From c7f7a9a62e45538443fa51f58a08e2d427554ff4 Mon Sep 17 00:00:00 2001
From: Andrea Mannocci <andremann@libero.it>
Date: Wed, 21 Apr 2021 16:51:59 +0200
Subject: [PATCH] updated requirements

---
 notebooks/01-Exploration.ipynb | 2653 ++++++++++++++++++++++++--------
 1 file changed, 2046 insertions(+), 607 deletions(-)

diff --git a/notebooks/01-Exploration.ipynb b/notebooks/01-Exploration.ipynb
index f8a79fb..939b2d0 100644
--- a/notebooks/01-Exploration.ipynb
+++ b/notebooks/01-Exploration.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -81,9 +81,11 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "import antispam\n",
-    "import profanity_check\n",
+    "# import antispam\n",
+    "# import profanity_check\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
     "import seaborn as sns\n",
     "import plotly\n",
     "from plotly.offline import iplot, init_notebook_mode\n",
@@ -826,7 +828,7 @@
       "text/plain": [
        "count                10989649\n",
        "unique               10989649\n",
-       "top       0000-0002-4206-6417\n",
+       "top       0000-0003-4717-4481\n",
        "freq                        1\n",
        "Name: orcid, dtype: object"
       ]
@@ -855,10 +857,10 @@
     {
      "data": {
       "text/plain": [
-       "count               124722\n",
-       "unique              124718\n",
-       "top       maykin@owasp.org\n",
-       "freq                     2\n",
+       "count                     124722\n",
+       "unique                    124718\n",
+       "top       opercin@erbakan.edu.tr\n",
+       "freq                           2\n",
        "Name: primary_email, dtype: object"
       ]
      },
@@ -2461,9 +2463,9 @@
        }
       },
       "text/html": [
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\"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Activation date distribution for all dup bios\"}, \"xaxis\": {\"tickangle\": 45, \"tickfont\": {\"size\": 12}}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('0e4d6eb7-8201-4c31-bbfd-345d8161da3d');\n",
+       "var gd = document.getElementById('3a57f18d-0642-4b14-a315-06f59980c2e0');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -62516,16 +62518,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "While in general it holds a seasonality (e.g. weekends and holidays)"
+    "While in general it holds a seasonality (e.g. weekends and holidays)\n",
+    "(Commented as the HTML generated is huge.. will sort this out)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# dup_bios_df = df[(df.biography.notna()) & (df.biography.duplicated(keep=False))]\n",
     "# YEAR = 2020\n",
     "# data = [\n",
     "#     go.Histogram(\n",
@@ -62544,13 +62546,6 @@
     "# plotly.offline.iplot(fig)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -62581,7 +62576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -62643,8 +62638,6 @@
        "      <th>biography_length</th>\n",
        "      <th>biography_n_sentences</th>\n",
        "      <th>biography_n_words</th>\n",
-       "      <th>date_diff</th>\n",
-       "      <th>ref_year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -62688,8 +62681,6 @@
        "      <td>165</td>\n",
        "      <td>2.0</td>\n",
        "      <td>28.0</td>\n",
-       "      <td>0.002594</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17800</th>\n",
@@ -62731,8 +62722,6 @@
        "      <td>369</td>\n",
        "      <td>1.0</td>\n",
        "      <td>60.0</td>\n",
-       "      <td>0.005230</td>\n",
-       "      <td>2019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26513</th>\n",
@@ -62774,8 +62763,6 @@
        "      <td>93</td>\n",
        "      <td>1.0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.000654</td>\n",
-       "      <td>2019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27456</th>\n",
@@ -62817,8 +62804,6 @@
        "      <td>69</td>\n",
        "      <td>1.0</td>\n",
        "      <td>7.0</td>\n",
-       "      <td>0.018818</td>\n",
-       "      <td>2017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30196</th>\n",
@@ -62860,8 +62845,6 @@
        "      <td>89</td>\n",
        "      <td>2.0</td>\n",
        "      <td>20.0</td>\n",
-       "      <td>0.001507</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -62903,8 +62886,6 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10988314</th>\n",
@@ -62946,8 +62927,6 @@
        "      <td>77</td>\n",
        "      <td>2.0</td>\n",
        "      <td>17.0</td>\n",
-       "      <td>0.003565</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10988449</th>\n",
@@ -62989,8 +62968,6 @@
        "      <td>98</td>\n",
        "      <td>1.0</td>\n",
        "      <td>21.0</td>\n",
-       "      <td>0.001775</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10988922</th>\n",
@@ -63032,8 +63009,6 @@
        "      <td>151</td>\n",
        "      <td>1.0</td>\n",
        "      <td>22.0</td>\n",
-       "      <td>0.006178</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10988929</th>\n",
@@ -63075,8 +63050,6 @@
        "      <td>150</td>\n",
        "      <td>1.0</td>\n",
        "      <td>24.0</td>\n",
-       "      <td>0.002207</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10989073</th>\n",
@@ -63118,12 +63091,10 @@
        "      <td>98</td>\n",
        "      <td>2.0</td>\n",
        "      <td>21.0</td>\n",
-       "      <td>0.002151</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>13663 rows × 40 columns</p>\n",
+       "<p>13663 rows × 38 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -63257,36 +63228,23 @@
        "10988929         False                NaN                 NaN   \n",
        "10989073         False                NaN                 NaN   \n",
        "\n",
-       "          biography_length  biography_n_sentences  biography_n_words  \\\n",
-       "613                    165                    2.0               28.0   \n",
-       "17800                  369                    1.0               60.0   \n",
-       "26513                   93                    1.0                3.0   \n",
-       "27456                   69                    1.0                7.0   \n",
-       "30196                   89                    2.0               20.0   \n",
-       "...                    ...                    ...                ...   \n",
-       "10988314                77                    2.0               17.0   \n",
-       "10988449                98                    1.0               21.0   \n",
-       "10988922               151                    1.0               22.0   \n",
-       "10988929               150                    1.0               24.0   \n",
-       "10989073                98                    2.0               21.0   \n",
+       "          biography_length  biography_n_sentences  biography_n_words  \n",
+       "613                    165                    2.0               28.0  \n",
+       "17800                  369                    1.0               60.0  \n",
+       "26513                   93                    1.0                3.0  \n",
+       "27456                   69                    1.0                7.0  \n",
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+       "...                    ...                    ...                ...  \n",
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+       "10988922               151                    1.0               22.0  \n",
+       "10988929               150                    1.0               24.0  \n",
+       "10989073                98                    2.0               21.0  \n",
        "\n",
-       "          date_diff  ref_year  \n",
-       "613        0.002594      2020  \n",
-       "17800      0.005230      2019  \n",
-       "26513      0.000654      2019  \n",
-       "27456      0.018818      2017  \n",
-       "30196      0.001507      2020  \n",
-       "...             ...       ...  \n",
-       "10988314   0.003565      2020  \n",
-       "10988449   0.001775      2020  \n",
-       "10988922   0.006178      2020  \n",
-       "10988929   0.002207      2020  \n",
-       "10989073   0.002151      2020  \n",
-       "\n",
-       "[13663 rows x 40 columns]"
+       "[13663 rows x 38 columns]"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -63303,216 +63261,1368 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Assign spam score from precanned library**"
+    "**Dup bios URLs**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# bios = df[df.biography.notna()][['orcid', 'biography']]"
+    "top_urls = dup_bios_df[['orcid', 'url_domains']]\\\n",
+    "                .explode('url_domains')\\\n",
+    "                .reset_index(drop=True)\\\n",
+    "                .groupby('url_domains')\\\n",
+    "                .count()\\\n",
+    "                .sort_values('orcid', ascending=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def score(bio):\n",
-    "#     try:\n",
-    "#         return antispam.score(bio)\n",
-    "#     except: # if len(bio) < 3 the filter doesn't know how to handle that\n",
-    "#         return -1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios['spam_score'] = bios.biography.apply(lambda bio: score(bio))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios[bios.spam_score == -1] # these are artefacts (no scoring possible)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios.spam_score.replace(to_replace=-1, value=np.nan, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios.spam_score.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios[bios.spam_score > 0.99]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Spam goes nowhere."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Search offending words, sexually explicit content, etc.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios['profanity_score'] = profanity_check.predict_prob(bios.biography)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# bios[bios.profanity_score > 0.90]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Profanity detection goes nowhere too."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
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+       "                            \n",
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       ]
      },
-     "execution_count": 121,
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "set_top_n(50)\n",
+    "data = [\n",
+    "    go.Bar(\n",
+    "        x=top_urls[:TOP_N].index,\n",
+    "        y=top_urls[:TOP_N]['orcid']\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title='Top-%s URL domains' % TOP_N,\n",
+    "    xaxis=dict(tickangle=45, tickfont=dict(size=12), range=TOP_RANGE)\n",
+    ")\n",
+    "fig = go.Figure(data=data, layout=layout)\n",
+    "plotly.offline.iplot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>orcid</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>url_domains</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>linkedin.com</th>\n",
+       "      <td>78418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>researchgate.net</th>\n",
+       "      <td>67823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>google.com</th>\n",
+       "      <td>44804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cnpq.br</th>\n",
+       "      <td>24635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>academia.edu</th>\n",
+       "      <td>21174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gil-sanzlab.com</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gilabola88.net</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gilabs.com</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>giladfeldman.org</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>​ http</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>199625 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  orcid\n",
+       "url_domains            \n",
+       "linkedin.com      78418\n",
+       "researchgate.net  67823\n",
+       "google.com        44804\n",
+       "cnpq.br           24635\n",
+       "academia.edu      21174\n",
+       "...                 ...\n",
+       "gil-sanzlab.com       1\n",
+       "gilabola88.net        1\n",
+       "gilabs.com            1\n",
+       "giladfeldman.org      1\n",
+       "​ http                1\n",
+       "\n",
+       "[199625 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[df.activation_date == df.last_update_date]['orcid'].count()"
+    "top_urls = df[['orcid', 'url_domains']]\\\n",
+    "                .explode('url_domains')\\\n",
+    "                .reset_index(drop=True)\\\n",
+    "                .groupby('url_domains')\\\n",
+    "                .count()\\\n",
+    "                .sort_values('orcid', ascending=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+       "\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>orcid</th>\n",
+       "      <th>url_domains</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000-0001-6097-3953</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0000-0001-6112-5550</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0000-0001-6152-2695</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0000-0001-6220-5683</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0000-0001-7071-8294</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10989644</th>\n",
+       "      <td>0000-0002-1686-1935</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10989645</th>\n",
+       "      <td>0000-0002-3800-6331</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10989646</th>\n",
+       "      <td>0000-0002-8783-5814</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10989647</th>\n",
+       "      <td>0000-0002-7584-2283</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10989648</th>\n",
+       "      <td>0000-0003-0529-3538</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11300438 rows × 2 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "124216"
+       "                        orcid url_domains\n",
+       "0         0000-0001-6097-3953         NaN\n",
+       "1         0000-0001-6112-5550         NaN\n",
+       "2         0000-0001-6152-2695         NaN\n",
+       "3         0000-0001-6220-5683         NaN\n",
+       "4         0000-0001-7071-8294         NaN\n",
+       "...                       ...         ...\n",
+       "10989644  0000-0002-1686-1935         NaN\n",
+       "10989645  0000-0002-3800-6331         NaN\n",
+       "10989646  0000-0002-8783-5814         NaN\n",
+       "10989647  0000-0002-7584-2283         NaN\n",
+       "10989648  0000-0003-0529-3538         NaN\n",
+       "\n",
+       "[11300438 rows x 2 columns]"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[df.activation_date > df.last_update_date]['orcid'].count()"
+    "exp = df[['orcid', 'url_domains']].explode('url_domains')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "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>orcid</th>\n",
+       "      <th>url_domains</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>48136</th>\n",
+       "      <td>0000-0001-6141-6446</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89320</th>\n",
+       "      <td>0000-0001-5691-4184</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91455</th>\n",
+       "      <td>0000-0001-7228-6456</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104766</th>\n",
+       "      <td>0000-0002-2219-4665</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142061</th>\n",
+       "      <td>0000-0002-3869-9561</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10864865</th>\n",
+       "      <td>0000-0002-2257-6612</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10874490</th>\n",
+       "      <td>0000-0001-8141-8158</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10970527</th>\n",
+       "      <td>0000-0002-5195-9647</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10974940</th>\n",
+       "      <td>0000-0002-7562-5465</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10977979</th>\n",
+       "      <td>0000-0002-8886-0069</td>\n",
+       "      <td>lucialpiazzale.com</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>548 rows × 2 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "3428336"
+       "                        orcid         url_domains\n",
+       "48136     0000-0001-6141-6446  lucialpiazzale.com\n",
+       "89320     0000-0001-5691-4184  lucialpiazzale.com\n",
+       "91455     0000-0001-7228-6456  lucialpiazzale.com\n",
+       "104766    0000-0002-2219-4665  lucialpiazzale.com\n",
+       "142061    0000-0002-3869-9561  lucialpiazzale.com\n",
+       "...                       ...                 ...\n",
+       "10864865  0000-0002-2257-6612  lucialpiazzale.com\n",
+       "10874490  0000-0001-8141-8158  lucialpiazzale.com\n",
+       "10970527  0000-0002-5195-9647  lucialpiazzale.com\n",
+       "10974940  0000-0002-7562-5465  lucialpiazzale.com\n",
+       "10977979  0000-0002-8886-0069  lucialpiazzale.com\n",
+       "\n",
+       "[548 rows x 2 columns]"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[(df.activation_date.dt.year == df.last_update_date.dt.year) &\n",
-    "   (df.activation_date.dt.month == df.last_update_date.dt.month) &\n",
-    "   (df.activation_date.dt.day == df.last_update_date.dt.day)]['orcid'].count()"
+    "exp[exp.url_domains == 'lucialpiazzale.com']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1.098965e+07\n",
-       "mean     5.663836e+02\n",
-       "std      7.434119e+02\n",
-       "min     -9.120370e-06\n",
-       "25%      1.145045e-02\n",
-       "50%      1.980872e+02\n",
-       "75%      9.430198e+02\n",
-       "max      3.084876e+03\n",
-       "Name: date_diff, dtype: float64"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['date_diff'] = (df.last_update_date - df.activation_date) / np.timedelta64(1, 'D')\n",
-    "df.date_diff.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [
     {
@@ -63576,6 +64686,378 @@
        "      <th>biography_n_words</th>\n",
        "      <th>date_diff</th>\n",
        "      <th>ref_year</th>\n",
+       "      <th>date_stale</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>142061</th>\n",
+       "      <td>0000-0002-3869-9561</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>kimble</td>\n",
+       "      <td>esterly</td>\n",
+       "      <td>when parents tackle additional dedications out...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020-12-25 20:10:07.336000+00:00</td>\n",
+       "      <td>2020-12-25 20:36:27.423000+00:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[lucialpiazzale.com]</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>1</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>150</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.018288</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>116.515419</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      orcid  verified_email  verified_primary_email  \\\n",
+       "142061  0000-0002-3869-9561            True                    True   \n",
+       "\n",
+       "       given_names family_name  \\\n",
+       "142061      kimble     esterly   \n",
+       "\n",
+       "                                                biography other_names  \\\n",
+       "142061  when parents tackle additional dedications out...         NaN   \n",
+       "\n",
+       "       primary_email keywords external_ids education employment  n_works  \\\n",
+       "142061          <NA>      NaN          NaN       NaN        NaN        0   \n",
+       "\n",
+       "       works_source                  activation_date  \\\n",
+       "142061          NaN 2020-12-25 20:10:07.336000+00:00   \n",
+       "\n",
+       "                       last_update_date  n_doi  n_arxiv  n_pmc  n_other_pids  \\\n",
+       "142061 2020-12-25 20:36:27.423000+00:00      0        0      0             0   \n",
+       "\n",
+       "        label primary_email_domain other_email_domains           url_domains  \\\n",
+       "142061  False                  NaN                 NaN  [lucialpiazzale.com]   \n",
+       "\n",
+       "        n_emails  n_urls  n_ids  n_keywords  n_education  n_employment  \\\n",
+       "142061      <NA>       1   <NA>        <NA>         <NA>          <NA>   \n",
+       "\n",
+       "       ext_works_source  n_ext_work_source authoritative  n_valid_education  \\\n",
+       "142061              NaN               <NA>         False                NaN   \n",
+       "\n",
+       "        n_valid_employment  biography_length  biography_n_sentences  \\\n",
+       "142061                 NaN               150                    1.0   \n",
+       "\n",
+       "        biography_n_words  date_diff  ref_year  date_stale  \n",
+       "142061               25.0   0.018288      2020  116.515419  "
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.orcid == '0000-0002-3869-9561']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Assign spam score from precanned library**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios = df[df.biography.notna()][['orcid', 'biography']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def score(bio):\n",
+    "#     try:\n",
+    "#         return antispam.score(bio)\n",
+    "#     except: # if len(bio) < 3 the filter doesn't know how to handle that\n",
+    "#         return -1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios['spam_score'] = bios.biography.apply(lambda bio: score(bio))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios[bios.spam_score == -1] # these are artefacts (no scoring possible)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios.spam_score.replace(to_replace=-1, value=np.nan, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios.spam_score.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios[bios.spam_score > 0.99]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Spam goes nowhere."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Search offending words, sexually explicit content, etc.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios['profanity_score'] = profanity_check.predict_prob(bios.biography)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bios[bios.profanity_score > 0.90]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Profanity detection goes nowhere too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "95398"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.activation_date == df.last_update_date]['orcid'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "124216"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.activation_date > df.last_update_date]['orcid'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3428336"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df.activation_date.dt.year == df.last_update_date.dt.year) &\n",
+    "   (df.activation_date.dt.month == df.last_update_date.dt.month) &\n",
+    "   (df.activation_date.dt.day == df.last_update_date.dt.day)]['orcid'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    1.098965e+07\n",
+       "mean     5.663836e+02\n",
+       "std      7.434119e+02\n",
+       "min     -9.120370e-06\n",
+       "25%      1.145045e-02\n",
+       "50%      1.980872e+02\n",
+       "75%      9.430198e+02\n",
+       "max      3.084876e+03\n",
+       "Name: date_diff, dtype: float64"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['date_diff'] = (df.last_update_date - df.activation_date) / np.timedelta64(1, 'D')\n",
+    "df.date_diff.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>orcid</th>\n",
+       "      <th>verified_email</th>\n",
+       "      <th>verified_primary_email</th>\n",
+       "      <th>given_names</th>\n",
+       "      <th>family_name</th>\n",
+       "      <th>biography</th>\n",
+       "      <th>other_names</th>\n",
+       "      <th>primary_email</th>\n",
+       "      <th>keywords</th>\n",
+       "      <th>external_ids</th>\n",
+       "      <th>education</th>\n",
+       "      <th>employment</th>\n",
+       "      <th>n_works</th>\n",
+       "      <th>works_source</th>\n",
+       "      <th>activation_date</th>\n",
+       "      <th>last_update_date</th>\n",
+       "      <th>n_doi</th>\n",
+       "      <th>n_arxiv</th>\n",
+       "      <th>n_pmc</th>\n",
+       "      <th>n_other_pids</th>\n",
+       "      <th>label</th>\n",
+       "      <th>primary_email_domain</th>\n",
+       "      <th>other_email_domains</th>\n",
+       "      <th>url_domains</th>\n",
+       "      <th>n_emails</th>\n",
+       "      <th>n_urls</th>\n",
+       "      <th>n_ids</th>\n",
+       "      <th>n_keywords</th>\n",
+       "      <th>n_education</th>\n",
+       "      <th>n_employment</th>\n",
+       "      <th>ext_works_source</th>\n",
+       "      <th>n_ext_work_source</th>\n",
+       "      <th>authoritative</th>\n",
+       "      <th>n_valid_education</th>\n",
+       "      <th>n_valid_employment</th>\n",
+       "      <th>biography_length</th>\n",
+       "      <th>biography_n_sentences</th>\n",
+       "      <th>biography_n_words</th>\n",
+       "      <th>date_diff</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -63620,7 +65102,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>-0.000009</td>\n",
-       "      <td>2020</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -63654,11 +65135,11 @@
        "          biography_length  biography_n_sentences  biography_n_words  \\\n",
        "10771774              <NA>                    NaN                NaN   \n",
        "\n",
-       "          date_diff  ref_year  \n",
-       "10771774  -0.000009      2020  "
+       "          date_diff  \n",
+       "10771774  -0.000009  "
       ]
      },
-     "execution_count": 125,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -63669,7 +65150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63678,7 +65159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63687,7 +65168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63707,19 +65188,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f574753d0>"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -63734,26 +65205,15 @@
     }
    ],
    "source": [
-    "%matplotlib inline\n",
     "plt.figure(figsize=(16, 6))\n",
-    "sns.violinplot(x='ref_year', y='date_diff', data=df)"
+    "ax = sns.violinplot(x='ref_year', y='date_diff', data=df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f4870e340>"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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5eDWREd3LJGWUC8WN43wAAADPmKIZPLRJcNAW/tEGmG4TGdG9wDn3NTOrds79c8krAgAAKGOVPEICoPyF5Rg1kRHdK/OPl5SyEAAA8PoxauIfbQCgXFRy6J3IiO4LZrZJ0pFmtr5ou0lyzrk3laY0AAAwUWZGwAqYSu5AApPF8QnT7TWDrnPucjObK+leSe8rfUkAAADlj449APgzkRFdOed2SzqlxLUAAIDXiXDlX6ENGNEFAH9eM+ia2R3OuQ+a2TOSit89mboMAACAwOOkAzC+Sj45OpER3U/nHy8qZSEAAODwVXJnpdwQqoKH/QMYFpb9YSLX6O7KP24rfTkAAACVISydySDjpAMwVvGxqZKPUxOZutyjkVOWR3DONU1pRQAA4LBVcqelXHCNLjAWx6bgKG6LbDbrsZLSmsiIbqMkmdlXJO2SdIty1+f+uaS2klYHAABQpgi6/hGugLHCMqIbmcRr3+ec+55zrsc51+2c+76ki0tVGAAAQDmr5A5kueGkAzCseBS3ko9Tkwm6fWb252YWNbOImf25pL5SFQYAAFDOCFfBUcmdeWCyGNEd688kfVDSnvzHB/LbAABAQFTy9VblppI7kOWGkw7AsJtvvnno+Xe+8x0tX77cYzWlM5HbC0mSnHMv61WmKpvZF5xz/zoVRQEAykN7e7sGBga0ePFi36Ugj3AVHIQr/9gfgLFeeeWVoedbt25VU1Nlri08mRHd1/KBKfxZAIAy8JnPfEYf//jHfZcBBBIhy79CG9AW/tEGwRGWtpjwiO4EcNoSAEJm7969vkvAKGHpwAATwa2egPCayhFd3lkBAPCMoOtfIVTRFv4xoguMFZb9YSqD7phTZWb2IzPba2bPFm37RzPbYWZr8x8XFn3tC2a22cxeNLPzi7ZfkN+22cz+bgprBgCgIhCugodRRP8Y0QXCayqD7s/H2fafki4YZ/u3nHOn5j/uliQzO0nSZZJOzn/P9/K3MopK+q6kP5F0kqTL868FAAB5BNzgoC2Cg7YAxgrLfjHhoGtmx5nZqsLorJm9ycy+VPi6c+5fRn+Pc+4RSQcn+E9cLOl251zSOfeSpM2Szsx/bHbObXXODUq6Xa+y+jMAAEAQMIoYHGHp2AcZbYDpNpkR3R9K+oKklCQ559YrNwJ7OD5pZuvzU5tb89vmSdpe9Jr2/LZDbQcAAKPQmQSGMXUZCK/JBN0659wTo7alD+Pf/L6kYyWdKmmXpG8cxs8Yl5ldZWarzWz1vn37purHAgBQNgi6wUFb+MdiVMBYYdkfJhN095vZscqvrmxmlyoXVCfFObfHOZdxzmWVGyU+M/+lHZIWFL10fn7bobaP97Ovd84tdc4tnT179mRLAwCg7IWlAxNkjB4GD23iH8cmTLfJBN2rJf1A0glmtkPSZyT978n+g2bWVvTp+yUVVmS+U9JlZlZtZkdLWiLpCUlPSlpiZkebWZVy06XvnOy/CwBAGNCZDA7awj/aABgrLPtFbBKvdc6588ysXlLEOdeTD6SHZGa3STpL0iwza5f0D5LOMrNTlRsZflnSJ/I//Dkzu0PS88pNib7aOZfJ/5xPSrpXUlTSj5xzz02ibgAAQiMsHZhywCiif0xdBsJrMkH3vySd7pzrK9q2QtKbD/UNzrnLx9l846u8/lpJ146z/W5Jd0+8VAAAwsXM6MwHDO0RHJx0AMLnNYOumZ2g3L1tm83sT4u+1CSpplSFAQAAlDOCrn+M6AYHbYDpNpER3eMlXSSpRdJ7i7b3SPqrUhQFAABQ7ujY+5fNZiUxoguE0WsGXefcryT9ysze5pz7wzTUBAAAJolQBQDAsMlco/u0mV2t3DTmoSnLzrmPTXlVAAAAZY6TD/4VRnRpCyB8JnN7oVskzZV0vqSHlbufbU8pigIAAIeHDn1wMF3Wv8L+QFsA4TOZoLvYOfdlSX3OuZskLZP0ltKUBQAADgdBFxjGiG5wFLcB7YHpMJmgm8o/dprZGyQ1Szpi6ksCAAAof4wi+seIbjARdDEdJnON7vVm1irpS5LulNQg6cslqQoAABwWOpDAMPaHYKJdMB0mch/dvyn69Mr843fzj/VTXhEAAAAwBZi6HEy0B6bDREZ0G/OPx0s6Q7nRXCl3T90nSlEUAAA4PHQgg4PpssFBWwDhM5H76P6TJJnZI5JOd8715D//R0krS1odAACYFIIuAACTW4xqjqTBos8H89sAAACAwOIEkH+suozpNpnFqG6W9ISZ/TL/+SWS/nPKKwIAlB3nHFMDgTw68cCrYx/BdJhw0HXOXWtmv5H0rvymK51zT5emLABAOUmn04rH477LgOhABglt4R9tEEy0C6bDZEZ05Zx7StJTJaoFAFCmCiubAhhe+IjOfHAw4wQYFpZj02Su0QUAYFyZTMZ3CcgLSwcmyAptQFsACKKwHJsIugCA142g6x8jVsFRaAtmOvjHSQcgvAi6AIDXjQ49MBb7RXBwIggYFpYTPwRdAMDrlkqlfJcABAajiMFBWwBjhWV/IOgCKDtbt25VZ2en7zJCr3i0Kp1Oe6wECCam9AcHI7rBEpagFVRh+fsTdAGUnY9+9KP6v//3//ouI/QGBweHnjOi619YOi4AgNcnLO8XBF0AZWnt2rW+Swi94qBb/BxATlg6k0HG1OVgoj0wHQi6AIDDMjAwMO5z+EUH0j+myQZH4RIL2sS/4mMTxym/wvL3J+gCAA5LMpkc9zmAnLB0JoOMEV0gvAi6AMrK8uXLfZeAPIIu8OoYRfTvwQcflERbAMXCcuKHoAugrGzcuNF3CcjjGt1goSMPjLVv3z7fJQDwhKALoKxwu47gKL69UPFzIOwKoyWcfAiOsIxgARhG0AVQVgi6wVHccSToBgcdemAYJx2AscLyPkHQBVBWCLrBUdxxjER4OwEKCFXBE5aOfbmgPTAd6JkAKCvFQZdRRL+KO/N07AEEGrkKCB2CLoCyUhx0uXcrAAAAxkPQBVBWiqc7sdKvX8Uj6kwpBxBsDOkGCVOXMR0IugDKSvGbYzqd9lgJik80pFIpj5UAwPiG3jO4vAIIHYIugLJSHHQZRfQrmUyO+xx+MVISHLRFgNAWQOgQdAGULYKuX319fUPP+/v7PVYCiQXBgoigGyDsH0DoEHQBlBWuCw2Onp6eoefd3d0eK4FEqAoi2gQA/CHoAigrLEYVHJ2dnZJybySF5wCGEXSDg7YAwqekQdfMfmRme83s2aJtM8zsfjPblH9szW83M1tuZpvNbL2ZnV70PVfkX7/JzK4oZc0Agq14RJfpsn51dnYqIikmqaOjw3c5QGAUQhXhyr9CG5iYugyETalHdP9T0gWjtv2dpFXOuSWSVuU/l6Q/kbQk/3GVpO9LuWAs6R8kvUXSmZL+oRCOAYRP8UrLvb29HitBV1eXosq9kTB1GRiLoBsctAUQPiUNus65RyQdHLX5Ykk35Z/fJOmSou03u5zHJLWYWZuk8yXd75w76JzrkHS/xoZnACFRfF0u4cqvvr4+mXJvJL20BTCEEd3gYbE2IHx8XKM7xzm3K/98t6Q5+efzJG0vel17ftuhto9hZleZ2WozW71v376prRpAIGQymaEjV/FiSJh+g4ODMkkmrpcOAkJVcBTaovhSCwDDOPGA6eB1MSqXeyeYsndm59z1zrmlzrmls2fPnqofCyBAMpmMXCR32EgkEp6rCTfn3NBVb4QsYBgBN3g4RgHh4yPo7slPSVb+cW9++w5JC4peNz+/7VDbAYSQc06FdMUool9mNnSmkrPzwUGHPjgIvADgj4+ge6ekwsrJV0j6VdH2v8ivvvxWSV35Kc73SnqPmbXmF6F6T34bgBAaCrpGJ9K3SCT3FuKKngMYPjZxjPKPEz/BxMlRTIdYKX+4md0m6SxJs8ysXbnVk/9N0h1m9nFJ2yR9MP/yuyVdKGmzpH5JV0qSc+6gmX1F0pP51/2zc270AlcAwob3SO+Kwy1BNzjo2PvHYlTBQ7ACwqekQdc5d/khvnTuOK91kq4+xM/5kaQfTWFpAAAAJcGIbvBw0gEIH07BAygrLn9VqMlG3GoI0694hITRkuAgXPlHqAoe2gQYFpb9gaALoKy4bP7gHJHS6bTfYkKueDEqpi77x8mG4AlLZxIAgoieCYCyknVZySSLmZLJpO9yQq14MSoj6HpHqAoOrtENItoCKAjLsYmeCYCyks3kgq7iUl9fn+9yQo3FqIIpLB2YICtMH6ctAARRWI5N9EwAlI10Op3rQEakbDyrzs5O3yWFGtfoAuMrdCK5Xjo4QtKvB1CEoAugbOzduzf3JCK5Wqfde3b7LSjkihcDY2Gw4AjLmfogK+wPBN3gYL8AhoVlfyDoAigb7e3tuSdRyTU47d61m4DlUTKZlCn3RpIcGPBdDhAY3F4oOLheGggvgi6AsrF9+/bck5ikxtxU5l27dnmtKcz6+/oUUe6S6UQi4bsc5NGh968QcDkRFyDsF8CQsLxPEHQBlI3t27fnUpVJrtENb4MX/X19QyO6/QRdYAgjusGTDUnHvlyEJWjBL4IugLKxe/duDQ0h1ue2DV23i2k3MDAw1BwDySQdFyCPVZeDh7YIFtoD04GgC6BsdHR2yEXyb47VuQdWXvanMC2zsN4yHRe/WPk6OAr7AlOX/Ru6RpfRda+WL1+eO1md94Mf/MBjNQgLgi6AspFIJIZTVUSyiHFtqEeFYOVGfQ6/mC7rH20QPJyG82vz5s1KJpNDn2/ZssVjNQjL+zVBF0DZSKfTIzdEGDHxqbq6Wk65DmQ8FgvNG2fQMbLuHyv9Bg8jusCwVCql2tpaXXrppaqtrVVfX5/vkkoi5rsAAJiobDY7PKIrScbIiU81tbXqUO6MaW1Nje9ykEe4Cg7awr+haeS8VwBDBgcHtWzZMl1zzTWSpEcffdRzRaVB0AVQNrJuZEfFzAi6HtXV1Q2N6BJ0g4Nw5R+LUQUPbREstIdfVVVVWrlypSRp5cqVmj9/vueKSoOgC6BsZDOjQi0jul7V1NbKScpKqiHoesd02eDg9kLBUdgfaAtgWDweV0dHh1asWCFJqq+v91xRaXCNLgDgsFRVVQ2N6FZVV/suB3kEXf8IusHDfgGED0EXQNmIREcespxzikajnqpBPB4fXowqHvddDhAYjqnLgUNbAOFD0AVQNqqrqkfeIyKdG1WEH7FYbKg5YgTdwGAU0b8s02UDY3jqMiv0A2FD0AVQNmpraoeDrpNc1qmaKbPeRCK5txAnMbIeIIxcAWOxWwDhQ9AFUDb2798vpSV1SpHf5g5fTJn1p/i+udxDNzgIusBY3EcXCB9WXQZQNhKJhMyZlJK033c1QLCYmZxzTJcFigytRi5OAAFhw4gugLIxXgd+YGDAQyUAcGiR/AwHpvQHBxMdgPAh6AIT4JzTL37xCx04cMB3KaGWTqdHbjCpu7vbTzEYceKBUcTgYOqyf5F8wGVKf3CwXwDhQ9AFJmDbtm36j//4D33ve9/zXUqojQm6Eamzs9NPMdCGDRuUljQoafOmTVq+fLnvkiA69EEQi8VGPCII2C+AsCHoAhMwODgoSXrppZc8VxJeg4ODYzvwJvX09PgpCEN/eyepr79fmzdv9ltQyA1di0jQ9W7nzp2Shlcmh3/ZLPsFEDYcgQGUhcLJhmLOnJLJpIdqIBGogopp5P4lEglJjOgCgE8EXQBl4VAjIyz24g9BN5gIusHBiC4wPt4/MB04AgMoC7W1tWO2WdZUX1/voRogeJi6HByFRagIugDgD0dgYAIymYwkOpA+mZmisVGjt05qbW31UxDYHwKmEK5ol+Bg1eXgiERoCyBsCLrABIxZ7RdexGPxkRuy0owZM/wUAwQUQRcYD0EXCBuCLjABhRFd+DXewi7Nzc0eKgGCi2t0gbEYXQfCh6ALTEAh6PJG6dd4C081NDR4qAQSI4dBRdANDtrCv6HrpXn/DhTeP/wKy9+foAtMACO6wTDewi5VVVUeKgGCi3AVHGHpTJYFgi4QOgRdYAIY0Q0ubi8E5BSOTwRd/woBl7YIDt6/gfAh6AITUFiMirPzfo3XUaHzAoxEuAoO3jOCg7cKYFhYjk3egq6ZvWxmz5jZWjNbnd82w8zuN7NN+ZVERIAAACAASURBVMfW/HYzs+VmttnM1pvZ6b7qRjgNDg76LgEa/8AcloM1MFEEXf+4p3HwGKsuBwr7BqaD7xHds51zpzrnluY//ztJq5xzSyStyn8uSX8iaUn+4ypJ35/2ShFqBN3golPvDx2VYGJNgeBgHwEQRGE5NvkOuqNdLOmm/PObJF1StP1ml/OYpBYza/NRIMIpkUhICs+BoZzQJv7wtw8WrgsNHvYR/4Yub2HuMhA6PoOuk3Sfma0xs6vy2+Y453bln++WNCf/fJ6k7UXf257fBkyLQtDlbdKv8TqNheunMf0cgSqQCFfBQVsA42PfwHSIefy33+mc22FmR0i638w2FH/ROefMbFJ7QT4wXyVJCxcunLpKEXqFoJvJEKp8Gm+kimnl/tBRAcbH6HpwDB2nOF4BQ8Ly/u1tRNc5tyP/uFfSLyWdKWlPYUpy/nFv/uU7JC0o+vb5+W2jf+b1zrmlzrmls2fPLmX5CJmBgYHcYz7wwo/xDszJZNJDJZDC80ZZbmiX4KAt/Fq+fLm6u7slST19vVq+fLnnigBMJy9B18zqzayx8FzSeyQ9K+lOSVfkX3aFpF/ln98p6S/yqy+/VVJX0RRnoOQKYYpQ5dd4oyO0iT904YOJcOVfoQ1YGMyvzZs3D13ekslktHnzZs8VoYDjFKaDr6nLcyT9Mr9AQEzST51z95jZk5LuMLOPS9om6YP5198t6UJJmyX1S7py+ktGmKVSqRGP8CPrxgZdrtEFcsxMzjk6kAAAyFPQdc5tlXTKONsPSDp3nO1O0tXTUBowrsJZ+QzXW/k1Tv+da+CAHO7dGiC0BfCq2Df8spCsQh602wthFEargFcXloM1MFFMl/Wv0IXnRBwA+EPQDbA1a9boggsu0JYtW3yXgjxClV+ZTEa1tbW69NJLVVtbK4k2AUYjXAUHbQEA/hB0A+yhhx7S4OCg1q1b57uU0ItGo5KkCKHKq2w2q2XLlumaa67RsmXLJDF6BYzGTCD/uL1QMDFdNjhoC0wHn/fRxWvgeqvgKATdwiP8sIhp5cqVkjT0yAJhwEic/AkO3r+DhfYAwoWgWwZ6e3t9l4A8izCi61sikdCKFStynxi3F/KJvSGYCLr+MaIbTATd4KAtMB2YuhxgiURCktTV1eW5EhRwXPZrTKfRpIGBAT/FAAFSfFshpi4HB0EXAPwh6AYYU5eDozBCQqfFL5cdtS8wousX16wHRvEoLkHXP0Z0AQRZWLIFQTfACqvKNjY2eq4EhetABwe5HtSn8TqNBF1/iLnBQdANJoIuML6wBC34RdANsMJtU5qbmz1XgsL02OTgIAdnT8btvDN1GZA0MuhyjW5w8H4BIIjCcmwi6AZYJBIZ8Qh/CtdLO+cYQfRkvEDrzKm/v99DNZDC80ZZDorDLaOIAIBXE5b3bxJUGQjLf8YgSyT6i54nPFYSXuOeYDBpIMmIri8cm4KDoAsAwEgE3QCjExkcA0XhlqmyfhzqukOuR/QnMypQEbD8Kf7bM3U5AFhMMpCMBfQCg33Dr7D8/Qm6AVY4IIflP2OQpVPDi1ARrIBcmBodbNk3/CluC044+Me7djDRnwJywrIvEHQDLCz/CctBJpOR5bsujJb4EY1GJ7UdpdXT0zNmG/uGPyxGFUyOkw4A4A1BN8AKQZepNv5lXVbRfDMwWuJHLBYbu9FJ8Vh8+ouBOjs7x2xjRNcfrtENJk5YA+Nj3/ArLH9/gm6AFQIuQde/dDqjqqjLP6cz78O4QVdSNMaIrg/jjeiyb/jDNbrBsXz58qHLXV7etk3Lly/3XBEQPGEJWvCLoBtgBN3gGEwmVR/LHZS5vZAfh7rNVjRC0PVhvNXHGUn0p/gkA0HXr82bNw9do5tIJLR582av9WAY4QoIF4JuGeDA7JdzTn39/ZpZk+vE9/X1ea4onA61H7B/+DHe6C1t4U9xuGVkHRgfx6jgoC0wHQi6Aea4PUEg9Pb2KpXOaH5DriPZ0dHhuaJwOuTthTJ06oHi/SNVtEo8AAQRfVtMB4JugHEQCIa9e/dKko5pyoz4HNNr3M67SYODg9NfDBSPj10EjMss/CneDwi6wPjoVwUHbYHpQNANsMI1iRwM/NqzZ48k6cj6jJprbOhzTK9DBVqCrh81NTWSpNraWl166aWqra0lYHlU2A8iFmUdAQCBR9/Wr7CcmB5/GVMECguL+FUItjNrsppZldbu3bs9VxRO405dNq5H9KWurk6StGzZMl1zzTWSpFWrVvksKdQKi4NVx+qUSAx4rgYIJsJVcNAWmA4E3TJwqNVmMT32798vM6m5yqm1OqP9+5i67MOhTvhwIsiP2tpaSdLKlSuHHmfMmOGzpFArBN2aeJ0S/f2eqwEABBkjuvCucLaL6YB+dXV1qbHKFDGpMe70UleX75JC6VC3rslkCbo+FKYuJxIJrVixQpJUX1/vs6RQK0xXro7VaiDZ7bkaIJgYRQwObkfnV1iCLkOFAdbb2yuJxY98SyQSqsnfQ7c25tTPtEAvxn1TNEZ0fYlGx96/OCxvnEE0MJA7LlXFarlG1zPCVHDRNsFBW/jV2NgoSTJzqqur0+LFiz1XVBoE3QDb8OJGSdLGjZs8VxJuqVRK8Xz/PRZxXBPqyaH+7tkMZ4V9GG+mCWfo/Sm0RzxWzSwgz+jABxdtExy0hV/HH3+8olEpHpcWL148tNZGpSHoBlRHR4d27miXJG3YsIGVZT3KZDKKWO6AHDUpnclwgPbgULcXolPvR+Ga0GIEXX8Ko7hV0RqlUoO0BTAO3ruDg7YIjkqejUXQDaj169dLkgbb3qRUalAbNmzwXFF4JZNJVUVyncZ4fo8hXE2/wtRMF3VycSc320k1UnIgyRumB319fWO2Ea78SSQSikZiikerJInpyx5xPAou2iY4eL/AdCDoBtTzzz8vRaJKzzlZkvTcc895rii8Dh7Yr4ZY7oDcGM89Hjx40GdJodTdnV9gp0lSi5Q9Kyu3wMk5N27oQmmN9zfneml/Cms67O56ecTnmH6EqeAiXAUHbeFXOp2WmWQmpdOVO3hD0A2oTZs2ydW1ylXXy2oatHnzZt8lhVIikdC2bds0ryHXgZ+ff2SEffoV7mc84qhVO+prmDY9PT1jtnH9uj+dnZ2SpIFU34jPMf1Gd+Dp0Psz+m/PyTh/Rp8AYr/wK5VKqTBjuZIvjyToBlT7jh3KVDdJktJVjdq5c6fnisLpnnvuUSqd0emzcme7jmnKqKlauvPOX3HWfpq9/PLLitRFRhy1XJMb+hqmV9c4t9miE+nPwYMdilhUEcutht3R0eG5ovAavR/QofdndFtwjPJn9CVfhcuR4EdunQ0nMymRqNx7rxN0AyqZTMpFcrc5dpGYBrjeatpt3bpVP/h//08ntqZ1fEtupCoWkd63qF+rV6/RL37xC88Vhstzzz+nTPOoTkqTZFHTCy+84KeoENu/f/+YbVy77s+BAwdyQTcSGfocfoy+Pprrpf0ZvWjeeIvoYXqMDrbJZJJZQB61t29XLCbFYtLu3Xsr9iQQQTegWppbZKncQSGSTqqludlzReGyZs0aXfOpT6rKJfSJk/r0k421uuXF3DzZ9yxI6rRZKX3729/WTTfdVLEHhyDp7e1V+/Z2uRmjRtEjkmtxuWvaMa3a29sVG7UtyRl6bzo7OhSxCCO6ATD6+nWul/Zn9Amfjo4ORhI9yGaz4558u+eeezxUg2QyqX379isWc4rFcrfN3L17t++ySoKgG1AtLc2yTO4scDSTVDNBd1r09PToW9/6lv7mb/4/1Wd79KXTuzSrNqttPVFt68l1ICMmfeqNvXr73KRuvPFGXfOpT2rLli2eK69sL730kqRcqB0t25LVlq1bmEo+jTKZjNavW6f4qO39iYT6+yt3ClRQHTx4UAPJAUUiUZlFVBWr0Y4dO3yXFUr33nuv9u3bN2Lbrl279Ic//MFTReHknNO999475hKLbDar73znOxynptmNN944pi1isZi+8Y1v6Omnn/ZUVXht3LhRzrmhEV2pcteeIegG0M6dO/XChg3KFq7RrW7S00+vZaXfEurv79ett96qyz70Qf3qv3+pd88f0FfO7NTcuvGvraqKSv/n5H594qQ+vbzxOX3sY1fq2muvpXNZIkMdx/pxvlgnJfoTTEmbRg888ID27d+vhlHbnXNM6Z9mzjk9+OCDkqSBVL86+/dKTlq1atW411GjNPbv369bb71V//Iv/6LYqHtSRs30pS99Sb/4xS9ok2nw8ssv65prrtG1116r6vzttgqaqxv161//Wh/+8w/rt7/9LSdIS8w5pzvvvFO33HKLqqpGtkV9fb1qamr0xS9+kcGCaXbvvfcqHjfV1EhVVVJDg+ne++71XVZJWKXu5EuXLnWrV6/2XcaEOee0fft2rVq1Srf/7A4NpNLqO+l9cjXNivTuU92GlWpsaNCfXX6Zzj77bLW1tfkuuSL09vbql7/8pX52+23q7unVKTNT+uDihBY1Dk9HvuXFWj2yM3eAXtSY0aLGjD5y/HCo6k2Zfv1yje5rr1XGmd797nfrwx/+sBYtWjTtv0+luu+++/TVr35VmQsyiqzJnZ/LnpU7CWGbTJG1Ed11111qamryWWbFc87pnnvu0de+9jUd6Zyizmlb0ddrJSUkfeITn9Dll18+dL0oSmPt2rW64YYbtH79ekUjMWWyI693q62t1Yc+9CF94AMfUGNjo6cqK1dXV5cefvhhrXrgAa1dt07OOc2srVYmk1Hn4HBbtFTF5CyiruSgopGIlp5xhs477zy9853vVH39eGfvcDj27dunFStW6I477lBNtErvX3yeHt+1Xps6ho9Sx7Uu0vuPO08/fWGltnfv1plnnKGP/+Vf6oQTTpCNOkGBw+ec02OPPaYf//jH2rBhg2bMmKFMJjPiRE9LS4tOPPFErVmzRqlUSuedd54+8pGP6KijjvJXeAgkk0ldcsn7NH9+r3p6c//n2+Zm9fTTVVqxYoVmzZrlucLJM7M1zrml436NoDu9nHPq6enRjh07tGPHDm3fvl2bNm3SCxte1IH9uVGrTMtCJRe9Ra5meLpypO+Aql95XJHu3OrLc+a26aQTT9Cxxx6r+fPna/78+TryyCNVX1/PwXoC+vv79fOf/1w/u/029fb165RZKb3/6IQWj17sSNJXVzdoQ+fwJM0TWlL60tKx11x1JE0rt9XowR21SmWdzj33PF155ZVasGBBSX+XMPjDH/6gz3/+88qck1HkmVFB91lT7MWYHnjgAcVio68axVRwzun3v/+9br7pJr2wYYOOkenP5PQTSS8XvW6RpEZJz0o6atEiffgjH9E555xDu7xOPT092rlzp3bt2qWdO3dq586d2rJli5577jnVVjXohLlv0faDL2p/b/vQ97TWzVV9dZPaOzaqvr5eZ555ptra2nTkkUcOfRxxxBG0zSTt379fTz75pB566EE9+cSTymSzaqiKa05NXHPra9QQj+mJPR3qSA4vzNZaHdcZR7SoJ5XWrr6k9gwMKpFKKx6L6W1vf7vOOussLV26VC0tLR5/s/K0f/9+Pfzww3rwwQf1zDPPSJLePu9U/elx71ZjVb2+8cSPtXFU0P3smVcqk83o4e2rdeeWh5RIDahtbpvOOfccnXPOOVq8eDH9qMPknNOjjz6qH//4x9q0aZNqa2u1cOFCtbW1afXq1cpkMlq2bJlWrlypaDSqM888U4ODg3rllVe0Y8cOZbNZnX322briiit09NFH+/51KtKqVav0T//0T7rkkkE98WTu+H/22Snd+pNq/fVf/7Uuu+wyzxVO3qsF3bJ6hzOzCyR9W1JU0g3OuX/zXJKk3I7d19enrq4udXV1qbOzUx0dHSM+9u/fr9179urAgf1Kjb5fVW2z0nUzlTlqiTItC+Sqx555z9bPVOLEC2UDXYp2vKIdvXu19w9r9NBDD414XXV1jWbNnqU5RxyhWbNmqaWlRa2trZoxY4ZaWlrU0tKi5uZmtbS0qLa2NpQH80ceeUTf+uY3dOBgh948e1CXnDygo5te/4JSrdVOHz4uofceNaDfbKvR/b9dpd8+9JA+dNll+tjHPqZ4fPQVjZiowui4dY79/2qdprYj2+iwl0AqldKDDz6o2376U2196SW1RiJ6r6TT5RSTSRp5otQkfUDSCZIeeWW7vvrVr+pHN9ygD11+uS644ALV1tZO/y9RBlKplPbs2TMizO7atUs7duzQzp07xyxuVBOvU11Vs05ZcJaOPeJUxSJxtXdsHPGaWDSuty++WB19e7Rh1+Na/dha9Q08rKwbvhwjEolo9qzZmjd/3lD4LYThtrY2NTc3h/I9olgikdC6deu0evVqPf7449q2LRea6uIxLWyo0dy6ajXGY6/5dzIzNVXF1VQV13HOqWswrV19A3ri97/XI488IjPT4mOP1RlnnqkzzjhDb3jDG1RdXT0dv2LZ6ejoGAq36/Ij6fMa5+h9i8/W0rlv0Jz6ma/5M6KRqM5Z9Ba99cg36em9G7R697O67ae36dZbb9X8efN0zrnn6uyzz9YxxxwT+n1gotauXatvf/vb2rJli+rq6nTCCSdo7ty5QzN70um0LrroIl1zzTWSpLvuukuSVFVVpcWLF2vhwoXavn27HnnkET344IP64z/+Y336058uyxHGIHvggQfU0GA6cl5WejK3rbXV6Yg5Tvfff19ZBt1XUzY9QzOLSvqupHdLapf0pJnd6Zyb8uVWC9MrOjo6dPDgQXV0dKizs3MoyBY+Ojo71dnZpZ6ebmUPsfKuxeJSvFaZWK2y8Xq5GccrW1UnV92obE2zXE2jFBm/Gao3rZIN9mlw4VuUbZwjSXI1zUq3vVFpSUlJyqQUGeiWJbtlyR6lBvvV19+n7Zt2KPrCZmmwXy4z/vLtsVhMjU1Nam5uUWtL81AILv6YMWPGUFBuamoq+6mIP/nJT3T99dfrqKasPnVG77gjuK9Xc5XTZUsSumDhgO7YXKtbb71Vzz37jP7969+g43KY2tradMScI7Rn556RX0hJkX0RnXHRGX4Kq1ADAwNasWKFVvz85zrY0aEjLKL/JemN2ayievVOX0SmUyS90WW1QdL/7Nmjb33rW7rhhz/UJe9/vz70oQ+Fcop5JpPR3r17tX379hEfr2x7RXv37ZMbEUCjaqhpUV28SXPrl6hhRovqq5tzH1UtqopN/DjSWj9Hb1v8PklS1mWVGOxVX7JTvcku9SU71Zfs0raNu/XCsy8qMTgyUNfW1mr+/PlatGiR5s+frwULFmjhwoWaP39+RU+5femll/Too4/qySee0DPPPqt0Oq1oxNRSFddxLfWaWVM1oXB7KGamluq4WqrjOiEfeg8MDGp/+yu6bcsW/fSnP1VVVZVOOeUUnXHGGXrnO9+p+fPnT/FvWZ6+853v6L9W/JeyLqu5DbO17Jg/0pvnnqwjG444rJ9XF6/VO+adpnfMO029g316es8GrdnznG655RbdfPPNOuH44/WNb36Tqf+v4fnnn9fnPvc5RaNRnXjiiZozZ86Y/mIsFtPKlSslSStXrhxzcrqqqkrHHnvsUOD93e9+p1deeUXf/e531dAwekUIHI7u7m49/vhjesMbUhrdnT9uSVq/+91mvfLKK1q4cKGfAkugbIKupDMlbXbObZUkM7td0sWSXlfQ7ezs1D333KOtW7eqPT+duLOjY/wFCsxk8Vq5WLUy0Wq5WLVc/Ai5IxbKxaulWI1crEYuXiMXr5WL1UrRw/sTR3r2qCGxRxddtEx33f0bdSw6eyjsjhCNK1s/U3q1M5iZlCw1IEslZOmELJ2U0gMaTA0okR7Qvo4BRfbvVCTzsiw9IJcakMb5/c1MM2bO0vx5R2revHlasmSJzj///LI5AO3atUvXX3+93jJnUP/n5D7FJpjZE2lTbW3t0HSbxATv+9ZS7XTVyf06sTWtH6xbr1//+te69NJLX8dvEF5mpgvOv0A333xz7hZD0fz2bSaXdjr//PP9Flhhvv/97+uXv/ylFst0kaTFLit7jYA7WkSmkySd6Jy2S3q0t1c/ueUWvbR1q/7lX/+1FGUHQl9fn7Zu3ToyzOan5RXfZzgeq1ZjdasaqmfoxLZj1FDdovrqXKCtjTdMOkSlMskRx6lUZuy9WyMWUX11k+qrmzReLEhlBtWf7BoKwb3JLvXsOajH2lerN7FKxSP4rS2tWrBwoRYtWjgiBC9YsKCsR8AGBgZ05ZVXKpvNqqk6rvm1VZpZ06DW6riikYn9XulsdkRbpNODh3xtceg9Nv+9B5MpHUgM6oV1a/Xkk0/qpptu0m9+85sp+g3L18MPP6yf//zneuuRb9J7jnqHjmw44jX/ryXSyVHv34e+p3FDVb3eteDNeteCN6s72avVu5/Vz1+8T8uXL9cXv/jFqf51KsbOnTv1+c9/XrFYTKeffvqYRacKYrGYent7tWLFCkk65FT9eDyuY445Ri0tLVq3bp2+/OUv69///d+ZtTUFnnnmGaXTGR119NiFVo8+Jqvf/S53e02Crh/zJG0v+rxd0luKX2BmV0m6StKEG+nqq6/W9u3bR2zL1M9Wtm6GsnWtyta2ylU1yMVrpGiVNE1v4NHuXbroomW65lOfkiT99A8vjR90J/TD4nLReG70eCKckzJJWWpAkWSPLNGhSKJTkb79OrB/nw7s36d169ZJkh566CFdd911h1fXNOvu7pYkLWrITDjkSlJ/2rTsomVD020evutnk/p3j21Oj/j3cXje//7367bbb9NgalBuppMyUvTFqE56w0k66aSTfJdXMZLJpO65+27Nk3S5nKomGXBHM5kWSjpSTrdLevT3v9e+ffs0e/bsqSg3cD760Su1Z8/Y+xE2185S28xjNKf5KDXVzlRNbGrXU0ilk1r2vuHj1G/uvH/SPyMerVJz3Ww1141tm0w2rd5kpw707tKuri3a07VN69ev0/r160a87m//9m910UUXHd4vEQDxeFzOOR3dVKfjWg7vJG4q63TRsuG2uOuXE1+JPBaJ6Ijaah1Rmxu1f+ZAt/rp4OvgwYP6+r9/XQub2/QXJ1+saCQ6oe9LpAZGvH//9q4HJvR9TdUNOmfRW9U72K+V996rd73rXfqjP/qjw66/UiWTSX3uc59Tf3//q4bcwzFjxgwdf/zxWrNmjb797W/rs5/97JT97LAqTANP5m8lPXvWcOAdSIx8TaWoqKOnc+56SddLucWoJvI9n/70p/XDH/5QBzs6dODAAWUzGUX79inaN3wfPItVSfEaZaLVykarpVh+NLfoIzeaW/R5tEqyw5/mm2lq0113587g3nX3b5RZdPZh/yxJkstK6UFZOilLD+Qfk7lAmx7+UHpA0cygIukBKTUgl0mN+VGxWEyzZs/WrJkzddVVV72+uqbRkiVLdOaZZ+jnTzyp7kHTnx6bUN0E9oC6mBsx3eaI2MQWcMs66dFdVfrJ5ga1NjfqwgsvfD3lh97MmTN16f+6VLfddpuyb83KNplcv9MnrvpEWY8gBU0kEtEpp56qxx5/XN+MRPS2bFZLJdUfZuAdkNNaSb+LRNSVzeqkE05QTU3NlNYcJJ///N9q7dq1am9v1/bt29Xe3q5EIqGuxH51JfZr457Vqq/JTUFurG5VQ03uo76qSXVVTYpFD+9a/nisesRxqjp2eFMtnXMaSPepP9mt3mSnegc61ZvsyH90KpkaXnHezDR79mwtWLBACxYs0Pz588s+DESjUc1obdWOri4NpDOaUVOlGTVVqo1GJnyciUdsRFvEJzgSLOXX/EhndHBgUAcHUjqQTOnoY5i2/NRTT6mru0vpqkH99IWVOrPtjVrSukiR1+hn1cZrRrTFrHjzq76+YE/fAT256xmt2ZubNLhq1aqy/79dColEQr29vUqlUtqyZYuOOeaYKZnl55zTvn371N6eW2Bv7969r/tnQkOLo3Z05I5J7/qj4RmKB/PbKm3V67JZddnM3ibpH51z5+c//4IkOefGnQN3OKsuZzIZHTx4ULt37x66Lrf4o6urS13d3erq6lJPT4/6Ry0QMqbmeG4ac26ac2E6c41cVZ1cvE6uqn7o+XgjxZGePYp271KmqW380VyXlQ325z5SffnH/tw05fSAIukBRdMDUnpALnXo6Tpmprr6BjU2Nqq5uUkt+etzC4tXFa7dnTlzpubMmaPW1tayvVZ3YGBA3/ve9/Tf//3faqgynT+/X+9ekFRD/ND7wS0v1mpDR0z9aVNdzOmE1vSI2wuNlslKT+yN69fb6vRKT0QnnniCvvSlL7P68hTo6enRpR+4VP0t/YocjOiMN52hr3/9677LqkjPPvusbr7pJj32+OOKSFos6RTlFpoqjPLeLaddRd/TJulCmdJy2iRpnaSNZko5pze+4Q264qMf1RlnnBGqExPOOXV0dKi9vV3t7e3asWOH2tvb9cor27WjvV0DhVPreTVVdaqL50JvXVWT6qub849NqqtqPuT1uU+/8qD29WxXKp1UPFat2Y0LdNrCc8a8rnCdbv9gl/qS3eof7M4/dimR6lFfsnvMbYpmz5qtBQsXDK3wX/hoa2uryHUHHn/8cd17771as3q1Ojo7JeUWn2qpimlmTVwzqqtUEzv0iOILHT3qGBhUKusUj5haa6p0Yuv4Jx6cc0pksvlgO6iOVEYDqdzff/asWXrz0qVatmyZTjnllKn/RctINpvV448/rgceeED/88j/aCA5oNbaJi2dc7LObHujFjS2jXtc+dkLv9Gmjm1KpAZUG6/RktZF+tCJfzLuv9GV7NGTu57Vk7uf1ctdOyRJp5xyis477zyde+65ZXOZ1nQr3MXitttuU39/v4444ggdffTRY67j37hxo3p7h+9Y0dDQoOOOO27Ea5xzOnDggF5++WV1d3dr/vz5+su//EudddZZZdvvDJoPu9RyrAAAEQZJREFUfvADikZ36f1/OvKSivvvi+ull2p1zz33lN008Yq4vZCZxSRtlHSupB3KrRX2Z86558Z7/XTcXiiTyai3t1fd3d1jPnp6eobCcUdHhw52dOQWruruGnv9byQq1TQpXdVwyIWpRsimFRvskQa6pezIefaRSESNTc1qbWnRjBmtQ4G1sLhUY2Ojmpqa1NTUlA+2zaqvr1c0OrFpQJVi48aNuvGGG/SHxx5TddT0zraELliQVFv92OsWJqo/Lf12R7Xua6/T/oS0YP48Xfmxj+ucc87hAD2FrrvuOt1xxx2SpG9+85taunTcYxumyJYtW3Tffffpgfvu074DB1RlplOd01mSGkeN8ibk9DtJT0YiSmSzam5s1Dnnnaf3vOc9Ovnkk32UH2iFTt2OHTu0Z88e7d69e+hx585d2rt3r1KpkZ2RqljNUAiuq2pSbbz+VS+pyWRTo4Jsz4iFrySpublFbW1z1dbWpjlz5mjOnDlqa2vT3LlzNW/evIoMsxPhnNO2bdv09NNP66mnntJTT61RT0+uo95QFdes6rjm1lWrqWpyC1M559SZTGl3Iqn9Ayn154Nta0uL3rx0qU4//XSddtppOvLII0N1UmiiEomEfv/73+v+++/XE48/oXQmrbkNs/SOI0/T2+edpoaqugn/rEw2o3V7X9Qj7au14cBLcnJasmSJzjvvPJ1zzjmaM+cwLxkLoe7ubv3sZz/THXfcocHBQc2dO1fHHHPMhI8fXV1d2rJlizo7OzV37lx97GMf03nnnVd2oSvo7rjjDl133XW6+OJBLViYey84cMB0+23V+uAHP6Srr77ac4WT9/+3d+9BWtX3HcffH2ABgYCA3AQDainGZhAVF9AoGmgGgaLWBuNAB1GnQyc2JhmbpE07rU1T2+q0juNMWwex3jBBRWXSgpCAIBURRYOIJrJ4AZabcQOryy57+faP50DWC8qze/a5nP28Znb22XPO/vj+9svZs9/n/M7vl4lCF0DSdOBOclPRLIqIHx/v2FJdR7e5uZmaZJj0e++9x/79+4+ti7hrdzVNJzDRUffuFYwYPvwjayEOHDjw2HJCLqpOXFVVFUuWLOHnq1bS2NTMuFMamTmynrP6n9iEUwC/qRfL3+nJM3tOor4pGHfOWL4++xouuugi56IDbNmyhZtuugmA1atX+yJYIC0tLWzZsoUVK1bw9IoVdI3gwgiGJ/sPkBuefDiCyZMnM336dMaPH+/8tMPRu8F79+79SBG8d+9e9uzZw759+zh8+PijSwCkLgwcMIBhp+YK16FDhzJkyJCPvO6shWy+Wlpa2LFjB5s3b2bjxo1sfuklmlta6FXRjcE9KxjSqyf9jlP0RgQ1DY3srWvgQH0j9U25dXQrJ0ygsrKSc889l5EjR7qwzdOhQ4dYu3Yty5cvZ+vWrVR07cb5Q/6AyaeN5/R+I47786ypP8izuzazfvdmDtbXMnjQYC6ffjlTp049tpydtU1NTQ0PP/wwS5cuJSKOTVR3vGtBXV0dVVVVHDhwgAEDBjB//nxmzJjha0cHaWhoYM6ca4EDfH12AxL8z88q2LevDz/96RL69Tux4f2lJDOFbj5KtdC10lRTU8OTTz7J0scf4+ChWs7u38Q1v1fHmZ+x/NDBBvHkWz1ZXd2ToAtTpkxh9uzZjBkzpoCRdz51dXVMmzaNkSNH8uCDDxY7nE5p586dLFy48BPreF9wwQUsWLCA0aNHFymyziUiOPLxddk/pmvXrv6DsYPU1tayfv161qxZw6ZNm2hubuakim707daV1oMdIuBgYxMNTc10796dSZMmcemllzJp0iR69Trxu4/22aqqqnjqqad4esXTHK4/zGl9h3LxiPMZdFL/Y8fUNx1h454tbDnwa4KgsrKSK6+8kokTJ3a6kW0drbq6mnvuuYfVq1fTo0cPRo0axbBhw47dAGhsbOTtt99m9+7dVFRUMHfuXGbPnu311gtg+fLl3HbbbfzRrCP06RM8srgHN9xwA/PmzSt2aG3iQtfsBNXX17Ns2TIeevABfnvwEFOGN3Dt79fRs9X1LwLW7enO4jf7UN8iZsyYydy5cxk6dGjxAu9ktm3bduyZcSue6upqamtrgdyaq1laksAsH7W1tWzYsIFn1qzhnXff/cT+M888k8suu4yJEyf6D/kOVldXx6pVq3jiiSfYsWPHJ/b369uPGTNnMGvWLE499dQiRNi5bNu2jbvvvputW7fSo0ePY2+8NTQ00NzczMyZM7n++usZOPAzlsm0VDU2NnLVVVcwePBBevcJXtvag6VLnzjukk+lzoWuWZ7q6upYtGgRjz66hBF9ghvOqqUiGYX88109WLO7B+POGcstf/k9/3FvZmZWYiKCt956iw9bTRzapUsXRo8eneoyOPb5IoJ169axdu3aY/PU9OrVi6uvvpozzjijyNF1TnfdddexNY0nT76EH/3oH4scUdu50DVroxdeeIG/+eEPqW/46KzVc+bM4cYbb/RQJzMzMzMrK1VVVcyfPx+A22+/nQkTJhQ5orZzoWvWDtXV1Wzfvv3Y1yeffDJjx44tYkRmZmZmZm23Y8cOGhsby35umc8qdD1DhdnnODq7tZmZmZlZFnSGYeNe+8TMzMzMzMwyxYWumZmZmZmZZYoLXTMzMzMzM8sUF7pmZmZmZmaWKS50zczMzMzMLFNc6JqZmZmZmVmmuNA1MzMzMzOzTHGha2ZmZmZmZpniQtfMzMzMzMwyxYWumZmZmZmZZYoLXTMzMzMzM8sUF7pmZmZmZmaWKYqIYsfQISQdAN4pdhwpOAV4r9hBGOBclBLnorQ4H6XDuSgdzkXpcC5Kh3NRWrKQj5ERMejTdmS20M0KSS9GxPhix2HORSlxLkqL81E6nIvS4VyUDueidDgXpSXr+fDQZTMzMzMzM8sUF7pmZmZmZmaWKS50S989xQ7AjnEuSodzUVqcj9LhXJQO56J0OBelw7koLZnOh5/RNTMzMzMzs0zxHV0zMzMzMzPLFBe6BSbpNElrJG2T9Jqkm5PtAyStkvRm8rl/sv0sSRskNUi6pVU7PSW9IOmXSTu3FqtP5SqtXLRqr6uklyX9rNB9KXdp5kLS25JelfSKpBeL0Z9yl3I+Tpb0mKQ3JL0uaVIx+lSuUrxmjEnOiaMfhyR9u1j9KkcpnxffSdrYKukRST2L0adylXIubk7y8JrPify1IRdzJG1JrtPPSTqnVVvTJP1K0nZJPyhWn8pZyvlYJGm/pK3F6k97eehygUkaBgyLiM2SvgC8BFwJXAe8HxH/nJzc/SPi+5IGAyOTY2oi4o6kHQG9I+IDSRXAeuDmiHi+CN0qS2nlolV73wXGA30jYmYh+1Lu0syFpLeB8RFR7uvCFU3K+bgfeDYiFkrqDvSKiN8Wuk/lKu3fU0mbXYHdwISIyMJ68wWR4vV7OLlr9tkRcVjSEuB/I+K/C9+r8pRiLr4M/ASoBI4AK4AFEbG94J0qU23IxYXA6xFRI+ly4O8jYkLye+nXwB8Cu4BNwLURsa0Y/SpXaeUjaesS4APggYj4clE61E6+o1tgEbEnIjYnr2uB14HhwBXA/clh95P7T0lE7I+ITUDjx9qJiPgg+bIi+fC7FnlIKxcAkkYAM4CFBQg9c9LMhbVfWvmQ1A+4BLg3Oe6Ii9z8dNC5MQWocpGbn5Rz0Q04SVI3oBdQ3cHhZ0qKufgSsDEi6iKiCVgL/HEBupAZbcjFcxFRk2x/HhiRvK4EtkfEjog4Qu4NiCsK04vsSDEfRMQ64P0Chd4hXOgWkaRRwLnARmBIROxJdu0FhpzA93eV9AqwH1gVERs7KNTMa28ugDuB7wEtHRFfZ5JCLgJYKeklSX/WIUF2Iu3Mx+nAAeA+5Yb1L5TUu6NizboUzo2jvgE8kmpwnUx7chERu4E7gHeBPcDBiFjZYcFmXDvPi63AxZIGSuoFTAdO66BQM68NubgBWJ68Hg7sbLVvV7LN2qid+cgEF7pFIqkP8Djw7Yg41Hpf5MaTf+7d2Yhojohx5N59qUyG4Fie2psLSTOB/RHxUsdF2TmkcV4AX4mI84DLgW8mQ2+sDVLIRzfgPOA/IuJc4EPAz121QUrnBsnw8VnAo6kH2UmkcM3oT+7uyunAqUBvSXM7KNxMa28uIuJ14F+AleSGLb8CNHdMtNmWby4kXUausPp+wYLsRJyPHBe6RZA8U/s48HBELE0270vG1R8dX7//RNtLhgKuAaalHWvWpZSLi4BZybOhPwG+KumhDgo5s9I6L5K7JUTEfuAJcsOhLE8p5WMXsKvVaJPHyBW+loeUrxmXA5sjYl/6kWZfSrmYCrwVEQciohFYClzYUTFnVYrXjHsj4vyIuASoIfecqOUh31xIGkvuUa8rIuI3yebdfPRu+ohkm+UppXxkggvdAksmkbqX3IPf/9Zq1zJgXvJ6HvDU57QzSNLJyeuTyD28/0b6EWdXWrmIiL+KiBERMYrckMDVEeF35/OQ4nnRO5l8gWSI7NfIDU2zPKR4buwFdkoak2yaAnhikTyklYtWrsXDltskxVy8C0yU1Ctpcwq55+jsBKV5XiQTVSHpi+Sez12cbrTZlm8ukp/zUuBPI6L1mwqbgNGSTk9GnnwjacPykGI+MsGzLheYpK8AzwKv8rvnOf+a3Pj5JcAXgXeA2RHxvqShwItA3+T4D4CzgVHkHibvSu4NiyUR8Q+F60n5SysXrYeESLoUuCU863JeUjwvTiF3Fxdyw2YXR8SPC9WPrEjz3JA0jtw7xd2BHcD8VhNf2OdIORe9yRVZZ0TEwcL2pPylnItbgWuAJuBl4MaIaChkf8pZyrl4FhhIbqKq70bELwramTLXhlwsBK5OtgE0RcT4pK3p5OY86Qos8vU7fynn4xHgUnJ/W+0D/i4i7i1QV1LhQtfMzMzMzMwyxUOXzczMzMzMLFNc6JqZmZmZmVmmuNA1MzMzMzOzTHGha2ZmZmZmZpniQtfMzMzMzMwyxYWumZmZmZmZZYoLXTMzsxIn6VuSXpf0cLFjMTMzKwdeR9fMzKwESBK563LLp+x7A5gaEbsKGE+3iGgq1L9nZmaWJt/RNTMzKxJJoyT9StIDwFbgbyVtkrRF0q3JMf8JnAEsl/SdT2mji6Q3JQ1q9fV2SYOSj8eTNjdJuig5plLSBkkvS3pO0phk+3WSlklaDfyiQD8GMzOz1HUrdgBmZmad3GhgHtAX+BOgEhCwTNIlEbFA0jTgsoh47+PfHBEtkh4C5gB3AlOBX0bEAUmLgX+PiPWSvgg8DXwJeAO4OCKaJE0F/gm4OmnyPGBsRLzfkZ02MzPrSC50zczMiuudiHhe0h3A14CXk+19yBXB606gjUXAU+QK3euB+5LtU4Gzc6OiAegrqQ/QD7hf0mgggIpWba1ykWtmZuXOha6ZmVlxfZh8FnBbRPxXvg1ExE5J+yR9ldwd4TnJri7AxIiob328pLuBNRFxlaRRwDOfEo+ZmVnZ8jO6ZmZmpeFp4PrkjiuShksanMf3LwQeAh6NiOZk20rgL44eIGlc8rIfsDt5fV17gjYzMytFLnTNzMxKQESsBBYDGyS9CjwGfCGPJpaRG+58X6tt3wLGJ5NbbQMWJNv/FbhN0st4dJeZmWWQlxcyMzPLAEnjyU08dXGxYzEzMys2v4trZmZW5iT9APhzfvdsrpmZWafmO7pmZmZlQtJ84OaPbf6/iPhmMeIxMzMrVS50zczMzMzMLFM8GZWZmZmZmZlligtdMzMzMzMzyxQXumZmZmZmZpYpLnTNzMzMzMwsU1zompmZmZmZWab8P8eTAxYLemjAAAAAAElFTkSuQmCC\n",
@@ -63770,31 +65230,30 @@
    "source": [
     "df['ref_year'] = df.last_update_date.dt.year\n",
     "\n",
-    "%matplotlib inline\n",
     "plt.figure(figsize=(16, 6))\n",
-    "sns.violinplot(x='ref_year', y='date_diff', data=df)"
+    "ax = sns.violinplot(x='ref_year', y='date_diff', data=df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "count    1.098965e+07\n",
-       "mean     4.938181e+02\n",
+       "mean     4.956143e+02\n",
        "std      5.441444e+02\n",
-       "min      2.592081e+01\n",
-       "25%      7.723253e+01\n",
-       "50%      2.568040e+02\n",
-       "75%      7.197954e+02\n",
-       "max      2.965898e+03\n",
+       "min      2.771697e+01\n",
+       "25%      7.902869e+01\n",
+       "50%      2.586001e+02\n",
+       "75%      7.215915e+02\n",
+       "max      2.967694e+03\n",
        "Name: date_stale, dtype: float64"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -63809,22 +65268,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f5787bf40>"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x432 with 1 Axes>"
       ]
@@ -63836,29 +65285,18 @@
     }
    ],
    "source": [
-    "%matplotlib inline\n",
     "plt.figure(figsize=(16, 6))\n",
-    "sns.violinplot(x='ref_year', y='date_stale', data=df)"
+    "ax= sns.violinplot(x='ref_year', y='date_stale', data=df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f2550bfa0>"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x432 with 1 Axes>"
       ]
@@ -63870,19 +65308,20 @@
     }
    ],
    "source": [
-    "df['ref_year'] = df.last_update_date.dt.year\n",
+    "df['ref_year'] = df.activation_date.dt.year\n",
     "\n",
-    "%matplotlib inline\n",
     "plt.figure(figsize=(16, 6))\n",
-    "sns.violinplot(x='ref_year', y='date_stale', data=df)"
+    "ax = sns.violinplot(x='ref_year', y='date_stale', data=df)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Todo:\n",
+    "- stale profiles with information initially set are likely to be fake?\n",
+    "- the more info is present"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -63893,7 +65332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -63983,8 +65422,8 @@
            0.06465027511100795,
            0.055840369923933694,
            0.17103410913261785,
-           0.2599894381407623,
-           -0.2656329665247711
+           0.022658381195495434,
+           -0.26563296652477103
           ],
           [
            0.9649829131836175,
@@ -64008,8 +65447,8 @@
            0.06711134086035722,
            0.05792821574427769,
            0.18256342138973428,
-           0.24972700405166764,
-           -0.25465810384977494
+           0.004382401997206513,
+           -0.25465810384977483
           ],
           [
            0.07899833525811681,
@@ -64033,8 +65472,8 @@
            0.11698458214656102,
            0.13164682662703245,
            0.2791714236915975,
-           0.11726253361300581,
-           -0.10956754111570942
+           -0.18761414495336803,
+           -0.10956754111570939
           ],
           [
            0.07259719921935885,
@@ -64058,8 +65497,8 @@
            0.09429723730634967,
            0.10609112655491064,
            0.26550066134398,
-           0.11270344440081602,
-           -0.10498244037738995
+           -0.17773494572670034,
+           -0.10498244037738991
           ],
           [
            0.0064613638682561435,
@@ -64083,8 +65522,8 @@
            0.002403168962484464,
            0.003025339152373764,
            0.019756527828553628,
-           0.011335927414831035,
-           -0.010450007598058798
+           -0.011377251319379719,
+           -0.010450007598058796
           ],
           [
            0.030614701011724112,
@@ -64108,8 +65547,8 @@
            0.03226052420254626,
            0.038540356154355394,
            0.10708605224889314,
-           0.04574404577605237,
-           -0.04234941816473596
+           -0.07142153774150983,
+           -0.04234941816473595
           ],
           [
            0.0606246420123506,
@@ -64133,8 +65572,8 @@
            0.09124719179253188,
            0.10486157551396036,
            0.21995081618224488,
-           0.09600045015556784,
-           -0.08929201861156615
+           -0.1460190598525363,
+           -0.08929201861156616
           ],
           [
            0.1240658917743258,
@@ -64158,7 +65597,7 @@
            0.055010262474370165,
            0.0601029924210191,
            0.5071876289916429,
-           0.24885938796815424,
+           -0.31637218163986053,
            -0.23119989325527732
           ],
           [
@@ -64183,8 +65622,8 @@
            0.042447732334981184,
            0.04161103151234445,
            0.061749919527358296,
-           0.03217571325625221,
-           -0.03146348660346865
+           -0.03657249514552719,
+           -0.03146348660346863
           ],
           [
            0.10712349577355784,
@@ -64208,8 +65647,8 @@
            0.22175814226273408,
            0.19693868136073678,
            0.14482640798133928,
-           0.08747802376691897,
-           -0.08555532728488985
+           -0.07613091173908271,
+           -0.08555532728488978
           ],
           [
            0.14475767748321952,
@@ -64233,8 +65672,8 @@
            0.12439619153280176,
            0.12871309557991262,
            0.44112707302264015,
-           0.171251774934463,
-           -0.16174776203112748
+           -0.3033997683189775,
+           -0.16174776203112778
           ],
           [
            0.10243044622702734,
@@ -64258,8 +65697,8 @@
            0.2239727795739842,
            0.21943621766746263,
            0.20608875126467818,
-           0.08371699764233723,
-           -0.08137348062024245
+           -0.1375367447333582,
+           -0.08137348062024251
           ],
           [
            0.22284375415000315,
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@@ -65452,7 +66891,7 @@
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   {
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+   "execution_count": 121,
    "metadata": {},
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\"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\", \"constrain\": \"domain\", \"domain\": [0.0, 1.0], \"scaleanchor\": \"y\"}, \"yaxis\": {\"anchor\": \"x\", \"autorange\": \"reversed\", \"constrain\": \"domain\", \"domain\": [0.0, 1.0]}},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('9d3c0b64-d22c-4512-9e48-44c286287357');\n",
+       "var gd = document.getElementById('2aa48001-cbe2-4aa6-8f5c-349c06f0a393');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
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@@ -68570,7 +70009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68592,7 +70031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {