2_double_well.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "d9a0a798",
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   "source": [
    "# 1D Double Well Potential"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb0335d",
   "metadata": {},
   "source": [
    "*Note: This is the second of a series of three jupyter notebooks. Generic explanations are given along with the code; however, for a more thorough explanation of the techniques used please visit the first notebook (linked next). The first and third notebooks can be found at this [GitHub repository](https://github.com/javier-rozalen/AJP-Article).*\n",
    "\n",
    "Here we solve the Double Well (DW) Potential in 1D for a single particle using the NQS method. The Hamiltonian reads, in HO units:\n",
    "\n",
    "$$\\hat{H}=-\\frac{1}{2}\\frac{d²}{dx²}+\\frac{1}{2}(|\\hat{x}|-b)^2,$$\n",
    "\n",
    "where $m$ is the mass of the particle and $w$ is the angular frequency of the oscillator. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104d8bce",
   "metadata": {},
   "source": [
    "## Imports\n",
    "We start our code by importing the necessary modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfa408db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "%matplotlib notebook\n",
    "import torch, time\n",
    "from torch import nn\n",
    "from torch.autograd import grad\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt # Plotting library\n",
    "from tqdm import tqdm # Progress bar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998e5043",
   "metadata": {},
   "source": [
    "## General parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b262b8e",
   "metadata": {},
   "source": [
    "We define the various parameters and hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e509882d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hardware (CPU or GPU)\n",
    "dev = 'cpu' # can be changed to 'cuda' to use GPU\n",
    "device = torch.device(dev)\n",
    "\n",
    "# Network hyperparameters\n",
    "Nin = 1   # Inputs to the neural network\n",
    "Nout = 1  # Outputs of the neural network\n",
    "Nhid = 8  # Nodes in the hidden layer\n",
    "\n",
    "# Network parameters.\n",
    "seed = 1\n",
    "torch.manual_seed(seed)\n",
    "W1 = torch.rand(Nhid, Nin, requires_grad=True) * (-1.) # First set of coefficients\n",
    "B = torch.rand(Nhid, requires_grad=True) * 2. - 1.    # Set of bias parameters\n",
    "W2 = torch.rand(Nout, Nhid, requires_grad=True)        # Second set of coefficients\n",
    "\n",
    "# Training hyperparameters\n",
    "epochs = 3000\n",
    "lr = 2e-2  # Learning rate\n",
    "\n",
    "# Mesh parameters\n",
    "Nx = 100                    # Mesh division\n",
    "train_a = -8                     # Mesh lower limit\n",
    "train_b = 8                        # Mesh upper limit\n",
    "h = (train_b - train_a) / (Nx - 1)  # Mesh parameter \"h\"\n",
    "grid = h*(np.arange(Nx)-Nx/2.+1)\n",
    "Q_train = torch.from_numpy(grid).requires_grad_().to(device).type(torch.float32)\n",
    "wi = torch.empty(Nx, 1).fill_(h).to(device)\n",
    "b = 1.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "149126a5",
   "metadata": {},
   "source": [
    "## Obtaining the benchmark functions\n",
    "\n",
    "In the notebook [Notebook: 1D Harmonic Oscillator](https://github.com/javier-rozalen/AJP-Article/blob/54ea0908b30eb471962e6d6d809d563211f83cd2/notebook1_JRS.ipynb) the solution to the problem was analytical, i.e., we had a closed-form expression for the wave function of all bound states. This time, however, the quartic term in the Hamiltonian prevents us from finding an exact solution, and we have to use computational tools. \n",
    "\n",
    "The idea is to obtain all Hamiltonian matrix elements $H_{ij}=\\langle x_i|\\hat{H}|x_j\\rangle$ and then diagonalize the resulting matrix numerically. The potential part of $H_{ij}$ is straightforward, for the potential is already diagonal in the position space grid. As for the kinetic part, it can be easily obtained by working in the momentum representation [ref appearing in Joan's TFM]. The final expression for the matrix elements can be written as:\n",
    "\n",
    "$$ H_{ij}=V(x_i)\\delta_{ij}+\\frac{1}{N_x}\\sum_{l=-N_x/2}^{N_x/2}T_l e^{(il2\\pi(i-j)/N)}, $$\n",
    "\n",
    "where $T_l=\\frac{2}{m}\\big(\\frac{\\hbar\\pi l}{N_x h}\\big)^2$. The following code cell implements this formula and diagonalizes the matrix using the numpy library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64c330c4",
   "metadata": {
    "scrolled": false
   },
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       "};\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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.canvas_div.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://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",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "# We define useful functions\n",
    "l_indices = [l for l in range(-int(Nx/2), int(Nx/2) + 1)]\n",
    "V = lambda x : 0.5*(abs(x)-b)**2\n",
    "delta = lambda i, j : 1 if i == j else 0\n",
    "pi = np.pi\n",
    "T = lambda l : 2.*(l*pi/(Nx*h))**2\n",
    "zi = complex(0., 1.) # imaginary unit\n",
    "\n",
    "# We construct the Hamiltonian matrix elements\n",
    "H = np.zeros((Nx, Nx))\n",
    "for i, xi in enumerate(grid):\n",
    "    for j, xj in enumerate(grid):\n",
    "        list_ = [T(l)*np.exp(zi*l*2.*pi*(i-j)/Nx) for l in l_indices]\n",
    "        H[i][j] = (1/Nx)*np.real(np.sum(list_)) + V(xi)*delta(i, j)\n",
    "        \n",
    "# We diagonalize H and obtain the ground-state eigenvalue and eigenvector\n",
    "eivals, eivecs = np.linalg.eigh(H)\n",
    "\n",
    "# We sort the eigenvalues and eigenvectors\n",
    "isort = np.argsort(eivals)\n",
    "eivals = eivals[isort]\n",
    "eivecs = eivecs[isort]\n",
    "\n",
    "ieig = 0\n",
    "neig = 4\n",
    "indx_origin = int(Nx/2 - 1)\n",
    "wf = np.zeros((Nx, neig))\n",
    "\n",
    "# We order the wavefunction values by setting the correct origin\n",
    "while ieig < neig :\n",
    "    # wavefunction at the origin\n",
    "    wf_at_origin = eivecs[indx_origin,ieig]\n",
    "    sss = eivecs[indx_origin+2,ieig]\n",
    "    sss = sss/np.abs(sss)\n",
    "    wf[:,ieig] = sss*eivecs[:,ieig]/np.sqrt(h);\n",
    "    ieig = ieig+1\n",
    "\n",
    "E0_th, psi0_th = eivals[0], wf[:,0]/np.sqrt(h*np.sum(wf[:,0]**2))\n",
    "E1_th, psi1_th = eivals[1], wf[:,1]/np.sqrt(h*np.sum(wf[:,1]**2))\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 5))\n",
    "for i in range(neig):\n",
    "    ax.plot(grid, wf[:, i], label=f'$\\psi_{i}$, E=' + format(eivals[i], '.4f'))\n",
    "ax.legend()\n",
    "fig.canvas.draw()\n",
    "time.sleep(0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d83f17",
   "metadata": {},
   "source": [
    "## The neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8b342a",
   "metadata": {},
   "source": [
    "We now create the neural network. The architecture is the same as that used for the HO, and therefore so is the code implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "049eb6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DWNQS(nn.Module):\n",
    "    def __init__(self, W1, B, W2):\n",
    "        super(DWNQS, self).__init__()\n",
    "        \n",
    "        # We set the operators \n",
    "        self.lc1 = nn.Linear(in_features=Nin, \n",
    "                             out_features=Nhid, \n",
    "                             bias=True)   # shape = (Nhid, Nin)\n",
    "        self.actfun = nn.Sigmoid()        # activation function\n",
    "        self.lc2 = nn.Linear(in_features=Nhid, \n",
    "                             out_features=Nout, \n",
    "                             bias=False)  # shape = (Nout, Nhid)\n",
    "        \n",
    "        # We set the parameters \n",
    "        with torch.no_grad():\n",
    "            self.lc1.weight = nn.Parameter(W1)\n",
    "            self.lc1.bias = nn.Parameter(B)\n",
    "            self.lc2.weight = nn.Parameter(W2)\n",
    "   \n",
    "    # We set the architecture\n",
    "    def forward(self, x): \n",
    "        o = self.lc2(self.actfun(self.lc1(x)))\n",
    "        return o\n",
    "    \n",
    "net = DWNQS(W1, B, W2).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a844a6",
   "metadata": {},
   "source": [
    "## The optimizer\n",
    "\n",
    "As in the first notebook, we use RMSprop as our optimizer. We can define our RMSprop optimizer in a single line of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "117b2faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.RMSprop(params=net.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb9c654b",
   "metadata": {},
   "source": [
    "## The loss function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d91dc3",
   "metadata": {},
   "source": [
    "The loss function is computed in much the same fashion as for the HO case, this is,\n",
    "\n",
    "$$\\newcommand{\\bra}[1]{{ \\left\\langle #1 \\right|} }\n",
    "\\newcommand{\\ket}[1]{{ \\left|  #1 \\right\\rangle } }\n",
    "\\mathcal{L}=\\frac{\\bra{\\psi_\\theta}\\hat{H}\\ket{\\psi_\\theta}}{\\bra{\\psi_\\theta}\\psi_\\theta\\rangle}. $$\n",
    "\n",
    "The only difference now is the potential term. Taking this into account, we can write our approximation to the integrals as:\n",
    "\n",
    "$$ \\bra{\\psi_\\theta}\\hat{H}\\ket{\\psi_\\theta}\\approx \\frac{1}{2}\\sum_{i=1}^{N_x} w_i \\bigg[\\bigg(\\frac{d\\psi_\\theta(x_i)}{dx}\\bigg)^2+(|x|-b)^2\\psi_\\theta^2(x_i)\\bigg], $$\n",
    "\n",
    "$$\\bra{\\psi_\\theta}\\psi_\\theta\\rangle\\approx \\sum_{i=1}^{N_x} w_i \\psi_\\theta^2(x_i)\\equiv N.$$\n",
    "\n",
    "The code implementation is, then:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c37b9312",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn():  \n",
    "    psi = net(X)\n",
    "    psi2 = net(X_det).pow(2)\n",
    "    dpsi_dx, = grad(outputs=psi, \n",
    "                    inputs=X,\n",
    "                    grad_outputs=torch.ones_like(psi), \n",
    "                    create_graph=True)            \n",
    "    \n",
    "    N = torch.tensordot(psi2, wi)                       \n",
    "    U =  (1/2) * torch.tensordot((torch.abs(X_det)-b).pow(2) * psi2, wi) / N # Potential energy\n",
    "    K = (1/2) * torch.tensordot(dpsi_dx.pow(2), wi) / N # Kinetic energy\n",
    "    E = U + K # Total energy\n",
    "    \n",
    "    return E, U, K, psi, psi / torch.sqrt(N) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f93b0c",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac66f49",
   "metadata": {},
   "source": [
    "We set the function that does the plots periodically. Please note that the syntax is slightly more intricate than strictly necessary; this is intended to improve the final visualization of the plots in a jupyter notebook environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "251df4b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pic(i, x, target, psi, psi_normalized, loss, U, K, ax1, ax2, ax3):   \n",
    "    \n",
    "    x_cpu = x.detach().cpu().numpy()\n",
    "    # Overlap\n",
    "    ax1.set_title('Normalized wavefunction')\n",
    "    wf = psi_normalized.detach().cpu().numpy()\n",
    "    ax1.set_ylim(np.min(wf), np.max(wf)*1.1)\n",
    "    if ax1.lines:\n",
    "        # This will run every time the function is called except the first one\n",
    "        ax1.lines[0].set_xdata(x_cpu)\n",
    "        ax1.lines[0].set_ydata(wf)\n",
    "        ax1.lines[1].set_xdata(grid)\n",
    "        ax1.lines[1].set_ydata(target)\n",
    "    else:\n",
    "        # This code will only run the first time the function is called\n",
    "        ax1.plot(x_cpu, wf, label='$\\psi_\\mathrm{NN}$', color='b')\n",
    "        ax1.plot(grid, target, linestyle=\"--\", \n",
    "                 label='$\\psi_\\mathrm{target}$', color='r')\n",
    "        ax1.legend()\n",
    "    \n",
    "    \n",
    "    # Wave function\n",
    "    ax2.set_title('NQS wavefunction')\n",
    "    wf = psi.detach().cpu().numpy()\n",
    "    maxwf = np.max(wf)\n",
    "    if maxwf < 0: wf = -wf \n",
    "    ax2.set_ylim(-0.05, maxwf*1.1)\n",
    "    if ax2.lines:\n",
    "        ax2.lines[0].set_xdata(x_cpu)\n",
    "        ax2.lines[0].set_ydata(wf)\n",
    "    else:\n",
    "        ax2.plot(x_cpu, wf, \n",
    "                 label='$\\psi_\\mathrm{NQS}$', color='b')\n",
    "        ax2.legend()\n",
    "\n",
    "    # Loss\n",
    "    ax3.set_title(\"Loss Function\")\n",
    "    ax3.set_xlim(0, i+1)\n",
    "    x = np.linspace(1, i+1, i+1)\n",
    "    if ax3.lines:\n",
    "        for line in ax3.lines:\n",
    "            line.set_xdata(x)\n",
    "            y = [loss, U, K, np.ones(i+1)*E0_th][ax3.lines.index(line)]\n",
    "            line.set_ydata(y)\n",
    "    else:\n",
    "        ax3.plot(x, loss, label='$E$', color='b')\n",
    "        ax3.plot(x, U, label='$U$', color='orange')\n",
    "        ax3.plot(x, K, label='$K$', color='g')\n",
    "        ax3.plot(x, np.ones(i+1)*E0_th, linestyle='--', label='$E_0$', color='b')\n",
    "        ax3.legend()\n",
    "        \n",
    "    fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9608aaa",
   "metadata": {},
   "source": [
    "## Training the network: ground state, $\\ket{\\psi_0}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1278b0f8",
   "metadata": {},
   "source": [
    "The training is carried out in the exact same manner as for the HO. Below is the code implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6d4cddb",
   "metadata": {},
   "outputs": [
    {
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       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
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       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
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       "        this.context.msBackingStorePixelRatio ||\n",
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       "        'canvas'\n",
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       "        'box-sizing: content-box;' +\n",
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       "                canvas.setAttribute(\n",
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       "                canvas.setAttribute(\n",
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       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
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       "                return fig.mouse_event(event, name);\n",
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       "        } else {\n",
       "            return function (event) {\n",
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       "\n",
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       "        'mousedown',\n",
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       "        on_mouse_event_closure('motion_notify')\n",
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       "\n",
       "    canvas_div.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
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       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
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       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.canvas_div.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://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",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training the NQS...: 100%|███████████████████████████████████████████████████████| 3000/3000 [00:05<00:00, 537.31it/s]\n"
     ]
    }
   ],
   "source": [
    "# Plotting\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(9, 5))\n",
    "ax1, ax2, ax3 = ax[0], ax[1], ax[2]\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "\n",
    "# Overlap\n",
    "ax1.set_xlabel(\"$x$\")\n",
    "ax1.set_ylabel(\"$\\psi(x)$\")\n",
    "\n",
    "# Wave function\n",
    "ax2.set_xlabel(\"$x$\")\n",
    "ax2.set_ylabel(\"$\\psi(x)$\")\n",
    "\n",
    "# Loss\n",
    "ax3.set_xlabel(\"Epoch\")\n",
    "ax3.set_ylabel(\"Cost\")\n",
    "ax3.set_ylim(0, 2)\n",
    "\n",
    "loss_accum = []\n",
    "U_accum = []\n",
    "K_accum = []\n",
    "X = Q_train.clone().unsqueeze(1)\n",
    "X_det = X.clone().detach()\n",
    "target = psi0_th\n",
    "\n",
    "# Training\n",
    "for i in tqdm(range(epochs), desc=\"Training the NQS...\"):\n",
    "    loss0, U, K, psi, psi_normalized = loss_fn()    # Energy and \\psi calculation\n",
    "\n",
    "    optimizer.zero_grad() # initialize gradients to zero at each epoch\n",
    "    loss0.backward()       # computation of the gradients\n",
    "    optimizer.step()      # update of the parameters\n",
    "\n",
    "    loss_accum.append(loss0.item())\n",
    "    U_accum.append(U.item())\n",
    "    K_accum.append(K.item())\n",
    "\n",
    "    if (i == epochs - 1) or (i % 100 == 0): # We plot periodically\n",
    "        pic(i, Q_train, target, psi, psi_normalized, loss_accum, U_accum, K_accum, ax1, ax2, ax3)\n",
    "        time.sleep(0.01)\n",
    "\n",
    "E0, psi0 = loss_accum[-1], psi.clone().detach()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389e119a",
   "metadata": {},
   "source": [
    "*Note: When running the above cell more than once, please take into account that the network is already trained from the first run. To restart the training from scratch, first re-run the code cells of sections [General parameters](#General-parameters), [The neural network](#The-neural-network) and [The optimizer](#The-optimizer).*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9f95cf5",
   "metadata": {},
   "source": [
    "## Training the network: first excited state, $\\ket{\\psi_1}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec6a656",
   "metadata": {},
   "source": [
    "We now go for the first excited state. As explained in the main article and in the HO notebook [include link], we want to make $\\psi_1$ antisymmetric (given that $\\psi_0$ is symmetric),\n",
    "\n",
    "$$ \\psi_{1,\\theta'}(x) \\longleftarrow \\psi_{1,\\theta'}(x)-\\psi_{1,\\theta'}(-x) .$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7afe351a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn_prime():  \n",
    "    psi = net1(X)-net1(-X)    \n",
    "    psi2 = psi.pow(2)\n",
    "    dpsi_dx, = grad(outputs=psi, \n",
    "                    inputs=X,\n",
    "                    grad_outputs=torch.ones_like(psi), \n",
    "                    create_graph=True)            \n",
    "    \n",
    "    N = torch.tensordot(psi2, wi)                       \n",
    "    U =  (1/2) * torch.tensordot((torch.abs(X_det)-b).pow(2) * psi2, wi) / N # Potential energy\n",
    "    K = (1/2) * torch.tensordot(dpsi_dx.pow(2), wi) / N # Kinetic energy\n",
    "    E = U + K # Total energy\n",
    "    \n",
    "    return E, U, K, psi, psi / torch.sqrt(N) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f5930f",
   "metadata": {},
   "source": [
    "Now we define a new neural network, which has the exact same architecture as the one used for the ground state. We can do this by creating a new instance of our [previous NQS](#The-neural-network). We also reset the optimizer parameters by redefining it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "affe0496",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Network hyperparameters\n",
    "Nin = 1   # Inputs to the neural network\n",
    "Nout = 1  # Outputs of the neural network\n",
    "Nhid = 8  # Nodes in the hidden layer\n",
    "\n",
    "# Network parameters.\n",
    "seed = 1\n",
    "torch.manual_seed(seed)\n",
    "W1 = torch.rand(Nhid, Nin, requires_grad=True) * (-1.) # First set of coefficients\n",
    "B = torch.rand(Nhid, requires_grad=True) * 2. - 1.    # Set of bias parameters\n",
    "W2 = torch.rand(Nout, Nhid, requires_grad=True)        # Second set of coefficients\n",
    "\n",
    "net1 = DWNQS(W1, B, W2).to(device)\n",
    "optimizer = torch.optim.RMSprop(params=net1.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81aeda3d",
   "metadata": {},
   "source": [
    "We define a new plotting function,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1953c128",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pic(i, x, target, psi, psi_normalized, loss, U, K, ax1, ax2, ax3):   \n",
    "    \n",
    "    x_cpu = x.detach().cpu().numpy()\n",
    "    # Overlap\n",
    "    ax1.set_title('Normalized wavefunction')\n",
    "    wf = psi_normalized.detach().cpu().numpy()\n",
    "    maxwf = np.max(wf)\n",
    "    ax1.set_ylim(np.min(wf)*1.1, maxwf*1.1)\n",
    "    if net1(torch.tensor([[-2.]]).to(device)).item() > 0.: wf *= -1\n",
    "    if ax1.lines:\n",
    "        # This will run every time the function is called except the first one\n",
    "        ax1.lines[0].set_xdata(x_cpu)\n",
    "        ax1.lines[0].set_ydata(wf)\n",
    "        ax1.lines[1].set_xdata(grid)\n",
    "        ax1.lines[1].set_ydata(target)\n",
    "    else:\n",
    "        # This code will only run the first time the function is called\n",
    "        ax1.plot(x_cpu, wf, label='$\\psi_\\mathrm{NQS}$', color='b')\n",
    "        ax1.plot(grid, target, linestyle=\"--\", \n",
    "                 label='$\\psi_\\mathrm{target}$', color='r')\n",
    "        ax1.legend()\n",
    "    \n",
    "    \n",
    "    # Wave function\n",
    "    ax2.set_title('NQS wavefunction')\n",
    "    wf = psi.detach().cpu().numpy()\n",
    "    maxwf = np.max(wf)\n",
    "    if maxwf < 0: wf = -wf \n",
    "    ax2.set_ylim(np.min(wf)*1.1, maxwf*1.1)\n",
    "    if ax2.lines:\n",
    "        ax2.lines[0].set_xdata(x_cpu)\n",
    "        ax2.lines[0].set_ydata(wf)\n",
    "    else:\n",
    "        ax2.plot(x_cpu, wf, \n",
    "                 label='$\\psi_\\mathrm{NQS}$', color='b')\n",
    "        ax2.legend()\n",
    "\n",
    "    # Loss\n",
    "    ax3.set_title(\"Loss Function\")\n",
    "    ax3.set_xlim(0, i+1)\n",
    "    x = np.linspace(1, i+1, i+1)\n",
    "    if ax3.lines:\n",
    "        for line in ax3.lines:\n",
    "            line.set_xdata(x)\n",
    "            y = [loss, U, K, np.ones(i+1)*E1_th][ax3.lines.index(line)]\n",
    "            line.set_ydata(y)\n",
    "    else:\n",
    "        ax3.plot(x, loss, label='$E$', color='b')\n",
    "        ax3.plot(x, U, label='$U$', color='orange')\n",
    "        ax3.plot(x, K, label='$K$', color='g')\n",
    "        ax3.plot(x, np.ones(i+1)*E1_th, linestyle='--', label='$E_1$', color='b')\n",
    "        ax3.legend()\n",
    "        \n",
    "    fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f25c7b",
   "metadata": {},
   "source": [
    "Now we are ready to train our ansatz $\\psi_{\\theta',1}=\\langle x | \\psi_{\\theta',1}\\rangle$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f3e8c78",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
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       "\n",
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       "        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",
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       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
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       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
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       "    this._root_extra_style(this.root);\n",
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       "    this.ws.onmessage = this._make_on_message_function(this);\n",
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       "    titlebar.classList =\n",
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       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
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       "    canvas_div.setAttribute('tabindex', '0');\n",
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       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
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       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;' +\n",
       "            'z-index: 2;'\n",
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       "        on_keyboard_event_closure('key_release')\n",
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       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
       "            'pointer-events: none;' +\n",
       "            'position: relative;' +\n",
       "            'z-index: 0;'\n",
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       "    var backingStore =\n",
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       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box;' +\n",
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       "            if (entry.devicePixelContentBoxSize) {\n",
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       "                canvas.setAttribute(\n",
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       "            canvas.style.width = width + 'px';\n",
       "            canvas.style.height = height + 'px';\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        /* User Agent sniffing is bad, but WebKit is busted:\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
       "         * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
       "         * The worst that happens here is that they get an extra browser\n",
       "         * selection when dragging, if this check fails to catch them.\n",
       "         */\n",
       "        var UA = navigator.userAgent;\n",
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       "                /* This prevents the web browser from automatically changing to\n",
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       "                 * want to control all of the cursor setting manually through\n",
       "                 * the 'cursor' event from matplotlib */\n",
       "                event.preventDefault()\n",
       "                return fig.mouse_event(event, name);\n",
       "            };\n",
       "        } else {\n",
       "            return function (event) {\n",
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       "            };\n",
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       "    canvas_div.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
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       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
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       "        on_mouse_event_closure('figure_enter')\n",
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       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
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       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    canvas_div.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
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       "            return fig.toolbar_button_onclick(name);\n",
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       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
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       "\n",
       "    fig.buttons = {};\n",
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       "    buttonGroup.classList = 'mpl-button-group';\n",
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       "        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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "    fig.canvas_div.style.cursor = msg['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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://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",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    // from https://stackoverflow.com/q/1114465\n",
       "    var boundingRect = this.canvas.getBoundingClientRect();\n",
       "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
       "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training the NQS...: 100%|███████████████████████████████████████████████████████| 3000/3000 [00:06<00:00, 495.49it/s]\n"
     ]
    }
   ],
   "source": [
    "# Plotting\n",
    "%matplotlib notebook\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(9, 5))\n",
    "ax1, ax2, ax3 = ax[0], ax[1], ax[2]\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "\n",
    "# Overlap\n",
    "ax1.set_xlabel(\"$x$\")\n",
    "ax1.set_ylabel(\"$\\psi(x)$\")\n",
    "\n",
    "# Wave function\n",
    "ax2.set_xlabel(\"$x$\")\n",
    "ax2.set_ylabel(\"$\\psi(x)$\")\n",
    "\n",
    "# Loss\n",
    "ax3.set_xlabel(\"Epoch\")\n",
    "ax3.set_ylabel(\"Cost\")\n",
    "ax3.set_ylim(0, 1.5)\n",
    "\n",
    "loss_accum = []\n",
    "U_accum = []\n",
    "K_accum = []\n",
    "X = Q_train.clone().unsqueeze(1)\n",
    "target = psi1_th\n",
    "\n",
    "# Training\n",
    "for i in tqdm(range(epochs), desc=\"Training the NQS...\"):\n",
    "    loss1, U, K, psi, psi_normalized = loss_fn_prime()    # Energy and \\psi calculation\n",
    "    \n",
    "    optimizer.zero_grad() # initialize gradients to zero at each epoch\n",
    "    loss1.backward()       # computation of the gradients\n",
    "    optimizer.step()      # update of the parameters\n",
    "    \n",
    "    loss_accum.append(loss1.item())\n",
    "    U_accum.append(U.item())\n",
    "    K_accum.append(K.item())\n",
    "    \n",
    "    if (i == epochs - 1) or (i % 100 == 0): # We plot periodically\n",
    "        pic(i, Q_train, target, psi, psi_normalized, loss_accum, U_accum, K_accum, ax1, ax2, ax3)\n",
    "        time.sleep(0.01)\n",
    "        \n",
    "E1, psi1 = loss_accum[-1], psi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "083e22b8",
   "metadata": {},
   "source": [
    "## Extra: Computation Graph\n",
    "\n",
    "Using the Python library found at this [GitHub repository](https://github.com/szagoruyko/pytorchviz) we construct the computation graph for the ground state. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57b9eabe",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Imports\n",
    "from torchviz import make_dot \n",
    "\n",
    "make_dot(loss0, params=dict(list(net.named_parameters())))"
   ]
  }
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