simple_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.data is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      "/Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.material is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      "/Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/importlib/_bootstrap.py:219: QAWarning: pyne.rxname is not yet QA compliant.\n",
      "  return f(*args, **kwds)\n",
      "/Users/wkerzend/miniconda/envs/tardis3/lib/python3.6/site-packages/pyne/ensdf.py:17: QAWarning: pyne.ensdf is not yet QA compliant.\n",
      "  warn(__name__ + \" is not yet QA compliant.\", QAWarning)\n"
     ]
    }
   ],
   "source": [
    "from tardisnuclear import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReadingFe56\n",
      "Fe57\n",
      "Co56\n",
      "Co57\n",
      "Ni56\n",
      "Ni57\n"
     ]
    }
   ],
   "source": [
    "sn_model = models.make_energy_injection_model(Ni56=0.6, Ni57=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
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       "window.mpl = {};\n",
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       "        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",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
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       "    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",
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       "            fig.request_resize(ui.size.width, ui.size.height);\n",
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       "            return fig.mouse_event(event, event['data']);\n",
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       "\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",
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       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
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       "    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",
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       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
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       "    this._resize_canvas(600, 600);\n",
       "\n",
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       "        canvas_div.focus();\n",
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       "\n",
       "    window.setTimeout(set_focus, 100);\n",
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       "\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",
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       "    function toolbar_mouse_event(event) {\n",
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       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
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       "        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",
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       "\n",
       "        nav_element.append(button);\n",
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       "\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",
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       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
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       "\n",
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       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
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       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
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       "\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",
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       "\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",
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       "\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",
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       "\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",
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       "    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",
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       "\n",
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       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
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       "\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",
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       "        break;\n",
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       "        break;\n",
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       "        break;\n",
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       "\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",
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       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
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       "\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",
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       "\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": [
       "Text(0, 0.5, 'Power erg/s')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pylab notebook\n",
    "t = np.arange(20, 1000)\n",
    "em_energy = sn_model.calculate_em_energy_per_s(t).sum(axis=1)\n",
    "lepton_energy = sn_model.calculate_lepton_energy_per_s(t).sum(axis=1)\n",
    "plot(t, em_energy, label='EM Energy')\n",
    "plot(t, lepton_energy, label='Leptron Energy')\n",
    "semilogy()\n",
    "legend()\n",
    "xlabel('Days')\n",
    "ylabel('Power erg/s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}