diff --git a/PATSTAT/patstat_analysis_pipeline.ipynb b/PATSTAT/patstat_analysis_pipeline.ipynb
index 73f55c7..b5508af 100644
--- a/PATSTAT/patstat_analysis_pipeline.ipynb
+++ b/PATSTAT/patstat_analysis_pipeline.ipynb
@@ -301,13 +301,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 297,
    "outputs": [
     {
      "data": {
       "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
      },
-     "execution_count": 48,
+     "execution_count": 297,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,10 +321,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 429,
    "outputs": [],
    "source": [
-    "fromdir=\"first_round\""
+    "fromdir=\"first_round\"\n",
+    "\n",
+    "os.makedirs(fr'{fromdir}/plots')"
    ],
    "metadata": {
     "collapsed": false
@@ -332,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 392,
    "outputs": [],
    "source": [
     "appln_pers_f = pd.read_csv(f\"{fromdir}/first-filings-with-persons-raw.csv\", header=None,\n",
@@ -340,6 +342,13 @@
     "\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": {
@@ -348,16 +357,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "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 == 'CN')|\n",
     "                      (tls_801.ctry_code == 'NO')|\n",
     "                      (tls_801.ctry_code == 'CH')|\n",
-    "                      (tls_801.ctry_code == 'UK'))]\n",
+    "                      (tls_801.ctry_code == 'GB'))]\n",
     "scope_countries = scope_df[\"ctry_code\"].unique()"
    ],
    "metadata": {
@@ -366,13 +407,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 396,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1116\n"
+      "1055\n"
      ]
     }
    ],
@@ -395,13 +436,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 397,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1116\n"
+      "1055\n"
      ]
     }
    ],
@@ -415,14 +456,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "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   \n828     410512383         EP        220            0             1   \n1454    333687134         EP      10329            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   \n828     SIEMENS AKTIENGESELLSCHAFT               DE    COMPANY    2709504   \n1454                Alcatel Lucent               FR    COMPANY      68848   \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  \n76      EVONIK DEGUSSA GMBH  \n87      EVONIK DEGUSSA GMBH  \n88      EVONIK DEGUSSA GMBH  \n828              SIEMENS AG  \n1454         ALCATEL LUCENT  \n...                     ...  \n147073        JONSSON, Tony  \n147074        JONSSON, Tony  \n147075        JONSSON, Tony  \n147076        JONSSON, Tony  \n147077  TRAN NGUYEN, Johnny  \n\n[4434 rows x 10 columns]",
-      "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>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>76</th>\n      <td>410448968</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>420386087</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>420386089</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n    </tr>\n    <tr>\n      <th>828</th>\n      <td>410512383</td>\n      <td>EP</td>\n      <td>220</td>\n      <td>0</td>\n      <td>1</td>\n      <td>SIEMENS AKTIENGESELLSCHAFT</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>2709504</td>\n      <td>SIEMENS AG</td>\n    </tr>\n    <tr>\n      <th>1454</th>\n      <td>333687134</td>\n      <td>EP</td>\n      <td>10329</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Alcatel Lucent</td>\n      <td>FR</td>\n      <td>COMPANY</td>\n      <td>68848</td>\n      <td>ALCATEL LUCENT</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    </tr>\n    <tr>\n      <th>147073</th>\n      <td>549690891</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147074</th>\n      <td>549690895</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147075</th>\n      <td>549690901</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147076</th>\n      <td>549690897</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147077</th>\n      <td>549788919</td>\n      <td>EP</td>\n      <td>85774895</td>\n      <td>2</td>\n      <td>0</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185774895</td>\n      <td>TRAN NGUYEN, Johnny</td>\n    </tr>\n  </tbody>\n</table>\n<p>4434 rows × 10 columns</p>\n</div>"
+      "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]",
+      "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>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector_primary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>76</th>\n      <td>410448968</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>420386087</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>420386089</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>337088926</td>\n      <td>EP</td>\n      <td>21790</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>974</th>\n      <td>410512383</td>\n      <td>EP</td>\n      <td>220</td>\n      <td>0</td>\n      <td>1</td>\n      <td>SIEMENS AKTIENGESELLSCHAFT</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>2709504</td>\n      <td>SIEMENS AG</td>\n      <td>COMPANY</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    </tr>\n    <tr>\n      <th>147073</th>\n      <td>549690891</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147074</th>\n      <td>549690895</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147075</th>\n      <td>549690901</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147076</th>\n      <td>549690897</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147077</th>\n      <td>549788919</td>\n      <td>EP</td>\n      <td>85774895</td>\n      <td>2</td>\n      <td>0</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185774895</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n<p>4367 rows × 11 columns</p>\n</div>"
      },
-     "execution_count": 71,
+     "execution_count": 398,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,21 +477,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 399,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1116\n"
+      "1055\n"
      ]
     },
     {
      "data": {
-      "text/plain": "         appln_id appln_auth appln_kind  filing_year  nb_applicants   \n51083   407234637         FI          A         2011              1  \\\n240847  442945435         FR          A         2015              1   \n685164  531137359         NL          A         2020              1   \n235203  438685261         EP          A         2015              1   \n430561  497548590         DE          A         2017              2   \n...           ...        ...        ...          ...            ...   \n721473  579480994         DE          A         2021              1   \n619328  523950441         EP          A         2019              2   \n484715  490602095         EP          A         2018              1   \n196502  450101287         DE          A         2014              1   \n367660  477378741         GB          A         2017              1   \n\n        nb_inventors  \n51083              2  \n240847             5  \n685164             3  \n235203             4  \n430561             8  \n...              ...  \n721473             3  \n619328             3  \n484715             1  \n196502             6  \n367660             1  \n\n[100 rows x 6 columns]",
-      "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_kind</th>\n      <th>filing_year</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>51083</th>\n      <td>407234637</td>\n      <td>FI</td>\n      <td>A</td>\n      <td>2011</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>240847</th>\n      <td>442945435</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>1</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>685164</th>\n      <td>531137359</td>\n      <td>NL</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>235203</th>\n      <td>438685261</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>430561</th>\n      <td>497548590</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>2</td>\n      <td>8</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    </tr>\n    <tr>\n      <th>721473</th>\n      <td>579480994</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>619328</th>\n      <td>523950441</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>484715</th>\n      <td>490602095</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>196502</th>\n      <td>450101287</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2014</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>367660</th>\n      <td>477378741</td>\n      <td>GB</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
+      "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]",
+      "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_kind</th>\n      <th>filing_year</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>114926</th>\n      <td>405478585</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>670680</th>\n      <td>532677899</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>568605</th>\n      <td>520224479</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>441101</th>\n      <td>483520394</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>687085</th>\n      <td>554756101</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>1</td>\n      <td>3</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    </tr>\n    <tr>\n      <th>235723</th>\n      <td>443704166</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>670840</th>\n      <td>532706785</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>415863</th>\n      <td>486019755</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>608249</th>\n      <td>520115105</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>753188</th>\n      <td>549690895</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
      },
-     "execution_count": 55,
+     "execution_count": 399,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 399,
    "outputs": [],
    "source": [],
    "metadata": {
@@ -485,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 399,
    "outputs": [],
    "source": [],
    "metadata": {
@@ -494,13 +535,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 400,
    "outputs": [
     {
      "data": {
-      "text/plain": "appln_kind\nA    1114\nU      13\nName: count, dtype: int64"
+      "text/plain": "appln_kind\nA    1059\nU       7\nName: count, dtype: int64"
      },
-     "execution_count": 57,
+     "execution_count": 400,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -514,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 401,
    "outputs": [],
    "source": [
     "# Co-patents per year"
@@ -525,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 430,
    "outputs": [
     {
      "data": {
@@ -546,16 +587,16 @@
          "orientation": "v",
          "showlegend": false,
          "text": [
-          52.0,
           48.0,
-          73.0,
+          42.0,
           64.0,
-          86.0,
-          97.0,
+          58.0,
+          83.0,
+          94.0,
           148.0,
-          179.0,
-          166.0,
-          145.0,
+          174.0,
+          151.0,
+          135.0,
           58.0
          ],
          "textfont": {
@@ -577,16 +618,16 @@
          ],
          "xaxis": "x",
          "y": [
-          52,
           48,
-          73,
+          42,
           64,
-          86,
-          97,
+          58,
+          83,
+          94,
           148,
-          179,
-          166,
-          145,
+          174,
+          151,
+          135,
           58
          ],
          "yaxis": "y",
@@ -608,16 +649,16 @@
          "showlegend": false,
          "text": [
           0.0,
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-          0.40384615384615385,
-          0.23076923076923078,
-          0.6538461538461539,
-          0.8653846153846154,
-          1.8461538461538463,
-          2.4423076923076925,
-          2.1923076923076925,
-          1.7884615384615385,
-          0.11538461538461539
+          -0.125,
+          0.3333333333333333,
+          0.20833333333333334,
+          0.7291666666666666,
+          0.9583333333333334,
+          2.0833333333333335,
+          2.625,
+          2.1458333333333335,
+          1.8125,
+          0.20833333333333334
          ],
          "textfont": {
           "size": 12
@@ -640,16 +681,16 @@
          "xaxis": "x2",
          "y": [
           0.0,
-          -0.07692307692307693,
-          0.40384615384615385,
-          0.23076923076923078,
-          0.6538461538461539,
-          0.8653846153846154,
-          1.8461538461538463,
-          2.4423076923076925,
-          2.1923076923076925,
-          1.7884615384615385,
-          0.11538461538461539
+          -0.125,
+          0.3333333333333333,
+          0.20833333333333334,
+          0.7291666666666666,
+          0.9583333333333334,
+          2.0833333333333335,
+          2.625,
+          2.1458333333333335,
+          1.8125,
+          0.20833333333333334
          ],
          "yaxis": "y2",
          "type": "scatter"
@@ -1574,7 +1615,7 @@
         "plotlyServerURL": "https://plotly.com"
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       },
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defaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"showgrid\":true,\"showline\":true,\"linewidth\":1,\"linecolor\":\"black\",\"mirror\":true,\"ticks\":\"outside\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.625,1.0],\"categoryorder\":\"total ascending\",\"showgrid\":true,\"showline\":true,\"linewidth\":1,\"linecolor\":\"black\",\"mirror\":true,\"ticks\":\"outside\",\"range\":[0,250]},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.0,1.0],\"showgrid\":true,\"showline\":true,\"linewidth\":1,\"linecolor\":\"black\",\"mirror\":true,\"ticks\":\"outside\"},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.0,0.375],\"showgrid\":true,\"showline\":true,\"linewidth\":1,\"linecolor\":\"black\",\"mirror\":true,\"ticks\":\"outside\",\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"range\":[-1,3.5]},\"annotations\":[{\"font\":{\"size\":16},\"showarrow\":false,\"text\":\"Co-patents per year\",\"x\":0.5,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{\"size\":16},\"showarrow\":false,\"text\":\"Relative growth of co-patents\",\"x\":0.5,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":0.375,\"yanchor\":\"bottom\",\"yref\":\"paper\"}],\"barmode\":\"relative\",\"font\":{\"family\":\"Montserrat\"}},                        {\"displayModeBar\": false, \"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('75ec3337-4e6d-4e58-a9c8-b65f37b2bb8e');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1632,7 +1673,9 @@
     "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
     "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
     "\n",
-    "figsuper.show(config= dict(displayModeBar = False, responsive = True))"
+    "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
@@ -1640,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 440,
    "outputs": [
     {
      "name": "stderr",
@@ -1649,43 +1692,6 @@
       "EP not found in ISO2\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": "     filing_year appln_auth  appln_id  filing_year_relative_growth   \n0           2011         AT       0.0                         2012  \\\n1           2012         AT       1.0                         2012   \n2           2013         AT       4.0                         2012   \n3           2014         AT       0.0                         2012   \n4           2015         AT       1.0                         2012   \n..           ...        ...       ...                          ...   \n160         2017         SE       3.0                         2015   \n161         2018         SE       1.0                         2015   \n162         2019         SE       0.0                         2015   \n163         2020         SE       1.0                         2015   \n164         2021         SE       0.0                         2015   \n\n     appln_id_relative_growth  appln_id_cumsum  Country  \n0                        -1.0              0.0  Austria  \n1                         0.0              1.0  Austria  \n2                         3.0              5.0  Austria  \n3                        -1.0              5.0  Austria  \n4                         0.0              6.0  Austria  \n..                        ...              ...      ...  \n160                       2.0              5.0   Sweden  \n161                       0.0              6.0   Sweden  \n162                      -1.0              6.0   Sweden  \n163                       0.0              7.0   Sweden  \n164                      -1.0              7.0   Sweden  \n\n[165 rows x 7 columns]",
-      "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>filing_year</th>\n      <th>appln_auth</th>\n      <th>appln_id</th>\n      <th>filing_year_relative_growth</th>\n      <th>appln_id_relative_growth</th>\n      <th>appln_id_cumsum</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>AT</td>\n      <td>0.0</td>\n      <td>2012</td>\n      <td>-1.0</td>\n      <td>0.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>AT</td>\n      <td>1.0</td>\n      <td>2012</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>AT</td>\n      <td>4.0</td>\n      <td>2012</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>AT</td>\n      <td>0.0</td>\n      <td>2012</td>\n      <td>-1.0</td>\n      <td>5.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>AT</td>\n      <td>1.0</td>\n      <td>2012</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>Austria</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    </tr>\n    <tr>\n      <th>160</th>\n      <td>2017</td>\n      <td>SE</td>\n      <td>3.0</td>\n      <td>2015</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>161</th>\n      <td>2018</td>\n      <td>SE</td>\n      <td>1.0</td>\n      <td>2015</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>162</th>\n      <td>2019</td>\n      <td>SE</td>\n      <td>0.0</td>\n      <td>2015</td>\n      <td>-1.0</td>\n      <td>6.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>163</th>\n      <td>2020</td>\n      <td>SE</td>\n      <td>1.0</td>\n      <td>2015</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>2021</td>\n      <td>SE</td>\n      <td>0.0</td>\n      <td>2015</td>\n      <td>-1.0</td>\n      <td>7.0</td>\n      <td>Sweden</td>\n    </tr>\n  </tbody>\n</table>\n<p>165 rows × 7 columns</p>\n</div>"
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "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",
-    "data"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "outputs": [
     {
      "data": {
       "application/vnd.plotly.v1+json": {
@@ -1730,21 +1736,21 @@
           2020,
           2021
          ],
-         "xaxis": "x13",
+         "xaxis": "x11",
          "y": [
           0.0,
           1.0,
-          4.0,
+          0.0,
           0.0,
           1.0,
           0.0,
           0.0,
           0.0,
           2.0,
-          3.0,
+          2.0,
           0.0
          ],
-         "yaxis": "y13",
+         "yaxis": "y11",
          "type": "scatter"
         },
         {
@@ -1787,7 +1793,7 @@
           2020,
           2021
          ],
-         "xaxis": "x14",
+         "xaxis": "x12",
          "y": [
           0.0,
           0.0,
@@ -1801,64 +1807,7 @@
           1.0,
           1.0
          ],
-         "yaxis": "y14",
-         "type": "scatter"
-        },
-        {
-         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
-         "hovertext": [
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China"
-         ],
-         "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,
-          0.0,
-          1.0,
-          2.0,
-          0.0,
-          0.0,
-          0.0,
-          0.0,
-          0.0,
-          0.0,
-          0.0
-         ],
-         "yaxis": "y15",
+         "yaxis": "y12",
          "type": "scatter"
         },
         {
@@ -1901,7 +1850,7 @@
           2020,
           2021
          ],
-         "xaxis": "x16",
+         "xaxis": "x13",
          "y": [
           0.0,
           0.0,
@@ -1915,7 +1864,7 @@
           1.0,
           0.0
          ],
-         "yaxis": "y16",
+         "yaxis": "y13",
          "type": "scatter"
         },
         {
@@ -1958,21 +1907,21 @@
           2020,
           2021
          ],
-         "xaxis": "x17",
+         "xaxis": "x14",
          "y": [
           0.0,
           0.0,
           0.0,
           0.0,
-          2.0,
-          1.0,
+          0.0,
+          0.0,
           0.0,
           0.0,
           2.0,
           0.0,
           0.0
          ],
-         "yaxis": "y17",
+         "yaxis": "y14",
          "type": "scatter"
         },
         {
@@ -2015,21 +1964,21 @@
           2020,
           2021
          ],
-         "xaxis": "x18",
+         "xaxis": "x15",
          "y": [
           17.0,
           15.0,
           16.0,
-          21.0,
+          19.0,
           28.0,
           34.0,
           56.0,
-          104.0,
-          115.0,
-          87.0,
+          102.0,
+          109.0,
+          86.0,
           33.0
          ],
-         "yaxis": "y18",
+         "yaxis": "y15",
          "type": "scatter"
         },
         {
@@ -2072,11 +2021,11 @@
           2020,
           2021
          ],
-         "xaxis": "x7",
+         "xaxis": "x6",
          "y": [
           3.0,
-          4.0,
-          3.0,
+          2.0,
+          1.0,
           1.0,
           3.0,
           1.0,
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ks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis10\":{\"anchor\":\"y10\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis10\":{\"anchor\":\"x10\",\"domain\":[0.35666666666666663,0.6433333333333333],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis11\":{\"anchor\":\"y11\",\"domain\":[0.0,0.18400000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis11\":{\"anchor\":\"x11\",\"domain\":[0.7133333333333333,0.9999999999999999],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis12\":{\"anchor\":\"y12\",\"domain\":[0.20400000000000001,0.388],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis12\":{\"anchor\":\"x12\",\"domain\":[0.7133333333333333,0.9999999999999999],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis13\":{\"anchor\":\"y13\",\"domain\":[0.40800000000000003,0.5920000000000001],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis13\":{\"anchor\":\"x13\",\"domain\":[0.7133333333333333,0.9999999999999999],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis14\":{\"anchor\":\"y14\",\"domain\":[0.6120000000000001,0.7960000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis14\":{\"anchor\":\"x14\",\"domain\":[0.7133333333333333,0.9999999999999999],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis15\":{\"anchor\":\"y15\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis15\":{\"anchor\":\"x15\",\"domain\":[0.7133333333333333,0.9999999999999999],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('b87c87b3-cee7-465b-a1b3-4f416385e429');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "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",
     "              y=record_col,\n",
     "              x='filing_year'\n",
-    "              ,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\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",
@@ -4360,7 +4187,7 @@
     "fig.update_xaxes(\n",
     "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
     "    ticks=\"outside\")\n",
-    "fig.update_layout({'template':\"plotly\"})\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",
@@ -4368,13 +4195,15 @@
     "figsuper_ppt = go.Figure(fig)\n",
     "\n",
     "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
+    "# figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n",
     "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
     "    a[\"font\"][\"size\"] = 22\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",
-    "figsuper_ppt.show()"
+    "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)"
    ],
    "metadata": {
     "collapsed": false
@@ -4382,7 +4211,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 442,
    "outputs": [
     {
      "data": {
@@ -4428,21 +4266,21 @@
           2020,
           2021
          ],
-         "xaxis": "x13",
+         "xaxis": "x11",
          "y": [
           -1.0,
           0.0,
-          3.0,
+          -1.0,
           -1.0,
           0.0,
           -1.0,
           -1.0,
           -1.0,
           1.0,
-          2.0,
+          1.0,
           -1.0
          ],
-         "yaxis": "y13",
+         "yaxis": "y11",
          "type": "scatter"
         },
         {
@@ -4485,7 +4323,7 @@
           2020,
           2021
          ],
-         "xaxis": "x14",
+         "xaxis": "x12",
          "y": [
           -1.0,
           -1.0,
@@ -4499,64 +4337,7 @@
           0.0,
           0.0
          ],
-         "yaxis": "y14",
-         "type": "scatter"
-        },
-        {
-         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
-         "hovertext": [
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China",
-          "China"
-         ],
-         "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": [
-          -1.0,
-          -1.0,
-          0.0,
-          1.0,
-          -1.0,
-          -1.0,
-          -1.0,
-          -1.0,
-          -1.0,
-          -1.0,
-          -1.0
-         ],
-         "yaxis": "y15",
+         "yaxis": "y12",
          "type": "scatter"
         },
         {
@@ -4599,7 +4380,7 @@
           2020,
           2021
          ],
-         "xaxis": "x16",
+         "xaxis": "x13",
          "y": [
           -1.0,
           -1.0,
@@ -4613,7 +4394,7 @@
           0.0,
           -1.0
          ],
-         "yaxis": "y16",
+         "yaxis": "y13",
          "type": "scatter"
         },
         {
@@ -4656,21 +4437,21 @@
           2020,
           2021
          ],
-         "xaxis": "x17",
+         "xaxis": "x14",
          "y": [
           -1.0,
           -1.0,
           -1.0,
           -1.0,
-          0.0,
-          -0.5,
+          -1.0,
+          -1.0,
           -1.0,
           -1.0,
           0.0,
           -1.0,
           -1.0
          ],
-         "yaxis": "y17",
+         "yaxis": "y14",
          "type": "scatter"
         },
         {
@@ -4713,21 +4494,21 @@
           2020,
           2021
          ],
-         "xaxis": "x18",
+         "xaxis": "x15",
          "y": [
           0.0,
           -0.11764705882352941,
           -0.058823529411764705,
-          0.23529411764705882,
+          0.11764705882352941,
           0.6470588235294118,
           1.0,
           2.2941176470588234,
-          5.117647058823529,
-          5.764705882352941,
-          4.117647058823529,
+          5.0,
+          5.411764705882353,
+          4.0588235294117645,
           0.9411764705882353
          ],
-         "yaxis": "y18",
+         "yaxis": "y15",
          "type": "scatter"
         },
         {
@@ -4770,11 +4551,11 @@
           2020,
           2021
          ],
-         "xaxis": "x7",
+         "xaxis": "x6",
          "y": [
           0.0,
-          0.3333333333333333,
-          0.0,
+          -0.3333333333333333,
+          -0.6666666666666666,
           -0.6666666666666666,
           0.0,
           -0.6666666666666666,
@@ -4784,7 +4565,7 @@
           -1.0,
           -1.0
          ],
-         "yaxis": "y7",
+         "yaxis": "y6",
          "type": "scatter"
         },
         {
@@ -4827,7 +4608,7 @@
           2020,
           2021
          ],
-         "xaxis": "x8",
+         "xaxis": "x7",
          "y": [
           0.0,
           -0.1,
@@ -4838,10 +4619,10 @@
           0.3,
           -0.4,
           0.1,
-          0.5,
+          0.4,
           -0.4
          ],
-         "yaxis": "y8",
+         "yaxis": "y7",
          "type": "scatter"
         },
         {
@@ -4884,21 +4665,21 @@
           2020,
           2021
          ],
-         "xaxis": "x9",
+         "xaxis": "x8",
          "y": [
           0.0,
           0.0,
-          1.1333333333333333,
-          1.0666666666666667,
-          0.6,
-          0.26666666666666666,
+          1.2142857142857142,
+          1.0714285714285714,
+          0.7142857142857143,
+          0.2857142857142857,
+          1.1428571428571428,
           1.0,
-          0.8666666666666667,
-          0.3333333333333333,
-          0.4,
-          0.0
+          0.35714285714285715,
+          0.35714285714285715,
+          0.07142857142857142
          ],
-         "yaxis": "y9",
+         "yaxis": "y8",
          "type": "scatter"
         },
         {
@@ -4941,7 +4722,7 @@
           2020,
           2021
          ],
-         "xaxis": "x10",
+         "xaxis": "x9",
          "y": [
           -1.0,
           -1.0,
@@ -4955,7 +4736,7 @@
           1.0,
           1.0
          ],
-         "yaxis": "y10",
+         "yaxis": "y9",
          "type": "scatter"
         },
         {
@@ -4998,7 +4779,7 @@
           2020,
           2021
          ],
-         "xaxis": "x11",
+         "xaxis": "x10",
          "y": [
           -1.0,
           -1.0,
@@ -5012,7 +4793,7 @@
           7.0,
           0.0
          ],
-         "yaxis": "y11",
+         "yaxis": "y10",
          "type": "scatter"
         },
         {
@@ -5055,7 +4836,7 @@
           2020,
           2021
          ],
-         "xaxis": "x12",
+         "xaxis": "x",
          "y": [
           -1.0,
           -1.0,
@@ -5069,7 +4850,7 @@
           -1.0,
           -1.0
          ],
-         "yaxis": "y12",
+         "yaxis": "y",
          "type": "scatter"
         },
         {
@@ -5112,10 +4893,10 @@
           2020,
           2021
          ],
-         "xaxis": "x",
+         "xaxis": "x2",
          "y": [
           0.0,
-          -0.5,
+          -0.75,
           -1.0,
           -0.75,
           -0.25,
@@ -5126,7 +4907,7 @@
           -1.0,
           -1.0
          ],
-         "yaxis": "y",
+         "yaxis": "y2",
          "type": "scatter"
         },
         {
@@ -5169,7 +4950,7 @@
           2020,
           2021
          ],
-         "xaxis": "x2",
+         "xaxis": "x3",
          "y": [
           -1.0,
           -1.0,
@@ -5183,7 +4964,7 @@
           0.0,
           -1.0
          ],
-         "yaxis": "y2",
+         "yaxis": "y3",
          "type": "scatter"
         },
         {
@@ -5226,21 +5007,21 @@
           2020,
           2021
          ],
-         "xaxis": "x3",
+         "xaxis": "x4",
          "y": [
+          -1.0,
+          -1.0,
           0.0,
-          -0.3333333333333333,
-          -0.3333333333333333,
-          -0.6666666666666666,
-          2.6666666666666665,
-          7.333333333333333,
-          11.0,
-          9.666666666666666,
+          0.0,
+          9.0,
+          23.0,
+          35.0,
+          28.0,
           3.0,
-          1.0,
+          0.0,
           -1.0
          ],
-         "yaxis": "y3",
+         "yaxis": "y4",
          "type": "scatter"
         }
        ],
@@ -5248,8 +5029,8 @@
         "annotations": [
          {
           "showarrow": false,
-          "text": "Spain",
-          "x": 0.075,
+          "text": "Portugal",
+          "x": 0.09200000000000001,
           "xanchor": "center",
           "xref": "paper",
           "y": 0.2866666666666666,
@@ -5261,8 +5042,8 @@
          },
          {
           "showarrow": false,
-          "text": "Sweden",
-          "x": 0.24499999999999997,
+          "text": "Spain",
+          "x": 0.29600000000000004,
           "xanchor": "center",
           "xref": "paper",
           "y": 0.2866666666666666,
@@ -5274,8 +5055,8 @@
          },
          {
           "showarrow": false,
-          "text": "United Kingdom",
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+          "text": "Sweden",
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      },
      "metadata": {},
      "output_type": "display_data"
@@ -7117,12 +6746,12 @@
     "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=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\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 output of co-patents by patent authority\",hover_name= \"Country\")\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",
@@ -7130,7 +6759,7 @@
     "fig.update_xaxes(\n",
     "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
     "    ticks=\"outside\")\n",
-    "fig.update_layout({'template':\"plotly\"})\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",
@@ -7146,7 +6775,8 @@
     "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()"
+    "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
@@ -7154,7 +6784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 405,
    "outputs": [],
    "source": [],
    "metadata": {
@@ -7163,7 +6793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 446,
    "outputs": [
     {
      "name": "stderr",
@@ -7173,75 +6803,6 @@
       "European Patent Office not found in regex\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": "         appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n136092  512548123         GB   55086687            0             1  \\\n140959  523940692         EP   69747486            0             1   \n145846  517778054         EP   77709457            3             0   \n111339  352908327         FR   46503468            4             0   \n102245  336208056         EP    5019334            1             0   \n...           ...        ...        ...          ...           ...   \n140952  513966940         EP   69747486            0             1   \n143598  546020617         DE   81337072            1             0   \n141356  512140061         NL   71325506            1             0   \n141273  488981411         EP   69749947            2             0   \n124034  407239903         EP   47903112            4             0   \n\n                                              person_name person_ctry_code   \n136092                          TCL Communication Limited               CN  \\\n140959  Ningbo Geely Automobile Research & Development...               CN   \n145846                                FORSBERG, Christian               SE   \n111339                                     GILIS, FABRICE               CN   \n102245                                      Carcone, Luca               IT   \n...                                                   ...              ...   \n140952  Ningbo Geely Automobile Research & Development...               CN   \n143598                                       Zhou, Hulian               CN   \n141356                                  JAN VAN DER HORST               NL   \n141273                                           Zuo, Fei               CN   \n124034                              Wichmann, Daniel, Dr.               DE   \n\n       psn_sector     han_id                            han_name   \n136092    COMPANY    3635036                        TCL COMM LTD  \\\n140959    COMPANY    3697072  NINGBO GEELY AUTOMOBILE R&D CO LTD   \n145846        NaN  177709457                 FORSBERG, Christian   \n111339        NaN  146503468                      GILIS, FABRICE   \n102245        NaN  105019334                       Carcone, Luca   \n...           ...        ...                                 ...   \n140952    COMPANY    3697072  NINGBO GEELY AUTOMOBILE R&D CO LTD   \n143598        NaN  181337072                        Zhou, Hulian   \n141356        NaN  171325506                   JAN VAN DER HORST   \n141273        NaN  169749947                            Zuo, Fei   \n124034        NaN  147903112               Wichmann, Daniel, Dr.   \n\n       person_ctry_code_fullname     appln_auth_fullname person_ctry_ISO3   \n136092                     China          United Kingdom              CHN  \\\n140959                     China  European Patent Office              CHN   \n145846                    Sweden  European Patent Office              SWE   \n111339                     China                  France              CHN   \n102245                     Italy  European Patent Office              ITA   \n...                          ...                     ...              ...   \n140952                     China  European Patent Office              CHN   \n143598                     China                 Germany              CHN   \n141356               Netherlands             Netherlands              NLD   \n141273                     China  European Patent Office              CHN   \n124034                   Germany  European Patent Office              DEU   \n\n       person_ctry_type ISO3  auth_ISO3    auth_ctry_type  \n136092            China  CHN        GBR  Non-EU associate  \n140959            China  CHN  not found               EPO  \n145846               EU  SWE  not found               EPO  \n111339            China  CHN        FRA                EU  \n102245               EU  ITA  not found               EPO  \n...                 ...  ...        ...               ...  \n140952            China  CHN  not found               EPO  \n143598            China  CHN        DEU                EU  \n141356               EU  NLD        NLD                EU  \n141273            China  CHN  not found               EPO  \n124034               EU  DEU  not found               EPO  \n\n[100 rows x 17 columns]",
-      "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>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>person_ctry_code_fullname</th>\n      <th>appln_auth_fullname</th>\n      <th>person_ctry_ISO3</th>\n      <th>person_ctry_type</th>\n      <th>ISO3</th>\n      <th>auth_ISO3</th>\n      <th>auth_ctry_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136092</th>\n      <td>512548123</td>\n      <td>GB</td>\n      <td>55086687</td>\n      <td>0</td>\n      <td>1</td>\n      <td>TCL Communication Limited</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>3635036</td>\n      <td>TCL COMM LTD</td>\n      <td>China</td>\n      <td>United Kingdom</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>GBR</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>140959</th>\n      <td>523940692</td>\n      <td>EP</td>\n      <td>69747486</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Ningbo Geely Automobile Research &amp; Development...</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>3697072</td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD</td>\n      <td>China</td>\n      <td>European Patent Office</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>145846</th>\n      <td>517778054</td>\n      <td>EP</td>\n      <td>77709457</td>\n      <td>3</td>\n      <td>0</td>\n      <td>FORSBERG, Christian</td>\n      <td>SE</td>\n      <td>NaN</td>\n      <td>177709457</td>\n      <td>FORSBERG, Christian</td>\n      <td>Sweden</td>\n      <td>European Patent Office</td>\n      <td>SWE</td>\n      <td>EU</td>\n      <td>SWE</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>111339</th>\n      <td>352908327</td>\n      <td>FR</td>\n      <td>46503468</td>\n      <td>4</td>\n      <td>0</td>\n      <td>GILIS, FABRICE</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>146503468</td>\n      <td>GILIS, FABRICE</td>\n      <td>China</td>\n      <td>France</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>FRA</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>102245</th>\n      <td>336208056</td>\n      <td>EP</td>\n      <td>5019334</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Carcone, Luca</td>\n      <td>IT</td>\n      <td>NaN</td>\n      <td>105019334</td>\n      <td>Carcone, Luca</td>\n      <td>Italy</td>\n      <td>European Patent Office</td>\n      <td>ITA</td>\n      <td>EU</td>\n      <td>ITA</td>\n      <td>not found</td>\n      <td>EPO</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    </tr>\n    <tr>\n      <th>140952</th>\n      <td>513966940</td>\n      <td>EP</td>\n      <td>69747486</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Ningbo Geely Automobile Research &amp; Development...</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>3697072</td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD</td>\n      <td>China</td>\n      <td>European Patent Office</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>143598</th>\n      <td>546020617</td>\n      <td>DE</td>\n      <td>81337072</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Zhou, Hulian</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>181337072</td>\n      <td>Zhou, Hulian</td>\n      <td>China</td>\n      <td>Germany</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>DEU</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>141356</th>\n      <td>512140061</td>\n      <td>NL</td>\n      <td>71325506</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JAN VAN DER HORST</td>\n      <td>NL</td>\n      <td>NaN</td>\n      <td>171325506</td>\n      <td>JAN VAN DER HORST</td>\n      <td>Netherlands</td>\n      <td>Netherlands</td>\n      <td>NLD</td>\n      <td>EU</td>\n      <td>NLD</td>\n      <td>NLD</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>141273</th>\n      <td>488981411</td>\n      <td>EP</td>\n      <td>69749947</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Zuo, Fei</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>169749947</td>\n      <td>Zuo, Fei</td>\n      <td>China</td>\n      <td>European Patent Office</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>124034</th>\n      <td>407239903</td>\n      <td>EP</td>\n      <td>47903112</td>\n      <td>4</td>\n      <td>0</td>\n      <td>Wichmann, Daniel, Dr.</td>\n      <td>DE</td>\n      <td>NaN</td>\n      <td>147903112</td>\n      <td>Wichmann, Daniel, Dr.</td>\n      <td>Germany</td>\n      <td>European Patent Office</td>\n      <td>DEU</td>\n      <td>EU</td>\n      <td>DEU</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 17 columns</p>\n</div>"
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "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[\"ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
-    "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"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",
-    "appln_pers_f.sample(100)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array(['DEU', 'FRA', 'SWE', 'ESP', 'BEL', 'CHN', 'CHE', 'AUT', 'FIN',\n       'NLD', 'GRC', 'ITA', 'CZE', 'HUN', 'DNK', 'IRL', 'POL', 'LUX',\n       'CYP', 'PRT', 'NOR', 'ROU'], dtype=object)"
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_pers_f[\"person_ctry_ISO3\"].unique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "outputs": [
     {
      "data": {
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-          "REN, CONG (CHN)",
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-          "VINCENT, GUILLAUME (CHN)",
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+          "NINGBO GEELY AUTOMOBILE R<br>D CO LTD (CHN)",
+          "TCL COMM LTD (CHN)",
+          "ZHEJIANG GEELY HOLDINGGROUP CO LTD (CHN)",
+          "HUAWEI TECH CO LTD (CHN)",
+          "CRRC QINGDAO SIFANG CO LTD (CHN)",
+          "BEIJING XIAOMI MOBILE SOFTWARE CO LTD (CHN)",
+          "ZF DRIVETECH SUZHOU CO LTD (CHN)",
+          "VENUS MEDTECH HANGZHOU INC (CHN)",
+          "ZHANGJIAGANG KANGDE XIN OPTRONICS MATERIAL CO LTD (CHN)",
+          "CNBM BENGBU DESIGN <br> RESEARCH INSTITUTE FOR GLASS IND CO LTD (CHN)",
+          "SHENZHEN GOODIX TECH CO LTD (CHN)",
+          "NOKIA SHANGHAI BELL CO LTD (CHN)",
+          "KEIPER SEATING MECHANISMS CO LTD (CHN)",
+          "HANERGY HOLDINGGROUP LTD (CHN)",
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-         "hovertemplate": "Country type=EU<br>Number of co-patents=%{x}<br>Name=%{y}<extra></extra>",
-         "legendgroup": "EU",
-         "marker": {
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-          13,
-          13,
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-         "xaxis": "x",
-         "y": [
-          "NILSSON, MAGNUS (SWE)",
-          "JOHANSSON, JONATHAN (SWE)",
-          "UMER SALIM (FRA)",
-          "GUANG LIU (FRA)",
-          "BENNY ASSOULINE (FRA)",
-          "OLIVIER MARCO (FRA)",
-          "LINDBERG NILSSON, ERIK (SWE)",
-          "EFSTATHIOS KATRANARAS (FRA)",
-          "BRUNO JECHOUX (FRA)",
-          "HUFENBACH, WERNER (DEU)",
-          "ULBRICHT ANDREAS (DEU)",
-          "ROY RON (FRA)",
-          "SETTERBERG, JOHAN (SWE)",
-          "SEEMANN, FRANK (DEU)",
-          "BORNTRÄGER, KAI (DEU)"
-         ],
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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"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "data": [
+        {
+         "coloraxis": "coloraxis",
+         "name": "0",
+         "texttemplate": "%{text}",
+         "x": [
+          "Sweden",
+          "Germany",
+          "France",
+          "Austria",
+          "Netherlands",
+          "Finland",
+          "Spain",
+          "Belgium",
+          "Italy"
+         ],
+         "y": [
+          "NINGBO GEELY AUTOMOBILE R&D CO LTD",
+          "TCL COMM LTD",
+          "ZHEJIANG GEELY HOLDINGGROUP CO LTD",
+          "HUAWEI TECH CO LTD",
+          "ZF DRIVETECH SUZHOU CO LTD",
+          "CRRC QINGDAO SIFANG CO LTD",
+          "BEIJING XIAOMI MOBILE SOFTWARE CO LTD",
+          "ZHANGJIAGANG KANGDE XIN OPTRONICS MATERIAL CO LTD",
+          "VENUS MEDTECH HANGZHOU INC",
+          "CNBM BENGBU DESIGN & RESEARCH INSTITUTE FOR GLASS IND CO LTD",
+          "SHENZHEN GOODIX TECH CO LTD",
+          "NOKIA SHANGHAI BELL CO LTD",
+          "KEIPER SEATING MECHANISMS CO LTD",
+          "HANERGY HOLDINGGROUP LTD",
+          "INKRON LTD",
+          "FOCUSLIGHT TECH INC",
+          "CHINA TRIUMPH INT ENGINEERING CO LTD",
+          "MICRO TECH NANJING CO LTD",
+          "ANHUI MEIZHI COMPRESSOR CO LTD",
+          "SHANGHAI HENGXU MATERIALS CO LTD"
+         ],
+         "z": [
+          [
+           3.399673721481038,
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+           0.0,
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      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "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": {
+    "collapsed": false
+   }
+  },
+  {
+   "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,
+   "outputs": [
     {
      "data": {
       "application/vnd.plotly.v1+json": {
        "data": [
         {
          "alignmentgroup": "True",
-         "hovertemplate": "Country type=EU<br>Number of co-patents=%{x}<br>Name=%{y}<extra></extra>",
+         "hovertemplate": "Country type=EU<br>Percent of related co-patents=%{x}<br>Inventor's country=%{y}<extra></extra>",
          "legendgroup": "EU",
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defaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Percent 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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':'#9F2B68'}\n",
+    "                     'EPO':'#7F00FF'}#CC8899\n",
+    "\n",
     "\n",
     "labeld_dict = dict({\n",
-    "                     record_col: 'Number of co-patents',\n",
-    "                     \"person_ctry_type\": \"Country type\",\n",
-    "                                  \"han_label\": \"Name\",\n",
-    "                                  \"Country_Type\":\"Country type\",\n",
-    "                                  \"Eurovoc_Class\":\"Region\"\n",
-    "                 },**c_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",
-    "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",
-    "        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',\"person_ctry_type\":\"first\"})\n",
-    "                 .sort_values(by=\"appln_id\", ascending=False)\n",
-    "                .head(15))\n",
-    "        data[\"han_label\"] = data[\"han_name\"].str.upper().str.strip(\".\")+ \" (\"+data[\"person_ctry_ISO3\"]+\")\"\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",
-    "        fig = px.bar(data, x=record_col, y=\"han_label\", color=\"person_ctry_type\",\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",
+    "            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",
@@ -14947,16 +15446,72 @@
     "            ticks=\"outside\")\n",
     "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
     "\n",
-    "        figsuper['layout'][\"font\"][\"size\"]=12\n",
+    "        figsuper['layout'][\"font\"][\"size\"]=14\n",
     "        for a in figsuper['layout'][\"annotations\"]:\n",
     "            a[\"font\"][\"size\"] = 14\n",
-    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\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",
-    "        figsuper.show(config= dict(displayModeBar = False, responsive = True))\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",
-    "        figsuper.show()"
+    "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
@@ -14964,3514 +15519,238 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "data": [
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-         "alignmentgroup": "True",
-         "hovertemplate": "Country type=EU<br>Percent of related co-patents=%{x}<br>Country=%{y}<extra></extra>",
-         "legendgroup": "EU",
-         "marker": {
-          "color": "#636EFA",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "EU",
-         "offsetgroup": "EU",
-         "orientation": "h",
-         "showlegend": true,
-         "textposition": "auto",
-         "texttemplate": "%{x}",
-         "x": [
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-          0.0037418147801683817,
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-          9.354536950420954E-4,
-          0.0205799812909261,
-          9.354536950420954E-4,
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-          0.025257249766136577,
-          0.25631431244153413
-         ],
-         "xaxis": "x",
-         "y": [
-          "Austria",
-          "Belgium",
-          "Czech Republic",
-          "Denmark",
-          "Finland",
-          "France",
-          "Germany",
-          "Greece",
-          "Hungary",
-          "Ireland",
-          "Italy",
-          "Luxembourg",
-          "Netherlands",
-          "Poland",
-          "Portugal",
-          "Romania",
-          "Spain",
-          "Sweden"
-         ],
-         "yaxis": "y",
-         "type": "bar"
-        },
-        {
-         "alignmentgroup": "True",
-         "hovertemplate": "Country type=China<br>Percent of related co-patents=%{x}<br>Country=%{y}<extra></extra>",
-         "legendgroup": "China",
-         "marker": {
-          "color": "#EF553B",
-          "pattern": {
-           "shape": ""
-          }
-         },
-         "name": "China",
-         "offsetgroup": "China",
+   "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": ""
+          }
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+         "name": "A-Human necessities",
+         "offsetgroup": "A-Human necessities",
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          "xaxis": "x",
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+          "A-Human necessities"
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          "yaxis": "y",
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         {
          "alignmentgroup": "True",
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+         "hovertemplate": "%{x:d}",
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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",
-    "c_dict = {\"count\":\"Number of co-publications\",\n",
-    "          \"percent_of_copubs\":\"Percent of co-publications\",\n",
-    "          \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\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\": \"Authority\",\n",
-    "                                    \"auth_ctry_type\": \"Authority type\",\n",
-    "                                  \"Country_Type\":\"Country type\",\n",
-    "                                  \"Eurovoc_Class\":\"Region\"\n",
-    "                 },**c_dict)\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",
-    "\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\"]=12\n",
-    "            for a in figsuper['layout'][\"annotations\"]:\n",
-    "                a[\"font\"][\"size\"] = 14\n",
-    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
-    "\n",
-    "            figsuper.update_layout(uniformtext_minsize=10)\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\"]=12\n",
-    "        for a in figsuper['layout'][\"annotations\"]:\n",
-    "            a[\"font\"][\"size\"] = 14\n",
-    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
-    "\n",
-    "        figsuper.update_layout(uniformtext_minsize=10)\n",
-    "        figsuper.show()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "   appln_auth  appln_id  appln_id_percent\n0          AT      6016          0.009362\n1          BE      1772          0.002758\n2          BG       229          0.000356\n3          CH      1926          0.002997\n4          CN    304654          0.474115\n5          CZ      1373          0.002137\n6          DE    124035          0.193028\n7          DK      1480          0.002303\n8          EE        46          0.000072\n9          EP     54082          0.084165\n10         ES      8484          0.013203\n11         FI      4719          0.007344\n12         FR     57391          0.089314\n13         GB     51069          0.079476\n14         GR       515          0.000801\n15         HR       135          0.000210\n16         HU       807          0.001256\n17         IE        70          0.000109\n18         IT      5535          0.008614\n19         LT        58          0.000090\n20         LU      1054          0.001640\n21         LV        60          0.000093\n22         MA       363          0.000565\n23         NL      7377          0.011480\n24         PL      1854          0.002885\n25         PT       385          0.000599\n26         RO       235          0.000366\n27         SE      6207          0.009660\n28         SI       430          0.000669\n29         SK       213          0.000331",
-      "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_auth</th>\n      <th>appln_id</th>\n      <th>appln_id_percent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AT</td>\n      <td>6016</td>\n      <td>0.009362</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BE</td>\n      <td>1772</td>\n      <td>0.002758</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BG</td>\n      <td>229</td>\n      <td>0.000356</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CH</td>\n      <td>1926</td>\n      <td>0.002997</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CN</td>\n      <td>304654</td>\n      <td>0.474115</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CZ</td>\n      <td>1373</td>\n      <td>0.002137</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>DE</td>\n      <td>124035</td>\n      <td>0.193028</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>DK</td>\n      <td>1480</td>\n      <td>0.002303</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>EE</td>\n      <td>46</td>\n      <td>0.000072</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>EP</td>\n      <td>54082</td>\n      <td>0.084165</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>ES</td>\n      <td>8484</td>\n      <td>0.013203</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>FI</td>\n      <td>4719</td>\n      <td>0.007344</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>FR</td>\n      <td>57391</td>\n      <td>0.089314</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>GB</td>\n      <td>51069</td>\n      <td>0.079476</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>GR</td>\n      <td>515</td>\n      <td>0.000801</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>HR</td>\n      <td>135</td>\n      <td>0.000210</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>HU</td>\n      <td>807</td>\n      <td>0.001256</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>IE</td>\n      <td>70</td>\n      <td>0.000109</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>IT</td>\n      <td>5535</td>\n      <td>0.008614</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>LT</td>\n      <td>58</td>\n      <td>0.000090</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>LU</td>\n      <td>1054</td>\n      <td>0.001640</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>LV</td>\n      <td>60</td>\n      <td>0.000093</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>MA</td>\n      <td>363</td>\n      <td>0.000565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>NL</td>\n      <td>7377</td>\n      <td>0.011480</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>PL</td>\n      <td>1854</td>\n      <td>0.002885</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>PT</td>\n      <td>385</td>\n      <td>0.000599</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>RO</td>\n      <td>235</td>\n      <td>0.000366</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>SE</td>\n      <td>6207</td>\n      <td>0.009660</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>SI</td>\n      <td>430</td>\n      <td>0.000669</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>SK</td>\n      <td>213</td>\n      <td>0.000331</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# top 25\n",
-    "# - inventor\n",
-    "# - applicant\n",
-    "# in china, EU, EUextended"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "        person_id      psn_sector     han_id   \n269495   48715290         COMPANY  148715290  \\\n225401   45096499  GOV NON-PROFIT     986555   \n426484   58455492             NaN  158455492   \n50805    15325724      INDIVIDUAL  115325724   \n112503   28064269             NaN  128064269   \n...           ...             ...        ...   \n464708   59491689             NaN  159491689   \n169950   42286280      INDIVIDUAL    4099527   \n485679   60214508             NaN  160214508   \n542210   62188978             NaN  162188978   \n752136   81407527             NaN  181407527   \n\n                                                 han_name  \n269495                            TECHFLOW MARINE LIMITED  \n225401  FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC...  \n426484                                       DING ZHIJIAO  \n50805                                   FOURNIER, THIERRY  \n112503                                      ADRIAN WALLER  \n...                                                   ...  \n464708                                         LIU YANZHI  \n169950                                     RIEMANN STEFAN  \n485679                                             WAN BO  \n542210                                            CHU WEI  \n752136                            GARCIA MONSONIS, Javier  \n\n[100 rows x 4 columns]",
-      "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>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>269495</th>\n      <td>48715290</td>\n      <td>COMPANY</td>\n      <td>148715290</td>\n      <td>TECHFLOW MARINE LIMITED</td>\n    </tr>\n    <tr>\n      <th>225401</th>\n      <td>45096499</td>\n      <td>GOV NON-PROFIT</td>\n      <td>986555</td>\n      <td>FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC...</td>\n    </tr>\n    <tr>\n      <th>426484</th>\n      <td>58455492</td>\n      <td>NaN</td>\n      <td>158455492</td>\n      <td>DING ZHIJIAO</td>\n    </tr>\n    <tr>\n      <th>50805</th>\n      <td>15325724</td>\n      <td>INDIVIDUAL</td>\n      <td>115325724</td>\n      <td>FOURNIER, THIERRY</td>\n    </tr>\n    <tr>\n      <th>112503</th>\n      <td>28064269</td>\n      <td>NaN</td>\n      <td>128064269</td>\n      <td>ADRIAN WALLER</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>464708</th>\n      <td>59491689</td>\n      <td>NaN</td>\n      <td>159491689</td>\n      <td>LIU YANZHI</td>\n    </tr>\n    <tr>\n      <th>169950</th>\n      <td>42286280</td>\n      <td>INDIVIDUAL</td>\n      <td>4099527</td>\n      <td>RIEMANN STEFAN</td>\n    </tr>\n    <tr>\n      <th>485679</th>\n      <td>60214508</td>\n      <td>NaN</td>\n      <td>160214508</td>\n      <td>WAN BO</td>\n    </tr>\n    <tr>\n      <th>542210</th>\n      <td>62188978</td>\n      <td>NaN</td>\n      <td>162188978</td>\n      <td>CHU WEI</td>\n    </tr>\n    <tr>\n      <th>752136</th>\n      <td>81407527</td>\n      <td>NaN</td>\n      <td>181407527</td>\n      <td>GARCIA MONSONIS, Javier</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
-    "appln_pers_haninfo.sample(100)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "          appln_id appln_auth appln_kind  filing_year  nb_applicants   \n1322402  523940692         EP          A         2019              2  \\\n765767   476494118         EP          A         2017              1   \n34130    378211797         DE          U         2011              1   \n789796   480385078         GB          A         2017              1   \n701348   451567585         NL          A         2016              1   \n...            ...        ...        ...          ...            ...   \n1361812  536054394         EP          A         2020              1   \n242632   424885398         DE          A         2013              1   \n1108772  503145148         EP          A         2018              1   \n538788   447597102         EP          A         2015              1   \n272481   412013115         EP          A         2013              1   \n\n         nb_inventors ipc_class_symbol ipc_class_level      cpc_id   \n1322402             2      B60W  50/14               A   B60W50/14  \\\n765767              4      B60H   1/34               A    B60H1/34   \n34130               0      F04B  17/04               A   F04B17/04   \n789796              2      H04W  76/14               A   H04W76/14   \n701348              3     E04F  13/077               A  E04F13/077   \n...               ...              ...             ...         ...   \n1361812             4      G05B  23/02               A   G05B23/02   \n242632              1      F02M  51/06               A   F02M51/06   \n1108772             2      G08G   1/01               A    G08G1/01   \n538788              6      H01F  38/30               A   H01F38/30   \n272481              3      C04B  28/04               A   C04B28/04   \n\n                          ipc_class             ipc_classname  \n1322402  B-Operations; transporting  Operations; transporting  \n765767   B-Operations; transporting  Operations; transporting  \n34130                 F-Engineering               Engineering  \n789796                H-Electricity               Electricity  \n701348        E-Fixed constructions       Fixed constructions  \n...                             ...                       ...  \n1361812                   G-Physics                   Physics  \n242632                F-Engineering               Engineering  \n1108772                   G-Physics                   Physics  \n538788                H-Electricity               Electricity  \n272481      C-Chemistry; metallurgy     Chemistry; metallurgy  \n\n[100 rows x 11 columns]",
-      "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_kind</th>\n      <th>filing_year</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n      <th>ipc_class_symbol</th>\n      <th>ipc_class_level</th>\n      <th>cpc_id</th>\n      <th>ipc_class</th>\n      <th>ipc_classname</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1322402</th>\n      <td>523940692</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>2</td>\n      <td>B60W  50/14</td>\n      <td>A</td>\n      <td>B60W50/14</td>\n      <td>B-Operations; transporting</td>\n      <td>Operations; transporting</td>\n    </tr>\n    <tr>\n      <th>765767</th>\n      <td>476494118</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>4</td>\n      <td>B60H   1/34</td>\n      <td>A</td>\n      <td>B60H1/34</td>\n      <td>B-Operations; transporting</td>\n      <td>Operations; transporting</td>\n    </tr>\n    <tr>\n      <th>34130</th>\n      <td>378211797</td>\n      <td>DE</td>\n      <td>U</td>\n      <td>2011</td>\n      <td>1</td>\n      <td>0</td>\n      <td>F04B  17/04</td>\n      <td>A</td>\n      <td>F04B17/04</td>\n      <td>F-Engineering</td>\n      <td>Engineering</td>\n    </tr>\n    <tr>\n      <th>789796</th>\n      <td>480385078</td>\n      <td>GB</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2</td>\n      <td>H04W  76/14</td>\n      <td>A</td>\n      <td>H04W76/14</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n    </tr>\n    <tr>\n      <th>701348</th>\n      <td>451567585</td>\n      <td>NL</td>\n      <td>A</td>\n      <td>2016</td>\n      <td>1</td>\n      <td>3</td>\n      <td>E04F  13/077</td>\n      <td>A</td>\n      <td>E04F13/077</td>\n      <td>E-Fixed constructions</td>\n      <td>Fixed constructions</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    </tr>\n    <tr>\n      <th>1361812</th>\n      <td>536054394</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>1</td>\n      <td>4</td>\n      <td>G05B  23/02</td>\n      <td>A</td>\n      <td>G05B23/02</td>\n      <td>G-Physics</td>\n      <td>Physics</td>\n    </tr>\n    <tr>\n      <th>242632</th>\n      <td>424885398</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>1</td>\n      <td>F02M  51/06</td>\n      <td>A</td>\n      <td>F02M51/06</td>\n      <td>F-Engineering</td>\n      <td>Engineering</td>\n    </tr>\n    <tr>\n      <th>1108772</th>\n      <td>503145148</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>2</td>\n      <td>G08G   1/01</td>\n      <td>A</td>\n      <td>G08G1/01</td>\n      <td>G-Physics</td>\n      <td>Physics</td>\n    </tr>\n    <tr>\n      <th>538788</th>\n      <td>447597102</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>1</td>\n      <td>6</td>\n      <td>H01F  38/30</td>\n      <td>A</td>\n      <td>H01F38/30</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n    </tr>\n    <tr>\n      <th>272481</th>\n      <td>412013115</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>3</td>\n      <td>C04B  28/04</td>\n      <td>A</td>\n      <td>C04B28/04</td>\n      <td>C-Chemistry; metallurgy</td>\n      <td>Chemistry; metallurgy</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 11 columns</p>\n</div>"
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "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\"]\n",
-    "appln_ipc_f.sample(100)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "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": 82,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    filing_year            ipc_class  appln_id  filing_year_relative_growth   \n0          2011  A-Human necessities       7.0                         2011  \\\n1          2012  A-Human necessities       5.0                         2011   \n2          2013  A-Human necessities      18.0                         2011   \n3          2014  A-Human necessities       9.0                         2011   \n4          2015  A-Human necessities      10.0                         2011   \n..          ...                  ...       ...                          ...   \n83         2017        H-Electricity      71.0                         2011   \n84         2018        H-Electricity      65.0                         2011   \n85         2019        H-Electricity      27.0                         2011   \n86         2020        H-Electricity      27.0                         2011   \n87         2021        H-Electricity      11.0                         2011   \n\n    appln_id_relative_growth  appln_id_cumsum  \n0                   0.000000              7.0  \n1                  -0.285714             12.0  \n2                   1.571429             30.0  \n3                   0.285714             39.0  \n4                   0.428571             49.0  \n..                       ...              ...  \n83                  3.437500            213.0  \n84                  3.062500            278.0  \n85                  0.687500            305.0  \n86                  0.687500            332.0  \n87                 -0.312500            343.0  \n\n[88 rows x 6 columns]",
-      "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>filing_year</th>\n      <th>ipc_class</th>\n      <th>appln_id</th>\n      <th>filing_year_relative_growth</th>\n      <th>appln_id_relative_growth</th>\n      <th>appln_id_cumsum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>A-Human necessities</td>\n      <td>7.0</td>\n      <td>2011</td>\n      <td>0.000000</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>A-Human necessities</td>\n      <td>5.0</td>\n      <td>2011</td>\n      <td>-0.285714</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>A-Human necessities</td>\n      <td>18.0</td>\n      <td>2011</td>\n      <td>1.571429</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>A-Human necessities</td>\n      <td>9.0</td>\n      <td>2011</td>\n      <td>0.285714</td>\n      <td>39.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>A-Human necessities</td>\n      <td>10.0</td>\n      <td>2011</td>\n      <td>0.428571</td>\n      <td>49.0</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    </tr>\n    <tr>\n      <th>83</th>\n      <td>2017</td>\n      <td>H-Electricity</td>\n      <td>71.0</td>\n      <td>2011</td>\n      <td>3.437500</td>\n      <td>213.0</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>2018</td>\n      <td>H-Electricity</td>\n      <td>65.0</td>\n      <td>2011</td>\n      <td>3.062500</td>\n      <td>278.0</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>2019</td>\n      <td>H-Electricity</td>\n      <td>27.0</td>\n      <td>2011</td>\n      <td>0.687500</td>\n      <td>305.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>2020</td>\n      <td>H-Electricity</td>\n      <td>27.0</td>\n      <td>2011</td>\n      <td>0.687500</td>\n      <td>332.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>2021</td>\n      <td>H-Electricity</td>\n      <td>11.0</td>\n      <td>2011</td>\n      <td>-0.312500</td>\n      <td>343.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>88 rows × 6 columns</p>\n</div>"
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "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",
-    "data"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "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": 87,
-   "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
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-         "textposition": "outside",
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-         "xaxis": "x",
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-         ],
-         "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": {
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-         "textposition": "outside",
-         "texttemplate": "%{x}",
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-         "xaxis": "x",
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-          "B-Operations; transporting"
+          "B-Operations; transporting"
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          "yaxis": "y",
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          "textposition": "outside",
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          "textposition": "outside",
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          "textposition": "outside",
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          "mode": "lines",
          "name": "A-Human necessities",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
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@@ -18724,7 +16003,7 @@
          "mode": "lines",
          "name": "B-Operations; transporting",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
          "stackgroup": "1",
          "x": [
           2011,
@@ -18741,17 +16020,17 @@
          ],
          "xaxis": "x2",
          "y": [
-          17.0,
-          26.0,
-          47.0,
-          64.0,
-          77.0,
-          97.0,
-          138.0,
-          199.0,
-          272.0,
-          333.0,
-          357.0
+          15.0,
+          23.0,
+          38.0,
+          52.0,
+          65.0,
+          85.0,
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+          186.0,
+          258.0,
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+          341.0
          ],
          "yaxis": "y2",
          "type": "scatter"
@@ -18771,7 +16050,7 @@
          "mode": "lines",
          "name": "C-Chemistry; metallurgy",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
          "stackgroup": "1",
          "x": [
           2011,
@@ -18790,15 +16069,15 @@
          "y": [
           9.0,
           20.0,
-          34.0,
-          43.0,
-          53.0,
-          63.0,
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-          114.0,
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          "yaxis": "y2",
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@@ -18818,7 +16097,7 @@
          "mode": "lines",
          "name": "D-Textiles; paper",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
          "stackgroup": "1",
          "x": [
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@@ -18844,8 +16123,8 @@
           9.0,
           11.0,
           13.0,
-          15.0,
-          15.0
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+          14.0
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          "yaxis": "y2",
          "type": "scatter"
@@ -18865,7 +16144,7 @@
          "mode": "lines",
          "name": "E-Fixed constructions",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
          "stackgroup": "1",
          "x": [
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@@ -18887,12 +16166,12 @@
           4.0,
           5.0,
           6.0,
-          10.0,
-          14.0,
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-          31.0,
-          45.0,
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+          49.0
          ],
          "yaxis": "y2",
          "type": "scatter"
@@ -18912,7 +16191,7 @@
          "mode": "lines",
          "name": "F-Engineering",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
          "stackgroup": "1",
          "x": [
           2011,
@@ -18929,17 +16208,17 @@
          ],
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          "yaxis": "y2",
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@@ -18959,7 +16238,7 @@
          "mode": "lines",
          "name": "G-Physics",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
          "stackgroup": "1",
          "x": [
           2011,
@@ -18976,17 +16255,17 @@
          ],
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@@ -19006,7 +16285,7 @@
          "mode": "lines",
          "name": "H-Electricity",
          "orientation": "v",
-         "showlegend": true,
+         "showlegend": false,
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@@ -19023,17 +16302,17 @@
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@@ -19068,15 +16347,15 @@
          "xaxis": "x3",
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@@ -19111,16 +16390,16 @@
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@@ -19157,14 +16436,14 @@
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@@ -19248,7 +16527,7 @@
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@@ -19287,15 +16566,15 @@
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@@ -19331,16 +16610,16 @@
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@@ -19375,16 +16654,16 @@
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of IPC classes\",\"x\":0.225,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{\"size\":14},\"showarrow\":false,\"text\":\"Cumulative sum of related co-patents\",\"x\":0.775,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{\"size\":14},\"showarrow\":false,\"text\":\"Related co-patents per year\",\"x\":0.225,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":0.375,\"yanchor\":\"bottom\",\"yref\":\"paper\"},{\"font\":{\"size\":14},\"showarrow\":false,\"text\":\"Relative growth of co-patents\",\"x\":0.775,\"xanchor\":\"center\",\"xref\":\"paper\",\"y\":0.375,\"yanchor\":\"bottom\",\"yref\":\"paper\"}],\"barmode\":\"relative\",\"font\":{\"family\":\"Montserrat\",\"size\":14}},                        {\"displayModeBar\": false, \"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('8cab11f8-edc4-41b0-b7d8-949efd888973');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
      },
      "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",
-    "    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\"]=12\n",
-    "for a in figsuper['layout'][\"annotations\"]:\n",
-    "    a[\"font\"][\"size\"] = 14\n",
-    "figsuper.show(config= dict(displayModeBar = False, responsive = True))"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "1964"
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_ipc_f[\"filing_year\"].min()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "615464"
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_ipc_f[\"appln_id\"].nunique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "591442"
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_ipc_f.merge(cpc_def, on=\"cpc_id\")[\"appln_id\"].nunique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array(['A', 'S', 'C'], dtype=object)"
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_ipc_f[\"ipc_class_level\"].unique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "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 == '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()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "array(['AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI',\n       'FR', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL',\n       'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK'], dtype=object)"
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scope_countries"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "    person_id   appln_id  applt_seq_nr  invt_seq_nr\n0           1  413601768             1            0\n1          21  332015605             1            0\n2          21  333490084             1            0\n3          21  335903805             1            0\n4          76  352908776             1            0\n..        ...        ...           ...          ...\n95        220  417147567             1            0\n96        220  417932952             1            0\n97        220  420712526             1            0\n98        220  420927947             1            0\n99        220  420947631             1            0\n\n[100 rows x 4 columns]",
-      "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>appln_id</th>\n      <th>applt_seq_nr</th>\n      <th>invt_seq_nr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>413601768</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21</td>\n      <td>332015605</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21</td>\n      <td>333490084</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>21</td>\n      <td>335903805</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>76</td>\n      <td>352908776</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>220</td>\n      <td>417147567</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>220</td>\n      <td>417932952</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>220</td>\n      <td>420712526</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>220</td>\n      <td>420927947</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>220</td>\n      <td>420947631</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 4 columns</p>\n</div>"
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "appln_pers.head(100)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'person_ctry_code'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3649\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3648\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
-      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
-      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
-      "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
-      "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
-      "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[26], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m \u001B[43mappln_pers\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroupby\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgr\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\n\u001B[0;32m      2\u001B[0m \u001B[43m                                                             \u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_auth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m      4\u001B[0m                                                               (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m      5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n",
-      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\groupby\\generic.py:1741\u001B[0m, in \u001B[0;36mDataFrameGroupBy.filter\u001B[1;34m(self, func, dropna, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1738\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m name, group \u001B[38;5;129;01min\u001B[39;00m gen:\n\u001B[0;32m   1739\u001B[0m     \u001B[38;5;28mobject\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__setattr__\u001B[39m(group, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mname\u001B[39m\u001B[38;5;124m\"\u001B[39m, name)\n\u001B[1;32m-> 1741\u001B[0m     res \u001B[38;5;241m=\u001B[39m func(group, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1743\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1744\u001B[0m         res \u001B[38;5;241m=\u001B[39m res\u001B[38;5;241m.\u001B[39msqueeze()\n",
-      "Cell \u001B[1;32mIn[26], line 1\u001B[0m, in \u001B[0;36m<lambda>\u001B[1;34m(gr)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m appln_pers\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m      2\u001B[0m                                                              (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m      3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m      4\u001B[0m                                                               (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m      5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n",
-      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\frame.py:3745\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3743\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m   3744\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3745\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   3746\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m   3747\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
-      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3651\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m   3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3651\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m   3652\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m   3653\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m   3654\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m   3655\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[0;32m   3656\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
-      "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'"
-     ]
-    }
-   ],
-   "source": [
-    "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",
-    "appln_scope[\"appln_id\"].nunique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_scope"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_scope[appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_scope[~appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_cpc[\"cpc_id\"] = appln_cpc[\"cpc_class_symbol\"].str.replace(\" \",\"\")\n",
+    "from plotly.subplots import make_subplots\n",
+    "import plotly.graph_objects as go\n",
     "\n",
-    "appln_cpc_merge = appln_cpc.merge(cpc_def,on='cpc_id')\n",
-    "print (f\"CPC Merge loss: {(1-len(appln_cpc_merge)/len(appln_cpc))*100}%\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "len(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True])"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")[\"appln_nr\"].nunique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\")[\"appln_id\"].nunique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "cpc_def[cpc_def[\"data_scope\"]==True]"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln.sample(100)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_title.sample(100)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_data = appln.merge(appln_title, on=\"appln_id\")\n",
-    "appln_data.head()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_data[\"appln_auth\"].unique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_pers"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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",
-    "persn = pers.merge(pers_sector_primary, on='han_id')\n",
-    "persn"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "pers_sector_primary"
+    "# 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
@@ -21193,47 +18104,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 425,
    "outputs": [],
    "source": [],
    "metadata": {
     "collapsed": false
    }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "appln_merge = appln.merge(appln_title, on=\"appln_id\")#.merge(appln_pers,on=\"appln_id\")\n",
-    "appln_merge.to_excel(\"appln_data.xlsx\", index=False)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "person_merge = appln_pers.merge(pers,on=\"person_id\")\n",
-    "person_merge.to_excel(\"person_data.xlsx\", index=False)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "pers[\"person_ctry_code\"].unique()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
   }
  ],
  "metadata": {