jupyter-notebooks/Cartopy-Clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"960\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "%matplotlib notebook\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import h5py as h5py\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.io.shapereader import Reader\n",
    "from cartopy.feature import ShapelyFeature\n",
    "from osgeo import gdal, osr\n",
    "\n",
    "projection = ccrs.PlateCarree()\n",
    "\n",
    "fig = plt.figure(dpi=150)\n",
    "\n",
    "ax = plt.axes(projection = projection)\n",
    "\n",
    "ax.coastlines('10m')\n",
    "ax.gridlines()\n",
    "\n",
    "extent = [-44.996, -44.009, -2.805, -1.809]\n",
    "\n",
    "ax.set_extent(extent, crs=ccrs.PlateCarree())\n",
    "\n",
    "ax.plot(-44.243317, -2.565823, 'bo', markersize=7, color='red', transform=ccrs.Geodetic())\n",
    "ax.text(-44.23, -2.54, 'São Luíz', color='black', size=10, transform=ccrs.Geodetic())\n",
    "\n",
    "plt.title('January 2019 Monthly Average Rain Rate')\n",
    "\n",
    "font = {'weight' : 'bold', 'size' : 5}\n",
    "\n",
    "plt.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = './resources/T23MNT_20190525T132241_TCI_60m.jp2'\n",
    "\n",
    "img = plt.imread(fname)\n",
    "\n",
    "ax.imshow(img, extent=extent, origin='upper', transform=ccrs.PlateCarree())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-44.95 -44.85 -44.75 -44.65 -44.55 -44.45 -44.35 -44.25 -44.15 -44.05]\n",
      "-44.95\n",
      "-44.05\n",
      "[-2.75 -2.65 -2.55 -2.45 -2.35 -2.25 -2.15 -2.05 -1.95 -1.85]\n",
      "-2.75\n",
      "-1.85\n"
     ]
    }
   ],
   "source": [
    "hdf5 = '/notebooks/resources/3B-MO.MS.MRG.3IMERG.20190101-S000000-E235959.01.V06A.HDF5'\n",
    "dataset = h5py.File(hdf5,'r')\n",
    "\n",
    "precip = dataset['Grid/precipitation'][:]\n",
    "precip = np.transpose(precip[0])\n",
    "\n",
    "theLats = dataset['Grid/lat'][:]\n",
    "theLons = dataset['Grid/lon'][:]\n",
    "\n",
    "# [-44.996, -44.009, -2.805, -1.809]\n",
    "\n",
    "condition = ((theLats < -44) & (theLats > -45))\n",
    "extractedLats = np.extract(condition, theLats)\n",
    "\n",
    "# print(theLats[(theLats < -44) & (theLats > -45)])\n",
    "# print(theLats[condition])\n",
    "print(extractedLats)\n",
    "print(extractedLats[0])\n",
    "print(extractedLats[-1])\n",
    "\n",
    "condition = ((theLons < -1.809) & (theLons > -2.805))\n",
    "extractedLons = np.extract(condition, theLons)\n",
    "\n",
    "# print(theLons[(theLons < -1.809) & (theLons > -2.805)])\n",
    "# print(theLons[condition])\n",
    "print(extractedLons)\n",
    "print(extractedLons[0])\n",
    "print(extractedLons[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:14: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  \n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-ffbf17e7fc3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# Extract\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mnewLons\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheLons\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == Lons[Xspan[0]:Xspan[1]+1, Yspan[0]:Yspan[1]+1]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mnewLats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheLats\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mnewData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecip\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "MLon = extractedLons[-1]\n",
    "mLon = extractedLons[0]\n",
    "MLat = extractedLats[-1]\n",
    "mLat = extractedLats[0]\n",
    "\n",
    "\n",
    "Xspan = np.where((theLons < MLon) & (theLons > mLon))[0][[0, -1]]\n",
    "Yspan = np.where((theLats < MLat) & (theLats > mLat))[0][[0, -1]]\n",
    "\n",
    "# Create a selection\n",
    "sel = [slice(Xspan[0], Xspan[1] + 1), slice(Yspan[0], Yspan[1] + 1)]\n",
    "\n",
    "# Extract\n",
    "newLons = theLons[sel]  # == Lons[Xspan[0]:Xspan[1]+1, Yspan[0]:Yspan[1]+1]\n",
    "newLats = theLats[sel]\n",
    "newData = precip[sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clevs = np.arange(0,0.1,0.01)\n",
    "\n",
    "x, y = np.float32(np.meshgrid(extractedLons, extractedLats))\n",
    "\n",
    "masked_array = np.ma.masked_where(precip < 0,precip)\n",
    "\n",
    "cmap = 'nipy_spectral'\n",
    "\n",
    "cs = ax.contourf(x, y, precip, clevs, transform=ccrs.PlateCarree(), alpha=0.3, cmap=cmap)\n",
    "\n",
    "plt.xlim(-2.805, -1.809)\n",
    "plt.ylim(-45, -44)\n",
    "\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap,norm=plt.Normalize(0,1))\n",
    "sm._A = []\n",
    "plt.colorbar(sm, ax=ax, label='mm/h', shrink=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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