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 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Map Reduce Example\n",
    "\n",
    "This demo shows how to use Map and Reduce to count the total number of atoms in PDB\n",
    "\n",
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkConf, SparkContext\n",
    "from mmtfPyspark.io import mmtfReader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = SparkConf().setMaster(\"local[*]\") \\\n",
    "                      .setAppName(\"MapReduceExample\")\n",
    "sc = SparkContext(conf = conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in MMTF files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"../../resources/mmtf_full_sample/\"\n",
    "\n",
    "pdb = mmtfReader.read_sequence_file(path, sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count number of atoms\n",
    "\n",
    "1) Map each mmtf structure to it's number of atoms\n",
    "\n",
    "2) Count total nubmer of atoms using reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of atoms in PDB: 29059439\n"
     ]
    }
   ],
   "source": [
    "numAtoms = pdb.map(lambda t: t[1].num_atoms).reduce(lambda a,b: a+b)\n",
    "\n",
    "print(f\"Total number of atoms in PDB: {numAtoms}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Terminate Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  }
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