notebook.ipynb

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   "id": "fb5413e5-2298-4e1f-b858-3e3c66fefffe",
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   "source": [
    "# Deconvolve Source Signals of ${}^{10}\\mathrm{C}\\text{-}\\alpha$ Scattering\n",
    "In this notebook we will generate a dataset containing the point cloud model inliers obtained from the signal data in the `TEvent` tree."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bfb10b1-e227-43df-a330-fef5a312a742",
   "metadata": {},
   "source": [
    "## Prepare source chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b91c96e-8633-480e-8d1a-b7b72db2e143",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'root_path_info' (list)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cabd6526-0278-40d7-8e4a-bc8014e6e1ee",
   "metadata": {},
   "source": [
    "Given that these early operations are constrained by memory, it is important that each unit of work consumes roughly the same amount of RAM for optimal throughput. This can be estimated from the multiplicity of each event (as the memory is largely determined by the `mmWaveformY` branch which scales as $n \\propto \\text{mul}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7cd6d1d4-8975-457d-aa98-dd2dc604088b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ae4769ac-2e65-41ea-8b42-0792cf8f8df3",
   "metadata": {},
   "source": [
    "Now we can read the events in memory-friendly chunks (ignoring empty chunks!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6fc77ae5-f6a9-4943-83be-3172b1be1e60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3398872b-a58a-4e70-89bf-bd8f2506c2e0",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Process TEvent TTrees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60211f1d-0c85-478d-a791-e5873dd967a3",
   "metadata": {},
   "source": [
    "Restructure the `TEvent` objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2faf2b96-911e-4123-8203-c33f089d0410",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "84e8223d-6736-484d-a93d-258cdaaf0217",
   "metadata": {},
   "source": [
    "Cleanup signals by removing saturated channels and attenuating the baseline noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ed0ad2a-7f0b-4f79-bf4f-843ee3fba858",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6efc810e-3532-4cea-bef1-56d81463a05b",
   "metadata": {},
   "source": [
    "Remove high frequency noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "68ed62d5-1dfa-49db-b137-dd1d392a4462",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8a414637-f692-4c5f-90e8-1ed529633d09",
   "metadata": {},
   "source": [
    "Compute the QT data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f18b0f42-5930-411e-89d4-29a4ba1e7ead",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4415f584-9b77-4df2-a49b-d398a4d1d842",
   "metadata": {},
   "source": [
    "Accumulate the results (generate the dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e1b7e21-9cf3-4603-b9c8-42a9149e494e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "834638e3-29ce-461b-b41a-b76773cbe584",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "abb977d3-0c07-4f43-9979-a5b1e7bfa42d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d37295a9-8c53-4dd6-81a5-7eec25283c29",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not dataset_path.exists():\n",
    "    dataset.compute()"
   ]
  }
 ],
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requirements.txt

jupyterlab >= 3.1.9
jlab-enhanced-cell-toolbar