{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GOLF timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import apollinaire as apn\n",
    "import matplotlib\n",
    "matplotlib.use ('TkAgg')\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.markers as markers\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining parameters for the cross-correlation operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining parameters for the cross-correlation operation\n",
    "sampling = 60 #GOLF and VIRGO series are sampled at 60s\n",
    "\n",
    "#subseries of 10 days with an overlap of one day will be considered\n",
    "sample_size = 14400 \n",
    "overlap = 1440\n",
    "\n",
    "#Cross correlation will be computed with a maximum lag of 20 minute\n",
    "maxlag=20\n",
    "\n",
    "#The guess frequency that will be considered for the gaussian cosinus\n",
    "#fit is 0.2 min^-1\n",
    "freq_init = 0.2\n",
    "\n",
    "shiftmax = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First steps\n",
    "\n",
    "Downloading (if necessary) timeseries and opening files.\n",
    "\n",
    "GOLF series starts on 11/04/1996.\n",
    "VIRGO series starts on 23/01/1996."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write a function that will download VIRGO and GOLF series if the user do not have it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seriesDir = '../timeseries/'\n",
    "file_golf = 'Res_bw_rw_3_960411_191202_F_dt60s.fits'\n",
    "file_virgogreen = 'VIRGO-SPM-GREEN-L2-MISSIONLONG.fits'\n",
    "file_virgored = 'VIRGO-SPM-RED-L2-MISSIONLONG.fits'\n",
    "file_virgoblue = 'VIRGO-SPM-BLUE-L2-MISSIONLONG.fits'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading GOLF fits file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu_golf = fits.open (seriesDir + file_golf) [0]\n",
    "data_1 = np.array (hdu_golf.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading VIRGO fits file and computing the mean of the three channels (blue, green and red)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdu_green = fits.open (seriesDir + file_virgogreen) [0]\n",
    "hdu_red = fits.open (seriesDir + file_virgored) [0]\n",
    "hdu_blue = fits.open (seriesDir + file_virgoblue) [0]\n",
    "data_red = np.array (hdu_red.data)\n",
    "data_green = np.array (hdu_green.data)\n",
    "data_blue = np.array (hdu_blue.data)\n",
    "\n",
    "\n",
    "data_2 = (data_red + data_green + data_blue) / 3.\n",
    "\n",
    "# Removing 79 first days of VIRGO series to make it start \n",
    "# on 11/04/1996 like GOLF. \n",
    "data_2 = data_2 [79*1440:]\n",
    "data_2 = np.nan_to_num (data_2, copy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determining the number of subseries that will be considered in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sample_1 = data_1.size // overlap - sample_size//overlap\n",
    "n_sample_2 = data_2.size // overlap - sample_size//overlap\n",
    "n_sample = min (n_sample_1, n_sample_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-correlation and gaussian cosinus fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = np.array ([i for i in range (-maxlag+1, maxlag)])\n",
    "shifts = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/students/Workspace_Sylvain/apollinaire/apollinaire/timing/compute_shift.py:149: RuntimeWarning: invalid value encountered in true_divide\n",
      "  cc = cc / (sub2.std () * sub1.std () * sub2.size)\n",
      "/Users/students/miniconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:787: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subseries 1812: envelope fitting failed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/students/Workspace_Sylvain/apollinaire/apollinaire/timing/compute_shift.py:37: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return A * np.exp ( - (x - alpha) * (x - alpha) / beta )\n",
      "/Users/students/Workspace_Sylvain/apollinaire/apollinaire/timing/compute_shift.py:37: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return A * np.exp ( - (x - alpha) * (x - alpha) / beta )\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subseries 6824: cos fitting failed\n"
     ]
    }
   ],
   "source": [
    "for ii in range (n_sample) :\n",
    "    sub1 = data_1 [ii*overlap :ii*(overlap)+sample_size]\n",
    "    sub2 = data_2 [ii*overlap:ii*(overlap)+sample_size]\n",
    "    # applying backward difference filter\n",
    "    sub1 = apn.processing.bdf (sub1)\n",
    "    sub2 = apn.processing.bdf (sub2)\n",
    "    tshift = apn.timing.compute_shift (sub1, sub2, maxlag, freq_init, \n",
    "                                       shiftmax, lags, plot=False, subindex=ii)\n",
    "    shifts.append (tshift)\n",
    "\n",
    "stamps = np.linspace (0, n_sample-1, n_sample)\n",
    "shifts = np.array (shifts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "        return MozWebSocket;\n",
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       "        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",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
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       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
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       "\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",
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       "\n",
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       "    var fig = this;\n",
       "\n",
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
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       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
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       "\n",
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       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
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       "    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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/students/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in less\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time shift between GOLF and VIRGO time series')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "plt.ion ()\n",
    "\n",
    "# Tick parameters\n",
    "plt.rcParams['ytick.right'] = True\n",
    "plt.rcParams['xtick.top'] = True\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "\n",
    "# Font defined to use Latex styles\n",
    "plt.rcParams['font.size'] = 14.\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "\n",
    "# Point size on the plot\n",
    "s = (144./fig.dpi)**2\n",
    "\n",
    "fig = plt.figure (figsize=(10,6))\n",
    "ax = fig.add_subplot (111)\n",
    "\n",
    "#removing \"SoHO vacations\" on the plot\n",
    "shifts[np.abs(shifts)<1e-5] = np.nan\n",
    "\n",
    "ax.scatter (stamps, shifts*60., marker=markers.MarkerStyle (marker='o', fillstyle='none'), s=s, color='firebrick')\n",
    "\n",
    "ax.set_xticks ([-71, 629, 1359, 2090, 2820, 3551, 4281, 5012, 5742, 6473, 7203, 7931, 8661])\n",
    "ax.set_xticklabels ([1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020])\n",
    "ax.set_ylabel ('Shift (s)')\n",
    "ax.set_xlabel ('Year')\n",
    "ax.set_ylim (-50, 20)\n",
    "\n",
    "ax.set_title ('Time shift between GOLF and VIRGO time series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
