test-attr-blobs.ipynb

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   "cell_type": "markdown",
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   "source": [
    "To make things easy I used one of the test arrays included with the repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiledbvcf\n",
    "uri = \"libtiledbvcf/test/inputs/arrays/v2/ingested_2samples_GT_DP_PL\"\n",
    "ds = tiledbvcf.Dataset(uri, mode = \"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting the `fmt` attribute for a single variant whose VCF record looks like this:\n",
    "\n",
    "```\n",
    "1\t13354\t.\tT\t<NON_REF>\t.\tLowQual\tEND=13374\tGT:GQ:MIN_DP:DP:PL\t0/0:42:14:15:0,24,360\n",
    "```\n",
    "\n",
    "Pulling out the `fmt` field returns the integer-representation of the data, similar to what you saw."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [2, 0, 0, 0, 71, 81, 0, 1, 0, 0, 0, 1, 0, 0, 0...\n",
       "Name: fmt, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.read(attrs = [\"pos_start\", \"fmt\"], regions=['1:13354-13354'], samples = [\"HG00280\"])\n",
    "df.fmt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting it back to bytes reveals the names of the 2 `info` attributes that are not explicitly stored as attributes: `GQ` and `MIN_DP`, which is good.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x02\\x00\\x00\\x00GQ\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x00\\x00*\\x00\\x00\\x00MIN_DP\\x00\\x01\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0e\\x00\\x00\\x00'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fmt[0].tobytes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " And accessing those attributes directly returns the expected values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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>pos_start</th>\n",
       "      <th>fmt_GQ</th>\n",
       "      <th>fmt_MIN_DP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13354</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pos_start  fmt_GQ  fmt_MIN_DP\n",
       "0      13354      42          14"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = tiledbvcf.Dataset(uri, mode = \"r\")\n",
    "ds.read(attrs = [\"pos_start\", \"fmt_GQ\", \"fmt_MIN_DP\"], regions=['1:13354-13354'], samples = [\"HG00280\"])"
   ]
  }
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