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   "source": [
    "# How to Store Simulations to HDF\n",
    "\n",
    "You can ask TARDIS to store the state of each iteration of the simulation you are running. We show examples of how this is done:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the simulation with the `tardis_example.yml` configuration file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tardis import run_tardis\n",
    "from tardis.io.atom_data import download_atom_data\n",
    "\n",
    "# We download the atomic data needed to run the simulation\n",
    "download_atom_data('kurucz_cd23_chianti_H_He')\n",
    "\n",
    "# We run the simulation\n",
    "simulation = run_tardis('tardis_example.yml')"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "You can now use the `to_hdf` method, to save properties to a HDF file.\n",
    "\n",
    "#### Parameters  \n",
    "\n",
    "`file_path`: Path where the HDF file should be stored. (Required)  \n",
    "`path`: Path inside the HDF store to store the elements. (Optional)  \n",
    "`name`: Name of the group inside HDF store, under which properties will be saved.(Optional)\n",
    "`overwrite`: If the HDF file already exists, do you overwrite the existing file (Optional, default `False`)\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "    \n",
    "Throughout this notebook, we set ``overwrite=True`` so that the notebook can be run repeatedly if needed.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation.to_hdf('/tmp/full_example.hdf', overwrite=True)\n",
    "\n",
    "# The commented out code below shows an example of to_hdf with more parameters\n",
    "#simulation.to_hdf(file_path='/tmp/full_example.hdf', path='/', name='simulation')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the stored HDF file with pandas and print a list of its entries using the `keys()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.HDFStore('/tmp/full_example.hdf', overwrite=True)\n",
    "\n",
    "data.keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access `model.density` under simulation, which is a one-dimensional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data['/simulation/simulation_state/density'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Scalars are stored in a `scalars` `pandas.Series` for every module. For example to access `model.t_inner` under simulation, one would need to do the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data['/simulation/simulation_state/scalars']['t_inner'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Breakdown of the various to_hdf methods\n",
    "Every module in TARDIS has its own `to_hdf` method responsible to store its own data to an HDF file."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plasma\n",
    "The following call will store every plasma property to `/tmp/plasma_output.hdf` under `/parent/plasma`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation.plasma.to_hdf('/tmp/plasma_output.hdf', path='parent', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "plasma_data = pandas.HDFStore('/tmp/plasma_output.hdf')\n",
    "\n",
    "plasma_data.keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plasma's `to_hdf` method can also accept a `collection` parameter which can specify which types of plasma properties will be stored. For example if we wanted to only store Input plasma properties, we would do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tardis.plasma.properties.base import Input\n",
    "simulation.plasma.to_hdf('/tmp/plasma_input_output.hdf', collection=[Input], overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "plasma_input_data = pandas.HDFStore('/tmp/plasma_input_output.hdf')\n",
    "\n",
    "plasma_input_data.keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model\n",
    "The following call will store properties of the `SimulationState` to `/tmp/model_output.hdf` under `/simulation_state`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation.simulation_state.to_hdf('/tmp/model_output.hdf', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "model_data = pandas.HDFStore('/tmp/model_output.hdf')\n",
    "\n",
    "model_data.keys()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MontecarloTransport\n",
    "The following call will store properties of the `MontecarloTransport` to `/tmp/transport_output.hdf` under `/transport`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation.transport.to_hdf('/tmp/transport_output.hdf', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "transport_data = pandas.HDFStore('/tmp/transport_output.hdf')\n",
    "\n",
    "transport_data.keys()"
   ]
  }
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