diff --git a/all_geo_beds/stats/bed_geo.ipynb b/all_geo_beds/stats/bed_geo.ipynb
new file mode 100644
index 0000000..490518f
--- /dev/null
+++ b/all_geo_beds/stats/bed_geo.ipynb
@@ -0,0 +1,1587 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "f1bde4e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "497b69ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/home/bnt4me/virginia/bedbase_data/bedbase_data_unique.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c0404845",
+   "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>gsm</th>\n",
+       "      <th>sample_name</th>\n",
+       "      <th>genome</th>\n",
+       "      <th>last_update_date</th>\n",
+       "      <th>submission_date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>gsm1000063</td>\n",
+       "      <td>licr_chipseq_kidney_h3k36me3_adult-8wks</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>gsm1000064</td>\n",
+       "      <td>licr_chipseq_smintestine_h3k27me3_adult-8wks</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>gsm1000065</td>\n",
+       "      <td>licr_chipseq_bmdm_h3k4me3_adult-8wks</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>gsm1000066</td>\n",
+       "      <td>licr_chipseq_bmdm_h3k4me1_adult-8wks</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>gsm1000067</td>\n",
+       "      <td>licr_chipseq_testis_h3k36me3_adult-8wks</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117408</th>\n",
+       "      <td>gsm999788</td>\n",
+       "      <td>bap1_flag_chip-seq</td>\n",
+       "      <td>mm9</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117409</th>\n",
+       "      <td>gsm999789</td>\n",
+       "      <td>hcf1_chip-seq</td>\n",
+       "      <td>mm9</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117410</th>\n",
+       "      <td>gsm999790</td>\n",
+       "      <td>ogt1_chip-seq</td>\n",
+       "      <td>mm9</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117411</th>\n",
+       "      <td>gsm999792</td>\n",
+       "      <td>pr-binding_leiomyoma_chip-seq</td>\n",
+       "      <td>hg19</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117412</th>\n",
+       "      <td>gsm999793</td>\n",
+       "      <td>pr-binding_t47d_chip-seq</td>\n",
+       "      <td>hg19</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "      <td>Sep 10 2012</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>117413 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               gsm                                   sample_name genome  \\\n",
+       "0       gsm1000063       licr_chipseq_kidney_h3k36me3_adult-8wks    NaN   \n",
+       "1       gsm1000064  licr_chipseq_smintestine_h3k27me3_adult-8wks    NaN   \n",
+       "2       gsm1000065          licr_chipseq_bmdm_h3k4me3_adult-8wks    NaN   \n",
+       "3       gsm1000066          licr_chipseq_bmdm_h3k4me1_adult-8wks    NaN   \n",
+       "4       gsm1000067       licr_chipseq_testis_h3k36me3_adult-8wks    NaN   \n",
+       "...            ...                                           ...    ...   \n",
+       "117408   gsm999788                            bap1_flag_chip-seq    mm9   \n",
+       "117409   gsm999789                                 hcf1_chip-seq    mm9   \n",
+       "117410   gsm999790                                 ogt1_chip-seq    mm9   \n",
+       "117411   gsm999792                 pr-binding_leiomyoma_chip-seq   hg19   \n",
+       "117412   gsm999793                      pr-binding_t47d_chip-seq   hg19   \n",
+       "\n",
+       "       last_update_date submission_date  \n",
+       "0           Sep 10 2012     Sep 10 2012  \n",
+       "1           Sep 10 2012     Sep 10 2012  \n",
+       "2           Sep 10 2012     Sep 10 2012  \n",
+       "3           Sep 10 2012     Sep 10 2012  \n",
+       "4           Sep 10 2012     Sep 10 2012  \n",
+       "...                 ...             ...  \n",
+       "117408      Sep 10 2012     Sep 10 2012  \n",
+       "117409      Sep 10 2012     Sep 10 2012  \n",
+       "117410      Sep 10 2012     Sep 10 2012  \n",
+       "117411      Sep 10 2012     Sep 10 2012  \n",
+       "117412      Sep 10 2012     Sep 10 2012  \n",
+       "\n",
+       "[117413 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "e25bbc89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"update_year\"] = pd.to_datetime(data[\"last_update_date\"]).dt.strftime('%Y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a3841a06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"submission_year\"] = pd.to_datetime(data[\"submission_date\"]).dt.strftime('%Y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "fe80208a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1 = data.groupby(\"submission_year\", as_index=False).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "d97b35c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data2 = data.groupby(\"update_year\", as_index=False).count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "2a27c529",
+   "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>submission_year</th>\n",
+       "      <th>gsm</th>\n",
+       "      <th>sample_name</th>\n",
+       "      <th>genome</th>\n",
+       "      <th>last_update_date</th>\n",
+       "      <th>submission_date</th>\n",
+       "      <th>update_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2009</td>\n",
+       "      <td>410</td>\n",
+       "      <td>410</td>\n",
+       "      <td>0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>410</td>\n",
+       "      <td>410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>469</td>\n",
+       "      <td>469</td>\n",
+       "      <td>0</td>\n",
+       "      <td>469</td>\n",
+       "      <td>469</td>\n",
+       "      <td>469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>1331</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>4215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1550</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2404</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3645</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8177</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7819</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>6942</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>7101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20639</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10300</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16465</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>14885</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>15500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>9866</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>10225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   submission_year    gsm  sample_name  genome  last_update_date  \\\n",
+       "0             2008     41           41       0                41   \n",
+       "1             2009    410          410       0               410   \n",
+       "2             2010    469          469       0               469   \n",
+       "3             2011   1985         1985      98              1985   \n",
+       "4             2012   4215         4215    1331              4215   \n",
+       "5             2013   1766         1766    1550              1766   \n",
+       "6             2014   2510         2510    2404              2510   \n",
+       "7             2015   3805         3805    3645              3805   \n",
+       "8             2016   8292         8292    8177              8292   \n",
+       "9             2017   7939         7939    7819              7939   \n",
+       "10            2018   7101         7101    6942              7101   \n",
+       "11            2019  20799        20799   20639             20799   \n",
+       "12            2020  10457        10457   10300             10457   \n",
+       "13            2021  16584        16584   16465             16584   \n",
+       "14            2022  15500        15500   14885             15500   \n",
+       "15            2023  10225        10225    9866             10225   \n",
+       "16            2024   5315         5315    5315              5315   \n",
+       "\n",
+       "    submission_date  update_year  \n",
+       "0                41           41  \n",
+       "1               410          410  \n",
+       "2               469          469  \n",
+       "3              1985         1985  \n",
+       "4              4215         4215  \n",
+       "5              1766         1766  \n",
+       "6              2510         2510  \n",
+       "7              3805         3805  \n",
+       "8              8292         8292  \n",
+       "9              7939         7939  \n",
+       "10             7101         7101  \n",
+       "11            20799        20799  \n",
+       "12            10457        10457  \n",
+       "13            16584        16584  \n",
+       "14            15500        15500  \n",
+       "15            10225        10225  \n",
+       "16             5315         5315  "
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "3b88cc7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_count = data1[[\"submission_year\", \"sample_name\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "cf1a3bd1",
+   "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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+       "\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>submission_year</th>\n",
+       "      <th>sample_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2009</td>\n",
+       "      <td>410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>1985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>4215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>1766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>2510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>3805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>20799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>10457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>16584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>15500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>5315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   submission_year  sample_name\n",
+       "0             2008           41\n",
+       "1             2009          410\n",
+       "2             2010          469\n",
+       "3             2011         1985\n",
+       "4             2012         4215\n",
+       "5             2013         1766\n",
+       "6             2014         2510\n",
+       "7             2015         3805\n",
+       "8             2016         8292\n",
+       "9             2017         7939\n",
+       "10            2018         7101\n",
+       "11            2019        20799\n",
+       "12            2020        10457\n",
+       "13            2021        16584\n",
+       "14            2022        15500\n",
+       "15            2023        10225\n",
+       "16            2024         5315"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "646f1691",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "03495e53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         41\n",
+       "1        451\n",
+       "2        920\n",
+       "3       2905\n",
+       "4       7120\n",
+       "5       8886\n",
+       "6      11396\n",
+       "7      15201\n",
+       "8      23493\n",
+       "9      31432\n",
+       "10     38533\n",
+       "11     59332\n",
+       "12     69789\n",
+       "13     86373\n",
+       "14    101873\n",
+       "15    112098\n",
+       "16    117413\n",
+       "Name: sample_name, dtype: int64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_count['sample_name'].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "69a8529a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_142310/635153305.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  data_count['summary'] = data_count['sample_name'].cumsum()\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_count['summary'] = data_count['sample_name'].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9dfbbae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data_count.loc[:, 'summary'] = data_count['sample_name'].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "f280a42f",
+   "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>submission_year</th>\n",
+       "      <th>sample_name</th>\n",
+       "      <th>summary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2009</td>\n",
+       "      <td>410</td>\n",
+       "      <td>451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>469</td>\n",
+       "      <td>920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>2905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>7120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>8886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>11396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>15201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>23493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>31432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>38533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>59332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>69789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>86373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>101873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>112098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>117413</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   submission_year  sample_name  summary\n",
+       "0             2008           41       41\n",
+       "1             2009          410      451\n",
+       "2             2010          469      920\n",
+       "3             2011         1985     2905\n",
+       "4             2012         4215     7120\n",
+       "5             2013         1766     8886\n",
+       "6             2014         2510    11396\n",
+       "7             2015         3805    15201\n",
+       "8             2016         8292    23493\n",
+       "9             2017         7939    31432\n",
+       "10            2018         7101    38533\n",
+       "11            2019        20799    59332\n",
+       "12            2020        10457    69789\n",
+       "13            2021        16584    86373\n",
+       "14            2022        15500   101873\n",
+       "15            2023        10225   112098\n",
+       "16            2024         5315   117413"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "27e7821d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0. , 0.2, 0.4, 0.6, 0.8, 1. ]),\n",
+       " [Text(0.0, 0, '0.0'),\n",
+       "  Text(0.2, 0, '0.2'),\n",
+       "  Text(0.4, 0, '0.4'),\n",
+       "  Text(0.6000000000000001, 0, '0.6'),\n",
+       "  Text(0.8, 0, '0.8'),\n",
+       "  Text(1.0, 0, '1.0')])"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "plt.xticks(rotation=45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "24d08f27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Cumulative number of BED files')"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.bar(data_count[\"submission_year\"], \n",
+    "       data_count[\"summary\"], \n",
+    "       label=data_count[\"submission_year\"], \n",
+    "       color=\"green\")\n",
+    "\n",
+    "ax.set_xlabel('Year')\n",
+    "ax.set_ylabel('Number of files')\n",
+    "ax.set_title('Cumulative number of BED files')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "106e9361",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rl3MOiSqZK1euiLFjx4p69eoJU1NTYWVlJTp37iy+/vprjVPLHz16JPz9/YWNjY2wsrISQ4YMEampqc89jf/evXsadfz8/ISlpWWR+t27dxfNmzfXWHbt2jXh7e0tzMzMhKOjo5gxY4aIiorS6pIKa9euFY0aNRJmZmbCzc1NrFu3TupTYZcvXxbdunUTFhYWAoB0KYHnXdJBCCFGjhwpAAhvb+/nPp7btm0TXbp0EZaWlsLS0lK4ubmJgIAAkZCQ8Ny/EeL5j1tx/Smr50J9Cv+mTZtEcHCwqFmzprCwsBD9+vWTLiVR2JkzZ8TAgQOFg4ODMDMzEy4uLmLIkCFi//79L+zTf7l27ZoYPHiwsLW1Febm5qJDhw5i165dRdqhFJdUMDY2FnXq1BHjxo0TKSkpGm3/65IKhZ8b9eOlvlWpUkXUqFFDdOvWTcybN0+kpqZqPebixqLtZUWEEGL16tWiXbt2wsLCQlhZWQl3d3cxdepUcefOHSGEEKdPnxbDhw8Xr7zyijAzMxM1a9YU/fv3F6dOndK6j1S5KIQowRGVRERERKSBx1QRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAi3+WIZVKhTt37sDKyqpUP0xLREREZUcIgYcPH8LZ2fk/L5bLUFWG7ty5g7p165Z3N4iIiKgEbt++jTp16jx3PUNVGbKysgLw9EmxtrYu594QERGRNpRKJerWrSt9jj8PQ1UZUu/ys7a2ZqgiIiIyMC86dIcHqhMRERHJgKGKiIiISAYMVUREREQyKNdQFR0djQEDBsDZ2RkKhQI7duyQ1uXl5WHatGlwd3eHpaUlnJ2dMWrUKNy5c0djG2lpaRg5ciSsra1ha2sLf39/ZGVlabQ5d+4cunbtCnNzc9StWxdhYWFF+rJ161a4ubnB3Nwc7u7u2LNnj8Z6IQRCQkJQq1YtWFhYwNvbG4mJifI9GERERGTQyjVUZWdno1WrVli5cmWRdY8ePcLp06cxc+ZMnD59Gr/++isSEhLwxhtvaLQbOXIk4uPjERUVhV27diE6Ohrjxo2T1iuVSvTq1QsuLi6IjY3F4sWLMWvWLKxevVpqc/ToUQwfPhz+/v44c+YMfH194evriwsXLkhtwsLCsGLFCoSHh+P48eOwtLSEj48PcnJy9PDIEBERkcERFQQAsX379v9sc+LECQFA3Lx5UwghxMWLFwUAcfLkSanNH3/8IRQKhfj333+FEEKsWrVK2NnZiSdPnkhtpk2bJpo0aSLdHzJkiOjXr59GLU9PTzF+/HghhBAqlUo4OTmJxYsXS+szMjKEmZmZ2LRpk9ZjzMzMFABEZmam1n9DRERE5Uvbz2+DOqYqMzMTCoUCtra2AICYmBjY2trCw8NDauPt7Q0jIyMcP35catOtWzeYmppKbXx8fJCQkID09HSpjbe3t0YtHx8fxMTEAACSkpKQnJys0cbGxgaenp5Sm+I8efIESqVS40ZERESVk8GEqpycHEybNg3Dhw+XrvGUnJyMmjVrarQzMTGBvb09kpOTpTaOjo4abdT3X9Sm8PrCf1dcm+IsWLAANjY20o1XUyciIqq8DCJU5eXlYciQIRBC4Ntvvy3v7mgtODgYmZmZ0u327dvl3SUiIiLSkwp/RXV1oLp58yYOHDigcSVyJycnpKamarTPz89HWloanJycpDYpKSkabdT3X9Sm8Hr1slq1amm0ad269XP7bmZmBjMzM12GS0RERAaqQs9UqQNVYmIi/vzzTzg4OGis9/LyQkZGBmJjY6VlBw4cgEqlgqenp9QmOjoaeXl5UpuoqCg0adIEdnZ2Upv9+/drbDsqKgpeXl4AAFdXVzg5OWm0USqVOH78uNSGiIiIXm7lGqqysrIQFxeHuLg4AE8PCI+Li8OtW7eQl5eHwYMH49SpU9iwYQMKCgqQnJyM5ORk5ObmAgCaNm2K3r17Y+zYsThx4gSOHDmCwMBADBs2DM7OzgCAESNGwNTUFP7+/oiPj8fmzZuxfPlyBAUFSf2YOHEiIiMjsWTJEly+fBmzZs3CqVOnEBgYCODpb/1MmjQJc+fOxe+//47z589j1KhRcHZ2hq+vb5k+ZkRERFRBlc3JiMU7ePCgAFDk5ufnJ5KSkopdB0AcPHhQ2saDBw/E8OHDRbVq1YS1tbUYPXq0ePjwoUads2fPii5duggzMzNRu3ZtsXDhwiJ92bJli2jcuLEwNTUVzZs3F7t379ZYr1KpxMyZM4Wjo6MwMzMTPXv2FAkJCTqNl5dUICIiMjzafn4rhBCiXNLcS0ipVMLGxgaZmZkax4YRERFRxaXt53eFPqaKiIiIyFBU+LP/iIiIqPJRzFbIvk0RWr473zhTRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDEzKuwNERERUMShmK/SyXREq9LLdioYzVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDk/LuABERET2fYrZCL9sVoUIv232ZcaaKiIiISAblGqqio6MxYMAAODs7Q6FQYMeOHRrrhRAICQlBrVq1YGFhAW9vbyQmJmq0SUtLw8iRI2FtbQ1bW1v4+/sjKytLo825c+fQtWtXmJubo27duggLCyvSl61bt8LNzQ3m5uZwd3fHnj17dO4LERERvbzKNVRlZ2ejVatWWLlyZbHrw8LCsGLFCoSHh+P48eOwtLSEj48PcnJypDYjR45EfHw8oqKisGvXLkRHR2PcuHHSeqVSiV69esHFxQWxsbFYvHgxZs2ahdWrV0ttjh49iuHDh8Pf3x9nzpyBr68vfH19ceHCBZ36QkRERC8vhRCiQuxUVSgU2L59O3x9fQE8nRlydnbGJ598gilTpgAAMjMz4ejoiIiICAwbNgyXLl1Cs2bNcPLkSXh4eAAAIiMj0bdvX/zzzz9wdnbGt99+i88++wzJyckwNTUFAEyfPh07duzA5cuXAQBDhw5FdnY2du3aJfWnY8eOaN26NcLDw7XqizaUSiVsbGyQmZkJa2trWR43IiKq3MrymCpDr6Wv48S0/fyusMdUJSUlITk5Gd7e3tIyGxsbeHp6IiYmBgAQExMDW1tbKVABgLe3N4yMjHD8+HGpTbdu3aRABQA+Pj5ISEhAenq61KZwHXUbdR1t+lKcJ0+eQKlUatyIiIiocqqwoSo5ORkA4OjoqLHc0dFRWpecnIyaNWtqrDcxMYG9vb1Gm+K2UbjG89oUXv+ivhRnwYIFsLGxkW5169Z9waiJiIjIUFXYUFUZBAcHIzMzU7rdvn27vLtEREREelJhQ5WTkxMAICUlRWN5SkqKtM7JyQmpqaka6/Pz85GWlqbRprhtFK7xvDaF17+oL8UxMzODtbW1xo2IiIgqpwobqlxdXeHk5IT9+/dLy5RKJY4fPw4vLy8AgJeXFzIyMhAbGyu1OXDgAFQqFTw9PaU20dHRyMvLk9pERUWhSZMmsLOzk9oUrqNuo66jTV+IiIjo5VauoSorKwtxcXGIi4sD8PSA8Li4ONy6dQsKhQKTJk3C3Llz8fvvv+P8+fMYNWoUnJ2dpTMEmzZtit69e2Ps2LE4ceIEjhw5gsDAQAwbNgzOzs4AgBEjRsDU1BT+/v6Ij4/H5s2bsXz5cgQFBUn9mDhxIiIjI7FkyRJcvnwZs2bNwqlTpxAYGAgAWvWFiIiIXm7l+jM1p06dwquvvirdVwcdPz8/REREYOrUqcjOzsa4ceOQkZGBLl26IDIyEubm5tLfbNiwAYGBgejZsyeMjIwwaNAgrFixQlpvY2ODffv2ISAgAO3atUP16tUREhKicS2rTp06YePGjfj8888xY8YMNGrUCDt27ECLFi2kNtr0hYiIiF5eFeY6VS8DXqeKiIh0ZejXjirLWrxOFREREVElwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA5Py7gAREZGhUcxW6GW7IlToZbtUNjhTRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpJBhQ5VBQUFmDlzJlxdXWFhYYEGDRpgzpw5EEJIbYQQCAkJQa1atWBhYQFvb28kJiZqbCctLQ0jR46EtbU1bG1t4e/vj6ysLI02586dQ9euXWFubo66desiLCysSH+2bt0KNzc3mJubw93dHXv27NHPwImIiMjgVOhQtWjRInz77bf45ptvcOnSJSxatAhhYWH4+uuvpTZhYWFYsWIFwsPDcfz4cVhaWsLHxwc5OTlSm5EjRyI+Ph5RUVHYtWsXoqOjMW7cOGm9UqlEr1694OLigtjYWCxevBizZs3C6tWrpTZHjx7F8OHD4e/vjzNnzsDX1xe+vr64cOFC2TwYREREVKEpROFpnwqmf//+cHR0xNq1a6VlgwYNgoWFBX766ScIIeDs7IxPPvkEU6ZMAQBkZmbC0dERERERGDZsGC5duoRmzZrh5MmT8PDwAABERkaib9+++Oeff+Ds7Ixvv/0Wn332GZKTk2FqagoAmD59Onbs2IHLly8DAIYOHYrs7Gzs2rVL6kvHjh3RunVrhIeHazUepVIJGxsbZGZmwtraWpbHiIiIyp5itkIv2xWhRT+SWat0deSg7ed3hZ6p6tSpE/bv348rV64AAM6ePYvDhw+jT58+AICkpCQkJyfD29tb+hsbGxt4enoiJiYGABATEwNbW1spUAGAt7c3jIyMcPz4calNt27dpEAFAD4+PkhISEB6errUpnAddRt1neI8efIESqVS40ZERESVk0l5d+C/TJ8+HUqlEm5ubjA2NkZBQQHmzZuHkSNHAgCSk5MBAI6Ojhp/5+joKK1LTk5GzZo1NdabmJjA3t5eo42rq2uRbajX2dnZITk5+T/rFGfBggWYPXu2rsMmIiIiA1ShZ6q2bNmCDRs2YOPGjTh9+jTWr1+PL7/8EuvXry/vrmklODgYmZmZ0u327dvl3SUiIiLSkwo9U/Xpp59i+vTpGDZsGADA3d0dN2/exIIFC+Dn5wcnJycAQEpKCmrVqiX9XUpKClq3bg0AcHJyQmpqqsZ28/PzkZaWJv29k5MTUlJSNNqo77+ojXp9cczMzGBmZqbrsImIiMgA6TxTdfr0aZw/f166/9tvv8HX1xczZsxAbm6urJ179OgRjIw0u2hsbAyVSgUAcHV1hZOTE/bv3y+tVyqVOH78OLy8vAAAXl5eyMjIQGxsrNTmwIEDUKlU8PT0lNpER0cjLy9PahMVFYUmTZrAzs5OalO4jrqNug4RERG93HQOVePHj5cOHL9+/TqGDRuGqlWrYuvWrZg6daqsnRswYADmzZuH3bt348aNG9i+fTuWLl2Kt956CwCgUCgwadIkzJ07F7///jvOnz+PUaNGwdnZGb6+vgCApk2bonfv3hg7dixOnDiBI0eOIDAwEMOGDYOzszMAYMSIETA1NYW/vz/i4+OxefNmLF++HEFBQVJfJk6ciMjISCxZsgSXL1/GrFmzcOrUKQQGBso6ZiIiIjJMOu/+u3LlirRrbevWrejWrRs2btyII0eOYNiwYVi2bJlsnfv6668xc+ZMfPTRR0hNTYWzszPGjx+PkJAQqc3UqVORnZ2NcePGISMjA126dEFkZCTMzc2lNhs2bEBgYCB69uwJIyMjDBo0CCtWrJDW29jYYN++fQgICEC7du1QvXp1hISEaFzLqlOnTti4cSM+//xzzJgxA40aNcKOHTvQokUL2cZLREREhkvn61RZW1sjNjYWjRo1wuuvv47+/ftj4sSJuHXrFpo0aYLHjx/rq68Gj9epIiKqHAz9ek6VtZbBXafKw8MDc+fOxY8//oi///4b/fr1A/D0mlHPXnKAiIiI6GWhc6hatmwZTp8+jcDAQHz22Wdo2LAhAOCXX35Bp06dZO8gERERkSHQ+Ziqli1bapz9p7Z48WIYGxvL0ikiIiIiQ1Oii39mZGRgzZo1CA4ORlpaGgDg4sWLRa4HRURERPSy0Hmm6ty5c+jZsydsbW1x48YNjB07Fvb29vj1119x69Yt/PDDD/roJxEREVGFpvNMVVBQEEaPHo3ExESNyxb07dsX0dHRsnaOiIiIyFDoHKpOnjyJ8ePHF1leu3bt//xxYSIiIqLKTOdQZWZmBqVSWWT5lStXUKNGDVk6RURERGRodA5Vb7zxBr744gvpd/IUCgVu3bqFadOmYdCgQbJ3kIiIiMgQ6ByqlixZgqysLNSsWROPHz9G9+7d0bBhQ1hZWWHevHn66CMRERFRhafz2X82NjaIiorC4cOHce7cOWRlZaFt27bw9vbWR/+IiIiIDILOoUqtS5cu6NKli5x9ISIiIjJYWoWqFStWaL3BCRMmlLgzRERERIZKq1D11VdfabUxhULBUEVEREQvJa1CVVJSkr77QURERGTQSvTbf0RERESkSauZqqCgIMyZMweWlpYICgr6z7ZLly6VpWNEREREhkSrUHXmzBnpYp+nT5+GQqEott3zlhMRERFVdlqFquXLl8Pa2hoA8Ndff+mzP0REREQGSatjqtq0aYP79+8DAOrXr48HDx7otVNEREREhkarUGVrayudAXjjxg2oVCq9doqIiIjI0Gi1+2/QoEHo3r07atWqBYVCAQ8PDxgbGxfb9vr167J2kIiIiMgQaBWqVq9ejYEDB+Lq1auYMGECxo4dCysrK333jYiIiMhgaP3bf7179wYAxMbGYuLEiQxVRERERIXo/IPK69at00c/iIiISk0xW/5L+4hQIfs2qXLiFdWJiIiIZMBQRURERCQDhioiIiIiGWgVqtq2bYv09HQAwBdffIFHjx7ptVNEREREhkarUHXp0iVkZ2cDAGbPno2srCy9doqIiIjI0Gh19l/r1q0xevRodOnSBUIIfPnll6hWrVqxbUNCQmTtIBEREZEh0CpURUREIDQ0FLt27YJCocAff/wBE5Oif6pQKBiqiIiI6KWkVahq0qQJfv75ZwCAkZER9u/fj5o1a+q1Y0RERESGROeLf/LHlImIiIiK0jlUAcC1a9ewbNkyXLp0CQDQrFkzTJw4EQ0aNJC1c0RERESGQufrVO3duxfNmjXDiRMn0LJlS7Rs2RLHjx9H8+bNERUVpY8+EhEREVV4Os9UTZ8+HZMnT8bChQuLLJ82bRpef/112TpHREREZCh0nqm6dOkS/P39iywfM2YMLl68KEuniIiIiAyNzqGqRo0aiIuLK7I8Li6OZwQSERHRS0vn3X9jx47FuHHjcP36dXTq1AkAcOTIESxatAhBQUGyd5CIiIjIEOgcqmbOnAkrKyssWbIEwcHBAABnZ2fMmjULEyZMkL2DRERERIZA51ClUCgwefJkTJ48GQ8fPgQAWFlZyd4xIiIiIkNSoutUqTFMERERET2l84HqRERERFQUQxURERGRDBiqiIiIiGSgU6jKy8tDz549kZiYqK/+EBERERkknUJVlSpVcO7cOX31hYiIiMhg6bz775133sHatWv10RciIiIig6VzqMrPz8e3334LDw8PjB8/HkFBQRo3uf37779455134ODgAAsLC7i7u+PUqVPSeiEEQkJCUKtWLVhYWMDb27vI7sm0tDSMHDkS1tbWsLW1hb+/P7KysjTanDt3Dl27doW5uTnq1q2LsLCwIn3ZunUr3NzcYG5uDnd3d+zZs0f28RIREZFh0jlUXbhwAW3btoWVlRWuXLmCM2fOSLfifhOwNNLT09G5c2dUqVIFf/zxBy5evIglS5bAzs5OahMWFoYVK1YgPDwcx48fh6WlJXx8fJCTkyO1GTlyJOLj4xEVFYVdu3YhOjoa48aNk9YrlUr06tULLi4uiI2NxeLFizFr1iysXr1aanP06FEMHz4c/v7+OHPmDHx9feHr64sLFy7IOmYiIiIyTAohhCjvTjzP9OnTceTIERw6dKjY9UIIODs745NPPsGUKVMAAJmZmXB0dERERASGDRuGS5cuoVmzZjh58iQ8PDwAAJGRkejbty/++ecfODs749tvv8Vnn32G5ORkmJqaSrV37NiBy5cvAwCGDh2K7Oxs7Nq1S6rfsWNHtG7dGuHh4VqNR6lUwsbGBpmZmbC2ti7x40JERMVTzFbIvk0RWvRjUh91WEs/deSg7ed3iS+pcPXqVezduxePHz8G8DTgyO3333+Hh4cH3n77bdSsWRNt2rTBd999J61PSkpCcnIyvL29pWU2Njbw9PRETEwMACAmJga2trZSoAIAb29vGBkZ4fjx41Kbbt26SYEKAHx8fJCQkID09HSpTeE66jbqOsV58uQJlEqlxo2IiIgqJ51D1YMHD9CzZ080btwYffv2xd27dwEA/v7++OSTT2Tt3PXr1/Htt9+iUaNG2Lt3Lz788ENMmDAB69evBwAkJycDABwdHTX+ztHRUVqXnJyMmjVraqw3MTGBvb29RpvitlG4xvPaqNcXZ8GCBbCxsZFudevW1Wn8REREZDh0DlWTJ09GlSpVcOvWLVStWlVaPnToUERGRsraOZVKhbZt22L+/Plo06YNxo0bh7Fjx2q9u628BQcHIzMzU7rdvn27vLtEREREeqLzDyrv27cPe/fuRZ06dTSWN2rUCDdv3pStYwBQq1YtNGvWTGNZ06ZNsW3bNgCAk5MTACAlJQW1atWS2qSkpKB169ZSm9TUVI1t5OfnIy0tTfp7JycnpKSkaLRR339RG/X64piZmcHMzEyrsRIRVVaGdOwMUWnoPFOVnZ2tMUOllpaWJnuA6Ny5MxISEjSWXblyBS4uLgAAV1dXODk5Yf/+/dJ6pVKJ48ePw8vLCwDg5eWFjIwMxMbGSm0OHDgAlUoFT09PqU10dDTy8vKkNlFRUWjSpIl0pqGXl5dGHXUbdR0iIiJ6uekcqrp27YoffvhBuq9QKKBSqRAWFoZXX31V1s5NnjwZx44dw/z583H16lVs3LgRq1evRkBAgFR70qRJmDt3Ln7//XecP38eo0aNgrOzM3x9fQE8ndnq3bs3xo4dixMnTuDIkSMIDAzEsGHD4OzsDAAYMWIETE1N4e/vj/j4eGzevBnLly/XuO7WxIkTERkZiSVLluDy5cuYNWsWTp06hcDAQFnHTERERIZJ591/YWFh6NmzJ06dOoXc3FxMnToV8fHxSEtLw5EjR2TtXPv27bF9+3YEBwfjiy++gKurK5YtW4aRI0dKbaZOnYrs7GyMGzcOGRkZ6NKlCyIjI2Fubi612bBhAwIDA9GzZ08YGRlh0KBBWLFihbTexsYG+/btQ0BAANq1a4fq1asjJCRE41pWnTp1wsaNG/H5559jxowZaNSoEXbs2IEWLVrIOmYiIiIyTCW6TlVmZia++eYbnD17FllZWWjbti0CAgI0jmuionidKiJ6GZXlMVW8TtXLXau8r1Ol80wV8HRm57PPPitx54iIiIgqmxKFqvT0dKxduxaXLl0CADRr1gyjR4+Gvb29rJ0jIiIiMhQ6H6geHR2NevXqYcWKFUhPT0d6ejpWrFgBV1dXREdH66OPRERERBWezjNVAQEBGDp0KL799lsYGxsDAAoKCvDRRx8hICAA58+fl72TRERERBWdzjNVV69exSeffCIFKgAwNjZGUFAQrl69KmvniIiIiAyFzqGqbdu20rFUhV26dAmtWrWSpVNEREREhkar3X/nzp2T/j1hwgRMnDgRV69eRceOHQEAx44dw8qVK7Fw4UL99JKIiIiogtMqVLVu3RoKhQKFL2k1derUIu1GjBiBoUOHytc7IiIiIgOhVahKSkrSdz+IiIiIDJpWoUr9A8ZEREREVLwSXfzzzp07OHz4MFJTU6FSqTTWTZgwQZaOERERERkSnUNVREQExo8fD1NTUzg4OECh+L/f7lEoFAxVRERE9FLSOVTNnDkTISEhCA4OhpGRzldkICIiIqqUdE5Fjx49wrBhwxioiIiIiArRORn5+/tj69at+ugLERERkcHSefffggUL0L9/f0RGRsLd3R1VqlTRWL906VLZOkdERERkKEoUqvbu3YsmTZoAQJED1YmIiIheRjqHqiVLluD777/He++9p4fuEBERERkmnY+pMjMzQ+fOnfXRFyIiIiKDpXOomjhxIr7++mt99IWIiIjIYOm8++/EiRM4cOAAdu3ahebNmxc5UP3XX3+VrXNEREREhkLnUGVra4uBAwfqoy9EREREBkvnULVu3Tp99IOIiIjIoPGy6EREREQy0HmmytXV9T+vR3X9+vVSdYiIiIjIEOkcqiZNmqRxPy8vD2fOnEFkZCQ+/fRTufpFREREZFB0DlUTJ04sdvnKlStx6tSpUneIiIiIyBDJdkxVnz59sG3bNrk2R0RERGRQZAtVv/zyC+zt7eXaHBEREZFB0Xn3X5s2bTQOVBdCIDk5Gffu3cOqVatk7RwRERGRodA5VPn6+mrcNzIyQo0aNdCjRw+4ubnJ1S8iIiIig6JzqAoNDdVHP4iIiIgMGi/+SURERCQDrWeqjIyM/vOinwCgUCiQn59f6k4RERERGRqtQ9X27dufuy4mJgYrVqyASqWSpVNEREREhkbrUPXmm28WWZaQkIDp06dj586dGDlyJL744gtZO0dERERkKEp0TNWdO3cwduxYuLu7Iz8/H3FxcVi/fj1cXFzk7h8RERGRQdApVGVmZmLatGlo2LAh4uPjsX//fuzcuRMtWrTQV/+IiIiIDILWu//CwsKwaNEiODk5YdOmTcXuDiQiIiJ6WWkdqqZPnw4LCws0bNgQ69evx/r164tt9+uvv8rWOSIiIiJDoXWoGjVq1AsvqUBERET0stI6VEVEROixG0RERESGjVdUJyIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYGFaoWLlwIhUKBSZMmSctycnIQEBAABwcHVKtWDYMGDUJKSorG3926dQv9+vVD1apVUbNmTXz66afIz8/XaPPXX3+hbdu2MDMzQ8OGDYu9LtfKlStRr149mJubw9PTEydOnNDHMImIiMgAGUyoOnnyJP73v/+hZcuWGssnT56MnTt3YuvWrfj7779x584dDBw4UFpfUFCAfv36ITc3F0ePHsX69esRERGBkJAQqU1SUhL69euHV199FXFxcZg0aRLef/997N27V2qzefNmBAUFITQ0FKdPn0arVq3g4+OD1NRU/Q+eiIiIKjyDCFVZWVkYOXIkvvvuO9jZ2UnLMzMzsXbtWixduhSvvfYa2rVrh3Xr1uHo0aM4duwYAGDfvn24ePEifvrpJ7Ru3Rp9+vTBnDlzsHLlSuTm5gIAwsPD4erqiiVLlqBp06YIDAzE4MGD8dVXX0m1li5dirFjx2L06NFo1qwZwsPDUbVqVXz//fdl+2AQERFRhWQQoSogIAD9+vWDt7e3xvLY2Fjk5eVpLHdzc8Mrr7yCmJgYAEBMTAzc3d3h6OgotfHx8YFSqUR8fLzU5tlt+/j4SNvIzc1FbGysRhsjIyN4e3tLbYrz5MkTKJVKjRsRERFVTlr/9l95+fnnn3H69GmcPHmyyLrk5GSYmprC1tZWY7mjoyOSk5OlNoUDlXq9et1/tVEqlXj8+DHS09NRUFBQbJvLly8/t+8LFizA7NmztRsoERERGbQKPVN1+/ZtTJw4ERs2bIC5uXl5d0dnwcHByMzMlG63b98u7y4RERGRnlToUBUbG4vU1FS0bdsWJiYmMDExwd9//40VK1bAxMQEjo6OyM3NRUZGhsbfpaSkwMnJCQDg5ORU5GxA9f0XtbG2toaFhQWqV68OY2PjYtuot1EcMzMzWFtba9yIiIiocqrQoapnz544f/484uLipJuHhwdGjhwp/btKlSrYv3+/9DcJCQm4desWvLy8AABeXl44f/68xll6UVFRsLa2RrNmzaQ2hbehbqPehqmpKdq1a6fRRqVSYf/+/VIbIiIierlV6GOqrKys0KJFC41llpaWcHBwkJb7+/sjKCgI9vb2sLa2xscffwwvLy907NgRANCrVy80a9YM7777LsLCwpCcnIzPP/8cAQEBMDMzAwB88MEH+OabbzB16lSMGTMGBw4cwJYtW7B7926pblBQEPz8/ODh4YEOHTpg2bJlyM7OxujRo8vo0SAiko9itkL2bYpQIfs2iQxJhQ5V2vjqq69gZGSEQYMG4cmTJ/Dx8cGqVauk9cbGxti1axc+/PBDeHl5wdLSEn5+fvjiiy+kNq6urti9ezcmT56M5cuXo06dOlizZg18fHykNkOHDsW9e/cQEhKC5ORktG7dGpGRkUUOXiciIqKXk0IIwa8WZUSpVMLGxgaZmZk8voqIylVZzlRVxlr6qMNa+qkjB20/vyv0MVVEREREhoKhioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBgxVRERERDIwKe8OEBHRU4rZCtm3KUKF7NskouJxpoqIiIhIBgxVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQDk/LuABFRRaaYrdDLdkWo0Mt2iaj8cKaKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZFChQ9WCBQvQvn17WFlZoWbNmvD19UVCQoJGm5ycHAQEBMDBwQHVqlXDoEGDkJKSotHm1q1b6NevH6pWrYqaNWvi008/RX5+vkabv/76C23btoWZmRkaNmyIiIiIIv1ZuXIl6tWrB3Nzc3h6euLEiROyj5mIiIgMU4UOVX///TcCAgJw7NgxREVFIS8vD7169UJ2drbUZvLkydi5cye2bt2Kv//+G3fu3MHAgQOl9QUFBejXrx9yc3Nx9OhRrF+/HhEREQgJCZHaJCUloV+/fnj11VcRFxeHSZMm4f3338fevXulNps3b0ZQUBBCQ0Nx+vRptGrVCj4+PkhNTS2bB4OIiIgqtAp9narIyEiN+xEREahZsyZiY2PRrVs3ZGZmYu3atdi4cSNee+01AMC6devQtGlTHDt2DB07dsS+fftw8eJF/Pnnn3B0dETr1q0xZ84cTJs2DbNmzYKpqSnCw8Ph6uqKJUuWAACaNm2Kw4cP46uvvoKPjw8AYOnSpRg7dixGjx4NAAgPD8fu3bvx/fffY/r06WX4qBAREVFFVKFnqp6VmZkJALC3twcAxMbGIi8vD97e3lIbNzc3vPLKK4iJiQEAxMTEwN3dHY6OjlIbHx8fKJVKxMfHS20Kb0PdRr2N3NxcxMbGarQxMjKCt7e31IaIiIhebhV6pqowlUqFSZMmoXPnzmjRogUAIDk5GaamprC1tdVo6+joiOTkZKlN4UClXq9e919tlEolHj9+jPT0dBQUFBTb5vLly8/t85MnT/DkyRPpvlKp1GHEREREZEgMZqYqICAAFy5cwM8//1zeXdHaggULYGNjI93q1q1b3l0iIiIiPTGIUBUYGIhdu3bh4MGDqFOnjrTcyckJubm5yMjI0GifkpICJycnqc2zZwOq77+ojbW1NSwsLFC9enUYGxsX20a9jeIEBwcjMzNTut2+fVu3gRMREZHBqNChSgiBwMBAbN++HQcOHICrq6vG+nbt2qFKlSrYv3+/tCwhIQG3bt2Cl5cXAMDLywvnz5/XOEsvKioK1tbWaNasmdSm8DbUbdTbMDU1Rbt27TTaqFQq7N+/X2pTHDMzM1hbW2vciIiIqHKq0MdUBQQEYOPGjfjtt99gZWUlHQNlY2MDCwsL2NjYwN/fH0FBQbC3t4e1tTU+/vhjeHl5oWPHjgCAXr16oVmzZnj33XcRFhaG5ORkfP755wgICICZmRkA4IMPPsA333yDqVOnYsyYMThw4AC2bNmC3bt3S30JCgqCn58fPDw80KFDByxbtgzZ2dnS2YBERET0cqvQoerbb78FAPTo0UNj+bp16/Dee+8BAL766isYGRlh0KBBePLkCXx8fLBq1SqprbGxMXbt2oUPP/wQXl5esLS0hJ+fH7744gupjaurK3bv3o3Jkydj+fLlqFOnDtasWSNdTgEAhg4dinv37iEkJATJyclo3bo1IiMjixy8TkRERC+nCh2qhBAvbGNubo6VK1di5cqVz23j4uKCPXv2/Od2evTogTNnzvxnm8DAQAQGBr6wT0RERPTyqdDHVBEREREZCoYqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZFChL/5JRFQcxWyFXrYrQl98wWEioufhTBURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiIiIZMBQRURERCQD/vYfEcmCv8dHRC87zlQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTASyoQVXL6uNQBL3NARFQUZ6qIiIiIZMBQRURERCQDhioiIiIiGTBUEREREcmAoYqIiIhIBjz7j6gc8Iw8IqLKhzNVRERERDJgqCIiIiKSAUMVERERkQwYqoiIiIhkwFBFREREJAOGKiIiIiIZMFQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDPjbf0T/H3+Pj4iISoMzVUREREQyYKgiIiIikgFDFREREZEMGKp0tHLlStSrVw/m5ubw9PTEiRMnyrtLREREVAEwVOlg8+bNCAoKQmhoKE6fPo1WrVrBx8cHqamp5d01IiIiKmc8+08HS5cuxdixYzF69GgAQHh4OHbv3o3vv/8e06dPL+feVU76OCMP4Fl5REQkP85UaSk3NxexsbHw9vaWlhkZGcHb2xsxMTHl2DMiIiKqCDhTpaX79++joKAAjo6OGssdHR1x+fLlYv/myZMnePLkiXQ/MzMTAKBUKvXX0TJgs8BGL9vNDM4sujBHL6WKfw70UOu5z3VlrGXgz1VlrVXur4vKWsvAXxeVtZa+Pl/V2xXiBXs5BGnl33//FQDE0aNHNZZ/+umnokOHDsX+TWhoqADAG2+88cYbb7xVgtvt27f/MytwpkpL1atXh7GxMVJSUjSWp6SkwMnJqdi/CQ4ORlBQkHRfpVIhLS0NDg4OUCj0c6zQiyiVStStWxe3b9+GtbU1a7FWmdZhLdYq7zqsxVolIYTAw4cP4ezs/J/tGKq0ZGpqinbt2mH//v3w9fUF8DQk7d+/H4GBgcX+jZmZGczMzDSW2dra6rmn2rG2ti6zFydrGU6tyjgm1jKsWpVxTKxleLWKY2Nj88I2DFU6CAoKgp+fHzw8PNChQwcsW7YM2dnZ0tmARERE9PJiqNLB0KFDce/ePYSEhCA5ORmtW7dGZGRkkYPXiYiI6OXDUKWjwMDA5+7uMwRmZmYIDQ0tsluStV7uWpVxTKxlWLUq45hYy/BqlZZCiBedH0hEREREL8KLfxIRERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFb0UeD5G6ZXlY1hQUFBmtcqCSqUqs1o5OTllXrOsXhv8f2w4yvK5KsvX+oswVJEkOzsbubm5SE9PB1B2L1R9/ud79sO5LMZU2d74c3NzAUD6cXB9ji85ORkAYGxsrNdglZiYiLi4OL1tv7Br167hm2++wb179/Re6+LFi3Bzc8PZs2dhZKTft3elUon09HQkJydDoVDo9f9Wfn4+gP977emz1rOv78r2/1nf1P9vy+Jxu3//PgDAyMiownwRY6giAE/fjIcMGYIePXrAx8cHx44d08ubckJCAkJDQ/Hee+/hm2++wfnz56FQKPTyH/DSpUv4+OOP4evrixkzZiA2NlZvHzT//PMPYmNjAUDvv+t49epVzJ8/H35+flizZg1u3Liht1qXL1/G+PHj4ePjg/Hjx+PChQt6G9+1a9fg7OyMvn37AtBfsDp79iyaNGmCmJgY2bf9rHPnzsHT0xM3b96UPgD0FQji4uLQtWtX3Lp1C1FRUXqtFR8fj/79+6Nnz55o2bIl9u3bp7f/W5cuXcKECRPw9ttvY/LkyYiJidFbrcLvT2vWrMHly5f1FhhTUlJw5coV2bf7rKSkJISHhyMoKAhRUVHS61Afrly5gilTpmDQoEGYO3cukpKS9Fqrfv36GDduHAD9fxHTFkMV4eLFi+jSpQsaN26MgQMHol69eggNDUVOTo6sYefChQvw8vLCP//8g4yMDOzevRvdu3fH3r17Zf+gvnz5Mjp27IhHjx7BxMQEsbGx6Ny5M3788UdZ6wBP34g9PDzw4Ycf4vDhw7Jvv7ALFy6gU6dOOHv2LBITE7F69WosWrQI2dnZstc6f/48OnXqBDMzMzRo0AApKSlYunQp8vLy9BKCU1NTUadOHVy9ehW9e/cG8PSNUs4PtLNnz6JTp06YOnUqPvzwQ9m2W5y7d+9i4MCB8PPzw5IlS9C0aVMA/zfjJ6ezZ8/Cy8sLkyZNwsSJExEeHo78/HwYGRnJ/lxdvnwZ3bt3R8eOHfHpp5/irbfeQmBgIJRKJQB5Zyji4+PRuXNnCCFQo0YNpKSkoFu3blizZo3sr/mLFy/C09MTFy9eRGJiItasWYPXX38d+/fvl/1xvHTpEjp06ICZM2ciPj5etu0+6/z58+jSpQt+//137Nq1Cx9//DG+//57qFQq2V8X6veL9PR0qFQq/PHHH9i0aROEEHp5v7h48SIsLCxw/vx5jB8/HoD87xclIuil9vjxY/HWW2+JDz/8UFq2du1aMXLkSJGbmyvu3bsnS52srCzh4+MjpkyZIi2LjY0VdnZ2wszMTGzZskUIIURBQYEs9T766CPh6+sr3U9JSRGff/65MDY2FqtWrRJCCKFSqUpd5+7du6JHjx6ic+fOok+fPqJXr14iOjq61Nstzq1bt0SzZs3E9OnTpWUrV64U9evXF//++6+sta5fvy4aNGggPvvsM2nZrFmzxJgxY4QQT59PIeR7vlQqlYiJiRFNmzYVGzduFI0bNxZ9+/aV1ssxvkuXLgkTExPp8VOpVGLbtm1i/vz5YtOmTSIhIaHUNQqLjIwUnTp1EkI8fZw+/vhj0a9fP9G+fXvxww8/iMePH8tS58yZM8LExEQEBwcLIYRISkoSdevWFWFhYbJsv7C8vDwxatQoMWrUKGlZVFSUGDhwoEhLSxO3b9+WrVZOTo4YNGiQ+Pjjj6Vld+7cEW5ubsLU1FQsWbJECCHP/+P8/HzxzjvviJEjR0rLzpw5I/z9/YWxsbHYtWuXEEKe1/u///4rOnXqJFq1aiU6dOgg/P39xfnz50u93WfduHFDNGrUSMyYMUPk5uYKIYSYPn26aNiwoWyvPbVr164JFxcXjfcLf39/MWHCBCHE09eN3Pbs2SMaN24sFi5cKNzd3cX48eOldQ8fPpS9nrY4U/WSy83NxbVr19C8eXNp2bVr13Do0CG0b98e7du3R0REBIDSfQPNycnB7du30bFjR2lbbdu2xWuvvQYvLy+88847OH78uGzT+snJyXBwcJDu16xZE3PmzMGcOXMQEBCAPXv2yLLb8Z9//oGxsTHCwsIwYcIEGBsbY+7cuTh06FBph6BBCIGDBw+icePG+OCDD6RvY/7+/gCefmuT08mTJ9G5c2dMmDBBWqZUKnHq1Cl4enqid+/e2Lt3r2zf4BUKBVq2bIlmzZqhe/fuWLRoEa5cuYKBAwdizJgxWL16NR49elSqGn///TcKCgrQpUsXqFQqvPrqq1i4cCHCw8Mxf/589O3bV9Zdgg8ePICJydNfAuvRowcSExPRqlUreHp6ws/PDwsXLgRQuv9XDx8+xOeff44pU6Zg/vz5AAAHBwe0bt0aBw8eLP0gnpGfn4+kpCTUr19fWnb48GEcPHgQ3bp1Q4sWLTB79mxZZuPy8vKQmJgovTfl5+ejVq1a6Ny5M7y9vTFlyhTs3r1blllulUqF27dvo27dutKy1q1bY8GCBRg3bhwGDx4s2yERly9fhpWVFdavX4+PPvoIZ86cwbJly3DhwoVSb1utoKAAv/32G9q0aYOPP/5Y6vekSZOQm5uLxMREWWtFRUWhZ8+e+OSTT6TXs4WFBS5cuIAePXpg9OjROHr0qGw1AcDd3R3t2rXD+++/j9GjRyMmJgaffPIJxowZgw0bNiAvL0/WelortzhHFYJKpRLDhw8X7u7u4pdffhFTpkwRVatWFREREWL37t1i/vz5wsjIqNSzL6mpqcLLy0vMnTtX+pZ0/fp14ezsLLZt2yZ69+4tRo4cKfLz82X55jlr1ixRt25daYZDvc3c3FzxwQcfiKZNm4q7d++Wuo4QQsTFxUn/3r17tzRj9ffff0vL1d9wS/NNd/fu3SI8PFy6r1KpxMOHD0Xt2rXF1q1bS7zd4qSnp4vLly9L98PCwoS5ublYtmyZCA8PFx9++KEwNTUV586dk61mTk6OaNOmjTQrcODAAWFraysUCoVUp7TfeGfNmiWMjY1FgwYNxKBBg0RCQoLIz88XJ06cEG+//bbw8PAQKSkppR6LEEL88ccfwtzcXKxfv14MHDhQY7s//PCDUCgU4vDhw6WuU3iGTf36Onz4sFAoFOKXX34p9fafNWHCBGFlZSVWrlwpAgIChIWFhdi0aZM4c+aM2LBhg1AoFOLXX38tdZ3c3FwxYMAA4e/vLzIzM4UQT2dfqlevLvbt2yfee+890blzZ5GdnV3qWkIIERAQILy8vERaWprG8lu3bolBgwaJvn37Sv0ojcePH4ujR49K97///nvRtm1b4e/vr/H/qbTvgxEREWL58uUay1JSUoStra04ePBgqbb9rOvXr4sLFy5I92fPni3Mzc3F/PnzRUhIiBg6dKioX7++uH79umw1s7OzRcuWLcWZM2dEdna2WL16tXBwcNB4v8jPz5etnrYYqkjs379fDBkyRPj6+oqGDRuK//3vf9K6J0+eiObNm4vQ0NBS15k0aZJo2bKlGDFihAgLCxPVqlUTAQEBQgghFi9eLJo3b16q/wSFA8vx48dF586dRWBgoPRhpl7/559/CmdnZ3HmzBlZaj1rz549onfv3sLHx0cKoxMnThTHjh0rUa3iHpPCb7jt2rUTv/32m3R//fr1Jd6VVVytJ0+eiLFjx4p9+/ZJy9SB+IcffihRHSE0H0P1ePz8/MT27duFEEIMHz5c2Nvbi1deeUVjV66unh3T3Llzhbu7e5Hnf+vWrcLBwaFUQbHwmAoKCsSwYcOEq6uraNq0qcjKyhL5+flSmzZt2oilS5eWuJZ6l86zVCqVUCqV4o033hDvvvuuePToUal3WxX++2vXromAgADxzjvviLZt24rFixdrtO3cubP44IMPZKm1bNky0bFjR9G1a1cRHBwsLC0tpW1v2rRJ1KtXT2RkZJS4VmGbN28Wbdq0EUuWLBFKpVJjXUREhHB2dha3bt2SpdazgSkiIkIKVupdgbNnzxZnz56Vtd7jx4+Fm5ubOH78uLTut99+k2Vc6ho5OTmib9++0pcjIYQ4dOiQqFmzpsZ7SGnk5uaK/Px80atXL3Ho0CEhhBBDhw4V1tbWolGjRtJux/JgUj7zY1Rebty4gaioKBgZGaF27dro3bs3XnvtNbz22mt48OABunTpgtq1awN4ulsiPz8f1tbWqFWrVonrODs7o0+fPvjqq68wf/58HDlyBH/88QdmzpyJqVOnAgBsbGxgYWFRol0hGRkZsLW1lU6rNTY2RocOHTBgwABs2bIFX375JSZOnCiNy83NDZaWliU60LVwLZVKpbE7QAgBhUKBPn36QKFQYMWKFZg/fz7Mzc3x22+/wc/Pr0S1jI2NkZ+fL+1KAjTPMCy8C+6zzz7D119/LZ2JWJJa6sdQPSZTU1OEh4drjFmhUKBWrVoau0t0rfXs9gCgefPmiIuLwy+//IKDBw9iz549ePDgAd59910MHToUmzdvLvWYPvvsM/Tr1w9ubm4AIPXB2dkZNWrUQNWqVWUZk5GREQYOHIiEhARcunQJ165dQ8uWLaWa1apVg52dXYlrValSpchrEHj62rCysoK3tzeCg4MREhKChg0bSq/Pko5L/RjWr18f33zzDXJyctC9e3c4OTkBeLobSAgBMzMzuLq6lnhcRkZG0ut94sSJsLOzw4EDB3DlyhXMmzcPEydOBACYmZnB2tpa5zoAcOfOHZw+fRq5ubl45ZVX4OHhgSFDhuCvv/7Cd999BwsLCwwdOhT29vYAgPbt26Nq1ap4+PBhqWq5uLigXbt20qEHQggYGRlJ7w0rVqzA8uXLoVQq8csvv2Dw4MGlGhMAjde++nWpfh3MmDED69atw/Hjx2UZU0FBAczMzLBz506N/wv29vZwdHSUHs+S1qpXrx7atm2LKlWqAADatWuHq1evYvXq1YiOjsbOnTtx/vx5LFy4ECYmJliyZInO9Uqt3OIclblz584JBwcH0bFjR9GgQQNRrVo1MWbMGHHnzh2pzVtvvSWCgoLE3bt3xePHj0VISIh45ZVXdJq2La6On5+fxjfKZ78JjhkzRgwaNEg8efJEpzFdvHhRuLq6ipkzZ0rLCn+DDwkJEZ6enmLAgAEiLi5OJCYmiunTpwsXFxedd/8VV+vZGYDC30B37twp7OzshK2trcYuQrlqCfH0m2f9+vXF9u3bxcKFC4W5ubk4deqU7LWe/WY9Y8YM0apVK9kfwzVr1giFQiEaNWokYmNjhRBPv/nu3r1bJCYmlqrOi2ZBP/nkE9GpUyeRnp6udZ3n1Sq8m/LHH38UTZo0EdbW1mLHjh3izz//FJ9//rmoU6eOzrtDdHmuVCqV6NSpk3j33XefO6ula61nH0N/f3/Rr18/kZSUJO7fvy9CQ0NF7dq1dXqunlfr2feCZ8fwwQcfiF69eolHjx7pVOvcuXOifv36okOHDqJ69erCw8NDbNq0SVr/3nvvCXd3dzFp0iRx9epVce/ePTF16lTRuHFjcf/+/VLXenZXfeHnb+3ataJKlSrCxsZGp5l0beoI8XS3fo0aNcSRI0fEnDlzhLm5uTh58qTsY3r2/WL69Omiffv2Op/49KJas2bNEgqFQri6ukrvF+np6WLVqlXi2rVrOtWSC0PVS+Lhw4fCy8tLOpPm7t274o8//hD29vaid+/e4urVq0KIp7tG2rdvL2rWrClee+014ezsLE6fPi1Lnddff12qoxYXFycmTpwobGxsdD4D5tatW6J169aiUaNGokWLFmL27NnSusJvyOvWrRN9+vQRCoVCtGjRQri4uOg0phfVKu5DraCgQEyaNElYWVnJOq5naxUUFIguXbqI5s2bi6pVq+r8BqlLLSGEuHz5spg8ebKws7PTOSj+V63CH9bTpk3TORiWpI7apUuXxKRJk4SdnZ3Ou1u0fQ0eOnRI+Pn5iWrVqolmzZqJli1b6vU1qDZ27Fjh6ekpna0pd62ffvpJdO/eXZiamoqOHTuKV155RdZxFQ6n6g/qI0eOiICAAGFtba3z83X16lVRp04dMXXqVJGRkSFOnTol/Pz8xJgxY0ROTo7Ubvbs2aJr165CoVCIdu3aCScnJ53H9V+1nj12VKVSifz8fDFhwgRhZ2encXySnHUePnwo2rRpI3r06FGiL2C61BJCiJs3b4pPP/20RP+3/quW+nWRl5cnPvroI3HixAkhxP+9RuQ6K7kkGKpeEo8fPxZt27YVP//8s8byhIQEUb16dfHGG29Iy3bv3i0WLVokwsPDdf4m/aI6b731lvSCz8jIED/++KNo06aNzsc3qVQqsWjRItG3b1+xb98+ERoaKtzc3J77oSbE0+Os4uPjdZ5d0abWsx/W586dE7Vr19b5TUvXWnl5eaJTp04letPStdaFCxekb5z6qCXHad66juncuXNi8uTJwt3dXeeQWJLXYGJiokhOThYPHjzQ67jUMjMzdf7Grk2twrNG58+fF2vXrhXbtm0TN2/e1Ou4CgoKxG+//Sa8vLx0fr6ePHkigoKCxJAhQzSel7Vr1woHB4cis1D3798Xf/zxhzh8+LDOl4rQtZYQQpw4cUIoFAqdvhjpWicjI0O4uLgIe3t7vT9+J0+eFB999JFo1aqV3mtVJAxVL4msrCxRu3btYt8Yz549KywtLWU5GF2bOnPmzJHWPXr0SOfdLWp3794VERERQoinZ7Wo35BnzZpVpHZpaVPr2W9HJT1TSNda33//vc67W0pa68KFCyU+O06bWnKcraPrmM6cOVPiM0G1qVXcbIu+ahUeV2nOlKzo/7eePXxAG48fPxZLly4V3333nRDi/56LS5cuaRwOIMcsh7a1nqXre2FJ6sydO1dcunRJpzolrRUdHa1xeIk+a5Xn7FRhDFUvkSVLlog6deqInTt3SsvUb4xz584Vnp6e4v79+9KLs6QfANrWkePSCYXduXOn2DfkHTt2yH5q7X/VUo9VrvE9r9a2bdtk2X5Fq7Vjxw5Z3yAr45gqSq3t27eX2f8tOWoVnnlX//+8e/euaNiwocYZcLru6pOrVkneM7Sto+uhAaWpVZrd97rWkuO5khPP/quk7t69i9u3byM9PR3e3t4wNjbGwIEDcezYMYSFhcHU1BS9evWSzqKoXr06lEolLCwspLOJtDlTqDR1dD0TqbhawNMzqdRno6l/B+rnn3+GEAKZmZlYvnw5/vnnHzg7O5dpLW3HZ2jjqmi1KuOYWEveWmlpaejVq5d0VmLhM+IyMzOlH5EHgJCQEHzzzTdITEyEvb29zv+P9V2rMo6prGvpVXmlOdKfs2fPChcXF9G4cWNhY2MjmjRpIjZt2iRyc3PFyZMnRf/+/UX79u2lM15yc3PF1KlTRffu3XWaVi+rOsXVcnNzExs3bpSOTSkoKJC+zdy5c0eEhIQIhUIh7OzsdP7WxFqGU6syjom1yqaWuk5CQoKoUaOGSEtLE3PmzBEWFhYVtlZlHFNZ19I3hqpKJjU1Vbi5uYkZM2aIa9euiX///VcMHTpUNG7cWMyePVvk5OSIuLg48cEHHwgTExPRqlUr0bFjR2FnZ6fTweJlVee/ajVt2lSEhoaK1NRUIYTm1Pm7774rrK2tRXx8PGtV0lqVcUysVba1hHh6HFebNm3E0KFDhampqc4f0mVVqzKOqaxrlQWGqkomPj5e1KtXr8iLbdq0aaJ58+biyy+/FCqVSmRlZYmYmBgxZ84cER4ervOBzmVV50W13N3dRVhYmMZPVaxZs0bY2tqWaF87axlOrco4JtYq+1oXL14UCoVCWFhYlOhXFsqqVmUcU1nXKgsMVZVMXFycqFOnjvTzKIUvjDdhwgTh4uIiy08flFUdbWq5urpq1EpOTi7xb0yxluHUqoxjYq2yr3X37l0REBBQojPiyrJWZRxTWdcqCwxVlVD79u3Fq6++Kt0vfFE7Dw8PMWzYMIOqo0stOc5EYi3DqVUZx8RaZVtLiNJfI62salXGMZV1LX0zevGh7FSRZWdn4+HDh1AqldKy//3vf4iPj8eIESMAPP2NrPz8fABAt27dSvSbd2VVp7S11GeJsFblq1UZx8Ra5V8LAMzNzStcrco4prKuVR4YqgzYxYsXMXDgQHTv3h1NmzbFhg0bAABNmzbF8uXLERUVhbfffht5eXnSZRJSU1NhaWmJ/Px8rX+8uKzqsBZrlXcd1mKtilCrMo6prGuVmzKfGyNZxMfHCwcHBzF58mSxYcMGERQUJKpUqSId1JmdnS1+//13UadOHeHm5iZ8fX3FkCFDhKWlpU6/RVdWdViLtcq7DmuxVkWoVRnHVNa1ypNCCEOIflRYWloahg8fDjc3Nyxfvlxa/uqrr8Ld3R0rVqyQlj18+BBz585FWloazM3N8eGHH6JZs2YVqg5rsVZ512Et1qoItSrjmMq6VnnjFdUNUF5eHjIyMjB48GAAT69EbGRkBFdXV6SlpQEAxNOTEGBlZYVFixZptKtodViLtcq7DmuxVkWoVRnHVNa1ypth9ZYAAI6Ojvjpp5/QtWtXAE8v4w8AtWvX1viJGSMjI42DAXW9hH9Z1WEt1irvOqzFWhWhVmUcU1nXKm8MVQaqUaNGAJ4mefXv6gkhkJqaKrVZsGAB1qxZI51FUZIXaFnVYS3WKu86rMVaFaFWZRxTWdcqT9z9Z+CMjIwghJBefOrUHxISgrlz5+LMmTMwMSn901xWdViLtcq7DmuxVkWoVRnHVNa1ygNnqioB9bkGJiYmqFu3Lr788kuEhYXh1KlTaNWqlcHVYS3WKu86rMVaFaFWZRxTWdcqa4YbB0miTvpVqlTBd999B2traxw+fBht27Y1yDqsxVrlXYe1WKsi1KqMYyrrWmVOh8svUAV38uRJoVAodP5F94pah7VYq7zrsBZrVYRalXFMZV2rrPA6VZVMdnY2LC0tK00d1mKt8q7DWqxVEWpVxjGVda2ywFBFREREJAMeqE5EREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIikgFDFREREZEMGKqIiAoRQsDb2xs+Pj5F1q1atQq2trb4559/yqFnRFTRMVQRERWiUCiwbt06HD9+HP/73/+k5UlJSZg6dSq+/vpr1KlTR9aaeXl5sm6PiMoHQxUR0TPq1q2L5cuXY8qUKUhKSoIQAv7+/ujVqxfatGmDPn36oFq1anB0dMS7776L+/fvS38bGRmJLl26wNbWFg4ODujfvz+uXbsmrb9x4wYUCgU2b96M7t27w9zcHBs2bCiPYRKRzPgzNUREz+Hr64vMzEwMHDgQc+bMQXx8PJo3b473338fo0aNwuPHjzFt2jTk5+fjwIEDAIBt27ZBoVCgZcuWyMrKQkhICG7cuIG4uDgYGRnhxo0bcHV1Rb169bBkyRK0adMG5ubmqFWrVjmPlohKi6GKiOg5UlNT0bx5c6SlpWHbtm24cOECDh06hL1790pt/vnnH9StWxcJCQlo3LhxkW3cv38fNWrUwPnz59GiRQspVC1btgwTJ04sy+EQkZ5x9x8R0XPUrFkT48ePR9OmTeHr64uzZ8/i4MGDqFatmnRzc3MDAGkXX2JiIoYPH4769evD2toa9erVAwDcunVLY9seHh5lOhYi0j+T8u4AEVFFZmJiAhOTp2+VWVlZGDBgABYtWlSknXr33YABA+Di4oLvvvsOzs7OUKlUaNGiBXJzczXaW1pa6r/zRFSmGKqIiLTUtm1bbNu2DfXq1ZOCVmEPHjxAQkICvvvuO3Tt2hUAcPjw4bLuJhGVE+7+IyLSUkBAANLS0jB8+HCcPHkS165dw969ezF69GgUFBTAzs4ODg4OWL16Na5evYoDBw4gKCiovLtNRGWEoYqISEvOzs44cuQICgoK0KtXL7i7u2PSpEmwtbWFkZERjIyM8PPPPyM2NhYtWrTA5MmTsXjx4vLuNhGVEZ79R0RERCQDzlQRERERyYChioiIiEgGDFVEREREMmCoIiIiIpIBQxURERGRDBiqiIiIiGTAUEVEREQkA4YqIiIiIhkwVBERERHJgKGKiIiISAYMVUREREQyYKgiIiIiksH/A42FLAYAJCmlAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "546b571b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig('./bed_geo_sep_24.svg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "dd6f8533",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Number of BED files')"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig1, ax1 = plt.subplots()\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "ax1.bar(data_count[\"submission_year\"], \n",
+    "       data_count[\"sample_name\"], \n",
+    "       label=data_count[\"submission_year\"], \n",
+    "       color=\"green\")\n",
+    "\n",
+    "ax1.set_xlabel('Year')\n",
+    "ax1.set_ylabel('Number of files')\n",
+    "ax1.set_title('Number of BED files')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "c683122b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# UPDATE DATE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "662d153b",
+   "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>update_year</th>\n",
+       "      <th>gsm</th>\n",
+       "      <th>sample_name</th>\n",
+       "      <th>genome</th>\n",
+       "      <th>last_update_date</th>\n",
+       "      <th>submission_date</th>\n",
+       "      <th>submission_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2009</td>\n",
+       "      <td>410</td>\n",
+       "      <td>410</td>\n",
+       "      <td>0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>410</td>\n",
+       "      <td>410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>469</td>\n",
+       "      <td>469</td>\n",
+       "      <td>0</td>\n",
+       "      <td>469</td>\n",
+       "      <td>469</td>\n",
+       "      <td>469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>1331</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>4215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1550</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>1766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2404</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>2510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3645</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>3805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8177</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>8292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7819</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>7939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>6942</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>7101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20639</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>20799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10300</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>10457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16465</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>16584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>14885</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>15500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>9866</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>10225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>5315</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   update_year    gsm  sample_name  genome  last_update_date  submission_date  \\\n",
+       "0         2008     41           41       0                41               41   \n",
+       "1         2009    410          410       0               410              410   \n",
+       "2         2010    469          469       0               469              469   \n",
+       "3         2011   1985         1985      98              1985             1985   \n",
+       "4         2012   4215         4215    1331              4215             4215   \n",
+       "5         2013   1766         1766    1550              1766             1766   \n",
+       "6         2014   2510         2510    2404              2510             2510   \n",
+       "7         2015   3805         3805    3645              3805             3805   \n",
+       "8         2016   8292         8292    8177              8292             8292   \n",
+       "9         2017   7939         7939    7819              7939             7939   \n",
+       "10        2018   7101         7101    6942              7101             7101   \n",
+       "11        2019  20799        20799   20639             20799            20799   \n",
+       "12        2020  10457        10457   10300             10457            10457   \n",
+       "13        2021  16584        16584   16465             16584            16584   \n",
+       "14        2022  15500        15500   14885             15500            15500   \n",
+       "15        2023  10225        10225    9866             10225            10225   \n",
+       "16        2024   5315         5315    5315              5315             5315   \n",
+       "\n",
+       "    submission_year  \n",
+       "0                41  \n",
+       "1               410  \n",
+       "2               469  \n",
+       "3              1985  \n",
+       "4              4215  \n",
+       "5              1766  \n",
+       "6              2510  \n",
+       "7              3805  \n",
+       "8              8292  \n",
+       "9              7939  \n",
+       "10             7101  \n",
+       "11            20799  \n",
+       "12            10457  \n",
+       "13            16584  \n",
+       "14            15500  \n",
+       "15            10225  \n",
+       "16             5315  "
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "5dd04140",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_count2 = data2[[\"update_year\", \"sample_name\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "64498911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_142310/609132149.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  data_count2['summary'] = data_count2['sample_name'].cumsum()\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_count2['summary'] = data_count2['sample_name'].cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "3a658fa8",
+   "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>update_year</th>\n",
+       "      <th>sample_name</th>\n",
+       "      <th>summary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2009</td>\n",
+       "      <td>410</td>\n",
+       "      <td>451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>469</td>\n",
+       "      <td>920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>2905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>4215</td>\n",
+       "      <td>7120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>1766</td>\n",
+       "      <td>8886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>11396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>3805</td>\n",
+       "      <td>15201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>8292</td>\n",
+       "      <td>23493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>7939</td>\n",
+       "      <td>31432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7101</td>\n",
+       "      <td>38533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>20799</td>\n",
+       "      <td>59332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>10457</td>\n",
+       "      <td>69789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>16584</td>\n",
+       "      <td>86373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>15500</td>\n",
+       "      <td>101873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>10225</td>\n",
+       "      <td>112098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>5315</td>\n",
+       "      <td>117413</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   update_year  sample_name  summary\n",
+       "0         2008           41       41\n",
+       "1         2009          410      451\n",
+       "2         2010          469      920\n",
+       "3         2011         1985     2905\n",
+       "4         2012         4215     7120\n",
+       "5         2013         1766     8886\n",
+       "6         2014         2510    11396\n",
+       "7         2015         3805    15201\n",
+       "8         2016         8292    23493\n",
+       "9         2017         7939    31432\n",
+       "10        2018         7101    38533\n",
+       "11        2019        20799    59332\n",
+       "12        2020        10457    69789\n",
+       "13        2021        16584    86373\n",
+       "14        2022        15500   101873\n",
+       "15        2023        10225   112098\n",
+       "16        2024         5315   117413"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_count2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "0104203e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Number of BED files')"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig2, ax2 = plt.subplots()\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "ax2.bar(data_count2[\"update_year\"], \n",
+    "       data_count2['summary'], \n",
+    "       label=data_count2[\"update_year\"], \n",
+    "       color=\"green\")\n",
+    "\n",
+    "ax2.set_xlabel('Year')\n",
+    "ax2.set_ylabel('Number of files')\n",
+    "ax2.set_title('Number of BED files')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/all_geo_beds/stats/bed_geo_sep_24.svg b/all_geo_beds/stats/bed_geo_sep_24.svg
new file mode 100644
index 0000000..b1de4eb
--- /dev/null
+++ b/all_geo_beds/stats/bed_geo_sep_24.svg
@@ -0,0 +1,1481 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="460.8pt" height="345.6pt" viewBox="0 0 460.8 345.6" xmlns="http://www.w3.org/2000/svg" version="1.1">
+ <metadata>
+  <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
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