ase-symmetry.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Crystal symmetry in ASE\n",
    "\n",
    "This notebook provides an introduction to some of the crystal symmetry features in ASE and spglib.\n",
    "\n",
    "It is intended as a self-guided tour, with small problems set in **bold text**. A few example systems are used, and readers are strongly encouraged to try out these tools with systems they are interested in, especially those with non-primitive unit cells and where data is available from geometry optimisation or structure prediction.\n",
    "\n",
    "> Notes written in quote blocks give tips that are related more to Python, IPython and Jupyter features.\n",
    "> Some familiarity with Python is assumed, but these tips should be helpful for completing the problems.\n",
    "\n",
    "The tutorial will first look at how we can apply known symmetry information when creating a system, then move on to analysing the symmetry of an existing structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bravais lattices\n",
    "The simplest crystal structures are defined entirely by their periodicity; the position of the atom within the unit cell is unimportant.\n",
    "\n",
    "Here we consider metallic silver: the list of available Bravais lattices in ASE is available [here](https://wiki.fysik.dtu.dk/ase/ase/lattice.html#available-crystal-lattices)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Made a lattice for Ag:  <class 'ase.lattice.FCC'>\n"
     ]
    }
   ],
   "source": [
    "import ase.lattice\n",
    "\n",
    "ag_lattice = ase.lattice.FCC(a=4.09) # Face-centered cubic\n",
    "print(\"Made a lattice for Ag: \", type(ag_lattice))\n",
    "\n",
    "assert isinstance(ag_lattice, ase.lattice.BravaisLattice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an instance of the FCC class which is derived from a parent class *BravaisLattice*. We don't work with BravaisLattice directly, but it provides features to its \"children\". Here we have set up an instance of FCC with lattice parameters matching metallic silver. To make an Atoms object, we convert to a unit cell and then to Atoms.\n",
    "> In Python, we can compare types with `type(x) == type(y)` but this will only succeed if they are exactly the same.\n",
    "> It is often more robust to use `isinstance()`, which will return True if the object type matches _or inherits from_ the compared type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell([[0.0, 2.045, 2.045], [2.045, 0.0, 2.045], [2.045, 2.045, 0.0]])\n"
     ]
    }
   ],
   "source": [
    "from ase import Atoms\n",
    "ag_cell = ag_lattice.tocell()\n",
    "ag_atoms = Atoms('Ag', cell=ag_cell, pbc=True)\n",
    "print(ag_atoms.cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that _Atoms_ needs to know what element we are using, while the _Cell_ does not. Because there is one atom and it doesn't matter where it goes, we didn't have to specify any positions.\n",
    "\n",
    "This operation has constructed a *primitive* cell of Ag rather than the cubic cell with 4.09Å sides.\n",
    "We can view the structure with ASE's inbuilt viewer. By default the `ase.visualize.view` command will create a pop-up window with the structure in it (you may need to move your browser window to spot it!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ase.visualize import view\n",
    "view(ag_atoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more notebook-friendly option is to plot with matplotlib. (Interactive notebook viewers are also avilable; see the [ASE docs](https://wiki.fysik.dtu.dk/ase/ase/visualize/visualize.html#viewer-for-jupyter-notebooks).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "    );\n",
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       "    nav_element.append(status_bar);\n",
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       "\n",
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       "}\n",
       "\n",
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       "\n",
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       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
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       "}\n",
       "\n",
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       "    // Updates the figure title.\n",
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       "\n",
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       "        break;\n",
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       "        break;\n",
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       "\n",
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       "\n",
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       "    // Request the server to send over a new figure.\n",
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       "\n",
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       "\n",
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       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122ad0e10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from ase.visualize.plot import plot_atoms\n",
    "plot_atoms(ag_atoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now try to build a tetragonal unit cell. Visualise it to see if it makes sense.**\n",
    "\n",
    "> It's not obvious what the lattice generating functions will be named.\n",
    "> We could look in the documentation or the source code, but from an iPython terminal\n",
    "> or Jupyter notebook there is another convenient option: tab-completion.\n",
    "> Type `ase.lattice.` and hit the tab key a few times to open a list of available options.\n",
    "> These are all the classes, functions and properties available in the `ase.lattice` module.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Space groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In crystallography, material structures are typically defined by a combination of\n",
    "- Bravais lattice\n",
    "- Symmetry operations\n",
    "- Atomic basis\n",
    "\n",
    "All the possible combinations of Bravais lattice types and symmetry operations have been determined by group theory and are assigned to crystallogaphic space groups. There are a few notation schemes, and arguably the easiest to work with are the sequential numbers 1-230 given in the *International Tables for Crystallography, vol. A*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine a public-domain .cif file from Crystallography Open Database. Put this file, downloaded from www.crystallography.net/cod/cif/1/52/68/1526860.cif, in the current working directory a rename it to *quartz.cif*.\n",
    "\n",
    "> From a Linux or Mac system it's possible to make a call to the Bash shell to display the file, even though this is primarily a Python notebook, with the \"cell magic\" `%%bash`. You can also view this file with a text editor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#------------------------------------------------------------------------------\n",
      "#$Date: 2016-02-13 21:28:24 +0200 (Sat, 13 Feb 2016) $\n",
      "#$Revision: 176429 $\n",
      "#$URL: svn://www.crystallography.net/cod/cif/1/52/68/1526860.cif $\n",
      "#------------------------------------------------------------------------------\n",
      "#\n",
      "# This file is available in the Crystallography Open Database (COD),\n",
      "# http://www.crystallography.net/\n",
      "#\n",
      "# All data on this site have been placed in the public domain by the\n",
      "# contributors.\n",
      "#\n",
      "data_1526860\n",
      "loop_\n",
      "_publ_author_name\n",
      "'Tucker, M.G.'\n",
      "'Dove, M.T.'\n",
      "'Keen, D.A.'\n",
      "_publ_section_title\n",
      ";\n",
      " A detailed structural characterization of quartz on heating through the\n",
      " alpha - beta transition\n",
      ";\n",
      "_journal_name_full               'Mineralogical Magazine (1969-)'\n",
      "_journal_page_first              489\n",
      "_journal_page_last               507\n",
      "_journal_volume                  65\n",
      "_journal_year                    2001\n",
      "_chemical_formula_sum            'O2 Si'\n",
      "_chemical_name_systematic        'Si O2'\n",
      "_space_group_IT_number           152\n",
      "_symmetry_space_group_name_Hall  'P 31 2\"'\n",
      "_symmetry_space_group_name_H-M   'P 31 2 1'\n",
      "_cell_angle_alpha                90\n",
      "_cell_angle_beta                 90\n",
      "_cell_angle_gamma                120\n",
      "_cell_formula_units_Z            3\n",
      "_cell_length_a                   4.9019\n",
      "_cell_length_b                   4.9019\n",
      "_cell_length_c                   5.3988\n",
      "_cell_volume                     112.346\n",
      "_citation_journal_id_ASTM        MNLMBB\n",
      "_cod_data_source_file            Tucker_MNLMBB_2001_1782.cif\n",
      "_cod_data_source_block           O2Si1\n",
      "_cod_original_cell_volume        112.3458\n",
      "_cod_original_formula_sum        'O2 Si1'\n",
      "_cod_database_code               1526860\n",
      "loop_\n",
      "_symmetry_equiv_pos_as_xyz\n",
      "x,y,z\n",
      "-y,x-y,z+1/3\n",
      "-x+y,-x,z+2/3\n",
      "y,x,-z\n",
      "-x,-x+y,-z+1/3\n",
      "x-y,-y,-z+2/3\n",
      "loop_\n",
      "_atom_site_label\n",
      "_atom_site_type_symbol\n",
      "_atom_site_fract_x\n",
      "_atom_site_fract_y\n",
      "_atom_site_fract_z\n",
      "_atom_site_occupancy\n",
      "_atom_site_U_iso_or_equiv\n",
      "O1 O-2 0.413 0.2711 0.2172 1 0.0\n",
      "Si1 Si+4 0.4673 0 0.3333 1 0.0\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cat quartz.cif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working from the bottom to the top of the file, we see in the last two lines that coordinates are only provided for two atoms in the cell. The \"loop_\" lines above define that format for these coordinates -- CIF is not the most concise format for structure data! Above these lines we see a loop over symmetry operations:\n",
    "```\n",
    "loop_\n",
    "_symmetry_equiv_pos_as_xyz\n",
    "x,y,z\n",
    "-y,x-y,z+1/3\n",
    "-x+y,-x,z+2/3\n",
    "y,x,-z\n",
    "-x,-x+y,-z+1/3\n",
    "x-y,-y,-z+2/3\n",
    "```\n",
    "\n",
    "`x,y,z` is the identity operation and `y,x,-z` is a rotation/reflection operation. The other operations include translations in the z axis - given that we only have one Si and one O in the basis, these *must* create some extra atoms.\n",
    "\n",
    "At the top of the file are lattice parameters and metadata, including several definitions of the spacegroup.\n",
    "```\n",
    "_space_group_IT_number           152\n",
    "_symmetry_space_group_name_Hall  'P 31 2\"'\n",
    "_symmetry_space_group_name_H-M   'P 31 2 1'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the `ase.spacegroup` module we find the command `ase.spacegroup.crystal` which allows us to build an _Atoms_ object using this crystallographic information.\n",
    "\n",
    "> This may not be the most obvious place as a user to look for this function: other high-symmetry \"structure builder\" functions live in `ase.build`. However, this is the module that contains the underlying symmetry tools, as we'll see in the next part of the tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124807320>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ase.spacegroup\n",
    "quartz = ase.spacegroup.crystal(symbols=['O', 'Si'],\n",
    "                                basis=[[0.413, 0.2711, 0.2172],\n",
    "                                       [0.4673, 0, 0.3333]],\n",
    "                                spacegroup=152,\n",
    "                                cellpar=[4.9019, 4.9019, 5.3988, 90, 90, 120])\n",
    "fig, ax = plt.subplots()\n",
    "plot_atoms(quartz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convince yourself that this structure is sensible**\n",
    "\n",
    "> Hopefully you can find out that this is a network of tetrahedral units, consisting of Si surrounded by O. It may be helpful to open the 3D viewer (using `view()`) and use the \"repeats\" option in the View menu to expand the viewing region. Alternatively, we can dump out a structure with e.g. `quartz.write('POSCAR', vasp5=True)` for viewing in your favourite atomistic structure viewer (e.g. VESTA)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also open up the original .cif file and see if the results agree.\n",
    "\n",
    "You don't *have* to make structures from .cif files this way. ASE has CIF support built-in, and we could have saved ourselves a lot of trouble with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ase.io\n",
    "atoms = ase.io.read('quartz.cif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but it's not unusual to find structures specified this way in research papers or textbooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Examine a .cif file for a high-symmetry material of interest to your research. How many symmetry operations are there? What happens when you construct the cell in ASE?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the symmetry operations\n",
    "How did ASE do that?\n",
    "\n",
    "ASE has a data structure for spacegroups that includes information about the symmetry operations, loaded from a data table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is a-quartz centrosymmetric? no\n",
      "Is the a-quartz lattice primitive? yes\n",
      "What symbol is this spacegroup? P 31 2 1\n"
     ]
    }
   ],
   "source": [
    "spg = ase.spacegroup.Spacegroup(152)\n",
    "print(\"Is a-quartz centrosymmetric? {}\".format(\"yes\" if spg.centrosymmetric else \"no\"))\n",
    "print(\"Is the a-quartz lattice primitive? {}\".format(\"yes\" if spg.lattice == 'P' else \"no\"))\n",
    "print(\"What symbol is this spacegroup? {}\".format(spg.symbol))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Class provides access to the actual symmetry operations as a list or iterator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Symmetry operation #1\n",
      "Rotation matrix:  [1 0 0]\n",
      "                  [0 1 0]\n",
      "                  [0 0 1]\n",
      "Translation vector:  [0. 0. 0.]\n",
      "\n",
      "Symmetry operation #2\n",
      "Rotation matrix:  [ 0 -1  0]\n",
      "                  [ 1 -1  0]\n",
      "                  [0 0 1]\n",
      "Translation vector:  [0.         0.         0.33333333]\n",
      "\n",
      "Symmetry operation #3\n",
      "Rotation matrix:  [-1  1  0]\n",
      "                  [-1  0  0]\n",
      "                  [0 0 1]\n",
      "Translation vector:  [0.         0.         0.66666667]\n",
      "\n",
      "Symmetry operation #4\n",
      "Rotation matrix:  [0 1 0]\n",
      "                  [1 0 0]\n",
      "                  [ 0  0 -1]\n",
      "Translation vector:  [0. 0. 0.]\n",
      "\n",
      "Symmetry operation #5\n",
      "Rotation matrix:  [ 1 -1  0]\n",
      "                  [ 0 -1  0]\n",
      "                  [ 0  0 -1]\n",
      "Translation vector:  [0.         0.         0.66666667]\n",
      "\n",
      "Symmetry operation #6\n",
      "Rotation matrix:  [-1  0  0]\n",
      "                  [-1  1  0]\n",
      "                  [ 0  0 -1]\n",
      "Translation vector:  [0.         0.         0.33333333]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, (rot, trans) in enumerate(spg.get_symop()):\n",
    "    print(\"Symmetry operation #{}\".format(i + 1))\n",
    "    print(\"Rotation matrix: \", rot[0,:])\n",
    "    print(\"                 \", rot[1,:])\n",
    "    print(\"                 \", rot[2,:])\n",
    "    print(\"Translation vector: \", trans)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hopefully these operations look familiar from the CIF file!\n",
    "\n",
    "**Have a look at the symmetry operations from some other spacegroups. Can you identify rotation, reflection and glide operations?**\n",
    "\n",
    "To generate a structure with these operations, ASE uses the symops in a method `Spacegroup.equivalent_sites`. This takes positions in fractional coordinates and applies the symmetry operations to derive a set of images. If we provide just the Si atom from the .cif file, we get our original site back, with two other images - this is consistent with the cell of three formula units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.4673    , 0.        , 0.3333    ],\n",
       "        [0.        , 0.4673    , 0.66663333],\n",
       "        [0.5327    , 0.5327    , 0.99996667]]), [0, 0, 0])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spg.equivalent_sites([0.4673, 0, 0.3333])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What does the [0, 0, 0] mean? This is not part of the equivalent_sites array. Have a look at the docstring...**\n",
    ">\n",
    "> To examine detailed help from a regular python terminal you could use `help(ase.spacegroup.Spacegroup)`.\n",
    "> To inspect the manually-written docstring part directly you could print `ase.spacegroup.Spacegroup.__doc__`.\n",
    "> From an iPython terminal or Jupyter notebook the most convenient option is the special syntax `ase.spacegroup.Spacegroup?`.\n",
    "> Most functions in ASE should have docstrings, so adding a `?` to a half-written line of code can be very helpful in the heat of the moment.\n",
    "\n",
    "**What happens if you include redundant sites in the input to *equivalent_sites()*?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing symmetry in existing structures\n",
    "\n",
    "Many atomistic codes are \"smart\" about how they use symmetry. They can identify symmetry and use this to:\n",
    "\n",
    "- reduce the amount of information to be calculated;\n",
    "- reduce the size of data files;\n",
    "- impose constraints on calculated structures and fields.\n",
    "\n",
    "However, there are several reasons reasearchers _should_ be aware (and possibly worried) about the symmetry of their system, including:\n",
    "\n",
    "- working with primitive/conventional unit cells as appropriate;\n",
    "- identifying unique sites for substitutions and defects;\n",
    "- identifying when symmetry was broken by numerical errors;\n",
    "- avoiding the imposition of excessive symmetry on top of the _magnetic_ spacegroup."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identifying the spacegroup\n",
    "\n",
    "Going back to the quartz cell that was imported from a .cif file, we can have a look at some metadata stored in the `info` attribute of *Atoms*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'spacegroup': Spacegroup(152, setting=1),\n",
       " 'unit_cell': 'conventional',\n",
       " 'occupancy': {0: {'O': 1}, 1: {'Si': 1}}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atoms.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spacegroup was actually set as a tag when it was imported. Let's break the symmetry slightly by making some random changes to the atom positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.36052887e+00,  1.15072730e+00,  1.17326705e+00],\n",
       "       [ 3.22672813e+00,  6.02155459e-01,  2.97198522e+00],\n",
       "       [ 2.76919195e+00,  2.49268218e+00,  4.77134989e+00],\n",
       "       [ 3.17205300e-01,  1.75279176e+00,  4.22571491e+00],\n",
       "       [ 7.74497067e-01,  3.64086703e+00,  6.25255722e-01],\n",
       "       [-1.09148013e+00,  3.09329153e+00,  2.42689489e+00],\n",
       "       [ 2.28974985e+00, -1.41230370e-03,  1.80088569e+00],\n",
       "       [-1.14555471e+00,  1.98383544e+00,  3.59759529e+00],\n",
       "       [ 1.30507668e+00,  2.26151294e+00,  5.39746905e+00]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atoms.rattle()\n",
    "atoms.positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This didn't change the tag, so to get the correct symmetry we need to analyse the structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spacegroup(1, setting=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ase.spacegroup.get_spacegroup(atoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have spacegroup 1, with zero symmetry. That's an accurate assessment of the system, but there are plenty of cases where we have a structure that isn't quite _perfect_ and we'd like to know what high-symmetry structure is nearby. To do this we can increase the tolerance used to determine if atoms are \"in the same position\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spacegroup(152, setting=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ase.spacegroup.get_spacegroup(atoms, symprec=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This useful function is actually provided by [spglib](https://atztogo.github.io/spglib), an external library written in C. The performance benefits of C are quite helpful here, as determining the spacegroup involves applying many symmetry operations and analysing the results. Spglib includes a Python API and the integration into ASE is not too complicated. We can also access the same function directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'P3_121 (152)'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spglib import get_spacegroup\n",
    "get_spacegroup((atoms.cell, atoms.get_scaled_positions(), atoms.numbers),\n",
    "               symprec=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a bit more cumbersome compared to calling directly from ASE because spglib doesn't know how to read an Atoms object, so we extract the necessary parts and put them in a `(tuple)`.\n",
    "**When might it be useful to call this function directly? Look at the docstring...**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning up a structure with standardize_cell\n",
    "\n",
    "There are a few more functions in the *spglib interface* that are not directly provided in ASE.\n",
    "\n",
    "Suppose your collaborator has asked you to calculate some properties and has sent you the structure file *mystery.cell* (included in the directory with this notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spacegroup(1, setting=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mystery_atoms = ase.io.read('mystery.cell')\n",
    "ase.spacegroup.get_spacegroup(mystery_atoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, you _could_ assume the structure is already perfect and we are simply dealing with a low-symmetry system.\n",
    "\n",
    "**Visualise the structure. Does this look low-symmetry to you?**\n",
    "\n",
    "It's not always easy to judge by eye. We don't know what spacegroup the material is _supposed_ to have. A useful procedure in this scenario is to look at how sensitive the spacegroup is to the distance threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold: 1e-05  Spacegroup: P 1\n",
      "Threshold: 0.0005  Spacegroup: P 1\n",
      "Threshold: 0.0001  Spacegroup: P 1\n",
      "Threshold: 0.001  Spacegroup: P 1\n",
      "Threshold: 0.005  Spacegroup: I -4\n",
      "Threshold: 0.01  Spacegroup: I -4\n",
      "Threshold: 0.1  Spacegroup: I -4\n"
     ]
    }
   ],
   "source": [
    "for symprec in (1e-5, 5e-4, 1e-4, 1e-3, 5e-3, 1e-2, 1e-1):\n",
    "    print(\"Threshold: {}  Spacegroup: {}\".format(symprec,\n",
    "                                                 ase.spacegroup.get_spacegroup(mystery_atoms,\n",
    "                                                                               symprec=symprec).symbol))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there is an I-4 system within a small atomic movement. It's possible that the symmetry-breaking is real, of course, but it would be nice to do some calculations with the high-symmetry system and get those efficiency improvements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spglib import standardize_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`standardize_cell` replaces the older `find_primitive` and `refine_cell` functions, and is generally recommended for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 5.38326953,  0.        ,  0.        ],\n",
       "        [ 0.        ,  5.38326953,  0.        ],\n",
       "        [ 0.        ,  0.        , 10.72708263]]),\n",
       " array([[0.        , 0.5       , 0.75      ],\n",
       "        [0.        , 0.        , 0.5       ],\n",
       "        [0.25965518, 0.76835118, 0.38004812],\n",
       "        [0.23164882, 0.25965518, 0.61995188],\n",
       "        [0.76835118, 0.74034482, 0.61995188],\n",
       "        [0.74034482, 0.23164882, 0.38004812],\n",
       "        [0.5       , 0.        , 0.75      ],\n",
       "        [0.5       , 0.5       , 0.5       ],\n",
       "        [0.5       , 0.        , 0.25      ],\n",
       "        [0.5       , 0.5       , 0.        ],\n",
       "        [0.75965518, 0.26835118, 0.88004812],\n",
       "        [0.73164882, 0.75965518, 0.11995188],\n",
       "        [0.26835118, 0.24034482, 0.11995188],\n",
       "        [0.24034482, 0.73164882, 0.88004812],\n",
       "        [0.        , 0.5       , 0.25      ],\n",
       "        [0.        , 0.        , 0.        ]]),\n",
       " array([29, 29, 16, 16, 16, 16, 50, 30, 29, 29, 16, 16, 16, 16, 50, 30],\n",
       "       dtype=int32))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mystery_cell = (mystery_atoms.cell, mystery_atoms.get_scaled_positions(), mystery_atoms.numbers)\n",
    "new_cell = standardize_cell(mystery_cell, to_primitive=False, symprec=5e-3)\n",
    "new_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that an spglib _cell_ is more analogous to _Atoms_ in ASE than to a _Cell_ in ASE. To convert this to an Atoms object we can feed the data into an Atoms constructor. This is made especially clear using Python's variable-unpacking syntax `x, y, z = a`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1255deb00>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_unit_cell, new_scaled_positions, new_numbers = new_cell\n",
    "new_atoms = Atoms(new_numbers, cell=new_unit_cell, scaled_positions=new_scaled_positions)\n",
    "fig, ax = plt.subplots()\n",
    "plot_atoms(new_atoms, rotation='90x,90y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Write this structure to a .cell file and compare it with the input mystery.cell file**\n",
    "\n",
    "This cell contains more atoms than the original data file. For many purposes this _conventional_ unit cell is preferred, but generally it will be more efficient to perform further calculations using the smaller _primitive_ cell.\n",
    "\n",
    "**Generate a primitive cell instead and write this to your preferred geometry file format.**\n",
    "> `spglib.standardize_cell` contains what you need to do this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Final exercise:**\n",
    "The \"mystery\" system is a photovoltaic absorber material which has been found to consistently under-perform compared to its theoretical efficiency limit. This might be due to its defect chemistry. The formation energies of isolated vacancies can be computed by constructing a supercell and removing appropriate atoms. How many inequivalent atom sites do we need to consider in this material?\n",
    "\n",
    "> Have a look at the available methods to Spacegroup and see which ones might be useful. There are several options!\n",
    "\n",
    "(This kind of study has been performed a few times, and recent work with hybrid DFT suggests that sulfur vacancies do play a significant role in this system.)"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,md"
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}