deskriptive_Statistik_de.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### deskriptive Statistik interaktiv\n",
    "\n",
    "Unterschiedlichen Verteilungsfunktionen können interaktiv parametriert und in einem Diagramm dargestellt werden. Zu der dargestellten Funktion können zudem weitere Kennwerte dargestellt werden.\n",
    "\n",
    "**Auswählbare Funktionen:**\n",
    "- Normalverteilung (Gauß-Funktion), [scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.norm.html#scipy.stats.norm)\n",
    "\n",
    "- Gamma-Poisson-Verteilung (negative Binomialverteilung), [scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.nbinom.html#scipy.stats.nbinom)\n",
    "\n",
    "- Poisson-Verteilung, [scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.poisson.html#scipy.stats.poisson)\n",
    "\n",
    "- Weibull-Verteilung, [scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.weibull_min.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resourcen\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import poisson, norm, nbinom, weibull_min, mode\n",
    "\n",
    "#set backend for interactive toolbar\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#interaktive Schalter\n",
    "\n",
    "# Signalauswahl\n",
    "#\n",
    "distribFuns = ['Poissonverteilung', 'Gamma-Poisson-Verteilung','Normalverteilung','Weibull-Verteilung']\n",
    "distrib_select = widgets.Dropdown(description='', options=distribFuns, value='Normalverteilung', layout= {'width': '40%'})\n",
    "signalSelect = widgets.VBox([\n",
    "    widgets.Label(value=\"Signalauswahl:\"),\n",
    "    distrib_select\n",
    "])\n",
    "\n",
    "#\n",
    "layout= widgets.Layout(width='90%',height='auto')\n",
    "style = {'description_width': 'initial'}\n",
    "\n",
    "# Funktionsparameter\n",
    "#\n",
    "# Anzahl der Zufallswerte\n",
    "size = widgets.BoundedIntText(value=1000, min=20, max=100000, layout= {'width': '30%'})\n",
    "sizeBox = widgets.VBox([\n",
    "    widgets.Label(value=\"Datensatzgröße (max.100000)\"),\n",
    "    size\n",
    "])\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Gauss: µ: Erwartungswert, \\sigma: Standardabweichung \n",
    "normLabel_1 = \"µ: (Erwartungswert)\"\n",
    "normLabel_2 = \"$\\sigma$: (Standardabweichung)\"\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Weibull : - Skalen- und Formparameter\n",
    "wblLabel_1 = \"k: Formparater (k > 0)\"\n",
    "wblLabel_2 = \"$\\lambda$: (Skalenparameter $\\lambda$.$1 \\over \\lambda > 0$.)\"\n",
    "wblLabel_3 = \"µ: (Verschiebeoperator)\"\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Gamma-Poisson => negative Binomialverteilung: n: Anzahl Erfolge, p: Einzelwahrscheinlichkeit \n",
    "nbLabel_1 = \"Anzahl der Erfolge n ($n > 0$).$mean \\over varianz$\"\n",
    "nbLabel_2 = \"Einzelwahrscheinlichkeit p (0...1). $(mean²\\over (var - mean))$\"\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Poisson: Erwartungswert und Varianz µ (geteilt mit Gauss - Erwartungswert)\n",
    "#\n",
    "poissonLabel = \"$\\lambda$: Erwartungswert und Varianz\"\n",
    "#\n",
    "# widget-box mit 3 Schiebereglern \n",
    "interakt = widgets.VBox([\\\n",
    "                         widgets.Label(value=\"\",layout=layout),\n",
    "                         widgets.FloatSlider(min=0.1,max=100.0,value=0.5,continuous_update=False, layout=layout),\n",
    "                         widgets.Label(value=\"\",layout=layout),\n",
    "                         widgets.FloatSlider(min=0.1,max=100.0,value=0.5,continuous_update=False, layout=layout),\n",
    "                         widgets.Label(value=\"\",layout=layout),\n",
    "                         widgets.FloatSlider(min=0.1,max=100.0,value=0.5,continuous_update=False, layout=layout)\n",
    "                         ]\n",
    "                       )\n",
    "\n",
    "# Quantil - Auswahl\n",
    "quantile = widgets.FloatSlider(min= 0, max=100, value=50)\n",
    "quantileBox = widgets.VBox([\n",
    "    widgets.Label(value=\"Quantil-Auswahl /%\"),\n",
    "    quantile\n",
    "])\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Visualisierungsoptionen\n",
    "toggle_1 = widgets.ToggleButtons(options=['pdf/pmf', 'cdf'],\\\n",
    "                                 description='',\\\n",
    "                                 disabled=False,\n",
    "                                 button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "                                 tooltips=['zeige Dichte- bzw. Wahrscheinlichkkeitsfunktion', 'zeige Verteilungsfunktion'],\n",
    "                                )\n",
    "toggleBox_1 = widgets.VBox([\n",
    "    widgets.Label(value=\"Funktionsauswahl: Dichtefunktion (pdf/pmf) bzw. kum. Wahrscheinlichkeit (cdf)\"),\n",
    "    toggle_1\n",
    "])\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "toggle_2 = widgets.ToggleButtons(options=['line','boxplot', 'violinplot'],\n",
    "                                 description='',\n",
    "                                 disabled=False,\n",
    "                                 button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "                                 tooltips=['line-plot','Boxer-und-Whisker Ansicht', 'Violin Ansicht'],\n",
    "                                )\n",
    "toggleBox_2 = widgets.VBox([\n",
    "    widgets.Label(value=\"Darstellung: line-plot' 'Boxer-und-Whisker' 'Violin'\"),\n",
    "    toggle_2\n",
    "])\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# plot limits\n",
    "x_range = widgets.IntRangeSlider(min= -200, max=200, value=[-5,5], continuous_update=False)\n",
    "xRangeBox = widgets.VBox([\n",
    "    widgets.Label(value=\"x-range\"),\n",
    "    x_range\n",
    "])\n",
    "#\n",
    "BtnReset = widgets.Button(description=\"reset\", button_style='warning')\n",
    "# ------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_rvs(dict_distribution, ax):\n",
    "# Datensatz der Verteilungsfunktion darstellen\n",
    "    x = dict_distribution['x']\n",
    "    y = dict_distribution['rvs']    \n",
    "    ax.clear()\n",
    "    ax.set_title(\"{}\".format(distrib_select.value))\n",
    "    ax.hist(y, 14)\n",
    "    ax.set_xlim(x_range.value[0], x_range.value[-1])\n",
    "\n",
    "#     ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"Wahrscheinlichkeit\")\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major',\\\n",
    "                  color='lightgrey',alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_params(dict_distribution, ax):\n",
    "    x = dict_distribution['x']\n",
    "    distrib_f = dict_distribution['distrib_f']    \n",
    "    var = dict_distribution['var']\n",
    "    std = dict_distribution['std']\n",
    "    rms = dict_distribution['rms']\n",
    "    \n",
    "    if toggle_1.value == 'pdf/pmf':\n",
    "        mode = dict_distribution['mode_cdf']\n",
    "        y = dict_distribution['prob_f']\n",
    "        mean = dict_distribution['mean_prob']\n",
    "        median = dict_distribution['median_prob']\n",
    "        mode = dict_distribution['mode_prob']\n",
    "    else:\n",
    "        mode = dict_distribution['mode_cdf']\n",
    "        y = dict_distribution['cdf']\n",
    "        mean = dict_distribution['mean_cdf']\n",
    "        median = dict_distribution['median_cdf']\n",
    "        mode = dict_distribution['mode_cdf']\n",
    "\n",
    "    # mean: vertikale Linie, gestrichelt, grau, Kommentar oben mit Pfeil, Text: µ (wert)\n",
    "    # varianz: standardabweichung: - horzontale linie für gültigen Bereich\n",
    "    # rms\n",
    "    # modus: gauß: 0\n",
    "    # median: gauß: mean\n",
    "    ax.clear()\n",
    "    ax.set_title(\"Parameter zur {}\".format(distrib_select.value))\n",
    "    #\n",
    "    #Verteilung nochmal zeichnen und dann die entsprechenden Informationen eintragen\n",
    "    if distrib_select.value in ['Poissonverteilung', 'Gamma-Poisson-Verteilung']:\n",
    "        if toggle_1.value == 'pdf/pmf':\n",
    "            ax.bar(x,y, color='green', width=0.25)\n",
    "    if distrib_select.value in ['Normalverteilung','Weibull-Verteilung'] or toggle_1.value == 'cdf':\n",
    "        ax.plot(x, y, color='C1', lw=2)\n",
    "    \n",
    "    # vertikale Line bis zum Funktionswert zeichnen\n",
    "    # angaben für axhline bzw. axvline in %!\n",
    "    yLim = ax.get_ylim()\n",
    "    yH = mean[-1]/yLim[-1] \n",
    "    #\n",
    "    # Mittelwert\n",
    "    ax.axvline(x=mean[0], ymin=0, ymax=yH, ls='--',color='grey',alpha=0.5)\n",
    "    ax.annotate(r'$\\bar{{x}}_{{{%.3f}}}$' % (mean[0]),\n",
    "                 xy=(mean[0], mean[-1]), xycoords='data',\n",
    "                 xytext=(-50,30), textcoords='offset points',\n",
    "                 bbox=dict(boxstyle=\"round\", fc=\"0.6\"),\n",
    "                 arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90,rad=10\"))\n",
    "    #\n",
    "    # mode\n",
    "    yH = mode[-1]/yLim[-1]    \n",
    "    #\n",
    "    if len(mode) > 2:\n",
    "        ax.axvline(x=mode[0], ymin=0, ymax=yH)\n",
    "        ax.axvline(x=mode[2], ymin=0, ymax=yH)\n",
    "        #\n",
    "        ax.annotate(\"mode_1\",\n",
    "                    xy=(mode[0], mode[1]), xycoords='data',\n",
    "                    xytext=(-50,30), textcoords='offset points',\n",
    "                    bbox=dict(boxstyle=\"round\", fc=\"0.6\"),\n",
    "                    arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90,rad=10\")\n",
    "                    )\n",
    "        ax.annotate(\"mode_2\",\n",
    "                    xy=(mode[2], mode[-1]), xycoords='data',\n",
    "                    xytext=(-50,30), textcoords='offset points',\n",
    "                    bbox=dict(boxstyle=\"round\", fc=\"0.6\"),\n",
    "                    arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90,rad=10\")\n",
    "                    )\n",
    "    else:\n",
    "        ax.axvline(x=mode[0], ymin=0, ymax=yH) \n",
    "        ax.annotate(\"mode\",\n",
    "                 xy=(mode[0], mode[-1]), xycoords='data',\n",
    "                 xytext=(-50,30), textcoords='offset points',\n",
    "                 bbox=dict(boxstyle=\"round\", fc=\"0.6\"),\n",
    "                 arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90,rad=10\"))\n",
    "    #\n",
    "    # median\n",
    "    yH = median[-1]/yLim[-1]\n",
    "    #\n",
    "    ax.axvline(x=median[0], ymin=0, ymax=yH)\n",
    "    ax.annotate(\"median\",\n",
    "                 xy=(median[0], median[-1]), xycoords='data',\n",
    "                 xytext=(50,30), textcoords='offset points',\n",
    "                 bbox=dict(boxstyle=\"round\", fc=\"0.6\"),\n",
    "                 arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90,rad=10\"))\n",
    "    #\n",
    "    # Normalverteilung:\n",
    "    # 1-sigma, 2-sigma und 3-sigma kennzeichnen\n",
    "    if (distrib_select.value == 'Normalverteilung' and toggle_1.value == 'pdf/pmf'):\n",
    "        #     \n",
    "        m_1_sigma = distrib_f.ppf(1-0.6827)\n",
    "        p_1_sigma = distrib_f.ppf(0.6827)\n",
    "        m_2_sigma = distrib_f.ppf(1-0.9545)\n",
    "        p_2_sigma = distrib_f.ppf(0.9545)\n",
    "        m_3_sigma = distrib_f.ppf(1-0.9973)\n",
    "        p_3_sigma = distrib_f.ppf(0.9973)\n",
    "        #\n",
    "        yH_s1n = distrib_f.pdf( m_1_sigma)\n",
    "        yH_s1p = distrib_f.pdf( p_1_sigma)\n",
    "        yH_s2n = distrib_f.pdf( m_2_sigma)\n",
    "        yH_s2p = distrib_f.pdf( p_2_sigma)\n",
    "        yH_s3n = distrib_f.pdf( m_3_sigma)\n",
    "        yH_s3p = distrib_f.pdf( p_3_sigma)\n",
    "        #\n",
    "        ax.axvline(x=m_1_sigma, ymin=0, ymax=yH_s1n/yLim[-1])\n",
    "        ax.axvline(x=p_1_sigma, ymin=0, ymax=yH_s1p/yLim[-1])\n",
    "        ax.axvline(x=m_2_sigma, ymin=0, ymax=yH_s2n/yLim[-1])\n",
    "        ax.axvline(x=p_2_sigma, ymin=0, ymax=yH_s2p/yLim[-1])\n",
    "        ax.axvline(x=m_3_sigma, ymin=0, ymax=yH_s3n/yLim[-1])\n",
    "        ax.axvline(x=p_3_sigma, ymin=0, ymax=yH_s3p/yLim[-1])\n",
    "        \n",
    "        # bereich markieren +/- sigma\n",
    "        # - hintergrund mit Farbe füllen\n",
    "        ax.fill_between(x, 0, y, where =(y >= (yH_s3n)),\\\n",
    "                   facecolor='green', alpha=0.3)\n",
    "        ax.fill_between(x,0,y, where =(y >= (yH_s2n)),\\\n",
    "                   facecolor='green', alpha=0.2)\n",
    "        ax.fill_between(x,0,y, where =(y >= (yH_s1n)),\\\n",
    "                   facecolor='green', alpha=0.1)\n",
    "        #\n",
    "        # horizontale Linien an den Stellen +/- sigma ziehen und beschriften\n",
    "        ax.plot((m_1_sigma,p_1_sigma),(yH_s1n, yH_s1n), ls='-', color='green')\n",
    "        ax.annotate(r'$\\pm\\sigma$', xy=(interakt.children[1].value, yH_s1n), xycoords='data')\n",
    "        #\n",
    "        ax.plot((m_2_sigma,p_2_sigma), (yH_s2n, yH_s2n),ls='-', color='green')\n",
    "        ax.annotate(r'$\\pm2\\sigma$', xy=(interakt.children[1].value, yH_s2n), xycoords='data')\n",
    "        #\n",
    "        ax.plot((m_3_sigma,p_3_sigma), (yH_s3n, yH_s3n),ls='-', color='green')\n",
    "        ax.annotate(r'$\\pm3\\sigma$', xy=(interakt.children[1].value, yH_s3n), xycoords='data')\n",
    "   \n",
    "    # Abszissenbereich entsprechend Bereichsauswahl einstellen\n",
    "    ax.set_xlim(x_range.value[0], x_range.value[-1])\n",
    "    #\n",
    "    # Gitterlinien andeuten\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',\n",
    "               alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_custom(dict_distribution, ax):\n",
    "    \n",
    "    x = dict_distribution['x']\n",
    "    distrib_f = dict_distribution['distrib_f']    \n",
    "    var = dict_distribution['var']\n",
    "    std = dict_distribution['std']\n",
    "    rms = dict_distribution['rms']\n",
    "    #\n",
    "    if toggle_1.value == 'pdf/pmf':\n",
    "        mode = dict_distribution['mode_cdf']\n",
    "        y = dict_distribution['prob_f'] \n",
    "        title_str = \"Dichtefunktion der {}\".format(distrib_select.value)\n",
    "        mean = dict_distribution['mean_prob']\n",
    "        mode = dict_distribution['mode_prob']\n",
    "        median = dict_distribution['median_prob']\n",
    "        quantil = dict_distribution['quantile_prob']\n",
    "    else:\n",
    "        mode = dict_distribution['mode_cdf']\n",
    "        y = dict_distribution['cdf']\n",
    "        title_str = \"Verteilungsfunktion der {}\".format(distrib_select.value)\n",
    "        mean = dict_distribution['mean_cdf']\n",
    "        mode = dict_distribution['mode_cdf']\n",
    "        median = dict_distribution['median_cdf']\n",
    "        quantil = dict_distribution['quantile_cdf']\n",
    "\n",
    "    ax.clear()\n",
    "        \n",
    "    ax.set_title(title_str)\n",
    "    if distrib_select.value in ['Poissonverteilung', 'Gamma-Poisson-Verteilung']:\n",
    "        if toggle_1.value == 'pdf/pmf':\n",
    "            ax.bar(x,y, color=\"darkgreen\", width=0.1)\n",
    "    if distrib_select.value in ['Normalverteilung','Weibull-Verteilung'] or toggle_1.value == \"cdf\":\n",
    "        ax.plot(x, y, ls='-',lw=2)\n",
    "    #\n",
    "    ax.set_xlim(x_range.value[0], x_range.value[-1])   \n",
    "\n",
    "    yLim = ax.get_ylim()\n",
    "    yq = np.max(y)*0.5\n",
    "    #\n",
    "    # info für quantil \n",
    "    txt_q1 = '\\n'.join((\n",
    "        r'$\\mathrm{%.2f}$ %s' % (quantile.value,'Prozent', ),\n",
    "        r'$\\mathrm{%.2f}$ %s' % (quantil[-1],'Wahrscheinlichkeit', )\n",
    "        ))\n",
    "    # \n",
    "    # quantile\n",
    "    ax.axvline(x=quantil[0], ymin=0, ymax=0.5, ls='--', color='darkgrey')\n",
    "    ax.annotate(txt_q1,\n",
    "                 xy=(quantil[0], yq), xycoords='data',\n",
    "                 xytext=(-2,20), textcoords='offset points',\n",
    "                 bbox=dict(boxstyle=\"round\", fc=\"0.6\"),\n",
    "                 arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"angle,angleA=0,angleB=90,rad=10\"))\n",
    "    \n",
    "    if toggle_2.value == 'boxplot':\n",
    "        ax.clear()\n",
    "        ax.boxplot(dict_distribution['rvs'])\n",
    "        ax.set_xticklabels([])\n",
    "    \n",
    "    if toggle_2.value == 'violinplot':\n",
    "        ax.clear()\n",
    "        ax.violinplot(dict_distribution['rvs'], showmeans=True, showmedians=True, showextrema=True)\n",
    "        ax.set_xticklabels([])\n",
    "      \n",
    "    ax.set_ylabel(\"Wahrscheinlichkeit\")\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',\n",
    "               alpha=0.5)\n",
    "    \n",
    "    # parameter in text box \n",
    "    if distrib_select.value in ['Normalverteilung', 'Poissonverteilung']:\n",
    "        if len(mode) > 2:\n",
    "            modeStr = r'%.2f;%.2f' % (mode[0], mode[2], )\n",
    "        else:\n",
    "            modeStr = r'%.2f' % (mode[1], )\n",
    "        textstr = '\\n'.join((\n",
    "            r'$%s$' % (distrib_select.value, ),\n",
    "            r'$\\mu=%.2f$' % (interakt.children[1].value, ),\n",
    "            r'$\\mathrm{median}=%.2f$' % (median[-1], ),\n",
    "            r'$\\mathrm{modus}=%s$' % (modeStr, ),\n",
    "            r'$\\mathrm{mean}=%.2f$' % (mean[-1], ),\n",
    "            r'$\\mathrm{\\sigma}=%.2f$' % (interakt.children[3].value, ),\n",
    "            r'$\\mathrm{\\sigma^2}=%.2f$' % (var, ),\n",
    "            r'$\\mathrm{rms}=%.2f$' % (rms, )))\n",
    "    if distrib_select.value in ['Gamma-Poisson-Verteilung']:\n",
    "            textstr = '\\n'.join((\n",
    "            r'$%s$' % (distrib_select.value, ),\n",
    "            r'$\\mathrm{median}=%.2f$' % (median[-1], ),\n",
    "            r'$\\mathrm{modus}=%.2f, %f$' % (mode[0],mode[-1], ),\n",
    "            r'$\\mathrm{mean}=%.2f$' % (mean[-1], ),\n",
    "            r'$\\mathrm{n}=%.2f$' % (interakt.children[1].value, ),\n",
    "            r'$\\mathrm{p}=%.2f$' % (interakt.children[3].value, ),\n",
    "            r'$\\mathrm{\\sigma^2}=%.2f$' % (var, ),\n",
    "            r'$\\mathrm{rms}=%.2f$' % (rms, )))\n",
    "#\n",
    "    if distrib_select.value in ['Weibull-Verteilung']:\n",
    "            textstr = '\\n'.join((\n",
    "            r'$%s$' % (distrib_select.value, ),\n",
    "            r'$\\mathrm{median}=%.2f$' % (median[-1], ),\n",
    "            r'$\\mathrm{modus}=%.2f$' % (mode[-1], ),\n",
    "            r'$\\mathrm{mean}=%.2f$' % (mean[-1], ),\n",
    "            r'$\\mathrm{\\lambda}=%.2f$' % (interakt.children[3].value, ),\n",
    "            r'$\\mathrm{k}=%.2f$' % (interakt.children[1].value, ),\n",
    "            r'$\\mathrm{var}=%.2f$' % (var, ),\n",
    "            r'$\\mathrm{rms}=%.2f$' % (rms, )))\n",
    "#\n",
    "    # these are matplotlib.patch.Patch properties\n",
    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
    "    # place a text box in upper left in axes coords\n",
    "    if distrib_select.value == 'Normalverteilung':\n",
    "        ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=10,\\\n",
    "                verticalalignment='top', bbox=props)\n",
    "    else:\n",
    "        ax.text(0.75, 0.95, textstr, transform=ax.transAxes, fontsize=10,\\\n",
    "                verticalalignment='top', bbox=props)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_controls(*btn):\n",
    "    # Reset Button:\n",
    "    distrib_select.value = 'Normalverteilung'\n",
    "    size.value = 1000\n",
    "    quantile.value = 50\n",
    "    toggle_1.value = 'pdf/pmf'\n",
    "    toggle_2.value = 'line'\n",
    "    x_range.value = [-5,5]\n",
    "    update_view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "daabd7b61cbe4a489d31183ad2f54fae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "78c2031c170c49febbe85be8d10bae7b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(VBox(children=(Label(value='Signalauswahl:'), Dropdown(index=2, layout=Layout(wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interaktive Visualisierung:\n",
    "#\n",
    "#- Figuren Zeichnen:\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8,8))\n",
    "\n",
    "distribType_old = \"\"\n",
    "p1_old = -1\n",
    "p2_old = -1\n",
    "p3_old = -1\n",
    "#\n",
    "# 1. create a callback which updates the plot when a control-value has changed\n",
    "def update_view(*args):\n",
    "    #\n",
    "    global distribType_old\n",
    "    global interakt\n",
    "    global p1_old, p2_old, p3_old\n",
    "    # Verteilungen und deren Eigenschaften als globale Variablen, da nicht immer eine Verteilung berechnet wird\n",
    "    global x_dist, distrib_f, mean, median, xQ1, dist_rvs, dist_prob, dist_cdf, mean_prob,\\\n",
    "    median_prob, mode_pdf, mean_cdf, median_cdf, mode_cdf, dist_std, dist_var, quantile_prob, quantile_cdf\n",
    "    #\n",
    "    # selections\n",
    "    distribType = distrib_select.value\n",
    "    #\n",
    "    initWidget = True\n",
    "    if distribType_old == distribType:\n",
    "        initWidget = False\n",
    "    #\n",
    "    # Zufallswerte werden nur dann berechnet, wennn sich die Funktionsparameter aendern\n",
    "    # - Abfrage in den jeweiligen Verteilungen\n",
    "    #-----------------------------------------------------------\n",
    "    # Datensatz nach der gewählten Verteilungsfunktion erzeugen\n",
    "    #   \n",
    "    # datensatz mit beliebiger größe: rvs({...}) Random variates\n",
    "    # abszisse: ppf({...}}) Percent point function (inverse of cdf — percentiles).\n",
    "    # Dichtefunktion/Wahrscheinlichkeitsfunktion: pdf({...}})/pmf({...}})\n",
    "    # kummulierte Dichtefunktion: cdf({...}}) Cumulative density function.\n",
    "    # Parameter sind teils in der Klasse der Verteilung enthalten:\n",
    "    # - Mittelwert, -Median, -Standardabweichung, -Varianz,\n",
    "    # mode: mode(a[, axis]) Returns an array of the modal (most common) value in the passed array.\n",
    "    # mode https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mode.html#scipy.stats.mode\n",
    "    # rms: sqrt(Mittelwert)\n",
    "    #\n",
    "    if (distribType == \"Normalverteilung\"):\n",
    "        if initWidget:\n",
    "            # µ und sigma\n",
    "            interakt.children[0].value = normLabel_1\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].min = -150\n",
    "            interakt.children[1].max = 150\n",
    "            interakt.children[1].value = 0.0\n",
    "            interakt.children[2].value = normLabel_2\n",
    "            interakt.children[3].disabled = False\n",
    "            interakt.children[3].min = 0.01\n",
    "            interakt.children[3].max = 50\n",
    "            interakt.children[3].value = 1\n",
    "            interakt.children[3].step = 0.05\n",
    "            interakt.children[3].style.handle_color = \"green\"\n",
    "            interakt.children[4].disabled = True\n",
    "            interakt.children[4].value = \"\"\n",
    "            interakt.children[4].disabled = True\n",
    "            interakt.children[4].value = \"\"\n",
    "        #\n",
    "        mu = interakt.children[1].value\n",
    "        sigma = interakt.children[3].value\n",
    "        if (p1_old != mu or p2_old != sigma):\n",
    "            x_dist = np.arange(norm.ppf(0.001,mu, sigma),norm.ppf(0.999, mu, sigma),1/100)\n",
    "            distrib_f = norm(mu, sigma)\n",
    "            mean = distrib_f.mean()\n",
    "            median = distrib_f.median()\n",
    "            #\n",
    "            dist_rvs = distrib_f.rvs(size=size.value)\n",
    "            dist_prob = distrib_f.pdf(x_dist)\n",
    "            dist_cdf = distrib_f.cdf(x_dist)\n",
    "            mean_prob = [mean, distrib_f.pdf(mean)]\n",
    "            median_prob = [median, distrib_f.pdf(median)]\n",
    "            mean_cdf = [mean, distrib_f.cdf(mean)]\n",
    "            median_cdf = [median, distrib_f.cdf(median)]\n",
    "            # Modus berechnen (= µ)\n",
    "            mode_cdf = [mu, distrib_f.cdf(mu)]\n",
    "            mode_pdf = [mu, distrib_f.pdf(mu)]\n",
    "            #\n",
    "            dist_std = distrib_f.std()\n",
    "            dist_var = distrib_f.var()\n",
    "        xQ1 = distrib_f.ppf(quantile.value/100)\n",
    "        quantile_prob = [xQ1, distrib_f.pdf(xQ1)]\n",
    "        quantile_cdf = [xQ1, distrib_f.cdf(xQ1)]\n",
    "#----------------------------------------------------------------------------------            \n",
    "    if (distribType == \"Poissonverteilung\"):\n",
    "        if initWidget:\n",
    "            # µ\n",
    "            interakt.children[0].value = poissonLabel\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].min = 0.0\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].value = 5.0\n",
    "            interakt.children[1].step = 0.5\n",
    "            interakt.children[2].disabled = True\n",
    "            interakt.children[2].value = \"\"\n",
    "            interakt.children[3].style.handle_color = None\n",
    "            interakt.children[3].disabled = True\n",
    "            interakt.children[4].disabled = True\n",
    "            interakt.children[4].value = \"\"\n",
    "            interakt.children[4].disabled = True\n",
    "            interakt.children[5].style.handle_color = None\n",
    "            interakt.children[5].disabled = True\n",
    "            # Diagram-Bereich umschalten damit Funktion sichtbar\n",
    "            x_range.min = 0\n",
    "        #\n",
    "        poiLambda = interakt.children[1].value\n",
    "        if poiLambda == 0:\n",
    "            poiLambda = 0.01\n",
    "        if p1_old != poiLambda:\n",
    "            x_dist = np.arange(poisson.ppf(0.001, poiLambda), poisson.ppf(0.999, poiLambda))\n",
    "            distrib_f = 'NaN' # die poisson-funktion entählt kein 'frozen'-object\n",
    "            mean = poisson.mean(poiLambda)\n",
    "            median = poisson.median(poiLambda)\n",
    "            #\n",
    "            dist_rvs = poisson.rvs(poiLambda, size = size.value)\n",
    "            dist_prob = poisson.pmf(x_dist, poiLambda)\n",
    "            dist_cdf = poisson.cdf(x_dist, poiLambda)\n",
    "            mean_prob = [mean, poisson.pmf(mean, poiLambda)]\n",
    "            median_prob = [median, poisson.pmf(median, poiLambda)]\n",
    "            mean_cdf = [mean, poisson.cdf(mean, poiLambda)]\n",
    "            median_cdf = [median, poisson.cdf(median, poiLambda)]\n",
    "            # Modus berechnen (modus = lambda wenn lambda nicht ganzzahlig, andernfalls 2 modi: lambda, lambda-1)\n",
    "            if poiLambda.is_integer():\n",
    "                mode_cdf = [poiLambda-1, poisson.cdf(poiLambda-1, poiLambda), poiLambda, poisson.cdf(poiLambda, poiLambda)]\n",
    "                mode_pdf = [poiLambda-1, poisson.pmf(poiLambda-1, poiLambda), poiLambda, poisson.pmf(poiLambda, poiLambda)]\n",
    "            else:\n",
    "                mode_cdf = [poiLambda, poisson.cdf(poiLambda, poiLambda)]\n",
    "                mode_pdf = [poiLambda, poisson.pmf(poiLambda, poiLambda)]\n",
    "            dist_std = poisson.std(poiLambda)\n",
    "            dist_var = poisson.var(poiLambda)\n",
    "        xQ1 = poisson.ppf(quantile.value/100, poiLambda)\n",
    "        quantile_prob = [xQ1, poisson.pmf(xQ1, poiLambda)]\n",
    "        quantile_cdf = [xQ1, poisson.cdf(xQ1, poiLambda)]\n",
    "#-----------------------------------------------------------------                            \n",
    "    if (distribType == \"Gamma-Poisson-Verteilung\"):\n",
    "        if initWidget:\n",
    "            # Diagram-Bereich umschalten damit Funktion sichtbar\n",
    "            x_range.min = 0\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].disabled = False\n",
    "            interakt.children[0].value = nbLabel_1\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].min = 1\n",
    "            interakt.children[1].max = 500\n",
    "            interakt.children[1].value = 10\n",
    "            interakt.children[1].step = 1.0\n",
    "            interakt.children[2].disabled = False\n",
    "            interakt.children[2].value = nbLabel_2\n",
    "            interakt.children[3].disabled = False\n",
    "            interakt.children[3].style.handle_color = \"green\"\n",
    "            interakt.children[3].min = 0\n",
    "            interakt.children[3].max = 1\n",
    "            interakt.children[3].value = 0.5\n",
    "            interakt.children[3].step = 0.05\n",
    "            interakt.children[4].disabled = True\n",
    "            interakt.children[4].value = \"\"\n",
    "            interakt.children[5].disabled = True\n",
    "            interakt.children[5].style.handle_color = None\n",
    "        #\n",
    "        n= interakt.children[1].value\n",
    "        p= interakt.children[3].value\n",
    "        if (p1_old != n or p2_old != p):\n",
    "            x_dist = np.arange(nbinom.ppf(0.001, n, p), nbinom.ppf(0.999, n, p))\n",
    "            #\n",
    "            distrib_f = nbinom(n, p)\n",
    "            mean = distrib_f.mean()\n",
    "            median = distrib_f.median()\n",
    "            #\n",
    "            dist_rvs = distrib_f.rvs(size=size.value)\n",
    "            dist_prob = distrib_f.pmf(x_dist)\n",
    "            dist_cdf = distrib_f.cdf(x_dist)\n",
    "            mean_prob = [mean, distrib_f.pmf(mean)]\n",
    "            median_prob = [median, distrib_f.pmf(median)]\n",
    "            mean_cdf = [mean, distrib_f.cdf(mean)]\n",
    "            median_cdf = [median, distrib_f.cdf(median)]\n",
    "            # Modus berechnen\n",
    "            if n > 1:\n",
    "                xm = ((1 - p) * (n - 1)) / (p)\n",
    "                # Toleranz von +- 0.01 zulassen    \n",
    "                if np.abs(xm - np.rint(xm)) < 0.011:\n",
    "                    xm = np.rint(xm)\n",
    "                mode_cdf = [xm, distrib_f.cdf(xm)]\n",
    "                mode_pdf = [xm, distrib_f.pmf(xm)]\n",
    "            else:\n",
    "                mode_cdf = [0, 0]\n",
    "                mode_pdf = [0, 0]\n",
    "            dist_std = distrib_f.std()\n",
    "            dist_var = distrib_f.var()\n",
    "        xQ1 = distrib_f.ppf(quantile.value/100)\n",
    "        quantile_prob = [xQ1, distrib_f.pmf(xQ1)]\n",
    "        quantile_cdf = [xQ1, distrib_f.cdf(xQ1)]\n",
    "#---------------------------------------------------\n",
    "    if (distribType == \"Weibull-Verteilung\"):\n",
    "        if initWidget:\n",
    "            # Diagram-Bereich umschalten damit Funktion sichtbar\n",
    "            x_range.min = 0\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].value = wblLabel_1\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].min = 0.1\n",
    "            interakt.children[1].max = 15\n",
    "            interakt.children[1].value = 1.69\n",
    "            interakt.children[1].step = 0.05\n",
    "            interakt.children[2].disabled = False\n",
    "            interakt.children[2].value = wblLabel_2\n",
    "            interakt.children[3].disabled = False\n",
    "            interakt.children[3].style.handle_color = \"green\"\n",
    "            interakt.children[3].min = 0.01\n",
    "            interakt.children[3].max = 100\n",
    "            interakt.children[3].value = 1\n",
    "            interakt.children[3].step = 0.05\n",
    "            interakt.children[4].disabled = False\n",
    "            interakt.children[4].value = wblLabel_3\n",
    "            interakt.children[5].disabled = False\n",
    "            interakt.children[5].style.handle_color = \"green\"\n",
    "            interakt.children[5].min = 0\n",
    "            interakt.children[5].value = 0\n",
    "            interakt.children[5].step = 0.5\n",
    "            interakt.children[5].max = 150\n",
    "        #\n",
    "        form_wbl = interakt.children[1].value\n",
    "        lambda_wbl = interakt.children[3].value\n",
    "        mu = interakt.children[5].value\n",
    "        if (p1_old != form_wbl or p2_old != lambda_wbl or p3_old != mu):\n",
    "            x_dist = np.linspace(weibull_min.ppf(0.01, form_wbl, loc= mu), weibull_min.ppf(0.99, form_wbl, loc=mu),100)\n",
    "            distrib_f = weibull_min(form_wbl, scale=lambda_wbl, loc= mu)\n",
    "            #\n",
    "            mean = distrib_f.mean()\n",
    "            median = distrib_f.median()\n",
    "            #\n",
    "            dist_rvs = distrib_f.rvs(size=size.value)\n",
    "            dist_prob = distrib_f.pdf(x_dist)\n",
    "            dist_cdf = distrib_f.cdf(x_dist)\n",
    "            mean_prob = [mean, distrib_f.pdf(mean)]\n",
    "            median_prob = [median, distrib_f.pdf(median)]\n",
    "            mean_cdf = [mean, distrib_f.cdf(mean)]\n",
    "            median_cdf = [median, distrib_f.cdf(median)]\n",
    "            # Modus berechnen\n",
    "            if form_wbl > 1:\n",
    "                mode_x = lambda_wbl*((form_wbl-1)/form_wbl)**(1/form_wbl) + mu\n",
    "                mode_cdf = [mode_x, distrib_f.cdf(mode_x)]\n",
    "                mode_pdf = [mode_x, distrib_f.pdf(mode_x)]\n",
    "            else:\n",
    "                mode_cdf = [0,0]\n",
    "                mode_pdf = [0, 0]\n",
    "            #\n",
    "            dist_std = distrib_f.std()\n",
    "            dist_var = distrib_f.var()\n",
    "        xQ1 = distrib_f.ppf(quantile.value/100)\n",
    "        quantile_prob = [xQ1, distrib_f.pdf(xQ1)]\n",
    "        quantile_cdf = [xQ1, distrib_f.cdf(xQ1)]\n",
    "#----------------------------------------------------------------------------------\n",
    "# diagramme aktualisieren\n",
    "#\n",
    "    distributionSelected = {\\\n",
    "                            'x': x_dist,\\\n",
    "                            'distrib_f': distrib_f,\\\n",
    "                            'rvs': dist_rvs,\\\n",
    "                            'prob_f': dist_prob,\\\n",
    "                            'cdf': dist_cdf,\\\n",
    "                            'mean_prob': mean_prob,\\\n",
    "                            'median_prob': median_prob,\\\n",
    "                            'mean_cdf': mean_cdf,\\\n",
    "                            'median_cdf': median_cdf,\\\n",
    "                            'mode_cdf': mode_cdf,\\\n",
    "                            'mode_prob':mode_pdf,\\\n",
    "                            'std': dist_std,\\\n",
    "                            'var': dist_var,\\\n",
    "                            'rms': np.sqrt(mean_prob[-1]),\\\n",
    "                            'quantile_prob': quantile_prob,\\\n",
    "                            'quantile_cdf': quantile_cdf\\\n",
    "                            }\n",
    "    #\n",
    "    draw_rvs(distributionSelected, axes[0])\n",
    "    draw_params(distributionSelected, axes[1])\n",
    "    draw_custom(distributionSelected, axes[2])\n",
    "    \n",
    "    distribType_old = distribType\n",
    "    p1_old = interakt.children[1].value\n",
    "    p2_old = interakt.children[3].value\n",
    "    p3_old = interakt.children[5].value\n",
    "\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    \n",
    "#--------------------------------------------------------\n",
    "# 2. die Callback-Funktion mit der Funktion 'observe' den Steuerelementen zuweisen\n",
    "    \n",
    "distrib_select.observe(update_view, 'value')\n",
    "size.observe(update_view, 'value')\n",
    "interakt.children[1].observe(update_view, 'value')\n",
    "interakt.children[3].observe(update_view, 'value')\n",
    "interakt.children[5].observe(update_view, 'value')\n",
    "quantile.observe(update_view, 'value')\n",
    "toggle_1.observe(update_view, 'value')\n",
    "toggle_2.observe(update_view, 'value')\n",
    "x_range.observe(update_view, 'value')\n",
    "BtnReset.on_click(reset_controls)\n",
    "\n",
    "#--------------------------------------------------------\n",
    "# Anwendung starten\n",
    "#\n",
    "# Diagramme einmal zeichnen\n",
    "update_view()\n",
    "#\n",
    "# mit 'widgets.VBox / .HBox' die Steuerelemente arangieren\n",
    "box_layout = widgets.Layout(display='inline-flex',flex_flow='row', align_items='stretch', width='99%')\n",
    "\n",
    "widgets.HBox([\n",
    "    widgets.VBox([\n",
    "        signalSelect,\n",
    "        sizeBox,\n",
    "        widgets.HTML(value=\"<hr>\"),\n",
    "        toggleBox_1,\n",
    "        toggleBox_2],\n",
    "        layout = widgets.Layout(width='50%')\n",
    "        ),\n",
    "    widgets.VBox([\n",
    "        interakt,\n",
    "        widgets.HTML(value=\"<hr>\"),\n",
    "        quantileBox,\n",
    "        xRangeBox,\n",
    "        BtnReset],\n",
    "    layout = widgets.Layout(width='50%'))\n",
    "    ])"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    }
   },
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   "metadata": {},
   "source": [
    "**Erläuterungen zur interaktiven Nutzung**\n",
    "<br>\n",
    "<br>\n",
    "**1**: $\\quad$ Signalauswahl () und Datensatzgröße <br>                                            \n",
    "**2**: $\\quad$ Einstellungen zur gewählten Funktion. Parameter werden entsprechend der gewählten Funktion bezeichnet.<br>\n",
    "**3**: $\\quad$ Darstellung umschalten: Auswahl zwischen Dichtefunktion und kummulierter Wahrscheinlichkeitsfunktion. Darstellung ändern zwischen line-plot, boxer-und Whisker-plot und Violin-plot. <br>\n",
    "**4**: $\\quad$ Quantil-Auswahl (in Diagramm 2 sichtbar) und x-Bereich wählen. X-Bereich ist für alle Diagramme gleich.\n",
    "\n",
    "\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IUBH Internationale Hochschule"
   ]
  }
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