475 lines
1.3 MiB
Plaintext
475 lines
1.3 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Zweidimensionale Flächen darstellen\n",
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"\n",
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"#### Erstellt von B. Miesch, 11.02.2021 für die Vorlesung Höhere Mathematik 2 der ZHAW SoE, mit Anpassungen von R. Knaack\n",
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"\n",
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"In diesem Tutorial wollen wir uns damit beschäftigen, wie wir Flächen darstellen können. Eine zweidimensionale Fläche ist zum Beispiel durch den Graphen einer Funktion $f\\colon \\mathbb{R}^2 \\to \\mathbb{R}$ gegeben. Wir definieren uns also zuerst eine Funktion $f(x,y) = x^2 + y^2$.\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"def f(x,y):\n",
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" return x**2 + y**2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Nun berechnen wir die Koordinaten für eine Menge von Stützpunkten. Dazu legen wir mit dem Befehl `numpy.meshgrid()` ein Gitter in die x-y-Ebene und berechnen anschliessend für jeden Punkt den Funktionswert $z=f(x,y)$."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(50, 50)\n",
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"(50, 50)\n",
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"(50, 50)\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"[x,y] = np.meshgrid(np.linspace(-5,5), np.linspace(-5,5));\n",
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"z = f(x,y)\n",
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"\n",
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"print(np.shape(x))\n",
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"print(np.shape(y))\n",
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"print(np.shape(z))\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wir sehen, dass x, y und z hier jeweils 50x50 Matrizen sind. Für jedes $x\\in[-5,5]$ und $y\\in[-5,5]$ steht der entsprechende Funktionswert $z=f(x,y)$ in dieser Matrix z, die zugehörige $x$-Koordinate in der Matrix x resp. die zugehörige $y$-Koordinate in der Matrix y. Nun stehen uns in Python verschiedene Möglichkeiten zu Verfügung, um diese Fläche $z$ (resp. die Matrix z) zu visualisieren. Wir schauen uns hier mal eine kleine Auswahl an."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Höhenlinien\n",
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"\n",
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"Die uns bekannte Bibliothek `matplotlib` stellt die Funktion `contour` zur Verfügung, welche die Höhenlinien der Fläche einzuzeichnet. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.contour(x, y, z)\n",
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"\n",
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"plt.title('Höhenlinien')\n",
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"plt.xlabel('x')\n",
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"plt.ylabel('y')\n",
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"\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wir können auch eine andere Einfärbung wählen und dazu eine Legende generieren."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from matplotlib import cm\n",
|
||
|
|
"\n",
|
||
|
|
"fig = plt.figure(0)\n",
|
||
|
|
"cont = plt.contour(x, y, z, cmap=cm.coolwarm)\n",
|
||
|
|
"fig.colorbar(cont, shrink=0.5, aspect=5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Höhenlinien')\n",
|
||
|
|
"plt.xlabel('x')\n",
|
||
|
|
"plt.ylabel('y')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Auch die Niveaus können selber ausgewähl werden."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(1)\n",
|
||
|
|
"cont = plt.contour(x, y, z, [1,4,9,16,25,36,49,64,81,100,121,144,169],cmap=cm.coolwarm)\n",
|
||
|
|
"fig.colorbar(cont, shrink=0.5, aspect=5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Höhenlinien')\n",
|
||
|
|
"plt.xlabel('x')\n",
|
||
|
|
"plt.ylabel('y')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Oberfläche im 3D\n",
|
||
|
|
"\n",
|
||
|
|
"Nun wollen wir die Fläche im dreidimensionalen Raum darstellen. Dazu verwenden wir das Paket `mpl_toolkits.mplot3d`, welches uns eine interaktive Ansicht bietet. Dazu müssen wir hier in jupyter noch mit dem Befehlt `%matplotlib notebook` die Einstellungen anpassen."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "07c5aa45d92240aaae64c3010b15bdef",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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" <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d5hk6V3fDX8q59g55zQ5x10hhIxASEhoAYPlBxPsy2BZLwjLNoZXRGHrwQIMZpEFliVQsCKCFasAWrFxcuiezjl3V3V1V87x+aO6TtXpntnpmame6d2+P9e1106fOnWfU6eq+3zrF74/RS6XyyEQCAQCgUAg2Dcon/QJCAQCgUAgEAgeL0IACgQCgUAgEOwzhAAUCAQCgUAg2GcIASgQCAQCgUCwzxACUCAQCAQCgWCfIQSgQCAQCAQCwT5DCECBQCAQCASCfYYQgAKBQCAQCAT7DCEABQKBQCAQCPYZQgAKBAKBQCAQ7DOEABQIBAKBQCDYZwgBKBAIBAKBQLDPEAJQIBAIBAKBYJ8hBKBAIBAIBALBPkMIQIFAIBAIBIJ9hhCAAoFAIBAIBPsMIQAFAoFAIBAI9hlCAAoEAoFAIBDsM4QAFAgEAoFAINhnCAEoEAgEAoFAsM8QAlAgEAgEAoFgnyEEoEAgEAgEAsE+QwhAgUAgEAgEgn2GEIACgUAgEAgE+wwhAAUCgUAgEAj2GUIACgQCgUAgEOwzhAAUCAQCgUAg2GcIASgQCAQCgUCwzxACUCAQCAQCgWCfIQSgQCAQCAQCwT5DCECBQCAQCASCfYYQgAKBQCAQCAT7DCEABQKBQCAQCPYZQgAKBAKBQCAQ7DOEABQIBG9aWltb+dmf/VnZto9//OMoFArm5uZ48cUXUSgUfPWrX33gtefm5lAoFHz84x8v09kKBALB40MIQIFA8IbkM5/5DAqF4q7//dqv/do9n/cjP/IjfPazn6Wqqoq+vj4++9nPcubMmcd45gKBQPDkUT/pExAIBIJH4Xd/93dpa2uTbTt06NA99+/r66Ovrw8Ak8nEv/yX/3JXz08gEAj2IkIACgSCNzQ//MM/zKlTp570aQgEAsEbCpECFggE+wav18uHP/xhDh8+jNlsxmq18kM/9EP09/dv2zcej/Pbv/3bdHd3o9frqaur433vex/T09Pb9v2Lv/gLOjo60Ol0nD59muvXr2/bZ2xsjB//8R/H6XSi1+s5deoUzz333G68TIFAILgvIgIoEAje0AQCAdbX12XbKisr77rvzMwMX//61/nJn/xJ2tracLvdfPKTn+Stb30rIyMj1NfXA5DJZHjXu97FCy+8wE/91E/xy7/8y4RCIf7xH/+RoaEhOjo6pDW/8IUvEAqF+Lf/9t+iUCj4gz/4A973vvcxMzODRqMBYHh4mIsXL9LQ0MCv/dqvYTKZ+PKXv8x73/tevva1r/FjP/Zju3R1BAKB4O4ocrlc7kmfhEAgEDwon/nMZ/i5n/u5uz5W+LPW2trKW9/6Vj7zmc8AkEgk0Gg0KJXF5Mfc3By9vb38xm/8Bh/5yEcA+PSnP83P//zP80d/9Ed86EMf2rZ2oYu4ra2NiooKJicncTgcADz33HO85z3v4Rvf+Abvete7AHj729/O2toa169fR6fTSes89dRTeDweJiYmyndhBAKBYAeICKBAIHhD8+yzz9Ld3b2jfQviC/JRPr/fj9lspqenh1u3bkmPfe1rX6OyspIPfvCD29ZQKBSyn//5P//nkvgDePrpp4F8tBHyaefvfe97/O7v/i6hUIhQKCTt+453vIPf+q3fYnl5mYaGhh29BoFAICgHQgAKBII3NGfOnNlxE0g2m+VP/uRP+PM//3NmZ2fJZDLSYxUVFdK/p6en6enpQa2+/5/I5uZm2c8FMejz+QCYmpoil8vxkY98RIowbmVtbU0IQIFA8FgRAlAgEOwb/ut//a985CMf4ed//uf5vd/7PZxOJ0qlkl/5lV8hm80+1Joqlequ2wtp6MK6H/7wh3nHO95x1307Ozsf6tgCgUDwsAgBKBAI9g1f/epX+f7v/34+9alPybb7/X5Z40hHRwdXr14llUpJjRwPS3t7OwAajYa3v/3tj7SWQCAQlAthAyMQCPYNKpWKrX1vX/nKV1heXpZte+aZZ1hfX+fP/uzPtq3xoH1z1dXVvPWtb+WTn/wkq6ur2x73eDwPtJ5AIBCUAxEBFAgE+4Z3vetd/O7v/i4/93M/x4ULFxgcHOTzn/+8FKUr8DM/8zP89V//Nb/6q7/KtWvXePrpp4lEInz3u9/l3/27f8d73vOeBzrus88+y1NPPcXhw4f5N//m39De3o7b7eby5cssLS0xMDBQzpcpEAgE90UIQIFAsG/49V//dSKRCF/4whf40pe+xIkTJ3j++ee3zQ5WqVR885vf5Pd///f5whe+wNe+9jUqKiokEfegHDhwgBs3bvA7v/M7fOYzn2FjY4Pq6mqOHz/Ob/7mb5br5QkEAsGOET6AAoFAIBAIBPsMUQMoEAgEAoFAsM8QAlAgEAgEAoFgnyEEoEAgEAgEAsE+QwhAgUAgEAgEgn2GEIACgUAgEAgE+wwhAAUCgUAgEAj2GUIACgQCgUAgEOwzhAAUCAQCgUAg2GcIASgQCAQCgUCwzxACUCAQCAQCgWCfIQSgQCAQCAQCwT5DCECBQCAQCASCfYYQgAKBQCAQCAT7DCEABQKBQCAQCPYZQgAKBAKBQCAQ7DOEABQIBAKBQCDYZwgBKBAIBAKBQLDPEAJQIBAIBAKBYJ8hBKBAIBAIBALBPkMIQIFAIBAIBIJ9hhCAAoFAIBAIBPsMIQAFAoFAIBAI9hlCAAoEAoFAIBDsM4QAFAgEAoFAINhnCAEoEAgEAoFAsM8QAlAgEAgEAoFgnyEEoEAgEAgEAsE+QwhAgUAgEAgEgn2GEIACgUAgEAgE+wwhAAUCgUAgEAj2GUIACgQCgUAgEOwzhAAUCAQCgUAg2GcIASgQCAQCgUCwzxACUCAQCAQCgWCfIQSgQCAQCAQCwT5D/aRPQCAQCHK5HJlMBgCVSoVCoXjCZyQQCARvboQAFAgET5RsNksqlSIWi5HL5VAqlWg0GlQqFWq1GqVSKQShQCAQlBlFLpfLPemTEAgE+49C1C+dTksisPDnKJvNAqBQKCRBqFarUalUQhAKBAJBGRACUCAQPHZyuRypVEpK+wJ4vV4MBgMajUbap/CfEIQCgUBQXoQAFAgEj5VsNksymSSbzaJUKkmn0wwNDeHxeMjlclgsFhwOBw6HA5vNhkqlAu4tCAupYiEIBQKBYOcIASgQCB4LhZRvIdWrVCrx+/0MDAxgNpvp7e0lk8ng9/vx+Xz4fD6SySRWq1UShFar9a6CsPCfUqlEqVQKQSgQCAT3QQhAgUCw62SzWdLptCzlOzs7y8zMDF1dXTQ3N5NOp8nlcpJYy+VyxONxSQz6fD7S6fQ2QahUKqX9S6ODUEwZC0EoEAgEcoQAFAgEu0ZBkBWifgqFgkQiwZ07d4jH4xw9ehSbzSbVBJYKwLutFYvFZIIwk8lgs9kkQWixWO4qCAvrlqaMC/8vbBcIBIL9hBCAAoFgV8jlcqTTadLpNJCPxq2vrzM4OEhlZSUHDhxArVZL+95PAN5t/UgkIksZ53I57HY7drtdEoSlEcWtgjAWi6FQKLDb7ZIoFIJQIBDsB4QAFAgEZacQ9ctkMpKYmpiYYHFxkQMHDtDQ0CDb/2EE4FZyuRzhcFgShH6/H0AmCM1ms0wQTk9Pk0ql6OzslEUICz6EhZSxQCAQvNkQAlAgEJSNrd5+SqWSaDTKwMAAAEePHsVkMt31eY8qAO+2ZigUkglChUIhpYvtdjsul4t0Ok1vb++2CCGwrX5QCEKBQPBmQQhAgUBQFrZ6+ykUClZXVxkeHqaxsZGenp57iqfdEIBbyWazhEIhKV0cCAQA0Gq1tLS0YLfbMRqN90wZgxCEAoHgzYMQgAKB4JHZ6u2XyWQYHR1lbW2Nw4cPU11d/brPfxwC8G7nPDo6SjQ
|
||
|
|
" </div>\n",
|
||
|
|
" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%matplotlib widget\n",
|
||
|
|
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||
|
|
"\n",
|
||
|
|
"fig = plt.figure(2)\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"ax.plot_surface(x,y,z)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Fläche')\n",
|
||
|
|
"ax.set_xlabel('x')\n",
|
||
|
|
"ax.set_ylabel('y')\n",
|
||
|
|
"ax.set_zlabel('z')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Mit der Option `projection` wählen wir den dreidimensionalen Plot aus. Auch hier können wir wieder mit der Einfärbung spielen."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "45a17e8e23854dadadcb34e87dc2810a",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" Figure\n",
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" </div>\n",
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" <img src='data:image/png;base64,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
|
||
|
|
" </div>\n",
|
||
|
|
" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(3)\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"surf = ax.plot_surface(x,y,z, cmap=cm.coolwarm, linewidth=0, antialiased=False)\n",
|
||
|
|
"\n",
|
||
|
|
"fig.colorbar(surf, shrink=0.5, aspect=5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Fläche')\n",
|
||
|
|
"ax.set_xlabel('x')\n",
|
||
|
|
"ax.set_ylabel('y')\n",
|
||
|
|
"ax.set_zlabel('z')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Um den Durchblick zu behalten, können wir alternativ auch nur ein Gitter plotten."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "ca4a3c7bf1524e5dac64de7e537006f8",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"image/png": "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
|
||
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"text/html": [
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|
"\n",
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|
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" <div style=\"display: inline-block;\">\n",
|
||
|
|
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
|
||
|
|
" Figure\n",
|
||
|
|
" </div>\n",
|
||
|
|
" <img src='data:image/png;base64,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
|
||
|
|
" </div>\n",
|
||
|
|
" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(4)\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"ax.plot_wireframe(x,y,z)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Gitter')\n",
|
||
|
|
"ax.set_xlabel('x')\n",
|
||
|
|
"ax.set_ylabel('y')\n",
|
||
|
|
"ax.set_zlabel('z')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Dabei lässt sich natürlich der Abstand zwischen den Linien einstellen."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "d6105cc818ce48258f4a9a028486f900",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
|
|
"image/png": "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"text/html": [
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"\n",
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" <div style=\"display: inline-block;\">\n",
|
||
|
|
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
|
||
|
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" Figure\n",
|
||
|
|
" </div>\n",
|
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|
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" <img src='data:image/png;base64,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
|
||
|
|
" </div>\n",
|
||
|
|
" "
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(5)\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"ax.plot_wireframe(x,y,z, rstride=5, cstride=5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Gitter')\n",
|
||
|
|
"ax.set_xlabel('x')\n",
|
||
|
|
"ax.set_ylabel('y')\n",
|
||
|
|
"ax.set_zlabel('z')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Zum Schluss zeichnen wir auch noch die Höhenlinien im dreidimensionalen Raum ein."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"application/vnd.jupyter.widget-view+json": {
|
||
|
|
"model_id": "75b4a0eb282d47698ef5b4edadce1443",
|
||
|
|
"version_major": 2,
|
||
|
|
"version_minor": 0
|
||
|
|
},
|
||
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" </div>\n",
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" "
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],
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
||
|
|
"fig = plt.figure(6)\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"surf = ax.contour(x,y,z, cmap=cm.coolwarm)\n",
|
||
|
|
"\n",
|
||
|
|
"fig.colorbar(surf, shrink=0.5, aspect=5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.title('Fläche')\n",
|
||
|
|
"ax.set_xlabel('x')\n",
|
||
|
|
"ax.set_ylabel('y')\n",
|
||
|
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"ax.set_zlabel('z')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"display_name": "ZHAWNotes-WN8_mYO5",
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"language": "python",
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"name": "python3"
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