Notebook Dynamic Stock Viz.ipynb

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   "source": [
    "# Dynamic MFA Visualisation"
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    "Script building an interactive Bokeh visualization\n",
    "for dynamic MFA models, where the user can:\n",
    "    - choose between stock and inflow-driven models\n",
    "    - choose different presets for the driver (inflow or stock)\n",
    "    - change the lifetime distribution \n",
    "      (normal distribution defined by mean and standard deviation)\n",
    "    - visualise the effet of decreasing or increasing lifetime\n",
    "    \n",
    "Based on the package dynamic_stock_model by Stefan Pauliuk:\n",
    "https://github.com/stefanpauliuk/dynamic_stock_model/\n",
    "\n",
    "The time and input data have to be specified in the file Sample_data.csv\n",
    "\n",
    "To run the file on a server, it is necessary to open the Anaconda prompt\n",
    "and type these two command lines \n",
    "(the first one should be mofidified to specifiy the actual path)\n",
    "cd *path*\n",
    "bokeh serve --show dynamic_stock_viz.py\n",
    "\n",
    "\n",
    "dependencies:\n",
    "    bokeh == 2.3.0\n",
    "    numpy == 1.19.2\n",
    "    pandas == 1.2.3\n",
    "    scipy == 1.6.2"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import stock_model_functions as smf\n",
    "\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.plotting import figure, curdoc, show \n",
    "from bokeh.layouts import row, column\n",
    "from bokeh.models.widgets import Slider, Select, Div\n",
    "from bokeh.palettes import Category20 \n",
    "from bokeh.application import Application\n",
    "from bokeh.application.handlers import FunctionHandler\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Initializing parameters for the model\n",
    "data = smf.data\n",
    "lifetime_start = 15\n",
    "lifetime_end = 15\n",
    "stdev=5\n",
    "driver='Inflow-driven'\n",
    "selected_input = 'Constant'\n",
    "\n",
    "lifetime = smf.range_lifetime(lifetime_start, lifetime_end, stdev, len(data['Time']))\n",
    "DSM = smf.compute_model(smf.data, lifetime)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def bkapp(curdoc):\n",
    "    global source, source_sc, source_bar, cohorts_matrix \n",
    "   \n",
    "    def update_driver(attrname, old, new):\n",
    "        \"\"\"\n",
    "        Callback function to dynamically update the value of \n",
    "        the driver type parameter\n",
    "        each time the user changes the value in the corresponding widget\n",
    "        It is necessary to update the value of each data source as well\n",
    "        \"\"\"\n",
    "        global source, source_sc, source_bar, cohorts_matrix, driver \n",
    "        driver = new\n",
    "\n",
    "        lifetime = smf.range_lifetime(lifetime_start, lifetime_end, stdev, len(smf.data['Time']))\n",
    "        DSM = smf.compute_model(smf.data, lifetime, driver, input_type=selected_input)\n",
    "        plot_data = {'time': DSM.t,\n",
    "                 'stock': DSM.s,\n",
    "                 'inflows': DSM.i,\n",
    "                 'outflows': DSM.o,\n",
    "                 'stock change': DSM.i - DSM.o\n",
    "                 }\n",
    "        source.data = plot_data\n",
    "        source_sc.data = {'image': [np.flipud(DSM.s_c)]}\n",
    "        cohorts_matrix = pd.DataFrame(\n",
    "            data=DSM.s_c,  \n",
    "            index=DSM.t.to_numpy(), \n",
    "            columns=[str(e) for e in plot_data[\"time\"].to_numpy().tolist()]\n",
    "            )\n",
    "        cohorts_matrix.insert(\n",
    "            0, \"time\",[str(e) for e in plot_data[\"time\"].to_numpy().tolist()])\n",
    "        source_bar = ColumnDataSource(data=cohorts_matrix)\n",
    "\n",
    "    def update_input(attrname, old, new):\n",
    "        \"\"\"\n",
    "        Callback function to dynamically update the drver distribution  \n",
    "        each time the user changes the value in the corresponding widget\n",
    "        It is necessary to update the value of each data source as well\n",
    "        \"\"\"\n",
    "        global source, source_sc, source_bar, cohorts_matrix, selected_input \n",
    "        selected_input = new\n",
    "\n",
    "        lifetime = smf.range_lifetime(lifetime_start, lifetime_end, stdev, len(smf.data['Time']))\n",
    "        DSM = smf.compute_model(smf.data, lifetime, driver, input_type=selected_input)\n",
    "        plot_data = {'time': DSM.t,\n",
    "                 'stock': DSM.s,\n",
    "                 'inflows': DSM.i,\n",
    "                 'outflows': DSM.o,\n",
    "                 'stock change': DSM.i - DSM.o\n",
    "                 }\n",
    "        source.data = plot_data\n",
    "        source_sc.data = {'image': [np.flipud(DSM.s_c)]}\n",
    "        cohorts_matrix = pd.DataFrame(\n",
    "            data=DSM.s_c,  \n",
    "            index=DSM.t.to_numpy(), \n",
    "            columns=[str(e) for e in plot_data[\"time\"].to_numpy().tolist()]\n",
    "            )\n",
    "        cohorts_matrix.insert(\n",
    "            0, \"time\",[str(e) for e in plot_data[\"time\"].to_numpy().tolist()])\n",
    "        source_bar = ColumnDataSource(data=cohorts_matrix)\n",
    "\n",
    "    def update_lifetime_start(attrname, old, new):\n",
    "        \"\"\"\n",
    "        Callback function to dynamically update the value of \n",
    "        parameter defining the lifetime of the first cohort\n",
    "        each time the user changes the value in the corresponding widget.\n",
    "        It is necessary to update the value of each data source as well\n",
    "        \"\"\"\n",
    "        global source, source_sc, source_bar, cohorts_matrix, lifetime_start\n",
    "        lifetime_start = new\n",
    "        lifetime = smf.range_lifetime(lifetime_start, lifetime_end, stdev, len(smf.data['Time']))\n",
    "\n",
    "        DSM = smf.compute_model(smf.data, lifetime, driver, input_type=selected_input)\n",
    "\n",
    "        plot_data = {'time': DSM.t,\n",
    "                 'stock': DSM.s,\n",
    "                 'inflows': DSM.i,\n",
    "                 'outflows': DSM.o,\n",
    "                 'stock change': DSM.i - DSM.o\n",
    "                 }\n",
    "        source.data = plot_data\n",
    "        lifetime_start = new\n",
    "        source_sc.data = {'image': [np.flipud(DSM.s_c)]}\n",
    "        cohorts_matrix = pd.DataFrame(\n",
    "            data=DSM.s_c,  \n",
    "            index=DSM.t.to_numpy(), \n",
    "            columns=[str(e) for e in plot_data[\"time\"].to_numpy().tolist()]\n",
    "            )\n",
    "        cohorts_matrix.insert(\n",
    "            0, \"time\",[str(e) for e in plot_data[\"time\"].to_numpy().tolist()])\n",
    "        source_bar = ColumnDataSource(data=cohorts_matrix)\n",
    "\n",
    "    def update_lifetime_end(attrname, old, new):\n",
    "        \"\"\"\n",
    "        Callback function to dynamically update the value of \n",
    "        parameter defining the lifetime of the last cohort\n",
    "        each time the user changes the value in the corresponding widget.\n",
    "        It is necessary to update the value of each data source as well\n",
    "        \"\"\"\n",
    "        global source, source_sc, source_bar, cohorts_matrix, lifetime_end \n",
    "        lifetime_end = new\n",
    "        lifetime = smf.range_lifetime(lifetime_start, lifetime_end, stdev, len(smf.data['Time']))\n",
    "\n",
    "        DSM = smf.compute_model(smf.data, lifetime, driver, input_type=selected_input)\n",
    "        plot_data = {'time': DSM.t,\n",
    "                 'stock': DSM.s,\n",
    "                 'inflows': DSM.i,\n",
    "                 'outflows': DSM.o,\n",
    "                 'stock change': DSM.i - DSM.o\n",
    "                 }\n",
    "        source.data = plot_data\n",
    "        source_sc.data = {'image': [np.flipud(DSM.s_c)]}\n",
    "        cohorts_matrix = pd.DataFrame(\n",
    "            data=DSM.s_c,  \n",
    "            index=DSM.t.to_numpy(), \n",
    "            columns=[str(e) for e in plot_data[\"time\"].to_numpy().tolist()]\n",
    "            )\n",
    "        cohorts_matrix.insert(\n",
    "            0, \"time\",[str(e) for e in plot_data[\"time\"].to_numpy().tolist()])\n",
    "        source_bar = ColumnDataSource(data=cohorts_matrix)\n",
    "\n",
    "\n",
    "    def update_stdev(attrname, old, new):\n",
    "        \"\"\"\n",
    "        Callback function to dynamically update the value of \n",
    "        the standard deviation parameter \n",
    "        each time the user changes the value in the corresponding widget\n",
    "        It is necessary to update the value of each data source as well\n",
    "        \"\"\"\n",
    "        global source, source_sc, source_bar, cohorts_matrix, stdev\n",
    "        stdev = new\n",
    "        lifetime = smf.range_lifetime(lifetime_start, lifetime_end, stdev, len(smf.data['Time']))\n",
    "\n",
    "        DSM = smf.compute_model(smf.data, lifetime, driver, input_type=selected_input)\n",
    "        plot_data = {'time': DSM.t,\n",
    "                 'stock': DSM.s,\n",
    "                 'inflows': DSM.i,\n",
    "                 'outflows': DSM.o,\n",
    "                 'stock change': DSM.i - DSM.o\n",
    "                 }\n",
    "        source.data = plot_data\n",
    "        source_sc.data = {'image': [np.flipud(DSM.s_c)]}\n",
    "        cohorts_matrix = pd.DataFrame(\n",
    "            data=DSM.s_c,  \n",
    "            index=DSM.t.to_numpy(), \n",
    "            columns=[str(e) for e in plot_data[\"time\"].to_numpy().tolist()]\n",
    "            )\n",
    "        cohorts_matrix.insert(\n",
    "            0, \"time\",[str(e) for e in plot_data[\"time\"].to_numpy().tolist()])\n",
    "        source_bar = ColumnDataSource(data=cohorts_matrix)\n",
    "\n",
    "\n",
    "    def make_line_plots(source):\n",
    "        \"\"\"\n",
    "        Creates 3 line graphs of:\n",
    "        1. Inflows and Outflows vs. Time\n",
    "        2. Stock Change vs. Time\n",
    "        3. Stock vs. Time\n",
    "        one positionnal parameter: source, a column data source containing \n",
    "        the information in plot_data from the Dynamic Stock Model\n",
    "        \"\"\"\n",
    "\n",
    "        options = dict(plot_width=300, plot_height=300,  toolbar_location=None)\n",
    "\n",
    "        # Inflows and outflows\n",
    "        p1 = figure(title=\"Inflows and outflows\",  **options)\n",
    "        p1.circle(\"time\", \"inflows\", color=\"green\", source=source)\n",
    "        p1.circle(\"time\", \"outflows\",  line_color=\"red\", fill_color=None, source=source)\n",
    "\n",
    "        # Stock change\n",
    "        p2 = figure(title=\"Stock change\", **options)\n",
    "        p2.circle(\"time\", \"stock change\",  line_color=\"black\", fill_color=None, source=source)\n",
    "\n",
    "        # Stock plot\n",
    "        p3 = figure(title=\"Stock\",  **options)\n",
    "        p3.circle(\"time\", \"stock\", color=\"blue\", source=source)\n",
    "\n",
    "        return [p1, p2, p3]\n",
    "\n",
    "\n",
    "    def make_bar_plots(source_sc, source_bar):\n",
    "        \"\"\"\n",
    "        Creates 2 graphs:\n",
    "        1. Time-cohorts matrix (heatmap image)\n",
    "        2. Stock Size and column composition (bar chart stacked by cohort)\n",
    "        two positionnal parameters: source_sc, and source_bar,\n",
    "        customs columndata source containing \n",
    "        information about  the stock by time and cohort s_c \n",
    "        from the Dynamic Stock Model\n",
    "        \"\"\"\n",
    "        # Image plot of the Stock by tie and cohort matrix\n",
    "        p4 = figure(x_range=(DSM.t[0], DSM.t[len(DSM.t)-1]),\n",
    "                y_range=(DSM.t[len(DSM.t)-1], DSM.t[0]),\n",
    "                y_axis_label='Time', x_axis_label='Cohorts',\n",
    "                title='Time-Cohorts Matrix',\n",
    "                plot_width=310, plot_height=300,\n",
    "                min_border_right = 20,\n",
    "                toolbar_location=None)\n",
    "\n",
    "        p4.image(\"image\", x=DSM.t[0], y=DSM.t[len(DSM.t)-1], \n",
    "             dw=len(DSM.t), dh=len(DSM.t),\n",
    "             source=source_sc, palette=\"Spectral11\")\n",
    "\n",
    "        stackers = [e for e in cohorts_matrix[\"time\"].to_numpy().tolist()]\n",
    "        palette = [Category20[20][i%19] for i in range(len(stackers))]\n",
    "\n",
    "        p5 = figure(plot_width=590, plot_height=300, x_range=stackers,\n",
    "                title = 'Stock size and cohort composition',\n",
    "                y_axis_location=\"right\", y_axis_label='Stock size',\n",
    "                toolbar_location=None)\n",
    "        p5.vbar_stack(stackers, x='time', width=0.9, color=palette,\n",
    "                             source=source_bar)\n",
    "        p5.xaxis.major_label_orientation = 3.14/2\n",
    "\n",
    "        return [p4, p5]\n",
    "\n",
    "    # Defining sliders for lifetime and selection widget for the input distribution\n",
    "    driver_selection = Select(title=\"Driver\", value=\"Inflow-driven\", \n",
    "                             options=['Inflow-driven','Stock-driven'], width=300)\n",
    "    input_selection = Select(title=\"Driver distribution\", value=\"Constant\", \n",
    "                             options=smf.data.columns.tolist()[1:], width=300)\n",
    "    lifetime_param_start = Slider(start=2, end=50, value=15, step=1, \n",
    "                            title=\"Mean lifetime of 1st cohort\", width=300)\n",
    "    lifetime_param_end = Slider(start=2, end=50, value=15, step=1, \n",
    "                            title=\"Mean lifetime of last cohort\", width=300)\n",
    "    stdev_param = Slider(start=0.5, end=15, value=5, step=.1, \n",
    "                         title=\"Standard dev.\", width=300)\n",
    "\n",
    "    # Launching the  calbacks every time the parameters are modified\n",
    "    driver_selection.on_change(\"value\", update_driver)\n",
    "    input_selection.on_change(\"value\", update_input)\n",
    "    lifetime_param_start.on_change('value_throttled', update_lifetime_start) \n",
    "    lifetime_param_end.on_change('value_throttled', update_lifetime_end) \n",
    "    stdev_param.on_change('value_throttled', update_stdev)\n",
    "\n",
    "\n",
    "    # Preparing data sources for the plots\n",
    "\n",
    "    # Source used for the line plots\n",
    "    plot_data = {'time': DSM.t,\n",
    "             'stock': DSM.s,\n",
    "             'inflows': DSM.i,\n",
    "             'outflows': DSM.o,\n",
    "             'stock change': DSM.i - DSM.o\n",
    "             }\n",
    "    source = ColumnDataSource(data=plot_data)\n",
    "\n",
    "    # Source used for the matrix plot\n",
    "    source_sc = ColumnDataSource(data={'image': [np.flipud(DSM.s_c)]})\n",
    "\n",
    "    # Data preparation for the bar chart of cohorts vs time\n",
    "    cohorts_matrix = pd.DataFrame(\n",
    "         data=DSM.s_c,  \n",
    "         index=DSM.t.to_numpy(), \n",
    "         columns=[str(e) for e in plot_data[\"time\"].to_numpy().tolist()]\n",
    "         )\n",
    "    cohorts_matrix.insert(\n",
    "         0, \"time\",[str(e) for e in plot_data[\"time\"].to_numpy().tolist()])\n",
    "    # Source used for the bar plot \n",
    "    source_bar = ColumnDataSource(data=cohorts_matrix)\n",
    "\n",
    "    # Putting the title and the widgets in a row container\n",
    "    widgets =column(row(Div(text=\"<h1> MFA Stock Dynamics Model</h1>\", width=400,),\n",
    "                        driver_selection, input_selection),\n",
    "                    row(lifetime_param_start, lifetime_param_end, stdev_param))\n",
    "\n",
    "    # Creating final layout and generating the plot\n",
    "    layout =column(\n",
    "            widgets,\n",
    "            row(make_line_plots(source)),\n",
    "            row(make_bar_plots(source_sc, source_bar))\n",
    "            )\n",
    "\n",
    "    curdoc.add_root(layout)\n",
    "    curdoc.title = \" Dynamic MFA model\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
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    "# Show the application\n",
    "# Make sure the URL matches your Jupyter instance\n",
    "show(bkapp)\n",
    "#, notebook_url=\"localhost:8888\", notebook_handle=True)\n"
   ]
  }
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