ZSI_Reconnect_China/PATSTAT/patstat_analysis_pipeline.ipynb

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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import janitor\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
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     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import janitor\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import math\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.offline as pyo\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "pyo.init_notebook_mode()\n",
    "\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"plotly_mimetype+notebook\"\n",
    "\n",
    "import country_converter as coco\n",
    "cc = coco.CountryConverter()\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "outdir=\"EU_CH_scope\"\n",
    "\n",
    "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 36,
     "metadata": {},
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    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "24638"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n8712   413791676         KR   20127034385         A         2011-06-03  \\\n15588  439148012         EP      15714777         A         2015-03-26   \n48628  536723783         RU    2019104609         A         2017-07-18   \n4458   380638095         US  201113218876         A         2011-08-26   \n43221  522842130         US  201829659634         F         2018-08-10   \n3836   378587773         WO    2011074635         W         2011-05-25   \n9237   414823192         EP      12882000         A         2012-08-03   \n6777   409424323         US  201113638187         A         2011-03-22   \n18826  446137103         WO    2015057861         W         2015-10-14   \n52265  545763074         BR  112020021641         A         2018-04-23   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n8712                2011     1020127034385       PI                   \\\n15588               2015          15714777       PI                    \n48628               2017        2019104609       PI                    \n4458                2011          13218876       PI                    \n43221               2018               NaN       DP                    \n3836                2011     CN2011/074635       PI               CN   \n9237                2012          12882000       PI                    \n6777                2011          13638187       PI                    \n18826               2015     IB2015/057861       PI               IB   \n52265               2018      112020021641       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n8712           339637678  ...          2013-11-04                2013  \\\n15588          438355565  ...          2017-02-15                2017   \n48628          488987246  ...          2020-08-20                2020   \n4458                   0  ...          2013-01-31                2013   \n43221                  0  ...          2019-12-03                2019   \n3836                   0  ...          2012-11-29                2012   \n9237           414790439  ...          2015-06-10                2015   \n6777           333698631  ...          2013-08-08                2013   \n18826                  0  ...          2016-04-21                2016   \n52265          521276805  ...          2021-01-26                2021   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n8712              413791677       Y         45067306          339616929  \\\n15588             473888077       Y         52814965          438355565   \n48628             536723784       Y         60991950          488987246   \n4458              380922724       N         47598050          380638095   \n43221             522842131       Y         68653880          522842130   \n3836              378816884       N         47216526          378587773   \n9237              439934604       N         50027133          414790439   \n6777              409424324       Y         43020401          319321099   \n18826             451530410       N         52014243          446137103   \n52265             545763075       N         68293428          521276805   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n8712                  6                   10              1            7  \n15588                 5                    1              1            2  \n48628                39                    3              1           19  \n4458                  2                    3              8            7  \n43221                 2                    0              1            1  \n3836                  3                    4              3            2  \n9237                  2                    0              1            2  \n6777                  6                    4              6            5  \n18826                 9                    3              2            1  \n52265                 6 
      "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>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8712</th>\n      <td>413791676</td>\n      <td>KR</td>\n      <td>20127034385</td>\n      <td>A</td>\n      <td>2011-06-03</td>\n      <td>2011</td>\n      <td>1020127034385</td>\n      <td>PI</td>\n      <td></td>\n      <td>339637678</td>\n      <td>...</td>\n      <td>2013-11-04</td>\n      <td>2013</td>\n      <td>413791677</td>\n      <td>Y</td>\n      <td>45067306</td>\n      <td>339616929</td>\n      <td>6</td>\n      <td>10</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>15588</th>\n      <td>439148012</td>\n      <td>EP</td>\n      <td>15714777</td>\n      <td>A</td>\n      <td>2015-03-26</td>\n      <td>2015</td>\n      <td>15714777</td>\n      <td>PI</td>\n      <td></td>\n      <td>438355565</td>\n      <td>...</td>\n      <td>2017-02-15</td>\n      <td>2017</td>\n      <td>473888077</td>\n      <td>Y</td>\n      <td>52814965</td>\n      <td>438355565</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>48628</th>\n      <td>536723783</td>\n      <td>RU</td>\n      <td>2019104609</td>\n      <td>A</td>\n      <td>2017-07-18</td>\n      <td>2017</td>\n      <td>2019104609</td>\n      <td>PI</td>\n      <td></td>\n      <td>488987246</td>\n      <td>...</td>\n      <td>2020-08-20</td>\n      <td>2020</td>\n      <td>536723784</td>\n      <td>Y</td>\n      <td>60991950</td>\n      <td>488987246</td>\n      <td>39</td>\n      <td>3</td>\n      <td>1</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>4458</th>\n      <td>380638095</td>\n      <td>US</td>\n      <td>201113218876</td>\n      <td>A</td>\n      <td>2011-08-26</td>\n      <td>2011</td>\n      <td>13218876</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2013-01-31</td>\n      <td>2013</td>\n      <td>380922724</td>\n      <td>N</td>\n      <td>47598050</td>\n      <td>380638095</td>\n      <td>2</td>\n      <td>3</td>\n      <td>8</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>43221</th>\n      <td>522842130</td>\n      <td>US</td>\n      <td>201829659634</td>\n      <td>F</td>\n      <td>2018-08-10</td>\n      <td>2018</td>\n      <td>NaN</td>\n      <td>DP</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-12-03</td>\n      <td>2019</td>\n      <td>522842131</td>\n      <td>Y</td>\n      <td>68653880</td>\n      <td>522842130</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3836</th>\n      <td>378587773</td>\n      <td>WO</td>\n      <td>2011074635</td>\n      <td>W</td>\n      <td>2011-05-25</td>\n      <td>2011</td>\n      <td>CN2011/074635</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2012-11-29</td>\n      <td>2012</td>\n      <td>378816884</td>\n      <td>N</td>\n      <td>47216526</td>\n      <td>378587773</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9237</th>\
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n56447  556331792         PE    2021000402         A         2019-09-26  \\\n41201  518451341         TW     107131654         A         2018-09-10   \n54094  550225934         TW     109119471         A         2017-07-06   \n9133   414556231         EP      12881920         A         2012-07-27   \n43037  522510076         EP      19306441         A         2019-11-07   \n...          ...        ...           ...        ...               ...   \n60095  569415925         TW     110116177         A         2021-05-05   \n61281  573442692         DE  102021213653         A         2021-12-02   \n41189  518448790         TW     108109981         A         2014-03-07   \n64973  587185640         CR      20220470         A         2021-03-18   \n8491   413374202         EP      13197783         A         2011-04-28   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n56447               2019        2021000402       PI                   \\\n41201               2018         107131654       PI                    \n54094               2017         109119471       PI                    \n9133                2012          12881920       PI                    \n43037               2019          19306441       PI                    \n...                  ...               ...      ...              ...   \n60095               2021         110116177       PI                    \n61281               2021      102021213653       PI                    \n41189               2014         108109981       PI                    \n64973               2021          20220470       PI                    \n8491                2011          13197783       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n56447          520193774  ...          2021-07-19                2021  \\\n41201                  0  ...          2019-06-16                2019   \n54094                  0  ...          2021-02-01                2021   \n9133           414544093  ...          2015-06-03                2015   \n43037                  0  ...          2021-05-12                2021   \n...                  ...  ...                 ...                 ...   \n60095                  0  ...          2021-12-16                2021   \n61281                  0  ...          2022-06-15                2022   \n41189                  0  ...          2019-06-16                2019   \n64973          549449130  ...          2022-11-14                2022   \n8491           376727956  ...          2014-08-20                2014   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n56447             556331793       N         68542579          520193774  \\\n41201             518451342       N         66100215          511344799   \n54094             550225935       N         59337896          481645536   \n9133              439682012       Y         49996511          414544093   \n43037             548841071       N         68655464          522510076   \n...                     ...     ...              ...                ...   \n60095             568265190       N         74845079          546468249   \n61281             573442693       N         81749879          573442692   \n41189             518448791       Y         51500341          421859342   \n64973             587185641       N         75660301          549449130   \n8491              420445397       N         45896256          352844875   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n56447                10                    0              1            3  \n41201                 2                    0              1            2  \n54094                 8                    3              1            7  \n9133                  4                    5              1            3  \n43037                 3                    0              1            2  \n...                 ...       
      "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>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>56447</th>\n      <td>556331792</td>\n      <td>PE</td>\n      <td>2021000402</td>\n      <td>A</td>\n      <td>2019-09-26</td>\n      <td>2019</td>\n      <td>2021000402</td>\n      <td>PI</td>\n      <td></td>\n      <td>520193774</td>\n      <td>...</td>\n      <td>2021-07-19</td>\n      <td>2021</td>\n      <td>556331793</td>\n      <td>N</td>\n      <td>68542579</td>\n      <td>520193774</td>\n      <td>10</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>41201</th>\n      <td>518451341</td>\n      <td>TW</td>\n      <td>107131654</td>\n      <td>A</td>\n      <td>2018-09-10</td>\n      <td>2018</td>\n      <td>107131654</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-06-16</td>\n      <td>2019</td>\n      <td>518451342</td>\n      <td>N</td>\n      <td>66100215</td>\n      <td>511344799</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>54094</th>\n      <td>550225934</td>\n      <td>TW</td>\n      <td>109119471</td>\n      <td>A</td>\n      <td>2017-07-06</td>\n      <td>2017</td>\n      <td>109119471</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-02-01</td>\n      <td>2021</td>\n      <td>550225935</td>\n      <td>N</td>\n      <td>59337896</td>\n      <td>481645536</td>\n      <td>8</td>\n      <td>3</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>9133</th>\n      <td>414556231</td>\n      <td>EP</td>\n      <td>12881920</td>\n      <td>A</td>\n      <td>2012-07-27</td>\n      <td>2012</td>\n      <td>12881920</td>\n      <td>PI</td>\n      <td></td>\n      <td>414544093</td>\n      <td>...</td>\n      <td>2015-06-03</td>\n      <td>2015</td>\n      <td>439682012</td>\n      <td>Y</td>\n      <td>49996511</td>\n      <td>414544093</td>\n      <td>4</td>\n      <td>5</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>43037</th>\n      <td>522510076</td>\n      <td>EP</td>\n      <td>19306441</td>\n      <td>A</td>\n      <td>2019-11-07</td>\n      <td>2019</td>\n      <td>19306441</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-05-12</td>\n      <td>2021</td>\n      <td>548841071</td>\n      <td>N</td>\n      <td>68655464</td>\n      <td>522510076</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>60095</th>\n      <td>569415925</td>\n      <td>TW</td>\n      <td
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]].sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "\n",
    "appln_title = pd.read_csv(f\"{outdir}/tls_202_scope.csv\")\n",
    "\n",
    "pers = pd.read_csv(f\"{outdir}/tls_206_scope.csv\")\n",
    "pers['psn_sector'] = pers['psn_sector'].fillna(\"UNKNOWN\")\n",
    "\n",
    "appln_pers = pd.read_csv(f\"{outdir}/tls_207_scope.csv\")\n",
    "\n",
    "appln_cpc = pd.read_csv(f\"{outdir}/tls_224_scope.csv\")\n",
    "\n",
    "cpc_def = pd.read_csv(\"CPC_data/cpc_defs.csv\", low_memory=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "        person_id                   person_name           person_name_orig_lg   \n99304    72271503                   SHE, HUIMIN                   SHE, HUIMIN  \\\n4389      6211563                    Zhou, Jian                    Zhou, Jian   \n59373    52078604              KIRCHNER, NICOLE              KIRCHNER, NICOLE   \n19688    16957554              BLAWERT, CARSTEN              BLAWERT, CARSTEN   \n25828    25701586                KNIESEL, SIMON                KNIESEL, SIMON   \n73679    55544020  WHITING, Tien Yi Theresa Hsu  WHITING, Tien Yi Theresa Hsu   \n39905    45320991                Zhang, Michael                Zhang, Michael   \n65003    53390230                 DONG, Hailong                 DONG, Hailong   \n113369   79456511                    LING, Jing                    LING, Jing   \n58383    51832619                  WEN JU ZHANG                  WEN JU ZHANG   \n\n                                           person_address person_ctry_code   \n99304                                            Shanghai               CN  \\\n4389                                              Beijing               CN   \n59373             SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE               DE   \n19688                                                 NaN               DE   \n25828                                                 NaN               DE   \n73679                                                 NaN               US   \n39905                                             Beijing               CN   \n65003                     Molenstraat 148,5701 KK Helmond               NL   \n113369  No. 6 North Of Binjiang Road, Taixing Economic...               CN   \n58383                                                  CN               CN   \n\n         nuts  nuts_level  doc_std_name_id                 doc_std_name   \n99304     NaN           9         32084338                   SHE HUIMIN  \\\n4389      NaN           9            33759                    ZHOU JIAN   \n59373      DE           0          2139104              KIRCHNER NICOLE   \n19688      DE           0           311260              BLAWERT CARSTEN   \n25828      DE           0           255684                KNIESEL SIMON   \n73679     NaN           9         21758228  WHITING TIEN YI THERESA HSU   \n39905     NaN           9           725806                ZHANG MICHAEL   \n65003   NL414           3          7196768                 DONG HAILONG   \n113369    NaN           9           429470                    LING JING   \n58383     NaN           9         23069059                 ZHANG WEN JU   \n\n          psn_id                      psn_name  psn_level  psn_sector   \n99304   29505777                   SHE, HUIMIN          0     UNKNOWN  \\\n4389    37354342                     ZHOU JIAN          1  INDIVIDUAL   \n59373   17194552              KIRCHNER, NICOLE          0     UNKNOWN   \n19688    2923498              BLAWERT, CARSTEN          1  INDIVIDUAL   \n25828   17406789                KNIESEL, SIMON          1  INDIVIDUAL   \n73679   35065052  WHITING, TIEN YI THERESA HSU          0     UNKNOWN   \n39905   37097378                ZHANG, MICHAEL          1  INDIVIDUAL   \n65003    7077131                 DONG, HAILONG          0     UNKNOWN   \n113369  19626776                    LING, JING          0     UNKNOWN   \n58383   34908417                  WEN JU ZHANG          0     UNKNOWN   \n\n           han_id                      han_name  han_harmonized  \n99304   172271503                   SHE, HUIMIN               0  \n4389    106211563                    Zhou, Jian               0  \n59373   152078604              KIRCHNER, NICOLE               0  \n19688   116957554              BLAWERT, CARSTEN               0  \n25828   125701586                KNIESEL, SIMON               0  \n73679   155544020  WHITING, Tien Yi Theresa Hsu               0  \n39905   145320991                Zhang, Michael               0  \n65003   153390230                 DONG, Hailong               0  \n113369  179456511                    
      "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>person_id</th>\n      <th>person_name</th>\n      <th>person_name_orig_lg</th>\n      <th>person_address</th>\n      <th>person_ctry_code</th>\n      <th>nuts</th>\n      <th>nuts_level</th>\n      <th>doc_std_name_id</th>\n      <th>doc_std_name</th>\n      <th>psn_id</th>\n      <th>psn_name</th>\n      <th>psn_level</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>han_harmonized</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>99304</th>\n      <td>72271503</td>\n      <td>SHE, HUIMIN</td>\n      <td>SHE, HUIMIN</td>\n      <td>Shanghai</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>32084338</td>\n      <td>SHE HUIMIN</td>\n      <td>29505777</td>\n      <td>SHE, HUIMIN</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>172271503</td>\n      <td>SHE, HUIMIN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4389</th>\n      <td>6211563</td>\n      <td>Zhou, Jian</td>\n      <td>Zhou, Jian</td>\n      <td>Beijing</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>33759</td>\n      <td>ZHOU JIAN</td>\n      <td>37354342</td>\n      <td>ZHOU JIAN</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>106211563</td>\n      <td>Zhou, Jian</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>59373</th>\n      <td>52078604</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>2139104</td>\n      <td>KIRCHNER NICOLE</td>\n      <td>17194552</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>152078604</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19688</th>\n      <td>16957554</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>NaN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>311260</td>\n      <td>BLAWERT CARSTEN</td>\n      <td>2923498</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>116957554</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25828</th>\n      <td>25701586</td>\n      <td>KNIESEL, SIMON</td>\n      <td>KNIESEL, SIMON</td>\n      <td>NaN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>255684</td>\n      <td>KNIESEL SIMON</td>\n      <td>17406789</td>\n      <td>KNIESEL, SIMON</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>125701586</td>\n      <td>KNIESEL, SIMON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73679</th>\n      <td>55544020</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>NaN</td>\n      <td>US</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>21758228</td>\n      <td>WHITING TIEN YI THERESA HSU</td>\n      <td>35065052</td>\n      <td>WHITING, TIEN YI THERESA HSU</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>155544020</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>39905</th>\n      <td>45320991</td>\n      <td>Zhang, Michael</td>\n      <td>Zhang, Michael</td>\n      <td>Beijing</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>725806</td>\n      <td>ZHANG MICHAEL</td>\n      <td>37097378</td>\n      <td>ZHANG, MICHAEL</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>145320991</td>\n      <td>Zhang, Michael</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>65003</th>\n      <td>53390230</td>\n      <td>DONG, Hailong</td>\n      <t
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pers.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "              cpc_id                                           cpc_name   \n0                  A                                  HUMAN NECESSITIES  \\\n1                A01  AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n2               A01B  SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n3           A01B1/00  Hand tools (edge trimmers for lawns A01G3/06  ...   \n4           A01B1/02  Spades; Shovels {(hand-operated dredgers E02F3...   \n...              ...                                                ...   \n260486  Y10T483/1873                                    Indexing matrix   \n260487  Y10T483/1882                                        Rotary disc   \n260488  Y10T483/1891                                      Chain or belt   \n260489    Y10T483/19                                      Miscellaneous   \n260490           NaN                                                NaN   \n\n       section  class subclass  group  main_group  cpc_version   \n0            A    NaN      NaN    NaN         NaN         2023  \\\n1            A    1.0      NaN    NaN         NaN         2023   \n2            A    1.0        B    NaN         NaN         2023   \n3            A    1.0        B    1.0         0.0         2023   \n4            A    1.0        B    1.0         2.0         2023   \n...        ...    ...      ...    ...         ...          ...   \n260486       Y   10.0        T  483.0      1873.0         2023   \n260487       Y   10.0        T  483.0      1882.0         2023   \n260488       Y   10.0        T  483.0      1891.0         2023   \n260489       Y   10.0        T  483.0        19.0         2023   \n260490     NaN    NaN      NaN    NaN         NaN         2022   \n\n               version https://git-lfs.github.com/spec/v1   \n0                                                     NaN  \\\n1                                                     NaN   \n2                                                     NaN   \n3                                                     NaN   \n4                                                     NaN   \n...                                                   ...   \n260486                                                NaN   \n260487                                                NaN   \n260488                                                NaN   \n260489                                                NaN   \n260490  oid sha256:f138d6bdf2939ba576b96b633d81366123b...   \n\n                                             cpc_taxonomy   \n0                            [('A', 'HUMAN NECESSITIES')]  \\\n1       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n2       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n3       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n4       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n...                                                   ...   \n260486  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260487  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260488  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260489  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260490                                                 []   \n\n                                             cpc_fullname   \n0                                       HUMAN NECESSITIES  \\\n1       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n2       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n3       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n4       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n...                                                   ...   \n260486  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260487  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260488  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260489  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260490                                                NaN   \n\n                                              tax_level_0   \n0                                       HUMAN NEC
      "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>cpc_id</th>\n      <th>cpc_name</th>\n      <th>section</th>\n      <th>class</th>\n      <th>subclass</th>\n      <th>group</th>\n      <th>main_group</th>\n      <th>cpc_version</th>\n      <th>version https://git-lfs.github.com/spec/v1</th>\n      <th>cpc_taxonomy</th>\n      <th>cpc_fullname</th>\n      <th>tax_level_0</th>\n      <th>tax_level_1</th>\n      <th>tax_level_2</th>\n      <th>tax_level_3</th>\n      <th>tax_level_4</th>\n      <th>tax_level_5</th>\n      <th>tax_level_6</th>\n      <th>tax_level_7</th>\n      <th>data_scope</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>A</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES')]</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A01</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A01B</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>B</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A01B1/00</td>\n      <td>Hand tools (edge trimmers for lawns A01G3/06  ...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>B</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>Hand tools (edge trimmers for lawns A01G3/06  ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A01B1/02</td>\n      <td>Spades; Shovels {(hand-operated dredgers E02F3...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>B</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTR
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc_def"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id cpc_class_symbol\n490979  560314579      B32B   7/12\n139994  445201240    C08G2261/3241\n505928  567683420     C25D   7/123\n130069  442410454      Y02D  10/00\n467350  551851575    H04B   7/0486\n318101  509884390    H01L  21/0228\n350171  519161420      G06F  21/42\n129829  442384375      H04W  88/04\n277164  496690078      H04L  27/26\n240979  487005701    F16F2230/0064",
      "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>appln_id</th>\n      <th>cpc_class_symbol</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>490979</th>\n      <td>560314579</td>\n      <td>B32B   7/12</td>\n    </tr>\n    <tr>\n      <th>139994</th>\n      <td>445201240</td>\n      <td>C08G2261/3241</td>\n    </tr>\n    <tr>\n      <th>505928</th>\n      <td>567683420</td>\n      <td>C25D   7/123</td>\n    </tr>\n    <tr>\n      <th>130069</th>\n      <td>442410454</td>\n      <td>Y02D  10/00</td>\n    </tr>\n    <tr>\n      <th>467350</th>\n      <td>551851575</td>\n      <td>H04B   7/0486</td>\n    </tr>\n    <tr>\n      <th>318101</th>\n      <td>509884390</td>\n      <td>H01L  21/0228</td>\n    </tr>\n    <tr>\n      <th>350171</th>\n      <td>519161420</td>\n      <td>G06F  21/42</td>\n    </tr>\n    <tr>\n      <th>129829</th>\n      <td>442384375</td>\n      <td>H04W  88/04</td>\n    </tr>\n    <tr>\n      <th>277164</th>\n      <td>496690078</td>\n      <td>H04L  27/26</td>\n    </tr>\n    <tr>\n      <th>240979</th>\n      <td>487005701</td>\n      <td>F16F2230/0064</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_cpc.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "325243"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln_pers)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "# Number of collaborative applications\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "record_col = \"appln_id\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "outputs": [
    {
     "data": {
      "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "outputs": [],
   "source": [
    "fromdir=\"first_round\"\n",
    "\n",
    "os.makedirs(fr'{fromdir}/plots')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "outputs": [],
   "source": [
    "appln_pers_f = pd.read_csv(f\"{fromdir}/first-filings-with-persons-raw.csv\", header=None,\n",
    "                      names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
    "\n",
    "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
    "\n",
    "appln_pers_haninfo[\"han_name\"] = appln_pers_haninfo[\"han_name\"].str.replace(\"Zhejiang Geely Holding Group Co., Ltd.\",\n",
    "                                                                            \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\")\n",
    "\n",
    "\n",
    "appln_pers_haninfo.loc[appln_pers_haninfo[\"han_name\"]==\"ZHEJIANG GEELY HOLDINGGROUP CO LTD\",\"han_id\"] = 3345915\n",
    "# appln_pers_haninfo[\"psn_sector\"] = appln_pers_haninfo[\"psn_sector\"].fillna(\"UNKNOWN\")\n",
    "\n",
    "appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "outputs": [
    {
     "data": {
      "text/plain": "han_id\n3345915    6\nName: count, dtype: int64"
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_haninfo[appln_pers_haninfo[\"han_name\"]==\"ZHEJIANG GEELY HOLDINGGROUP CO LTD\"][\"han_id\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "outputs": [],
   "source": [
    "pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n",
    "    lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n",
    "appln_pers_f = appln_pers_f.merge(pers_sector_primary, on='han_id')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "outputs": [],
   "source": [
    "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
    "# tls_801.head()\n",
    "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
    "                      (tls_801.ctry_code == 'NO')|\n",
    "                      (tls_801.ctry_code == 'CH')|\n",
    "                      (tls_801.ctry_code == 'GB'))]\n",
    "scope_countries = scope_df[\"ctry_code\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1055\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n",
    "#                                                              (gr[\"appln_auth\"]==\"CN\")).any())\n",
    "# appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n",
    "#                                                               (gr[\"appln_auth\"].isin(scope_countries))).any())\n",
    "\n",
    "appln_ch = appln_pers_f.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"]==\"CN\").any())\n",
    "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"].isin(scope_countries)).any())\n",
    "\n",
    "print(appln_scope[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1055\n"
     ]
    }
   ],
   "source": [
    "appln_pers_f = appln_scope[appln_scope[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])].copy()\n",
    "print(appln_pers_f[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n76      410448968         DE    7067540            0             1  \\\n87      420386087         DE    7067540            0             1   \n88      420386089         DE    7067540            0             1   \n173     337088926         EP      21790            0             1   \n974     410512383         EP        220            0             1   \n...           ...        ...        ...          ...           ...   \n147073  549690891         EP   85841201            1             0   \n147074  549690895         EP   85841201            1             0   \n147075  549690901         EP   85841201            1             0   \n147076  549690897         EP   85841201            1             0   \n147077  549788919         EP   85774895            2             0   \n\n                       person_name person_ctry_code psn_sector     han_id   \n76             Evonik Degussa GmbH               DE    COMPANY     840063  \\\n87             Evonik Degussa GmbH               DE    COMPANY     840063   \n88             Evonik Degussa GmbH               DE    COMPANY     840063   \n173            Evonik Degussa GmbH               DE    COMPANY     840063   \n974     SIEMENS AKTIENGESELLSCHAFT               DE    COMPANY    2709504   \n...                            ...              ...        ...        ...   \n147073               JONSSON, Tony               SE    UNKNOWN  185841201   \n147074               JONSSON, Tony               SE    UNKNOWN  185841201   \n147075               JONSSON, Tony               SE    UNKNOWN  185841201   \n147076               JONSSON, Tony               SE    UNKNOWN  185841201   \n147077         TRAN NGUYEN, Johnny               SE    UNKNOWN  185774895   \n\n                   han_name psn_sector_primary  \n76      EVONIK DEGUSSA GMBH            COMPANY  \n87      EVONIK DEGUSSA GMBH            COMPANY  \n88      EVONIK DEGUSSA GMBH            COMPANY  \n173     EVONIK DEGUSSA GMBH            COMPANY  \n974              SIEMENS AG            COMPANY  \n...                     ...                ...  \n147073        JONSSON, Tony            UNKNOWN  \n147074        JONSSON, Tony            UNKNOWN  \n147075        JONSSON, Tony            UNKNOWN  \n147076        JONSSON, Tony            UNKNOWN  \n147077  TRAN NGUYEN, Johnny            UNKNOWN  \n\n[4367 rows x 11 columns]",
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     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1055\n"
     ]
    },
    {
     "data": {
      "text/plain": "         appln_id appln_auth appln_kind  filing_year  nb_applicants   \n114926  405478585         EP          A         2013              1  \\\n670680  532677899         EP          A         2020              2   \n568605  520224479         EP          A         2019              1   \n441101  483520394         FR          A         2017              1   \n687085  554756101         DE          A         2020              1   \n...           ...        ...        ...          ...            ...   \n235723  443704166         FR          A         2015              3   \n670840  532706785         EP          A         2020              2   \n415863  486019755         EP          A         2017              1   \n608249  520115105         EP          A         2019              1   \n753188  549690895         EP          A         2021              2   \n\n        nb_inventors  \n114926             4  \n670680             2  \n568605             2  \n441101             2  \n687085             3  \n...              ...  \n235723             3  \n670840             2  \n415863             2  \n608249             4  \n753188             1  \n\n[100 rows x 6 columns]",
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     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_f = pd.read_csv(f\"{fromdir}/first-filings-raw.csv\", header=None,\n",
    "                      names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors'])\n",
    "\n",
    "appln_f[\"filing_year\"] = appln_f[\"filing_year\"].astype(int)\n",
    "\n",
    "appln_f = appln_f[appln_f[\"filing_year\"]>2010]\n",
    "\n",
    "appln_f[\"appln_auth\"] = appln_f[\"appln_auth\"].str.replace(\"MA\",\"CN\")\n",
    "\n",
    "appln_f = appln_f[appln_f[\"appln_id\"].isin(appln_scope['appln_id'])]\n",
    "print(appln_f[\"appln_id\"].nunique())\n",
    "\n",
    "appln_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_kind\nA    1059\nU       7\nName: count, dtype: int64"
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_f[\"appln_kind\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "outputs": [],
   "source": [
    "# Co-patents per year"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "outputs": [
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   ],
   "source": [
    "data = (appln_f.groupby(['filing_year'])[record_col].nunique(dropna=False)\n",
    "        .reset_index()\n",
    "        .rename(columns={0:record_col}))\n",
    "data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True)[record_col][0]\n",
    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
    "\n",
    "data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n",
    "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n",
    "\n",
    "year_output = px.line(data,x=\"filing_year\", y=record_col, markers=True, text=record_col)\n",
    "year_output.update_traces(textposition='top center',textfont_size=12)\n",
    "year_output.update_traces(hovertemplate='Year:%{x:d}<br>Number of co-publications:%{y:d}')\n",
    "\n",
    "year_rel_output = px.line(data,x=\"filing_year\", y=record_col+\"_relative_growth\", markers=True, text=record_col+\"_relative_growth\")\n",
    "year_rel_output.update_traces(textposition='top center',textfont_size=12, texttemplate='%{y:.0%}')\n",
    "year_rel_output.update_traces(hovertemplate='Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}')\n",
    "\n",
    "figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\",\"Relative growth of co-patents\"],\n",
    "                         specs=[\n",
    "                            [{\"type\": \"xy\"}],\n",
    "                            [{\"type\": \"xy\"}]\n",
    "                         ])\n",
    "\n",
    "for trace in list(year_output.select_traces()):\n",
    "    figsuper.add_trace(trace,\n",
    "        row=1, col=1\n",
    "    )\n",
    "\n",
    "for trace in list(year_rel_output.select_traces()):\n",
    "    figsuper.add_trace(trace,\n",
    "        row=2, col=1\n",
    "    )\n",
    "\n",
    "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n",
    "figsuper.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "figsuper.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n",
    "# for i in[\"xaxis\",\"xaxis2\"]:\n",
    "#     figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n",
    "#\n",
    "figsuper['layout'][\"yaxis\"][\"range\"] = [0,250]\n",
    "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,3.5]\n",
    "#\n",
    "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
    "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
    "\n",
    "figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "\n",
    "figsuper.write_image(f\"{fromdir}/plots/overall_distr&trends.png\",height=900,width=900,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n"
     ]
    },
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   ],
   "source": [
    "data = (appln_f.groupby(['filing_year',\"appln_auth\"])[record_col]\n",
    "        .nunique(dropna=False).unstack()\n",
    "        .fillna(0)\n",
    "        .stack()\n",
    "        .reset_index()\n",
    "        .rename(columns={0:record_col}))\n",
    "\n",
    "data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n",
    "\n",
    "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n",
    "                  on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n",
    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
    "data = data.sort_values(by =[\"appln_auth\",\"filing_year\"], ascending=[True,True])\n",
    "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n",
    "data[\"Country\"] = cc.pandas_convert(series=data[\"appln_auth\"], to='name_short')\n",
    "data[\"Country\"] = data[\"Country\"].str.replace(\"not found\",\"European Patent Office\")\n",
    "\n",
    "\n",
    "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
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    "              x='filing_year'\n",
    "              ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
    "              markers=True,\n",
    "              labels={\n",
    "                     record_col: 'Number of co-patents',\n",
    "                 },\n",
    "                title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n",
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    "fig.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
    "fig.update_yaxes(title='')\n",
    "fig.update_xaxes(title='')\n",
    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
    "figsuper_ppt = go.Figure(fig)\n",
    "\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
    "# figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n",
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    "    a[\"font\"][\"size\"] = 22\n",
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    "s=16\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_outputs.png\",height=800,width=1600,scale = 4)"
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   }
  },
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
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   "cell_type": "code",
   "execution_count": 442,
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      "text/html": "<div>                            <div id=\"a2954aef-dd42-43a0-9428-0ec9fae980c8\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"a2954aef-dd42-43a0-9428-0ec9fae980c8\")) {                    Plotly.newPlot(                        \"a2954aef-dd42-43a0-9428-0ec9fae980c8\",                        [{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x11\",\"y\":[-1.0,0.0,-1.0,-1.0,0.0,-1.0,-1.0,-1.0,1.0,1.0,-1.0],\"yaxis\":\"y11\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x12\",\"y\":[-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,0.0,0.0],\"yaxis\":\"y12\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x13\",\"y\":[-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,0.0,1.0,0.0,-1.0],\"yaxis\":\"y13\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x14\",\"y\":[-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,0.0,-1.0,-1.0],\"yaxis\":\"y14\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x15\",\"y\":[0.0,-0.11764705882352941,-0.058823529411764705,0.11764705882352941,0.6470588235294118,1.0,2.2941176470588234,5.0,5.411764705882353,4.0588235294117645,0.9411764705882353],\"yaxis\":\"y15\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Finland\",\"Finland\",\"Finland\",\"Finland\",\"Finland\",\"Finland\",\"Finland\",\"Finland
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     "metadata": {},
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   ],
   "source": [
    "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
    "              y=record_col+\"_relative_growth\",\n",
    "              x='filing_year'\n",
    "              ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
    "              markers=True,\n",
    "              labels={\n",
    "                    record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n",
    "                 },\n",
    "                title=\"Yearly rel. output of co-patents by patent authority\",hover_name= \"Country\")\n",
    "fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}')\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
    "fig.update_yaxes(title='')\n",
    "fig.update_xaxes(title='')\n",
    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "figsuper_ppt = go.Figure(fig)\n",
    "\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 22\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n",
      "European Patent Office not found in regex\n"
     ]
    },
    {
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   "source": [
    "# appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n",
    "#                       names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
    "\n",
    "# appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
    "#\n",
    "# appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n",
    "#\n",
    "# tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
    "# # tls_801.head()\n",
    "# scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
    "#                       (tls_801.ctry_code == 'CN')|\n",
    "#                       (tls_801.ctry_code == 'NO')|\n",
    "#                       (tls_801.ctry_code == 'CH')|\n",
    "#                       (tls_801.ctry_code == 'UK'))]\n",
    "# scope_countries = scope_df[\"ctry_code\"].unique()\n",
    "#\n",
    "# appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n",
    "\n",
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n",
    "\n",
    "appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n",
    "appln_pers_f[\"person_ctry_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
    "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"person_ctry_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
    "\n",
    "appln_pers_f[\"auth_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"appln_auth_fullname\"], to='ISO3')\n",
    "appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\" if x!='not found' else \"EPO\")\n",
    "\n",
    "# top 25\n",
    "# - inventor\n",
    "# - applicant\n",
    "# in china, EU, EUextended\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#9F2B68'}\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col: 'Number of co-patents',\n",
    "                     \"person_ctry_type\": \"Country type\",\n",
    "    \"psn_sector_primary\":\"Sector\",\n",
    "                                  \"han_label\": \"Name\",\n",
    "                                  \"Country_Type\":\"Country type\",\n",
    "                                  \"Eurovoc_Class\":\"Region\"\n",
    "                 })\n",
    "for trait in  [\"person_ctry_type\",\"psn_sector_primary\"]:\n",
    "    for ctry in [\"China\",'EU']:\n",
    "        ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
    "        # for role in [\"invt\",\"applt\"]:\n",
    "        if True:\n",
    "            role = \"applt\"\n",
    "            pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
    "            data = (pers_sub\n",
    "                     .groupby([\"han_id\",'han_name'],as_index=False)\n",
    "                     .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n",
    "                     .sort_values(by=\"appln_id\", ascending=False)\n",
    "                    .head(15))\n",
    "            data[\"han_label\"] = (data[\"han_name\"]\n",
    "                                 .str.upper()\n",
    "                                 .str.strip(\".\")\n",
    "                                 .str.replace(\"&\",\"<br>\")\n",
    "                                 + \" (\"+data[\"person_ctry_ISO3\"]+\")\")\n",
    "\n",
    "            fig = px.bar(data, x=record_col, y=\"han_label\", color=trait,\n",
    "                         color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n",
    "            fig.update_layout(font_family=\"Montserrat\",\n",
    "                          yaxis={'categoryorder':'total ascending'})\n",
    "\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=12\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 12\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "\n",
    "            figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "            figsuper.write_image(f\"{fromdir}/plots/top15_applt_{trait}_{ctry}.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log10\n",
      "\n"
     ]
    },
    {
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   ],
   "source": [
    "applt_sub = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]!=0)&\n",
    "                          (appln_pers_f[\"psn_sector_primary\"]!=\"INDIVIDUAL\"))]\n",
    "invt_sub = appln_pers_f[appln_pers_f[\"invt_seq_nr\"]!=0]\n",
    "\n",
    "eu_applt_subset = applt_sub[~applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "ch_applt_subset = applt_sub[applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "\n",
    "eu_invt_subset = invt_sub[~invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "ch_invt_subset = invt_sub[invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "\n",
    "#\n",
    "# patstat_applt_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "# patstat_applt_dipol.sample(100)\n",
    "\n",
    "patstat_applt_dipol = eu_applt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "patstat_applt_inv_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "\n",
    "# co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
    "#                             values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
    "                            values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "\n",
    "\n",
    "co_occur = co_occur.T\n",
    "co_occur[\"sum\"] = co_occur.sum(axis=1)\n",
    "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
    "\n",
    "co_occur = (co_occur\n",
    "            .sort_values(by=\"sum\", ascending=False)\n",
    "            .head(20)\n",
    "            .drop(columns=\"sum\")\n",
    "            .replace(0,np.nan)\n",
    "            .dropna(axis=1,how=\"all\")\n",
    "            .replace(np.nan,0)\n",
    "            .astype(int))\n",
    "\n",
    "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
    "                labels=dict(x=\"Inventor (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
    "                x=list(co_occur.columns),\n",
    "                text_auto=True,\n",
    "                y=list(co_occur.index))\n",
    "\n",
    "\n",
    "fig.update_layout(title_x=0.5,\n",
    "                   width=1000, height=1000,\n",
    "                   xaxis_showgrid=False,\n",
    "                   yaxis_showgrid=False,\n",
    "                   yaxis_autorange='reversed',\n",
    "                  font_family=\"Montserrat\",\n",
    "                  template='plotly_white',\n",
    "                  coloraxis_colorbar=dict(\n",
    "                            thicknessmode=\"pixels\", thickness=25,\n",
    "                            ticks=\"outside\", ticksuffix=\" \",\n",
    "                            dtick=20,outlinewidth=1,\n",
    "                        ))\n",
    "\n",
    "# fig.update_traces(texttemplate='%{z}')\n",
    "# fig.update_traces(z=co_occur.values)\n",
    "# fig.update(data=[{'customdata': co_occur}])\n",
    "# fig.update(data=[{'customdata': co_occur,\n",
    "#     'hovertemplate': \"(x) %{x} product: %{customdata[0]}<br>(y) %{y} product: %{customdata[1]}<br>%{z}\"}])\n",
    "# fig.update_traces(texttemplate='%{customdata}')\n",
    "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.update_xaxes(tickangle= -45)\n",
    "fig.update_yaxes(\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    ticks=\"outside\")\n",
    "\n",
    "figsuper_ppt = fig\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 16\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "\n",
    "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_applt_ctry.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
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  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "outputs": [],
   "source": [
    "patstat_applt_dipol_2 = (patstat_applt_dipol\n",
    "                         .groupby([\"han_name_ch\",\"han_name_eu\"], as_index=False)\n",
    "                         .agg({\"psn_sector_primary_ch\":\"first\",\n",
    "                               \"psn_sector_primary_eu\":\"first\",\n",
    "                               \"person_ctry_ISO3_eu\":\"first\",\n",
    "                               record_col:\"nunique\"})\n",
    "                         .sort_values(by=record_col, ascending=False)\n",
    "                         .head(-1))\n",
    "patstat_applt_dipol_2[\"applt_eu_label\"] = (patstat_applt_dipol_2[\"han_name_eu\"]\n",
    "                                           +\" (\"\n",
    "                                           +patstat_applt_dipol_2[\"person_ctry_ISO3_eu\"]\n",
    "                                           +\"; \"\n",
    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_eu\"]\n",
    "                                           +\")\")\n",
    "\n",
    "patstat_applt_dipol_2[\"applt_ch_label\"] = (patstat_applt_dipol_2[\"han_name_ch\"]\n",
    "                                           +\" (\"\n",
    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_ch\"]\n",
    "                                           +\")\")\n",
    "\n",
    "patstat_applt_dipol_2[[\"applt_ch_label\",\"applt_eu_label\",\"appln_id\"]].to_excel(rf\"{fromdir}/plots/applt_table.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 456,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# percent of publications related to\n",
    "# -authority country\n",
    "# -inventor country\n",
    "# -applicant country\n",
    "\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#7F00FF'}#CC8899\n",
    "\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col+\"_percent\": 'Percent of related co-patents',\n",
    "                    \"person_ctry_code_fullname\": \"Country\",\n",
    "                    \"person_ctry_type\": \"Country type\",\n",
    "                    \"appln_auth_fullname\": \"Application authority\",\n",
    "                    \"auth_ctry_type\": \"Authority type\",\n",
    "                    \"Country_Type\":\"Country type\",\n",
    "                    \"Eurovoc_Class\":\"Region\"\n",
    "                 })\n",
    "\n",
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    pers_sub = appln_pers_f\n",
    "    if c == \"person_ctry_code\":\n",
    "        for role in [\"invt\",\"applt\"]:\n",
    "            pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
    "            role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n",
    "            labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n",
    "\n",
    "\n",
    "            data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
    "            data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "            fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "            fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=14\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "            figsuper.write_image(rf\"{fromdir}/plots/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
    "            figsuper.show()\n",
    "    else:\n",
    "        data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
    "        data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "        fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "        fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "        figsuper = fig\n",
    "        figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "        figsuper.update_yaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_xaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "        figsuper['layout'][\"font\"][\"size\"]=14\n",
    "        for a in figsuper['layout'][\"annotations\"]:\n",
    "            a[\"font\"][\"size\"] = 14\n",
    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
    "\n",
    "        figsuper.update_layout(uniformtext_minsize=10)\n",
    "        figsuper.write_image(rf\"{fromdir}/plots/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
    "        figsuper.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "outputs": [],
   "source": [
    "appln_ipc_f = pd.read_csv(f\"{fromdir}/first-filings-with-IPC-raw.csv\", header=None,\n",
    "                      names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors',\n",
    "                             'ipc_class_symbol','ipc_class_level'])\n",
    "\n",
    "appln_ipc_f = appln_ipc_f[appln_ipc_f[\"filing_year\"]>2010]\n",
    "appln_ipc_f = appln_ipc_f[appln_ipc_f[\"appln_id\"].isin(appln_f[\"appln_id\"])]\n",
    "\n",
    "appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n",
    "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n",
    "appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n",
    "                                                                 \"B\":\"Performing operations; transporting\",\n",
    "                                                                 \"B\":\"Operations; transporting\",\n",
    "                                                                 \"C\":\"Chemistry; metallurgy\",\n",
    "                                                                 \"D\":\"Textiles; paper\",\n",
    "                                                                 \"E\":\"Fixed constructions\",\n",
    "                                                                 \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n",
    "                                                                 \"F\":\"Engineering\",\n",
    "                                                                 \"G\":\"Physics\",\n",
    "                                                                 \"H\":\"Electricity\"})\n",
    "\n",
    "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "outputs": [],
   "source": [
    "group = 'ipc_class'\n",
    "data = appln_ipc_f.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n",
    "\n",
    "fig = px.bar(data.sort_values(by=group), x=record_col, y=group, color=group,barmode='relative',text_auto=True,\n",
    "                              labels={\n",
    "                     record_col: 'Number of co-publications',\n",
    "                     group: \"\",\n",
    "                 },\n",
    "                title=\"Distribution of Domains\", template='plotly')\n",
    "fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n",
    "fig.update_traces(hovertemplate='%{x:d}')\n",
    "fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=True)\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_layout(yaxis={'categoryorder':'total ascending'})\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "dom_distr = go.Figure(fig)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "outputs": [],
   "source": [
    "data = (appln_ipc_f.groupby(['filing_year','ipc_class'])[record_col].nunique(dropna=False).unstack()\n",
    "        .fillna(0)\n",
    "        .stack()\n",
    "        .reset_index()\n",
    "        .rename(columns={0:record_col}))\n",
    "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset='ipc_class'),\n",
    "                  on='ipc_class', suffixes=[None,\"_relative_growth\"])\n",
    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
    "\n",
    "data = data.sort_values(by =[\"ipc_class\",\"filing_year\"], ascending=[True,True])\n",
    "data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "outputs": [],
   "source": [
    "group = ['filing_year','ipc_class']\n",
    "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True,                             labels={\n",
    "                     record_col: 'Number of co-publications',\n",
    "                     group[-1]: \"Domain\",\n",
    "                 },\n",
    "                title=\"Yearly output of co-publications\", template='plotly')\n",
    "fig.update_traces(hovertemplate='%{y:d}')\n",
    "fig.update_layout(hovermode='x unified')\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "year_output_by_domain = go.Figure(fig)\n",
    "\n",
    "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True,                             labels={\n",
    "                     record_col+\"_relative_growth\": 'Rel. growth<br>in co-publications (%)',\n",
    "                     group[-1]: \"Domain\",\n",
    "                 },\n",
    "                title=\"Relative growth in the output of co-publications\", template='plotly')\n",
    "\n",
    "fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n",
    "fig.update_traces(hovertemplate='%{y:.0f}00%')\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "rel_output_by_domain = go.Figure(fig)\n",
    "\n",
    "\n",
    "fig = px.area(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_cumsum\",x=group[0], color=group[-1],line_group=group[-1],\n",
    "              labels={\n",
    "                     record_col+\"_cumsum\": 'Cumulative number of co-publications',\n",
    "                     group[-1]: \"Domain\",\n",
    "                 },\n",
    "                title=\"Cumulative number of co-publications\", template='plotly')\n",
    "fig.update_traces(hovertemplate='%{y:d}')\n",
    "fig.update_layout(hovermode='x unified')\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "cumsum_by_domain = go.Figure(fig)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "A-Human necessities",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "A-Human necessities",
         "offsetgroup": "A-Human necessities",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          99
         ],
         "xaxis": "x",
         "y": [
          "A-Human necessities"
         ],
         "yaxis": "y",
         "type": "bar"
        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "B-Operations; transporting",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "B-Operations; transporting",
         "offsetgroup": "B-Operations; transporting",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          341
         ],
         "xaxis": "x",
         "y": [
          "B-Operations; transporting"
         ],
         "yaxis": "y",
         "type": "bar"
        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "C-Chemistry; metallurgy",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "C-Chemistry; metallurgy",
         "offsetgroup": "C-Chemistry; metallurgy",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          145
         ],
         "xaxis": "x",
         "y": [
          "C-Chemistry; metallurgy"
         ],
         "yaxis": "y",
         "type": "bar"
        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "D-Textiles; paper",
         "marker": {
          "color": "#ab63fa",
          "pattern": {
           "shape": ""
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         },
         "name": "D-Textiles; paper",
         "offsetgroup": "D-Textiles; paper",
         "orientation": "h",
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         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          14
         ],
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          "D-Textiles; paper"
         ],
         "yaxis": "y",
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        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "E-Fixed constructions",
         "marker": {
          "color": "#FFA15A",
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         },
         "name": "E-Fixed constructions",
         "offsetgroup": "E-Fixed constructions",
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         },
         "textposition": "outside",
         "texttemplate": "%{x}",
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         ],
         "xaxis": "x",
         "y": [
          "E-Fixed constructions"
         ],
         "yaxis": "y",
         "type": "bar"
        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# dom_distr\n",
    "# year_output_by_domain\n",
    "# rel_output_by_domain\n",
    "# cumsum_by_domain\n",
    "\n",
    "figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of IPC classes\",\"Cumulative sum of related co-patents\",\n",
    "                                                         \"Related co-patents per year\",\"Relative growth of co-patents\"])\n",
    "\n",
    "\n",
    "for trace in list(dom_distr.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    # trace.barmode\n",
    "    figsuper.add_trace(trace,\n",
    "        row=1, col=1\n",
    "    )\n",
    "\n",
    "for trace in list(cumsum_by_domain.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    figsuper.add_trace(trace,\n",
    "        row=1, col=2\n",
    "    )\n",
    "\n",
    "for trace in list(year_output_by_domain.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    figsuper.add_trace(trace,\n",
    "        row=2, col=1\n",
    "    )\n",
    "\n",
    "for trace in list(rel_output_by_domain.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    figsuper.add_trace(trace,\n",
    "        row=2, col=2\n",
    "    )\n",
    "\n",
    "\n",
    "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n",
    "figsuper.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "figsuper.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n",
    "figsuper['layout']['yaxis2'].update(tickformat=\".0f\")\n",
    "figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n",
    "figsuper['layout'][f'xaxis'][\"range\"] = [0,400]\n",
    "for i in range(2,5):\n",
    "    figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n",
    "figsuper['layout'][\"font\"][\"size\"]=14\n",
    "for a in figsuper['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 14\n",
    "figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "figsuper.write_image(rf\"{fromdir}/plots/ipc_classes.png\",height=900,width=1600,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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