{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "focal_countries_list = [\"Peoples R china\", \"Hong Kong\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "country_mode = \"CU\" #CU-country-region AU-address"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"computer vision\") OR TS=(\"pattern recognition\")) AND"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "'TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")'"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords_source = r'..\\ai_scope_keywords.txt'\n",
    "with open(keywords_source,'r') as f:\n",
    "    keywords = f.readlines()\n",
    "\n",
    "keywords = [c.strip() for c in keywords[0].split(\",\")]\n",
    "\n",
    "keywords_str = ' OR '.join('TS=(\\\"'+k+'\\\")' for k in keywords)\n",
    "keywords_str"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "scope_country_source = r'..\\eu_scope_countries.txt'\n",
    "\n",
    "with open(scope_country_source,'r') as f:\n",
    "    coop_countries = f.readlines()\n",
    "coop_countries = [c.strip().upper() for c in coop_countries[0].split(\",\")]\n",
    "focal_countries = [c.strip().upper() for c in focal_countries_list]\n",
    "eu_countries = coop_countries[0:-7]\n",
    "assoc_countries = coop_countries[-7:]\n",
    "\n",
    "nor_c = [coop_countries[-7],]\n",
    "swi_c = [coop_countries[-6],]\n",
    "uk_c = coop_countries[-5:]\n",
    "\n",
    "foc_str = ' OR '.join([country_mode+'='+c for c in focal_countries])\n",
    "coop_str = ' OR '.join([country_mode+'='+c for c in coop_countries])\n",
    "eu_str = ' OR '.join([country_mode+'='+c for c in eu_countries])\n",
    "assoc_str = ' OR '.join([country_mode+'='+c for c in assoc_countries])\n",
    "\n",
    "nor_str =' OR '.join([country_mode+'='+c for c in nor_c])\n",
    "swi_str =' OR '.join([country_mode+'='+c for c in swi_c])\n",
    "uk_str =' OR '.join([country_mode+'='+c for c in uk_c])\n",
    "eu_sub_str = eu_str.split(' OR ')\n",
    "# eu_sub_str"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "['UNITED KINGDOM', 'ENGLAND', 'WALES', 'SCOTLAND', 'N IRELAND']"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coop_countries[-5:]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "'CU=PEOPLES R CHINA OR CU=HONG KONG'"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foc_str"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")) AND PY=(2011-2022)'"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scope_query = f'({foc_str}) AND ({coop_str}) AND ({keywords_str}) AND PY=(2011-2022)'\n",
    "scope_query"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\"))'"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch_scope_query = f'({foc_str}) AND ({keywords_str})'\n",
    "ch_scope_query"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "'(CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\"))'"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_scope_query = f'({eu_str}) AND ({keywords_str})'\n",
    "eu_scope_query"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "sub_queries = [f'PY=(2011-2022) AND ({i_str}) AND ({keywords_str})' for i_str in [foc_str,eu_str,assoc_str,nor_str,swi_str,uk_str]+eu_sub_str]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "from wossel_miners import wos_fetch_entries,wos_fetch_yearly_output"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 33/33 [12:49<00:00, 23.31s/it]\n"
     ]
    }
   ],
   "source": [
    "wos_fetch_yearly_output(query_str_list=sub_queries)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "'(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")) AND PY=(2011-2022)'"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scope_query"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hoooold...\n",
      "27672 records found! Here we go in 93 steps...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 92/92 [09:38<00:00,  6.29s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final batch of 27601-27672\n"
     ]
    }
   ],
   "source": [
    "wos_fetch_entries(query_str=scope_query)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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