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plotting.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "- pylab enthält Plotting Funktionen und auch Arrays, die wir brauchen, um die Höhe des Balls in Abhängigkeit von der Zeit abzubilden.\n",
+    "- Arrays enthalten eine Liste von Zahlen, z.B. [0 1 2 3 4 5 6 7 8 9]\n",
+    "- linspace(min, max, n) erzeugt ein Array von n Zahlen zwischen min und max.\n",
+    "- um einen Plot zu erzeugen, braucht man ein eindimensionales Array für die x-Achse und eins für die y-Achse.\n",
+    "- dann wird für jedes Paar aus x und y ein Punkt im Plot abgebildet\n",
+    "- und diese Punkte werden durch Geraden verbunden"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Diagonale plotten\n",
+    "- erst einmal plotten wir eine Diagonale\n",
+    "- dazu brauchen wir ein x-Array und ein y-Array, beide der Form [0 1 2 3 4 5 6 7 8 9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pylab import * \n",
+    "x = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "y = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "xlabel(\"x\")\n",
+    "ylabel(\"y\")\n",
+    "plot(x,y,\"x\")\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Flugkurve eines Balls\n",
+    "jetzt plotten wir die Flugkurve eines Balls mit einer bestimmten Anfangsgeschwindigkeit v0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.         0.1019368  0.2038736  0.3058104  0.4077472  0.509684\n",
+      " 0.6116208  0.71355759 0.81549439 0.91743119 1.01936799]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pylab import * # importiert plotting und array Funktionen\n",
+    "v0 = 5   # initial velocity\n",
+    "g = 9.81 # acceleration of gravity\n",
+    "n = 11 # number of points\n",
+    "t = linspace(0, 2*v0/g, n)\n",
+    "print(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.         0.09989806 0.19571865 0.28746177 0.37512742 0.4587156\n",
+      " 0.5382263  0.61365953 0.68501529 0.75229358 0.81549439 0.87461774\n",
+      " 0.92966361 0.98063201 1.02752294 1.07033639 1.10907238 1.14373089\n",
+      " 1.17431193 1.20081549 1.22324159 1.24159021 1.25586137 1.26605505\n",
+      " 1.27217125 1.27420999 1.27217125 1.26605505 1.25586137 1.24159021\n",
+      " 1.22324159 1.20081549 1.17431193 1.14373089 1.10907238 1.07033639\n",
+      " 1.02752294 0.98063201 0.92966361 0.87461774 0.81549439 0.75229358\n",
+      " 0.68501529 0.61365953 0.5382263  0.4587156  0.37512742 0.28746177\n",
+      " 0.19571865 0.09989806 0.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = v0*t - 0.5*g*t**2\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(t, y)\n",
+    "xlabel(\"t\")\n",
+    "ylabel(\"y\")\n",
+    "show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aufgaben\n",
+    "- Schreibe an die y Achse \"Ballposition in Meter\"\n",
+    "- Plotte eine Diagonale die von links oben nach rechts unten geht.\n",
+    "- Plotte eine Gerade, die parallel zu x Achse verläuft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Slideshow",
+  "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}