2 - Differential training.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Differential training of a GeodesyNet\n",
    "In this notebook we show the differential trainin: how to train a neural density field to represent the difference between a known, homogeneously filled, asteroid and a heterogeneous ground truth.\n",
    "\n",
    "**NOTE: With respect to a *normal* training (see Starter Notebook) the differences in the code are minimal**. Conceptually, we are now taking advantage of a ground truth 3D model of the asteroid surface.\n",
    "\n",
    "We suggest to run this notebook in the same conda environment as the one described in the Starter Notebook."
   ]
  },
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   "execution_count": 2,
   "metadata": {
    "scrolled": true
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    {
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     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Available devices  1\n",
      "__pyTorch VERSION: 1.11.0\n",
      "__CUDNN VERSION: 8200\n",
      "__Number CUDA Devices: 1\n",
      "Active CUDA Device: GPU 0\n",
      "Setting default tensor type to Float32\n",
      "Will use device  cuda:0\n"
     ]
    }
   ],
   "source": [
    "# core stuff\n",
    "import gravann\n",
    "import numpy as np\n",
    "import pickle as pk\n",
    "import os\n",
    "from collections import deque\n",
    "\n",
    "# pytorch\n",
    "from torch import nn\n",
    "import torch\n",
    "\n",
    "# plotting stuff\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "%matplotlib notebook\n",
    "\n",
    "# Ensure that changes in imported module (gravann most importantly) are autoreloaded\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# If possible enable CUDA\n",
    "gravann.enableCUDA()\n",
    "gravann.fixRandomSeeds()\n",
    "device = os.environ[\"TORCH_DEVICE\"]\n",
    "print(\"Will use device \",device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and visualizing the ground truth asteroid\n",
    "Here we load the ground truth model (mascon) used to generate synthetic acceleration readings. Note that the ground truth is heterogenous, that is its internal mass distribution is not uniform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nu will be added\n",
    "# one of \"bennu\" \"itokawa\" \"planetesimal\"\n",
    "name_of_gt = \"itokawa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:  Itokawa non-uniform\n",
      "Number of points:  41748\n",
      "Total mass (homogenous):  tensor(1.0000, dtype=torch.float64)\n",
      "Total mass (heterogeneous):  tensor(1.0000, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# We load the ground truth (a mascon model of some heterogenous body)\n",
    "with open(\"mascons/\"+name_of_gt+\"_nu.pk\", \"rb\") as file:\n",
    "    mascon_points, mascon_masses_nu, mascon_name = pk.load(file)\n",
    "# We also load the homogenous version of the same body\n",
    "with open(\"mascons/\"+name_of_gt+\".pk\", \"rb\") as file:\n",
    "    _, mascon_masses_u, _ = pk.load(file)\n",
    "    \n",
    "mascon_points = torch.tensor(mascon_points)\n",
    "mascon_masses_nu = torch.tensor(mascon_masses_nu)\n",
    "mascon_masses_u = torch.tensor(mascon_masses_u)\n",
    "\n",
    "\n",
    "# Print some information on the loaded ground truth \n",
    "# (non-dimensional units assumed. All mascon coordinates\n",
    "# are thus in -1,1 and the mass is 1)\n",
    "print(\"Name: \", mascon_name)\n",
    "print(\"Number of points: \", len(mascon_points))\n",
    "print(\"Total mass (homogenous): \", sum(mascon_masses_u))\n",
    "print(\"Total mass (heterogeneous): \", sum(mascon_masses_nu))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* 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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we visualize the loaded ground truth. Darker areas,\n",
    "# the head in the case of Itokawa, correspond to heavier densities.\n",
    "gravann.plot_mascon(mascon_points, mascon_masses_nu,alpha=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Representing an asteroid via a neural network\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Defining the network architecture\n",
    "Here the only change w.r.t. a normal training is the activation function of the last layer\n",
    "which needs to allow for negative numbers (to be able to remove mass from a homogeneous reference distribution) and is thus an hyperbolic tangent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding: direct encoding (i.e. feeding the network directly with the Cartesian coordinates in the unit hypercube)\n",
    "# was found to work well in most cases. But more options are implemented in the module.\n",
    "encoding = gravann.direct_encoding()\n",
    "\n",
    "# The model is here a SIREN network (FFNN with sin non linearities and a final hyperbolic tangent to predict the density)\n",
    "model = gravann.init_network(encoding, n_neurons=100, model_type=\"siren\", activation =  nn.Tanh())\n",
    "\n",
    "# When a new network is created we init empty training logs\n",
    "loss_log = []\n",
    "weighted_average_log = []\n",
    "running_loss_log = []\n",
    "n_inferences = []\n",
    "# .. and we init a loss trend indicators\n",
    "weighted_average = deque([], maxlen=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Miniconda3\\envs\\geodesynet\\lib\\site-packages\\torch\\functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\TensorShape.cpp:2228.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
     ]
    }
   ],
   "source": [
    "## ---------------------------------------------------------------------------------------\n",
    "## IF YOU WANT TO LOAD AN ALREADY TRAINED NETWORK UNCOMMENT HERE.\n",
    "## 300000 points used for the quadrature, 1000 for batches and trained for 20000 epochs\n",
    "## If a model is preloaded skip to the later interpretation of results cells\n",
    "## ---------------------------------------------------------------------------------------\n",
    "\n",
    "model.load_state_dict(torch.load(\"models/\"+name_of_gt+\"_nu_dl.mdl\"))\n",
    "# Once a model is loaded the learned constant c (named kappa in the paper) is unknown \n",
    "# and must be relearned (ideally it should also be saved at the end of the training as it is a learned parameter)\n",
    "c = gravann.compute_c_for_model(model, encoding, mascon_points, mascon_masses_nu, use_acc = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Visualizing an asteroid represented by the network\n",
    "The network output is the neural density in the unit cube. Since we are now modelling a density difference, negative values are also present and a rejection plot must be called twice to visualize the positive values (red) and the negative (blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fig = gravann.plot_model_rejection(model, encoding, views_2d=False, N=2500, progressbar=True, c=2, bw=True, color = 'b')\n",
    "gravann.plot_model_rejection(model, encoding, views_2d=False, N=2500, progressbar=True, c=2, bw=True, color = 'r', figure = fig);\n",
    "\n",
    "plt.title(\"Differential neural density field: untrained\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differential training of a geodesyNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EXPERIMENTAL SETUP ------------------------------------------------------------------------------------\n",
    "# Number of points to be used to evaluate numerically the triple integral\n",
    "# defining the acceleration. \n",
    "# Use <=30000 to for a quick training ... 300000 was used to produce most of the paper results\n",
    "n_quadrature = 300000\n",
    "\n",
    "# Dimension of the batch size, i.e. number of points\n",
    "# where the ground truth is compared to the predicted acceleration\n",
    "# at each training epoch.\n",
    "# Use 100 for a quick training. 1000  was used to produce most of the paper results\n",
    "batch_size = 1000\n",
    "\n",
    "# Loss. The normalized L1 loss (kMAE in the paper) was\n",
    "# found to be one of the best performing choices.\n",
    "# More are implemented in the module\n",
    "loss_fn = gravann.normalized_L1_loss\n",
    "\n",
    "# The numerical Integration method. \n",
    "# Trapezoidal integration is here set over a dataset containing acceleration values,\n",
    "# (it is possible to also train on values of the gravity potential, results are similar)\n",
    "mc_method = gravann.ACC_trap\n",
    "\n",
    "# The sampling method to decide what points to consider in each batch.\n",
    "# In this case we sample points unifromly in a sphere and reject those that are inside the asteroid\n",
    "targets_point_sampler = gravann.get_target_point_sampler(batch_size, \n",
    "                                                         limit_shape_to_asteroid=\"3dmeshes/\"+name_of_gt+\"_lp.pk\", \n",
    "                                                         method=\"spherical\", \n",
    "                                                         bounds=[0.0,1.0])\n",
    "# Here we set the optimizer\n",
    "learning_rate = 1e-6\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,factor = 0.8, patience = 200, min_lr = 1e-6,verbose=True)\n",
    "\n",
    "# And init the best results\n",
    "best_loss = np.inf\n",
    "best_model_state_dict = model.state_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... so far all is identical to the normal training ... in the next cell, note how the only difference is on the definition of the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# TRAINING LOOP (differential training, use of any prior shape information)------------------------\n",
    "# This cell can be stopped and started again without loosing memory of the training nor its logs\n",
    "torch.cuda.empty_cache()\n",
    "# The main training loop\n",
    "for i in range(20000):\n",
    "    # Each ten epochs we resample the target points\n",
    "    if (i % 10 == 0):\n",
    "        target_points = targets_point_sampler()\n",
    "        # We compute the labels whenever the target points are changed\n",
    "        # These are the difference between a homogeneous and an inhomogenous ground truth\n",
    "        labels_u = gravann.ACC_L(target_points, mascon_points, mascon_masses_u)\n",
    "        labels_nu = gravann.ACC_L(target_points, mascon_points, mascon_masses_nu)\n",
    "        labels = labels_nu - labels_u\n",
    "    \n",
    "    # We compute the values predicted by the neural density field\n",
    "    predicted = mc_method(target_points, model, encoding, N=n_quadrature)\n",
    "    \n",
    "    # We learn the scaling constant (k in the paper)\n",
    "    c = torch.sum(predicted*labels)/torch.sum(predicted*predicted)\n",
    "    \n",
    "    # We compute the loss (note that the contrastive loss needs a different shape for the labels)\n",
    "    if loss_fn == gravann.contrastive_loss:\n",
    "       loss = loss_fn(predicted, labels)\n",
    "    else:\n",
    "       loss = loss_fn(predicted.view(-1), labels.view(-1))\n",
    "    \n",
    "    # We store the model if it has the lowest fitness \n",
    "    # (this is to avoid losing good results during a run that goes wild)\n",
    "    if loss < best_loss:\n",
    "        best_model_state_dict = model.state_dict()\n",
    "        best_loss = loss\n",
    "        print('New Best: ', loss.item())  \n",
    "        \n",
    "    # Update the loss trend indicators\n",
    "    weighted_average.append(loss.item())\n",
    "    \n",
    "    # Update the logs\n",
    "    weighted_average_log.append(np.mean(weighted_average))\n",
    "    loss_log.append(loss.item())\n",
    "    n_inferences.append((n_quadrature*batch_size) // 100000)\n",
    "    \n",
    "    # Print every i iterations\n",
    "    if i % 25 == 0:\n",
    "        wa_out = np.mean(weighted_average)\n",
    "        print(f\"It={i}\\t loss={loss.item():.3e}\\t  weighted_average={wa_out:.3e}\\t  c={c:.3e}\")\n",
    "        \n",
    "    # Zeroes the gradient (necessary because of things)\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Backward pass: compute gradient of the loss with respect to model parameters\n",
    "    loss.backward()\n",
    "\n",
    "    # Calling the step function on an Optimizer makes an update to its\n",
    "    # parameters\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Perform a step in LR scheduler to update LR\n",
    "    scheduler.step(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we restore the learned parameters of the best model of the run\n",
    "for layer in model.state_dict():\n",
    "    model.state_dict()[layer] = best_model_state_dict[layer]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Interpretation of the neural density field learned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# First lets have a look at the training loss history\n",
    "plt.figure()\n",
    "abscissa = np.cumsum(n_inferences)\n",
    "plt.semilogy(abscissa, loss_log)\n",
    "plt.semilogy(abscissa, weighted_average_log)\n",
    "plt.xlabel(\"Thousands of model evaluations\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend([\"Loss\",\"Weighted Average Loss\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling points...: 2562it [00:03, 717.69it/s]                                                                         \n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* 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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes3DSubplot:title={'center':'3D View'}, xlabel='X', ylabel='Y'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then overlaying a heatmap to the mascons \n",
    "gravann.plot_model_vs_mascon_contours(model, encoding, mascon_points, mascon_masses_nu,c=c, progressbar = True, N=2500, heatmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing validation...:  62%|██████████████████████████████▉                   | 48700/78672 [00:53<00:32, 927.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discarding 5977 of 16220 points in altitude sampler which did not meet requested altitude.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing validation...:  75%|█████████████████████████████████████▎            | 58800/78672 [01:29<00:51, 383.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discarding 10858 of 16220 points in altitude sampler which did not meet requested altitude.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing validation...:  87%|███████████████████████████████████████████▋      | 68800/78672 [02:01<00:25, 385.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discarding 14419 of 16220 points in altitude sampler which did not meet requested altitude.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing validation...: 78800it [02:32, 518.19it/s]                                                                   \n"
     ]
    }
   ],
   "source": [
    "# Computes the Validation table with rel and abs errors on the predicted acceleration (w.r.t. ground truth) \n",
    "# at low, med, high altitudes (see paper). is requires sampling quite a lot, so it takes time \n",
    "results_geodesyNet = gravann.validation(model, encoding, mascon_points, mascon_masses_u, \n",
    "                mascon_masses_nu=mascon_masses_nu, \n",
    "                use_acc=True, \n",
    "                asteroid_pk_path=\"3dmeshes/\"+name_of_gt+\".pk\", \n",
    "                N=10000, \n",
    "                N_integration=300000,  # This needs to be the same as the number used during training, else precision will be lost\n",
    "                batch_size=100, \n",
    "                progressbar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Altitude</th>\n",
       "      <th>Normalized L1 Loss</th>\n",
       "      <th>Normalized Relative Component Loss</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>relRMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low Altitude</td>\n",
       "      <td>0.00577</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>0.023571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High Altitude</td>\n",
       "      <td>0.002641</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.071316</td>\n",
       "      <td>0.016004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Altitude_0</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.00142</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.00209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Altitude_1</td>\n",
       "      <td>0.001129</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>0.005883</td>\n",
       "      <td>0.002106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Altitude_2</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.001065</td>\n",
       "      <td>0.002696</td>\n",
       "      <td>0.001495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Altitude Normalized L1 Loss Normalized Relative Component Loss  \\\n",
       "0   Low Altitude            0.00577                             0.0043   \n",
       "1  High Altitude           0.002641                           0.002229   \n",
       "2     Altitude_0           0.001608                            0.00142   \n",
       "3     Altitude_1           0.001129                           0.001204   \n",
       "4     Altitude_2           0.000619                           0.001065   \n",
       "\n",
       "       RMSE   relRMSE  \n",
       "0  0.105047  0.023571  \n",
       "1  0.071316  0.016004  \n",
       "2    0.0072   0.00209  \n",
       "3  0.005883  0.002106  \n",
       "4  0.002696  0.001495  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_geodesyNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor_planetesimal = 9.982e12 * 6.67430e-11 / 3126.6064453124995**2\n",
    "factor_itokawa = 3.51e10 * 6.67430e-11 / 350.438691675663**2\n",
    "factor_bennu = (7.329e10   * 6.67430e-11  / 352.1486930549145**2)\n",
    "factor = factor_itokawa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "absolute = results_geodesyNet[\"Normalized L1 Loss\"] * factor\n",
    "relative = results_geodesyNet[\"Normalized Relative Component Loss\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.07e-08 & 2.15e-08 & 1.18e-08 & 0.14 & 0.12 & 0.106\n"
     ]
    }
   ],
   "source": [
    "# Prints the line for the table 1 in the paper\n",
    "print(f\"{absolute[2]:.2e} & {absolute[3]:.2e} & {absolute[4]:.2e} & {relative[2]*100:.2f} & {relative[3]*100:.2f} & {relative[4]*100:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment to save to models/\n",
    "torch.save(model.state_dict(), \"models/\"+name_of_gt+\"_nu_dl.mdl\")"
   ]
  }
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