4b83368403", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "After executing the code you should see a marker at the address Johannes-Selenka-Platz 1, 38118 Braunschweig.\n", + "\n", + "Since this is relatively boring we will now try to adapt the marker to our needs." + ] + }, + { + "cell_type": "markdown", + "id": "071b4725-8f13-4d38-9ea6-8f62826baf17", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-0fd8e8915f7f9613", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### Popup & Tooltip\n", + "\n", + "The marker accepts strings as _tooltip_ & _popup_ parameters, as the following example demonstrates.\n", + "This is the most primitive form of how a simple marker can be created with information.\n", + "\n", + "To understand what the _tooltip_ parameter does, run the next example and hover over the marker. Clicking on the marker will display the contents of the _popup_ parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "33a5ffa1-7f39-46d4-9a17-35b3c8eb26ef", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-8b6f8ca79a9a05c3", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = folium.Map(\n", + " location=(52.264150, 10.526420),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=16,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "# Schloss Braunschweig\n", + "castle_popup = \"Ritterbrunnen 1, 38100 Braunschweig\"\n", + "castle_tooltip = \"More about the castle\"\n", + "\n", + "\n", + "castle_marker = folium.Marker(\n", + " location=(52.2643, 10.529),\n", + " popup=castle_popup,\n", + " tooltip=castle_tooltip\n", + " )\n", + "castle_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "cadd0223-d6d5-445a-ac7b-ead3b010260f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-cfc72d893f2572ff", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### HTML Popups\n", + "\n", + "To do justice to the inner artist as well, markers can also be designed in Folium using the tools of the modern internet.\n", + "\n", + "The Folium object [Popup](https://python-visualization.github.io/folium/modules.html#folium.map.Popup) can be used to display simple HTML strings. \n", + "\n", + "Before we get to the main features of HTML for Folium, we will first take a look at the other optional parameters of the Popup object.\n", + "\n", + "These include:\n", + "\n", + "- _parse_html_ (bool, default False) not normally needed, forces Folium to interpret the HTML string first. Useful for any customization via JavaScript.\n", + "- _max_width_ (int or str, default '100%') sets the maximum width of the popup. For the str parameter it is important to include the '%' character.\n", + "- _show_ (bool, default False) if this parameter is set to _True_, the popup will load when the map is opened.\n", + "- _sticky_ (bool, default False) if this parameter is set to _True_, the popup will not be closed.\n", + "\n", + "\n", + "Mandatory for creating a popup is the _html_ parameter. This parameter requires a (multi-)string containing HTML code.\n", + "\n", + "As an example we will create a HBK BS popup which renders the following HTML:\n", + "\n", + "---\n", + "

\n", + "\"HBK\n", + "

\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de

\n", + "\n", + "---\n", + "\n", + "\n", + "and the associated HTML:\n", + "\n", + "\n", + "```html\n", + "

\n", + "\"HBK\n", + "

\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de

\n", + "```\n", + "\n", + "\n", + "Do not let this confuse you. The statements in between `<> & ` are HTML tags, for example `

` represents a 'Paragraph' & `` represents a hyperlink. \n", + "For the text semantic elements I recommend the following [reference](https://www.w3.org/html/wiki/Elements).\n", + "\n", + "In Python, no HTML can be displayed directly. For this, the entire HTML must be within a string. To simplify the readability Python offers the multiline string notated with 3 `'''`:\n", + "\n", + "\n", + "\n", + "'''\n", + "hi I am\n", + "\n", + "\n", + "a\n", + "\n", + "multiline string\n", + "'''\n", + "\n", + "\n", + "And as with the already known string, this one supports all string format options. But more about that later in the Factory Patterns part.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "db9725a6-c438-4a23-9e5b-5b401d412832", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-b683105305025808", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# HBK Braunschweig\n", + "hbk_popup_html = folium.Popup(\n", + " '''\n", + "

\n", + " \"HBK\n", + "

\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de

\n", + " ''',\n", + " show=True\n", + " )\n", + "\n", + "hbk_tooltip = \"More about the university\"" + ] + }, + { + "cell_type": "markdown", + "id": "2773d557-e6ca-406c-9de1-b9c0675e0604", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-cf6a889f7ebf8a54", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### Icon\n", + "\n", + "The last step to complete the marker is the [Icon](https://python-visualization.github.io/folium/modules.html#folium.map.Icon) object.\n", + "\n", + "Unlike the other objects discussed, this one has no mandatory parameters.\n", + "\n", + "As usual, here is an explanation of the parameters:\n", + "- _color_ (str, default 'blue') sets the color of the marker.\n", + "\n", + " Possible colors are: \n", + " - red, blue, green,\n", + " - purple, orange, darkred,\n", + " - lightred, beige, darkblue,\n", + " - darkgreen, cadetblue, darkpurple,-\n", + " - white, pink, lightblue,\n", + " - lightgreen, gray, black,\n", + " - lightgray\n", + " \n", + " \n", + " or any hexadecimal value noted with '#XXXXXX'.\n", + "\n", + "- _icon_color_ (str, default 'white') sets the color of the glyphicon. The possible color values are the same as for _color_.\n", + "- _angle_ (int, default 0) sets the rotation of the glyphicon. The possible values are limited to the range 0-359 integer.\n", + "- _prefix_ (str, default 'glyphicon) can take two values 'fa' for the icons of the website [Font Awesome](https://fontawesome.com/icons) (Attention not all icons are free) and 'glyphicon' for the icons of the website [Bootstrap](https://getbootstrap.com/docs/3.3/components/) (All icons behind this link are free). The value in _prefix_ specifies which website is queried. \n", + "- _icon_ (str, default 'info-sign') specifies the name of the icon to be displayed and is therefore dependent on the _prefix_ parameter. The default icon is 'glyphicon glyphicon-info-sign'.\n", + "\n", + "To design a reasonable icon for the HBK marker the following example should be understood:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8f79bd13-82f0-478c-8409-1e97d79d4b26", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-5e1cf782bc0512c1", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [], + "source": [ + "hbk_icon = folium.Icon(\n", + " color='black',\n", + " icon_color='#deddda',\n", + " prefix='glyphicon',\n", + " icon='glyphicon-home',\n", + " angle=0\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "4a2ffb76-8045-4cba-a416-5555f521889b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-8d4b5a5cb9cc90cc", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "After successfully creating the three variables _hbk_tooltip_, _hbk_html_popup_ & _hbk_icon_, we now display the customized marker:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "852785cc-0dff-4c2c-b6bb-20d08e26349f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-62068ff221befbb2", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = folium.Map(\n", + " location=(52.258, 10.5),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=16,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "hbk_marker = folium.Marker(\n", + " location=(52.257770, 10.502490),\n", + " popup=hbk_popup_html,\n", + " tooltip=hbk_tooltip,\n", + " icon=hbk_icon\n", + " )\n", + "\n", + "hbk_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "6a7a1f4d-720b-4152-a90b-c9a194494cbe", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-a6152b205ccf50ed", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "## Exercise 2: Design your own Marker\n", + "\n", + "In the following exercise you will design your own marker.\n", + "\n", + "In case of problems, please check the Folium API:\n", + "- [Marker](https://python-visualization.github.io/folium/modules.html#folium.map.Marker)\n", + "- [Popup](https://python-visualization.github.io/folium/modules.html#folium.map.Popup)\n", + "- [Icon](https://python-visualization.github.io/folium/modules.html#folium.map.Icon)\n", + "- [Tooltip](https://python-visualization.github.io/folium/modules.html#folium.map.Tooltip)" + ] + }, + { + "cell_type": "markdown", + "id": "093f0526-114f-45da-947a-550f2163030a", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d5aa18ce07303756", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### 2.1: Defining a Tooltip\n", + "\n", + "Define a tooltip variable named `tooltip` with the text `More about TU Braunschweig`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "725abb1e-f8c4-4cca-be6f-326f23126062", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-04f53b3f7eb6c201", + "locked": false, + "schema_version": 3, + "solution": true, + "task": false + }, + "tags": [] + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "tooltip = \"More about TU Braunschweig\"\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d0c93b34-79de-4a90-b595-dec419b3a930", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-bfcb53d3e2aba304", + "locked": true, + "points": 1, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "# Your Solutions are tested here...\n", + "assert tooltip == \"More about TU Braunschweig\"" + ] + }, + { + "cell_type": "markdown", + "id": "97aa8013-defd-408d-8b80-2389ff92904c", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-e943331e564dd5c6", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### 2.2: Defining a Popup\n", + "\n", + "Define a popup object with HTML text named `tu_popup_html` and the [TU BS Logo](https://www.google.com/url?sa=i&url=https%3A%2F%2Fde.m.wikipedia.org%2Fwiki%2FDatei%3ASiegel_TU_Braunschweig_transparent.svg&psig=AOvVaw3PFFLWsIPyXrT81Jo4F6ot&ust=1669222516763000&source=images&cd=vfe&ved=0CA8QjRxqFwoTCIjR-MqgwvsCFQAAAAAdAAAAABAE).\n", + "\n", + "Address: _Universitätspl. 2, 38106 Braunschweig_\n", + "\n", + "Logo URL: https://upload.wikimedia.org/wikipedia/commons/thumb/9/9d/Siegel_TU_Braunschweig_transparent.svg/1200px-Siegel_TU_Braunschweig_transparent.svg.png\n", + "\n", + "You can also use the Template from the Explanation.\n", + "\n", + "You can find a HTML reference [here](https://www.w3.org/html/wiki/Elements). If you write better in Markdown I recommend the following [converter](https://markdowntohtml.com/) and this [Markdown reference](https://www.markdownguide.org/cheat-sheet/)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "599b4313-9d53-46d8-be0e-dbf0ab707105", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-1f5f10be1f0c3e95", + "locked": false, + "points": 1, + "schema_version": 3, + "solution": true, + "task": false + }, + "tags": [] + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "tu_popup_html = folium.Popup(\n", + " '''\n", + "

\n", + " \"TU\n", + "

\n", + "

Universitätspl. 2

\n", + "

38106 Braunschweigg

\n", + "

Germany, DE

\n", + "

Visit: tu-bs.de

\n", + " ''',\n", + " show=False\n", + " )\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "541653fa-f882-4f15-90bb-2ad7665d380f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d0ad985cc098c85f", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "### 2.3 Defining an Icon\n", + "\n", + "Next, a _red_ icon named `tu_icon` should be defined.\n", + "\n", + "The color for the glyphicon should be a gray hexcode that you can choose freely. To make the color selection easier you can use the [Color Picker](https://htmlcolorcodes.com/color-picker/).\n", + "\n", + "As glyph, _glyphicon-education_, should be used." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7adb36ce-9012-4a9a-b025-2b7a91b4d2ac", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-3ec775c00a9d80a2", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + }, + "tags": [] + }, + "outputs": [], + "source": [ + "tu_icon = None\n", + "### BEGIN SOLUTION\n", + "tu_icon = folium.Icon(\n", + " color='red',\n", + " icon_color='#eeeeee',\n", + " prefix='glyphicon',\n", + " icon='glyphicon-education',\n", + " angle=0\n", + " )\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "287924de-5810-4f3c-8b36-e3308798b3ea", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-b16e407b56f6a000", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### 2.4 Defining a Marker\n", + "\n", + "All previously created objects should now be combined into one marker named `tu_bs_marker`.\n", + "\n", + "As the location data use `(52.273460, 10.529231)`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c7dd4f94-e7d2-4c11-9426-f911462e122c", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-43ce13ffa68645bc", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + }, + "tags": [] + }, + "outputs": [], + "source": [ + "tu_bs_marker = None\n", + "### BEGIN SOLUTION\n", + "tu_bs_marker = folium.Marker(\n", + " location=(52.273460, 10.529231),\n", + " popup=tu_popup_html,\n", + " tooltip=tooltip,\n", + " icon=tu_icon\n", + " )\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "13fa1a0f-f06e-4042-bae6-254fc99640e7", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-365a8eef0b32decd", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your Solutions are tested and displayed here...\n", + "m = folium.Map(\n", + " location=(52.274, 10.53),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=17,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "tu_bs_marker.add_to(m)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "6639280d-2c23-421b-9170-418895482fb3", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-8cf61a16719f867b", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "source": [ + "### Circle & Circle Marker\n", + "\n", + "Another possibility to mark places on maps is the [Circle](https://python-visualization.github.io/folium/modules.html#folium.vector_layers.Circle) and the [CircleMarker](https://python-visualization.github.io/folium/modules.html#folium.vector_layers.CircleMarker). The only difference between the two is the parameter _radius_ which is not fixed for the circle and has a default value of 10 for the circle marker which is specified in pixels, the circle marker scales with the map when zooming. Otherwise the objects _popup_ & _tooltip_ can be set with the marker. For the coloring the parameters _color_ & _fill_color_ are to be assigned. _color_ describes the color of the outer ring of the circle, while _fill_color_ specifies the fill color of the circle, whose default value is copied from the parameter_color_. to use _fill_color_ the parameter _fill_ must also be set to _True_.\n", + "\n", + "Here is an example.\n", + "\n", + "The HBK is marked with a red filled circle marker, which has the radius 100 pixel. To highlight the area inside the HBK, black is used as fill color.\n", + "Also here a tooltip, as well as the usual HBK popup is specified." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "511b9c23-f335-4639-8120-289ee9d4fee2", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-06a4d0cb95f20d93", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Map Setup\n", + "m = folium.Map(\n", + " location=(52.258, 10.5),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=16,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "# HBK Braunschweig\n", + "hbk_popup_html = folium.Popup(\n", + " '''\n", + "

\n", + " \"HBK\n", + "

\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de

\n", + " ''',\n", + " show=False\n", + " )\n", + "\n", + "# Defining tooltip\n", + "hbk_tooltip = \"More about the university\"\n", + "\n", + "# Create Circle Marker\n", + "hbk_circle_marker = folium.Circle(\n", + " location=(52.2572, 10.501),\n", + " popup=hbk_popup_html,\n", + " tooltip=hbk_tooltip,\n", + " radius=100,\n", + " fill=True,\n", + " fill_color='black',\n", + " color='red'\n", + " )\n", + "\n", + "# Attach Circle Marker to map\n", + "hbk_circle_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "c8bcbdc6-3b3a-4421-b78f-8c62f7f03d34", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-90296cc00bae2968", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "### Rectangle \n", + "\n", + "Rectangles can also be defined in the same way. Instead of a location with a radius, the two corner points must be specified. The data structure used for this is a list of tuples (_[tuple, tuple]_).\n", + "\n", + "Consider the following example:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "eee0d9ff-1ada-4f62-9675-39064b68fa32", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-641478b6b1df4ae5", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Map Setup\n", + "m = folium.Map(\n", + " location=(52.258, 10.5),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=16,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "# HBK Braunschweig\n", + "hbk_popup_html = folium.Popup(\n", + " '''\n", + "

\n", + " \"HBK\n", + "

\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de

\n", + " ''',\n", + " show=False\n", + " )\n", + "\n", + "# Defining tooltip\n", + "hbk_tooltip = \"More about the university\"\n", + "\n", + "# Create Rectangle Marker\n", + "hbk_rectangle_marker = folium.Rectangle(\n", + " bounds=[(52.258077, 10.498424), (52.255896, 10.504092)], # List of tuples defining the Corner Points\n", + " popup=hbk_popup_html,\n", + " tooltip=hbk_tooltip,\n", + " fill=True,\n", + " fill_color='black',\n", + " color='red'\n", + " )\n", + "\n", + "# Attach Rectangle Marker to map\n", + "hbk_rectangle_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "09574eea-8007-4740-b3f5-e81a40c28cbd", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-5d436ff9290eb683", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "## Builder Pattern\n", + "\n", + "To simplify working with Folium, certain techniques can be used to simplify the creation of objects. \n", + "\n", + "Design patterns are known from the technical literature that lead to a simpler, error-free and clearer way of developing software. \n", + "One of these design patterns, the creation pattern, will be presented below and used for future tasks.\n", + "\n", + "The technical literature states freely quoted:\n", + "\"The creation pattern serves to decouple the construction of objects from their representation!\" - [_Design Patterns: Elements of Reusable Object-Oriented Software\n", + "by Erich Gamma, Ralph Johnson, Richard Helm, Ralph E. Johnson, John Vlissides_](https://books.google.de/books?hl=de&lr=&id=tmNNfSkfTlcC&oi=fnd&pg=PR11&dq=software+design+patterns&ots=e_iImZT2d3&sig=DtkhOov5t0Ot6lf7QubDGNhWzz0#v=onepage&q=software%20design%20patterns&f=false)\n", + "\n", + "A builder is a function (function of an object) that is used to create new objects. In our case, these will be markers and popups." + ] + }, + { + "cell_type": "markdown", + "id": "5f27eaae-3e5b-4f6c-854a-2c37c9fbffac", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-6473594d5d9882b0", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "### A simple Popup Factory\n", + "\n", + "Anyone who remembers the HTML popups in the previous chapter will quickly realise that with a larger data set, setting individual HTML tags for images, addresses or other information soon becomes a tedious task.\n", + "\n", + "Assuming we want to plot all the universities in Lower Saxony on a map, a separate popup would have to be created for each marker. To change this, the following function will be introduced:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ec9a37e4-1dd3-4673-b36f-122adafadeb2", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-069cd8284c5bc36c", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "def popup_factory(adr: str, zipc: str, country: str, pic: str):\n", + " html = '''\n", + "

\n", + "

{}

\n", + "

{}

\n", + "

{}

\n", + " '''.format(pic, adr, zipc, country)\n", + " return folium.Popup(html)" + ] + }, + { + "cell_type": "markdown", + "id": "1740ce5e-f7a8-47f9-8211-7a62b19cf08b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-de4c7e1824c4e5a6", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "The _popup_factory_ function just defined takes the four string parameters _adr_ (address), _zipc_ (postcode), _country_ & _pic_ (picture URL) and generates an HTML-compliant string with the given information from the string specified in the _html_ variable. The return value of the function is a Folium popup object.\n", + "\n", + "To get closer to the goal of plotting all universities in Lower Saxony, a few objects are still missing.\n", + "\n", + "The _hbk_icon_ I created above is to be used as the standard icon. It is defined as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9dddf885-ffa7-4e3b-b948-43ded7f3bd7b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-a700169d2ec58d8c", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "def icon_factory(is_public=True):\n", + " icon = folium.Icon(\n", + " color='black' if is_public else 'white',\n", + " icon_color = 'white' if is_public else 'black',\n", + " icon='glyphicon-home'\n", + " )\n", + " return icon" + ] + }, + { + "cell_type": "markdown", + "id": "d4f4be32-8d71-41b0-9d8e-5ec2e32416a8", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-3fd735bc0f0cba70", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "The only thing missing in the next step is the factory for creating markers.\n", + "\n", + "This is defined as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "07a0df3e-92f9-4e25-89f3-3e27048d2c6b", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-393cf1e2b37be7a6", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "def marker_factory(loc, popup, is_public=True):\n", + " std_tooltip = 'Click for more information'\n", + " std_icon = icon_factory(is_public)\n", + " return folium.Marker(loc, popup=popup, icon=std_icon, tooltip=std_tooltip)" + ] + }, + { + "cell_type": "markdown", + "id": "1e7f7f3a-d234-427d-ad06-3026c00312a4", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-2b6d551c3e806181", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "With these two functions it is now easy to replicate the map created in the previous chapter:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6a60ccee-4728-4bf1-93db-1158deae037f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-7166c54f214ed13e", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create Map\n", + "m = folium.Map(\n", + " location=(52.258, 10.5),\n", + " tiles='OpenStreetMap',\n", + " zoom_start=16,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "# Define popup\n", + "pp = popup_factory(\n", + " adr='Johannes-Selenka-Platz 1',\n", + " zipc='38118 Braunschweig',\n", + " country='Germany, DE',\n", + " pic=\"https://www.hbk-bs.de/fileadmin/_processed_/5/1/csm_HBK_Logo_9f3f898a2b.png\",\n", + " )\n", + "\n", + "# Define Marker\n", + "marker = marker_factory(\n", + " loc=(52.257770, 10.502490),\n", + " popup=pp\n", + " )\n", + "\n", + "# Attach Marker to Map\n", + "marker.add_to(m)\n", + "\n", + "# Display Map\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "1110a947-36cb-43c8-9348-fe3bafabe185", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-a1045f8d87d79df5", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Exercise 3: Mapping the Universties in Lower Saxony\n", + "\n", + "## 3.1: Reading the Dataset\n", + "The data set for this notebook is *unis_nd.csv*.\n", + "\n", + "Read this into the variable `df` using the _pandas_ `read_csv` function.\n", + "\n", + "(I recommend that you take a look at the data set)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b50d076d-4b5f-4137-93df-846144b119b7", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-c55c41f6b9c03dd6", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "16c81c07-5893-42a7-9b1d-a081deea998c", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d58de9c13e900a3b", + "locked": false, + "schema_version": 3, + "solution": true, + "task": false + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + "
University nameType of universitySponsorshipRight of promotionFounding yearNumber of studentsAddresslatlonplzpic
0Hochschule für Bildende Künste BraunschweigArtistic universitypublicyes1963976.000Johannes-Selenka-Platz 152.25773810.50231538118 Braunschweighttps://www.hbk-bs.de/fileadmin/_processed_/5/...
1Technische Universität Carolo-Wilhelmina zu Br...Universitypublicyes174517709.000Universitätspl. 252.27355010.53009738106 Braunschweighttps://upload.wikimedia.org/wikipedia/commons...
2Hochschule 21University of Applied Sciencesprivatno20051084.000Harburger Str. 653.4776509.70465021614 Buxtehudehttps://upload.wikimedia.org/wikipedia/commons...
3Technische Universität ClausthalUniversitypublicyes17753446.000Adolph-Roemer-Straße 2A51.80484010.33411038678 Clausthal-Zellerfeldhttps://www.presse.tu-clausthal.de/fileadmin/T...
4Hochschule Emden/LeerUniversity of Applied Sciencespublicno20094481.000Constantiapl. 453.3681607.18141026723 Emdenhttps://sta-hisweb.hs-emden-leer.de/QIS/images...
5PFH – Private Hochschule GöttingenUniversity of Applied Sciencesprivatno19954226.000Weender Landstraße 3-751.5389109.93322037073 Göttingenhttps://goettingen-campus.de/fileadmin/_proces...
6Georg-August-Universität GöttingenUniversitypublicyes173728614.000Wilhelmsplatz 151.5340709.93785037073 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
7Fachhochschule für die Wirtschaft HannoverUniversity of Applied Sciencesprivatno1996641.000Freundallee 1552.3662009.77247030173 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
8Hochschule HannoverUniversity of Applied Sciencespublicno19719209.000Ricklinger Stadtweg 12052.3541909.72238030459 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
9Hochschule für Musik, Theater und Medien HannoverArtistic universitypublicyes18971409.000Neues Haus 152.3773809.75392030175 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
10Leibniz-FachhochschuleUniversity of Applied Sciencesprivatno1920589.000Expo Plaza 1152.3211509.81868030539 Hannoverhttps://www.visit-hannover.com/var/storage/ima...
11Medizinische Hochschule Hannover (MHH)Universitypublicyes19633778.000Carl-Neuberg-Straße 152.3840509.80603030625 Hannoverhttps://upload.wikimedia.org/wikipedia/commons...
12Stiftung Tierärztliche Hochschule HannoverUniversitypublicyes17782381.000Bünteweg 252.3546809.79773030559 Hannoverhttps://upload.wikimedia.org/wikipedia/de/thum...
13Gottfried Wilhelm Leibniz Universität HannoverUniversitypublicyes183128935.000Welfengarten 152.3822509.71777030167 Hannoverhttps://www.uni-hannover.de/fileadmin/_process...
14Fachhochschule für Interkulturelle Theologie H...University of Applied Sciencesprivatno201291.000Missionsstraße 3-552.70884310.14071029320 Südheidehttps://cdn.max-e5.info/damfiles/logo/fh_herma...
15Universität HildesheimUniversitypublicyes19788378.000Universitätspl. 152.1340109.97469031141 Hildesheimhttps://www.uni-hildesheim.de/media/_processed...
16HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Hohnsen 452.1424609.95798031134 Hildesheimhttps://upload.wikimedia.org/wikipedia/commons...
17HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Haarmannpl. 351.8272609.45069037603 Holzmindenhttps://upload.wikimedia.org/wikipedia/commons...
18HAWK Hochschule für angewandte Wissenschaft un...University of Applied Sciencespublicno19716495.000Von-Ossietzky-Straße 9951.5217509.96967037085 Göttingenhttps://upload.wikimedia.org/wikipedia/commons...
19Leuphana Universität LüneburgUniversitypublicyes19466497.000Universitätsallee 153.22853110.40171021335 Lüneburghttps://upload.wikimedia.org/wikipedia/commons...
20Norddeutsche Hochschule für Rechtspflege – Nie...University of Administrationpublicno20076409.000Godehardspl. 652.1448409.94923031134 Hildesheimhttps://static.studycheck.de/media/images/inst...
21Kommunale Hochschule für Verwaltung in Nieders...University of Administrationpublicno20071570.000Wielandstraße 852.3705009.72239030169 Hannoverhttps://www.nsi-hsvn.de/fileadmin/user_upload/...
22Carl von Ossietzky Universität Oldenburg\\nUniversitypublicyes197315635.000Uhlhornsweg 49-5553.1473408.17902026129 Oldenburghttps://upload.wikimedia.org/wikipedia/commons...
23Hochschule OsnabrückUniversity of Applied Sciencespublicno197113620.000Albrechtstraße 3052.2826808.02501049076 Osnabrückhttps://login.hs-osnabrueck.de/nidp/hsos/image...
24Universität OsnabrückUniversitypublicyes197313640.000Neuer Graben 2952.2713708.04454049074 Osnabrückhttps://www.eh-tabor.de/sites/default/files/st...
25Hochschule Braunschweig/Wolfenbüttel, Ostfalia...University of Applied Sciencespublicno197111577.000Salzdahlumer Str. 46/4852.17683010.54865038302 Wolfenbüttelhttps://www.ostfalia.de/export/system/modules/...
26Hochschule Wolfsburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Robert-Koch-Platz 8A52.42595010.78711038440 Wolfsburghttps://www.ostfalia.de/export/system/modules/...
27Hochschule Suderburg, Ostfalia Hochschule für ...University of Applied Sciencespublicno197111577.000Herbert-Meyer-Straße 752.89761010.44659029556 Suderburghttps://www.ostfalia.de/export/system/modules/...
28Hochschule Salzgitter, Ostfalia Hochschule für...University of Applied Sciencespublicno197111577.000Karl-Scharfenberg-Straße 55/5752.08724010.38055038229 Salzgitterhttps://www.ostfalia.de/export/system/modules/...
29Hochschule für Künste im Sozialen, OttersbergUniversity of Applied Sciencesprivatno1967342.000Große Str. 10753.1066809.16310028870 Ottersberghttps://upload.wikimedia.org/wikipedia/commons...
30Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Rombergstraße 4052.7212508.27891049377 Vechtahttps://www.phwt.de/wp-content/uploads/2020/09...
31Private Hochschule für Wirtschaft und Technik ...University of Applied Sciencesprivatno1998558.000Schlesier Str. 13A52.6117108.36334049356 Diepholzhttps://www.phwt.de/wp-content/uploads/2020/09...
32Universität VechtaUniversitypublicyes19954.551Driverstraße 2252.7211708.29380049377 Vechtahttps://upload.wikimedia.org/wikipedia/commons...
33Hochschule WeserberglandUniversity of Applied Sciencesprivatno2010485.000Am Stockhof 252.0987509.35542031785 Hamelnhttps://upload.wikimedia.org/wikipedia/commons...
34Jade Hochschule – WilhelmshavenUniversity of Applied Sciencespublicno20096789.000Friedrich-Paffrath-Straße 10153.5478708.08804026389 Wilhelmshavenhttps://www.jade-hs.de/fileadmin/layout2016/as...
35Jade Hochschule – OldenburgUniversity of Applied Sciencespublicno20096789.000Ofener Str. 16/1953.1417908.20213026121 Oldenburghttps://www.jade-hs.de/fileadmin/layout2016/as...
36Jade Hochschule – ElsflethUniversity of Applied Sciencespublicno20096789.000Weserstraße 5253.2424408.46651026931 Elsflethhttps://www.jade-hs.de/fileadmin/layout2016/as...
37Steuerakademie Niedersachsen RintelnUniversity of Administrationpublicno2006500.000Wilhelm-Busch-Weg 2952.2069609.09112031737 Rintelnhttps://www.steuerakademie.niedersachsen.de/as...
38Steuerakademie Niedersachsen Bad EilsenUniversity of Administrationpublicno2006500.000Bahnhofstraße 552.2398109.10423031707 Bad Eilsenhttps://www.steuerakademie.niedersachsen.de/as...
\n", + "
" + ], + "text/plain": [ + " University name \\\n", + "0 Hochschule für Bildende Künste Braunschweig \n", + "1 Technische Universität Carolo-Wilhelmina zu Br... \n", + "2 Hochschule 21 \n", + "3 Technische Universität Clausthal \n", + "4 Hochschule Emden/Leer \n", + "5 PFH – Private Hochschule Göttingen \n", + "6 Georg-August-Universität Göttingen \n", + "7 Fachhochschule für die Wirtschaft Hannover \n", + "8 Hochschule Hannover \n", + "9 Hochschule für Musik, Theater und Medien Hannover \n", + "10 Leibniz-Fachhochschule \n", + "11 Medizinische Hochschule Hannover (MHH) \n", + "12 Stiftung Tierärztliche Hochschule Hannover \n", + "13 Gottfried Wilhelm Leibniz Universität Hannover \n", + "14 Fachhochschule für Interkulturelle Theologie H... \n", + "15 Universität Hildesheim \n", + "16 HAWK Hochschule für angewandte Wissenschaft un... \n", + "17 HAWK Hochschule für angewandte Wissenschaft un... \n", + "18 HAWK Hochschule für angewandte Wissenschaft un... \n", + "19 Leuphana Universität Lüneburg \n", + "20 Norddeutsche Hochschule für Rechtspflege – Nie... \n", + "21 Kommunale Hochschule für Verwaltung in Nieders... \n", + "22 Carl von Ossietzky Universität Oldenburg\\n \n", + "23 Hochschule Osnabrück \n", + "24 Universität Osnabrück \n", + "25 Hochschule Braunschweig/Wolfenbüttel, Ostfalia... \n", + "26 Hochschule Wolfsburg, Ostfalia Hochschule für ... \n", + "27 Hochschule Suderburg, Ostfalia Hochschule für ... \n", + "28 Hochschule Salzgitter, Ostfalia Hochschule für... \n", + "29 Hochschule für Künste im Sozialen, Ottersberg \n", + "30 Private Hochschule für Wirtschaft und Technik ... \n", + "31 Private Hochschule für Wirtschaft und Technik ... \n", + "32 Universität Vechta \n", + "33 Hochschule Weserbergland \n", + "34 Jade Hochschule – Wilhelmshaven \n", + "35 Jade Hochschule – Oldenburg \n", + "36 Jade Hochschule – Elsfleth \n", + "37 Steuerakademie Niedersachsen Rinteln \n", + "38 Steuerakademie Niedersachsen Bad Eilsen \n", + "\n", + " Type of university Sponsorship Right of promotion \\\n", + "0 Artistic university public yes \n", + "1 University public yes \n", + "2 University of Applied Sciences privat no \n", + "3 University public yes \n", + "4 University of Applied Sciences public no \n", + "5 University of Applied Sciences privat no \n", + "6 University public yes \n", + "7 University of Applied Sciences privat no \n", + "8 University of Applied Sciences public no \n", + "9 Artistic university public yes \n", + "10 University of Applied Sciences privat no \n", + "11 University public yes \n", + "12 University public yes \n", + "13 University public yes \n", + "14 University of Applied Sciences privat no \n", + "15 University public yes \n", + "16 University of Applied Sciences public no \n", + "17 University of Applied Sciences public no \n", + "18 University of Applied Sciences public no \n", + "19 University public yes \n", + "20 University of Administration public no \n", + "21 University of Administration public no \n", + "22 University public yes \n", + "23 University of Applied Sciences public no \n", + "24 University public yes \n", + "25 University of Applied Sciences public no \n", + "26 University of Applied Sciences public no \n", + "27 University of Applied Sciences public no \n", + "28 University of Applied Sciences public no \n", + "29 University of Applied Sciences privat no \n", + "30 University of Applied Sciences privat no \n", + "31 University of Applied Sciences privat no \n", + "32 University public yes \n", + "33 University of Applied Sciences privat no \n", + "34 University of Applied Sciences public no \n", + "35 University of Applied Sciences public no \n", + "36 University of Applied Sciences public no \n", + "37 University of Administration public no \n", + "38 University of Administration public no \n", + "\n", + " Founding year Number of students Address \\\n", + "0 1963 976.000 Johannes-Selenka-Platz 1 \n", + "1 1745 17709.000 Universitätspl. 2 \n", + "2 2005 1084.000 Harburger Str. 6 \n", + "3 1775 3446.000 Adolph-Roemer-Straße 2A \n", + "4 2009 4481.000 Constantiapl. 4 \n", + "5 1995 4226.000 Weender Landstraße 3-7 \n", + "6 1737 28614.000 Wilhelmsplatz 1 \n", + "7 1996 641.000 Freundallee 15 \n", + "8 1971 9209.000 Ricklinger Stadtweg 120 \n", + "9 1897 1409.000 Neues Haus 1 \n", + "10 1920 589.000 Expo Plaza 11 \n", + "11 1963 3778.000 Carl-Neuberg-Straße 1 \n", + "12 1778 2381.000 Bünteweg 2 \n", + "13 1831 28935.000 Welfengarten 1 \n", + "14 2012 91.000 Missionsstraße 3-5 \n", + "15 1978 8378.000 Universitätspl. 1 \n", + "16 1971 6495.000 Hohnsen 4 \n", + "17 1971 6495.000 Haarmannpl. 3 \n", + "18 1971 6495.000 Von-Ossietzky-Straße 99 \n", + "19 1946 6497.000 Universitätsallee 1 \n", + "20 2007 6409.000 Godehardspl. 6 \n", + "21 2007 1570.000 Wielandstraße 8 \n", + "22 1973 15635.000 Uhlhornsweg 49-55 \n", + "23 1971 13620.000 Albrechtstraße 30 \n", + "24 1973 13640.000 Neuer Graben 29 \n", + "25 1971 11577.000 Salzdahlumer Str. 46/48 \n", + "26 1971 11577.000 Robert-Koch-Platz 8A \n", + "27 1971 11577.000 Herbert-Meyer-Straße 7 \n", + "28 1971 11577.000 Karl-Scharfenberg-Straße 55/57 \n", + "29 1967 342.000 Große Str. 107 \n", + "30 1998 558.000 Rombergstraße 40 \n", + "31 1998 558.000 Schlesier Str. 13A \n", + "32 1995 4.551 Driverstraße 22 \n", + "33 2010 485.000 Am Stockhof 2 \n", + "34 2009 6789.000 Friedrich-Paffrath-Straße 101 \n", + "35 2009 6789.000 Ofener Str. 16/19 \n", + "36 2009 6789.000 Weserstraße 52 \n", + "37 2006 500.000 Wilhelm-Busch-Weg 29 \n", + "38 2006 500.000 Bahnhofstraße 5 \n", + "\n", + " lat lon plz \\\n", + "0 52.257738 10.502315 38118 Braunschweig \n", + "1 52.273550 10.530097 38106 Braunschweig \n", + "2 53.477650 9.704650 21614 Buxtehude \n", + "3 51.804840 10.334110 38678 Clausthal-Zellerfeld \n", + "4 53.368160 7.181410 26723 Emden \n", + "5 51.538910 9.933220 37073 Göttingen \n", + "6 51.534070 9.937850 37073 Göttingen \n", + "7 52.366200 9.772470 30173 Hannover \n", + "8 52.354190 9.722380 30459 Hannover \n", + "9 52.377380 9.753920 30175 Hannover \n", + "10 52.321150 9.818680 30539 Hannover \n", + "11 52.384050 9.806030 30625 Hannover \n", + "12 52.354680 9.797730 30559 Hannover \n", + "13 52.382250 9.717770 30167 Hannover \n", + "14 52.708843 10.140710 29320 Südheide \n", + "15 52.134010 9.974690 31141 Hildesheim \n", + "16 52.142460 9.957980 31134 Hildesheim \n", + "17 51.827260 9.450690 37603 Holzminden \n", + "18 51.521750 9.969670 37085 Göttingen \n", + "19 53.228531 10.401710 21335 Lüneburg \n", + "20 52.144840 9.949230 31134 Hildesheim \n", + "21 52.370500 9.722390 30169 Hannover \n", + "22 53.147340 8.179020 26129 Oldenburg \n", + "23 52.282680 8.025010 49076 Osnabrück \n", + "24 52.271370 8.044540 49074 Osnabrück \n", + "25 52.176830 10.548650 38302 Wolfenbüttel \n", + "26 52.425950 10.787110 38440 Wolfsburg \n", + "27 52.897610 10.446590 29556 Suderburg \n", + "28 52.087240 10.380550 38229 Salzgitter \n", + "29 53.106680 9.163100 28870 Ottersberg \n", + "30 52.721250 8.278910 49377 Vechta \n", + "31 52.611710 8.363340 49356 Diepholz \n", + "32 52.721170 8.293800 49377 Vechta \n", + "33 52.098750 9.355420 31785 Hameln \n", + "34 53.547870 8.088040 26389 Wilhelmshaven \n", + "35 53.141790 8.202130 26121 Oldenburg \n", + "36 53.242440 8.466510 26931 Elsfleth \n", + "37 52.206960 9.091120 31737 Rinteln \n", + "38 52.239810 9.104230 31707 Bad Eilsen \n", + "\n", + " pic \n", + "0 https://www.hbk-bs.de/fileadmin/_processed_/5/... \n", + "1 https://upload.wikimedia.org/wikipedia/commons... \n", + "2 https://upload.wikimedia.org/wikipedia/commons... \n", + "3 https://www.presse.tu-clausthal.de/fileadmin/T... \n", + "4 https://sta-hisweb.hs-emden-leer.de/QIS/images... \n", + "5 https://goettingen-campus.de/fileadmin/_proces... \n", + "6 https://upload.wikimedia.org/wikipedia/commons... \n", + "7 https://upload.wikimedia.org/wikipedia/commons... \n", + "8 https://upload.wikimedia.org/wikipedia/commons... \n", + "9 https://upload.wikimedia.org/wikipedia/commons... \n", + "10 https://www.visit-hannover.com/var/storage/ima... \n", + "11 https://upload.wikimedia.org/wikipedia/commons... \n", + "12 https://upload.wikimedia.org/wikipedia/de/thum... \n", + "13 https://www.uni-hannover.de/fileadmin/_process... \n", + "14 https://cdn.max-e5.info/damfiles/logo/fh_herma... \n", + "15 https://www.uni-hildesheim.de/media/_processed... \n", + "16 https://upload.wikimedia.org/wikipedia/commons... \n", + "17 https://upload.wikimedia.org/wikipedia/commons... \n", + "18 https://upload.wikimedia.org/wikipedia/commons... \n", + "19 https://upload.wikimedia.org/wikipedia/commons... \n", + "20 https://static.studycheck.de/media/images/inst... \n", + "21 https://www.nsi-hsvn.de/fileadmin/user_upload/... \n", + "22 https://upload.wikimedia.org/wikipedia/commons... \n", + "23 https://login.hs-osnabrueck.de/nidp/hsos/image... \n", + "24 https://www.eh-tabor.de/sites/default/files/st... \n", + "25 https://www.ostfalia.de/export/system/modules/... \n", + "26 https://www.ostfalia.de/export/system/modules/... \n", + "27 https://www.ostfalia.de/export/system/modules/... \n", + "28 https://www.ostfalia.de/export/system/modules/... \n", + "29 https://upload.wikimedia.org/wikipedia/commons... \n", + "30 https://www.phwt.de/wp-content/uploads/2020/09... \n", + "31 https://www.phwt.de/wp-content/uploads/2020/09... \n", + "32 https://upload.wikimedia.org/wikipedia/commons... \n", + "33 https://upload.wikimedia.org/wikipedia/commons... \n", + "34 https://www.jade-hs.de/fileadmin/layout2016/as... \n", + "35 https://www.jade-hs.de/fileadmin/layout2016/as... \n", + "36 https://www.jade-hs.de/fileadmin/layout2016/as... \n", + "37 https://www.steuerakademie.niedersachsen.de/as... \n", + "38 https://www.steuerakademie.niedersachsen.de/as... " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### BEGIN SOLUTION\n", + "df = pd.read_csv('unis_nd.csv')\n", + "df\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a30598a7-c7d4-42cc-a9b8-dc6356d060aa", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-3e80e16a38e85d29", + "locked": true, + "points": 1, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "# Your Solutions are tested here..\n", + "assert isinstance(df, pd.DataFrame)" + ] + }, + { + "cell_type": "markdown", + "id": "da44f50f-0c2d-404d-8083-289742c5497a", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-237bfc25448676b2", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "## 3.2: Defining the Map\n", + "\n", + "Before we plot the dataset, define a map with the name `lower_saxony`. \n", + "\n", + "- The location should be the georaphic centre of Lower Saxony in _Wehrenberg 27318 Hoyerhagen_ `(52.806390, 9.135110)`.\n", + "- Use a suitable tileset from the documentation\n", + "- Use a suitable zoom setting" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bfe04668-a597-4f93-b6e4-11ef17d18dcd", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-0d7c995b3fcc368e", + "locked": false, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "lower_saxony = folium.Map(\n", + " location=(52.806390, 9.135110), # Georaphical centre Point of Lower Saxony Wehrenberg 27318 Hoyerhagen\n", + " tiles='OpenStreetMap',\n", + " zoom_start=7\n", + " )\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a2f05ce6-7942-4c71-b1e2-a709c88b0f36", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-77f8257bb3c1308f", + "locked": true, + "points": 4, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [], + "source": [ + "# Your Solutions are tested here..\n", + "assert isinstance(lower_saxony, folium.Map)\n", + "assert len(lower_saxony.location) == 2" + ] + }, + { + "cell_type": "markdown", + "id": "357c71bb-923a-4de6-bcbd-b9d2741498fc", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d66a439bbfb3778a", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "## 3.3 Plotting the Dataset\n", + "\n", + "Write a for loop which reads the values from the dataset `df` and add the markers to the map `lower_saxony` using the already defined functions `popup_factory` and `marker_factory`. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b392f48f-f0ae-4158-806f-859e71009c04", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-b744358aaaa7db44", + "locked": false, + "points": 5, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### BEGIN SOLUTION\n", + "for index, row in df.iterrows():\n", + " pp = popup_factory(\n", + " adr=row['Address'],\n", + " zipc=row['plz'],\n", + " country='Germany, DE',\n", + " pic=row['pic'],\n", + " )\n", + " location = (float(row['lat']), float(row['lon']))\n", + " \n", + " is_public = False\n", + " if row['Sponsorship'] == 'public':\n", + " is_public = True\n", + " \n", + " marker = marker_factory(location, pp, is_public) \n", + " marker.add_to(lower_saxony)\n", + " \n", + "lower_saxony\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "ab37a764-3718-4327-976c-146e5531a048", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-49864685eac331d1", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your Solutions are tested and displayed here..\n", + "lower_saxony" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + }, + "toc-autonumbering": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Material/wise_24_25/lernmaterial/people_in_germany.csv b/Material/wise_24_25/lernmaterial/people_in_germany.csv index 1caa8f1..890d3c9 100644 --- a/Material/wise_24_25/lernmaterial/people_in_germany.csv +++ b/Material/wise_24_25/lernmaterial/people_in_germany.csv @@ -1,1001 +1,1001 @@ gender,height,weight -False,199.3,109.9 -True,188.3,76.1 -False,193.7,103.7 -True,165.1,88.2 -False,177.5,66.6 -True,156.6,62.5 -False,190.6,81.5 -True,200.5,90.7 -False,179.4,92.0 -True,164.1,61.5 -False,203.7,71.7 -True,162.9,82.3 -False,161.5,44.9 -True,170.6,59.8 -False,180.8,105.6 -True,152.7,97.1 -False,165.8,94.4 -True,173.5,73.1 -False,191.6,143.4 -True,187.0,64.9 -False,183.9,46.2 -True,169.4,86.8 -False,189.0,109.9 -True,174.4,77.1 -False,172.3,106.0 -True,146.8,85.6 -False,193.3,75.9 -True,169.1,93.0 -False,187.3,109.5 -True,178.0,84.4 -False,181.3,128.1 -True,163.1,84.3 -False,184.2,80.4 -True,171.5,68.8 -False,173.0,68.8 -True,158.2,74.3 -False,191.2,101.3 -True,181.5,71.1 -False,173.9,89.4 -True,168.8,74.5 -False,183.3,64.6 -True,167.8,80.3 -False,173.5,97.1 -True,166.6,60.1 -False,168.3,124.7 -True,182.6,74.0 -False,172.8,79.5 -True,153.4,54.9 -False,172.8,106.6 -True,171.0,91.7 -False,166.5,67.9 -True,184.2,64.9 -False,176.0,61.9 -True,157.5,51.2 -False,155.4,88.0 -True,167.5,68.8 -False,169.0,103.6 -True,172.0,58.1 -False,192.0,45.1 -True,174.3,17.7 -False,187.1,130.2 -True,175.6,57.4 -False,181.0,46.0 -True,165.9,41.0 -False,186.1,98.0 -True,166.8,54.5 -False,190.2,85.0 -True,177.2,95.5 -False,195.1,37.5 -True,154.6,54.0 -False,170.6,126.5 -True,174.7,53.3 -False,186.0,23.0 -True,157.1,58.6 -False,174.4,34.2 -True,189.1,65.9 -False,172.3,38.7 -True,176.8,50.5 -False,186.3,88.8 -True,175.4,67.6 -False,192.6,85.4 -True,164.5,97.3 -False,192.1,80.6 -True,192.8,69.5 -False,181.7,75.9 -True,157.6,23.0 -False,170.5,60.4 -True,160.7,61.7 -False,166.9,110.7 -True,150.7,43.8 -False,169.3,100.9 -True,163.9,44.4 -False,174.6,79.3 -True,186.2,36.8 -False,203.8,91.2 -True,154.8,54.6 -False,187.9,108.2 -True,153.6,62.9 -False,196.6,59.5 -True,163.7,80.0 -False,174.5,58.6 -True,174.8,17.1 -False,168.7,69.2 -True,190.6,55.3 -False,185.2,68.0 -True,180.7,80.6 -False,186.8,94.9 -True,147.7,111.8 -False,181.6,83.1 -True,168.0,66.3 -False,198.8,101.8 -True,158.2,53.7 -False,172.3,116.7 -True,160.2,68.2 -False,172.7,105.8 -True,159.2,74.4 -False,194.1,144.4 -True,158.4,78.4 -False,172.5,111.8 -True,184.4,25.9 -False,181.2,150.3 -True,161.9,77.6 -False,194.9,90.2 -True,145.7,79.2 -False,184.0,107.0 -True,155.3,19.6 -False,167.1,65.6 -True,175.1,61.6 -False,166.4,66.6 -True,175.4,65.3 -False,181.3,97.2 -True,178.0,65.2 -False,189.1,90.1 -True,158.9,56.4 -False,177.5,63.0 -True,168.7,58.5 -False,179.3,53.0 -True,181.2,95.9 -False,185.8,132.6 -True,174.6,62.4 -False,170.0,105.0 -True,154.3,55.5 -False,176.6,91.8 -True,167.5,65.4 -False,178.8,93.9 -True,159.8,33.4 -False,168.0,78.2 -True,172.5,69.5 -False,171.4,26.7 -True,177.0,83.9 -False,177.0,119.5 -True,172.7,94.8 -False,176.4,81.4 -True,155.5,67.5 -False,175.7,84.9 -True,164.7,68.2 -False,184.1,96.3 -True,152.8,74.3 -False,171.4,100.3 -True,149.7,73.9 -False,178.5,102.7 -True,144.7,73.1 -False,176.3,97.9 -True,160.4,72.0 -False,194.6,100.9 -True,171.6,72.1 -False,190.9,121.6 -True,146.8,58.0 -False,168.6,94.0 -True,159.7,71.9 -False,186.7,88.8 -True,162.2,70.0 -False,183.7,119.6 -True,157.2,42.7 -False,178.4,110.6 -True,174.9,69.8 -False,175.4,105.3 -True,169.4,71.0 -False,192.5,144.0 -True,168.3,49.3 -False,174.3,98.2 -True,157.5,84.2 -False,169.1,122.3 -True,174.1,85.0 -False,182.7,116.5 -True,145.9,74.5 -False,177.5,90.8 -True,156.9,73.9 -False,172.7,82.8 -True,184.8,42.0 -False,185.4,49.6 -True,174.4,42.9 -False,159.3,66.9 -True,135.5,77.3 -False,180.7,117.7 -True,157.0,29.6 -False,180.2,59.8 -True,164.9,99.2 -False,175.6,13.6 -True,169.2,64.7 -False,187.0,84.2 -True,175.6,44.7 -False,181.1,106.3 -True,156.5,78.8 -False,188.5,99.5 -True,147.4,53.7 -False,172.5,80.4 -True,150.4,66.0 -False,204.5,119.2 -True,129.3,64.0 -False,171.5,49.0 -True,180.4,94.0 -False,173.8,64.1 -True,181.4,98.5 -False,178.7,22.7 -True,175.6,59.3 -False,195.7,102.7 -True,169.0,83.2 -False,163.9,47.4 -True,172.4,32.8 -False,171.5,39.6 -True,174.4,80.5 -False,184.2,56.7 -True,150.6,84.2 -False,186.7,130.9 -True,169.1,77.7 -False,193.5,59.5 -True,167.3,102.7 -False,175.4,102.3 -True,167.9,24.8 -False,171.6,82.7 -True,191.7,113.0 -False,170.4,131.6 -True,163.7,70.8 -False,174.3,71.5 -True,170.7,63.9 -False,174.6,89.4 -True,140.7,95.6 -False,182.6,81.4 -True,158.1,61.6 -False,181.8,75.3 -True,186.1,81.5 -False,167.8,46.7 -True,179.1,83.9 -False,171.4,103.7 -True,161.8,69.3 -False,186.6,90.6 -True,174.0,75.9 -False,160.7,69.2 -True,163.6,56.2 -False,185.2,89.0 -True,147.2,95.1 -False,179.8,91.4 -True,170.1,94.0 -False,168.6,94.6 -True,151.3,39.5 -False,170.0,109.3 -True,153.5,50.0 -False,180.1,159.9 -True,169.4,35.4 -False,162.2,74.7 -True,174.9,67.5 -False,187.4,93.2 -True,177.3,100.0 -False,171.2,76.6 -True,156.2,78.4 -False,190.9,100.8 -True,172.3,69.3 -False,173.0,53.0 -True,162.9,24.5 -False,163.1,129.9 -True,180.4,77.5 -False,176.6,109.6 -True,150.1,57.5 -False,182.0,62.5 -True,157.6,58.7 -False,185.1,64.7 -True,184.3,65.5 -False,175.3,59.1 -True,151.0,44.4 -False,178.2,95.4 -True,174.8,61.1 -False,179.6,98.3 -True,158.3,37.7 -False,172.0,100.2 -True,162.7,75.0 -False,181.9,82.2 -True,183.5,63.0 -False,174.1,102.1 -True,166.5,61.0 -False,173.5,71.5 -True,170.3,71.7 -False,170.6,120.0 -True,168.5,36.0 -False,180.1,89.7 -True,159.9,57.3 -False,159.1,108.2 -True,158.2,109.1 -False,183.3,47.8 -True,174.5,60.1 -False,185.1,73.7 -True,160.9,67.6 -False,191.7,110.0 -True,171.1,81.0 -False,164.9,105.3 -True,158.7,51.1 -False,159.5,80.4 -True,160.1,68.8 -False,165.0,94.3 -True,173.9,80.9 -False,191.7,57.0 -True,175.2,70.8 -False,182.5,78.5 -True,154.6,99.3 -False,172.9,77.9 -True,161.9,46.0 -False,171.3,112.6 -True,146.9,35.4 -False,188.0,92.3 -True,177.9,87.7 -False,159.1,68.6 -True,153.7,83.8 -False,164.9,54.9 -True,156.9,55.3 -False,168.5,111.0 -True,174.2,71.0 -False,184.9,82.5 -True,160.2,60.9 -False,177.7,139.8 -True,181.8,50.3 -False,180.9,123.4 -True,191.2,70.8 -False,191.6,92.9 -True,170.9,76.8 -False,191.8,74.6 -True,168.1,74.5 -False,161.2,103.7 -True,169.3,68.2 -False,181.4,61.9 -True,167.2,86.6 -False,176.0,58.9 -True,173.7,82.5 -False,189.0,41.3 -True,174.2,54.2 -False,189.1,88.5 -True,164.7,37.6 -False,188.5,99.0 -True,146.4,34.8 -False,156.8,126.0 -True,174.6,74.6 -False,181.2,113.4 -True,160.2,57.6 -False,204.1,43.4 -True,163.6,66.8 -False,180.5,89.8 -True,150.9,113.6 -False,182.6,89.7 -True,161.3,113.1 -False,187.3,72.3 -True,130.3,78.7 -False,170.4,85.0 -True,173.9,56.7 -False,180.8,75.5 -True,162.7,90.0 -False,163.9,108.5 -True,159.9,87.1 -False,172.7,22.2 -True,163.7,44.1 -False,185.2,110.7 -True,179.5,63.2 -False,158.1,89.7 -True,177.5,62.6 -False,174.8,85.8 -True,149.7,82.2 -False,184.1,95.2 -True,173.0,75.8 -False,186.6,51.0 -True,165.0,56.5 -False,166.7,91.6 -True,164.0,32.3 -False,186.9,86.0 -True,162.5,97.5 -False,180.3,120.6 -True,154.2,43.3 -False,170.4,77.7 -True,190.7,103.5 -False,178.3,88.4 -True,154.5,37.9 -False,179.9,65.2 -True,173.9,57.0 +False,185.5,99.3 +True,179.7,69.6 +False,189.7,51.7 +True,146.8,71.0 +False,166.9,64.2 +True,152.8,27.8 +False,177.5,100.7 +True,165.2,44.5 +False,166.7,98.1 +True,167.1,55.9 +False,180.6,56.4 +True,172.5,53.7 +False,185.9,79.5 +True,170.0,88.4 +False,166.4,113.3 +True,167.1,42.6 +False,155.9,73.8 +True,163.2,73.9 +False,161.7,63.0 +True,155.0,59.0 +False,169.4,104.1 +True,179.6,73.2 +False,172.6,97.6 +True,149.1,39.5 +False,167.8,86.6 +True,175.5,71.8 +False,178.9,120.5 +True,166.4,56.2 +False,174.7,107.7 +True,167.1,87.7 +False,187.7,99.5 +True,167.7,61.3 +False,180.3,75.4 +True,186.6,57.5 +False,180.6,104.8 +True,159.1,54.4 +False,179.1,147.4 +True,168.3,53.0 +False,172.8,63.7 +True,170.5,74.6 +False,179.2,110.1 +True,158.2,47.5 +False,186.5,133.9 +True,173.0,63.8 +False,169.2,57.4 +True,195.1,56.6 +False,176.5,76.2 +True,171.1,69.3 +False,169.7,102.0 +True,182.8,120.7 +False,173.6,72.6 +True,164.7,58.9 +False,185.6,94.3 +True,163.0,51.0 +False,159.6,97.3 +True,172.3,77.5 +False,176.9,95.9 +True,145.7,60.2 +False,173.4,97.6 +True,175.6,39.9 +False,187.4,98.0 +True,140.8,68.9 +False,188.8,126.0 +True,177.3,54.7 +False,175.9,140.7 +True,167.3,47.3 +False,165.9,52.1 +True,170.4,72.1 +False,168.7,71.4 +True,156.7,77.2 +False,178.6,107.3 +True,157.2,74.0 +False,184.4,98.4 +True,166.4,77.2 +False,173.4,83.2 +True,160.0,87.7 +False,172.1,72.2 +True,165.5,69.3 +False,165.6,120.5 +True,182.8,86.0 +False,189.1,89.5 +True,189.4,78.8 +False,165.6,58.3 +True,156.7,57.5 +False,176.3,89.7 +True,155.6,54.4 +False,196.6,128.5 +True,166.2,67.9 +False,187.2,122.0 +True,194.0,67.4 +False,155.5,114.7 +True,156.8,77.4 +False,174.2,35.9 +True,158.5,78.0 +False,182.1,38.5 +True,165.6,43.3 +False,177.7,86.8 +True,168.4,54.4 +False,182.6,53.6 +True,171.9,88.5 +False,181.4,104.2 +True,154.8,96.8 +False,173.6,87.9 +True,164.9,95.1 +False,167.6,35.5 +True,149.1,74.5 +False,166.6,104.6 +True,169.1,44.7 +False,180.6,50.8 +True,179.0,99.7 +False,177.2,30.8 +True,152.5,51.5 +False,167.4,132.9 +True,168.7,50.3 +False,182.5,86.4 +True,182.3,71.1 +False,169.2,118.6 +True,173.1,122.2 +False,181.9,59.9 +True,164.6,42.1 +False,188.2,84.4 +True,142.0,37.3 +False,177.2,67.6 +True,164.1,55.7 +False,196.0,36.5 +True,163.8,86.0 +False,182.7,117.9 +True,183.7,53.8 +False,190.3,99.1 +True,165.7,83.8 +False,172.8,94.0 +True,156.3,58.0 +False,181.5,91.2 +True,161.1,85.5 +False,183.4,64.9 +True,158.7,57.9 +False,170.1,86.1 +True,160.8,32.8 +False,162.0,83.6 +True,170.8,53.8 +False,192.4,89.9 +True,170.3,40.6 +False,180.8,84.7 +True,173.4,72.4 +False,189.2,102.1 +True,163.2,40.3 +False,184.8,136.9 +True,156.4,85.9 +False,166.9,85.4 +True,138.2,52.2 +False,185.6,93.2 +True,151.2,56.5 +False,194.5,76.6 +True,164.8,64.1 +False,175.5,95.8 +True,161.3,60.1 False,168.2,69.0 -True,174.5,66.5 -False,176.2,85.7 -True,164.2,68.1 -False,167.2,111.1 -True,152.7,74.4 -False,176.7,41.0 -True,153.5,121.6 -False,167.2,67.3 -True,151.5,74.9 -False,195.9,73.6 -True,160.1,80.6 -False,169.1,77.8 -True,160.3,70.5 -False,193.9,94.1 -True,162.1,70.4 -False,179.5,93.8 -True,176.8,65.4 -False,185.9,73.0 -True,152.0,87.3 -False,187.8,75.9 -True,164.3,59.5 -False,189.3,95.3 -True,174.1,74.3 -False,178.5,66.2 -True,165.5,67.0 -False,169.0,57.0 -True,177.2,79.7 -False,193.1,65.1 -True,172.4,58.7 -False,186.3,107.0 -True,160.2,42.4 -False,176.8,153.9 -True,181.8,68.1 -False,186.9,85.1 -True,159.2,83.7 -False,181.6,67.6 -True,146.5,79.3 -False,165.3,87.2 -True,171.7,70.3 -False,190.4,73.3 -True,182.2,54.8 -False,163.3,113.0 -True,181.6,82.7 -False,187.7,55.6 -True,167.8,65.6 -False,172.0,86.5 -True,158.2,71.6 -False,186.2,66.1 -True,161.0,79.7 -False,166.1,131.3 -True,178.7,85.8 -False,190.4,94.6 -True,148.8,91.6 -False,173.5,56.0 -True,148.9,86.8 -False,181.2,71.1 -True,155.8,89.0 -False,179.9,78.5 -True,175.3,28.6 -False,170.4,73.0 -True,163.8,59.8 -False,180.4,85.8 -True,173.8,68.6 -False,165.7,116.7 -True,159.7,107.4 -False,196.3,122.9 -True,150.1,44.2 -False,183.9,107.2 -True,145.3,12.8 -False,186.9,88.4 -True,162.0,61.9 -False,171.9,93.6 -True,150.5,91.8 -False,171.4,77.1 -True,172.5,36.5 -False,174.0,129.1 -True,165.6,53.6 -False,179.6,91.7 -True,157.9,76.5 -False,170.0,108.7 -True,169.2,73.2 -False,185.4,114.8 -True,165.8,91.3 -False,190.2,80.3 -True,168.1,67.2 -False,183.2,60.1 -True,176.0,46.4 -False,160.0,63.0 -True,146.5,85.9 -False,195.9,109.5 -True,161.4,93.6 -False,176.1,45.5 -True,188.7,48.1 -False,188.7,75.1 -True,178.2,61.0 -False,172.5,101.8 -True,161.0,65.0 -False,184.0,75.3 -True,166.3,112.3 -False,175.3,76.0 -True,157.9,73.5 -False,164.1,123.3 -True,145.8,75.6 -False,172.1,82.6 -True,177.0,43.1 -False,171.8,65.3 -True,165.2,55.2 -False,177.5,71.5 -True,155.1,57.0 -False,182.2,57.0 -True,180.3,42.4 -False,173.5,81.5 -True,168.2,71.2 -False,161.0,93.2 -True,137.1,76.7 -False,180.5,59.4 -True,181.6,74.3 -False,172.1,106.8 -True,174.6,62.8 -False,167.3,96.1 -True,171.2,45.1 -False,173.9,84.0 -True,141.7,42.5 -False,175.5,108.9 -True,171.5,22.8 -False,180.7,117.1 -True,176.6,86.4 -False,174.8,86.4 -True,154.7,61.1 -False,188.7,100.2 -True,143.6,55.4 -False,199.1,53.5 -True,161.3,53.9 -False,190.6,75.8 -True,157.9,67.7 -False,174.3,67.5 -True,152.2,57.0 -False,183.2,96.7 -True,177.6,68.1 -False,175.7,74.0 -True,177.1,47.1 -False,195.5,114.7 -True,154.4,20.8 -False,179.2,62.9 -True,168.8,62.5 -False,168.6,68.5 -True,180.0,66.6 -False,168.2,90.1 -True,176.8,89.7 -False,184.9,143.4 -True,177.2,48.6 -False,189.4,67.8 -True,170.6,85.6 -False,209.0,106.6 -True,156.5,60.5 -False,164.4,66.0 -True,152.4,71.8 -False,177.6,40.8 -True,172.1,55.9 -False,187.2,112.0 -True,171.2,65.6 -False,177.8,77.9 -True,166.9,49.4 -False,184.8,64.6 -True,165.4,76.9 -False,167.8,95.0 -True,175.0,65.8 -False,173.0,93.1 -True,172.1,45.1 -False,171.6,66.3 -True,180.2,84.4 -False,173.2,118.0 -True,170.9,85.2 -False,181.1,66.0 -True,165.1,63.4 -False,172.4,81.7 -True,164.1,79.5 -False,189.0,65.3 -True,164.5,44.1 -False,173.4,95.4 -True,161.8,78.9 -False,190.4,81.1 -True,171.4,32.7 -False,169.3,69.5 -True,162.0,74.5 -False,189.6,106.8 -True,162.4,12.1 -False,207.2,123.1 -True,142.6,46.1 -False,159.9,71.3 -True,164.5,67.7 -False,177.8,62.5 -True,169.9,49.1 -False,174.5,142.4 -True,156.7,59.9 -False,185.0,97.9 -True,143.2,73.0 -False,175.6,75.7 -True,170.8,62.4 -False,188.4,90.2 -True,172.8,48.8 -False,165.5,90.9 -True,180.4,75.5 -False,174.2,87.6 -True,152.1,63.7 -False,170.9,84.3 -True,156.7,80.3 -False,189.5,53.9 -True,177.0,86.8 -False,177.6,109.0 -True,171.6,121.1 -False,189.6,112.9 -True,161.0,67.0 -False,178.2,89.3 -True,178.9,48.5 -False,187.5,93.4 -True,162.6,46.8 -False,175.0,77.8 -True,179.4,91.9 -False,167.9,122.8 -True,139.3,39.3 -False,180.6,75.2 -True,161.6,75.8 -False,191.7,79.7 -True,163.2,53.9 -False,180.1,110.9 -True,170.6,50.4 -False,183.9,51.9 -True,155.1,69.1 -False,178.0,81.9 -True,142.9,68.1 -False,190.0,94.6 -True,164.6,60.9 -False,179.8,82.8 -True,168.4,86.5 -False,189.7,121.9 -True,185.5,63.3 -False,189.1,87.3 -True,171.3,64.0 -False,183.3,98.6 -True,183.3,101.7 -False,199.7,99.1 -True,145.0,86.5 -False,167.0,80.9 -True,193.3,86.8 -False,192.3,73.5 -True,176.7,72.0 -False,164.0,73.5 -True,156.7,64.0 -False,170.5,87.0 -True,151.7,79.4 -False,174.2,102.7 -True,175.8,52.5 -False,172.0,92.6 -True,169.3,48.5 -False,190.2,92.7 -True,143.8,49.5 -False,180.9,107.0 -True,158.4,65.4 -False,180.3,70.6 -True,151.7,68.9 -False,159.9,91.1 -True,162.6,69.5 -False,202.6,123.3 -True,158.6,107.0 -False,178.1,104.7 -True,141.8,74.0 -False,167.7,98.8 -True,150.5,63.8 -False,159.2,82.9 -True,176.5,61.4 -False,173.4,78.3 -True,166.2,87.3 -False,170.5,82.9 -True,174.1,80.4 -False,177.9,80.2 -True,154.7,76.1 -False,190.9,46.9 -True,157.3,80.1 -False,181.5,90.9 -True,158.8,35.5 -False,185.6,144.0 -True,181.2,50.5 -False,158.6,37.0 -True,166.8,86.9 -False,166.6,94.9 -True,151.4,90.6 -False,176.5,73.8 -True,133.7,100.1 -False,194.7,45.2 -True,168.6,53.2 -False,182.0,92.3 -True,169.0,99.8 -False,188.7,79.4 -True,164.3,79.7 -False,190.1,105.1 -True,163.2,94.3 -False,191.2,101.3 -True,161.5,52.7 -False,198.4,132.2 -True,164.7,102.4 -False,181.8,80.9 -True,152.7,45.1 -False,178.8,119.6 -True,159.7,59.0 -False,179.4,133.5 -True,147.5,81.0 -False,166.6,69.1 -True,172.9,52.0 -False,186.1,48.9 -True,184.3,105.7 -False,173.7,82.4 -True,176.7,56.6 -False,189.7,55.2 -True,178.2,40.2 -False,194.9,91.1 -True,155.8,84.7 -False,177.6,64.6 -True,165.1,45.0 -False,182.4,104.1 -True,169.5,111.2 -False,185.4,84.8 -True,146.0,73.6 -False,167.6,84.9 -True,175.0,73.9 -False,205.9,100.3 -True,180.0,61.6 -False,190.9,128.5 -True,165.3,48.1 -False,195.5,54.9 -True,176.1,69.8 -False,181.3,130.0 -True,155.1,103.5 -False,176.4,106.4 -True,147.3,99.3 -False,190.2,35.1 -True,169.1,48.4 -False,172.9,86.2 -True,181.9,72.9 -False,181.6,139.9 -True,163.9,67.0 -False,176.0,54.3 -True,165.0,73.8 -False,184.5,104.3 -True,153.5,43.1 -False,183.6,48.7 -True,176.3,60.2 -False,193.2,134.6 -True,177.8,56.9 -False,186.8,81.4 -True,152.8,43.0 -False,193.3,90.2 -True,165.1,50.7 -False,187.3,130.2 -True,169.0,55.1 -False,162.0,92.3 -True,154.7,46.4 -False,170.0,111.7 -True,150.4,71.7 -False,186.7,21.1 -True,177.4,75.0 -False,169.0,76.7 -True,160.9,43.5 -False,176.3,59.0 -True,167.2,61.6 -False,167.2,80.8 -True,168.7,59.3 -False,174.8,88.6 -True,157.9,96.8 -False,194.9,101.2 -True,183.4,63.3 -False,168.0,104.7 -True,155.1,67.6 -False,189.7,76.7 -True,151.1,92.5 -False,177.8,112.7 -True,171.3,79.5 -False,201.3,49.7 -True,176.4,85.1 -False,171.8,59.8 -True,177.0,79.0 -False,192.5,128.5 -True,154.5,60.6 -False,175.9,85.7 -True,188.1,70.2 -False,194.0,40.1 -True,173.2,120.3 -False,178.2,111.1 -True,156.8,51.8 -False,168.1,26.4 -True,175.9,55.8 -False,174.0,102.0 -True,162.8,84.7 -False,181.3,103.3 -True,172.6,54.1 -False,177.1,66.0 -True,168.2,51.8 -False,176.1,76.9 -True,181.9,77.0 -False,161.5,68.6 -True,173.2,108.0 -False,163.1,73.8 -True,161.2,90.9 -False,166.5,61.0 -True,165.6,59.2 -False,183.9,59.3 -True,161.5,106.1 -False,167.5,94.9 -True,155.0,76.3 -False,170.0,86.9 -True,184.0,56.5 -False,180.5,59.7 -True,157.5,62.7 -False,188.5,74.4 -True,167.9,69.8 -False,185.0,64.1 -True,169.5,85.3 -False,181.7,117.5 -True,160.2,55.0 -False,180.4,94.5 -True,156.6,82.9 -False,170.6,94.1 -True,159.3,38.4 -False,170.7,87.4 -True,184.2,63.6 -False,177.6,74.4 -True,163.5,59.8 -False,169.0,48.1 -True,154.5,93.1 -False,180.5,107.3 -True,161.6,70.5 -False,174.7,124.0 -True,169.7,95.8 -False,173.8,51.5 -True,162.5,91.0 -False,201.6,57.2 -True,166.6,94.4 -False,186.5,61.3 -True,177.3,81.8 -False,190.6,66.6 -True,180.0,38.6 -False,167.9,109.0 -True,148.0,59.4 -False,184.1,91.1 -True,157.0,73.8 -False,168.7,89.1 -True,159.8,73.8 -False,177.9,45.9 -True,150.0,64.0 -False,186.5,91.5 -True,184.2,61.0 -False,165.5,127.1 -True,165.8,76.7 -False,171.7,130.8 -True,157.0,64.5 -False,198.2,45.4 -True,164.3,53.4 -False,167.0,66.1 -True,182.4,104.3 -False,190.2,107.5 -True,173.0,30.7 -False,167.9,118.0 -True,180.8,33.4 -False,152.0,99.8 -True,166.1,67.1 -False,190.2,112.4 -True,161.5,82.0 -False,186.5,77.2 -True,163.6,28.4 -False,158.3,82.5 -True,171.6,84.5 -False,174.4,114.5 -True,160.2,41.7 -False,179.6,100.4 -True,149.0,44.8 -False,177.3,58.9 -True,178.4,58.5 -False,176.7,108.7 -True,164.6,52.6 -False,182.9,89.3 -True,154.0,81.0 -False,188.1,68.9 -True,163.3,47.7 -False,161.2,59.7 -True,168.8,33.4 -False,179.9,92.5 -True,151.6,95.2 -False,203.0,127.1 -True,156.9,69.4 -False,187.4,77.6 -True,165.7,61.3 -False,179.2,71.1 -True,155.9,54.3 -False,180.0,76.7 -True,170.9,68.8 -False,188.5,65.2 -True,167.0,60.7 -False,182.9,30.8 -True,156.7,88.1 -False,190.0,43.7 -True,155.2,60.2 -False,179.3,86.4 -True,181.8,80.5 -False,188.7,62.8 -True,169.9,61.5 -False,192.1,105.6 -True,171.5,85.6 -False,190.0,92.5 -True,178.0,62.1 -False,196.3,88.1 -True,174.6,57.0 -False,165.4,41.9 -True,159.7,70.7 -False,167.5,103.8 -True,150.7,89.9 -False,166.0,94.7 -True,184.5,75.8 -False,195.6,151.3 -True,165.4,49.5 -False,180.5,100.8 -True,169.4,52.9 -False,186.3,91.5 -True,167.0,43.7 -False,189.7,89.8 -True,168.2,72.4 -False,187.6,41.6 -True,160.5,88.3 -False,190.0,97.8 -True,153.7,40.9 -False,193.8,102.9 -True,166.0,48.2 -False,183.5,97.7 -True,172.7,111.5 -False,183.8,46.5 -True,161.7,77.1 -False,174.2,119.6 -True,151.1,77.0 -False,192.1,109.3 -True,178.4,25.9 -False,171.5,70.9 -True,147.4,88.8 -False,179.0,123.2 -True,146.8,49.5 -False,171.5,110.9 -True,167.9,55.5 -False,192.1,80.1 -True,144.0,73.7 -False,165.8,86.0 -True,177.5,63.6 -False,190.4,51.1 -True,166.1,60.3 -False,183.4,109.7 -True,181.4,65.9 -False,194.6,112.6 -True,133.8,83.2 -False,158.0,83.4 -True,189.6,34.7 -False,186.7,114.6 -True,163.3,116.0 -False,173.2,95.0 -True,180.9,74.6 -False,195.4,64.1 -True,177.4,47.2 -False,174.5,123.5 -True,156.3,77.3 -False,186.1,99.6 -True,168.4,67.4 -False,174.5,29.6 -True,153.4,110.9 -False,169.1,66.2 -True,179.9,74.0 -False,170.5,75.4 -True,159.7,81.6 -False,168.3,52.5 -True,162.1,35.4 -False,191.3,47.9 -True,165.5,90.0 -False,181.4,81.3 -True,170.9,64.7 -False,190.5,88.1 -True,175.3,60.5 -False,184.5,44.0 -True,147.3,37.0 -False,181.7,115.4 -True,171.1,42.1 -False,176.6,62.7 -True,166.6,80.8 -False,174.9,107.0 -True,157.3,57.7 -False,176.1,93.2 -True,157.5,49.5 -False,186.9,156.4 -True,154.3,59.7 -False,176.7,111.1 -True,157.1,68.0 -False,164.2,103.9 -True,158.4,60.3 -False,215.2,162.2 -True,164.8,64.3 -False,190.4,89.6 -True,179.3,82.3 -False,163.4,80.2 -True,158.7,65.8 -False,183.2,28.6 -True,155.8,76.8 -False,197.6,36.9 -True,164.0,56.2 -False,180.5,35.3 -True,152.8,70.0 +True,182.4,83.1 +False,175.9,75.7 +True,165.6,31.9 +False,197.2,130.6 +True,173.4,99.0 +False,188.3,113.0 +True,160.6,61.3 +False,191.1,84.8 +True,167.6,66.6 +False,175.1,114.4 +True,154.6,98.1 +False,187.4,87.4 +True,168.8,87.4 +False,188.5,67.5 +True,163.7,64.0 +False,170.1,120.0 +True,174.3,55.2 +False,170.7,61.4 +True,169.9,88.6 +False,181.3,89.7 +True,165.6,57.4 +False,198.3,44.8 +True,166.2,65.9 +False,164.4,81.3 +True,167.8,63.7 +False,173.6,83.5 +True,180.2,75.3 +False,186.9,143.1 +True,156.7,66.5 +False,196.1,105.2 +True,176.8,62.6 +False,198.7,98.8 +True,149.8,50.6 +False,168.1,80.2 +True,160.5,31.1 +False,173.7,85.2 +True,184.5,75.5 +False,188.6,67.1 +True,153.5,78.2 +False,174.9,87.1 +True,162.0,92.3 +False,170.1,53.3 +True,153.8,54.4 +False,172.6,74.4 +True,152.8,67.4 +False,188.0,96.5 +True,174.9,62.3 +False,186.1,68.7 +True,173.1,57.2 +False,172.6,79.1 +True,161.0,35.3 +False,181.3,118.0 +True,168.5,66.2 +False,179.3,107.4 +True,156.0,58.9 +False,185.6,122.8 +True,191.4,51.2 +False,179.0,132.7 +True,179.1,56.8 +False,173.9,105.1 +True,160.2,96.4 +False,194.5,60.7 +True,170.9,74.3 +False,157.6,42.1 +True,174.6,83.4 +False,170.5,67.5 +True,175.4,87.3 +False,177.5,81.5 +True,172.7,112.5 +False,202.8,111.3 +True,177.5,96.8 +False,174.4,75.5 +True,153.4,94.3 +False,176.2,92.2 +True,165.3,85.1 +False,166.4,68.8 +True,177.0,95.5 +False,177.9,132.8 +True,175.6,82.1 +False,167.2,90.0 +True,175.0,73.1 +False,166.3,45.4 +True,161.7,54.4 +False,186.4,97.0 +True,161.5,55.4 +False,173.8,86.2 +True,150.2,90.3 +False,171.2,40.0 +True,154.0,38.9 +False,200.4,66.8 +True,161.8,16.1 +False,180.1,84.8 +True,173.3,85.9 +False,180.7,94.0 +True,174.0,82.8 +False,166.2,88.7 +True,177.8,66.6 +False,193.2,84.2 +True,156.6,47.2 +False,182.7,43.5 +True,136.3,89.3 +False,175.2,76.9 +True,151.4,82.4 +False,185.7,121.5 +True,149.4,86.2 +False,185.8,86.2 +True,165.4,78.4 +False,169.1,79.3 +True,152.2,67.5 +False,178.6,140.3 +True,164.7,35.1 +False,188.7,80.0 +True,173.7,48.4 +False,184.1,52.6 +True,166.9,69.4 +False,178.5,108.7 +True,159.1,76.6 +False,176.8,86.5 +True,190.3,67.3 +False,175.3,88.0 +True,172.9,81.6 +False,187.0,121.7 +True,169.3,59.2 +False,177.8,52.1 +True,162.5,53.8 +False,188.4,99.9 +True,169.0,103.0 +False,170.1,84.0 +True,157.5,88.2 +False,160.7,124.5 +True,147.9,67.1 +False,184.1,100.2 +True,160.7,95.0 +False,174.6,70.1 +True,165.3,121.5 +False,164.4,66.2 +True,172.5,59.7 +False,177.4,85.0 +True,172.9,52.3 +False,195.6,121.0 +True,163.7,48.5 +False,191.8,82.6 +True,171.8,31.4 +False,172.3,99.3 +True,193.5,80.3 +False,178.5,75.5 +True,159.1,87.9 +False,163.8,85.2 +True,161.5,112.4 +False,165.5,93.8 +True,168.7,7.7 +False,184.2,106.9 +True,162.6,97.7 +False,188.1,83.7 +True,161.2,73.8 +False,170.0,64.2 +True,163.4,81.2 +False,186.9,109.6 +True,165.3,62.1 +False,168.0,76.4 +True,158.2,82.2 +False,187.9,97.6 +True,165.6,73.4 +False,149.9,114.5 +True,157.9,66.3 +False,177.6,54.1 +True,156.2,101.1 +False,171.9,90.7 +True,182.6,63.9 +False,163.7,21.5 +True,170.1,90.6 +False,177.9,91.3 +True,154.3,68.9 +False,179.2,56.6 +True,157.6,47.3 +False,186.5,80.9 +True,146.8,106.2 +False,158.9,87.5 +True,180.9,100.8 +False,177.4,79.4 +True,152.0,57.7 +False,174.8,71.6 +True,156.9,70.5 +False,167.9,96.0 +True,170.5,47.8 +False,168.6,49.0 +True,138.1,92.1 +False,169.8,124.2 +True,174.4,54.0 +False,179.3,96.4 +True,149.6,70.3 +False,157.7,84.2 +True,165.6,59.0 +False,180.4,100.9 +True,158.5,76.6 +False,175.4,101.9 +True,148.8,57.2 +False,185.6,73.1 +True,158.0,67.1 +False,194.1,131.9 +True,158.6,52.2 +False,196.7,61.3 +True,154.2,91.4 +False,174.1,79.5 +True,154.2,86.2 +False,161.9,115.1 +True,179.8,98.2 +False,180.2,123.6 +True,163.1,64.8 +False,200.5,65.0 +True,155.6,61.8 +False,164.9,111.2 +True,176.4,88.3 +False,178.8,68.3 +True,169.5,69.0 +False,176.3,85.4 +True,158.1,64.1 +False,171.0,49.3 +True,164.9,76.4 +False,167.6,53.1 +True,156.2,80.3 +False,184.0,74.3 +True,184.9,89.3 +False,178.6,84.7 +True,162.1,83.3 +False,162.6,86.0 +True,168.5,36.4 +False,179.4,60.7 +True,167.7,77.5 +False,170.9,92.7 +True,171.9,88.3 +False,180.6,79.0 +True,163.9,67.5 +False,182.8,90.6 +True,175.2,43.5 +False,195.2,62.4 +True,178.6,60.2 +False,184.9,84.4 +True,159.0,73.5 +False,180.1,64.6 +True,163.7,63.3 +False,175.5,130.3 +True,174.2,110.8 +False,193.4,75.6 +True,164.3,67.4 +False,178.3,112.4 +True,174.1,67.7 +False,193.4,42.6 +True,179.1,95.1 +False,144.2,104.7 +True,176.3,57.1 +False,182.1,110.2 +True,167.7,103.8 +False,181.5,64.5 +True,176.6,60.0 +False,181.2,100.0 +True,148.1,87.7 +False,179.1,60.2 +True,166.3,53.7 +False,184.2,129.2 +True,156.8,45.8 +False,175.4,50.9 +True,161.4,69.3 +False,187.8,45.3 +True,158.6,91.5 +False,188.0,50.3 +True,179.8,43.3 +False,188.5,86.2 +True,172.0,73.8 +False,159.5,129.4 +True,145.2,81.3 +False,181.0,113.2 +True,190.2,56.7 +False,187.4,70.6 +True,170.9,82.8 +False,193.2,102.9 +True,145.5,62.6 +False,177.7,114.6 +True,175.3,75.2 +False,184.3,65.6 +True,142.6,67.3 +False,179.8,74.1 +True,159.0,69.5 +False,155.6,37.4 +True,158.6,68.2 +False,179.2,80.3 +True,175.8,81.5 +False,187.0,84.9 +True,162.5,73.1 +False,191.6,83.3 +True,168.6,59.2 +False,151.8,76.3 +True,184.9,63.7 +False,183.5,63.3 +True,167.0,42.3 +False,187.8,73.5 +True,169.6,81.3 +False,191.4,92.5 +True,163.6,76.6 +False,179.7,56.1 +True,166.9,40.2 +False,183.5,79.9 +True,171.7,89.7 +False,180.3,140.3 +True,191.7,79.0 +False,188.2,72.2 +True,157.0,100.5 +False,184.4,89.7 +True,165.4,79.2 +False,162.4,64.2 +True,167.6,68.5 +False,160.6,109.4 +True,167.1,70.3 +False,167.0,46.0 +True,175.3,87.9 +False,187.7,33.9 +True,161.7,89.9 +False,188.2,96.1 +True,164.8,93.7 +False,178.6,42.7 +True,156.4,96.5 +False,182.5,84.6 +True,183.5,19.7 +False,161.5,110.0 +True,178.5,78.6 +False,170.4,106.5 +True,168.1,67.0 +False,185.8,76.9 +True,161.6,41.7 +False,185.4,52.6 +True,140.4,68.2 +False,171.2,53.2 +True,167.9,81.1 +False,186.2,137.0 +True,150.7,51.9 +False,176.6,46.4 +True,165.9,79.5 +False,180.8,102.6 +True,182.6,41.8 +False,180.5,116.1 +True,169.8,65.0 +False,159.1,113.2 +True,146.2,59.6 +False,180.5,75.9 +True,171.1,95.0 +False,164.8,84.5 +True,156.3,103.9 +False,167.5,69.1 +True,152.8,75.2 +False,184.7,98.3 +True,168.4,74.2 +False,163.3,117.7 +True,163.7,53.3 +False,170.7,53.3 +True,171.2,46.7 +False,179.3,142.4 +True,167.1,54.5 +False,180.9,63.7 +True,174.0,70.2 +False,179.6,71.3 +True,147.0,56.5 +False,174.9,57.4 +True,178.0,65.1 +False,170.4,72.0 +True,166.1,57.4 +False,174.4,52.9 +True,170.0,92.1 +False,168.7,55.4 +True,164.3,78.6 +False,178.9,154.8 +True,146.3,81.1 +False,168.8,72.4 +True,159.5,48.1 +False,173.1,64.1 +True,161.9,73.9 +False,181.4,55.6 +True,167.2,62.9 +False,191.9,77.0 +True,148.2,110.5 +False,183.5,72.0 +True,150.4,84.4 +False,172.8,90.6 +True,172.0,61.6 +False,170.2,52.2 +True,160.5,81.7 +False,178.3,81.0 +True,171.6,57.9 +False,200.7,92.8 +True,172.8,92.7 +False,183.5,90.2 +True,176.0,61.3 +False,172.4,94.6 +True,177.3,49.0 +False,166.5,65.8 +True,151.7,81.4 +False,171.9,96.8 +True,164.4,80.2 +False,200.0,106.2 +True,153.4,66.7 +False,183.4,70.5 +True,184.4,108.8 +False,181.9,48.5 +True,152.1,59.2 +False,154.9,104.8 +True,164.9,76.3 +False,184.9,77.4 +True,151.0,77.2 +False,214.6,98.3 +True,160.5,87.3 +False,190.2,83.0 +True,172.6,61.9 +False,181.5,84.1 +True,181.7,68.5 +False,185.0,35.3 +True,146.5,74.2 +False,178.9,97.7 +True,157.5,91.3 +False,182.6,116.5 +True,165.6,103.5 +False,185.8,94.7 +True,177.1,80.7 +False,175.4,79.8 +True,152.1,56.1 +False,178.9,75.5 +True,166.9,74.4 +False,171.9,76.4 +True,155.5,73.9 +False,192.6,61.3 +True,165.2,59.9 +False,182.5,61.9 +True,179.7,87.4 +False,173.4,63.7 +True,169.4,53.2 +False,187.7,112.0 +True,151.7,53.7 +False,169.7,90.4 +True,182.9,31.7 +False,180.8,67.4 +True,193.1,45.7 +False,169.5,97.7 +True,158.5,77.3 +False,186.4,37.4 +True,163.6,50.9 +False,171.5,133.6 +True,152.5,81.5 +False,198.3,37.2 +True,170.3,59.0 +False,185.8,88.2 +True,168.2,65.7 +False,175.0,52.1 +True,167.8,17.6 +False,175.8,68.8 +True,178.9,42.9 +False,185.9,61.6 +True,163.1,71.9 +False,182.6,153.5 +True,148.7,81.1 +False,175.8,100.0 +True,166.9,81.0 +False,179.2,109.7 +True,149.8,86.3 +False,188.0,58.8 +True,171.0,65.1 +False,186.9,74.3 +True,176.4,61.0 +False,197.3,69.8 +True,170.5,103.2 +False,181.0,58.9 +True,168.4,87.8 +False,183.4,60.6 +True,168.2,100.9 +False,177.3,40.3 +True,178.3,110.6 +False,191.3,88.4 +True,147.8,58.7 +False,189.8,71.9 +True,168.6,75.3 +False,173.5,84.5 +True,166.7,61.1 +False,168.0,78.3 +True,157.2,85.6 +False,183.9,67.9 +True,159.4,91.0 +False,176.1,58.4 +True,159.4,51.8 +False,181.2,92.8 +True,178.4,43.3 +False,202.6,86.9 +True,161.1,63.9 +False,172.5,113.4 +True,157.8,88.8 +False,169.5,76.9 +True,149.1,53.6 +False,161.8,84.1 +True,167.7,38.3 +False,193.8,124.4 +True,176.7,59.9 +False,167.6,91.2 +True,170.0,47.7 +False,167.2,78.0 +True,176.1,35.7 +False,182.1,141.1 +True,149.9,70.5 +False,172.8,41.3 +True,167.7,72.1 +False,166.8,87.9 +True,158.0,75.5 +False,183.4,83.9 +True,155.8,53.9 +False,165.2,66.8 +True,174.9,68.7 +False,175.0,108.9 +True,171.7,93.5 +False,182.8,85.5 +True,168.9,53.6 +False,167.7,64.2 +True,175.6,54.6 +False,168.8,73.7 +True,170.7,92.8 +False,180.1,92.2 +True,171.6,76.3 +False,181.4,50.0 +True,149.4,72.4 +False,182.9,95.7 +True,165.7,87.7 +False,159.0,75.4 +True,175.0,60.5 +False,150.6,80.0 +True,150.2,59.6 +False,189.2,65.8 +True,182.4,107.6 +False,198.8,73.1 +True,167.2,59.1 +False,164.5,84.2 +True,171.1,77.3 +False,168.1,122.5 +True,155.1,83.0 +False,176.0,104.5 +True,172.0,82.1 +False,181.6,65.3 +True,185.0,79.4 +False,178.5,97.6 +True,177.4,39.1 +False,179.7,154.6 +True,162.0,81.5 +False,177.4,86.9 +True,181.5,110.4 +False,169.4,112.3 +True,157.8,92.5 +False,169.0,42.3 +True,172.9,71.5 +False,194.3,111.8 +True,163.5,59.6 +False,190.4,104.6 +True,172.4,50.6 +False,151.2,63.1 +True,155.3,46.7 +False,181.3,51.2 +True,161.5,35.6 +False,179.5,102.9 +True,167.0,79.4 +False,178.3,100.2 +True,162.1,75.2 +False,181.6,95.8 +True,178.9,87.3 +False,174.7,80.0 +True,150.7,93.5 +False,175.6,62.1 +True,179.7,63.6 +False,186.0,110.1 +True,162.8,50.4 +False,179.2,58.4 +True,168.1,51.0 +False,180.6,103.2 +True,155.9,41.9 +False,170.2,80.8 +True,156.9,78.5 +False,177.5,87.3 +True,158.0,82.3 +False,192.0,70.1 +True,158.5,96.8 +False,163.1,42.6 +True,149.6,62.9 +False,175.5,61.9 +True,161.9,60.9 +False,202.5,126.1 +True,157.6,64.1 +False,184.0,103.1 +True,146.0,46.5 +False,176.0,42.8 +True,168.7,78.1 +False,190.8,43.9 +True,163.5,70.8 +False,167.7,82.0 +True,172.9,56.0 +False,161.7,137.4 +True,150.9,67.7 +False,164.3,107.9 +True,182.1,62.5 +False,194.8,49.2 +True,190.7,94.6 +False,177.4,108.7 +True,176.4,68.6 +False,176.0,35.6 +True,157.7,72.0 +False,189.0,98.3 +True,153.7,69.8 +False,175.7,71.1 +True,164.3,74.3 +False,177.1,68.2 +True,163.8,54.8 +False,166.9,80.7 +True,163.1,54.3 +False,182.7,107.7 +True,149.3,78.1 +False,182.8,48.6 +True,163.9,77.6 +False,187.8,73.6 +True,163.6,96.6 +False,178.7,118.7 +True,170.4,104.3 +False,174.8,75.2 +True,155.2,60.3 +False,170.1,59.9 +True,147.0,79.8 +False,161.1,80.2 +True,151.2,58.7 +False,188.6,86.4 +True,185.6,84.3 +False,185.2,73.2 +True,177.8,36.0 +False,183.0,89.4 +True,142.7,52.1 +False,176.9,98.2 +True,173.4,73.7 +False,160.3,134.5 +True,167.5,85.8 +False,168.6,76.2 +True,186.0,38.9 +False,193.1,81.8 +True,192.3,76.2 +False,182.3,81.3 +True,158.2,56.4 +False,189.9,99.6 +True,169.3,40.7 +False,169.6,150.4 +True,157.9,76.2 +False,189.5,103.5 +True,164.8,63.7 +False,195.2,76.2 +True,180.2,60.4 +False,177.2,38.0 +True,159.0,58.5 +False,176.0,111.9 +True,163.0,62.6 +False,200.2,87.8 +True,143.1,93.4 +False,190.5,85.5 +True,177.6,105.0 +False,164.5,126.0 +True,157.0,62.9 +False,189.7,68.9 +True,166.2,85.5 +False,193.2,86.2 +True,180.7,85.9 +False,182.8,58.2 +True,161.8,97.8 +False,175.5,56.4 +True,166.1,103.3 +False,189.9,68.6 +True,165.8,51.8 +False,187.8,72.9 +True,159.3,94.5 +False,191.7,114.8 +True,145.8,66.9 +False,167.8,105.9 +True,150.1,58.0 +False,188.4,69.9 +True,160.5,53.6 +False,189.9,71.9 +True,143.6,64.4 +False,185.3,59.5 +True,166.1,73.4 +False,184.5,129.1 +True,157.3,51.3 +False,177.3,82.1 +True,154.6,99.3 +False,178.7,72.8 +True,177.5,69.2 +False,164.4,91.5 +True,170.9,94.1 +False,190.7,116.2 +True,177.9,75.1 +False,174.5,103.0 +True,156.4,111.6 +False,171.5,105.4 +True,160.3,98.4 +False,174.0,101.1 +True,164.6,33.3 +False,176.2,81.6 +True,167.6,55.2 +False,198.9,44.9 +True,161.1,68.7 +False,181.0,82.6 +True,156.8,89.8 +False,180.0,87.4 +True,173.1,60.2 +False,175.8,86.7 +True,168.1,100.1 +False,171.3,49.1 +True,159.0,84.4 +False,178.4,69.2 +True,164.0,75.0 +False,190.8,48.4 +True,153.3,65.3 +False,191.4,86.4 +True,167.6,86.6 +False,167.3,114.0 +True,179.5,109.2 +False,187.5,83.2 +True,154.4,70.9 +False,175.0,79.8 +True,159.1,99.6 +False,196.2,88.9 +True,160.0,91.6 +False,189.9,106.4 +True,172.8,63.7 +False,188.8,83.7 +True,157.3,112.2 +False,187.2,61.1 +True,156.6,73.9 +False,187.7,71.9 +True,163.0,64.1 +False,182.9,60.5 +True,177.1,74.5 +False,188.9,42.9 +True,153.2,80.9 +False,180.2,65.2 +True,139.8,99.5 +False,153.2,72.6 +True,159.4,85.0 +False,188.8,88.3 +True,171.2,92.3 +False,179.4,33.2 +True,164.4,90.8 +False,173.9,127.1 +True,170.5,63.6 +False,152.8,101.9 +True,171.7,71.1 +False,176.0,46.2 +True,172.3,34.0 +False,171.7,96.1 +True,164.0,49.8 +False,190.3,72.1 +True,179.6,83.2 +False,199.4,68.2 +True,148.9,97.4 +False,199.7,126.5 +True,161.5,94.9 +False,186.9,96.9 +True,169.4,91.0 +False,195.4,69.1 +True,175.3,65.3 +False,182.8,93.1 +True,167.0,59.9 +False,157.4,87.6 +True,174.0,101.1 +False,202.8,85.5 +True,167.7,48.6 +False,165.8,83.5 +True,167.6,47.3 +False,205.7,155.7 +True,169.7,80.9 +False,179.8,38.2 +True,174.2,65.6 +False,173.2,128.0 +True,172.1,62.4 +False,184.2,42.9 +True,168.1,104.7 +False,167.1,69.6 +True,163.4,67.5 +False,171.4,102.2 +True,163.0,50.9 +False,195.3,111.3 +True,152.8,61.2 +False,184.4,144.1 +True,157.5,48.0 +False,169.5,99.1 +True,151.6,102.6 +False,168.5,45.7 +True,153.6,91.3 +False,178.4,89.3 +True,168.9,60.4 +False,166.9,65.4 +True,160.3,45.4 +False,169.8,107.9 +True,151.4,100.7 +False,176.5,106.7 +True,169.6,88.0 +False,177.3,57.2 +True,171.3,65.9 +False,175.0,94.8 +True,137.1,62.2 +False,192.9,70.9 +True,149.1,97.5 +False,200.4,77.8 +True,158.3,72.4 +False,175.1,47.3 +True,169.1,47.2 +False,167.8,77.3 +True,168.4,65.2 +False,178.3,151.2 +True,149.4,79.8 +False,176.0,57.3 +True,178.1,63.1 +False,166.5,74.1 +True,154.3,95.5 +False,191.6,39.3 +True,169.1,97.5 +False,184.5,108.3 +True,154.6,44.3 +False,182.5,122.2 +True,176.9,88.4 +False,186.9,133.4 +True,180.9,62.8 +False,169.8,77.5 +True,166.0,105.4 +False,172.1,63.9 +True,177.0,70.7 +False,187.1,73.8 +True,166.5,66.6 +False,187.5,92.0 +True,182.0,77.1 +False,168.4,112.0 +True,179.9,82.8 +False,182.2,86.5 +True,191.9,57.4 +False,173.0,115.3 +True,185.6,76.6 +False,181.3,106.5 +True,174.7,78.2 +False,171.1,57.6 +True,181.3,50.7 diff --git a/Material/wise_24_25/v8.ipynb b/Material/wise_24_25/v8.ipynb new file mode 100644 index 0000000..927af8f --- /dev/null +++ b/Material/wise_24_25/v8.ipynb @@ -0,0 +1,775 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "737a2a07-5247-42d5-b06a-ff6a3bf21306", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import folium" + ] + }, + { + "cell_type": "markdown", + "id": "dc86d298-4a92-41e2-8e9a-bcb2dd36d80f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "[LatLon](https://www.latlong.net/)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "dbed7e42-d95e-4b25-91b8-248cd95f22ea", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = folium.Map(\n", + " location=(52.264150, 10.526420),\n", + " tiles='OpenStreetMap',\n", + " #iles='Stamen Toner',\n", + " zoom_start=13,\n", + " prefer_canvas=False\n", + " )\n", + "\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2b08e2f2-cf81-439b-b47f-733e123d72d3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_marker = folium.Marker(\n", + " location=(52.25802230834961, 10.503097534179688)\n", + " )\n", + "\n", + "my_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "71dbf9b1-2217-40d5-bfa1-c38b2bbc6362", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "castle_popup = \"Ritterbrunnen 1, 38100 Braunschweig\"\n", + "castle_tooltip = \"More about the castle\"\n", + "\n", + "\n", + "castle_marker = folium.Marker(\n", + " location=(52.2643, 10.529),\n", + " popup=castle_popup,\n", + " tooltip=castle_tooltip\n", + " )\n", + "castle_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "60c6bc4b-c339-4cd5-806d-66c879807b6e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "hbk_popup_html = folium.Popup(\n", + " '''\n", + "

\n", + " \"HBK\n", + "

\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de

\n", + " ''',\n", + " show=False\n", + " )\n", + "\n", + "hbk_tooltip = \"More about the university\"" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "19123dd9-c88d-4713-ab84-4cc692e3f663", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "hbk_icon = folium.Icon(\n", + " color='black',\n", + " icon_color='#deddda',\n", + " prefix='glyphicon',\n", + " icon='glyphicon-home',\n", + " angle=0\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7f496828-e49a-4253-98e3-aaa59d0cdc1c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hbk_marker = folium.Marker(\n", + " location=(52.257770, 10.502490),\n", + " popup=hbk_popup_html,\n", + " tooltip=hbk_tooltip,\n", + " icon=hbk_icon\n", + " )\n", + "\n", + "hbk_marker.add_to(m)\n", + "\n", + "m" + ] + }, + { + "cell_type": "markdown", + "id": "78002786-20fb-4b92-be9a-b92b678f50ff", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "

\n", + " \"HBK\n", + "

Johannes-Selenka-Platz 1

\n", + "

38118 Braunschweig

\n", + "

Germany, DE

\n", + "

Visit: hbk-bs.de\n", + "

" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0547528b-b97f-4e94-aab4-d1d069be020c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def popup_factory(adr: str, zipc: str, country: str, pic: str):\n", + " html = '''\n", + "

\n", + "

{}

\n", + "

{}

\n", + "

{}

\n", + " '''.format(pic, adr, zipc, country)\n", + " return folium.Popup(html)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2ec42850-9c12-4fb4-8b94-024bca9ad9b1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def icon_factory(is_public=True):\n", + " icon = folium.Icon(\n", + " color='black' if is_public else 'white',\n", + " icon_color = 'white' if is_public else 'black',\n", + " icon='glyphicon-home'\n", + " )\n", + " return icon" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b3d3d3c8-0039-4f80-9f08-5de61172b588", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def marker_factory(loc, popup, is_public=True):\n", + " std_tooltip = 'Click for more information'\n", + " std_icon = icon_factory(is_public)\n", + " return folium.Marker(loc, popup=popup, icon=std_icon, tooltip=std_tooltip)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18701dc2-4409-486d-ad7d-7bde6fc3273e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Timetable.pdf b/Timetable.pdf index 952369169eb36e97ce123719afbda7d669aa75f1..a68151520bd73ab26b12ff3a2875a637ca748038 100644 GIT binary patch delta 3006 zcmZXWcRUpSAIF_@84)F%@#Sn6&Mn-@9*JzCPFD81WMn%lE9K72_6;c`n{3IrL}z85 zWE9FMLUQq|?_a-1eg67n-DqrdMhyc`M%L&D@qOOhfOD2tqyud{={`z^e-gTFIGN?QpD zN1_o(1PXybV^EkhX#g+F|2>TW_5?UmRTbjr|10Os5|r0xJ@-IN@?3azHF-57Su`?I zy-p_JtpTOJDsvy$-c*|5A|85Thtu$7hEzz_KW_39d|W{$0~sV?4C!MMC4|#5HMF8$ zt0o*mt}DEQ7Vv^@&>dH)<18F8#)RYb3>xh(zG?h=q08?0Fy(_&%pT~*X9 zT8cn83NhW7f$f~wb;WG7FnyLDclXDGoNg=9berWE*oZ z1G4|Xh8HD#fIN$3VIz=H9J?b|H$v5at&Nt9> zDIn@X>PC#C4Zx((>;!?kr&f5Sx<|Muzu`yTUL$v+59KSFMhw0r z7{!sid8C=p!Tz;8OP?f}bpNa;FnM!Y_*QqInY8k)Zex45k3{fXZ*JF(h{VGe98^N1 zgbByb!ZY?3bgmD|7>NuKKkF)maC|=DZM5$mJi|%DJNpBmsTEMx^T~$xUn^y>r%_(d zb;+%zi!%1lfHIp2vTA^7>yRa`V4m)rACVB1krH%dE%n`du6yY~_)2X@-Tt}rO2*T( zRUHR$rF)51YyU8Mu-k64N*^2_RuK#Y<}sehhVHXD0IA+Ax&*qlQ*@`)V zrwe9i$^H{BX5+Z&cN%x>dkkJy5gZmWW08#E@PeWZV`fB^hjt9Nd(nP zEl2WjNv)NPJgtRJEIkrdDrqRHpy-d6uobw~1V4CaSmZ9PI*E5MdCz+gx&EleNo~qY zO(AB0lCCz;bu7r6OV3Xb60&-KWE7C2>u5WEe{?3J-O0M=ZPkdRgLU(U(HB9vJjVbD zD0x#HL=pqtFZpuJ1P!}A;XZn3YUZ?=|C)*Krnkx3RA1F1$x4aHJvz{TZR(N1kKAG- ze{mhySh*RqRjWosb>On0!X-4aN*3F*!C-i;xJ~@-n`qA35U>ED3Rnqg8DG0?o;zHS zb)+G|PMdwFf*!Ft``9n{r}FGr9L<|;p;mA8_*LR@eo(VL+e6cczRx?!2bP_6)7nBl z!@HDSBgf$3?;-h;Euj(JDCvAJ-B$Vv%-21wuqZ&+I1kuojOY0a9`Gg)B}wZ7DV)=w zO?bAl)4tT*#6h00r#XJ1=Cua8QD5k54NmCJIYY;ki_D+$wd+VSxt+#0_yzi^oG<0e z&SZ)NZl_OV$3;97d3QK;z_yx>tWUnaDVWn?Sx7q4^P30TDL$S{B7Mvnut2JqziSL9 zq#j;d2_ixmi@pLX`(*(=IxKFD?8}Nd~pRQ{1qs z%!O#^D3N=ag+r#UkraX?Ml&UJ%3YbLyK?3RrEH_Eb3S?CyineEQcOolDcs8qc~dNpa9TyO z)72635^HU`aN`r&XcM#dRl&A@IxAr-6lk=~-NemVV)(Mmn#x-%5ia?RS$BC`s>uln zynkONsKUrmhkIT&!w1(`*fy;v9anj?2KJ3G14~j@Id3zlinGhrJO@wc*b7f_0!n)t zB^RGLXcc43=V@Vsz2a4VpPC0~;R#@GD=F_b7j+pogj!6>7D_P;GR-_6E$FZ^%Ltyrk?0Du$36W=~!X~sCUVaMm5WMS6u(@D6xnM`!^XXPzOzj6& zoNav@6dWc$DARdT(7$};Y^NVoxn;3wh>2&qc~HivF-aktF)O;! z{Ap`|C2g-k@i>IviHz2w?HymH?FkZ2)i7Q+;R^C_C|mz%?e2%2tC6J-sdBoJ|Hy8J zexkm)&ijk!zD5|D6(8!h?pYx<2by%1j4L^8#ORspGqd%lF z{jQ6wxHL3S;@fHyFSM=PY`v%=G@;*{7j!z~=lvGP*;Snz|X3*YqC zL+JwSWS`Mp=N`i~ALH~*QEIgWT!@gf`1_hZ_==WD!bTQsxz>dC-q z{095zT=CErt@s{;r{9+`+@huBB0K4eDiV2iUwUr8Y94GIzCXDX;$F9 z#z<=xlnl@C@Kwt*1mhgm;m`nD%TzE45C%ne=NLAY4R7JNp=Mhs@O`_j^pZv!I= z#iSPd%SRbQSz_nI)WJM8t6DemfMM$1;?z9{)#tTN0da+HgII&Ol#{*>XwtWrTF*Y8 zrSN|_Uf1bxzJT}4yswW7i!0m;8rb6Vi`dqor7z|LCeIs>O`2beAB?JeUIIwiu{ZT= zjkM=0q9)DOp`Bf!P30dNGI~wf69Iu;vU(SfW%<`U~m))js8nRDd%5mc}1*3n!TGgGYZQhF0NyI GiRFJh2x#vB delta 3050 zcmZXOc{mh`8pe$+*|H^BCxu3gSkkeH66oT|tOUYY2OvxP;Pj z%kb?9lsMiEg=Asm+u)yD@1Jnk`_TnEb#xf8JI5hEaTEx&N}PTuB=``qL5-T=ZWId@ z9e%`%%e4hXPR6zG@^WZ5HS{xh-UR7%zx8&giX73kl~cw!TKRAeF~2%8jOFUtZ)HB^ z2|b7IjLl?yc4Mh{(1Vcg&oE#wt{$$gvr@sP?X98)-c(DA`sj4);>qZ&Qjre&7~4pz zl@P0!m9GLx+E}B7;s#9M04o*)(h^n3ao-&@#KYO1TPDO0P=d&DqJ>CfHLVtDdJi4p ze*R=Uo!{iGLowt|ZfXbJp}&p!9hw&9t+DLF=ffwQ=mr#5Bs9UPKYf@DJZlV^yZi34 zGt4LB6Nd5RXefU(T5<<=Qt2cY9iAv5pzvQIqYva>b5q2Zt_!ml5WrqF$EYIbejNo; zl1qOZr$B$$*YLBoq@Qs*;^CN)Q2s3GSzGCIGT8t;D?7sA*mrk4{oCE39_kvYALQ+hF_V-bsZ&(1)bfc_jf}$#K;EzcV?ZK5 z15a4|;z}^1{i(NKj@k%wI7a~U5Y(B0lYwo{msyQj?L^oDdFDz=`LR;)QJvIfg16+I z@M*_;X3^&ayXz>db^{jc)qEvo#p3(DsjDGpuQ;` z;jF|gDn7>0h`Vnzf)0!T9bU&YUPx_AKq|h#L0xsrSYa*{sPhbi0nuONUSnK zI4tlZ-Ne@Jny|uNGu1-sk*Hxh3`H5&O*I)|c*aF}I*=kDg+eCD_skawiIx$ss)Cp> zr%y!8f1RcaixB#{xNXMM6L^&ZpD+uPY_ZY0~Jl4N`@Q}0xle=nQd!X!;cX6l{)|(sL|qrpYe(eEcrH7FRHd?`tsO*VAkJxuj1*= zU9`-EpihFK-`GBDCa>Po>~f#PcR6zDoXd;HrN7m_VYlVmj`=Gfc0w_vzEvN$Mqbrc zJXYm(BUJ30>&Gd#Or|IY_I^yZio_FTi}y6P#f(Y(l@DvrLJ>2gqH1KB6@RyoF^bV8 zopHNc=7yUz)vh1GnH%W41^}mPLG{@5pfm{{576rsFzRA6c=mydNqfEU^#N0OkqQml z)2M(bu3GCJbdr#(3H~c5v71A-X{L6hB_ruHp(nm7z|gA?BiGR5>+>>7mUns_ARk{Hh{Oo<+mn{A+Z>Dy@@}+=j-wB$9JCtyFLK}=;FDXtCH6QE_4r`-B1rDI{B5c|O&pf~D*$dy|yx?xz+ zj`Pfj$P$=VBv>;}{UzYZzlb^z7!&&CTz+vfk+TSevs+5DZ zBS?Pg%S_2&zVp;nTH<(&A?+Su{2`u@b)ZV2PqpZuFIbE=7F(kVtQiMNhnjv6yPKOn zP*ZhRHm=tbLrcEn{FFQJd2`Cj;?9dJrp;YH&hF-%(Ak#G35w?sAl0V`2I_In&p(7cb zS@A>>nOhCE@tCqXU;8!qYuCJJA+d8EESqAh6oS{=%HBAWy(NxHZxh?H z<1ez+bJ(#uUurhFYyq;XdhaW!a`$nBAw$4RV++&AGugeScW(#XWAAwT{M+~ju~QjW z!;va6SHQfcfsz`=rwmLMT%gX|5xx~fm{70PrjK18oVCgP_&(|N0!@*ln$ZY3<9ETs zdEsn>c7N|6!TEGv(KS!=A}i`Ox7;Vh*-v)nL3~EvDsrkoE$EpT1ADZ3wLqbtQMyyf zSWn+8TYF^|x10nUmD#b;<|kg(<8508a2RnxTrCBxK^D};<3OKMFc zK|N*M_`8uf)id;pzh;7|eU-=9Zs@z1m)zp#a{agaO{V0qD@K|$uV6!(S6s`Qg)37g zsHtbX@=kGZTtpRU;dMs2;`>_sU0gg7Kq@TTTMW2i?rYmvK(scl?@Irq#()K0&4Vb)b z#k$<#@;m%>o!W-jsbKwr8v#NIJbIb_p4DeIEr^STG|&Q+JMVK(`@?eA?eRoE3XL3+ zf7L_7GFPOu$(sM5^s7|Zi+=M|Wi)Ns7fbp`oOWF*Hl)_p2THUMknqrR3+os6g7Z0q z7nDJ7uB2Wb=E#kVDw7t%!SF$bA6KiGg6hcY>0pbGHIHO*+#-hhTdZc=b1mB4XXw10 zW2H*DUPFJXsLVPB&~HMwzpwgnsWc3Z6f}~>(qZix)A4l;nd0e`t1@KXy5*-40qyP$ zh_YA{VjndcWR zEM0#&>9}7rV=;btnz=UZ;yTRq&_Zhsks;-wHBw^V%a>oY4*ix~khi2Fp1qxQm&no# zHrQ$oo-4~jzEQit*+fsQ3+7CaTz!m*QKRTOOq#k2+VtcSuz28HJf@fu%qff%8?1NQ zl9QI{PzbirNqPhnTHWM%;$eWg49<=6!y$;9tCoE&l}0wZt`F|j1t-3%y0hT^@Nmm6 zzuw0(d2wgS8dD^aFduX<`dH6@&1<8w610X_25hnds44k%3$Tuu{lq4J-dr1X`X|2LJU+qzu2$%|IdQL tpoo9zU?|An4E@hi2sHe6GYknq|6UM=M8i=e6jtjb8p19mrERRk{vW^on{)sG