howto.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "# Einführung in nbinteract\n",
    "\n",
    "### 1.) Erstellen eines Git Accounts und eines Reposetories \n",
    "\n",
    "Gehen sie auf https://github.com/ und erstellen sie sich ein Account sowie ein Reposetory.\n",
    "\n",
    "Benennen wir das Repo als \"wmd\"\n",
    "\n",
    "    - Das Repo muss aud Public stehen \n",
    "    - Das Repo muss mit einer README initialisiert werden\n",
    "\n",
    "Nachfolgend ist in die Einstellungen des Repos zu sehen\n",
    "\n",
    "<img style=\"float: left;\" src=\"./PIC/image.png\" >\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.) Initialisieren des Reposetories auf mybinder.org\n",
    "\n",
    "Gehen sie auf  http://mybinder.org/ und initialisieren sie ihr Reposetory.\n",
    "Geben sie hierzu den Pfad ihres Gits ein\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img style=\"float: left;\" src=\"./PIC/image2.PNG\"  width=\"400\" >\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nachfolgend öffnet der Binder Server eine Jupyter Notebook Fenster in dem sie den Ihr Aotebook online ohne offline Installation von Jupyter Notebook benötigen \n",
    "\n",
    "In diesem Beispiel betrachten wir jedoch die Verwendung mit einem offline am PC intsalliertn Jupyter Notebook "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.) Clonen des Reposetories (local)\n",
    "\n",
    "Hierzu sollten sie zunächst ihr Reposetory local clonen. Öffnen sie ein Terminal (z.B. Anaconda Prompt Shell).  \n",
    "\n",
    "Hinweis: Sollten sie noch nie mit Git gearbeitet haben sollten sie zunächst Git im Terminal installieren. \n",
    "\n",
    "Im Fall von Anaconda: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```terminal\n",
    "conda install -c anaconda git\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nachfolgenden können Sie die das Repo in ihren gewünschten Ordner clonen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```terminal\n",
    "# Clone the repo\n",
    "{your folder}: git clone https://github.com/{username}/wmd\n",
    "# Move into the repo directory\n",
    "cd wmd\n",
    "# Start Jupyter\n",
    "jupyter notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.) Installieren und Initialisieren von nbinteract \n",
    "\n",
    "Gehen sie wieder in das locale Terminal. Hier ab besten das Terminal in Jupyter drirekt\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: left;\" src=\"./PIC/image3.PNG\"  width=\"300\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Installieren sie nbinteract:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "pip install nbinteract\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gehen sie im Terminal nun in den Ordner in dem sich auch das Repo befindet\n",
    "(über cd {yourpath})\n",
    "\n",
    "Initialisieren sie nun ihr Verzeichnismit folgender Eingabe:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializes nbinteract.\n",
    "nbinteract init\n",
    "\n",
    "git add -A\n",
    "git commit -m \"Setup nbinteract\"\n",
    "git push origin master"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun sollte sich in ihrem localen Verzeichnis eine requirements.txt  erstellt haben in dieser sollten sie alle benutzen pakete eintragen z.b. numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Erstellen eines ersten Notebooks\n",
    "\n",
    "Zunächst erstellen wir eine Notebook was interaktiv auf die Eingabe der Benutzer reagiert. Hierzu gibt es ein generelles vorgehen.\n",
    "    - Import aller benötigten Pakete \n",
    "    - Erstellung von Funktionen die später interaktiv in der Html Seite aus-geführt werden sollen \n",
    "    - Aufruf der geeigneten Eingabemethode\n",
    "    \n",
    "#### IMPORT    \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact\n",
    "import nbinteract as nbi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BSP 1:  Flächenberechnung mittels Schieberegler   \n",
    "\n",
    "Wir wollen berechnen \n",
    "\n",
    "$$A=a*b$$\n",
    "<div>\n",
    "<img style=\"float: left;\" src=\"./PIC/image4.PNG\" width=\"300\" >\n",
    "</div>    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Erstellen der Funktion mittels def"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square(a,b):\n",
    "    return 'Die Fläche A beträgt {} m^2'.format(a * float(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Erstellen der Eingabemaske 2 Beispiele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1ab0e991cf547edbcd671f5d6afe773",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=50, description='a', step=5), Text(value='1.5', description='b'), Output…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nbi:left\n",
    "interact(square, a=(0,100,5),b='1.5');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a81a5e61777445ab81fb084e6fcd0d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=50, description='a', step=5), Dropdown(description='b', options={'One': …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nbi:right\n",
    "interact(square, a=(0,100,5),b={'One': 1, 'Five': 5, 'Ten': 10});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BSP 1:  Funktion Plotten mittels Schieberegler   \n",
    "\n",
    "Wir wollen berechnen \n",
    "\n",
    "$$f(x)=a\\cdot x+b$$ \n",
    "\n",
    "oder \n",
    "\n",
    "$$f(x)=a\\cdot x^2+b*x + c$$ \n",
    "\n",
    "und anschließend plotten \n",
    "\n",
    "#### Einbinden des Matplotlib paketes \n",
    "\n",
    "Hinweis: Binden sie das Paket auch in die requierements.txt ein --> hier einfach ''matplotlib'' eintragen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Erstellen der Funktion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lin(a,b,x):\n",
    "    return a*x+b\n",
    "def quad(a,b,c,x):\n",
    "    return a*x**2+b*x+c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Erstellen der Plot funktion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def funk_plot(Bereich,Funktion,a,b,c=0):\n",
    "    x=np.zeros(int(Bereich)*100)\n",
    "    y=np.zeros(int(Bereich)*100)\n",
    "    for i in range(0,int(Bereich)*100):\n",
    "        x[i]=i/100\n",
    "        if Funktion =='Linear':\n",
    "            y[i]=lin(a,b,x[i])\n",
    "            title='Lineare'\n",
    "            label='$f(x)=$'+str(a)+'$\\cdot x+$'+str(b)\n",
    "        if Funktion =='Quadratisch':\n",
    "            y[i]=quad(a,b,c,x[i])\n",
    "            title='Quadratische'\n",
    "            label='$f(x)=$'+str(a)+'$\\cdot x^2+$'+str(b)+'$\\cdot x+$'+str(c)\n",
    "           \n",
    "    plt.plot(x,y, label=r'Funktion: '+label)\n",
    "    plt.title(title+'-Funktion') \n",
    "    plt.xlabel('$x$')\n",
    "    plt.ylabel('$y$')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testen der beiden Funktion (statisch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "funk_plot(10,'Linear',1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "funk_plot(10,'Quadratisch',1,2,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Erstellen der Interaktiven Funktionen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "919537debc4642a7834ddef953ffaa02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='10', description='Bereich'), Dropdown(description='Funktion', options=('Line…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.funk_plot(Bereich, Funktion, a, b, c=0)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "interact(funk_plot,a=(0,100,5),b=(0,100,5),c=(0,100,5),Bereich='10',Funktion={'Quadratisch','Linear'});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Versteckte Funktionen in gesonderten Datein\n",
    "\n",
    "Um eine übersuchtliche Seite zu erstellen gliedern wir Funktionben aus und speichern Sie in eine gesonderte *.py* Datei ab um sie später für interaktive felder zu nutzen.\n",
    "\n",
    "Die Idee:\n",
    "\n",
    "Wir möchen Nachfolgenden Code (Funktion und Sinn irrelevant) komplett auserhalb des Notebooks laufen lassen aber die Ausgabe mit variabel einzustellenden Werten innerhallb des Notebooks haben.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "hide_output"
    ]
   },
   "outputs": [],
   "source": [
    "import random as rd\n",
    "from numpy import sqrt,zeros\n",
    "from matplotlib.pyplot import plot,close\n",
    "\n",
    "close\n",
    "def huge_plot(E=70000,SIG_Y=300 ,v=0.3,eta_min=-0.005,eta_max=0.005,NC=500):\n",
    "    eta_min=float(eta_min)\n",
    "    eta_max=float(eta_max)\n",
    "    G=float(E)/(2.*(1+v))\n",
    "    SIG_R_1=zeros(NC)\n",
    "    SIG_G_1=zeros(NC)\n",
    "    SIG_R_2=zeros(NC)\n",
    "    SIG_G_2=zeros(NC)\n",
    "    for i in range(1,NC):\n",
    "       EPS_XX=rd.uniform(eta_min,eta_max)\n",
    "       EPS_YY=rd.uniform(eta_min,eta_max)\n",
    "       GAM_XY=rd.uniform(eta_min,eta_max)\n",
    "       SIG_XX=(E/(1-v**2))*(EPS_XX+v*EPS_YY)\n",
    "       SIG_YY=(E/(1-v**2))*(EPS_YY+v*EPS_XX)\n",
    "       TAU_XY=G*GAM_XY    \n",
    "       SIG_1=((SIG_XX+SIG_YY)/2)+sqrt(((SIG_XX-SIG_YY)/2)**2+TAU_XY**2)\n",
    "       SIG_2=((SIG_XX+SIG_YY)/2)-sqrt(((SIG_XX-SIG_YY)/2)**2+TAU_XY**2)\n",
    "       SIG_v3=sqrt(0.5*((SIG_1-SIG_2)**2+SIG_1**2+SIG_2**2))\n",
    "       f=SIG_v3-SIG_Y   \n",
    "       if f<0:\n",
    "           SIG_G_1[i]=SIG_1\n",
    "           SIG_G_2[i]=SIG_2\n",
    "       elif f>0: \n",
    "           SIG_R_1[i]=SIG_1\n",
    "           SIG_R_2[i]=SIG_2       \n",
    "    plot(SIG_G_1,SIG_G_2,'go')\n",
    "    plot(SIG_R_1,SIG_R_2,'ro')  \n",
    "    plot(-SIG_G_1,-SIG_G_2,'go')\n",
    "    plot(-SIG_R_1,-SIG_R_2,'ro')     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9de20108e687445c93491675e5cc23b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=70000, description='E', max=210000, min=-70000), IntSlider(value=300, de…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.huge_plot(E=70000, SIG_Y=300, v=0.3, eta_min=-0.005, eta_max=0.005, NC=500)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(huge_plot,eta_min='-0.005',eta_max='0.005');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importieren einer verstekten Funktion aus einem gesonderten Notebook und gleichzeitiges ausführen durch \"if __name__ =='__main__':\" Zeile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run wmdfunctions.ipynb #Jupyternotebook Datei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sind mehrer Funktionen zu versteken sollte ein Python Skript angelegt werden und dieses als Modul importiert werden.\n",
    "Nun kann die in der verstekten Datei vorhandene Funktion huge_plot_2 ausgeführt werden. Diese ist exakt die gleiche wie in dem vorherigen Code-Abschnitt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hiddencode as hd#Python Skript\n",
    "# Aufrufen der Funktion \n",
    "hd.huge_plot_2(E=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Ausblenden von Code um Interaktionen zu erlauben\n",
    "\n",
    "Sobald sich jedoch der Code nicht in dem Notebook befindet das später in eine HTML umgewandelt werden soll können wir keine interaktiven Module einbauen. Dies ist geschuldet dadurch das der dezedierte Code nur einmalig ausgeführt wird. Hierzu können jedoch die Metadaten der einzelnen Zellen beeinflusst werde. Nbinteract bietet hierzu den Befehl:\n",
    "\n",
    "```python\n",
    "# nbi:hide_in\n",
    "\n",
    "#some code \n",
    "import ... as ..\n",
    "a=b\n",
    "```\n",
    "Dieser wird einfach vor den code gesetzt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hide_input": false,
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a31119a692d4439390a5738c34d24932",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=600, description='E', max=1800, min=-600), IntSlider(value=300, descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.huge_plot_3(E=70000, SIG_Y=300, v=0.3, eta_min=-0.005, eta_max=0.005, NC=500)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nbi:hide_in\n",
    "\n",
    "import random as rd\n",
    "from numpy import sqrt,zeros\n",
    "from matplotlib.pyplot import plot,close\n",
    "\n",
    "close\n",
    "def huge_plot_3(E=70000,SIG_Y=300 ,v=0.3,eta_min=-0.005,eta_max=0.005,NC=500):\n",
    "    eta_min=float(eta_min)\n",
    "    eta_max=float(eta_max)\n",
    "    G=float(E)/(2.*(1+v))\n",
    "    SIG_R_1=zeros(NC)\n",
    "    SIG_G_1=zeros(NC)\n",
    "    SIG_R_2=zeros(NC)\n",
    "    SIG_G_2=zeros(NC)\n",
    "    for i in range(1,NC):\n",
    "       EPS_XX=rd.uniform(eta_min,eta_max)\n",
    "       EPS_YY=rd.uniform(eta_min,eta_max)\n",
    "       GAM_XY=rd.uniform(eta_min,eta_max)\n",
    "       SIG_XX=(E/(1-v**2))*(EPS_XX+v*EPS_YY)\n",
    "       SIG_YY=(E/(1-v**2))*(EPS_YY+v*EPS_XX)\n",
    "       TAU_XY=G*GAM_XY    \n",
    "       SIG_1=((SIG_XX+SIG_YY)/2)+sqrt(((SIG_XX-SIG_YY)/2)**2+TAU_XY**2)\n",
    "       SIG_2=((SIG_XX+SIG_YY)/2)-sqrt(((SIG_XX-SIG_YY)/2)**2+TAU_XY**2)\n",
    "       SIG_v3=sqrt(0.5*((SIG_1-SIG_2)**2+SIG_1**2+SIG_2**2))\n",
    "       f=SIG_v3-SIG_Y   \n",
    "       if f<0:\n",
    "           SIG_G_1[i]=SIG_1\n",
    "           SIG_G_2[i]=SIG_2\n",
    "       elif f>0: \n",
    "           SIG_R_1[i]=SIG_1\n",
    "           SIG_R_2[i]=SIG_2       \n",
    "    plot(SIG_G_1,SIG_G_2,'go')\n",
    "    plot(SIG_R_1,SIG_R_2,'ro')  \n",
    "    plot(-SIG_G_1,-SIG_G_2,'go')\n",
    "    plot(-SIG_R_1,-SIG_R_2,'ro') \n",
    "interact(huge_plot_3,E=600,eta_min='-0.005',eta_max='0.005');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Und wir sehen nur noch die Ausgabe. \n",
    "\n",
    "So kann z.B. eine Interaktive Seite wie folgt aussehen"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<a href=\"tutorial.html\">Showing Text</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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