{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 500px; float: left;\"> \n",
    "        <h1>Ingenieurinformatik – Übung</h1>\n",
    "        Lehrstuhl <b>Computational Civil Engineering</b><br>\n",
    "        Kontakt: <a href = \"mailto: cce-inginf@uni-wuppertal.de\">Email senden</a> | Individuelle Kontakte siehe Webseite des Lehrstuhls<br>\n",
    "        Links: \n",
    "        <a href=\"cce.uni-wuppertal.de/inginf\">Vorlesungsskript</a> | \n",
    "        <a href=\"cce.uni-wuppertal.de/\">Webseite des Lehrstuhls</a>\n",
    "    </div>\n",
    "    <div style=\"float:right;\"> \n",
    "        <img src=\"logo_cce_combined.png\" style=\"width:150px;\"/>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperaturdaten II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In dieser Aufgabe betrachten wir nochmal die Probleme aus Aufgabe 2.11, nur dass durch die Verwendung von numpy diese wesentlich vereinfacht werden.\n",
    "\n",
    "Eine Tabelle mit allen Messdaten des Experiments ist gespeichert in der Datei {download}`TC_all.csv <./TC_all.csv>`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabenstellung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Benutzen Sie `np.loadtxt()` um die Daten aus der Datei in einem numpy Array zu speichern.\n",
    "1. Verwenden Sie `np.mean()` und `np.amax()` um jeweils in einer Zeile die mittlere und maximale Temperatur pro Zeitschritt zu bestimmen.\n",
    "1. Geben Sie beide Arrays aus. Was müssen sie tun, um die gesamten Arrays betrachten zu können?\n",
    "1. Erstellen Sie mithilfe von `plt.imshow(np.ndarray, cmap='hot')` eine [Heatmap](https://de.wikipedia.org/wiki/Heatmap) der Temperaturdaten. Wieso erkennen Sie nichts?\n",
    "1. Reskalieren Sie ihre Graphik und verwenden Sie nur eine sinnvolle Auswahl der Messwerte, um einen lesbaren Plot zu erhalten.\n",
    "1. Was haben Sie hier veranschaulicht?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lösungshinweise\n",
    "\n",
    "zu 2.: Schlagen sie die Methoden, in den Dokumentationen von Numpy und nach und verstehen Sie, wie Sie mean und max entlang unterschiedlicher Achsen von numpy Arrays finden.\n",
    "\n",
    "zu 5.: Ihre Graphik könnte wie folgt aussehen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# laden der csv datei\n",
    "data = np.loadtxt('TC_all.csv', delimiter=',')\n",
    "\n",
    "# wähle zum Visualisieren nur jede 20. Messung\n",
    "steps = 20\n",
    "cropped_data = data[0::steps]\n",
    "\n",
    "# plotte den zeitlichen Verlauf der Wärmeausbreitung im Stahlträger als Heatmap\n",
    "plt.figure(figsize=(10,2))\n",
    "plt.imshow(cropped_data.transpose(), cmap='hot')\n",
    "\n",
    "# plottet die Farbkodierung und gibt eine Referenz zu dieser zurück\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Temperature in °C')\n",
    "\n",
    "plt.xticks()\n",
    "plt.xlabel(f't / {steps}s')\n",
    "plt.ylabel('Thermometer ID')\n",
    "plt.title('zeitlicher Verlauf der Wärmeausbreitung in einem Stahlträger')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "source": [
    "### Lösungsvorschlag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "loesung",
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximale Temperatur pro Zeitschritt: [26.  26.  27.6 ... 25.6 25.6 25.5]\n",
      "Mittlere Temperatur pro Zeitschritt: [22.41666667 22.4        22.53333333 ... 25.09166667 25.08333333\n",
      " 25.06666667]\n",
      "Maximale Temperatur pro Zeitschritt als Liste:\n",
      "[26.0, 35.4, 45.7, 54.4, 62.0, 69.7, 75.7, 81.1, 86.9, 92.2, 96.2, 100.6, 104.4, 107.8, 111.1, 112.0, 105.1, 98.4, 91.4, 85.9, 80.4, 75.9, 72.6, 68.7, 66.1, 63.3, 60.8, 58.8, 56.1, 54.1, 52.3, 51.0, 49.5, 48.0, 46.6, 45.3, 44.3, 43.2, 42.2, 41.3, 40.3, 39.4, 38.6, 37.9, 37.2, 36.5, 35.9, 35.2, 34.6, 34.1, 33.6, 33.1, 32.6, 32.2, 31.8, 31.4, 31.0, 30.7, 30.4, 30.0, 29.7, 29.3, 28.9, 28.7, 28.3, 28.0, 27.7, 27.5, 27.2, 27.0, 26.7, 26.5, 26.3, 26.0, 25.8, 25.6]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# laden der csv datei\n",
    "data = np.loadtxt('TC_all.csv', delimiter=',')\n",
    "\n",
    "# wähle zum Visualisieren nur jede 20. Messung\n",
    "steps = 20\n",
    "cropped_data = data[0::steps]\n",
    "\n",
    "# plotte den zeitlichen Verlauf der Wärmeausbreitung im Stahlträger als Heatmap\n",
    "plt.figure(figsize=(10,2))\n",
    "plt.imshow(cropped_data.transpose(), cmap='hot')\n",
    "\n",
    "# plottet die Farbkodierung und gibt eine Referenz zu dieser zurück\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Temperature in °C')\n",
    "\n",
    "plt.xticks()\n",
    "plt.xlabel(f't / {steps}s')\n",
    "plt.ylabel('Thermometer ID')\n",
    "plt.title('zeitlicher Verlauf der Wärmeausbreitung in einem Stahlträger')\n",
    "plt.show()\n",
    "\n",
    "dmax = np.amax(data, 1)\n",
    "print(f'Maximale Temperatur pro Zeitschritt: {dmax}')\n",
    "dmean = np.mean(data, 1)\n",
    "print(f'Mittlere Temperatur pro Zeitschritt: {dmean}')\n",
    "\n",
    "# um ein 1D-Array komplett auszugeben reicht ein Cast auf eine Liste\n",
    "print(f'Maximale Temperatur pro Zeitschritt als Liste:\\n{list(np.amax(cropped_data, 1))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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