{
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   "cell_type": "code",
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   "source": [
    "import pandas as pd  # pandas import for data manipulation\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "# Jupyter import for iteractive widgets ()\n",
    "from ipywidgets import interact, interact_manual, interactive, HBox, VBox, Layout\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "\n",
    "# import bokeh palette to color graphs\n",
    "from bokeh.palettes import Category10_10 as palette\n",
    "import itertools  # import itertool to loop over palette\n",
    "\n",
    "# %matplotlib inline\n",
    "raw_data = pd.read_csv('data.csv',\n",
    "                       sep=',', skiprows=0, index_col=False)  # read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "def wm(df):\n",
    "    wm = sum(df['price']*df['results'])/sum(df['results'])\n",
    "    return wm\n",
    "\n",
    "\n",
    "def std(df):\n",
    "    p_mean = wm(df)\n",
    "    std = (sum(df['results']*(df['price']-(p_mean))**2) /\n",
    "           (sum(df['results'])-1))**0.5\n",
    "    return std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "def color_picker(models, model, colors):\n",
    "    if len(models) <= len(palette):\n",
    "        # give each model in the list sequential palette color\n",
    "        current_color = palette[models.index(model)]\n",
    "    else:\n",
    "        # cycle through palette if more models than colors\n",
    "        current_color = next(colors)\n",
    "    return current_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    "def default_graph():\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "    ax.grid(True)\n",
    "    ax.set_title('Trend', fontsize=16)\n",
    "    ax.set_xlabel('Year', fontsize=16)\n",
    "    ax.set_ylabel('Price', fontsize=16)\n",
    "    ax.autoscale(tight=True)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "\n",
    "def default_dist():\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
    "    ax.grid(True)\n",
    "    ax.set_title('Distribution', fontsize=16)\n",
    "    ax.set_xlabel('Price', fontsize=16)\n",
    "    ax.set_ylabel('Offers', fontsize=16)\n",
    "    ax.autoscale(tight=True)\n",
    "    return fig, ax"
   ]
  },
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       "HBox(children=(VBox(children=(SelectMultiple(description='Model', options=('opel corsa (FR)', 'opel corsa (DE)…"
      ]
     },
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   ],
   "source": [
    "# make a cyclable palette for large models lists\n",
    "colors = itertools.cycle(palette)\n",
    "\n",
    "\n",
    "def update(models=['renault clio (FR)'], year=2012, checkbox_status=False):\n",
    "\n",
    "    # create 2 output widgets to intercept graphs\n",
    "    out1 = widgets.Output()\n",
    "    out2 = widgets.Output()\n",
    "\n",
    "    # create a Tab widget to arrange Output widgets\n",
    "    tab = widgets.Tab(children=[out2, out1])\n",
    "    tab.set_title(0, 'Distribution')\n",
    "    tab.set_title(1, 'Trend')\n",
    "\n",
    "    # redirect mpl output to one Output widget (tabs separation)\n",
    "    with out1:\n",
    "        fig, ax = default_graph()\n",
    "        for model in models:     # loop through the selected models\n",
    "\n",
    "            # set color scheme for selected models\n",
    "            current_color = color_picker(models, model, colors)\n",
    "\n",
    "            price_by_year = pd.DataFrame()  # create empty data frame\n",
    "\n",
    "            price_by_year['year'] = raw_data[raw_data['model']\n",
    "                                             == model]['year'].unique()  # get years for a specific model\n",
    "\n",
    "            price_by_year['price'] = raw_data[raw_data['model'] == model][[\n",
    "                'year', 'results', 'price']].groupby('year').apply(wm).values  # get weighted mean price per year\n",
    "\n",
    "            price_by_year['std'] = raw_data[raw_data['model'] == model][[\n",
    "                'year', 'results', 'price']].groupby('year').apply(std).values  # get standard deviation\n",
    "\n",
    "            price_by_year['lower'] = price_by_year['price'] - \\\n",
    "                price_by_year['std']  # get lower band limit\n",
    "\n",
    "            price_by_year['upper'] = price_by_year['price'] + \\\n",
    "                price_by_year['std']  # get upper band limit\n",
    "\n",
    "            if checkbox_status:\n",
    "\n",
    "                ax.fill_between(price_by_year['year'], price_by_year['lower'],\n",
    "                                price_by_year['upper'], facecolor=current_color, alpha=0.3)\n",
    "            ax.plot(price_by_year['year'], price_by_year['price'], lw=3,\n",
    "                    color=current_color, marker=\".\", markersize=10, label=model)\n",
    "\n",
    "        # display(fig)\n",
    "        plt.show(fig)\n",
    "\n",
    "    # redirect mpl output to the second Output widget (tabs separation)\n",
    "    with out2:\n",
    "        fig2, ax2 = default_dist()\n",
    "\n",
    "        for model in models:     # loop through the selected models\n",
    "\n",
    "            # set color scheme for selected models\n",
    "            current_color = color_picker(models, model, colors)\n",
    "\n",
    "            # prepare price distribution for a selected year\n",
    "\n",
    "            df = raw_data[(raw_data['model'] == model) & (\n",
    "                raw_data['year'] == year)]  # create filtered df\n",
    "            # introduce shift of bars in bar plot for better visibility\n",
    "            shift = models.index(model)*200/len(models)\n",
    "            # smoothen results by rolling mean with window 5\n",
    "            x = df['price'].values+shift\n",
    "            # create array of bar width parameters for mpl bar plot\n",
    "            width_arr = np.ones(len(x))*150\n",
    "            # calculate sliding window mean with winndow size 5\n",
    "            y_rolling_mean = df['results'].rolling(5).mean().values\n",
    "            # create bar plot\n",
    "            ax2.bar(x, y_rolling_mean, color=current_color,\n",
    "                    width=width_arr, linewidth=1, label=model, alpha=1.0)\n",
    "\n",
    "        plt.show(fig2)\n",
    "\n",
    "    # add legends to the plots\n",
    "    ax2.legend(loc='best')\n",
    "    ax.legend(loc='best')\n",
    "    # display graphs wrapped in Tab widgets\n",
    "    display(tab)\n",
    "\n",
    "    return\n",
    "\n",
    "\n",
    "# create instance of Interactive widget acting as a container for other widgets\n",
    "widget = interactive(update, models=widgets.SelectMultiple(\n",
    "    options=list(raw_data['model'].unique()),\n",
    "    value=[],\n",
    "    # rows=8,\n",
    "    description='Model',\n",
    "    disabled=False),\n",
    "    year=widgets.IntSlider(\n",
    "    value=2012,\n",
    "    min=2008,\n",
    "    max=2016,\n",
    "    step=1,\n",
    "    description='year',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'),\n",
    "    checkbox_status=widgets.Checkbox(\n",
    "    value=False,\n",
    "    description='Show trend error bar',\n",
    "    disabled=False)\n",
    ")\n",
    "\n",
    "# create vertical column with control elements\n",
    "controls = VBox(widget.children[:-1],\n",
    "                layout=Layout(width='35%', height='400px'))\n",
    "# declare output window as the Output widget (last in child list) of interactive widget\n",
    "output = widget.children[-1]\n",
    "\n",
    "# display created design\n",
    "display(HBox([controls, output]))\n",
    "\n",
    "#show(update(), notebook_handle=True)"
   ]
  },
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