RDA_hov_plots_final.ipynb

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   "source": [
    "## Make series of three hovmoller plots of binned frequency, intensity, and f*i\n",
    "\n",
    "#PLOT 1: Climatology\n",
    "#PLOT 2: Changes, 1980-2007 v 1951-1979\n",
    "#PLOT 3: Changes, 1994-2007 v 1980-1993"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   },
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "import os\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "%load_ext autotime\n",
    "from bootstrap import *\n",
    "import numpy.ma as ma\n",
    "from chr_matplotlib import remappedColorMap\n",
    "from shutil import copy\n",
    "\n",
    "from jd_matplotlib_tools import add_double_calendar_axis, mark_time_periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "text": [
      "51\n",
      "07\n",
      "time: 3.22 ms\n"
     ]
    }
   ],
   "source": [
    "## CLIMATOLOGY PLOT ##\n",
    "\n",
    "## LOAD FROM NETCDF ##\n",
    "ferretdir = \"/Users/Siwen/Desktop/Ferret/bin/\"\n",
    "\n",
    "#bounding years for climatology\n",
    "yb=1951 \n",
    "ye=2007\n",
    "\n",
    "#load 57-year climatology\n",
    "yb_last2= str(yb-1900)\n",
    "print(yb_last2)\n",
    "ye_last2= str(\"{0:0=2d}\".format((ye-1900)%100))\n",
    "print(ye_last2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 23.5 ms\n"
     ]
    }
   ],
   "source": [
    "## LOAD FREQUENCY HOVMOLLER ##\n",
    "loadfile = \"RDA_freq_hov\"\n",
    "loadname = ferretdir + loadfile + '.nc'\n",
    "\n",
    "#import from netCDF file\n",
    "freq_climo_netcdf = nc.Dataset(loadname, \"r\")\n",
    "freq_climo = freq_climo_netcdf['lat_hov_smth_' + yb_last2 + ye_last2][:]\n",
    "\n",
    "#close it\n",
    "freq_climo_netcdf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 20.5 ms\n"
     ]
    }
   ],
   "source": [
    "## LOAD INTENSITY HOVMOLLER CLIMATOLOGY ##\n",
    "loadfile = \"RDA_int_climo\"\n",
    "loadname = ferretdir + loadfile + '.nc'\n",
    "\n",
    "#import from netCDF file and load 57-year climatology\n",
    "int_climo_netcdf = nc.Dataset(loadname, \"r\")\n",
    "int_climo = int_climo_netcdf['int_mean_' + yb_last2 + ye_last2][:]\n",
    "\n",
    "#close it\n",
    "int_climo_netcdf.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 11.8 ms\n"
     ]
    }
   ],
   "source": [
    "## LOAD EFFECTIVE RAINFALL CLIMATOLOGY ##\n",
    "loadfile = \"RDA_effective_rainfall\"\n",
    "loadname = ferretdir + loadfile + '.nc'\n",
    "\n",
    "#import from netCDF file and load 57-year climatology\n",
    "rain_climo_netcdf = nc.Dataset(loadname, \"r\")\n",
    "rain_climo = rain_climo_netcdf['rain_effective_' + yb_last2 + ye_last2][:]\n",
    "\n",
    "#close it\n",
    "rain_climo_netcdf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.07 ms\n"
     ]
    }
   ],
   "source": [
    "## DEFINE TIME PERIODS OF INTEREST\n",
    "time_periods = [[80.5,120.5],[120.5,160.5],[160.5,200.5],[200.5,273.5],[273.5,320.5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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       "        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;\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",
       "\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 overriden (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",
       "    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",
       "\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/text.py:2130: UserWarning: You have used the `textcoords` kwarg, but not the `xytext` kwarg.  This can lead to surprising results.\n",
      "  warnings.warn(\"You have used the `textcoords` kwarg, but not \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting existing version of file in RDA manuscript directory...\n",
      "Overwriting existing version of file in RDA Python directory...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/Users/Siwen/RDA/Figures/hov_freq_int_climo.pdf'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.92 s\n"
     ]
    }
   ],
   "source": [
    "## CLIMATOLOGY PLOT ##\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"Climatology, 1951-2007\", fontsize=16, y = 1.08, x=.43)\n",
    "mypalette = 'RdYlBu_r'\n",
    "ax.set_axis_off()\n",
    "\n",
    "## Frequency climatology\n",
    "ax1 = fig.add_subplot(3,1,1)\n",
    "plot1 = ax1.pcolormesh(100*freq_climo[:,1:21].T, cmap = mypalette); \n",
    "plt.ylabel('Latitude ($\\!^\\circ\\!$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar1 = plt.colorbar(plot1, pad=0.02)\n",
    "cbar1.set_ticks(np.linspace(0, 8, 9))\n",
    "\n",
    "ax1.set_title(\"a) Probability of rainband centered at given latitude (%)\", fontsize=13, y=1.12)\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes')\n",
    "\n",
    "\n",
    "## Intensity climatology\n",
    "ax2 = fig.add_subplot(3,1,2)\n",
    "plot2 = ax2.pcolormesh(int_climo[:,1:21].T, cmap = mypalette); \n",
    "plt.ylabel('Latitude ($\\!^\\circ\\!$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar2 = plt.colorbar(plot2, pad=0.02)\n",
    "ax2.set_title(\"b) Mean rainband intensity (mm day$^{-1}$)\", fontsize=13, y=1.12)\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes')\n",
    "\n",
    "\n",
    "## Effective rainfall climatology\n",
    "ax3 = fig.add_subplot(3,1,3)\n",
    "plot3 = ax3.pcolormesh(rain_climo[:,1:21].T, cmap = mypalette); \n",
    "plt.ylabel('Latitude ($\\!^\\circ\\!$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar3 = plt.colorbar(plot3, pad=0.02)\n",
    "cbar3.set_ticks(np.linspace(0.4,2.5,8))\n",
    "\n",
    "ax3.set_title(\"c) Effective rainfall (freq * intensity)\", fontsize=13, y=1.12)\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes')\n",
    "\n",
    "\n",
    "\n",
    "## final formatting\n",
    "plt.tight_layout(pad=0, w_pad=0, h_pad=0)\n",
    "\n",
    "\n",
    "## SAVE COPY TO MANUSCRIPT AND ANALYSIS DIRECTORIES\n",
    "figure_name = \"hov_freq_int_climo.pdf\"\n",
    "\n",
    "savepath = \"/Users/Siwen/Desktop/RDA/Figures/\" + figure_name\n",
    "\n",
    "if os.path.exists(savepath):\n",
    "    print(\"Overwriting existing version of file in RDA manuscript directory...\")\n",
    "    os.remove(savepath)\n",
    "\n",
    "plt.savefig(savepath)\n",
    "    \n",
    "copypath = \"/Users/Siwen/RDA/Figures/\" + figure_name\n",
    "    \n",
    "if os.path.exists(copypath):\n",
    "    print(\"Overwriting existing version of file in RDA Python directory...\")\n",
    "    os.remove(copypath)\n",
    "    \n",
    "copy(savepath,copypath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5179\n",
      "8007\n",
      "8093\n",
      "9407\n",
      "time: 54.9 ms\n"
     ]
    }
   ],
   "source": [
    "## LOAD 1980-2007 v 1951-1979 changes FROM NETCF ##\n",
    "\n",
    "## NETCDF FILENAMES\n",
    "freq_diff_file = \"RDA_freq_diff_tau_seasonal\"\n",
    "freq_diff_name = ferretdir + freq_diff_file + '.nc'\n",
    "\n",
    "int_diff_file = \"RDA_int_diff\"\n",
    "int_diff_name = ferretdir + int_diff_file + '.nc'\n",
    "\n",
    "int_pval_file = \"RDA_int_diff_bootstrap_2000iter\"\n",
    "int_pval_name = ferretdir + int_pval_file + '.nc'\n",
    "\n",
    "\n",
    "\n",
    "#import from netCDF file and load 57-year climatology\n",
    "freq_diff_netcdf = nc.Dataset(freq_diff_name, \"r\")\n",
    "int_diff_netcdf = nc.Dataset(int_diff_name, \"r\")\n",
    "int_pval_netcdf = nc.Dataset(int_pval_name, \"r\")\n",
    "\n",
    "#limits of different time periods that we're comparing\n",
    "p1_beg = [1951,1980]\n",
    "p1_end = [1979,1993]\n",
    "p2_beg = [1980,1994]\n",
    "p2_end = [2007,2007]\n",
    "\n",
    "#in this case, we're just going to load the smoothed plots of hovmoller frequency\n",
    "freq_diff = []\n",
    "freq_pval = []\n",
    "int_diff = []\n",
    "int_pval = []\n",
    "\n",
    "for p1_b, p1_e, p2_b, p2_e in zip(p1_beg, p1_end, p2_beg, p2_end):\n",
    "\n",
    "    years_p1= str(p1_b-1900) + str(\"{0:0=2d}\".format((p1_e-1900)%100))\n",
    "    print(years_p1)\n",
    "    years_p2= str(p2_b-1900) + str(\"{0:0=2d}\".format((p2_e-1900)%100))\n",
    "    print(years_p2)\n",
    "    \n",
    "    freq_diff.append(freq_diff_netcdf['lat_diff_tau_' + years_p2 + '_' + years_p1][:])\n",
    "    freq_pval.append(freq_diff_netcdf['lat_pval_tau_' + years_p2 + '_' + years_p1][:])\n",
    "    int_diff.append(int_diff_netcdf['int_diff_' + years_p2 + '_' + years_p1][:])\n",
    "    int_pval.append(int_pval_netcdf['int_pval_' + years_p2 + '_' + years_p1][:])\n",
    "    \n",
    "#close it\n",
    "freq_diff_netcdf.close()\n",
    "int_diff_netcdf.close()\n",
    "int_pval_netcdf.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 12 ms\n"
     ]
    }
   ],
   "source": [
    "## USE P-VALS TO CREATE MASKS APPLIED TO ONLY SHOW SIGNIFICANT DIFFERENCES\n",
    "## 1980-2007 versus 1951-1979 - clearly cuts out a lot of random noise!\n",
    "\n",
    "## frequency masking by analytic p-value\n",
    "freq_diff_mask_95 = []\n",
    "freq_diff_mask_99 = []\n",
    "\n",
    "for fq, pval in zip(freq_diff, freq_pval):\n",
    "    freq_diff_mask_95.append(ma.array(fq, mask = ~(abs(pval-.5) > .475)))\n",
    "    freq_diff_mask_99.append(ma.array(fq, mask = ~(abs(pval-.5) > .495)))\n",
    "\n",
    "#intensity masking by bootstrapped p-value\n",
    "int_diff_mask_95 = []\n",
    "int_diff_mask_99 = []   \n",
    "    \n",
    "for ints, pval in zip(int_diff, int_pval):\n",
    "    int_diff_mask_95.append(ma.array(ints, mask = ~(abs(pval-.5) > .475)))\n",
    "    int_diff_mask_99.append(ma.array(ints, mask = ~(abs(pval-.5) > .495)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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",
       "\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 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",
       "\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'];\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",
       "\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;\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",
       "\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 overriden (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",
       "    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",
       "\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\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": {
      "text/html": [
       "<div id='4a079216-cef3-46e4-a923-c59cd548c12b'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/text.py:2130: UserWarning: You have used the `textcoords` kwarg, but not the `xytext` kwarg.  This can lead to surprising results.\n",
      "  warnings.warn(\"You have used the `textcoords` kwarg, but not \"\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'rain_diff_mask_95' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-892045be965b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;31m## C) EFFECTIVE RAINFALL SUBPLOT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0max3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mplot3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrain_diff_mask_95\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmypalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrain_diff_mask_99\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"c) Change in Band Rainfall (freq*intensity)\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'rain_diff_mask_95' is not defined"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 643 ms\n"
     ]
    }
   ],
   "source": [
    "## CREATE FIGURE OF 1980-2007 v 1951-1979 changes\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"1980-2007 versus 1951-1979\", fontsize=16, y = 1.08, x=.43)\n",
    "mypalette = 'bwr'\n",
    "ax.set_axis_off()\n",
    "\n",
    "\n",
    "## A) FREQUENCY SUBPLOT\n",
    "ax1 = fig.add_subplot(3,1,1)\n",
    "plot1 = ax1.pcolormesh(100*freq_diff_mask_95[0][:,1:21].T, cmap = mypalette, vmin = -3, vmax = 3); \n",
    "plt.contour(freq_diff_mask_99[0][:,1:21].mask.T,1,colors = 'gray')\n",
    "plt.ylabel('Latitude ($\\!^\\circ\\!$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar1 = plt.colorbar(plot1, pad=0.02)\n",
    "cbar1.set_ticks(np.linspace(-3,3,7))\n",
    "\n",
    "ax1.set_title(\"a) Change in Band Frequency (%)\", fontsize=13, y=1.12)\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes', box = 'no')\n",
    "\n",
    "\n",
    "\n",
    "## B) INTENSITY SUBPLOT\n",
    "ax2 = fig.add_subplot(3,1,2)\n",
    "plot2 = ax2.pcolormesh(int_diff_mask_95[0][:,1:21].T, cmap = mypalette, vmin = -5, vmax = 5); \n",
    "plt.contour(int_diff_mask_99[0][:,1:21].mask.T,1,colors = 'gray')\n",
    "ax2.set_title(\"b) Change in Band Intensity (mm day$^{-1}$)\", fontsize=13, y=1.12)\n",
    "plt.ylabel('Latitude ($\\!^\\circ\\!$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar2 = plt.colorbar(plot2, pad=0.02)\n",
    "cbar2.set_ticks(np.linspace(-4.5,4.5,7))\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes', box = 'no')\n",
    "\n",
    "\n",
    "\n",
    "## C) EFFECTIVE RAINFALL SUBPLOT\n",
    "ax3 = fig.add_subplot(3,1,3)\n",
    "plot3 = ax3.pcolormesh(rain_diff_mask_95[0][:,1:21].T, cmap = mypalette, vmin = -1, vmax = 1); \n",
    "plt.contour(rain_diff_mask_99[0][:,1:21].T,1,colors = 'gray')\n",
    "ax3.set_title(\"c) Change in Band Rainfall (freq*intensity)\", fontsize=13, y=1.12)\n",
    "plt.ylabel('Latitude ($^\\circ$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar3 = plt.colorbar(plot3, pad=0.02)\n",
    "cbar3.set_ticks(np.linspace(-1,1,6))\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes', box = 'no')\n",
    "\n",
    "\n",
    "## Final tweak to adjust plot fit.\n",
    "plt.tight_layout(pad=0, w_pad=0, h_pad=0)\n",
    "\n",
    "## SAVE FIGURE TO MANUSCRIPT AND ANALYSIS DIRECTORIES\n",
    "figure_name = \"hov_freq_int_8007_5179.pdf\"\n",
    "\n",
    "savepath = \"/Users/Siwen/Desktop/RDA/Figures/\" + figure_name\n",
    "\n",
    "if os.path.exists(savepath):\n",
    "    print(\"Overwriting existing version of file in RDA manuscript directory...\")\n",
    "    os.remove(savepath)\n",
    "    \n",
    "plt.savefig(savepath)\n",
    "    \n",
    "copypath = \"/Users/Siwen/RDA/Figures/\" + figure_name\n",
    "    \n",
    "if os.path.exists(copypath):\n",
    "    print(\"Overwriting existing version of file in RDA Python directory...\")\n",
    "    os.remove(copypath)\n",
    "    \n",
    "copy(savepath,copypath)\n",
    "\n",
    "plt.savefig(savepath, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## CREATE FIGURE OF 1994-2007 versus 1980-1993 changes\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"1994-2007 versus 1980-1993\", fontsize=16, y = 1.08, x=.43)\n",
    "mypalette = 'bwr'\n",
    "ax.set_axis_off() #make sure big axis doesn't show up\n",
    "\n",
    "\n",
    "## a) Frequency changes\n",
    "ax1 = fig.add_subplot(3,1,1)\n",
    "plot1 = ax1.pcolormesh(100*freq_diff_mask_95[1][:,1:21].T, cmap = mypalette, vmin = -3, vmax = 3); \n",
    "plt.contour(freq_diff_mask_99[1][:,1:21].mask.T,1,colors = 'gray')\n",
    "ax1.set_title(\"a) Change in Band Frequency (%))\", fontsize=13, y=1.12)\n",
    "plt.ylabel('Latitude ($^\\circ$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar1 = plt.colorbar(plot1, pad=0.02)\n",
    "cbar1.set_ticks(np.linspace(-3,3,7))\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes', box = 'no')\n",
    "\n",
    "\n",
    "\n",
    "## b) Intensity changes\n",
    "ax2 = fig.add_subplot(3,1,2)\n",
    "plot2 = ax2.pcolormesh(int_diff_mask_95[1][:,1:21].T, cmap = mypalette, vmin = -5, vmax = 5); \n",
    "plt.contour(int_diff_mask_99[1][:,1:21].mask.T,1,colors = 'gray')\n",
    "ax2.set_title(\"b) Change in Band Intensity (mm day$^{-1}$)\", fontsize=13, y=1.12)\n",
    "plt.ylabel('Latitude ($^\\circ$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar2 = plt.colorbar(plot2, pad=0.02)\n",
    "cbar2.set_ticks(np.linspace(-4.5,4.5,7))\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes', box = 'no')\n",
    "\n",
    "\n",
    "\n",
    "## c) Effective changes\n",
    "ax3 = fig.add_subplot(3,1,3)\n",
    "plot3 = ax3.pcolormesh(rain_diff_mask_95[1][:,1:21].T, cmap = mypalette, vmin = -1, vmax = 1); \n",
    "plt.contour(rain_diff_mask_99[1][:,1:21].T,1,colors = 'gray')\n",
    "ax3.set_title(\"c) Change in Band Rainfall (freq*intensity)\", fontsize=13, y=1.12)\n",
    "plt.ylabel('Latitude ($^\\circ$)', fontsize=12, labelpad=0)\n",
    "plt.yticks(np.linspace(0,20,6),np.linspace(20,40,6).astype(int))\n",
    "\n",
    "cbar3 = plt.colorbar(plot3, pad=0.02)\n",
    "cbar3.set_ticks(np.linspace(-1,1,6))\n",
    "\n",
    "add_double_calendar_axis()\n",
    "mark_time_periods(time_periods, numbers = 'yes', box = 'no')\n",
    "\n",
    "\n",
    "\n",
    "## we have separate axis for whole figure and for each subplot\n",
    "plt.tight_layout(pad=0, w_pad=0, h_pad=0)\n",
    "\n",
    "\n",
    "## SAVE COPIES OF FIGURE TO MANUSCRIPT AND ANALYSIS DIRECTORIES\n",
    "figure_name = \"hov_freq_int_9407_8093.pdf\"\n",
    "\n",
    "savepath = \"/Users/Siwen/Desktop/RDA/Figures/\" + figure_name\n",
    "\n",
    "if os.path.exists(savepath):\n",
    "    print(\"Overwriting existing version of file in RDA manuscript directory...\")\n",
    "    os.remove(savepath)\n",
    "    \n",
    "plt.savefig(savepath)\n",
    "    \n",
    "copypath = \"/Users/Siwen/RDA/Figures/\" + figure_name\n",
    "    \n",
    "if os.path.exists(copypath):\n",
    "    print(\"Overwriting existing version of file in RDA Python directory...\")\n",
    "    os.remove(copypath)\n",
    "    \n",
    "copy(savepath,copypath)\n",
    "\n",
    "plt.savefig(savepath, bbox_inches='tight')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
   "source": []
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