poster_overview.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "02d5ebeb-e475-44ac-bbc2-7b3512aaad9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf5b3d99-b484-429f-b2c1-d72415fcf262",
   "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>poster</th>\n",
       "      <th>datatype</th>\n",
       "      <th>software</th>\n",
       "      <th>analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>416</td>\n",
       "      <td>PET</td>\n",
       "      <td>CMA</td>\n",
       "      <td>ROI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>497</td>\n",
       "      <td>VBM</td>\n",
       "      <td>GingerAle</td>\n",
       "      <td>Coordinate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>591</td>\n",
       "      <td>tfMRI</td>\n",
       "      <td>SDM</td>\n",
       "      <td>Image</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608</td>\n",
       "      <td>VBM</td>\n",
       "      <td>GingerAle</td>\n",
       "      <td>Coordinate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>608</td>\n",
       "      <td>DWI</td>\n",
       "      <td>GingerAle</td>\n",
       "      <td>Coordinate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   poster datatype   software    analysis\n",
       "0     416      PET        CMA         ROI\n",
       "1     497      VBM  GingerAle  Coordinate\n",
       "2     591    tfMRI        SDM       Image\n",
       "3     608      VBM  GingerAle  Coordinate\n",
       "4     608      DWI  GingerAle  Coordinate"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posters = pd.read_csv('./posters.tsv', sep='\\t')\n",
    "posters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "096508c8-c014-4305-84a2-d7d92a4aa4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Input Data'}>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "posters['datatype'].value_counts().plot(kind='bar', title=\"Input Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64019565-5aee-40dd-a9b9-9aae3262813a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since posters could include multiple input data types for their meta-analysis, we want the software and analysis type counts\n",
    "# to only be one\n",
    "single_posters = posters.drop_duplicates(subset=[\"poster\"], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df11443c-588a-4085-b193-961e66337280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Software'}>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sb7Htb9YBdlviciZixbfAk6zlh6H9fWB/YJ0jUBau/8p/G/CqreOUk+wCvA64e1WdPGR9LUuyL92kKb8NvLqq3j5wSdoJrOgAT3IucE/gXXSjJi5LcmVVHTBwaU1KcgnwyKq6fcb6VcCXquphw1TWtiSH0TUwngZsBv6yqi4ZtqrloR959hzghKo6Zuh6Fmqln3S6ge5E5j78cCD/yv2NNr7bZoY3dLM3Ad8boJ6mJfmTJJvpWt0b6b4Znmh4jyfJ6iTPSfJeugv2jgb+duCyFmVFt8DhB/22P0fXwjkQ2Av46W1cWq85JPky3XGceUY/wD/ZAl+YfljmlcB3+lU/8p+1qh655EU1LMnT6X4+nw58iu6cwpuqau2QdY1jxQf4qCT3BX6R7h95TVXtN3BJTUnyKbqQ2Rrgoz9cqaqnLH1V7UpyIN23w2tnbNoPuL6qLl/6qtrV/0L8T+DFVXVlv+6KqnrgsJUt3krvQiHJrkleD1BVN1bVqVV1JPD4gUtr0auB51fVU/qwPgO4lW4o3HMHraxNfwXcXFVXjz6Am/ttWpjDgHOB/0jy8SQn0k3I3qwVH+D9DZge31/dNrr+6oFKatnf0vd1J3ki8Fq6EL+Zxmf/Hsg+VfWlmSv7dWuXvpy2VdWFVXVKVT0IeA3wKODHkvxbkvXDVrc4dqEASU4DHgC8l5FLwavq/YMV1aAkX6iqn+yfvxmYrqo/6pcvrKpHDVhec5JcVlUHbmfb5VX14KWuabnph7k+FTi+qn5l6HoWasW3wHs/DnwdOAr42f5x7KAVtWnXfsggdGf2PzmyzYvGFm5TkpfMXJnkV+mGE2oRkrw/yTOT7FJVd1bVx1oMb7AFrglK8gfAM+lue7oGOKyqKsmDgTP6cwuap37eyw/QXRy1NbDXAauB51TV9UPV1rIkTwV+GTiC7lv326rq0mGrWhwDHOfEnKQkR9BdAv6xqvp2v+4hwO5VdcGgxTUqyVPobtMLcHFVfXK2/TU//RDiE4A/oBvpczrdcNfvD1rYAhjgQJKN9HNiVtWh/bqLquqQ2V8pqUVJ7gO8EHgB3Y3C3kE38uwRVfXkAUtbEPslO7tV1fkzBqI4J6a0DCX5AN3MRm8Hjh3pinp3kk3DVbZwBnjHOTGllWMD8HDgSGBdkk8Dp1XV/1VVU3cgtQsF58SUVpIk7wFuoes2gW6aur2q6heGq2pxDPARzokpLX9JLqmqg+da1wK7ULjr3Jh9X/jNwOaqunCImiTtMBckOaKqPgeQ5HCgqb7vrWyBA0neSTe+9kP9qmOBL9JdrvzeqvqLgUqTNGFJttCdxLymX7UGuJRu4EK1dJdHAxxIcg7wzKq6tV/eHfgI8Ay6VnhzX60kbVuS/Wfb3tJ9kOxC6dyXH51w4Pt0F/V8N4kTEUjLSEsBPRcDvPMO4LwkH+yXfxZ4Z39S09lPJO2U7ELpJXkM3TBCgM9UVZMnNSStHAZ4L8mudLOf/OBbSVVds/1XSNKw7EIBkpxMd4P3G4A76KYEK6CZs9GSVh5b4HQ3xwcOr6qvD12LJM2XEzp0rqW7cEeSmmEXSucK4OwkH2FkOGFVvWG4kiRpdgZ455r+sbp/SNJOzz5wSWrUim6BJ3ljVb08yYfo7wU+qqqeNUBZkjQvKzrA6WbkAHj9yLqtQR4kaSe20gN83/62km8GSHI+MEUX4q8etDJJmsNKH0b4KuCskeXVdLeVfTLwsiEKkqT5Wukt8NVVde3I8qf7i3m+3t/ISpJ2Wiu9BX6v0YWqOmlkcWqJa5GkBVnpAX5ekpfMXJnkpcD5A9QjSfO2oseBJ7kv8C90V19e0K9+NHA34NlVdcNApUnSnFZ0gG+V5Cjg4f3ixVX1ySHrkaT5MMAlqVErvQ9ckpplgEtSowxwSWqUAS5JjTLAJalR/w+G/4jdgdXYEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_posters['software'].value_counts().plot(kind='bar', title=\"Software\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f1cffe7-80d8-4290-9801-aa981525551f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Analysis Type'}>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T0SraQfm1a9tUfv1SVZOuQZI0BK/ElKRGGeCS1CgDXJIaZYCPQJLdJl2DpOmzzS/k2ZEleRZwEvBIYH2SQ4B/V1W/MdnKtJwkfwo85Lv3VfXmMZajISUJ8Grgp6rqD5KsBx5XVRdPuLSxMcD7eR/wQrr171V1eZLnTbYkrcLmSRegkfgQ8CPgcOAPgDuBvwKeMcmixskA76mqbhxMBO73w0nVotWpqlPnP07yyG7se5OrSEN6ZlUdluQrAFV1R3dR4dSwB97PjV0bpZLsnORtwDWTLkorS/Lvk9wAXA9cn+T6JLa+2nJvd1vrAkgyw2BGPjUM8H5+HfgPDO7MeDNwKGAIbOeS/FfgpcC/qarHVtVjgecDL+r2qQ3vB84E9k7yh8CXgT+abEnj5ZWYPSR5dlVdsNKYti/dLY0Pqaq7F43vClxeVU+ZTGVaqyQHAUcAAc6tqqn6DdgA7yHJpVV12Epj2r4kubaqDlrrPm1fkjxmieE7q+resRczIb6JOYQkPws8C5hJ8h8X7NqDwY29tH27OckRVXXuwsEkhwNbJ1ST1u5SBre0voPBDHxP4JYktwJvrKotE6xtLAzw4ezCYO33OmD3BePfBY6ZSEVaizcDZyX5MjD/TT4LPBsfC9iSc4AzqurzAEl+Afgl4CMMlhg+6AlhOxpbKD0keWJVXT/pOrR2SR4OvAr4F93Q1cBpi/vi2n4luaKqnrZo7KtV9TNJLquqQydU2tg4A+/nriTvYRACD58frKrDJ1eSVqML6pMXjiV5WJJXV9VpEypLa7M1yX9m8EB1gF8Bbu2WFk7FckKXEfZzGnAtsD/w+8B1DJ5UpO1Ykj2SvCPJB5K8IAO/CXwDeMWk69OqvYrBoxz/T/dnfTe2E1PydbSF0kOSLVX1L+d/bevGLqmqqbmUt0VJzmLwxteFDJag7c3gTbC3VNVlEyxNWhNbKP3ML1famuQo4J+BpZY2afvyU/O90yQnMVh5st7+d1u6Ky/fzhS3MG2h9POuJI8CTgDexuDOhL812ZK0CvevE66qHwI3Gd5NmvoWpi0UTZ0kPwS+P78J7Arc1X1cVbXHpGrT6tnCtIXSS/cr3BuBDSz4v6yqX5tUTVpZVXmx1Y5h6luYBng/ZwHnA1/A28hK47awhfmnDK6EnqoWpi2UHqblYgFJ2ydn4P18JsmLq+qvJ12ING2S7A+8iQe3MF82qZrGzRl4D0nuBB4B/IBBP843waQxSXI58GHgChZceVlVfzexosbMAJfUpCQXVdUOf8Oq5RjgQ0hyUFVdm2TJ+35X1aXjrkmaNkleBRwAnM3gt2Bgur7/7IEP5wQGywffu8S+YvCUbEnb1tOA1zL4fptvoUzV958zcElNSvJ14OCqumfStUyKM/AhJPm3y+2vqk+NqxZpil3J4Ck8t024jokxwIfz0u7vvRk8Wu28bvv5wP8FDHBp29sTuDbJJTywBz41ywgN8CFU1a8CJDmbwa9wW7vtxwOnTLA0aZq8c9IFTJoB3s8T5sO7cyuDm8pL2samab33QzHA+zk3yeeB07vtX2FwXxRJ20h3Ad1Sqy+m7kI6V6H0lOTlwPO6zS9V1ZmTrEfS9DDAh9Q9OPWqqjpo0rVImk4+kWdI3ZNcvpbEnrekibAH3s+jgauSXMyPn/AyVcuYJE2OAd7P70y6AEnTyx54T0n2AeafwXdxVU3tVWGSxsseeA9JXgFcDPwy8ArgoiTHTLYqSdPCGXgP3Q3lXzA/6+4ecvyFqjpkspVJmgbOwPt52KKWyTfx/1TSmPgmZj+fW+JKTJ+PKWksbKH01N1a9jnd5vleiSlpXJyB93cBgwcaF4M3NCVpLOzX9rBgFcoxuApF0pjZQunBVSiSJskZeD+uQpE0MfbA+3EViqSJsYUyhCRPBvapqgsWrUL5NnBaVf3TxIqTNDUM8CEk+Qzwjqq6YtH404A/qqqXLv2ZkjQ69muHs8/i8AboxjaMvxxJ08gAH86ey+zbdVxFSJpuBvhwNid54+LBJG8AtkygHklTyB74ELp7gJ8J3MOPA3sW2AV4eVXdMqnaJE0PA7yHJM8HntptXlVV502yHknTxQCXpEbZA5ekRhngktQoA1ySGmWAS1KjDHBJatT/B1q9iEj3nbgiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_posters['analysis'].value_counts().plot(kind='bar', title=\"Analysis Type\")"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}