{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating structures in pyiron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section gives a brief introduction about some of the tools available in pyiron to construct atomic structures. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the sake of compatibility, our structure class is written to be compatible with the popular Atomistic Simulation Environment package ([ASE](https://wiki.fysik.dtu.dk/ase/)). This makes it possible to use routines from ASE to help set-up structures.\n",
    "\n",
    "Furthermore, pyiron uses the [NGLview](http://nglviewer.org/nglview/latest/api.html) package to visualize the structures and trajectories interactively in 3D using NGLview-widgets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As preparation for the following discussion we import a few python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-10T08:32:28.838135Z",
     "start_time": "2018-02-10T08:32:26.787708Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and create a pyiron project named 'structures':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron.project import Project\n",
    "pr = Project(path='structures')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bulk crystals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we discuss various possibilities to create bulk crystal structures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `create_structure()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way to generate simple crystal structures is using the inbuilt `create_structure()` function specifying the element symbol, Bravais basis and the lattice constant(s)\n",
    "\n",
    "Note: The output gives a cubic cell rather than the smallest non-orthogonal unit cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-10T08:36:09.587677Z",
     "start_time": "2018-02-10T08:36:08.971155Z"
    }
   },
   "outputs": [],
   "source": [
    "structure = pr.create_structure('Al', \n",
    "                            bravais_basis='fcc', \n",
    "                            lattice_constant=4.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the structure interactively in 3D simply use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6d41f5f787f458f963db58d887f69cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_ColormakerRegistry()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c729cf4c00094e12bf31d78184355e12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `create_ase_bulk()`\n",
    "\n",
    "Another convenient way to set up structures is using the `create_ase_bulk()` function which is built on top of the ASE build package for [bulk crystals](https://wiki.fysik.dtu.dk/ase/ase/build/build.html#ase.build.bulk). This function returns an object which is of the pyiron structure object type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example:** fcc bulk aluminum in a cubic cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-11T08:48:16.795460Z",
     "start_time": "2018-02-11T08:48:16.525956Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c3a009bffd14d8287125e8ec46bc64a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure = pr.create_ase_bulk('Al', cubic=True)\n",
    "structure.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example:** wurtzite GaN in a 3x3x3 repeated orthorhombic cell.\n",
    "\n",
    "Note: \n",
    "- In contrast to new_structure = structure.repeat() which creates a new object, set_repeat() modifies the existing structure object.\n",
    "- Setting `spacefill=False` in the `plot3d()` method changes the atomic structure style to \"ball and stick\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-11T08:51:10.251891Z",
     "start_time": "2018-02-11T08:51:09.957494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b5439dadaa242a682b8271bab6bdf1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure = pr.create_ase_bulk('AlN', \n",
    "                           crystalstructure='wurtzite', \n",
    "                           a=3.5, orthorhombic=True)\n",
    "structure.set_repeat([3,3,3])\n",
    "structure.plot3d(spacefill=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating surfaces (using ASE)\n",
    "\n",
    "Surfaces can be created using the `create_surface()` function which is also built on top of the ASE build package for [surfaces](https://wiki.fysik.dtu.dk/ase/_modules/ase/build/surface.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example:** Creating a 3x4 fcc Al(111) surface with 4 layers and a vacuum of 10 Ångström"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea0f7bf09600445fa596d9b6f22ff3bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Al_111 = pr.create_surface(\"Al\", surface_type=\"fcc111\", \n",
    "                           size=(3, 4, 4), vacuum=10, orthogonal=True)\n",
    "Al_111.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating structures without importing the project class\n",
    "\n",
    "In all the examples shown above, the structures are create from the pyiron `Project` object. It is also possible to do this without importing/initializing this object. For this the appropriate imports must be made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron import create_ase_bulk, create_surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16b554dd32cb4639ac3e2233b9b511f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure = create_ase_bulk('AlN', \n",
    "                            crystalstructure='wurtzite', \n",
    "                            a=3.5, orthorhombic=True)\n",
    "structure.set_repeat([3,3,3])\n",
    "structure.plot3d(spacefill=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "255d7e806b574609a6a8210d79ed90d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Al_111 = create_surface(\"Al\", surface_type=\"fcc111\", \n",
    "                        size=(3, 4, 4), vacuum=10, orthogonal=True)\n",
    "Al_111.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the ASE spacegroup class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-11T09:37:06.542461Z",
     "start_time": "2018-02-11T09:37:06.310283Z"
    }
   },
   "outputs": [],
   "source": [
    "from ase.spacegroup import crystal\n",
    "from pyiron import ase_to_pyiron\n",
    "\n",
    "a = 9.04\n",
    "skutterudite = crystal(('Co', 'Sb'),\n",
    "                       basis=[(0.25, 0.25, 0.25), (0.0, 0.335, 0.158)],\n",
    "                       spacegroup=204,\n",
    "                       cellpar=[a, a, a, 90, 90, 90])\n",
    "skutterudite = ase_to_pyiron(skutterudite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-11T09:37:47.604586Z",
     "start_time": "2018-02-11T09:37:47.457434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96c71cd15e774a329a7d57f3ccba1012",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skutterudite.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing the properties of the structure object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the bulk aluminum fcc example from before the structure object can be created by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure = pr.create_ase_bulk('Al', cubic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A summary of the information about the structure is given by using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Al: [0. 0. 0.]\n",
      "Al: [0.    2.025 2.025]\n",
      "Al: [2.025 0.    2.025]\n",
      "Al: [2.025 2.025 0.   ]\n",
      "pbc: [ True  True  True]\n",
      "cell: \n",
      "[[4.05 0.   0.  ]\n",
      " [0.   4.05 0.  ]\n",
      " [0.   0.   4.05]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(structure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell vectors of the structure object can be accessed and edited through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.05, 0.  , 0.  ],\n",
       "       [0.  , 4.05, 0.  ],\n",
       "       [0.  , 0.  , 4.05]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure.cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The positions of the atoms in the structure object can be accessed and edited through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.   , 0.   ],\n",
       "       [0.   , 2.025, 2.025],\n",
       "       [2.025, 0.   , 2.025],\n",
       "       [2.025, 2.025, 0.   ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "structure.positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Point defects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a single vacancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by setting up a 4x4x4 supercell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-12T09:07:41.713136Z",
     "start_time": "2018-02-12T09:07:41.358933Z"
    }
   },
   "outputs": [],
   "source": [
    "structure = pr.create_ase_bulk('Al', cubic=True)\n",
    "structure.set_repeat([4,4,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the vacancy at position index \"0\" simply use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del structure[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the structure that now contains a vacancy run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f8d6aa814494b14877625310afb05d3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structure.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating multiple vacancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-12T09:17:37.707055Z",
     "start_time": "2018-02-12T09:17:37.321281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of atoms in the repeat unit:  256\n"
     ]
    }
   ],
   "source": [
    "# First create a 4x4x4 supercell\n",
    "structure = pr.create_ase_bulk('Al', cubic=True)\n",
    "structure.set_repeat([4,4,4])\n",
    "print('Number of atoms in the repeat unit: ',structure.get_number_of_atoms())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `del` command works for passing a list of indices to the structure object. For example, a random set of n$_{\\text{vac}}$ vacancies can be created by using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a list of indices for the vacancies\n",
    "n_vac = 24\n",
    "vac_ind_lst = np.random.permutation(len(structure))[:n_vac]\n",
    "\n",
    "# Remove atoms according to the \"vac_ind_lst\"\n",
    "del structure[vac_ind_lst]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of atoms in the repeat unit:  232\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b351697dfa9e45f085cfbbaec04e8439",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the structure\n",
    "print('Number of atoms in the repeat unit: ',structure.get_number_of_atoms())\n",
    "structure.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random substitutial alloys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 4x4x4 supercell\n",
    "structure = pr.create_ase_bulk('Al', cubic=True)\n",
    "structure.set_repeat([4,4,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Substitutional atoms can be defined by changing the atomic species accessed through its position index.\n",
    "\n",
    "Here, we set $n_{\\text{sub}}$ magnesium substitutional atoms at random positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-12T09:19:45.031085Z",
     "start_time": "2018-02-12T09:19:44.598754Z"
    }
   },
   "outputs": [],
   "source": [
    "n_sub = 24\n",
    "structure[np.random.permutation(len(structure))[:n_sub]] = 'Mg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of atoms in the repeat unit:  256\n",
      "Chemical formula:  Al232Mg24\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8b46b23d7e8488f9a6cd413cac88321",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the structure and print some additional information about the structure\n",
    "print('Number of atoms in the repeat unit: ',structure.get_number_of_atoms())\n",
    "print('Chemical formula: ',structure.get_chemical_formula())\n",
    "structure.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicit definition of the structure\n",
    "\n",
    "You can also set-up structures through the explicit input of the cell parameters and positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1356d7f3e2224fb19dd4db4ef7c9397f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell = 10.0 * np.eye(3) # Specifying the cell dimensions\n",
    "positions = [[0.25, 0.25, 0.25], [0.75, 0.75, 0.75]]\n",
    "elements = ['O', 'O']\n",
    "\n",
    "# Now use the Atoms class to create the instance.\n",
    "O_dimer = pr.create_atoms(elements=elements, scaled_positions=positions, cell=cell)\n",
    "\n",
    "O_dimer.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing from cif/other file formats\n",
    "\n",
    "Parsers from ASE can be used to import structures from other formats. In this example, we will download and import a Nepheline structure from the [Crystallography Open Database (COD)](http://www.crystallography.net/cod/index.php)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The COD structures can be accessed through their unique COD identifier\n",
    "cod = 1008753\n",
    "filename = '{}.cif'.format(cod)\n",
    "url = 'http://www.crystallography.net/cod/{}'.format(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download and save the structure file locally\n",
    "import urllib\n",
    "urllib.request.urlretrieve(url=url, filename='strucs.'+filename);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/surendralal/miniconda3/envs/pyiron_workshop/lib/python3.7/site-packages/ase/io/cif.py:375: UserWarning: crystal system 'hexagonal' is not interpreted for space group Spacegroup(173, setting=1). This may result in wrong setting!\n",
      "  setting_name, spacegroup))\n"
     ]
    }
   ],
   "source": [
    "# Using ase parsers to read the structure and then convert to a pyiron instance\n",
    "import ase\n",
    "from pyiron import ase_to_pyiron\n",
    "\n",
    "structure = ase_to_pyiron(ase.io.read(filename='strucs.'+filename,\n",
    "                                      format='cif'))\n",
    "structure.info[\"cod\"] = cod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "The package nglview needs to be installed for the plot3d() function!",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m~/science/phd/dev/pyiron/pyiron/atomistics/structure/atoms.py\u001b[0m in \u001b[0;36mplot3d\u001b[0;34m(self, show_cell, show_axes, camera, spacefill, particle_size, select_atoms, background, color_scheme, colors, scalar_field, scalar_start, scalar_end, scalar_cmap, vector_field, vector_color, magnetic_moments, custom_array, custom_3darray, view_plane, distance_from_camera)\u001b[0m\n\u001b[1;32m   1326\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# If the graphical packages are not available, the GUI will not work.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m             \u001b[0;32mimport\u001b[0m \u001b[0mnglview\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'nglview'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-184096eccb97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstructure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/science/phd/dev/pyiron/pyiron/atomistics/structure/atoms.py\u001b[0m in \u001b[0;36mplot3d\u001b[0;34m(self, show_cell, show_axes, camera, spacefill, particle_size, select_atoms, background, color_scheme, colors, scalar_field, scalar_start, scalar_end, scalar_cmap, vector_field, vector_color, magnetic_moments, custom_array, custom_3darray, view_plane, distance_from_camera)\u001b[0m\n\u001b[1;32m   1327\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnglview\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m             raise ImportError(\n\u001b[0m\u001b[1;32m   1330\u001b[0m                 \u001b[0;34m\"The package nglview needs to be installed for the plot3d() function!\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m             )\n",
      "\u001b[0;31mImportError\u001b[0m: The package nglview needs to be installed for the plot3d() function!"
     ]
    }
   ],
   "source": [
    "structure.plot3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Structures can be stored indepently from jobs in HDF5 by using the special `StructureContainer` job.  To save to disk, call `run()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job nepheline was saved and received the ID: 47\n"
     ]
    }
   ],
   "source": [
    "container = pr.create_job(pr.job_type.StructureContainer, \"nepheline\")\n",
    "container.structure = structure\n",
    "container.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to store multiple structures in one container and to store directly from a job.  Let's use this here to store the equilibrated structures at finite temperatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This group does not exist in the HDF5 file al_temp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-08-22 14:05:03,496 - pyiron_log - WARNING - The job T_400 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2020-08-22 14:05:04,282 - pyiron_log - WARNING - The job T_600 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n",
      "2020-08-22 14:05:05,065 - pyiron_log - WARNING - The job T_800 is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The job al_temp was saved and received the ID: 51\n"
     ]
    }
   ],
   "source": [
    "al_container = pr.create_job(pr.job_type.StructureContainer, \"al_temp\", delete_existing_job=True)\n",
    "for T in (400, 600, 800):\n",
    "    j = pr.create_job(pr.job_type.Lammps, \"T_{}\".format(T))\n",
    "    j.structure = pr.create_ase_bulk(\"Al\", cubic = True)\n",
    "    j.potential = j.list_potentials()[0]\n",
    "    j.calc_md(temperature=T, n_ionic_steps=1000, pressure=0)\n",
    "    j.run()\n",
    "    structure = j.get_structure(-1)\n",
    "    structure.info[\"T\"] = T\n",
    "    structure.info[\"P\"] = 0\n",
    "    al_container.append(structure)\n",
    "    \n",
    "al_container.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'T': 400, 'P': 0}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al_container.structure_lst[0].info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": [
       "Al: [0.13389146 3.96541338 4.05893092]\nAl: [3.99018226 2.0071096  1.95618182]\nAl: [1.98560236 3.88778884 2.0465924 ]\nAl: [2.04906472 2.05913422 0.09311447]\npbc: [ True  True  True]\ncell: \nCell([[4.079370396328773, 2.497893949200251e-16, 2.497893949200251e-16], [0.0, 3.973148678151775, 2.4328519056175543e-16], [0.0, 0.0, 4.077409804014061]])\n",
       "Al: [0.0070279  4.03832899 0.08383998]\nAl: [4.08339864 2.06533333 2.03444326]\nAl: [2.20534808 4.07618808 1.94632881]\nAl: [1.91118709 2.15964157 0.05514228]\npbc: [ True  True  True]\ncell: \nCell([[4.103480856873615, 2.5126573483663555e-16, 2.5126573483663555e-16], [0.0, 4.113163987813141, 2.518586556021763e-16], [0.0, 0.0, 4.11975432838738]])\n",
       "Al: [3.7382874  0.12171228 4.27645154]\nAl: [0.05199557 1.91099383 2.20493355]\nAl: [1.92074788 0.03592662 2.13915097]\nAl: [1.89264518 1.93451826 0.04368514]\npbc: [ True  True  True]\ncell: \nCell([[3.801838019513017, 2.3279543807366644e-16, 2.3279543807366644e-16], [0.0, 4.003150985990484, 2.4512230207484086e-16], [0.0, 0.0, 4.332110602330073]])\n"
      ],
      "text/plain": [
       "InputList([Al: [0.13389146 3.96541338 4.05893092]\n",
       "Al: [3.99018226 2.0071096  1.95618182]\n",
       "Al: [1.98560236 3.88778884 2.0465924 ]\n",
       "Al: [2.04906472 2.05913422 0.09311447]\n",
       "pbc: [ True  True  True]\n",
       "cell: \n",
       "Cell([[4.079370396328773, 2.497893949200251e-16, 2.497893949200251e-16], [0.0, 3.973148678151775, 2.4328519056175543e-16], [0.0, 0.0, 4.077409804014061]])\n",
       ", Al: [0.0070279  4.03832899 0.08383998]\n",
       "Al: [4.08339864 2.06533333 2.03444326]\n",
       "Al: [2.20534808 4.07618808 1.94632881]\n",
       "Al: [1.91118709 2.15964157 0.05514228]\n",
       "pbc: [ True  True  True]\n",
       "cell: \n",
       "Cell([[4.103480856873615, 2.5126573483663555e-16, 2.5126573483663555e-16], [0.0, 4.113163987813141, 2.518586556021763e-16], [0.0, 0.0, 4.11975432838738]])\n",
       ", Al: [3.7382874  0.12171228 4.27645154]\n",
       "Al: [0.05199557 1.91099383 2.20493355]\n",
       "Al: [1.92074788 0.03592662 2.13915097]\n",
       "Al: [1.89264518 1.93451826 0.04368514]\n",
       "pbc: [ True  True  True]\n",
       "cell: \n",
       "Cell([[3.801838019513017, 2.3279543807366644e-16, 2.3279543807366644e-16], [0.0, 4.003150985990484, 2.4512230207484086e-16], [0.0, 0.0, 4.332110602330073]])\n",
       "])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "al_container.structure_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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