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   "source": [
    "# Storing 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:"
   ]
  },
  {
   "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.util 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')"
   ]
  },
  {
   "cell_type": "markdown",
   "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')"
   ]
  },
  {
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access `model.homologous_density.density_0` under simulation, which is a one-dimensional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data['/simulation/model/homologous_density/density_0'])"
   ]
  },
  {
   "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/model/scalars']['t_inner'])"
   ]
  },
  {
   "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."
   ]
  },
  {
   "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()"
   ]
  },
  {
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model\n",
    "The following call will store properties of the `Radial1DModel` to `/tmp/model_output.hdf` under `/model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation.model.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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MontecarloRunner\n",
    "The following call will store properties of the `MontecarloRunner` to `/tmp/runner_output.hdf` under `/runner`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulation.runner.to_hdf('/tmp/runner_output.hdf', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "runner_data = pandas.HDFStore('/tmp/runner_output.hdf')\n",
    "\n",
    "runner_data.keys()"
   ]
  }
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