Polar_Average.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "05f9cd64",
   "metadata": {},
   "source": [
    "# Polar Average\n",
    "The following notebook is for plotting average pattern of divergence-free currents and radial magnetic field in mlt and mlat co-ordinates, as shown in the left panel of Figure 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ba3b58",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cd67eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2261e502",
   "metadata": {},
   "source": [
    "## Polar Plot Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a7dc3df",
   "metadata": {},
   "outputs": [],
   "source": [
    "class polar():\n",
    "    def __init__(self, ax, hemisphere='Northern'):\n",
    "        self.__dict__=ax.__dict__.copy()\n",
    "        self.get_figure=ax.get_figure\n",
    "        self.get_position= ax.get_position\n",
    "        self._set_position= ax._set_position\n",
    "        self.set_anchor= ax.set_anchor\n",
    "        self.set_mlt_zero_location= ax.set_theta_zero_location\n",
    "        self.set_title=ax.set_title\n",
    "        self.set_zorder= ax.set_zorder\n",
    "        if hemisphere.lower()=='northern':\n",
    "            self.Hadj= +1 # Hemispheric Adjuster\n",
    "        elif hemisphere.lower()=='southern':\n",
    "            self.Hadj= -1 # Hemispheric Adjuster\n",
    "        else:\n",
    "            raise ArgumentError(f\"hemisphere argument incorrect. Must be 'northern' or southern. argument was set to be {hemisphere}\")\n",
    "        ticks=list(range(0, 100, 10))\n",
    "        ax.set_rticks(ticks[:int(len(ax.get_yticklabels())/2)+2])\n",
    "        ax.set_xticklabels([0, 3, 6, 9, 12, 15, 18, 21])\n",
    "        labels= np.array((range(90, -10, -10)))*self.Hadj\n",
    "        ax.set_yticklabels(labels[:len(ax.get_yticklabels())])\n",
    "        ax.set_theta_zero_location('S')\n",
    "        self.old_plot= ax.plot\n",
    "        self.old_scatter= ax.scatter\n",
    "        self.set_colatmax= ax.set_rmax\n",
    "        self.set_colatmin= ax.set_rmin\n",
    "        self.old_contourf= ax.contourf\n",
    "        self.old_pcolormesh= ax.pcolormesh\n",
    "        self.old_fillbetween= ax.fill_between\n",
    "        self.in_axes= ax.in_axes\n",
    "        self.ax= ax\n",
    "        self.old_quiver= ax.quiver\n",
    "        self.in_axes= ax.in_axes\n",
    "        self.set_xlabel= self.ax.set_xlabel\n",
    "        self.set_ylabel= self.ax.set_ylabel\n",
    "        ax.format_coord= self.make_format()\n",
    "        ax.autoscale(enable=False)\n",
    "        self.legend= ax.legend\n",
    "    def set_xlim(self, *args):\n",
    "    \tprint(f'set xlim not supported \\n {args}')\n",
    "    \treturn args\n",
    "    def set_ylim(self, *args):\n",
    "    \tprint(f'set ylim not supported \\n {args}')\n",
    "    \treturn args\n",
    "    def set_xlabel(self, *args):\n",
    "    \tprint(f'set_xlabel not supported \\n {args}')\n",
    "    \treturn args\n",
    "    def set_ylabel(self, *args):\n",
    "    \tprint(f'set_ylabel not supported \\n {args}')\n",
    "    \treturn args\n",
    "    def set_latticks(self, ticks, labels=False, **kwargs):\n",
    "        if not labels:\n",
    "            labels=ticks\n",
    "        self.ax.set_rticks(90-ticks, **kwargs)\n",
    "        self.ax.set_yticklabels(labels)\n",
    "    def set_mltrange(self, mltrange, **kwargs):\n",
    "        print(mltrange)\n",
    "        theta, _ =self.conv(mltrange, [80]*2)\n",
    "        theta= np.rad2deg(theta)\n",
    "        theta[theta<0]+=360\n",
    "        theta= np.round(theta)\n",
    "        print(min(theta), max(theta))\n",
    "        self.ax.set_thetamin(min(theta), **kwargs)\n",
    "        self.ax.set_thetamax(max(theta), **kwargs)\n",
    "        mlt_labels=np.arange(0, 24, 3)\n",
    "        mlt_labels=mlt_labels[(mlt_labels>=mltrange[0]) & (mlt_labels<=mltrange[-1])]\n",
    "        theta2, _= self.conv(mlt_labels, [80]*len(mlt_labels))\n",
    "        self.ax.set_xticks(theta2)\n",
    "        self.ax.set_xticklabels(mlt_labels)\n",
    "        self.ax.set_thetamin(min(theta), **kwargs)\n",
    "        self.ax.set_thetamax(max(theta), **kwargs)\n",
    "    def conv(self, mlt, mlat):\n",
    "        mlt=np.array(mlt)\n",
    "        mlat=np.array(mlat*self.Hadj)\n",
    "        theta=mlt*np.pi/12\n",
    "        x= -np.sin(theta-np.pi/2)\n",
    "        y= np.sin(theta)\n",
    "        θ = np.arctan2(y, x)\n",
    "        return θ, 90-mlat\n",
    "    def conv_inv(self, theta, r):\n",
    "        mlat= 90-np.array([r])\n",
    "        mlt= np.array([theta]) *(12/np.pi)\n",
    "        mlt[mlt<-0]+=24\n",
    "        return mlt, mlat\n",
    "    def vec_theta(self, mlt):\n",
    "        theta=mlt*np.pi/12\n",
    "        x= np.sin(theta)\n",
    "        y= np.sin(theta-np.pi/2)\n",
    "        θ = np.arctan2(y, x)\n",
    "        return θ\n",
    "    def vec_conv(self, dr, dt, theta):\n",
    "        return dr * np.cos(theta) - dt*np.sin(theta), dr*np.sin(theta) + dt*np.cos(theta)\n",
    "    def plot(self, mlt, mlat, **kwargs):\n",
    "        return self.old_plot(*self.conv(mlt, mlat), **kwargs)\n",
    "    def scatter(self, mlt, mlat, **kwargs):\n",
    "        return self.old_scatter(*self.conv(mlt, mlat), **kwargs)\n",
    "    def set_rmin(self, mlat):\n",
    "        return self.set_colatmax(90-mlat)\n",
    "    def set_rmax(self, mlat):\n",
    "        return self.set_colatmin(90-mlat)\n",
    "    def contourf(self, mlt, mlat, z, **kwargs):\n",
    "        return self.old_contourf(*self.conv(mlt, mlat), z, **kwargs)\n",
    "    def pcolormesh(self, mlt, mlat, z, **kwargs):\n",
    "        return self.old_pcolormesh(*self.conv(mlt, mlat), z, **kwargs)\n",
    "    def fill_between(self, mlt, mlat, **kwargs):\n",
    "        return self.old_fillbetween(*self.conv(mlt, mlat), **kwargs)\n",
    "    def quiver(self, mlt, mlat, East, North, **kwargs):\n",
    "        v_theta= self.vec_theta(mlt)\n",
    "        theta, r= self.conv(mlt, mlat)\n",
    "        return self.old_quiver(theta, r, *self.vec_conv(-North, East, v_theta), **kwargs)\n",
    "    def hist2d(self, mlt, mlat , bins, hist_kwargs={}, pc_kwargs={}):\n",
    "        c, x, y=np.histogram2d(mlt, mlat, bins= bins, **hist_kwargs)\n",
    "        if 'weights' in hist_kwargs:\n",
    "            hist_kwargs['weights']=None\n",
    "            c2, x2, y2= np.histogram2d(mlt, mlat, bins=bins, **hist_kwargs)\n",
    "            c/=c2\n",
    "        return x, y, c, self.pcolormesh(*np.meshgrid(np.append(x, x[0]), y), np.append(c, [c[0]], axis=0).T, **pc_kwargs)\n",
    "    def imshow(self, image, mlt=None, mlat=None, image_format='xarray', inimage='image', **kwargs):\n",
    "        if image_format=='xarray':\n",
    "            mlt, mlat= image.mlt.values, image.mlat.values\n",
    "        mlt_ind, mlat_ind= np.isfinite(mlt), np.isfinite(mlat)\n",
    "        self.pcolormesh(mlt[mlt_ind&mlat_ind], mlat[mlt_ind&mlat_ind], image['image'].values[mlt_ind&mlat_ind], **kwargs)\n",
    "    def make_format(current):\n",
    "        def format_coord(theta, r):\n",
    "            display_coord = current.conv_inv(theta,r)\n",
    "            ax_coord= (float(i) for i in display_coord)\n",
    "            string= 'mlt={:.2f}, mlat={:.2f}'.format(*ax_coord) \n",
    "            return (string)\n",
    "        return format_coord\n",
    "    def get_projected_coastlines(self, datetime, height=0, **kwargs):\n",
    "        import cartopy.io.shapereader as shpreader\n",
    "        from apexpy import Apex\n",
    "        \"\"\" generate coastlines in projected coordinates \"\"\"\n",
    "\n",
    "        if 'resolution' not in kwargs.keys():\n",
    "            kwargs['resolution'] = '50m'\n",
    "        if 'category' not in kwargs.keys():\n",
    "            kwargs['category'] = 'physical'\n",
    "        if 'name' not in kwargs.keys():\n",
    "            kwargs['name'] = 'coastline'\n",
    "\n",
    "        shpfilename = shpreader.natural_earth(**kwargs)\n",
    "        reader = shpreader.Reader(shpfilename)\n",
    "        coastlines = reader.records()\n",
    "        multilinestrings = []\n",
    "        A = Apex(date=datetime)\n",
    "        for coastline in coastlines:\n",
    "            if coastline.geometry.geom_type == 'MultiLineString':\n",
    "                multilinestrings.append(coastline.geometry)\n",
    "                continue\n",
    "            lon, lat = np.array(coastline.geometry.coords[:]).T \n",
    "            mlat, mlon= A.geo2apex(lat, lon, height)\n",
    "            mlt= A.mlon2mlt(mlon, datetime)\n",
    "            yield mlt,mlat\n",
    "\n",
    "        for mls in multilinestrings:\n",
    "            for ls in mls:\n",
    "                lon, lat = np.array(ls.coords[:]).T\n",
    "                mlat, mlon= A.geo2apex(lat, lon, height)\n",
    "                mlt= A.mlon2mlt(mlon, datetime)\n",
    "                yield mlt, mlat\n",
    "    def coastlines(self, datetime, height=0, map_kwargs=None, plot_kwargs=None):\n",
    "        if (plot_kwargs is None):\n",
    "            plot_kwargs= {'color':'k'}\n",
    "        elif not('color' in plot_kwargs.keys()):\n",
    "            plot_kwargs.update({'color':'k'})\n",
    "        plots=[]\n",
    "        if map_kwargs is None:\n",
    "            for line in self.get_projected_coastlines(datetime,height=height):\n",
    "                plots.extend(self.plot(line[0], line[1], **plot_kwargs))\n",
    "        else:\n",
    "            for line in self.get_projected_coastlines(datetime,height=height, **map_kwargs):\n",
    "                plots.extend(self.plot(line[0], line[1], **plot_kwargs))\n",
    "            \n",
    "        return plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e9d962",
   "metadata": {},
   "source": [
    "## Loading the data and making selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2bf93d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('Meridian_Data.csv', \n",
    "                index_col=0, parse_dates=['Date_UTC'])\n",
    "# Using a selection of December for this example, to use solar wind data we recommend using \n",
    "# the OMNI data outlined in the paper\n",
    "df= df[df.Date_UTC.dt.month==12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa09ff6a",
   "metadata": {},
   "source": [
    "## Binning data and finding average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f4d97dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "East= np.array([np.histogram(df.MLT, bins=48, range=(0, 24), weights=df[f'Je_{i}'])[0] for i in range(50)])/np.histogram(df.MLT, bins=48, range=(0, 24))[0]\n",
    "North=np.array([np.histogram(df.MLT, bins=48, range=(0, 24), weights=df[f'Jn_{i}'])[0] for i in range(50)])/np.histogram(df.MLT, bins=48, range=(0, 24))[0]\n",
    "Br= np.array([np.histogram(df.MLT, bins=48, range=(0, 24), weights=df[f'Br_{i}'])[0] for i in range(50)])/np.histogram(df.MLT, bins=48, range=(0, 24))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d836e6",
   "metadata": {},
   "source": [
    "## Bin Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffb553b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlt_centers= np.arange(0.25, 24.25, .5)\n",
    "mlt_edges= np.histogram(df.MLT, bins=48, range=(0, 24))[1]\n",
    "mlat_centers= np.linspace(49, 81, 50) #Location of the evaluation points along the meridian\n",
    "mlat_edges= np.append(mlat_centers-np.diff(mlat_centers)[0]/2, mlat_centers[-1]+np.diff(mlat_centers)[0]/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a995de",
   "metadata": {},
   "source": [
    "## Creating Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca5ed22a",
   "metadata": {},
   "outputs": [
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       "                );\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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_178819/2703536824.py:19: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([0, 3, 6, 9, 12, 15, 18, 21])\n"
     ]
    }
   ],
   "source": [
    "fig= plt.figure()\n",
    "gs= fig.add_gridspec(2, 1, height_ratios=[1, .1], hspace=0.2)\n",
    "ax = polar(fig.add_subplot(gs[0], projection='polar'))\n",
    "cax= fig.add_subplot(gs[1])\n",
    "ax.ax.set_title('SECS Inversion Average')\n",
    "cbar=fig.colorbar(mpl.cm.ScalarMappable(norm = mpl.colors.Normalize(vmin=-150, vmax=150), cmap=mpl.cm.seismic), \n",
    "                  cax=cax, orientation='horizontal', extend='both')\n",
    "cbar.set_label(r'Br On Ground (nT)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e3875d",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "Plotting the vectors as pins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82f55b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting the start points of the pins, reduced to every 4th mlat point to make the plot easier to interpret\n",
    "scat=ax.scatter(np.array([mlt_centers]*len(mlat_centers))[::4, :], np.concatenate([np.vstack(mlat_centers[::4])]*len(mlt_centers), axis=1), color='black', s=1/2)\n",
    "# Plotting the vector represented by the length of the pin, reduced to every 4th mlat point\n",
    "quiv=ax.quiver(mlt_centers, mlat_centers[::4], East[::4,:], North[::4, :], scale=3, headaxislength=0, headlength=0, zorder=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e023f46",
   "metadata": {},
   "source": [
    "Plotting Br as a pcolormesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ecf5428",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_178819/2703536824.py:104: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  return self.old_pcolormesh(*self.conv(mlt, mlat), z, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Using the mlt bin edges and artificial mlat edges that contain one evaluation point to show the radial magnetic field\n",
    "mesh=ax.pcolormesh(mlt_edges, mlat_edges, Br*1e9, cmap='seismic', vmin=-150, vmax=150, zorder=0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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