pysgrid_example.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# pysgrid"
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "from netCDF4 import Dataset\n\nurl = ('http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/'\n       'jcwarner/Projects/Sandy/triple_nest/00_dir_NYB05.ncml')\n\nnc = Dataset(url)",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The pysgrid object can be instantiated from a `netCDF4-python` object or the file/URL."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pysgrid\n\nsgrid = pysgrid.from_nc_dataset(nc)",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The `sgrid` object knows about the center of the grid and prepare a slicing object that can be used to get the data at the center of the grid."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "sgrid.u.center_slicing",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(slice(None, None, None),\n slice(None, None, None),\n slice(1, -1, None),\n slice(None, None, None))"
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "sgrid.v.center_slicing",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(slice(None, None, None),\n slice(None, None, None),\n slice(None, None, None),\n slice(1, -1, None))"
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Let's get some data. (Using integer indexes for convenience.)"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "time_idx = 120\nvertical_idx = 0\n\nu = nc['u'][time_idx, vertical_idx, sgrid.u.center_slicing[-2], sgrid.u.center_slicing[-1]]\nv = nc['v'][time_idx, vertical_idx, sgrid.v.center_slicing[-2], sgrid.v.center_slicing[-1]]",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The raw API is just that: an interpretation of the SGRID conventions that translate to a center slicing object.\nIt does not look like much, but anyone familiar with staggered grids models know the amount of code that goes into performing that slice.\n\nThe API is meant to be used by higher level tools like `iris` and `xray` where plotting/slicing would be done automatically.\n\nHowever, `pysgrid` **does** bring some convenience processing methods like:\n\n- avg_to_cell_center"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "from pysgrid.processing_2d import avg_to_cell_center\n\nu = avg_to_cell_center(u, sgrid.u.center_axis)\nv = avg_to_cell_center(v, sgrid.v.center_axis)",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "- rotate_vectors"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "from pysgrid.processing_2d import rotate_vectors\n\nangles = nc.variables[sgrid.angle.variable][sgrid.angle.center_slicing]\nu, v = rotate_vectors(u, v, angles)",
   "execution_count": 7,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "- vector_sum"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "from pysgrid.processing_2d import vector_sum\n\nspeed = vector_sum(u, v)",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/home/filipe/miniconda/envs/IOOS/lib/python2.7/site-packages/pysgrid/processing_2d.py:22: RuntimeWarning: invalid value encountered in sqrt\n  vector_sum = np.sqrt(x_arr**2 + y_arr**2)\n",
     "name": "stderr"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We need to get the grid cell centers before plotting.\nA high level object could use pysgrid's API to get the cell centers and its coordinates variable names."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "grid_cell_centers = sgrid.centers\n\nlon_var_name, lat_var_name = sgrid.face_coordinates\n\nsg_lon = getattr(sgrid, lon_var_name)\nsg_lat = getattr(sgrid, lat_var_name)",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "And then perform the same center slicing we did for the variables."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "lon = grid_cell_centers[..., 0][sg_lon.center_slicing]\nlat = grid_cell_centers[..., 1][sg_lat.center_slicing]",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Now that we have data and coordinates at the grid center we can finally plot the data."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport matplotlib.pyplot as plt\n\nimport cartopy.crs as ccrs\nfrom cartopy.io import shapereader\nfrom cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n\n\ndef make_map(projection=ccrs.PlateCarree(), figsize=(9, 9)):\n    fig, ax = plt.subplots(figsize=figsize,\n                           subplot_kw=dict(projection=projection))\n    gl = ax.gridlines(draw_labels=True)\n    gl.xlabels_top = gl.ylabels_right = False\n    gl.xformatter = LONGITUDE_FORMATTER\n    gl.yformatter = LATITUDE_FORMATTER\n    return fig, ax",
   "execution_count": 12,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "%matplotlib notebook\n\nsub = 10\nscale = 0.06\n\nfig, ax = make_map()\n\nkw = dict(scale=1.0/scale, pivot='middle', width=0.003, color='black')\nq = plt.quiver(lon[::sub, ::sub], lat[::sub, ::sub],\n               u[::sub, ::sub], v[::sub, ::sub], zorder=2, **kw)\n\ncs = plt.pcolormesh(lon[::sub, ::sub],\n                    lat[::sub, ::sub],\n                    speed[::sub, ::sub], zorder=1, cmap=plt.cm.rainbow)\n\n_ = ax.coastlines('10m')",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\nmpl.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\nmpl.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            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        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.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 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);\n        canvas.attr('height', height);\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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'];\n    var y0 = fig.canvas.height - msg['y0'];\n    var x1 = msg['x1'];\n    var y1 = fig.canvas.height - msg['y1'];\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\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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;\n    var y = canvas_pos.y;\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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 overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.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\nmpl.figure.prototype.handle_close = function(fig, msg) {\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 + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (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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      "text/plain": "<IPython.core.display.HTML object>",
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\">"
     },
     "metadata": {}
    }
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python2",
   "display_name": "Python 2",
   "language": "python"
  },
  "language_info": {
   "mimetype": "text/x-python",
   "nbconvert_exporter": "python",
   "name": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11",
   "file_extension": ".py",
   "codemirror_mode": {
    "version": 2,
    "name": "ipython"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}