Conduction.ipynb

{
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   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "from numba import njit\n",
    "import matplotlib.pyplot as plt\n",
    "from HydroWeno.Weno import reconstruct_weno, reconstruct_weno_z, reconstruct_weno_nm_z\n",
    "from scipy.special import gamma\n",
    "from scipy.optimize import newton_krylov, root\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smooth Non-Linear Heat Conduction Problem\n",
    "\n",
    "Presented in [Rempel 2016](http://doi.org/10.3847/1538-4357/834/1/10)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = np.linspace(0, 1, 257)\n",
    "temp = 0.1 + 0.9 * grid**5\n",
    "dx = grid[1] - grid[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "@njit('float64[:](float64[:],float64)')\n",
    "def compute_rempel_source(temperature, beta):\n",
    "    source = np.zeros_like(grid)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    tTilde = reconstruct_weno(padded)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "    \n",
    "    source[1:-1] = 1 / dx**2 * (tTilde[1,1:-1]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) - \n",
    "                             tTilde[0,1:-1]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]))\n",
    "    source[1:-1] += 1 / dx**2 * (tTilde[0,2:]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) - \n",
    "                             tTilde[1,0:-2]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]))\n",
    "    source *= 0.5\n",
    "\n",
    "    return source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Time taken: 35.693027 s, per step: 0.035693 s\n"
     ]
    }
   ],
   "source": [
    "temps = [temp]\n",
    "Theta = 0.55\n",
    "dt = 1e-3\n",
    "currentTime = 0\n",
    "start = time.time()\n",
    "steps = int(np.ceil(1.0 / dt))\n",
    "for s in tqdm(range(steps)):\n",
    "#     print(currentTime)\n",
    "    prevTemp = np.copy(temps[-1])\n",
    "    \n",
    "    def residual(temp):\n",
    "        dPlus = compute_rempel_source(temp, 2.5)\n",
    "        d = compute_rempel_source(prevTemp, 2.5)\n",
    "\n",
    "        return temp - prevTemp - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "#     newTemp = newton_krylov(residual, prevTemp)\n",
    "    newTemp = root(residual, prevTemp, method='df-sane').x\n",
    "    temps.append(newTemp)\n",
    "    currentTime += dt\n",
    "end = time.time()\n",
    "print('Time taken: %f s, per step: %f s' % (end - start, (end-start)/(len(temps)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
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       "\n",
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       "    // Updates the figure title.\n",
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       "    // Request the server to send over a new figure.\n",
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       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1294bed10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(grid, temps[0], label='Starting Solution')\n",
    "plt.plot(grid, temps[-1], '+', label='Final Solution')\n",
    "finalTemp = (0.1**3.5 + (1 - 0.1**3.5)*grid)**(2/7)\n",
    "plt.plot(grid, finalTemp, '--', label='Asymptotic Analytic Solution')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Barenblatt Problem\n",
    "\n",
    "Solution of the non-linear, non-dimensionalised Barenblatt problem presented in [Viallet et al 2011](http://doi.org/10.1051/0004-6361/201016374) (A.2.2). As this problem is represented by an infinitely fine pile of energy at $\\bar{t}=0$ s we choose to use $\\bar{t}=0.01$ s as our starting solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barenblatt(alpha, beta, Q, x, t):\n",
    "    xi0 = ((beta+2)**(beta+1) * 2**(1-beta) / (beta * np.pi**(0.5*beta)) * gamma(0.5 + 1/beta) / gamma(1/beta))**(1/(beta+2))\n",
    "    f = lambda xi: np.where(np.abs(xi) < xi0, beta * xi0**2 / (2 * (beta+2))**(1/beta)*(1-(xi/xi0)**2)**(1/beta), 0)\n",
    "    temp = (Q**2/(alpha*t))**(1/(beta+2)) * f(x / (alpha * Q**beta * t))\n",
    "    return temp\n",
    "\n",
    "def barenblatt_nondim(beta, x, t):\n",
    "    temp = t**(-1/(beta+2))\n",
    "    a = (1-(x/t**(1/(beta+2)))**2)\n",
    "    a[a<0] = 0\n",
    "    temp *= a**(1/beta)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-1.5, 1.5, 101)\n",
    "dx = x[1] - x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit('float64[:](float64[:],float64,float64)')\n",
    "def compute_barenblatt_source(temperature, alpha, beta):\n",
    "    source = np.zeros_like(x)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    tTilde = reconstruct_weno(padded)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "\n",
    "    source[1:-1] = alpha / dx**2 * (tTilde[1,1:-1]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) - \n",
    "                             tTilde[0,1:-1]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]))\n",
    "    source[1:-1] += alpha / dx**2 * (tTilde[0,2:]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) - \n",
    "                             tTilde[1,0:-2]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]))\n",
    "    source *= 0.5\n",
    "\n",
    "    return source\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abf71328c4674ddbaab3cda2d8cca0a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=2000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Time taken: 4.877753 s, per step: 0.002439 s\n"
     ]
    }
   ],
   "source": [
    "# Change beta to explore the different soluitions. Some can use larger timesteps.\n",
    "bBeta = 5.0\n",
    "bAlpha = bBeta / (2 * (bBeta+2))\n",
    "\n",
    "initialTime = 0.01\n",
    "bTemps = [barenblatt_nondim(bBeta,x,initialTime)]\n",
    "Theta = 0.55\n",
    "dt = 5e-4\n",
    "currentTime = initialTime\n",
    "start = time.time()\n",
    "steps = int(np.ceil(1.0 / dt))\n",
    "for s in tqdm(range(steps)):\n",
    "#     print(currentTime)\n",
    "    prevTemp = np.copy(bTemps[-1])\n",
    "    \n",
    "    def residual(temp):\n",
    "        dPlus = compute_barenblatt_source(temp, bAlpha, bBeta)\n",
    "        d = compute_barenblatt_source(prevTemp, bAlpha, bBeta)\n",
    "\n",
    "        return temp - prevTemp - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "#     newTemp = newton_krylov(residual, prevTemp, f_rtol=1e-7)\n",
    "    newTemp = root(residual, prevTemp, method='df-sane').x\n",
    "    bTemps.append(newTemp)\n",
    "    currentTime += dt\n",
    "end = time.time()\n",
    "print('Time taken: %f s, per step: %f s' % (end - start, (end-start)/(len(bTemps)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12bf01650>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x,bTemps[0], label='Starting Solution')\n",
    "plt.plot(x,bTemps[-1], label='Final Solution')\n",
    "plt.plot(x,barenblatt_nondim(bBeta,x,1.0),'--', label='Analytic Solution')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conductive Flux Limiting\n",
    "We adopt the form described in [Bradshaw & Cargill 2013](http://doi.org/10.1088/0004-637X/770/1/12) (Appendix) -- for electrons only. In this example we look at the temperature evolution in a slab of plasma with constant density ($n_e$). Decreasing this density, decreases the collisionality of the plasma, increasing its conductivity (as the mean free path increases). Units are CGS. In a complete system the problem will be slightly more non-linear as $n_e$ will not be constant, and alpha will therefore be an array too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ne = 1e10 # electron density cm^{-3}\n",
    "\n",
    "nGrid = 301\n",
    "sigRange = 120 # Controls the steepness of the temperature transition, increase to make steeper.\n",
    "z = np.linspace(0, 3e8, nGrid)\n",
    "dz = z[1] - z[0]\n",
    "# Initial temperature profile\n",
    "temperature = (np.tanh(np.linspace(-sigRange, sigRange, nGrid)) + 1.0) / 4.0 + 0.1\n",
    "temperature *= 1.55e7\n",
    "\n",
    "# Physical parameters needed for simultaion\n",
    "Gamma = 1.4\n",
    "GammaM = Gamma - 1.0\n",
    "Boltzmann = 1.3806e-16\n",
    "Kappa0 = 1.1e-6\n",
    "EleCv = Boltzmann * ne / GammaM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c934410>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(temperature, '+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit('float64[:](float64[:], float64, float64, float64)')\n",
    "def compute_temperature_source(temperature, alpha, beta, kappa0):\n",
    "    source = np.zeros_like(z)\n",
    "    fCond = np.zeros_like(z)\n",
    "    fSat = np.zeros_like(z)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    tTilde = reconstruct_weno(padded)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "\n",
    "    source[1:-1] = alpha / dz**2 * (kappa0 * tTilde[1,1:-1]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) - \n",
    "                             kappa0 * tTilde[0,1:-1]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]))\n",
    "    source[1:-1] += alpha / dz**2 * (kappa0 * tTilde[0,2:]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) - \n",
    "                             kappa0 * tTilde[1,0:-2]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]))\n",
    "    source *= 0.5\n",
    "\n",
    "    return source\n",
    "\n",
    "@njit('float64[:](float64[:], float64, float64, float64)')\n",
    "def compute_temperature_limited(temperature, alpha, beta, kappa0):\n",
    "    source = np.zeros_like(z)\n",
    "    fCond = np.zeros_like(z)\n",
    "    fSat = np.zeros_like(z)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    tTilde = reconstruct_weno(padded)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "\n",
    "    fCond[1:-1] = kappa0 * tTilde[1,1:-1]**beta * ((temperature[2:] - temperature[1:-1])) / dz\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[1, 1:-1])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] = fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[0,1:-1]**beta * ((temperature[1:-1] - temperature[0:-2])) / dz\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[0, 1:-1])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] -= fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[0, 2:]**beta * ((temperature[2:] - temperature[1:-1])) / dz\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[0, 2:])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] += fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[1, 0:-2]**beta * ((temperature[1:-1] - temperature[0:-2])) / dz\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[1, 0:-2])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] -= fCond[1:-1]\n",
    "    \n",
    "    source *= 0.5 * alpha / dz\n",
    "    \n",
    "    return source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8740b0ab0e354a6fbbc9a9a325b56612",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Time taken: 51.310024 s, per step: 0.025655 s\n"
     ]
    }
   ],
   "source": [
    "cBeta = 2.5\n",
    "cAlpha = 1 / EleCv\n",
    "\n",
    "initialTime = 0\n",
    "cTemps = [temperature]\n",
    "lTemps = [temperature]\n",
    "Theta = 0.55\n",
    "dt = 1e-4\n",
    "currentTime = initialTime\n",
    "start = time.time()\n",
    "steps = int(np.ceil(0.4 / dt))\n",
    "for s in tqdm(range(steps)):\n",
    "#     print(currentTime)\n",
    "    prevTemp = np.copy(cTemps[-1])\n",
    "    prevTempLim = np.copy(lTemps[-1])\n",
    "    \n",
    "    def residual(temp):\n",
    "        dPlus = compute_temperature_source(temp, cAlpha, cBeta, Kappa0)\n",
    "        d = compute_temperature_source(prevTemp, cAlpha, cBeta, Kappa0)\n",
    "\n",
    "        return temp - prevTemp - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "    def residual_lim(temp):\n",
    "        dPlus = compute_temperature_limited(temp, cAlpha, cBeta, Kappa0)\n",
    "        d = compute_temperature_limited(prevTempLim, cAlpha, cBeta, Kappa0)\n",
    "\n",
    "        return temp - prevTempLim - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "#     newTemp = newton_krylov(residual, prevTemp)\n",
    "    newTemp = root(residual, prevTemp, method='df-sane').x\n",
    "#     newTempLim = newton_krylov(residual_lim, prevTempLim)\n",
    "    newTempLim = root(residual_lim, prevTempLim, method='df-sane').x\n",
    "    cTemps.append(newTemp)\n",
    "    lTemps.append(newTempLim)\n",
    "    currentTime += dt\n",
    "end = time.time()\n",
    "print('Time taken: %f s, per step: %f s' % (end - start, (end-start)/(len(bTemps)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" 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     "data": {
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       "<matplotlib.legend.Legend at 0x12c75bb90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(z, cTemps[0], label='Starting Solution')\n",
    "plt.plot(z, cTemps[40], label='Non-limited Solution')\n",
    "plt.plot(z, lTemps[40], '--', label='Limited Solution')\n",
    "plt.grid()\n",
    "plt.xlim(1.0e8, 2.0e8)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non-uniform Grids\n",
    "This scheme is not very sensitive to grid non-uniformity in the reconstruction, as it uses a central differencing scheme for the temperature. Nevertheless, the results are slightly improved by the use of of WENO-NM and the solution remains very good. (This is to say the NM solution is slightly better behaved in the conduction limited case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save solutions from the uniform case\n",
    "cTempsUnif = cTemps\n",
    "lTempsUnif = lTemps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the same problem again using a non-uniform grid.\n",
    "ne = 1e10 # electron density cm^{-3}\n",
    "\n",
    "nGrid = 201\n",
    "sigRange = 120 # Controls the steepness of the temperature transition, increase to make steeper.\n",
    "z_nonuniform = np.linspace(0, 3e8, nGrid)\n",
    "dz = z_nonuniform[1] - z_nonuniform[0]\n",
    "# These offsets control the the grid non-uniformity\n",
    "z_nonuniform[1:-1:2] += 0.4 * dz\n",
    "z_nonuniform[1:-1:5] -= 0.35 * dz\n",
    "# cell-centres\n",
    "zcc = 0.5 * (z_nonuniform[1:] + z_nonuniform[:-1])\n",
    "dz = z_nonuniform[1:] - z_nonuniform[:-1]\n",
    "# Initial temperature profile\n",
    "temperature = (np.tanh(np.linspace(-sigRange, sigRange, zcc.shape[0])) + 1.0) / 4.0 + 0.1\n",
    "# temperature = (np.tanh(np.linspace(-sigRange, sigRange, nGrid)) + 1.0) / 4.0 + 0.1\n",
    "temperature *= 1.55e7\n",
    "\n",
    "# Physical parameters needed for simultaion\n",
    "Gamma = 1.4\n",
    "GammaM = Gamma - 1.0\n",
    "Boltzmann = 1.3806e-16\n",
    "Kappa0 = 1.1e-6\n",
    "EleCv = Boltzmann * ne / GammaM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equations are the same, but reframed around the central difference on non-uniform grid\n",
    "@njit('float64[:](float64[:], float64[:], float64, float64, float64)')\n",
    "def compute_temperature_source(temperature, dz, alpha, beta, kappa0):\n",
    "    source = np.zeros_like(temperature)\n",
    "    fCond = np.zeros_like(temperature)\n",
    "    fSat = np.zeros_like(temperature)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    tTilde = reconstruct_weno_z(padded)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "\n",
    "    source[1:-1] = alpha / dz[1:-1] * (kappa0 * tTilde[1,1:-1]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) / (zcc[2:] - zcc[1:-1]) - \n",
    "                             kappa0 * tTilde[0,1:-1]**beta * \n",
    "                             (temperature[1:-1] - temperature[:-2]) / (zcc[1:-1] - zcc[:-2]))\n",
    "    source[1:-1] += alpha / dz[1:-1] * (kappa0 * tTilde[0,2:]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) / (zcc[2:] - zcc[1:-1]) - \n",
    "                             kappa0 * tTilde[1,0:-2]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]) / (zcc[1:-1] - zcc[0:-2]))\n",
    "    source *= 0.5\n",
    "\n",
    "    return source\n",
    "\n",
    "@njit('float64[:](float64[:], float64[:], float64, float64, float64)')\n",
    "def compute_temperature_source_nm(temperature, dz, alpha, beta, kappa0):\n",
    "    source = np.zeros_like(temperature)\n",
    "    fCond = np.zeros_like(temperature)\n",
    "    fSat = np.zeros_like(temperature)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    dzPad = np.empty(padded.shape[1])\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    dzPad[2:-2] = dz\n",
    "    dzPad[:2] = dz[0]\n",
    "    dzPad[-2:] = dz[-1]\n",
    "    tTilde = reconstruct_weno_nm_z(padded, dzPad)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "\n",
    "    #     source[1:-1] = alpha / dz[1:-1] * (kappa0 * tTilde[1,1:-1]**beta * \n",
    "    #                              (temperature[2:] - temperature[1:-1]) / dz[1:-1] - \n",
    "    #                              kappa0 * tTilde[0,1:-1]**beta * \n",
    "    #                              (temperature[1:-1] - temperature[:-2]) / dz[0:-2])\n",
    "    #     source[1:-1] += alpha / dz[1:-1] * (kappa0 * tTilde[0,2:]**beta * \n",
    "    #                              (temperature[2:] - temperature[1:-1]) / dz[1:-1] - \n",
    "    #                              kappa0 * tTilde[1,0:-2]**beta * \n",
    "    #                              (temperature[1:-1] - temperature[0:-2]) / dz[0:-2])\n",
    "    source[1:-1] = alpha / dz[1:-1] * (kappa0 * tTilde[1,1:-1]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) / (zcc[2:] - zcc[1:-1]) - \n",
    "                             kappa0 * tTilde[0,1:-1]**beta * \n",
    "                             (temperature[1:-1] - temperature[:-2]) / (zcc[1:-1] - zcc[:-2]))\n",
    "    source[1:-1] += alpha / dz[1:-1] * (kappa0 * tTilde[0,2:]**beta * \n",
    "                             (temperature[2:] - temperature[1:-1]) / (zcc[2:] - zcc[1:-1]) - \n",
    "                             kappa0 * tTilde[1,0:-2]**beta * \n",
    "                             (temperature[1:-1] - temperature[0:-2]) / (zcc[1:-1] - zcc[0:-2]))\n",
    "    \n",
    "    source *= 0.5\n",
    "\n",
    "    return source\n",
    "\n",
    "@njit('float64[:](float64[:], float64[:], float64, float64, float64)')\n",
    "def compute_temperature_limited(temperature, dz, alpha, beta, kappa0):\n",
    "    source = np.zeros_like(temperature)\n",
    "    fCond = np.zeros_like(temperature)\n",
    "    fSat = np.zeros_like(temperature)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    tTilde = reconstruct_weno_z(padded)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[1,1:-1]**beta * ((temperature[2:] - temperature[1:-1])) / (zcc[2:] - zcc[1:-1]) #dz[1:-1]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[1, 1:-1])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] = fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[0,1:-1]**beta * ((temperature[1:-1] - temperature[0:-2])) / (zcc[1:-1] - zcc[0:-2]) # dz[0:-2]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[0, 1:-1])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] -= fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[0, 2:]**beta * ((temperature[2:] - temperature[1:-1])) / (zcc[2:] - zcc[1:-1]) #dz[1:-1]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[0, 2:])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] += fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[1, 0:-2]**beta * ((temperature[1:-1] - temperature[0:-2])) / (zcc[1:-1] - zcc[0:-2]) #dz[0:-2]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[1, 0:-2])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] -= fCond[1:-1]\n",
    "\n",
    "    source *= 0.5 * alpha / dz\n",
    "    \n",
    "    return source\n",
    "\n",
    "@njit('float64[:](float64[:], float64[:], float64, float64, float64)')\n",
    "def compute_temperature_limited_nm(temperature, dz, alpha, beta, kappa0):\n",
    "    source = np.zeros_like(temperature)\n",
    "    fCond = np.zeros_like(temperature)\n",
    "    fSat = np.zeros_like(temperature)\n",
    "    \n",
    "    padded = np.zeros((1, temperature.shape[0] + 4))\n",
    "    dzPad = np.empty(padded.shape[1])\n",
    "    padded[0, 2:-2] = temperature\n",
    "    padded[0, :2] = temperature[0]\n",
    "    padded[0, -2:] = temperature[-1]\n",
    "    dzPad[2:-2] = dz\n",
    "    dzPad[:2] = dz[0]\n",
    "    dzPad[-2:] = dz[-1]\n",
    "    tTilde = reconstruct_weno_nm_z(padded, dzPad)\n",
    "    tTilde = tTilde[0, :, 2:-2]\n",
    "\n",
    "    fCond[1:-1] = kappa0 * tTilde[1,1:-1]**beta * ((temperature[2:] - temperature[1:-1])) / (zcc[2:] - zcc[1:-1]) #dz[1:-1]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[1, 1:-1])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] = fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[0,1:-1]**beta * ((temperature[1:-1] - temperature[0:-2])) / (zcc[1:-1] - zcc[0:-2]) # dz[0:-2]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[0, 1:-1])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] -= fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[0, 2:]**beta * ((temperature[2:] - temperature[1:-1])) / (zcc[2:] - zcc[1:-1]) #dz[1:-1]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[0, 2:])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] += fCond[1:-1]\n",
    "    \n",
    "    fCond[1:-1] = kappa0 * tTilde[1, 0:-2]**beta * ((temperature[1:-1] - temperature[0:-2])) / (zcc[1:-1] - zcc[0:-2]) #dz[0:-2]\n",
    "    fSat[1:-1] = 3 /( 2 * np.sqrt(9.1e-28)) * ne * (Boltzmann * tTilde[1, 0:-2])**1.5\n",
    "    fCond = fCond * fSat / (np.sqrt(fCond**2 + fSat**2))\n",
    "    \n",
    "    source[1:-1] -= fCond[1:-1]\n",
    "    \n",
    "    source *= 0.5 * alpha / dz\n",
    "    \n",
    "    return source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cb5cde190204c7fa443df1fc50164e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Time taken: 167.045293 s, per step: 0.041761 s\n"
     ]
    }
   ],
   "source": [
    "cBeta = 2.5\n",
    "cAlpha = 1 / EleCv\n",
    "\n",
    "initialTime = 0\n",
    "cTemps = [temperature]\n",
    "nmTemps = [temperature]\n",
    "lTemps = [temperature]\n",
    "lNmTemps = [temperature]\n",
    "Theta = 0.55\n",
    "dt = 1e-4\n",
    "currentTime = initialTime\n",
    "start = time.time()\n",
    "steps = int(np.ceil(0.4 / dt))\n",
    "for s in tqdm(range(steps)):\n",
    "#     print(currentTime)\n",
    "    prevTemp = np.copy(cTemps[-1])\n",
    "    prevTempNm = np.copy(nmTemps[-1])\n",
    "    prevTempLim = np.copy(lTemps[-1])\n",
    "    prevTempLimNm = np.copy(lNmTemps[-1])\n",
    "    \n",
    "    def residual(temp):\n",
    "        dPlus = compute_temperature_source(temp, dz, cAlpha, cBeta, Kappa0)\n",
    "        d = compute_temperature_source(prevTemp, dz, cAlpha, cBeta, Kappa0)\n",
    "\n",
    "        return temp - prevTemp - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "    def residual_nm(temp):\n",
    "        dPlus = compute_temperature_source_nm(temp, dz, cAlpha, cBeta, Kappa0)\n",
    "        d = compute_temperature_source_nm(prevTempNm, dz, cAlpha, cBeta, Kappa0)\n",
    "\n",
    "        return temp - prevTempNm - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "    def residual_lim(temp):\n",
    "        dPlus = compute_temperature_limited(temp, dz, cAlpha, cBeta, Kappa0)\n",
    "        d = compute_temperature_limited(prevTempLim, dz, cAlpha, cBeta, Kappa0)\n",
    "\n",
    "        return temp - prevTempLim - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "    def residual_lim_nm(temp):\n",
    "        dPlus = compute_temperature_limited_nm(temp, dz, cAlpha, cBeta, Kappa0)\n",
    "        d = compute_temperature_limited_nm(prevTempLimNm, dz, cAlpha, cBeta, Kappa0)\n",
    "\n",
    "        return temp - prevTempLimNm - dt * ((Theta)*dPlus + (1.0 - Theta)*d)\n",
    "    \n",
    "#     newTemp = newton_krylov(residual, prevTemp)\n",
    "    newTemp = root(residual, prevTemp, method='df-sane').x\n",
    "#     newTempNm = newton_krylov(residual_nm, prevTempNm)\n",
    "    newTempNm = root(residual_nm, prevTempNm, method='df-sane').x\n",
    "#     newTempLim = newton_krylov(residual_lim, prevTempLim)\n",
    "    newTempLim = root(residual_lim, prevTempLim, method='df-sane').x\n",
    "#     newTempLimNm = newton_krylov(residual_lim_nm, prevTempLimNm)\n",
    "    newTempLimNm = root(residual_lim_nm, prevTempLimNm, method='df-sane').x\n",
    "    cTemps.append(newTemp)\n",
    "    nmTemps.append(newTempNm)\n",
    "    lTemps.append(newTempLim)\n",
    "    lNmTemps.append(newTempLimNm)\n",
    "    currentTime += dt\n",
    "end = time.time()\n",
    "print('Time taken: %f s, per step: %f s' % (end - start, (end-start)/(len(cTemps)-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12e849110>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(zcc, cTemps[0], label='Starting Solution')\n",
    "plt.plot(zcc, cTemps[40], label='Non-limited Solution')\n",
    "plt.plot(zcc, nmTemps[40], label='NM Non-limited Solution')\n",
    "plt.plot(zcc, lTemps[40], '--', label='Limited Solution')\n",
    "plt.plot(z, lTempsUnif[40], '--', label='Limited Solution (Uniform)')\n",
    "plt.plot(zcc, lNmTemps[40], '--', label='NM Limited Solution')\n",
    "plt.grid()\n",
    "plt.xlim(1.0e8, 2.0e8)\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}