{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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        "row": 0,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "# Got Scotch?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
        "col": null,
        "height": 2,
        "hidden": true,
        "locked": false,
        "row": null,
        "width": 2
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
        "col": null,
        "height": 2,
        "hidden": true,
        "locked": false,
        "row": null,
        "width": 2
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from traitlets import Unicode, List, Instance, link, HasTraits\n",
    "from IPython.display import display, clear_output, HTML, Javascript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "extensions": {
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      "version": 1,
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       "grid_default": {
        "col": null,
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        "hidden": true,
        "locked": false,
        "row": null,
        "width": 2
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "features = [[2, 2, 2, 0, 0, 2, 1, 2, 2, 2, 2, 2],\n",
    " [3, 3, 1, 0, 0, 4, 3, 2, 2, 3, 3, 2],\n",
    " [1, 3, 2, 0, 0, 2, 0, 0, 2, 2, 3, 1],\n",
    " [4, 1, 4, 4, 0, 0, 2, 0, 1, 2, 1, 0],\n",
    " [2, 2, 2, 0, 0, 1, 1, 1, 2, 3, 1, 3],\n",
    " [2, 3, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1],\n",
    " [0, 2, 0, 0, 0, 1, 1, 0, 2, 2, 3, 1],\n",
    " [2, 3, 1, 0, 0, 2, 1, 2, 2, 2, 2, 2],\n",
    " [2, 2, 1, 0, 0, 1, 0, 0, 2, 2, 2, 1],\n",
    " [2, 3, 2, 1, 0, 0, 2, 0, 2, 1, 2, 3],\n",
    " [4, 3, 2, 0, 0, 2, 1, 3, 3, 0, 1, 2],\n",
    " [3, 2, 1, 0, 0, 3, 2, 1, 0, 2, 2, 2],\n",
    " [4, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2],\n",
    " [2, 2, 1, 0, 0, 2, 2, 0, 0, 2, 3, 1],\n",
    " [3, 2, 2, 0, 0, 3, 1, 1, 2, 3, 2, 2],\n",
    " [2, 2, 2, 0, 0, 2, 2, 1, 2, 2, 2, 2],\n",
    " [1, 2, 1, 0, 0, 0, 1, 1, 0, 2, 2, 1],\n",
    " [2, 2, 2, 0, 0, 1, 2, 2, 2, 2, 2, 2],\n",
    " [2, 2, 3, 1, 0, 2, 2, 1, 1, 1, 1, 3],\n",
    " [1, 1, 2, 2, 0, 2, 2, 1, 2, 2, 2, 3],\n",
    " [1, 2, 1, 1, 0, 1, 1, 1, 1, 2, 2, 1],\n",
    " [3, 1, 4, 2, 1, 0, 2, 0, 2, 1, 1, 0],\n",
    " [1, 3, 1, 0, 0, 1, 1, 0, 2, 2, 2, 1],\n",
    " [3, 2, 3, 3, 1, 0, 2, 0, 1, 1, 2, 0],\n",
    " [2, 2, 2, 0, 1, 2, 2, 1, 2, 2, 1, 2],\n",
    " [2, 3, 2, 1, 0, 0, 1, 0, 2, 2, 2, 1],\n",
    " [4, 2, 2, 0, 0, 1, 2, 2, 2, 2, 2, 2],\n",
    " [3, 2, 2, 1, 0, 1, 2, 2, 1, 2, 3, 2],\n",
    " [2, 2, 2, 0, 0, 2, 1, 0, 1, 2, 2, 1],\n",
    " [2, 2, 1, 0, 0, 2, 1, 1, 1, 3, 2, 2],\n",
    " [2, 3, 1, 1, 0, 0, 0, 0, 1, 2, 2, 1],\n",
    " [2, 3, 1, 0, 0, 2, 1, 1, 4, 2, 2, 2],\n",
    " [2, 3, 1, 1, 1, 1, 1, 2, 0, 2, 0, 3],\n",
    " [2, 3, 1, 0, 0, 2, 1, 1, 1, 1, 2, 1],\n",
    " [2, 1, 3, 0, 0, 0, 3, 1, 0, 2, 2, 3],\n",
    " [1, 2, 0, 0, 0, 1, 0, 1, 2, 1, 2, 1],\n",
    " [2, 3, 1, 0, 0, 1, 2, 1, 2, 1, 2, 2],\n",
    " [1, 2, 1, 0, 0, 1, 2, 1, 2, 2, 2, 1],\n",
    " [3, 2, 1, 0, 0, 1, 2, 1, 1, 2, 2, 2],\n",
    " [2, 2, 2, 2, 0, 1, 0, 1, 2, 2, 1, 3],\n",
    " [1, 3, 1, 0, 0, 0, 1, 1, 1, 2, 0, 1],\n",
    " [1, 3, 1, 0, 0, 1, 1, 0, 1, 2, 2, 1],\n",
    " [4, 2, 2, 0, 0, 2, 1, 4, 2, 2, 2, 2],\n",
    " [3, 2, 1, 0, 0, 2, 1, 2, 1, 2, 3, 2],\n",
    " [2, 4, 1, 0, 0, 1, 2, 3, 2, 3, 2, 2],\n",
    " [1, 3, 1, 0, 0, 0, 0, 0, 0, 2, 2, 1],\n",
    " [1, 2, 0, 0, 0, 1, 1, 1, 2, 2, 3, 1],\n",
    " [1, 2, 1, 0, 0, 1, 2, 0, 0, 2, 2, 1],\n",
    " [2, 3, 1, 0, 0, 2, 2, 2, 1, 2, 2, 2],\n",
    " [1, 2, 1, 0, 0, 1, 2, 0, 1, 2, 2, 1],\n",
    " [2, 2, 1, 1, 0, 1, 2, 0, 2, 1, 2, 1],\n",
    " [2, 3, 1, 0, 0, 1, 1, 2, 1, 2, 2, 2],\n",
    " [2, 3, 1, 0, 0, 2, 2, 2, 2, 2, 1, 2],\n",
    " [2, 2, 3, 1, 0, 2, 1, 1, 1, 2, 1, 3],\n",
    " [1, 3, 1, 1, 0, 2, 2, 0, 1, 2, 1, 1],\n",
    " [2, 1, 2, 2, 0, 1, 1, 0, 2, 1, 1, 3],\n",
    " [2, 3, 1, 0, 0, 2, 2, 1, 2, 1, 2, 2],\n",
    " [4, 1, 4, 4, 1, 0, 1, 2, 1, 1, 1, 0],\n",
    " [4, 2, 4, 4, 1, 0, 0, 1, 1, 1, 0, 0],\n",
    " [2, 3, 1, 0, 0, 1, 1, 2, 0, 1, 3, 1],\n",
    " [1, 1, 1, 1, 0, 1, 1, 0, 1, 2, 1, 1],\n",
    " [3, 2, 1, 0, 0, 1, 1, 1, 3, 3, 2, 2],\n",
    " [4, 3, 1, 0, 0, 2, 1, 4, 2, 2, 3, 2],\n",
    " [2, 1, 1, 0, 0, 1, 1, 1, 2, 1, 2, 1],\n",
    " [2, 4, 1, 0, 0, 1, 0, 0, 2, 1, 1, 1],\n",
    " [3, 2, 2, 0, 0, 2, 3, 3, 2, 1, 2, 2],\n",
    " [2, 2, 2, 2, 0, 0, 2, 0, 2, 2, 2, 3],\n",
    " [1, 2, 2, 0, 1, 2, 2, 1, 2, 3, 1, 3],\n",
    " [2, 1, 2, 2, 1, 0, 1, 1, 2, 2, 2, 3],\n",
    " [2, 3, 2, 1, 1, 1, 2, 1, 0, 2, 3, 1],\n",
    " [3, 2, 2, 0, 0, 2, 2, 2, 2, 2, 3, 2],\n",
    " [2, 2, 1, 1, 0, 2, 1, 1, 2, 2, 2, 2],\n",
    " [2, 4, 1, 0, 0, 2, 1, 0, 0, 2, 1, 1],\n",
    " [2, 2, 1, 0, 0, 1, 0, 1, 2, 2, 2, 1],\n",
    " [2, 2, 2, 2, 0, 2, 2, 1, 2, 1, 0, 3],\n",
    " [2, 2, 1, 0, 0, 2, 2, 2, 3, 3, 3, 2],\n",
    " [2, 3, 1, 0, 0, 0, 2, 0, 2, 1, 3, 1],\n",
    " [4, 2, 3, 3, 0, 1, 3, 0, 1, 2, 2, 0],\n",
    " [1, 2, 1, 0, 0, 2, 0, 1, 1, 2, 2, 1],\n",
    " [1, 3, 2, 0, 0, 0, 2, 0, 2, 1, 2, 1],\n",
    " [2, 2, 2, 1, 0, 0, 2, 0, 0, 0, 2, 3],\n",
    " [1, 1, 1, 0, 0, 1, 0, 0, 1, 2, 2, 1],\n",
    " [2, 3, 2, 0, 0, 2, 2, 1, 1, 2, 0, 3],\n",
    " [0, 3, 1, 0, 0, 2, 2, 1, 1, 2, 1, 1],\n",
    " [2, 2, 1, 0, 0, 1, 0, 1, 2, 1, 0, 3],\n",
    " [2, 3, 0, 0, 1, 0, 2, 1, 1, 2, 2, 1]]\n",
    "\n",
    "feature_names = ['Body', 'Sweetness', 'Smoky', \n",
    "                 'Medicinal', 'Tobacco', 'Honey',\n",
    "                 'Spicy', 'Winey', 'Nutty',\n",
    "                 'Malty', 'Fruity', 'cluster']\n",
    "\n",
    "brand_names = ['Aberfeldy',\n",
    " 'Aberlour',\n",
    " 'AnCnoc',\n",
    " 'Ardbeg',\n",
    " 'Ardmore',\n",
    " 'ArranIsleOf',\n",
    " 'Auchentoshan',\n",
    " 'Auchroisk',\n",
    " 'Aultmore',\n",
    " 'Balblair',\n",
    " 'Balmenach',\n",
    " 'Belvenie',\n",
    " 'BenNevis',\n",
    " 'Benriach',\n",
    " 'Benrinnes',\n",
    " 'Benromach',\n",
    " 'Bladnoch',\n",
    " 'BlairAthol',\n",
    " 'Bowmore',\n",
    " 'Bruichladdich',\n",
    " 'Bunnahabhain',\n",
    " 'Caol Ila',\n",
    " 'Cardhu',\n",
    " 'Clynelish',\n",
    " 'Craigallechie',\n",
    " 'Craigganmore',\n",
    " 'Dailuaine',\n",
    " 'Dalmore',\n",
    " 'Dalwhinnie',\n",
    " 'Deanston',\n",
    " 'Dufftown',\n",
    " 'Edradour',\n",
    " 'GlenDeveronMacduff',\n",
    " 'GlenElgin',\n",
    " 'GlenGarioch',\n",
    " 'GlenGrant',\n",
    " 'GlenKeith',\n",
    " 'GlenMoray',\n",
    " 'GlenOrd',\n",
    " 'GlenScotia',\n",
    " 'GlenSpey',\n",
    " 'Glenallachie',\n",
    " 'Glendronach',\n",
    " 'Glendullan',\n",
    " 'Glenfarclas',\n",
    " 'Glenfiddich',\n",
    " 'Glengoyne',\n",
    " 'Glenkinchie',\n",
    " 'Glenlivet',\n",
    " 'Glenlossie',\n",
    " 'Glenmorangie',\n",
    " 'Glenrothes',\n",
    " 'Glenturret',\n",
    " 'Highland Park',\n",
    " 'Inchgower',\n",
    " 'Isle of Jura',\n",
    " 'Knochando',\n",
    " 'Lagavulin',\n",
    " 'Laphroig',\n",
    " 'Linkwood',\n",
    " 'Loch Lomond',\n",
    " 'Longmorn',\n",
    " 'Macallan',\n",
    " 'Mannochmore',\n",
    " 'Miltonduff',\n",
    " 'Mortlach',\n",
    " 'Oban',\n",
    " 'OldFettercairn',\n",
    " 'OldPulteney',\n",
    " 'RoyalBrackla',\n",
    " 'RoyalLochnagar',\n",
    " 'Scapa',\n",
    " 'Speyburn',\n",
    " 'Speyside',\n",
    " 'Springbank',\n",
    " 'Strathisla',\n",
    " 'Strathmill',\n",
    " 'Talisker',\n",
    " 'Tamdhu',\n",
    " 'Tamnavulin',\n",
    " 'Teaninich',\n",
    " 'Tobermory',\n",
    " 'Tomatin',\n",
    " 'Tomintoul',\n",
    " 'Tormore',\n",
    " 'Tullibardine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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        "locked": false,
        "row": null,
        "width": 2
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "features_df = pd.DataFrame(features, columns=feature_names, index=brand_names)\n",
    "features_df = features_df.drop('cluster', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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        "row": null,
        "width": 2
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "norm = (features_df ** 2).sum(axis=1).apply('sqrt')\n",
    "normed_df = features_df.divide(norm, axis=0)\n",
    "sim_df = normed_df.dot(normed_df.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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        "row": null,
        "width": 2
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def radar(df, ax=None):\n",
    "    # calculate evenly-spaced axis angles\n",
    "    num_vars = len(df.columns)\n",
    "    theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars)\n",
    "    # rotate theta such that the first axis is at the top\n",
    "    theta += np.pi/2\n",
    "    if not ax:\n",
    "        fig = plt.figure(figsize=(4, 4))\n",
    "\n",
    "        ax = fig.add_subplot(1,1,1, projection='polar')\n",
    "    else:\n",
    "        ax.clear()\n",
    "    for d, color in zip(df.itertuples(), sns.color_palette()):\n",
    "        ax.plot(theta, d[1:], color=color, alpha=0.7)\n",
    "        ax.fill(theta, d[1:], facecolor=color, alpha=0.5)\n",
    "    ax.set_xticklabels(df.columns)\n",
    "\n",
    "    legend = ax.legend(df.index, loc=(0.9, .95))\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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        "locked": false,
        "row": null,
        "width": 2
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def get_similar(name, n, top=True):\n",
    "    a = sim_df[name].sort_values(ascending=False)\n",
    "    a.name = 'Similarity'\n",
    "    df = pd.DataFrame(a) #.join(features_df).iloc[start:end]\n",
    "    return df.head(n) if top else df.tail(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
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       "grid_default": {
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       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def on_pick_scotch(Scotch):\n",
    "    name = Scotch\n",
    "    # Get top 6 similar whiskeys, and remove this one\n",
    "    top_df = get_similar(name, 6).iloc[1:]\n",
    "    # Get bottom 5 similar whiskeys\n",
    "    df = top_df\n",
    "    \n",
    "    # Make table index a set of links that the radar widget will watch\n",
    "    df.index = ['''<a class=\"scotch\" href=\"#\" data-factors_keys='[\"{}\",\"{}\"]'>{}</a>'''.format(name, i, i) for i in df.index]\n",
    "    \n",
    "    tmpl = f'''<p>If you like {name} you might want to try these five brands. Click one to see how its taste profile compares.</p>'''\n",
    "    prompt_w.value = tmpl\n",
    "    table.value = df.to_html(escape=False)\n",
    "    lines.x = features_df.loc[Scotch].index.values\n",
    "    lines.y = features_df.loc[Scotch].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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      "version": 1,
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       "grid_default": {
        "col": 0,
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        "locked": false,
        "row": 2,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9793f6c5c68647e29bbfa50519370ea2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='Aberfeldy')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prompt_w = widgets.HTML(value='Aberfeldy')\n",
    "display(prompt_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
        "col": 0,
        "height": 6,
        "hidden": false,
        "locked": false,
        "row": 7,
        "width": 4
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "219ba9be2b984eaf9aa3432da29c2aae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='Hello <b>World</b>')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = widgets.HTML(\n",
    "    value=\"Hello <b>World</b>\"\n",
    ")\n",
    "display(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
        "col": 4,
        "height": 9,
        "hidden": false,
        "locked": false,
        "row": 4,
        "width": 8
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51195e849bb740e9a6cfe5edcb244c96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(animation_duration=500, axes=[Axis(scale=OrdinalScale(), tick_rotate=45, tick_style={'font-size': 20}),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bqplot import (OrdinalScale, LinearScale, Bars, Lines,\n",
    "                    Figure, Axis, ColorScale, ColorAxis, CATEGORY10)\n",
    "x_ord = OrdinalScale()\n",
    "y_sc = LinearScale()\n",
    "\n",
    "lines = Lines(x=features_df.loc['Aberfeldy'].index.values,\n",
    "              y=features_df.loc['Aberfeldy'].values, scales={'x': x_ord, 'y': y_sc},\n",
    "              fill='bottom', fill_colors=['#aaaaff'], fill_opacities=[0.4],\n",
    "              stroke_width=3)\n",
    "ax_x = Axis(scale=x_ord, tick_rotate=45, tick_style={'font-size': 20})\n",
    "ax_y = Axis(scale=y_sc, tick_format='0.2f', orientation='vertical')\n",
    "\n",
    "Figure(marks=[lines], axes=[ax_x, ax_y], animation_duration=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "extensions": {
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      "version": 1,
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       "grid_default": {
        "col": 0,
        "height": 3,
        "hidden": false,
        "locked": false,
        "row": 4,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3cce0f1be5540c999a2975031ae887e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Scotch', options=('Aberfeldy', 'Aberlour', 'AnCnoc', 'Ardbeg', 'Ar…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "picker_w = widgets.interact(on_pick_scotch, Scotch=list(sim_df.index))"
   ]
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