{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the `supernova_explosion_64` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data/train/supernova_explosion_Msun_0.1_dim64_file_00.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_01.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_02.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_03.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_04.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_05.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_06.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_07.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_08.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_09.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_10.hdf5', 'data/train/supernova_explosion_Msun_0.1_dim64_file_11.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_00.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_01.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_02.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_03.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_04.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_05.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_06.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_07.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_08.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_09.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_10.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_11.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_12.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_13.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_14.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_15.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_16.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_17.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_18.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_19.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_20.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_21.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_22.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_23.hdf5', 'data/train/supernova_explosion_Msun_1_dim64_file_24.hdf5']\n"
     ]
    }
   ],
   "source": [
    "# print the list of paths of files in the training set\n",
    "set_path = \"train\"\n",
    "paths = sorted(glob.glob(f\"data/{set_path}/*.hdf5\"))\n",
    "print(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<KeysViewHDF5 ['boundary_conditions', 'dimensions', 'scalars', 't0_fields', 't1_fields', 't2_fields']>\n"
     ]
    }
   ],
   "source": [
    "# select the tenth path (more visual choice)\n",
    "p = paths[10]\n",
    "\n",
    "# print the first layer of keys\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    print(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "print bc available: <KeysViewHDF5 ['x_open', 'y_open', 'z_open']>\n",
      "print attributes of the bc: <KeysViewHDF5 ['associated_dims', 'associated_fields', 'bc_type', 'sample_varying', 'time_varying']>\n",
      "get the bc type: OPEN\n"
     ]
    }
   ],
   "source": [
    "# In 'boundary_conditions' is stored the information about the boundary conditions:\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    print(\"print bc available:\", f[\"boundary_conditions\"].keys())\n",
    "    print(\"print attributes of the bc:\", f[\"boundary_conditions\"][\"x_open\"].attrs.keys())\n",
    "    print(\"get the bc type:\", f[\"boundary_conditions\"][\"x_open\"].attrs[\"bc_type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t0_fields: <KeysViewHDF5 ['density', 'pressure', 'temperature']>\n",
      "t1_fields: <KeysViewHDF5 ['velocity']>\n",
      "t2_fields: <KeysViewHDF5 []>\n"
     ]
    }
   ],
   "source": [
    "# Reminder: 't0_fields', 't1_fields', 't2_fields' are respectively scalar fields, vector fields and tensor fields\n",
    "# print the different fields available in the dataset\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    print(\"t0_fields:\", f[\"t0_fields\"].keys())\n",
    "    print(\"t1_fields:\", f[\"t1_fields\"].keys())\n",
    "    print(\"t2_fields:\", f[\"t2_fields\"].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of the selected t0_field:  (59, 64, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "# The data is of shape (n_trajectories, n_timesteps, x, y, z)\n",
    "# Get the first t0_field and save it as a numpy array\n",
    "traj = 3  # select the trajectory as the data is quite big\n",
    "with h5py.File(p, \"r\") as f:\n",
    "    temperature = f[\"t0_fields\"][\"temperature\"][traj, :]# HDF5 datasets can be sliced like a numpy array\n",
    "    print(\"shape of the selected t0_field: \", temperature.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "x_slice = 32  # select the middle slice\n",
    "traj_toplot = temperature[:, x_slice, :, :]\n",
    "\n",
    "# field is now of shape (n_timesteps, y, z).\n",
    "# Let's do a subplot to plot it at t= 0, t= T/3, t= 2T/3 and t= T:\n",
    "fig, axs = plt.subplots(1, 4, figsize=(20, 5))\n",
    "T = traj_toplot.shape[0]\n",
    "\n",
    "# fix colorbar for all subplots:\n",
    "normalize_plots = True\n",
    "cmap = \"magma\"\n",
    "\n",
    "if normalize_plots:\n",
    "    vmin = np.min(traj_toplot)\n",
    "    vmax = np.max(traj_toplot)\n",
    "    norm = LogNorm(vmin=vmin, vmax=vmax)\n",
    "    for i, t in enumerate([0, T // 3, (2 * T) // 3, T - 1]):\n",
    "        axs[i].imshow(traj_toplot[t], cmap=cmap, norm=norm)\n",
    "        axs[i].set_xticks([])\n",
    "        axs[i].set_yticks([])\n",
    "        axs[i].set_title(f\"t={t}\")\n",
    "else:\n",
    "    for i, t in enumerate([0, T // 3, (2 * T) // 3, T - 1]):\n",
    "        axs[i].imshow(np.log(traj_toplot[t]), cmap=cmap)\n",
    "        axs[i].set_xticks([])\n",
    "        axs[i].set_yticks([])\n",
    "        axs[i].set_title(f\"t={t}\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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