GOLD_projection_cartopy.ipynb

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     "start_time": "2022-10-14T19:23:02.679790Z"
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    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%matplotlib notebook \n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "import cartopy\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "measured-saying",
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "fname = '~/WORK_201909_GOLD_TIEGCM/GOLD_data/level2/gold_l2_on2_2018_303_v02_r01_c01.nc'\n",
    "data_xr = xr.open_dataset(fname)"
   ]
  },
  {
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   "id": "amino-california",
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    "ExecuteTime": {
     "end_time": "2022-10-14T19:23:06.536080Z",
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   },
   "outputs": [
    {
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       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (nlats: 104, nlons: 92, nmask: 4001, nscans: 68)\n",
       "Dimensions without coordinates: nlats, nlons, nmask, nscans\n",
       "Data variables:\n",
       "    mask_oi_1356        (nmask) int32 ...\n",
       "    n2_lbh_unc_ran      (nscans, nlats, nlons) float32 ...\n",
       "    lookup_table        (nscans) |S50 ...\n",
       "    mask_wavelength     (nmask) float32 ...\n",
       "    radiance_oi_1356    (nscans, nlats, nlons) float32 ...\n",
       "    channel             (nscans) |S50 ...\n",
       "    latitude            (nlats, nlons) float32 ...\n",
       "    on2                 (nscans, nlats, nlons) float32 ...\n",
       "    oi_1356_unc_ran     (nscans, nlats, nlons) float32 ...\n",
       "    on2_unc_ran         (nscans, nlats, nlons) float32 ...\n",
       "    dqi                 (nscans) int32 ...\n",
       "    scan_start_time     (nscans) |S50 ...\n",
       "    solar_zenith_angle  (nscans, nlats, nlons) float32 ...\n",
       "    longitude           (nlats, nlons) float32 ...\n",
       "    on2_dqi             (nscans, nlats, nlons) int32 ...\n",
       "    on2_unc_sys         (nscans, nlats, nlons) float32 ...\n",
       "    on2_unc_mod         (nscans, nlats, nlons) float32 ...\n",
       "    n2_lbh_unc_sys      (nscans, nlats, nlons) float32 ...\n",
       "    time_utc            (nscans, nlats, nlons) |S50 ...\n",
       "    mask_n2_lbh         (nmask) int32 ...\n",
       "    input_l1c_file      (nscans) |S50 ...\n",
       "    radiance_n2_lbh     (nscans, nlats, nlons) float32 ...\n",
       "    emission_angle      (nscans, nlats, nlons) float32 ...\n",
       "    scan_stop_time      (nscans) |S50 ...\n",
       "    oi_1356_unc_sys     (nscans, nlats, nlons) float32 ...\n",
       "    hemisphere          (nscans) |S50 ...\n",
       "Attributes:\n",
       "    Data_revision:               01\n",
       "    Logical_source:              GOLD_L2_ON2\n",
       "    PI_name:                     Richard Eastes\n",
       "    TEXT:                        The GOLD mission of opportunity flies an ult...\n",
       "    Mission_group:               GOLD\n",
       "    Data_version:                02\n",
       "    Data_type:                   APIDx? &gt; GOLD Application ID 0x?: Level 2 Da...\n",
       "    Generation_date:             20190808\n",
       "    date_start:                  2018-10-30T06:10:31Z\n",
       "    dqi:                         0\n",
       "    Instrument_type:             UV Imaging Spectrograph (Space)\n",
       "    PI_affiliation:              University of Colorado/LASP\n",
       "    Source_name:                 GOLD&gt;Global-scale Observations of the Limb a...\n",
       "    Data_cycle:                  01\n",
       "    Acknowledgement:             This is a data product from the NASA Global-...\n",
       "    Logical_source_description:  GOLD ON2 L2 Daily File\n",
       "    Discipline:                  Space Physics &gt; Ionospheric Science\n",
       "    Logical_file_id:             GOLD_L2_ON2_2018_303_v02_r01_c01\n",
       "    date_end:                    2018-10-30T23:03:45Z\n",
       "    Descriptor:                  ON2&gt;Column density ratio of atomic oxygen to...\n",
       "    Project:                     NASA &gt; GOLD\n",
       "    Software_version:            f5220e6</pre>"
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (nlats: 104, nlons: 92, nmask: 4001, nscans: 68)\n",
       "Dimensions without coordinates: nlats, nlons, nmask, nscans\n",
       "Data variables:\n",
       "    mask_oi_1356        (nmask) int32 ...\n",
       "    n2_lbh_unc_ran      (nscans, nlats, nlons) float32 ...\n",
       "    lookup_table        (nscans) |S50 ...\n",
       "    mask_wavelength     (nmask) float32 ...\n",
       "    radiance_oi_1356    (nscans, nlats, nlons) float32 ...\n",
       "    channel             (nscans) |S50 ...\n",
       "    latitude            (nlats, nlons) float32 ...\n",
       "    on2                 (nscans, nlats, nlons) float32 ...\n",
       "    oi_1356_unc_ran     (nscans, nlats, nlons) float32 ...\n",
       "    on2_unc_ran         (nscans, nlats, nlons) float32 ...\n",
       "    dqi                 (nscans) int32 ...\n",
       "    scan_start_time     (nscans) |S50 ...\n",
       "    solar_zenith_angle  (nscans, nlats, nlons) float32 ...\n",
       "    longitude           (nlats, nlons) float32 ...\n",
       "    on2_dqi             (nscans, nlats, nlons) int32 ...\n",
       "    on2_unc_sys         (nscans, nlats, nlons) float32 ...\n",
       "    on2_unc_mod         (nscans, nlats, nlons) float32 ...\n",
       "    n2_lbh_unc_sys      (nscans, nlats, nlons) float32 ...\n",
       "    time_utc            (nscans, nlats, nlons) |S50 ...\n",
       "    mask_n2_lbh         (nmask) int32 ...\n",
       "    input_l1c_file      (nscans) |S50 ...\n",
       "    radiance_n2_lbh     (nscans, nlats, nlons) float32 ...\n",
       "    emission_angle      (nscans, nlats, nlons) float32 ...\n",
       "    scan_stop_time      (nscans) |S50 ...\n",
       "    oi_1356_unc_sys     (nscans, nlats, nlons) float32 ...\n",
       "    hemisphere          (nscans) |S50 ...\n",
       "Attributes:\n",
       "    Data_revision:               01\n",
       "    Logical_source:              GOLD_L2_ON2\n",
       "    PI_name:                     Richard Eastes\n",
       "    TEXT:                        The GOLD mission of opportunity flies an ult...\n",
       "    Mission_group:               GOLD\n",
       "    Data_version:                02\n",
       "    Data_type:                   APIDx? > GOLD Application ID 0x?: Level 2 Da...\n",
       "    Generation_date:             20190808\n",
       "    date_start:                  2018-10-30T06:10:31Z\n",
       "    dqi:                         0\n",
       "    Instrument_type:             UV Imaging Spectrograph (Space)\n",
       "    PI_affiliation:              University of Colorado/LASP\n",
       "    Source_name:                 GOLD>Global-scale Observations of the Limb a...\n",
       "    Data_cycle:                  01\n",
       "    Acknowledgement:             This is a data product from the NASA Global-...\n",
       "    Logical_source_description:  GOLD ON2 L2 Daily File\n",
       "    Discipline:                  Space Physics > Ionospheric Science\n",
       "    Logical_file_id:             GOLD_L2_ON2_2018_303_v02_r01_c01\n",
       "    date_end:                    2018-10-30T23:03:45Z\n",
       "    Descriptor:                  ON2>Column density ratio of atomic oxygen to...\n",
       "    Project:                     NASA > GOLD\n",
       "    Software_version:            f5220e6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xr"
   ]
  },
  {
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   "id": "technical-march",
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    "ExecuteTime": {
     "end_time": "2022-10-14T19:49:39.209138Z",
     "start_time": "2022-10-14T19:49:36.442115Z"
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   },
   "outputs": [
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       "        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",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.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",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\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 http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\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",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\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.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"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, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* 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.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = 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.one(\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",
       "\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",
       "    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",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.gridliner.Gridliner at 0x7f14ed0bccd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot a scan\n",
    "\n",
    "from cartopy import config\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "'''Chose scan number 2 of the day'''\n",
    "scan_num=2\n",
    "\n",
    "'''Setup the format if needed'''\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 20,\n",
    "    \"lines.color\": \"black\",\n",
    "    \"patch.edgecolor\": \"black\",\n",
    "    \"text.color\": \"white\",\n",
    "    \"axes.facecolor\": \"white\",\n",
    "    \"axes.edgecolor\": \"black\",\n",
    "    \"axes.labelcolor\": \"black\",\n",
    "    \"xtick.color\": \"black\",\n",
    "    \"ytick.color\": \"black\",\n",
    "    \"grid.color\": \"lightgray\",\n",
    "    \"figure.facecolor\": \"white\",\n",
    "    \"figure.edgecolor\": \"white\",\n",
    "    \"savefig.facecolor\": \"white\",\n",
    "    \"savefig.edgecolor\": \"white\"})\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "plt.rcParams['axes.labelpad']=15\n",
    "\n",
    "'''Setup the size of the figure'''\n",
    "fig= plt.figure(figsize=(7,6))\n",
    "\n",
    "'''Title'''\n",
    "fig.suptitle('%s to \\n %s' % (str(data_xr.scan_start_time[scan_num].values)[2:-2],str(data_xr.scan_stop_time[scan_num].values)[2:-2]),color='black')\n",
    "\n",
    "'''Setup the projection of your plot, I use 'geostationary' as a example, you can also try NearsidePerspective if you want to focus on other latitue '''\n",
    "# Nearside_ax=ccrs.NearsidePerspective(\n",
    "#                             central_latitude=10.,\n",
    "#                             central_longitude=-50.,\n",
    "#                             satellite_height=35785831)\n",
    "geostationary_ax = ccrs.Geostationary(central_longitude=-30, satellite_height=65785831)\n",
    "\n",
    "\n",
    "'''Select the data you like to plot'''\n",
    "r1356 = data_xr['radiance_oi_1356'][scan_num, :, :]\n",
    "lats = data_xr['latitude']\n",
    "lons = data_xr['longitude']\n",
    "\n",
    "'''Simply using pcolor with a proper projection, you can make the plot with data on the globe.'transform=ccrs.PlateCarree()' place the data on accordingly in a geographic longitude vs. latitude coordinate'''\n",
    "ax=plt.subplot(projection=geostationary_ax)\n",
    "im=ax.pcolor(lons, lats, r1356, transform=ccrs.PlateCarree(),cmap='plasma')#,edgecolor='black')#,vmax=1.5,vmin=0.5)\n",
    "\n",
    "\n",
    "'''As an example, I marked Boston and Madrid in the map using thier lon and lat '''\n",
    "Boston_lon=360-71.0589\n",
    "Boston_lat=42.3601\n",
    "Madrid_lon=360-3.7038\n",
    "Madrid_lat=40.4168\n",
    "ax.plot([Boston_lon,Madrid_lon],[Boston_lat,Madrid_lat],'ro',transform=ccrs.PlateCarree())\n",
    "\n",
    "'''Creat a color bar'''\n",
    "cb=plt.colorbar(im)\n",
    "cb.set_label('O I 135.6 nm Brightness (Rayleighs)')\n",
    "\n",
    "ax.set_global()\n",
    "ax.add_feature(cartopy.feature.OCEAN,color='white')\n",
    "ax.add_feature(cartopy.feature.LAND,color='white')\n",
    "ax.coastlines(color='lightgrey',linewidth=1)\n",
    "ax.gridlines(color='lightgrey')\n",
    "\n",
    "#'''If you want to save the plot'''\n",
    "# plt.savefig('plot location')\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "proved-cabin",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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