{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction \n",
    "Demonstrating widget two-way communication."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">> clustergrammer2 backend version 0.17.1\n"
     ]
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from clustergrammer2 import Network, CGM2, net\n",
    "import json\n",
    "import pandas as pd\n",
    "df = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 29)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clean'] = pd.read_csv('rc_two_cat_clean.csv', index_col=0)\n",
    "df['meta_col'] = pd.read_csv('meta_col.csv', index_col=0)\n",
    "df['clean'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cat</th>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dog</th>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shark</th>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snake</th>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lizard</th>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         color\n",
       "Cat        red\n",
       "Dog     yellow\n",
       "Shark    black\n",
       "Snake     blue\n",
       "Lizard   green"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['meta_cat_col'] = pd.DataFrame()\n",
    "df['meta_cat_col'].loc['Cat', 'color'] = 'red'\n",
    "df['meta_cat_col'].loc['Dog', 'color'] = 'yellow'\n",
    "df['meta_cat_col'].loc['Shark', 'color'] = 'black'\n",
    "df['meta_cat_col'].loc['Snake', 'color'] = 'blue'\n",
    "df['meta_cat_col'].loc['Lizard', 'color'] = 'green'\n",
    "df['meta_cat_col']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['meta_cat_col'].to_csv('meta_cat_col.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['meta_col']['Values'] = pd.Series(1.1, index=df['meta_col'].index.tolist())\n",
    "df['meta_col'].loc['H1781', 'Values'] = -2\n",
    "df['meta_col'].loc['H838', 'Values'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_cats = ['Category', 'Gender', 'Values']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c70069bfa734970ae90e24e9ef8652b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "CGM2(manual_cat='{\"col\": {\"col_cat_colors\": {\"Category: five\": \"#393b79\", \"Category: four\": \"#ff7f0e\", \"Catego…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1 = Network(CGM2)\n",
    "n1.set_global_cat_colors(df['meta_cat_col'])\n",
    "n1.load_df(df['clean'], meta_col=df['meta_col'], col_cats=viz_cats)\n",
    "n1.set_manual_category(col='Category', preferred_cats=df['meta_cat_col'])\n",
    "n1.widget()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "470f05c447744ce7b492829caa66a02d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "CGM2(manual_cat='{\"col\": {\"col_cat_colors\": {\"Category: five\": \"#393b79\", \"Category: four\": \"#ff7f0e\", \"Catego…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n2 = Network(CGM2)\n",
    "n2.load_df(df['clean'], meta_col=df['meta_col'], col_cats=viz_cats)\n",
    "n2.set_global_cat_colors(df['meta_cat_col'])\n",
    "n2.set_manual_category(col='Category', preferred_cats=df['meta_cat_col'])\n",
    "n2.widget(link_net=n1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "5378b9aa213e45359dae3430d5902286": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "TextModel",
      "state": {
       "layout": "IPY_MODEL_fdaefa734607402497280e2282a2f371",
       "style": "IPY_MODEL_f80779fabb804e64985001b96c7c7f71",
       "value": "col-cat-0 -> Category: five"
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     "55bdd8653fe6437f951af10952419ed6": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "7abe84e2c5934c888242b62d7937b9b2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "LinkModel",
      "state": {
       "source": [
        "IPY_MODEL_5378b9aa213e45359dae3430d5902286",
        "value"
       ],
       "target": [
        "IPY_MODEL_92607433eb15404bbb31b3aa572b10ff",
        "value"
       ]
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     },
     "92607433eb15404bbb31b3aa572b10ff": {
      "model_module": "clustergrammer2",
      "model_module_version": "^0.5.19",
      "model_name": "ExampleModel",
      "state": {
       "_model_module_version": "^0.5.19",
       "_view_module_version": "^0.5.19",
       "layout": "IPY_MODEL_55bdd8653fe6437f951af10952419ed6",
       "mat_string": "ini_mat_string",
       "network": "{\"row_nodes\": [{\"name\": \"Gene: CDK4\", \"ini\": 38, \"clust\": 14, \"rank\": 30, \"rankvar\": 20, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 0, \"group\": [10.0, 9.0, 8.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: LMTK3\", \"ini\": 37, \"clust\": 20, \"rank\": 28, \"rankvar\": 24, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 17, \"group\": [21.0, 20.0, 19.0, 13.0, 11.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: LRRK2\", \"ini\": 36, \"clust\": 26, \"rank\": 9, \"rankvar\": 10, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 18, \"group\": [26.0, 25.0, 22.0, 16.0, 13.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: UHMK1\", \"ini\": 35, \"clust\": 25, \"rank\": 26, \"rankvar\": 14, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 19, \"group\": [28.0, 27.0, 24.0, 18.0, 14.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: EGFR\", \"ini\": 34, \"clust\": 13, \"rank\": 33, \"rankvar\": 7, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 1, \"group\": [12.0, 11.0, 10.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: STK32A\", \"ini\": 33, \"clust\": 32, \"rank\": 36, \"rankvar\": 32, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 2, \"group\": [33.0, 32.0, 29.0, 22.0, 17.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: NRK\", \"ini\": 32, \"clust\": 15, \"rank\": 35, \"rankvar\": 33, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 3, \"group\": [11.0, 10.0, 9.0, 5.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: ERBB2\", \"ini\": 31, \"clust\": 34, \"rank\": 24, \"rankvar\": 9, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 20, \"group\": [35.0, 34.0, 31.0, 23.0, 18.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: ERBB4\", \"ini\": 30, \"clust\": 30, \"rank\": 6, \"rankvar\": 2, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 21, \"group\": [31.0, 30.0, 27.0, 20.0, 16.0, 7.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: AAK1\", \"ini\": 29, \"clust\": 36, \"rank\": 18, \"rankvar\": 8, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 22, \"group\": [37.0, 36.0, 33.0, 25.0, 19.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: SRPK3\", \"ini\": 28, \"clust\": 4, \"rank\": 8, \"rankvar\": 15, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 23, \"group\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: STK39\", \"ini\": 27, \"clust\": 35, \"rank\": 7, \"rankvar\": 4, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 4, \"group\": [36.0, 35.0, 32.0, 24.0, 18.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: GRK4\", \"ini\": 26, \"clust\": 3, \"rank\": 1, \"rankvar\": 3, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 24, \"group\": [3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: TBK1\", \"ini\": 25, \"clust\": 12, \"rank\": 34, \"rankvar\": 26, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 25, \"group\": [13.0, 12.0, 11.0, 6.0, 5.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: INSRR\", \"ini\": 24, \"clust\": 23, \"rank\": 13, \"rankvar\": 5, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 26, \"group\": [23.0, 22.0, 20.0, 14.0, 12.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: IRAK1\", \"ini\": 23, \"clust\": 11, \"rank\": 20, \"rankvar\": 30, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 5, \"group\": [14.0, 13.0, 12.0, 7.0, 6.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: KDR\", \"ini\": 22, \"clust\": 33, \"rank\": 19, \"rankvar\": 11, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 27, \"group\": [34.0, 33.0, 30.0, 22.0, 17.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: NPR1\", \"ini\": 21, \"clust\": 16, \"rank\": 27, \"rankvar\": 29, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 6, \"group\": [20.0, 19.0, 18.0, 12.0, 10.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: PAK3\", \"ini\": 20, \"clust\": 9, \"rank\": 11, \"rankvar\": 12, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 28, \"group\": [16.0, 15.0, 14.0, 9.0, 8.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: PDGFRA\", \"ini\": 19, \"clust\": 7, \"rank\": 2, \"rankvar\": 0, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 7, \"group\": [7.0, 6.0, 5.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: PDK4\", \"ini\": 18, \"clust\": 24, \"rank\": 22, \"rankvar\": 22, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 29, \"group\": [24.0, 23.0, 20.0, 14.0, 12.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: ULK4\", \"ini\": 17, \"clust\": 28, \"rank\": 16, \"rankvar\": 19, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 8, \"group\": [29.0, 28.0, 25.0, 19.0, 15.0, 7.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: PRKCE\", \"ini\": 16, \"clust\": 21, \"rank\": 14, \"rankvar\": 1, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 30, \"group\": [22.0, 21.0, 19.0, 13.0, 11.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: PRKG2\", \"ini\": 15, \"clust\": 2, \"rank\": 25, \"rankvar\": 18, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 31, \"group\": [4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: MAPK4\", \"ini\": 14, \"clust\": 18, \"rank\": 10, \"rankvar\": 16, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 9, \"group\": [17.0, 16.0, 15.0, 10.0, 9.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: MAPK11\", \"ini\": 13, \"clust\": 8, \"rank\": 17, \"rankvar\": 34, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 10, \"group\": [8.0, 7.0, 6.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: STK31\", \"ini\": 12, \"clust\": 31, \"rank\": 37, \"rankvar\": 31, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 11, \"group\": [32.0, 31.0, 28.0, 21.0, 16.0, 7.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: GRK1\", \"ini\": 11, \"clust\": 19, \"rank\": 15, \"rankvar\": 23, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 32, \"group\": [18.0, 17.0, 16.0, 10.0, 9.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: ROS1\", \"ini\": 10, \"clust\": 37, \"rank\": 31, \"rankvar\": 37, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 12, \"group\": [38.0, 37.0, 34.0, 26.0, 20.0, 8.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: MAP2K4\", \"ini\": 9, \"clust\": 10, \"rank\": 23, \"rankvar\": 28, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 13, \"group\": [15.0, 14.0, 13.0, 8.0, 7.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: SRC\", \"ini\": 8, \"clust\": 27, \"rank\": 21, \"rankvar\": 21, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 14, \"group\": [27.0, 26.0, 23.0, 17.0, 13.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: TGFBR1\", \"ini\": 7, \"clust\": 17, \"rank\": 12, \"rankvar\": 13, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 15, \"group\": [19.0, 18.0, 17.0, 11.0, 9.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: CAMK2B\", \"ini\": 6, \"clust\": 5, \"rank\": 3, \"rankvar\": 17, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 33, \"group\": [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: STK24\", \"ini\": 5, \"clust\": 6, \"rank\": 0, \"rankvar\": 36, \"cat-0\": \"Gene Type: Interesting\", \"cat_0_index\": 16, \"group\": [9.0, 8.0, 7.0, 4.0, 4.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: DCLK3\", \"ini\": 4, \"clust\": 1, \"rank\": 5, \"rankvar\": 25, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 34, \"group\": [5.0, 4.0, 3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: LATS1\", \"ini\": 3, \"clust\": 22, \"rank\": 29, \"rankvar\": 27, \"cat-0\": \"Gene Type: Not Interesting\", \"cat_0_index\": 35, \"group\": [25.0, 24.0, 21.0, 15.0, 12.0, 6.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, {\"name\": \"Gene: 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