probabilityfunctions_en.ipynb

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    "# Probability distributions\n",
    "\n",
    "## Interactive visualization of various distribution functions\n",
    "\n",
    "### Different functions can be selected and set interactively.\n",
    "\n",
    "**functions:**\n",
    "- Poisson distribution\n",
    " - https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.poisson.html#scipy.stats.poisson\n",
    "- Gamma - Poisson distribution\n",
    " - https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.nbinom.html#scipy.stats.nbinom\n",
    "- Binomial distribution\n",
    " - https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.binom.html#scipy.stats.binom\n",
    "- density function (Gauss function)\n",
    " - https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.stats.norm.html\n",
    "- Landau distribution\n",
    " - https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.moyal.html\n",
    "- Gamma distribution\n",
    " - https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.gamma.html#scipy.stats.gamma\n",
    "- Weibull distribution\n",
    " - https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.weibull_min.html#scipy.stats.weibull_min"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "# resources\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import poisson, gamma, moyal, norm, binom, nbinom, weibull_min\n",
    "\n",
    "#set backend for interactive toolbar\n",
    "%matplotlib nbagg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "lang": "de"
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   "source": [
    "# Warnings are issued during the calculation of the distributions.\n",
    "# These are switched off here\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "lang": "de"
   },
   "outputs": [],
   "source": [
    "# interactive switches\n",
    "#\n",
    "# Signal selection\n",
    "distribFuns = ['Poisson distribution', 'Gamma-Poisson distribution',\n",
    "               'Binomial distribution', 'normal distribution', 'Landau distribution',\n",
    "               'Gamma distribution', 'Weibull distribution']\n",
    "distrib_select = widgets.Dropdown(description='', options=distribFuns,\n",
    "                                  value='normal distribution',\n",
    "                                  layout= widgets.Layout(width='55%',height='auto')\n",
    "                                 )\n",
    "#----------------------------------------------------------------------------------\n",
    "layout= widgets.Layout(width='95%',height='auto')\n",
    "style = {'description_width': 'initial'}\n",
    "# Function parameters\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Gauss: µ: expected value, \\ sigma: standard deviation\n",
    "mu = widgets.FloatSlider(min=-150, max=150, value=0, continuous_update=False, layout= layout)\n",
    "sigma = widgets.FloatSlider(min=0.1, max=15, value=1, layout= layout)\n",
    "#\n",
    "normLabel_1 = \"µ: (expected value)\"\n",
    "normLabel_2 = \"$\\sigma$: (standard deviation)\"\n",
    "\n",
    "#------------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Poisson: expected value and variance µ (divided by Gauss expected value), µ> 0\n",
    "poisLabel = \"µ: (expected value and variance)\"\n",
    "#------------------------------------------------------------------------------------------------------------------------------\n",
    "# Gamma-Poisson => negative binomial distribution:\n",
    "# n: number of successes, alternatively adjustable via µ n = (µ² / {\\ sigma} ² - µ)\n",
    "# p: individual probability, alternatively adjustable via µ and sigma: p = (µ / {\\ sigma} ²)\n",
    "#\n",
    "#         \n",
    "gammaPoisLabel_1 = \"n: (number of successes n. n > 0 (mean²/(var - mean))\"\n",
    "gammaPoisLabel_2 = \"p: (probability of a single success. $0 \\leq p \\leq 1$ (mean/varianz)\"\n",
    "\n",
    "# ----------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Binomial distribution: n: number of successes, p: individual probability\n",
    "# (Buttons divided by gamma-Poisson distribution)\n",
    "#\n",
    "binomLabel_1 = \"n: (number of successes n. n > 0 (mean²/(var - mean))\"\n",
    "binomLabel_2 = \"p: (probability of a single success. $0 \\leq p \\leq 1$ (mean/varianz)\"\n",
    "\n",
    "# ----------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Landau distribution: location parameter µ; Scale parameter c\n",
    "# (Buttons divided by normal distribution and Poisson distribution)\n",
    "landauLabel_1 = \"µ: (expected value)\"\n",
    "landauLabel_2 = \"$\\sigma$: (standard deviation)\"\n",
    "# ----------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Gamma distribution: inverse scale parameter b; Shape parameters p\n",
    "# ----------------------------------------------------------------------------------------------------------------------------\n",
    "p_gamma = widgets.FloatSlider(min=0, max=150, value=1, step=0.01, continuous_update=False, layout= layout)\n",
    "#\n",
    "gammaLabel_1 = \"p: form parameter (p>0)\"\n",
    "gammaLabel_2 = \"b: (scaling factor)\"\n",
    "gammaLabel_3 = \"µ: (average)\"\n",
    "#\n",
    "# ----------------------------------------------------------------------------------------------------------------------------\n",
    "#\n",
    "# Weibull: - Scale and shape parameters:\n",
    "#                      1 / lambda> 0\n",
    "wblLabel_1 = \"k: form parameter (k > 0)\"\n",
    "wblLabel_2 = \"$\\lambda$: (scaling parameter $\\lambda$.$1 \\over \\lambda > 0$.)\"\n",
    "wblLabel_3 = \"µ: (shift operator)\"\n",
    "#\n",
    "# ---------------------------------------------------------------------------------------------------------------------------\n",
    "# interactive buttons summarized in widget 'interact'\n",
    "# - the buttons are adapted to each selected distribution function\n",
    "#\n",
    "label_1 = widgets.Label(vlaue=\"\", tooltips=\"\")\n",
    "label_2 = widgets.Label(vlaue=\"\", tooltips=\"\")\n",
    "label_3 = widgets.Label(vlaue=\"\", tooltips=\"\")\n",
    "\n",
    "interakt = widgets.VBox([\\\n",
    "                         label_1,\n",
    "                         widgets.FloatSlider(min=0.1, max=1.0,continuous_update=False),\n",
    "                         label_2,\n",
    "                         widgets.FloatSlider(min = 0.0, max=1.1, continuous_update=False),\n",
    "                         label_3,\n",
    "                         widgets.FloatSlider(min=0.1,max=1.1, continuous_update=False)\n",
    "                        ]\n",
    "                       )\n",
    "# plot limits\n",
    "x_range = widgets.IntRangeSlider(min= -200, max=200, value=[-5,5],description='$x_{range}$',\\\n",
    "                                 continuous_update=False, layout= layout)\n",
    "#\n",
    "BtnReset = widgets.Button(description=\"reset\", button_style='warning')\n",
    "# ---------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lang": "de"
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   "source": [
    "def draw_ProbabilityDensity(x, f_distribution, ax):\n",
    "    ax.clear()\n",
    "    ax.set_title(\"density function for {}\".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', 'Binomial distribution']:\n",
    "        ax.bar(x, f_distribution, color='green', width=0.25)\n",
    "    else:\n",
    "        ax.plot(x, f_distribution, color='green', lw=2)\n",
    "        ax.fill_between(x, 0, f_distribution, where =(f_distribution >= 0),\\\n",
    "                   facecolor='green', alpha=0.3)\n",
    "    # Set the abscissa area according to the area selection\n",
    "    ax.set_xlim(x_range.value[0], x_range.value[-1])\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"probability\")\n",
    "    #\n",
    "    # Indicate grid lines\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',\n",
    "               alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lang": "de"
   },
   "outputs": [],
   "source": [
    "def draw_cumulativeProbability(x, f_distribution, ax):\n",
    "    ax.clear()\n",
    "    ax.set_title(\"cumulative distribution for {}\".format(distrib_select.value))\n",
    "    ax.plot(x, f_distribution, color='C1', lw=2)\n",
    "    ax.fill_between(x, 0, f_distribution, where =(f_distribution >= 0),\\\n",
    "                   facecolor='C1', alpha=0.3)\n",
    "    # Set the abscissa area according to the area selection\n",
    "    ax.set_xlim(x_range.value[0], x_range.value[-1])\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"probability\")\n",
    "    # Indicate grid lines\n",
    "    ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',\n",
    "               alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_controls(*btn):\n",
    "    # Reset Button:\n",
    "    global interakt\n",
    "    distrib_select.value = 'normal distribution'\n",
    "    interakt.children[0].value = normLabel_1\n",
    "    interakt.children[2].value = normLabel_2\n",
    "    interakt.children[1].disabled = False\n",
    "    interakt.children[1].min = -150\n",
    "    interakt.children[1].max = 150\n",
    "    interakt.children[1].style.handle_color = \"green\"\n",
    "    interakt.children[1].value = 0.0\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",
    "    x_range.value = [-5,5]\n",
    "    update_view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "lang": "de",
    "scrolled": false
   },
   "outputs": [
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       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04b1fd8ce9bb47fe97f0aaaf6e82e874",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Dropdown(index=3, layout=Layout(height='auto', width='55%'), options=('Poisson distribution', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# interactive visualization\n",
    "#\n",
    "# draw figure:\n",
    "#\n",
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(6,6))\n",
    "\n",
    "distribType_old = \"\"\n",
    "#\n",
    "# 1. create a callback which updates the plot when a control-value has changed\n",
    "def update_view(*args):\n",
    "    global distribType_old\n",
    "    global interakt\n",
    "    \n",
    "    # selections\n",
    "    distribType = distrib_select.value\n",
    "    \n",
    "    initWidgets = True\n",
    "    if distribType == distribType_old:\n",
    "        initWidgets = False\n",
    "    #-----------------------------------------------------------\n",
    "    # Create data record according to the selected distribution function\n",
    "    #\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",
    "    #\n",
    "    if (distribType == \"normal distribution\"):\n",
    "        if initWidgets:\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].value = normLabel_1\n",
    "            interakt.children[2].value = normLabel_2\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].min = -150\n",
    "            interakt.children[1].max = 150\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].value = 0.0\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",
    "        #\n",
    "        x_dist = np.arange(norm.ppf(0.01, mu, sigma), norm.ppf(0.99, mu, sigma), 1/100)\n",
    "        distrib_f = norm(mu, sigma)\n",
    "        #\n",
    "        dist_prob = distrib_f.pdf(x_dist)\n",
    "        dist_cdf = distrib_f.cdf(x_dist)\n",
    "#------------------------------------------------ \n",
    "    if (distribType == \"Poisson distribution\"):\n",
    "        if initWidgets:\n",
    "            x_range.min = 0\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].value = poisLabel\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].min = 0.1\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].value = 3.76\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",
    "            #\n",
    "        mu = interakt.children[1].value\n",
    "        x_dist = np.arange(poisson.ppf(0.001, mu), poisson.ppf(0.999, mu))\n",
    "        dist_prob = poisson.pmf(x_dist, mu)\n",
    "        dist_cdf = poisson.cdf(x_dist, mu)\n",
    "#--------------------------------------------------------       \n",
    "    if (distribType == \"Gamma-Poisson distribution\"):\n",
    "        if initWidgets:\n",
    "            # Toggle diagram area so function is visible\n",
    "            x_range.min = 0\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].disabled = False\n",
    "            interakt.children[0].value = gammaPoisLabel_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 = gammaPoisLabel_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= interakt.children[1].value\n",
    "        p= interakt.children[3].value\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",
    "        dist_prob = distrib_f.pmf(x_dist)\n",
    "        dist_cdf = distrib_f.cdf(x_dist)\n",
    "#------------------------------------------------------    \n",
    "    if (distribType == \"Binomial distribution\"):\n",
    "        if initWidgets:\n",
    "            # Toggle diagram area so function is visible\n",
    "            x_range.min = 0\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].disabled = False\n",
    "            interakt.children[0].value = binomLabel_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 = binomLabel_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",
    "        #\n",
    "        x_dist = np.arange(binom.ppf(0.01, n, p), binom.ppf(0.99, n, p))\n",
    "        distrib_f = binom(n, p)\n",
    "        dist_prob = distrib_f.pmf(x_dist)\n",
    "        dist_cdf = distrib_f.cdf(x_dist)\n",
    "#-----------------------------------------------------------       \n",
    "    if (distribType == \"Gamma distribution\"):\n",
    "        if initWidgets:\n",
    "            # Toggle diagram area so function is visible\n",
    "            x_range.min = 0\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].disabled = False\n",
    "            interakt.children[0].value = gammaLabel_1\n",
    "            interakt.children[1].disabled = False\n",
    "            interakt.children[1].style.handle_color = \"green\"\n",
    "            interakt.children[1].min = 0\n",
    "            interakt.children[1].max = 150\n",
    "            interakt.children[1].value = 1\n",
    "            interakt.children[1].step = 0.05\n",
    "            interakt.children[2].disabled = False\n",
    "            interakt.children[2].value = gammaLabel_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 = 15\n",
    "            interakt.children[3].value = 0.05\n",
    "            interakt.children[3].step = 0.05\n",
    "            interakt.children[4].disabled = False\n",
    "            interakt.children[4].value = gammaLabel_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 = 1.0\n",
    "            interakt.children[5].max = 150\n",
    "        #\n",
    "        p = interakt.children[1].value\n",
    "        sigma = interakt.children[3].value\n",
    "        mu = interakt.children[5].value\n",
    "        x_dist = np.arange(gamma.ppf(0.001, p, loc=mu), gamma.ppf(0.999, p, loc=mu), 1/100)\n",
    "        #\n",
    "        distrib_f = gamma(p, loc= mu, scale= sigma)\n",
    "        dist_prob = distrib_f.pdf(x_dist)\n",
    "        dist_cdf = distrib_f.cdf(x_dist)\n",
    "#----------------------------------------------------------\n",
    "    if (distribType == \"Landau distribution\"):\n",
    "        if initWidgets:\n",
    "            # Toggle diagram area so function is visible\n",
    "            x_range.min = -10\n",
    "            # init intaeraktWidget\n",
    "            interakt.children[0].disabled = False\n",
    "            interakt.children[0].value = landauLabel_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 = 1\n",
    "            interakt.children[1].step = 0.1\n",
    "            interakt.children[2].disabled = False\n",
    "            interakt.children[2].value = landauLabel_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 = 15\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",
    "            interakt.children[5].min = 0\n",
    "            interakt.children[5].value = 0\n",
    "        #\n",
    "        mu = interakt.children[1].value\n",
    "        sigma = interakt.children[3].value\n",
    "        x_dist = np.arange(moyal.ppf(0.001), moyal.ppf(0.999), 1/100)\n",
    "        #\n",
    "        dist_prob = moyal.pdf(x_dist,loc=mu, scale=sigma)\n",
    "        dist_cdf = moyal.cdf(x_dist,loc=mu, scale=sigma)\n",
    "#--------------------------------------------------------\n",
    "    if (distribType == \"Weibull distribution\"):\n",
    "        if initWidgets:\n",
    "            # init intaeraktWidget\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",
    "        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",
    "        dist_prob = distrib_f.pdf(x_dist)\n",
    "        dist_cdf = distrib_f.cdf(x_dist)\n",
    "#---------------------------------------------------------------------------\n",
    "# update diagrams\n",
    "#\n",
    "#\n",
    "    draw_ProbabilityDensity(x_dist, dist_prob, axes[0])\n",
    "    draw_cumulativeProbability(x_dist, dist_cdf, axes[1])\n",
    "    \n",
    "    distribType_old = distribType\n",
    "    #\n",
    "    fig.tight_layout()\n",
    "    #  \n",
    "#--------------------------------------------------------\n",
    "# 2. Assign the callback function to the controls using the 'observe' function\n",
    "\n",
    "distrib_select.observe(update_view, 'value')\n",
    "x_range.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",
    "BtnReset.on_click(reset_controls)\n",
    "#--------------------------------------------------------\n",
    "# Start the application\n",
    "#\n",
    "# Draw diagrams once\n",
    "update_view()\n",
    "#\n",
    "# arrange the controls with 'widgets.VBox / .HBox'\n",
    "widgets.VBox([\\\n",
    "             distrib_select,\n",
    "             interakt,\n",
    "             x_range,\n",
    "             BtnReset])"
   ]
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   "source": [
    "Copyright © 2020 IUBH Internationale Hochschule"
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