{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Of Using PySpark To Find Metal Interactions \n",
    "\n",
    "<br>\n",
    "<img src=\"./figures/metal_interaction.png\" width=600, align=\"left\">\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pyspark import SparkConf, SparkContext                    \n",
    "from mmtfPyspark.io import mmtfReader\n",
    "from mmtfPyspark.interactions import InteractionFilter, GroupInteractionExtractor\n",
    "from mmtfPyspark.filters import ContainsLProteinChain, Resolution\n",
    "from mmtfPyspark.webfilters import Pisces\n",
    "from mmtfPyspark.structureViewer import group_interaction_viewer, metal_distance_widget\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import py3Dmol\n",
    "import time\n",
    "                                                               \n",
    "# Create variables                                             \n",
    "APP_NAME = \"MMTF_Spark\"                                        \n",
    "path = \"../../resources/mmtf_full_sample/\"\n",
    "\n",
    "# Configure Spark                                              \n",
    "conf = SparkConf().setAppName(APP_NAME).setMaster(\"local[*]\")  \n",
    "sc = SparkContext(conf=conf)                                   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read PDB and create PISCES non-redundant set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb = mmtfReader.read_sequence_file(path, sc)\n",
    "pdb = pdb.filter(Pisces(sequenceIdentity = 30, resolution = 2.5))         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup criteria for metal interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chemical component codes of metals in different oxidation states\n",
    "metals = {\"V\",\"CR\",\"MN\",\"MN3\",\"FE\",\"FE2\",\"CO\",\"3CO\",\"NI\",\"3NI\", \"CU\",\"CU1\",\"CU3\",\"ZN\",\"MO\",\"4MO\",\"6MO\"}\n",
    "\n",
    "interactions_filter = InteractionFilter(distanceCutoff = 3.0, minInteractions=4, maxInteractions=6)\n",
    "interactions_filter.set_query_groups(True, metals)\n",
    "\n",
    "# Exclude non-polar interactions\n",
    "interactions_filter.set_target_elements(False, ['H','C','P'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabulate interactions in a Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal interactions: 1577\n"
     ]
    }
   ],
   "source": [
    "interactions = GroupInteractionExtractor().get_interactions(pdb,interactions_filter).cache()\n",
    "print(f\"Metal interactions: {interactions.count()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select interacting atoms and orientational order parameters (q4-q6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>pdbId</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>element0</th>\n",
       "      <th>groupNum0</th>\n",
       "      <th>chain0</th>\n",
       "      <th>element1</th>\n",
       "      <th>groupNum1</th>\n",
       "      <th>chain1</th>\n",
       "      <th>...</th>\n",
       "      <th>chain4</th>\n",
       "      <th>distance4</th>\n",
       "      <th>element5</th>\n",
       "      <th>groupNum5</th>\n",
       "      <th>chain5</th>\n",
       "      <th>distance5</th>\n",
       "      <th>element6</th>\n",
       "      <th>groupNum6</th>\n",
       "      <th>chain6</th>\n",
       "      <th>distance6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1M4L</td>\n",
       "      <td>0.810257</td>\n",
       "      <td>0.425694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>1308</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>196</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.245983</td>\n",
       "      <td>N</td>\n",
       "      <td>69</td>\n",
       "      <td>A</td>\n",
       "      <td>2.034816</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1YIX</td>\n",
       "      <td>0.683122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>601</td>\n",
       "      <td>A</td>\n",
       "      <td>O</td>\n",
       "      <td>205</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.179354</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2ETV</td>\n",
       "      <td>0.622655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ni</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>O</td>\n",
       "      <td>495</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.234011</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3VK6</td>\n",
       "      <td>0.995256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>103</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.352950</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1ZKP</td>\n",
       "      <td>0.922594</td>\n",
       "      <td>0.455911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>245</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>59</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>1.983932</td>\n",
       "      <td>O</td>\n",
       "      <td>155</td>\n",
       "      <td>A</td>\n",
       "      <td>2.543866</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  pdbId        q4        q5  q6 element0 groupNum0 chain0 element1 groupNum1  \\\n",
       "0  1M4L  0.810257  0.425694 NaN       Zn      1308      A        N       196   \n",
       "1  1YIX  0.683122       NaN NaN       Zn       601      A        O       205   \n",
       "2  2ETV  0.622655       NaN NaN       Ni         1      A        O       495   \n",
       "3  3VK6  0.995256       NaN NaN       Zn       103      A        S         4   \n",
       "4  1ZKP  0.922594  0.455911 NaN       Zn       245      A        N        59   \n",
       "\n",
       "  chain1    ...     chain4 distance4 element5 groupNum5  chain5 distance5  \\\n",
       "0      A    ...          A  2.245983        N        69       A  2.034816   \n",
       "1      A    ...          A  2.179354     None      None    None  0.000000   \n",
       "2      A    ...          A  2.234011     None      None    None  0.000000   \n",
       "3      A    ...          A  2.352950     None      None    None  0.000000   \n",
       "4      A    ...          A  1.983932        O       155       A  2.543866   \n",
       "\n",
       "  element6 groupNum6  chain6 distance6  \n",
       "0     None      None    None       0.0  \n",
       "1     None      None    None       0.0  \n",
       "2     None      None    None       0.0  \n",
       "3     None      None    None       0.0  \n",
       "4     None      None    None       0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interactions = interactions.select(\"pdbId\", \\\n",
    "                \"q4\",\"q5\",\"q6\", \\\n",
    "                \"element0\",\"groupNum0\",\"chain0\", \\\n",
    "                \"element1\",\"groupNum1\",\"chain1\",\"distance1\", \\\n",
    "                \"element2\",\"groupNum2\",\"chain2\",\"distance2\", \\\n",
    "                \"element3\",\"groupNum3\",\"chain3\",\"distance3\", \\\n",
    "                \"element4\",\"groupNum4\",\"chain4\",\"distance4\", \\\n",
    "                \"element5\",\"groupNum5\",\"chain5\",\"distance5\", \\\n",
    "                \"element6\",\"groupNum6\",\"chain6\",\"distance6\").cache();\n",
    "\n",
    "# show some example interactions\n",
    "ds = interactions.dropDuplicates([\"pdbId\"])\n",
    "df = ds.toPandas() # convert to pandas dataframe to fit table in jupyter notebook cell\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count Unique interactions by metal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique interactions by metal: \n",
      "+--------+-----+\n",
      "|element0|count|\n",
      "+--------+-----+\n",
      "|      Cu|   30|\n",
      "|      Co|   42|\n",
      "|      Ni|  122|\n",
      "|      Fe|  147|\n",
      "|      Mn|  205|\n",
      "|      Zn| 1031|\n",
      "+--------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique interactions by metal: \")\n",
    "unique_ds = interactions.groupBy(['element0']).count().sort(\"count\")\n",
    "unique_ds.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin plot using Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marshuang80/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:588: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  kde_data = remove_na(group_data)\n",
      "/home/marshuang80/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:816: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  violin_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# tranform Dataset to pandas DataFrame\n",
    "df = interactions.toPandas()\n",
    "\n",
    "# Set fonts\n",
    "sns.set(font_scale=2)\n",
    "\n",
    "# Make subplots\n",
    "fig, ax = plt.subplots(1,3, sharey = True, figsize = (30,5))\n",
    "\n",
    "# Loop through subplots\n",
    "for i in range(3):\n",
    "    subplot = sns.violinplot(x=\"element0\", y=f\"q{i+4}\", palette=\"muted\", data=df, ax = ax[i])\n",
    "    subplot.set(xlabel=\"Metals\", ylabel=\"\", title=f\"q{i+4}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Violin plots for Metal-Elements distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4afa77a8ab25455384585d0242b6e9d1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Metals', options=('Zn', 'Ni', 'Mn', 'Fe', 'Co', 'Cu'), value='Zn')…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function mmtfPyspark.structureViewer.metal_distance_widget.<locals>.metal_distance_violinplot(metal)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe subsets for elements 1-6\n",
    "df_sub = [df[['element0', f'element{i}', f'distance{i}']]\\\n",
    "          .rename(columns={'element0':'Metal', f'element{i}':'Element', f'distance{i}':'Distance'}) \\\n",
    "          for i in range(1,7)]\n",
    "\n",
    "# Vertically concating the dataframe subsets\n",
    "df_concat = pd.concat(df_sub)\n",
    "\n",
    "# Drop rows with NaN values\n",
    "df_concat.dropna(inplace = True)\n",
    "                  \n",
    "metal_distance_widget(df_concat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create subset based on Metal and q values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>pdbId</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>element0</th>\n",
       "      <th>groupNum0</th>\n",
       "      <th>chain0</th>\n",
       "      <th>element1</th>\n",
       "      <th>groupNum1</th>\n",
       "      <th>chain1</th>\n",
       "      <th>...</th>\n",
       "      <th>chain4</th>\n",
       "      <th>distance4</th>\n",
       "      <th>element5</th>\n",
       "      <th>groupNum5</th>\n",
       "      <th>chain5</th>\n",
       "      <th>distance5</th>\n",
       "      <th>element6</th>\n",
       "      <th>groupNum6</th>\n",
       "      <th>chain6</th>\n",
       "      <th>distance6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>5GRQ</td>\n",
       "      <td>0.999569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>212</td>\n",
       "      <td>A</td>\n",
       "      <td>N</td>\n",
       "      <td>96</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.064547</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>2YHO</td>\n",
       "      <td>0.998952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>1002</td>\n",
       "      <td>G</td>\n",
       "      <td>S</td>\n",
       "      <td>418</td>\n",
       "      <td>G</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>2.338642</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>2YHO</td>\n",
       "      <td>0.998921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>1002</td>\n",
       "      <td>E</td>\n",
       "      <td>N</td>\n",
       "      <td>404</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>E</td>\n",
       "      <td>2.310509</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>1ZY7</td>\n",
       "      <td>0.998688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>802</td>\n",
       "      <td>B</td>\n",
       "      <td>S</td>\n",
       "      <td>451</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>2.058088</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>2YHO</td>\n",
       "      <td>0.998687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>1002</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>421</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.301190</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1OQJ</td>\n",
       "      <td>0.998562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>183</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>113</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.371518</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>3ZVS</td>\n",
       "      <td>0.998459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>1162</td>\n",
       "      <td>B</td>\n",
       "      <td>S</td>\n",
       "      <td>132</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>2.310093</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>3UFF</td>\n",
       "      <td>0.998175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>N</td>\n",
       "      <td>231</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>2.315904</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>1Q08</td>\n",
       "      <td>0.998095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>401</td>\n",
       "      <td>A</td>\n",
       "      <td>O</td>\n",
       "      <td>301</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.426980</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>3H0N</td>\n",
       "      <td>0.997777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zn</td>\n",
       "      <td>201</td>\n",
       "      <td>A</td>\n",
       "      <td>S</td>\n",
       "      <td>168</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>2.297602</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pdbId        q4  q5  q6 element0 groupNum0 chain0 element1 groupNum1  \\\n",
       "614   5GRQ  0.999569 NaN NaN       Zn       212      A        N        96   \n",
       "874   2YHO  0.998952 NaN NaN       Zn      1002      G        S       418   \n",
       "872   2YHO  0.998921 NaN NaN       Zn      1002      E        N       404   \n",
       "760   1ZY7  0.998688 NaN NaN       Zn       802      B        S       451   \n",
       "868   2YHO  0.998687 NaN NaN       Zn      1002      A        S       421   \n",
       "496   1OQJ  0.998562 NaN NaN       Zn       183      A        S       113   \n",
       "1429  3ZVS  0.998459 NaN NaN       Zn      1162      B        S       132   \n",
       "1269  3UFF  0.998175 NaN NaN       Zn         3      B        N       231   \n",
       "1316  1Q08  0.998095 NaN NaN       Zn       401      A        O       301   \n",
       "1209  3H0N  0.997777 NaN NaN       Zn       201      A        S       168   \n",
       "\n",
       "     chain1    ...     chain4 distance4 element5 groupNum5  chain5 distance5  \\\n",
       "614       B    ...          A  2.064547     None      None    None       0.0   \n",
       "874       G    ...          G  2.338642     None      None    None       0.0   \n",
       "872       E    ...          E  2.310509     None      None    None       0.0   \n",
       "760       B    ...          B  2.058088     None      None    None       0.0   \n",
       "868       A    ...          A  2.301190     None      None    None       0.0   \n",
       "496       A    ...          A  2.371518     None      None    None       0.0   \n",
       "1429      B    ...          B  2.310093     None      None    None       0.0   \n",
       "1269      B    ...          B  2.315904     None      None    None       0.0   \n",
       "1316      A    ...          A  2.426980     None      None    None       0.0   \n",
       "1209      A    ...          A  2.297602     None      None    None       0.0   \n",
       "\n",
       "     element6 groupNum6  chain6 distance6  \n",
       "614      None      None    None       0.0  \n",
       "874      None      None    None       0.0  \n",
       "872      None      None    None       0.0  \n",
       "760      None      None    None       0.0  \n",
       "868      None      None    None       0.0  \n",
       "496      None      None    None       0.0  \n",
       "1429     None      None    None       0.0  \n",
       "1269     None      None    None       0.0  \n",
       "1316     None      None    None       0.0  \n",
       "1209     None      None    None       0.0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub = df[df[\"element0\"] == 'Zn']    # Fitler only Zinc interactinos \n",
    "df_sub = df_sub.sort_values([\"q4\"], ascending = False).dropna(subset=['q4'])    #Sort by q4 values and drop NaN\n",
    "df_sub = df_sub[df_sub['q5'] != np.nan]    # Revove interactions where q5 has values\n",
    "\n",
    "df_sub.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "904c57af8a434d518d2419adb6504e44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, continuous_update=False, description='Structure', max=1030), Output()…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function mmtfPyspark.structureViewer.group_interaction_viewer.<locals>.view3d(i=0)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_interaction_viewer(df_sub, 'q4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terminate Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  }
 ],
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