deskriptive_Statistik_en.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "#### interactive descriptive statistics\n",
    "\n",
    "Different distribution functions can be parameterized and visualized interactively.\n",
    "\n",
    "**Selectable functions:**\n",
    "- normal distribution (Gaussian function), [scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.norm.html#scipy.stats.norm)\n",
    "\n",
    "- Gamma-Poisson distribution (negative binomial distribution),[scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.nbinom.html#scipy.stats.nbinom)\n",
    "\n",
    "- Poisson distribution, [scipy-link](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.poisson.html#scipy.stats.poisson)\n",
    "\n",
    "- Weibull distribution, [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": [
    "# resources\n",
    "#\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 nbagg\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Warnings are issued while the samples are being calculated and the t-test is being performed.\n",
    "# These are switched off here\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "lang": "de"
   },
   "outputs": [],
   "source": [
    "#interactive switches\n",
    "\n",
    "#\n",
    "# Signal selection\n",
    "distribFuns = ['Poisson distribution', 'Gamma-Poisson distribution', 'Normal distribution', 'Weibull distribution']\n",
    "distrib_select = widgets.Dropdown(description = '', options = distribFuns, value = 'Normal distribution',\\\n",
    "                                   layout = widgets.Layout(width='75%', height='auto'))\n",
    "signalSelect = widgets.VBox([\n",
    "    widgets.Label(value=\"signal selection:\"),\n",
    "    distrib_select\n",
    "])\n",
    "\n",
    "\n",
    "layout= widgets.Layout(width='90%',height='auto')\n",
    "style = {'description_width': 'initial'}\n",
    "#\n",
    "# Function parameters\n",
    "#\n",
    "# Number of random values\n",
    "size = widgets.BoundedIntText(value=1000, min=20, max=100000,layout= {'width': '30%'})\n",
    "sizeBox = widgets.VBox([\n",
    "    widgets.Label(value=\"sample size (max.100000)\"),\n",
    "    size\n",
    "])\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Gauss: µ: expected value, \\ sigma: standard deviation\n",
    "normLabel_1 = \"µ: (expected value)\"\n",
    "normLabel_2 = \"$\\sigma$: (standard deviation)\"\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Weibull: - Scale and shape parameters\n",
    "wblLabel_1 = \"k: shape parameter (k > 0)\"\n",
    "wblLabel_2 = \"$\\lambda$: (scale parameter $\\lambda$.$1 \\over \\lambda > 0$.)\"\n",
    "wblLabel_3 = \"µ: (location parameter)\"\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Gamma-Poisson => negative binomial distribution: n: number of successes, p: individual probability\n",
    "nbLabel_1 = \"Number of successes n ($n > 0$).$mean \\over varianz$\"\n",
    "nbLabel_2 = \"individual probability p (0 ... 1). $(mean²\\over (var - mean))$\"\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Poisson: expected value and variance µ (divided by Gauss - expected value)\n",
    "#\n",
    "poissonLabel = \"$\\lambda$: expected value and variance\"\n",
    "#\n",
    "# widget-box with 3 sliders \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=\"quantile selection /%\"),\n",
    "    quantile\n",
    "])\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# toggle buttons for visualization options\n",
    "toggle_1 = widgets.ToggleButtons(options=['pdf/pmf', 'cdf'],\\\n",
    "                                 description='',\\\n",
    "                                 disabled=False,\n",
    "                                 button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "                                 tooltips = ['show density or probability function', 'show distribution function'],\n",
    "                                )\n",
    "toggleBox_1 = widgets.VBox([\n",
    "    widgets.Label(value=\"function selection: density / probability (pdf/pmf) or distribution function (cdf)\"),\n",
    "    toggle_1\n",
    "])\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 view', 'Violin view'],\n",
    "                                )\n",
    "toggleBox_2 = widgets.VBox([\n",
    "    widgets.Label(value=\"plots: 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": {
    "lang": "de"
   },
   "outputs": [],
   "source": [
    "def draw_rvs(dict_distribution, ax):\n",
    "# Display data record of the distribution function\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(\"probability\")\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major',\\\n",
    "                  color='lightgrey',alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lang": "de"
   },
   "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: vertical line, dashed, gray, comment above with arrow, text: µ (value)\n",
    "    # variance: standard deviation: - horizontal line for valid range\n",
    "    # rms\n",
    "    # mode: gauss: 0\n",
    "    # median: gauss: mean\n",
    "    ax.clear()\n",
    "    ax.set_title(\"Parameter of {}\".format(distrib_select.value))\n",
    "    # Draw the distribution again and then enter the relevant information\n",
    "    if distrib_select.value in ['Poisson distribution', 'Gamma-Poisson distribution']:\n",
    "        if toggle_1.value == 'pdf/pmf':\n",
    "            ax.bar(x,y, color='green', width=0.25)\n",
    "    if distrib_select.value in ['Normal distribution','Weibull distribution'] or toggle_1.value == 'cdf':\n",
    "        ax.plot(x, y, color='C1', lw=2)\n",
    "    \n",
    "    # Draw the vertical line up to the function value\n",
    "    # information for axhline or axvline in%!\n",
    "    yLim = ax.get_ylim()\n",
    "    yH = mean[-1]/yLim[-1] \n",
    "    #\n",
    "    # mean value\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",
    "    # normal distributaion:\n",
    "    # mark 1-sigma, 2-sigma und 3-sigma\n",
    "    if (distrib_select.value == 'Normal distribution' 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",
    "        # mark area +/- sigma\n",
    "        # - fill the background with paint\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",
    "        # Draw and label horizontal lines at the +/- sigma points\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",
    "    # Set the abscissa area according to the area selection\n",
    "    ax.set_xlim(x_range.value[0], x_range.value[-1])\n",
    "    #\n",
    "    # Indicate grid lines\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',\n",
    "               alpha=0.5)\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "lang": "de"
   },
   "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 = \"probability function of {}\".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 = \"cumulated distribution function of {}\".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 ['Poisson distribution', 'Gamma-Poisson distribution']:\n",
    "        if toggle_1.value == 'pdf/pmf':\n",
    "            ax.bar(x,y, color=\"darkgreen\", width=0.1)\n",
    "    if distrib_select.value in ['Normal distribution','Weibull distribution'] 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 for quantile \n",
    "    txt_q1 = '\\n'.join((\n",
    "        r'$\\mathrm{%.2f}$ %s' % (quantile.value,'percent', ),\n",
    "        r'$\\mathrm{%.2f}$ %s' % (quantil[-1],'probability', )\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(\"probability\")\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 ['Normal distribution', 'Poisson distribution']:\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 distribution']:\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 distribution']:\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 == 'normal distribution':\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": {
    "lang": "de"
   },
   "outputs": [],
   "source": [
    "def reset_controls(*btn):\n",
    "    # Reset Button:\n",
    "    distrib_select.value = 'Normal distribution'\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": {
    "lang": "de"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18904c40ad974d05bfc88e51c6f7ec28",
       "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": "fb4dda0db0ea420e95f9c6c74d9a3786",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(VBox(children=(Label(value='signal selection:'), Dropdown(index=2, layout=Layout…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interaktive visualising:\n",
    "#\n",
    "#- draw figure:\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",
    "    # as a distribution is not always calculated, distributions and their characteristics ar global variables\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",
    "    # Random values are only calculated if the function parameters change\n",
    "    # - Query in the respective distributions\n",
    "    # ------------------------------------------------- ----------\n",
    "    # Create data record according to the selected distribution function\n",
    "    #\n",
    "    # record of any size: rvs ({...}) random variates\n",
    "    # abscissa: ppf ({...}}) Percent point function (inverse of cdf - percentiles).\n",
    "    # Density function / probability function: pdf ({...}}) / pmf ({...}})\n",
    "    # cumulative density function: cdf ({...}}) Cumulative density function.\n",
    "    # Parameters are partly included in the distribution class:\n",
    "    # - mean, median, standard deviation, variance,\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 (mean)\n",
    "    #\n",
    "    if (distribType == \"Normal distribution\"):\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 == \"Poisson distribution\"):\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",
    "            # switch diagram limits to show distribution\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' # no 'frozen'-object in poisson\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",
    "            # Calculate mode (mode = lambda if lambda is not an integer, otherwise 2 modes: 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 distribution\"):\n",
    "        if initWidget:\n",
    "            # switch diagram limits to show distribution\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\n",
    "            if n > 1:\n",
    "                xm = ((1 - p) * (n - 1)) / (p)\n",
    "                # allow tolerance of +- 0.01    \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 distribution\"):\n",
    "        if initWidget:\n",
    "            # switch diagram limits to show distribution\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 \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",
    "# update diagrams\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. Assign the callback function to the controls using the 'observe' function\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",
    "# run app\n",
    "#\n",
    "# draw diagram\n",
    "update_view()\n",
    "#\n",
    "# arange controls with 'widgets.VBox / .HBox'\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.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "lang": "de"
   },
   "source": [
    "**Explanations for interactive use**\n",
    "<br>\n",
    "<br>\n",
    "**1**: $\\quad$ signalselection and sample size<br>\n",
    "**2**: $\\quad$ settings to the current function. parameters are labeled according the selection.<br>\n",
    "**3**: $\\quad$ switch diagram: Select between probability density and commulative density function. Switch between line-plot, boxer-und Whisker-plot and Violin-plot. <br>\n",
    "**4**: $\\quad$ Quantil and x-range -selection (as seen in diagram 2). x-range is valid for all plots.\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IUBH Internationale Hochschule"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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