{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae9ab3671af05fbe",
   "metadata": {},
   "source": [
    "# Post-run analysis\n",
    "\n",
    "In the last tutorial, we simulated a Lennard-Jones crystal and stored data in the file `'LJ_T0.70.h5'`.\n",
    "In this tutorial, we will analyse this data. Below, we will do some standard imports and load the data from the file on the disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e92268e37823bf5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.016038Z",
     "start_time": "2025-08-23T08:17:09.567624Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:57.248735Z",
     "iopub.status.busy": "2026-05-18T13:26:57.248497Z",
     "iopub.status.idle": "2026-05-18T13:26:57.756493Z",
     "shell.execute_reply": "2026-05-18T13:26:57.756166Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found .h5 file (./LJ_T0.70.h5), loading to gamdpy as output dictionary\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import gamdpy as gp\n",
    "\n",
    "# Select h5 file\n",
    "filename='./LJ_T0.70.h5'\n",
    "output = gp.tools.TrajectoryIO(filename).get_h5()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873b2c19-d819-479b-9913-a142c1a87e04",
   "metadata": {},
   "source": [
    "## Brief about the h5 data file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c66719-95ca-4a86-b7ba-2c0f36545533",
   "metadata": {},
   "source": [
    "### Large data sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e5d1df-a632-4b09-95b4-d33de1356c64",
   "metadata": {},
   "source": [
    "Large data sets, such as the trajectory of particle positions, are stored as an HDF5 dataset (a data structure similar to a NumPy array, but data is stored on the disk)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2877306999e9ffdf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.029402Z",
     "start_time": "2025-08-23T08:17:10.026013Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:57.758038Z",
     "iopub.status.busy": "2026-05-18T13:26:57.757858Z",
     "iopub.status.idle": "2026-05-18T13:26:57.761143Z",
     "shell.execute_reply": "2026-05-18T13:26:57.760860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of timeblocks:          nblocks = 8\n",
      "Configurations per timeblock:  nconfs = 12\n",
      "Number of particles:           N = 2048\n",
      "Number of spatial dimensions:  D = 3\n"
     ]
    }
   ],
   "source": [
    "nblocks, nconfs, N, D = output['trajectory/positions'].shape\n",
    "print(f'Number of timeblocks:          {nblocks = }')\n",
    "print(f'Configurations per timeblock:  {nconfs = }')\n",
    "print(f'Number of particles:           {N = }')\n",
    "print(f'Number of spatial dimensions:  {D = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dbb271c-74d7-4c6b-b548-87aadee834ad",
   "metadata": {},
   "source": [
    "### Information in attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21cca96b-5158-4a22-9d4a-d2b6827a00d0",
   "metadata": {},
   "source": [
    "Some information, such as details about the simulation box, is stored as attributes. Below, we fetch such data to get the density of the NVT simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f11c4bca2bb1ec0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.080637Z",
     "start_time": "2025-08-23T08:17:10.078104Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:57.762399Z",
     "iopub.status.busy": "2026-05-18T13:26:57.762309Z",
     "iopub.status.idle": "2026-05-18T13:26:57.764573Z",
     "shell.execute_reply": "2026-05-18T13:26:57.764257Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density:  rho = 0.97300\n"
     ]
    }
   ],
   "source": [
    "simbox = output['initial_configuration'].attrs['simbox_data']\n",
    "volume = np.prod(simbox)\n",
    "rho = N/volume\n",
    "print(f'Density:  {rho = :.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b90577-a614-42dd-8f41-83d48ecb0935",
   "metadata": {},
   "source": [
    "## Thermodynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d93c369c3edc37",
   "metadata": {},
   "source": [
    "The gamdpy package contains helper functions to read data in .h5 files.\n",
    "Thermodynamic data, stored in the `'scalar_saver'` group, can be extracted using the `gp.extract_scalars()` function (we skip the first block with `first_block=1` since the system is not equilibrated)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30dd986d-613d-409d-8480-cc2b60fc8b52",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.307493Z",
     "start_time": "2025-08-23T08:17:10.127550Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:57.765577Z",
     "iopub.status.busy": "2026-05-18T13:26:57.765487Z",
     "iopub.status.idle": "2026-05-18T13:26:58.091482Z",
     "shell.execute_reply": "2026-05-18T13:26:58.091147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract thermodynamic data\n",
    "U, W, K = gp.ScalarSaver.extract(output, ['U', 'W', 'K'], per_particle=False, first_block=1)\n",
    "\n",
    "# Get the associated times\n",
    "times = gp.ScalarSaver.get_times(output, first_block=1)\n",
    "\n",
    "# Plot potential energy per particle as a function of time\n",
    "plt.figure()\n",
    "plt.plot(times, U/N)\n",
    "plt.xlabel(r'Time, $t$')\n",
    "plt.ylabel('Potential energy per particle, $u=U/N$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92ac37ec2c888f1",
   "metadata": {},
   "source": [
    "Some properties need to be computed from the data stored. Examples are the kinetic temperature and pressure (in LJ units)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c168a51d98cd9da7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.331435Z",
     "start_time": "2025-08-23T08:17:10.329263Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:58.092853Z",
     "iopub.status.busy": "2026-05-18T13:26:58.092736Z",
     "iopub.status.idle": "2026-05-18T13:26:58.095105Z",
     "shell.execute_reply": "2026-05-18T13:26:58.094674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean kinetic temperature:  np.mean(T_kin) = 0.70031\n",
      "Mean pressure:             np.mean(P) = 1.40649\n"
     ]
    }
   ],
   "source": [
    "# Compute instantaneous kinetic temperature\n",
    "dof = D * N - D  # degrees of freedom\n",
    "T_kin = 2 * K / dof\n",
    "\n",
    "# Compute instantaneous pressure\n",
    "P = rho * T_kin + W / volume\n",
    "\n",
    "print(f'Mean kinetic temperature:  {np.mean(T_kin) = :.5f}')\n",
    "print(f'Mean pressure:             {np.mean(P) = :.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46be63f-ce41-420a-9b61-47fa7590f2b0",
   "metadata": {},
   "source": [
    "## Structure\n",
    "\n",
    "To investigate the structure and dynamics of particles, we need to analyse data in the `'trajectory_saver'` group. Again, gamdpy has some built-in functionality to compute various measures. Let us calculate the radial distribution function. We will do this by inserting positions into a configuration object, and using the `gamdpy.CalculatorRadialDistribution()` class. The calculation is done on the GPU (for efficency)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58c5f624-bbf3-489f-bfc9-73644a7777ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.952819Z",
     "start_time": "2025-08-23T08:17:10.379815Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:58.096089Z",
     "iopub.status.busy": "2026-05-18T13:26:58.095978Z",
     "iopub.status.idle": "2026-05-18T13:26:59.701896Z",
     "shell.execute_reply": "2026-05-18T13:26:59.701643Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create configuration object\n",
    "configuration = gp.Configuration(D=D, N=N)\n",
    "configuration.simbox = gp.Orthorhombic(D, output['initial_configuration'].attrs['simbox_data'])\n",
    "configuration.ptype = output['initial_configuration/ptype']\n",
    "configuration.copy_to_device()\n",
    "\n",
    "# Make a radial distribution (RDF) calculator object\n",
    "calc_rdf = gp.CalculatorRadialDistribution(configuration, bins=1000)\n",
    "\n",
    "# Loop over the last (-1) configuration in each time block\n",
    "positions = output['trajectory/positions']  # Shape: nblocks, nconfs, N, D\n",
    "first_timeblock = 1\n",
    "for pos in positions[first_timeblock:, -1, :, :]:\n",
    "    configuration['r'] = pos\n",
    "    configuration.copy_to_device()\n",
    "    calc_rdf.update()\n",
    "rdf_data = calc_rdf.read()\n",
    "\n",
    "# Plot RDF\n",
    "plt.figure()\n",
    "plt.plot(rdf_data['distances'], rdf_data['rdf'][:,0,0])\n",
    "plt.xlabel(r'Pair distance, $r_{ij}$')\n",
    "plt.ylabel('Radial Distribution Function')\n",
    "plt.xlim(0, None)\n",
    "plt.ylim(0, None)\n",
    "plt.savefig(filename+'_rdf.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3509171d-8f19-454f-80e4-efbba01b59b6",
   "metadata": {},
   "source": [
    "## Dynamics\n",
    "\n",
    "There are also built-in tools for analysing the trajectory. For example, the `gamdpy.tools.calc_dynamics` function computes several dynamical measures, including the mean squared displacement and the intermediate scattering function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "166dbf6e-69ac-4fd8-9d9d-f4848e95f17f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:10.991050Z",
     "start_time": "2025-08-23T08:17:10.959951Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:59.703361Z",
     "iopub.status.busy": "2026-05-18T13:26:59.703164Z",
     "iopub.status.idle": "2026-05-18T13:26:59.733636Z",
     "shell.execute_reply": "2026-05-18T13:26:59.733012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['times', 'msd', 'alpha2', 'qvalues', 'Fs', 'count'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qvalues = 7.5\n",
    "dynamics = gp.tools.calc_dynamics(output, first_block=1, qvalues=qvalues)  # Dictionary with dynamics\n",
    "dynamics.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eeb37bb4-c1e5-4913-9b40-39801a39806e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:11.109164Z",
     "start_time": "2025-08-23T08:17:11.006969Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:59.735655Z",
     "iopub.status.busy": "2026-05-18T13:26:59.735459Z",
     "iopub.status.idle": "2026-05-18T13:26:59.845099Z",
     "shell.execute_reply": "2026-05-18T13:26:59.844503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(dynamics['times'], dynamics['msd'], 'o')\n",
    "plt.xscale('log')\n",
    "plt.ylim(0, None)\n",
    "plt.xlabel(r'Time, $t$')\n",
    "plt.ylabel(r'Mean squared displacement')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12c2aaba-aa41-47c4-8a28-99245f287700",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:11.214911Z",
     "start_time": "2025-08-23T08:17:11.114751Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:59.846249Z",
     "iopub.status.busy": "2026-05-18T13:26:59.846148Z",
     "iopub.status.idle": "2026-05-18T13:26:59.950011Z",
     "shell.execute_reply": "2026-05-18T13:26:59.949763Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(dynamics['times'], dynamics['Fs'], 'o')\n",
    "plt.xscale('log')\n",
    "plt.ylim(0, 1.1)\n",
    "plt.xlabel(r'Time, $t$')\n",
    "plt.ylabel(r'Intermediate scattering function, $F(t, q = ' f'{qvalues}' r'$)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7a37b6-312a-437a-899b-ac0838bc2f3d",
   "metadata": {},
   "source": [
    "## Details about stored data\n",
    "\n",
    "### Structure of h5 file\n",
    "Below is a function to print the structure of a given h5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31e30363397fed86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:11.236649Z",
     "start_time": "2025-08-23T08:17:11.221779Z"
    },
    "execution": {
     "iopub.execute_input": "2026-05-18T13:26:59.951134Z",
     "iopub.status.busy": "2026-05-18T13:26:59.951040Z",
     "iopub.status.idle": "2026-05-18T13:26:59.961341Z",
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial_configuration/ (Group)\n",
      "    ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "    r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "    scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "    topology/ (Group)\n",
      "        angles  (Dataset, shape=(0,), dtype=int32)\n",
      "        bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "        dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "        molecules/ (Group)\n",
      "    vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "restarts/ (Group)\n",
      "    restart0000/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0001/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0002/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0003/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0004/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0005/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0006/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "    restart0007/ (Group)\n",
      "        ptype  (Dataset, shape=(2048,), dtype=int32)\n",
      "        r_im  (Dataset, shape=(2048, 3), dtype=int32)\n",
      "        scalars  (Dataset, shape=(2048, 4), dtype=float32)\n",
      "        topology/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        vectors  (Dataset, shape=(3, 2048, 3), dtype=float32)\n",
      "scalars/ (Group)\n",
      "    scalars  (Dataset, shape=(8, 64, 3), dtype=float32)\n",
      "    steps  (Dataset, shape=(65,), dtype=int32)\n",
      "trajectory/ (Group)\n",
      "    images  (Dataset, shape=(8, 12, 2048, 3), dtype=int32)\n",
      "    positions  (Dataset, shape=(8, 12, 2048, 3), dtype=float32)\n",
      "    ptypes  (Dataset, shape=(8, 12, 2048), dtype=int32)\n",
      "    steps  (Dataset, shape=(12,), dtype=int32)\n",
      "    topologies/ (Group)\n",
      "        block0000/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0001/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0002/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0003/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0004/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0005/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0006/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n",
      "        block0007/ (Group)\n",
      "            angles  (Dataset, shape=(0,), dtype=int32)\n",
      "            bonds  (Dataset, shape=(0,), dtype=int32)\n",
      "            dihedrals  (Dataset, shape=(0,), dtype=int32)\n",
      "            molecules/ (Group)\n"
     ]
    }
   ],
   "source": [
    "gp.tools.print_h5_structure(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d99f3cbe-eb60-450e-ab57-c54b9db18603",
   "metadata": {},
   "source": [
    "### Attributes inside the H5 file\n",
    "Below is a function that will print the attributes in a given h5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d48dc708-79d6-43ae-8232-b67127b0f1a6",
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    "ExecuteTime": {
     "end_time": "2025-08-23T08:17:11.285209Z",
     "start_time": "2025-08-23T08:17:11.270953Z"
    },
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     "shell.execute_reply": "2026-05-18T13:26:59.974692Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes at /:\n",
      "    - dt: 0.005\n",
      "    - script_content: ...\n",
      "    - script_name: ...\n",
      "Attributes at /initial_configuration/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /initial_configuration/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /initial_configuration/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /initial_configuration/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/:\n",
      "    - timeblocks_between_restarts: 1\n",
      "Attributes at /restarts/restart0000/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0000/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0000/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0000/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0001/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0001/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0001/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0001/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0002/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0002/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0002/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0002/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0003/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0003/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0003/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0003/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0004/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0004/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0004/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0004/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0005/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0005/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0005/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0005/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0006/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0006/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0006/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0006/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /restarts/restart0007/:\n",
      "    - simbox_data: [12.815602 12.815602 12.815602]\n",
      "    - simbox_name: Orthorhombic\n",
      "Attributes at /restarts/restart0007/scalars:\n",
      "    - scalar_columns: ['U' 'W' 'K' 'm']\n",
      "Attributes at /restarts/restart0007/topology/molecules/:\n",
      "    - names: []\n",
      "Attributes at /restarts/restart0007/vectors:\n",
      "    - vector_columns: ['r' 'v' 'f']\n",
      "Attributes at /scalars/:\n",
      "    - compression_info: gzip with opts 4\n",
      "    - scalar_columns: ['U' 'W' 'K']\n",
      "    - scheduler: Lin\n",
      "    - scheduler_info: {\"steps_between\": 16, \"npoints\": null}\n",
      "    - steps_between_output: 16\n",
      "Attributes at /trajectory/:\n",
      "    - compression_info: gzip with opts 4\n",
      "    - num_timeblocks: 8\n",
      "    - scheduler: Log2\n",
      "    - scheduler_info: {}\n",
      "    - steps_per_timeblock: 1024\n",
      "    - trajectory_columns: ['r' 'img']\n",
      "    - update_ptype: False\n",
      "    - update_topology: False\n",
      "Attributes at /trajectory/topologies/block0000/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0001/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0002/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0003/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0004/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0005/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0006/molecules/:\n",
      "    - names: []\n",
      "Attributes at /trajectory/topologies/block0007/molecules/:\n",
      "    - names: []\n"
     ]
    }
   ],
   "source": [
    "gp.tools.print_h5_attributes(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a62831-a9b6-4fa7-a34a-82ad0e4965ed",
   "metadata": {},
   "source": [
    "## Concluding remarks\n",
    "\n",
    "You have now conducted your first simulation and done some post-analysis of the trajectory. You now understand the basics of how gamdpy works, and are ready for more advanced simulations and analysis - see examples."
   ]
  }
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