Data_Analysis_of_arxiv_for_python3.x_v1.0.ipynb

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    "# Data-Analysis of Arxiv website research papers - v1.0\n",
    "\n",
    "This python book is created for Python2.x version programmers.Published on 5-March-2017.\n",
    "\n",
    "In this book we have total 3 section.\n",
    "\n",
    "* Section 1: **Fetching meta data from Arxiv.org**\n",
    "* Section 2: **Cleaning and Analysis of data with visualization of patterns**\n",
    "* Section 3: **Downloading research all research paper(optional if any want to download)**\n",
    "\n",
    "Created by: \n",
    "\n",
    "* 1) Neel Shah - Know about him more:  [neelshah18.github.io](https://neelshah18.github.io/) | [Linkedin](https://www.linkedin.com/in/neel-shah-7b5495104/) | [GitHub](https://github.com/NeelShah18) | Email:neelknightme@gmail.com\n",
    "\n",
    "Special thanks to :\n",
    "\n",
    "* 1) Malaikannan Sankarasubbu - Know about him more:  [Linkedin](https://www.linkedin.com/in/malaikannan/) | [GitHub](href=\"https://github.com/malaikannan)\n",
    "\n",
    "* 2) Dr. Jacob Minz - Know about him more:  [Linkedin](https://www.linkedin.com/in/jacob-minz-16762a3/) | [GitHub](https://github.com/jrminz)\n",
    "\n",
    "* 3) Anirban Santara - Know about him more:  [Linkedin](https://www.linkedin.com/in/anirbansantara/) | [GitHub](https://github.com/Santara)\n",
    "\n",
    "If you have any question, problem to run this code, suggetion or feedback please feel free to mail Neel Shah. We will try to contact you as soon as possible."
   ]
  },
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   "source": [
    "** Warning: This code is implemented using 3.6+version with latest tool available at that time so please check the compatibility before running it.**\n",
    "\n",
    "#### Question: What is arxiv.org ? \n",
    "\n",
    "arXiv is an e-print service in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance and statistics. Submissions to arXiv should conform to Cornell University academic standards. arXiv is owned and operated by Cornell University, a private not-for-profit educational institution. arXiv is funded by Cornell University Library, the Simons Foundation and by the member institutions. [1](https://arxiv.org/)\n",
    "\n",
    "#### What data-analysis are we doing?\n",
    "\n",
    "We are doing this analysis for [IDLI](http://idli.group/) group. And analysis was done specially for Machine Learning, Deep Learning and Artificial Intelligence and related field only. We use around 24,000+ research paper dataset for data analysis. \n",
    "\n",
    "#### What to except from this data-analysis?\n",
    "\n",
    "We are giving 5 types of pattern from this data. Which help researcher's who are in ML, DL or AI field. All pattern code are available free.\n",
    "\n",
    "** I try my best to use default library for data analysis so anyone who is not from this field can use this without difficulty ** \n"
   ]
  },
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   "source": [
    "## Section 1: Fetching meta data from Arxiv.org and storing in database"
   ]
  },
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   "source": [
    "** I already download all data put in this folder, so you don't need to download it to reproduce this result. You need to change the path of the database **\n",
    "\n",
    "\n",
    "- Below code download meta data from arvix python API by parsing query and getting XML response for metadata. You can change the query as you want. You also can change the number of metadata download of papers, We findout that their are around 26000 pages available for Machine learning, Deep learning and Artificial Intelligence related field. So we dowload around 26,000 meta data.\n",
    "\n",
    "**Please, note arxiv have rate limit so you can download certain number of papers per day. Right now limit is 10,000 for further information please read [this link](https://arxiv.org/help/api/index).**\n",
    "\n",
    "**To contruct the query and search for specific papers, I encourage to read [this link](https://arxiv.org/help/api/user-manual).**\n",
    "\n",
    "**To edit this code, Please read all the comment in the code carefully and change code as per requirement.(we assume you have basic knowledge of urllib,feedparser and sqllite)**\n",
    "\n",
    "Note:\n",
    "\n",
    "- What the data you get is describe at this link . Please read it. [link](https://arxiv.org/help/api/user-manual).**"
   ]
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    "import urllib.request\n",
    "import feedparser\n",
    "import sqlite3\n",
    "import time\n",
    "\n",
    "# Base api query url\n",
    "base_url = 'http://export.arxiv.org/api/query?';\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "\n",
    "# Search parameters\n",
    "search_query = 'cat:cs.CV+OR+cat:cs.AI+OR+cat:cs.LG+OR+cat:cs.CL+OR+cat:cs.NE+OR+cat:stat.ML' # search for CV,CL,AI,LG,NE,ML field papers\n",
    "start = 0                     \n",
    "# retreive the first 1000 results\n",
    "max_results = 1000\n",
    "count = 0\n",
    "fail_count = 0\n",
    "while start < 24000:\n",
    "    time.sleep(5)\n",
    "    query = 'search_query=%s&start=%i&max_results=%i' % (search_query,start,max_results)\n",
    "    start = start + 1000\n",
    "\n",
    "    # Opensearch metadata such as totalResults, startIndex, \n",
    "    # and itemsPerPage live in the opensearch namespase.\n",
    "    # Some entry metadata lives in the arXiv namespace.\n",
    "    # This is a hack to expose both of these namespaces in\n",
    "    # feedparser v4.1\n",
    "    feedparser._FeedParserMixin.namespaces['http://a9.com/-/spec/opensearch/1.1/'] = 'opensearch'\n",
    "    feedparser._FeedParserMixin.namespaces['http://arxiv.org/schemas/atom'] = 'arxiv'\n",
    "\n",
    "    # perform a GET request using the base_url and query\n",
    "    response = urllib.request.urlopen(base_url+query).read()\n",
    "\n",
    "    # parse the response using feedparser\n",
    "    feed = feedparser.parse(response)\n",
    "\n",
    "    # print out feed information\n",
    "    print('Feed title: %s' % feed.feed.title)\n",
    "    print('Feed last updated: %s' % feed.feed.updated)\n",
    "\n",
    "    # print opensearch metadata\n",
    "    # print('totalResults for this query: %s' % feed.feed.opensearch_totalresults)\n",
    "    # print('itemsPerPage for this query: %s' % feed.feed.opensearch_itemsperpage)\n",
    "    # print('startIndex for this query: %s'   % feed.feed.opensearch_startindex)\n",
    "\n",
    "    # Run through each entry, and print out information\n",
    "    for entry in feed.entries:\n",
    "        print('e-print metadata')\n",
    "        print('arxiv-id: %s' % entry.id.split('/abs/')[-1])\n",
    "        c1_data = \"'\"+str(entry.id.split('/abs/')[-1])+\"'\"\n",
    "        print('Published: %s' % entry.published)\n",
    "        c2_data = \"'\"+str(entry.published)+\"'\"\n",
    "        print('Title:  %s' % entry.title)\n",
    "        old_title = str(entry.title)\n",
    "        new_title = old_title.replace(\"'\",\"\")\n",
    "        c3_data = \"'\"+str(new_title)+\"'\"\n",
    "    \n",
    "        # feedparser v4.1 only grabs the first author\n",
    "        author_string = entry.author\n",
    "    \n",
    "        # grab the affiliation in <arxiv:affiliation> if present\n",
    "        # - this will only grab the first affiliation encountered\n",
    "        #   (the first affiliation for the first author)\n",
    "        # Please email the list with a way to get all of this information!\n",
    "        try:\n",
    "            author_string += ' (%s)' % entry.arxiv_affiliation\n",
    "        except AttributeError:\n",
    "            pass\n",
    "    \n",
    "        print('Last Author:  %s' % author_string)\n",
    "    \n",
    "        # feedparser v5.0.1 correctly handles multiple authors, print them all\n",
    "        try:\n",
    "            old_author_list = ', '.join(author.name for author in entry.authors)\n",
    "            new_author_list = old_author_list.replace(\"'\",\"\")\n",
    "            print('Authors:  %s' % ', '.join(author.name for author in entry.authors))\n",
    "            c4_data = \"'\"+str(new_author_list)+\"'\"\n",
    "        except AttributeError:\n",
    "            pass\n",
    "\n",
    "        # get the links to the abs page and pdf for this e-print\n",
    "        for link in entry.links:\n",
    "            if link.rel == 'alternate':\n",
    "                print('abs page link: %s' % link.href)\n",
    "            elif link.title == 'pdf':\n",
    "                print('pdf link: %s' % link.href)\n",
    "                c5_data = \"'\"+str(link.href)+\"'\"\n",
    "    \n",
    "        # The journal reference, comments and primary_category sections live under \n",
    "        # the arxiv namespace\n",
    "        try:\n",
    "            journal_ref = entry.arxiv_journal_ref\n",
    "        except AttributeError:\n",
    "            journal_ref = 'No journal ref found'\n",
    "        print('Journal reference: %s' % journal_ref)\n",
    "        c6_data = \"'\"+str(journal_ref)+\"'\"\n",
    "        \n",
    "        try:\n",
    "            comment = entry.arxiv_comment\n",
    "        except AttributeError:\n",
    "            comment = 'No comment found'\n",
    "        # print('Comments: %s' % comment)\n",
    "        c7_data = \"'\"+str('none')+\"'\"\n",
    "        \n",
    "        # Since the <arxiv:primary_category> element has no data, only\n",
    "        # attributes, feedparser does not store anything inside\n",
    "        # entry.arxiv_primary_category\n",
    "        # This is a dirty hack to get the primary_category, just take the\n",
    "        # first element in entry.tags.  If anyone knows a better way to do\n",
    "        # this, please email the list!\n",
    "        print('Primary Category: %s' % entry.tags[0]['term'])\n",
    "        c8_data = \"'\"+str(entry.tags[0]['term'])+\"'\"\n",
    "        \n",
    "        # Lets get all the categories\n",
    "        all_categories = [t['term'] for t in entry.tags]\n",
    "        print('All Categories: %s' % (', ').join(all_categories))\n",
    "    \n",
    "        # The abstract is in the <summary> element\n",
    "        # print('Abstract: %s' %  entry.summary)\n",
    "        c9_data = \"'\"+str('Summary')+\"'\"\n",
    "        # primary key of database\n",
    "        c0_data = \"'\"+str(count)+\"'\"\n",
    "        try:\n",
    "            c.execute(\"INSERT INTO arxiv_data VALUES ({c1},{c2},{c3},{c4},{c5},{c6},{c7},{c8},{c9})\".format(c1=c1_data,c2=c2_data,c3=c3_data,c4=c4_data,c5=c5_data,c6=c6_data,c7=c7_data,c8=c8_data,c9=c9_data))\n",
    "            conn.commit()\n",
    "            print('-------------Data is stored in database-----------------')\n",
    "            count += 1\n",
    "        except:\n",
    "            fail_count += 1\n",
    "            pass\n",
    "        \n",
    "        \n",
    "        \n",
    "conn.close()        \n",
    "print('Fail count: '+str(fail_count))\n",
    "print('Connection is closed!')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Below code and it's output give the simple example what is the data stored in database."
   ]
  },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '1603.03827v1', '2016-03-12T00:02:51Z', 'Sequential Short-Text Classification with Recurrent and Convolutional\\n  Neural Networks', 'Ji Young Lee, Franck Dernoncourt', 'http://arxiv.org/pdf/1603.03827v1', 'No journal ref found', 'none', 'cs.CL', 'Summary')\n",
      "(1, '1606.00776v2', '2016-06-02T17:37:31Z', 'Multiresolution Recurrent Neural Networks: An Application to Dialogue\\n  Response Generation', 'Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron Courville', 'http://arxiv.org/pdf/1606.00776v2', 'No journal ref found', 'none', 'cs.CL', 'Summary')\n",
      "(2, '1504.00923v1', '2015-04-03T19:57:06Z', 'A Unified Deep Neural Network for Speaker and Language Recognition', 'Fred Richardson, Douglas Reynolds, Najim Dehak', 'http://arxiv.org/pdf/1504.00923v1', 'No journal ref found', 'none', 'cs.CL', 'Summary')\n",
      "(3, '1609.06492v1', '2016-09-21T10:52:03Z', 'Document Image Coding and Clustering for Script Discrimination', 'Darko Brodic, Alessia Amelio, Zoran N. Milivojevic, Milena Jevtic', 'http://arxiv.org/pdf/1609.06492v1', 'ICIC Express Letters Vol. 10 n. 7 July 2016 pp. 1561-1566', 'none', 'cs.CV', 'Summary')\n",
      "(4, '1610.01076v1', '2016-10-04T16:29:28Z', 'Tutorial on Answering Questions about Images with Deep Learning', 'Mateusz Malinowski, Mario Fritz', 'http://arxiv.org/pdf/1610.01076v1', 'No journal ref found', 'none', 'cs.CV', 'Summary')\n"
     ]
    }
   ],
   "source": [
    "import sqlite3\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "data=[]\n",
    "c.execute(\"SELECT * FROM arxiv_data LIMIT 5\")\n",
    "data = c.fetchall()\n",
    "for line in data:\n",
    "    print(line)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 2: data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we start do data analysis.Below is the different area which are included in data analysis:\n",
    "\n",
    "* Computer Vision and Pattern Recognition\n",
    "* Artificial Intelligence\n",
    "* Learning\n",
    "* Computation and Language\n",
    "* Neural and Evolutionary Computing\n",
    "* Machine Learning\n",
    "\n",
    "** Below is the list of patterns we find from this dataset.**\n",
    "\n",
    "* 1) Publish papers in different area during 2010.\n",
    "* 2) Publish papers in different area during 2011.\n",
    "* 3) Publish papers in different area during 2012.\n",
    "* 4) Publish papers in different area during 2013.\n",
    "* 5) Publish papers in different area during 2014.\n",
    "* 6) Publish papers in different area during 2015.\n",
    "* 7) Publish papers in different area during 2016.\n",
    "* 8) Trend analysis from 2010 to 2016 in different field.\n",
    "* 9) Top 10 author for arxiv publiction from 1992 to 2017"
   ]
  },
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g/Nw5uwDDImLv9Hxn4GRgALAV8MkK/fQAJkTEQOAvwNdy/f93RHwMeK7DrsrM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\nJzZmZmZWGE5szMzMrDCc2JiZmVlh/H9YXO4+PQxx0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc2d1a4278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[0])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2010\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RwLmS1gUMDK+dc5Xtx2vPJ9p+EEDSVGAM8IeWfv4FXFYeTwZ2KY+3AfYoj38C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc2d281ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[1])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2011\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZERgaEXuk5/2Ak4HewBbALmX66QyMjYg+wD+A43P9/z4iPg+82GJnZWZmzeKp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc2d354390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[2])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2012\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A2cAFuWP6AUMjYq/0vC9wKtAb2BLYrUw/nYDxEbET8HfgxFz//xURXwJearaz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc309e7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[3])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2013\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QoQ4hYsBImmx7i/6cm30HEQvnjcB5kpYB/gl8aojjiYiIFkl2IhaC7TuAzXqtGBERQyZ7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc309e7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[4])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2014\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AzYHdy/TTFRgfEf2BvwPH5/r/bUR8Gniuzc7KzMxaxVNRtqrbF+iXBlkA1pXU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\nExszMzMrDCc2ZmZmVhj/H6Ujwf7JgZiQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc331ca4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[5])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2015\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wZ0TsBJwFnJ87Z2dgRETsnZ7vBJwC9Ae2BHYv009XYGJEDAD+AZyQ6/9/IuIz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\nMzOzwnBiY2ZmZoXx/wEltsT69d+QhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc30741e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT * FROM `freq_data`;\")\n",
    "publish_date = c.fetchall()\n",
    "s = str(publish_date[6])\n",
    "s1 = s[6:len(s)-1]\n",
    "c1,c2,c3,c4,c5,c6  = s1.split(\",\")\n",
    "\n",
    "\n",
    "objects = ('Computer Vision and Pattern Recognition', 'Artificial Intelligence', 'Learning', 'Computation and Language', 'Neural and Evolutionary Computing', 'Machine Learning')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(c1),int(c2),int(c3),int(c4),int(c5),int(c6)]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Publish Papers in different area during 2016\\n')\n",
    " \n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7diyTJ0+madOmREREMHjwYEqXLk2jRo3IysqiR48eODk5sXXrVlJTU5kwYUKu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\nR0F0DN/KsfN4Rxz72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aTk1eQkLMCkR9xH0TVx9QRoOuvjbL0Xpz2AJUpjyaoErPEjf8Oi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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f220a045668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import numpy as np\n",
    "\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "publish_date = []\n",
    "c.execute(\"SELECT `publish_date`,`primary_categories` FROM `arxiv_data` ORDER BY `_rowid_` ASC LIMIT 0, 50000;\")\n",
    "publish_date = c.fetchall()\n",
    "text = str(publish_date[0])\n",
    "conn.close()\n",
    "count = 0 \n",
    "\n",
    "\n",
    "data_CV = {}\n",
    "data_AI = {}\n",
    "data_LG = {}\n",
    "data_CL = {}\n",
    "data_NE = {}\n",
    "data_ML = {}\n",
    "\n",
    "y_CV = []\n",
    "y_AI = []\n",
    "y_LG = []\n",
    "y_CL = []\n",
    "y_NE = []\n",
    "y_ML = []\n",
    "\n",
    "for data in publish_date:\n",
    "    text = str(data)\n",
    "    year = text[2:6] \n",
    "    field = text[-4:-2] \n",
    "    if field == 'CV':\n",
    "        try:\n",
    "            data_CV[year] = int(data_CV[year]) + 1\n",
    "        except:\n",
    "            data_CV[year] = 1\n",
    "            pass\n",
    "    if field == 'AI':\n",
    "        try:\n",
    "            data_AI[year] = int(data_AI[year]) + 1\n",
    "        except:\n",
    "            data_AI[year] = 1\n",
    "            pass\n",
    "    if field == 'LG':\n",
    "        try:\n",
    "            data_LG[year] = int(data_LG[year]) + 1\n",
    "        except:\n",
    "            data_LG[year] = 1\n",
    "            pass\n",
    "    if field == 'CL':\n",
    "        try:\n",
    "            data_CL[year] = int(data_CL[year]) + 1\n",
    "        except:\n",
    "            data_CL[year] = 1\n",
    "            pass\n",
    "    if field == 'NE':\n",
    "        try:\n",
    "            data_NE[year] = int(data_NE[year]) + 1\n",
    "        except:\n",
    "            data_NE[year] = 1\n",
    "            pass\n",
    "    if field == 'ML':\n",
    "        try:\n",
    "            data_ML[year] = int(data_ML[year]) + 1\n",
    "        except:\n",
    "            data_ML[year] = 1\n",
    "            pass\n",
    "\n",
    "x_xix = []        \n",
    "        \n",
    "x = 2010\n",
    "zero = 0\n",
    "while int(x) < 2017:\n",
    "    try:\n",
    "        #print(x)\n",
    "        y_CV.append(data_CV[str(x)])\n",
    "        #print(data_CV[x])\n",
    "    except:\n",
    "        y_CV.append(int(zero))\n",
    "        pass\n",
    "    try:\n",
    "        #print(x)\n",
    "        y_AI.append(data_AI[str(x)])\n",
    "        #print(data_CV[x])\n",
    "    except:\n",
    "        y_AI.append(int(zero))\n",
    "        pass\n",
    "    try:\n",
    "        #print(x)\n",
    "        y_CL.append(data_CL[str(x)])\n",
    "        #print(data_CV[x])\n",
    "    except:\n",
    "        y_CL.append(int(zero))\n",
    "        pass\n",
    "    try:\n",
    "        #print(x)\n",
    "        y_LG.append(data_LG[str(x)])\n",
    "        #print(data_CV[x])\n",
    "    except:\n",
    "        y_LG.append(int(zero))\n",
    "        pass\n",
    "    try:\n",
    "        #print(x)\n",
    "        y_NE.append(data_NE[str(x)])\n",
    "        #print(data_CV[x])\n",
    "    except:\n",
    "        y_NE.append(int(zero))\n",
    "        pass\n",
    "    try:\n",
    "        #print(x)\n",
    "        y_ML.append(data_ML[str(x)])\n",
    "        #print(data_CV[x])\n",
    "    except:\n",
    "        y_ML.append(int(zero))\n",
    "        pass\n",
    "    \n",
    "    x_xix.append(int(x))\n",
    "    x += 1\n",
    "\n",
    "\n",
    "style.use('ggplot')\n",
    "plt.plot(x_xix,y_CV,label=\"Computer Vision and Pattern Recognition\")\n",
    "\n",
    "plt.plot(x_xix,y_AI,label=\"Artificial Intelligence\")\n",
    "plt.plot(x_xix,y_CL,label=\"Computation and Language\")\n",
    "plt.plot(x_xix,y_LG,label=\"Learning\")\n",
    "plt.plot(x_xix,y_NE,label=\"Neural and Evolutionary Computing\")\n",
    "plt.plot(x_xix,y_ML,label=\"Machine Learning\")\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Trend of research in different area')\n",
    "plt.ylabel('Number of publish paper')\n",
    "plt.xlabel('Years: 1993 - 2017')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yxDFHAn+S9AtgHnAwaVq14mvAkZLmAa8CZ+Xy4jVWAUdHxAJJDcYQEVMl/RK4\nT9IC4HHgmOafqpmZtQZF+BJYRzJwk94xZvhJ9Q7DzA9st3ZF0viIGFSr3rI0PWtmZrZEnDTNzMxK\nWpauaS4T1L2Xp8XMzFqJR5pmZmYlOWmamZmV5KRpZmZWkq9pdjAxZwbzRg+tdxhmDfL1dmvvPNI0\nMzMryUnTzMysJCdNMzOzkpw0zczMSiqVNCXtLynyih216p5YvRpIWyDpEkkHNfPYXSXd3FLtNTOG\nUZJqPhfRzMxaT9mR5mHAg/l7LScCbS5pmpmZLamaSVNSD2BH4FvAobls1zzyuUbSk5IuV3ICsDZw\nr6R7c93DJE2WNEXSOYV2Z0v6paSJksZI6iVpFUn/krRcrtNd0kuSukg6VtKjuf61ldFsHvGdJ+lh\nSc9XRn85nvMlPSXpLuBThb4HSrpP0nhJt0taK5ePknSOpLGSnpa0U5k3cXHbk7SipH9KmibpekmP\nVEaRkr4gabSkxyRdnd9/MzNrA8qMNPcDRkbE08Abkgbm8i1Jo8pNgfWAHfJizC8Du0XEbpLWBs4B\ndgcGAIMrizoD3YExEbEFcD9wbES8DUwAdsl19gFuj4h5wHURMTjXf4KUxCvWIiX2fYCzc9kBwEY5\nvqOA7QEkdQH+ABwUEQOB4cAvC211joit87mdVijfSdKEyhew7xK0913gzYjYFPgZMDC31RP4KbBH\nRGxFWnPT63yZmbURZR5ucBjw+7x9ZX59MzA2IqYD5CTSlzSFWzQYGBURr+d6lwM7AzcAH+Z2AMYD\ne+btq4BDgHtJI9sLc3k/SWcCqwI9gNsL/dwQEQuBaZJ65bKdgSsiYgHwsqR7cvlGQD/gTkkAnYBX\nCm1dV4ipb6H8gYjYp/JC0iVL0N6O5Pc0IqZImpTLtyUl+YdyW8sDo6lB0hBgCECfXqvVqm5mZs3U\nZNKUtDpplLi5pCAlhABuAeYWqi6o1VYD5sWiFbCLx48Azsp9DwQqye4SYP+ImCjpGGDXQlvFWFSj\nXwFTI2K7RvZX2ip7Ti3ZnoA7I6LMteOPRMQwYBikRagX51gzMyuv1vTsQcDfI+IzEdE3InoDLwBN\nXet7F1gpb48FdpHUU1In0ij1vqY6jIjZwKOkkdjNeaRIbvOVPB16eI24IU35HiKpU77GuFsufwpY\nU9J2kKZXJW1Wor3GNKe9h4Cv5fqbApvn8jHADpI2yPu6S/rcEsRmZmYtqFbSPAy4vqrsWpq+i3YY\nMFLSvRF0nU/FAAAW5ElEQVTxCnAKaap1IjA+Im4sEddVwBH5e8XPgEdICefJEm1cDzwDTAP+Rp7m\njIgPSX8MnCNpIuka6vYl2mtQM9u7kJRopwFnAlOBt/M09jHAFXnKdjRQ82M+Zma2dGjRDKktLXnU\n3SUiPpC0PnAXsFFOwEtk4Ca9Y8xw3ztkbZMf2G5tlaTxEVHzs/Be5aQ+ViR9LKcL6Trmd1siYZqZ\nWety0qyDiHgX8NN9zMzaGT971szMrCSPNDsYde/l60ZmZq3EI00zM7OSnDTNzMxKctI0MzMrydc0\nO5iYM4N5o4fWOwyzuvD1fGttHmmamZmV5KRpZmZWkpOmmZlZSR0iaUrqK2lKVdnpkk5ugbZvkDSm\nquw4SUc1ccyukm5ubH9T9SXtK+mU5kdsZmatxTcCNUHSqqQ1PWdLWi8ingeIiItaq8+IGEFaU9TM\nzNqYDjHSbMJyksYDSNpCUkjqk18/J2lFSWtKulbSo/lrh8LxXwVuAq4EDq0UFkexkjaQdJekiZIe\ny6uWUKg7WNLjktbP62MOlzQ2l+1XHbCkYySdn7cvkXSepIclPS/poJZ+g8zMrLyOnjQXAl0lrUxa\nOHscsJOkzwCvRcR7pMWuh0bEYOBA4OLC8YcBV+SvxtYQvRy4ICK2IK2j+Uplh6TtgYuA/SLiOeBU\n4J6I2Jq0KPavJXWvcQ5rATsC+wBnlz5zMzNrcR1leraxRUEDeBjYAdgZOAvYi7Qc1wO5zh7AppIq\nx6wsqQfQHdgQeDAiQtI8Sf0i4qNrp5JWAtaJiOsBIuKDXA6wCWlB7i9ExMv5kC8A+xautXYF+tQ4\ntxsiYiEwTVKvhipIGgIMAejTa7UazZmZWXN1lKT5BlCdLVYHXgDuJ40yPwPcCPyIlExvyfWWA7at\nJLwKSd/Ibb6Qk+DKpNHmqSVjeoWUFLcEKklTwIER8VRVXw0mw2xusWpDFSJiGClBM3CT3l5V3Mys\nlXSI6dmImA28Iml3AEmrk0aUD5JGlEcAz+QR2yzgS3kfwB3A8ZW2JA3Im4cBe0VE34joS7oh6KPr\nmrnfd4HpkvbPx64gacW8+y3gy8CvJO2ay24HjlfOwpK2bJE3wMzMlooOkTSzo4CfSZoA3AOcERHP\nRcSLpBHa/bneg8BbEfFmfn0CMEjSJEnTgOMk9SWNTD/6qElEvAC8LWmbqn6PBE6QNIk0FfzpwjEz\nSNciL8jH/S/QBZgkaWp+bWZm7YQiPJvXkQzcpHeMGX5SvcMwqws/e9aaS9L4iBhUq15HGmmamZm1\nKidNMzOzkpw0zczMSuooHzmxTN17+bqOmVkr8UjTzMysJCdNMzOzkpw0zczMSvI1zQ4m5sxg3uih\n9Q7DrO58bd9ag0eaZmZmJTlpmpmZleSkaWZmVlK7SZqSQtJlhdedJb0u6eY6xTO76vUxks6vcUxf\nSV8vvB4g6UutFaOZmbWsdpM0gTlAP0nd8us9gf8sTgOS6n3jU1/g64XXA0jLlJXWBs7BzGyZ1Z6S\nJsCtpDUqIa13eUVlh6StJY2W9LikhyVtlMuPkTRC0j3A3bnsR5ImS5oo6excNkrSoLzdU9KLeXsz\nSWMlTcjLh21YK0hJl0g6qPC6Mio9G9gpt/Uj4BfAIfn1IZK6Sxqe+3tc0n6NnYOZmS197W3UciXw\n8zwl2x8YDuyU9z0J7BQR8yXtAZwFHJj3bQX0j4hZkvYG9gO2iYj38oLVTTkO+H1EXC5peaBTLu+W\n1+6sWB0YUaOtU4CTI2IfAEkzgEER8b38+izgnoj4pqRVgbGS7qo+hxp9mJlZK2lXSTMiJuUFog8j\njTqLVgEuzSPBIC32XHFnIdnsAfw1It7LbdZKQqOBUyWtC1wXEc/k8vcjYkClkqRjgJprsdXwBWBf\nSSfn112BPg2cw8dIGgIMAejTa7UlDMHMzBrT3qZnIY3mfkNhajb7X+DeiOgHfIWUcCrmlGh3Pove\nj4+OjYh/APsC7wO3Stp9cdqStBywfIljAAQcGBED8lefiHii1jlExLCIGBQRg3qu1r1kV2Zmtrja\nY9IcDpwREZOryldh0Y1BxzRx/J3ANyStCFCYnn0RGJi3i9cj1wOej4jzgBtJ08K1FNval0Wj3neB\nlQr1ql/fDhwvSbnvLUv0ZWZmS0m7S5oRMT0nsGrnAr+S9DhNTDtHxEjSaHVcviZZmQr9DfCdfHzP\nwiFfA6bkuv2Av5UI88/ALpImAtuxaJQ4CViQb0D6AXAvsGnlRiDSaLkLMEnS1PzazMzaCEVEvWOw\nFjRwk94xZvhJ9Q7DrO787FlbHJLGR0TN+1La3UjTzMysXpw0zczMSnLSNDMzK6ldfU7TalP3Xr6W\nY2bWSjzSNDMzK8lJ08zMrCQnTTMzs5J8TbODiTkzmDd6aL3DMLMOwPdHfJJHmmZmZiU5aZqZmZXk\npGlmZlaSk6aZmVlJ7SJpSvq0pCslPSdpvKRbJQ2RdHMLtd9X0pQWams5SedJmiJpsqRHJX0275vd\nEn2YmVl9tPm7Z/PaktcDl0bEoblsC9I6lW3RIcDaQP+IWChpXcotgm1mZm1cexhp7gbMi4iLKgUR\nMRF4AOgh6RpJT0q6vLB484uSeubtQZJG5e3TJQ2XNErS85JOKPTTSdKfJU2VdIekbvmYUZIG5e2e\nkl7M230lPSDpsfy1fW5nLeCViFiYY50eEW9WOpH0y7ye5hhJvXLZmpKuzaPSRyXtUCJeMzNbytpD\n0uwHjG9k35bAicCmwHrADiXa2xj4IrA1cJqkLrl8Q+CCiNgMeAs4sEY7rwF7RsRWpNFlZWHsfwJf\nyQtL/1bSloVjugNjImIL4H7g2Fz+e2BoRAzO/V5cIt6P5KnqcZLGzXzTg1ozs9bS5qdnaxgbEdMB\nJE0A+gIP1jjmloiYC8yV9BrQK5e/EBET8vb43FZTugDnSxoALAA+B2lkKWkjYPf8dbekgyPibuBD\noHIddjywZ97eA9g0D5QBVpbUo4l4pxcDiYhhwDBIi1DXiNvMzJqpPSTNqcBBjeybW9hewKLzmc+i\nUXTXksdUl3er0dYPgBnAFnn/B5UdOcndBtwmaQawP3A3aZq5ktSKfS8HbBsRH7UBkJNoY/GamdlS\n1h6mZ+8BVpA0pFIgqT+wUxPHvAgMzNu1pllrKbZVTN6rsOja5ZFApxzbVpLWztvLAf2Bf9Xo4w7g\n+MqLPHo1M7M2ps0nzTwyOwDYI3/kZCrwK+DVJg47A/i9pHGk0dmS+A3wHUmPAz0L5RcCR0uaSLru\nWLmY+CngpvwRlkmkker5Nfo4ARgkaZKkacBxSxizmZm1Ai2aLbSOYOAmvWPM8JPqHYaZdQDL0gPb\nJY2PiEG16rX5kaaZmVlb4aRpZmZWku/E7GDUvdcyNaViZrY0eaRpZmZWkpOmmZlZSU6aZmZmJfma\nZgcTc2Ywb/TQeodhZrZULa17OTzSNDMzK8lJ08zMrCQnTTMzs5KcNM3MzEpq00lTyYOS9i6UHSxp\n5GK2M13Sqi0U03RJk/Mi05MlfaUl2q3qo7ekq1q6XTMzWzJt+u7ZiAhJxwFXS7qXFO9ZwF71jYyd\nIuItSZsBI4CbWrLxiHgJOKQl2zQzsyXXpkeaABExhZSUfgT8HPhbRDwn6YeSpuSv4wEkrSTpNkkT\nc3lx/csTJT2el9/6XK5/pqQTKxUkPSlp3bx9k6TxkqZK+nYj4a0MvFk4/mhJY/Mo9EJJy0nqLOkt\nSWfnuEZL+lSuv6GkR/KI9ZeS3srlG0iakLe7Sbo013lM0s4t9NaamdliavNJMzsD+DqwN3CupG2A\nw4HBwHbAdyVtDnwJeDEitoiIfsCdhTZmRMSWwMVAmbWzjo6IgbmPkyStVtj3QF7X827gpwCS+pHW\n/dw+IgaQRsWH5vqrAPdFxBbAaOCbufwPwG8iYnPglUbiOAGYm+scCfxd0vLFCpKGSBonadzMN+c0\n2IiZmS25dpE0I2IOcBXw94iYC+wIXBsR70fEu8ANwE6kRZ/3yqO6HSLi7UIz1+Xv44G+Jbr9QV5g\nejSwLrB+Yd9OEbEZMAD4o6QVgT1ICXZcHiXuUjjm/Yi4rYH+twGuzdv/aCSOHYHL8vswFXgZ2KBY\nISKGRcSgiBjUc7XuJU7NzMyao01f06yyMH81KiKekDSINOI8W9JtEXFW3j03f1/AovOez8f/cOgK\nIGkPYGdg24h4X9KDlX1V/T0taRawMSBgeET8rFhHUmfgw0JRsX8zM2tH2sVIswEPAAfk6309gP1I\nU6brALMj4u/Ab4GtarTzIjAQQNLWQO9cvgowKyfMzUgjyE+Q9GmgD/Bv4C7ga5J65n1rSOpTo/+x\npCldWDSV29C5Hp7b3ARYC3i2RrtmZtYK2uWIJyLGSroCeDQX/TEiJkuqjDAXkkZ3x9Vo6mrgCElT\ngDHA87n8FmCIpGnAU8AjVcc9IGkB0AU4OSJmAjMlnQHcJWk5YF7u/+Um+j+BdI3yNOB24O0G6vwB\n+JOkybnNoyLiwwbqmZlZK1NE1DuGZZak7sB7+aM1RwAHRMSBS9LmwE16x5jhZe5zMjPrOJb0ge2S\nxkfEoFr12uVIswMZDPwuj0zfBL5R53jMzKwJTpp1FBGjSHfgmplZO+Ck2cGoe6+ltq6cmdmypr3e\nPWtmZrbUOWmamZmV5KRpZmZWkpOmmZlZSU6aZmZmJTlpmpmZleSkaWZmVpKTppmZWUlOmmZmZiX5\nge0djKR3SSuztFc9gZn1DmIJOP76cvz11Z7j/0xErFmrkh+j1/E8VeZJ/W2VpHGOv34cf305/rbP\n07NmZmYlOWmamZmV5KTZ8QyrdwBLyPHXl+OvL8ffxvlGIDMzs5I80jQzMyvJSdPMzKwkJ80ORNJe\nkp6S9KykU+odTy2Seku6V9I0SVMlfT+Xry7pTknP5O+r1TvWxkjqJOlxSTfn1+0mdgBJq0q6RtKT\nkp6QtF17OQdJP8j/bqZIukJS17Yeu6Thkl6TNKVQ1mjMkn6c/z8/JemL9Yl6kUbi/3X+9zNJ0vWS\nVi3sa1PxtwQnzQ5CUifgAmBvYFPgMEmb1jeqmuYD/x0RmwLbAv8vx3wKcHdEbAjcnV+3Vd8Hnii8\nbk+xA/weGBkRGwNbkM6lzZ+DpHWAE4BBEdEP6AQcStuP/RJgr6qyBmPO/xcOBTbLx1yY/5/X0yV8\nMv47gX4R0R94GvgxtNn4l5iTZsexNfBsRDwfER8CVwL71TmmJkXEKxHxWN5+l/QLex1S3JfmapcC\n+9cnwqZJWhf4MnBxobhdxA4gaRVgZ+AvABHxYUS8Rfs5h85AN0mdgRWBl2njsUfE/cCsquLGYt4P\nuDIi5kbEC8CzpP/nddNQ/BFxR0TMzy/HAOvm7TYXf0tw0uw41gFeKryensvaBUl9gS2BR4BeEfFK\n3vUq0KtOYdXyO+CHwMJCWXuJHeCzwOvAX/MU88WSutMOziEi/gP8Bvg38ArwdkTcQTuIvQGNxdwe\n/09/E7gtb7fH+Gty0rS6k9QDuBY4MSLeKe6L9JmoNve5KEn7AK9FxPjG6rTV2As6A1sBf4yILYE5\nVE1nttVzyNf99iMl/rWB7pKOKNZpq7E3pT3GXCHpVNIll8vrHUtrctLsOP4D9C68XjeXtWmSupAS\n5uURcV0uniFprbx/LeC1esXXhB2AfSW9SJoK313SZbSP2CumA9Mj4pH8+hpSEm0P57AH8EJEvB4R\n84DrgO1pH7FXayzmdvN/WtIxwD7A4bHow//tJv7F4aTZcTwKbCjps5KWJ12AH1HnmJokSaTraU9E\nxP8Vdo0Ajs7bRwM3Lu3YaomIH0fEuhHRl/Re3xMRR9AOYq+IiFeBlyRtlIs+D0yjfZzDv4FtJa2Y\n/x19nnRNvD3EXq2xmEcAh0paQdJngQ2BsXWIr0mS9iJdptg3It4r7GoX8S+2iPBXB/kCvkS6e+05\n4NR6x1Mi3h1JU1GTgAn560vAGqS7CJ8B7gJWr3esNc5jV+DmvN3eYh8AjMs/gxuA1drLOQBnAE8C\nU4C/Ayu09diBK0jXYOeRRvrfaipm4NT8//kpYO82Gv+zpGuXlf/DF7XV+Fviy4/RMzMzK8nTs2Zm\nZiU5aZqZmZXkpGlmZlaSk6aZmVlJTppmZmYlOWmamZmV5KRpZmZWkpOmmZlZSU6aZmZmJTlpmpmZ\nleSkaWZmVpKTppmZWUlOmmZmZiU5aZqZmZXkpGlmZlaSk6aZmVlJTppmS5GkkPTbwuuTJZ3eQm1f\nIumglmirRj8HS3pC0r1V5X0lvS9pgqRpki6S1OTvGEkvSurZQPnpkk7O27+QtEcTbZQ6b0kLcmxT\nJF0tacVax5hVc9I0W7rmAl9tKFHUk6TOi1H9W8CxEbFbA/uei4gBQH9gU2D/JY0tIn4eEXctaTvA\n+xExICL6AR8Cx7VAmw1azPfT2hEnTbOlaz4wDPhB9Y7qEZOk2fn7rpLuk3SjpOclnS3pcEljJU2W\ntH6hmT0kjZP0tKR98vGdJP1a0qOSJkn6r0K7D0gaAUxrIJ7DcvtTJJ2Ty34O7Aj8RdKvGzvJiJgP\nPAxskPu5udDu+ZKOKVT/Ye5nrKQNmnpf8rlPy+fxm0K1nSU9nN+fMqPtB4ANcps3SBovaaqkIYV+\nZ0samsvvlrRmLl9f0sh8zAOSNi7EeZGkR4BzJe2SR7YTJD0uaaUScVkb57+GzJa+C4BJks5djGO2\nADYBZgHPAxdHxNaSvg8cD5yY6/UFtgbWB+7NSego4O2IGCxpBeAhSXfk+lsB/SLihWJnktYGzgEG\nAm8Cd0jaPyJ+IWl34OSIGNdYsHnq8/PAz0uc29sRsbmko4DfAfs00uYawAHAxhERklYt7F6LlMw3\nBkYA1zQRW2dgb2BkLvpmRMyS1A14VNK1EfEG0B0YFxE/yH8snAZ8j/RHz3ER8YykbYALgd1zW+sC\n20fEAkk3Af8vIh6S1AP4oMR7YW2cR5pmS1lEvAP8DThhMQ57NCJeiYi5wHNAJelNJiXKin9GxMKI\neIaUXDcGvgAcJWkC8AiwBrBhrj+2OmFmg4FREfF6HjVeDuxcIs71cz8PAbdExG0ljrmi8H27Juq9\nTUo8f5H0VeC9wr4b8nlPA3o1cny3HNs44N/AX3L5CZImAmOA3ix6bxYCV+Xty4Adc/LbHrg6t/Un\nUsKuuDoiFuTth4D/k3QCsGp+H62d80jTrD5+BzwG/LVQNp/8h2y+gWb5wr65he2FhdcL+fj/46jq\nJwABx0fE7cUdknYF5jQv/EZVrmkWfXReWdcGYmxo++OVIuZL2po0gj2INOqrjPCK748aaeL96tjy\ne7AHsF1EvCdpVAPxFWNbDnirgXOs+Oj9jIizJd0CfIk0uv9iRDzZ2PlZ++CRplkdRMQs4J+km2oq\nXiRNhwLsC3RpRtMHS1ouX+dcD3gKuB34jqQuAJI+J6l7jXbGArtI6impE3AYcF8z4gH4F7CppBXy\nlOrnq/YfUvg+urFG8ihvlYi4lXRNeItmxlO0CvBmTpgbA9sW9i1HSs4AXwcezLMEL0g6OMckSQ3G\nIWn9iJgcEecAj5JG/dbOeaRpVj+/JY2WKv4M3JinCkfSvFHgv0kJb2XSdbcPJF1MmsJ9TJKA16lx\nV2tEvCLpFOBe0sjtloi4sRnxEBEvSfonMAV4AXi8qspqkiaRRouHNdHUSqT3p2uO6aTmxFNlJHCc\npCdIf2CMKeybA2wt6afAayxK7ocDf8zlXYArgYkNtH2ipN1IswFTgTJT1dbGKaLR2RAzs2WWpNkR\n0aPecVjb4ulZMzOzkjzSNDMzK8kjTTMzs5KcNM3MzEpy0jQzMyvJSdPMzKwkJ00zM7OSnDTNzMxK\n+v8Id+8RzSk/7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc2cffaa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import operator\n",
    "\n",
    "data = []\n",
    "author_dic = {}\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT `publish_date`,`authors` FROM `arxiv_data`;\")\n",
    "data = c.fetchall()\n",
    "#print(data[0])\n",
    "text = str(data[0])\n",
    "#print(text[2:6])\n",
    "#print(text[26:len(text)-2])\n",
    "#print(len(data))\n",
    "x = 0\n",
    "\n",
    "\n",
    "while x < len(data):\n",
    "    text = str(data[x])\n",
    "    author_list = (text[26:len(text)-2].replace(\" \", \"\")).split(\",\")\n",
    "    for single_author in author_list:\n",
    "        try:\n",
    "            author_dic[str(single_author)] = int(author_dic[str(single_author)]) + 1\n",
    "        except:\n",
    "            author_dic[str(single_author)] = 1\n",
    "            pass\n",
    "    x += 1\n",
    "    #print(author_list)\n",
    "\n",
    "#print(author_dic)    \n",
    "rev_author_dic = {v: k for k, v in author_dic.items()}\n",
    "#print(rev_author_dic)\n",
    "sort_rev_author_dic=sorted(rev_author_dic.items(), key=operator.itemgetter(0),reverse=True)\n",
    "#print(sort_rev_author_dic[0][0])\n",
    "#print(sort_rev_author_dic[0][1])\n",
    "\n",
    "objects = (str(sort_rev_author_dic[0][1]),str(sort_rev_author_dic[1][1]),str(sort_rev_author_dic[2][1]),str(sort_rev_author_dic[3][1]),str(sort_rev_author_dic[4][1]),str(sort_rev_author_dic[5][1]),str(sort_rev_author_dic[6][1]),str(sort_rev_author_dic[7][1]),str(sort_rev_author_dic[8][1]),str(sort_rev_author_dic[9][1]))\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [int(sort_rev_author_dic[0][0]),int(sort_rev_author_dic[1][0]),int(sort_rev_author_dic[2][0]),int(sort_rev_author_dic[3][0]),int(sort_rev_author_dic[4][0]),int(sort_rev_author_dic[5][0]),int(sort_rev_author_dic[6][0]),int(sort_rev_author_dic[7][0]),int(sort_rev_author_dic[8][0]),int(sort_rev_author_dic[9][0])]\n",
    " \n",
    "plt.barh(y_pos, performance, align='center', alpha=0.5,color='#F78F1E')\n",
    "plt.yticks(y_pos, objects)\n",
    "plt.xlabel('\\nNumber of Publish Papers')\n",
    "plt.title('Top 10 author for arxiv publiction\\n from 1992 to 2017\\n')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5XwAAANjQruGuu1+f5HDno/vqw9RckOSCbaYfTHLWXhYQAACA3e1ptEwAAABumoQ7AACA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9513d433c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "import operator\n",
    "\n",
    "data = []\n",
    "author_dic = {}\n",
    "sqlite_database = '/home/neel/arxiv_data/test.sqlite'\n",
    "conn = sqlite3.connect(sqlite_database)\n",
    "c = conn.cursor()\n",
    "c.execute(\"SELECT `publish_date`,`title` FROM `arxiv_data`;\")\n",
    "data = c.fetchall()\n",
    "#print(data[0])\n",
    "#text = str(data[0])\n",
    "#print(text[2:6])\n",
    "#print(text[26:len(text)-2])\n",
    "#print((text[26:len(text)-2].replace(\"\\\\n\", \"\")).split(\" \"))\n",
    "conn.close()\n",
    "\n",
    "word_dic = {}\n",
    "\n",
    "x = 0\n",
    "\n",
    "\n",
    "while x < len(data):\n",
    "    text = str(data[x])\n",
    "    word_list = (text[26:len(text)-2].replace(\"\\\\n\", \"\")).split(\" \")\n",
    "    for single_word in word_list:\n",
    "        try:\n",
    "            word_dic[str(single_word)] = int(word_dic[str(single_word)]) + 1\n",
    "        except:\n",
    "            word_dic[str(single_word)] = 1\n",
    "            pass\n",
    "    x += 1\n",
    "\n",
    "#print(word_dic)\n",
    "#print('************************************************************')\n",
    "lis_1 = sorted(word_dic, key=word_dic.get, reverse=True)\n",
    "#print(lis_1)\n",
    "#print(lis_1[1])\n",
    "#print(word_dic[str(lis_1[1])])\n",
    "lis_extra_word=['Methods','Method','Using','for','of','and','in','with','A','the','to','a','on','using','from','data','by','via','An','The','model','Approach','based','Algorithm','On','Feature','Based']\n",
    "\n",
    "draw_word_dic = {}\n",
    "z = 1\n",
    "count = 0\n",
    "\n",
    "while count < 50:\n",
    "    if str(lis_1[z]) not in lis_extra_word:\n",
    "        draw_word_dic[str(lis_1[z])] = (word_dic[str(lis_1[z])])\n",
    "        #print(str(lis_1[z])+\" --> \"+str(word_dic[str(lis_1[z])]))\n",
    "        count += 1\n",
    "    \n",
    "    z += 1\n",
    "\n",
    "#print(draw_word_dic)\n",
    "# Get current size\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    " \n",
    "# Prints: [8.0, 6.0]\n",
    "#print \"Current size:\", fig_size\n",
    " \n",
    "# Set figure width to 20 and height to 12\n",
    "fig_size[0] = 15\n",
    "fig_size[1] = 10\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "plt.bar(range(len(draw_word_dic)),draw_word_dic.values(),align='center')\n",
    "plt.xticks(range(len(draw_word_dic)),list(draw_word_dic.keys()),rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  }
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