lookup_pure.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import difflib\n",
    "from fuzzywuzzy import fuzz\n",
    "\n",
    "def append_row(df, row):\n",
    "    return pd.concat([\n",
    "                df, \n",
    "                pd.DataFrame([row], columns=row.index)]\n",
    "           ).reset_index(drop=True)\n",
    "                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_names = pd.read_csv('people_in_S4S.csv',index_col='Index')\n",
    "# df_names['fullname_abb'] = df_names['firstname'].apply(lambda x: x[0].upper() + '.') + ' ' + df_names['lastname']\n",
    "del df_names['faculty'], df_names['department']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\torbj\\.conda\\envs\\newenv\\Lib\\site-packages\\openpyxl\\styles\\stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default\n",
      "  warn(\"Workbook contains no default style, apply openpyxl's default\")\n"
     ]
    }
   ],
   "source": [
    "#read and format the PURE data\n",
    "col_names = ['solisID','pureID','lastname','firstname', 'scopusID','orcID', 'jobtitle','organisationalUnit','numberResearchOutputs','sampleTitle','sampleDOI','sampleType']\n",
    "df_pure = pd.read_excel('pure_data.xlsx',names=col_names,keep_default_na='')\n",
    "df_pure['firstname'] = df_pure['firstname'].apply(lambda x: 'no firstname' if len(str(x)) == 0 else str(x).split(',')[-1].strip()+ ' ')\n",
    "df_pure['fullname'] = df_pure['firstname'] + df_pure['lastname']\n",
    "df_pure.fillna('',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using fuzzywuzzy to compare names on the list (people_in_S4S) with people in PURE. \n",
    "tuples_list = [max([(fuzz.token_set_ratio(i,j),j) for j in df_pure['fullname']]) for i in df_names['fullname']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity_score, fuzzy_match = map(list,zip(*tuples_list))\n",
    "df_fuzzymatch = pd.DataFrame({\"fullname\":df_names['fullname'], \"fuzzyMatch\": fuzzy_match, \"similarityScore\":similarity_score,'source':df_names['source']})\n",
    "df_fuzzymatch['fuzzyMatch'] = df_fuzzymatch['fuzzyMatch'].apply(lambda x: str(x).strip())\n",
    "\n",
    "#the list loaded in the next line is a list which involved the manual lookup of names (with low similarity scores)\n",
    "# df_lookup = pd.read_csv(\"lookup_table_new.csv\",encoding='utf-16',delimiter='\\t',index_col=[0]).sort_index()\n",
    "# df_fuzzymatch['note'] = df_lookup['note']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fuzzymatch = df_fuzzymatch[df_fuzzymatch['similarityScore'] > 95]\n",
    "df_fuzzymatch.drop_duplicates(subset='fuzzyMatch',keep='first',inplace=True)\n",
    "df_fuzzymatch.reset_index(drop=True,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_reduced = df_pure.set_index('fullname').loc[df_fuzzymatch['fuzzyMatch'].to_list()].reset_index()\n",
    "df_reduced = df_reduced.drop_duplicates('fullname').sort_index() #sort_values('numberResearchOutputs', ascending=False\n",
    "df_reduced.reset_index(drop=True,inplace=True)\n",
    "df_reduced['source'] = df_fuzzymatch['source']\n",
    "\n",
    "df_reduced.to_csv('people_in_S4S_pureFiltered.csv',index_label='Index')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "retrieving author information (similar to retrieve_orcid_scopusid.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from tqdm import tqdm\n",
    "import json\n",
    "client_orcid = open('orcid_config.json')\n",
    "client_orcid = json.load(client_orcid)\n",
    "\n",
    "client_scopus = open('scopus_config.json')\n",
    "client_scopus = json.load(client_scopus)\n",
    "\n",
    "df_persons = df_reduced\n",
    "\n",
    "#orcid start up\n",
    "client_id = client_orcid['client_id']\n",
    "client_secret = client_orcid['client_secret']\n",
    "\n",
    "resp = requests.post(url=\"https://orcid.org/oauth/token\",\n",
    "                     headers={'Accept':'application/json'},\n",
    "                     data={'client_id':client_id, 'client_secret':client_secret,'grant_type': 'client_credentials', 'scope':'/read-public'}\n",
    "                     )\n",
    "access_token = resp.json()['access_token']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) Take scopusId provided by PURE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) Retrieve scopusId with doi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 109/109 [02:03<00:00,  1.13s/it]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(df_persons[df_persons['scopusId'] == ''].index):\n",
    "    # if df_persons['sampleDoi'][i] != '':\n",
    "    first_name = df_persons['firstname'][i].strip()\n",
    "    last_name = df_persons['lastname'][i].strip()\n",
    "\n",
    "    resp = requests.get(url=f'http://api.elsevier.com/content/search/author?query=AUTHLASTNAME({last_name})+AND+AUTHFIRST({first_name})+AND+AFFIL(Utrecht)', \n",
    "            headers={'X-ELS-APIKey':client_scopus['apikey'], 'X-ELS-Insttoken':client_scopus['insttoken'], 'accept':'application/json'},)\n",
    "    try:\n",
    "        #saving the scopusid\n",
    "        scopus_id = resp.json()['search-results']['entry'][0]['dc:identifier'][10:]\n",
    "        df_persons['scopusId'].loc[i] = scopus_id\n",
    "    except:\n",
    "        df_persons['scopusId'].loc[i] = ''\n",
    "\n",
    "\n",
    "        #see if there is an orcid associated\n",
    "    try:\n",
    "        orcid =  resp.json()['search-results']['entry'][0]['orcid']\n",
    "        df_persons['orcid'].loc[i] = orcid            \n",
    "    except:\n",
    "        df_persons['orcid'].loc[i] = ''  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_persons.to_csv('person_names_from_pure.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) Retrieve scopusId with NAME and AFFIL and KEYWORD(?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FIGURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_persons = pd.read_csv('person_names_from_pure.csv',keep_default_na=False,index_col=[0])\n"
   ]
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             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#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
          }
         }
        },
        "width": 1200
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "# Define nodes\n",
    "nodes = [\"Mailinglist\",     #0\n",
    "         \"Website\",         #1\n",
    "         \"In Pure\",         #2\n",
    "         \"Not in Pure\",     #3\n",
    "         \"In Scopus\",       #4\n",
    "         \"Not in Scopus\"]   #5\n",
    "\n",
    "# Define links\n",
    "links = [\n",
    "    {\"source\": 0, \"target\": 2, \"value\": len(df_persons[df_persons['source'] =='mailinglist'])},  # Mailinglist True -> In Pure True\n",
    "    {\"source\": 0, \"target\": 3, \"value\": len(df_names[df_names['source'] =='mailinglist']) - len(df_persons[df_persons['source'] =='mailinglist'])},   # Mailinglist True -> In Pure False\n",
    "    {\"source\": 1, \"target\": 2, \"value\": len(df_persons[df_persons['source'] =='website'])},  # Mailinglist False -> In Scopus True\n",
    "    {\"source\": 1, \"target\": 3, \"value\": len(df_names[df_names['source'] =='website']) - len(df_persons[df_persons['source'] =='website'])},  # Mailinglist False -> In Scopus False\n",
    "    {\"source\": 2, \"target\": 4, \"value\": len(df_persons[df_persons['scopusId'] !=''])},   # Website True -> In Pure True\n",
    "    {\"source\": 2, \"target\": 5, \"value\": len(df_persons[df_persons['scopusId'] ==''])},  # Website True -> In Pure False\n",
    "    {\"source\": 3, \"target\": 4, \"value\": 0},   # Website False -> In Scopus True\n",
    "    {\"source\": 3, \"target\": 5, \"value\": len(df_names)-len(df_persons)}   # Website False -> In Scopus False\n",
    "]\n",
    "\n",
    "# Create the Sankey diagram\n",
    "# fig.update_layout(title_text=\"S4S people\")\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node=dict(\n",
    "        pad=25,\n",
    "        thickness=30,\n",
    "        # line=dict(color=\"black\", width=0.5),\n",
    "        # label=nodes,\n",
    "        color = [\"skyblue\",\"violet\",\"#8ED973\",\"salmon\",'forestgreen','firebrick']\n",
    "    ),\n",
    "    link=dict(\n",
    "        source=[link['source'] for link in links],\n",
    "        target=[link['target'] for link in links],\n",
    "        value=[link['value'] for link in links],\n",
    "        # color='lightgrey'\n",
    "   )\n",
    ")],\n",
    "   layout=go.Layout(autosize=False,\n",
    "    width=1200,\n",
    "    height=700,))\n",
    "# color_for_nodes = [\"skyblue\",\"violet\",\"#8ED973\",\"salmon\",'forestgreen','firebrick']\n",
    "# fig.update_traces(node_color = color_for_nodes)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig.show()\n",
    "fig.write_html(\"plots/sankey_peopleS4S.html\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PLOTTING TO CHART STUDIO (IN ORDER TO EMBED THE WEBVIEW INTO POWERPOINT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio\n",
    "chart_studio.tools.set_credentials_file(username='Tkagel', api_key='GzCJTnpLg05gfCd1hBUD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plotly.com/~Tkagel/1/'"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import chart_studio.plotly as py\n",
    "test = data=[go.Sankey(\n",
    "    node=dict(\n",
    "        pad=25,\n",
    "        thickness=30,\n",
    "        # line=dict(color=\"black\", width=0.5),\n",
    "        label=nodes,\n",
    "        color = [\"skyblue\",\"violet\",\"#8ED973\",\"salmon\",'forestgreen','firebrick']\n",
    "    ),\n",
    "    link=dict(\n",
    "        source=[link['source'] for link in links],\n",
    "        target=[link['target'] for link in links],\n",
    "        value=[link['value'] for link in links],\n",
    "        # color='lightgrey'\n",
    "   )\n",
    ")]\n",
    "py.plot(test, filename = 'basic-line', auto_open=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JOB TITLE PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df_lookup = pd.read_csv('lookup_table_new.csv',encoding='utf-16',delimiter='\\t',index_col=[0]).sort_index()\n",
    "df_roles = df_lookup\n",
    "df_roles['role'] = df_lookup['note']\n",
    "test_list = (df_roles['role'] == 'correct match').to_list()\n",
    "k=0\n",
    "for i in range(len(test_list)):\n",
    "    if test_list[i] == True:\n",
    "        test_list[i] = df_reduced['jobTitle'].loc[k]\n",
    "        k+=1\n",
    "    if test_list[i] == False:\n",
    "        test_list[i] = df_roles['role'].loc[i]\n",
    "        \n",
    "df_roles['role'] = test_list\n",
    "df_roles.to_csv('person_roles.csv',encoding='utf-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_roles = pd.read_csv('person_roles.csv',encoding='utf-16',delimiter='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Assistant Professor\n",
       "1                     PhD Student\n",
       "2                      Researcher\n",
       "3                          Decaan\n",
       "4             Assistant Professor\n",
       "                  ...            \n",
       "264                   PhD Student\n",
       "265                     not at UU\n",
       "266                       Student\n",
       "267    Education/Research Officer\n",
       "268                     not at UU\n",
       "Name: role, Length: 269, dtype: object"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_roles['role']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Assistant Professor\n",
       "1                     PhD Student\n",
       "2                      Researcher\n",
       "3                          Decaan\n",
       "4             Assistant Professor\n",
       "                  ...            \n",
       "264                   PhD Student\n",
       "265                     not at UU\n",
       "266                       Student\n",
       "267    Education/Research Officer\n",
       "268                     not at UU\n",
       "Name: role, Length: 269, dtype: object"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_roles['role'].apply(lambda x: str(x).strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "counted_roles = df_roles['role'].apply(lambda x: str(x).strip()).value_counts()\n",
    "counted_roles_plot = counted_roles[counted_roles > 5]\n",
    "counted_roles_plot.loc['Misc']  = counted_roles[counted_roles <= 5].sum()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "counted_roles_df = pd.DataFrame(counted_roles_plot)\n",
    "counted_roles_df['order'] = [3,8,5,1,2,7,6,9,4,10,0]\n",
    "counted_roles_df.reset_index(inplace=True)\n",
    "counted_roles_df.set_index('order',inplace=True)\n",
    "counted_roles_df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_lookup = plt.cm.Greens(np.linspace(0.3,0.7,6))\n",
    "plt.pie(counted_roles_df['count'],\n",
    "        startangle=90,\n",
    "        counterclock=False,\n",
    "        labels=counted_roles_df['role'],\n",
    "        labeldistance=1.1,\n",
    "        radius=2,\n",
    "        colors=['indianred','indianred','indianred','skyblue',color_lookup[0],color_lookup[1],color_lookup[2],color_lookup[3],color_lookup[4],color_lookup[5],'lightgrey'],\n",
    "        wedgeprops={\"edgecolor\":\"white\",'linewidth': 1, 'linestyle': 'solid', 'antialiased': True}\n",
    "\n",
    "        );"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "newenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}