rscha-701 days extended hd final mix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "import rscha2d\n",
    "import fibonacci_sphere\n",
    "import trig\n",
    "import constants\n",
    "import coords\n",
    "import gauge\n",
    "\n",
    "from matplotlib import pyplot, colors, ticker\n",
    "import matplotlib\n",
    "%matplotlib nbagg\n",
    "matplotlib.rcParams[\"font.family\"] = \"Fira Sans\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "db_path = \"../data/rscha2d/bd_11mayo_naveg_piracy.csv\"\n",
    "filtered_db_path = \"../data/rscha2d/bd_11mayo_naveg_piracy_q.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lat_c = 42.0\n",
    "lon_c = 20.0\n",
    "theta_c, phi_c, theta_0d, theta_0 = numpy.deg2rad((90-lat_c, lon_c, 26.0, 50.0))\n",
    "knots_bd = numpy.arange(-2100, 2050, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#THIS IS A DUMMY MODEL !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
    "model = rscha2d.Model()\n",
    "model.set_model_params(theta_c, phi_c, theta_0d,\n",
    "                       cap_edge=0, kmax_int=3, kmax_ext=3, m_max=2, g10_ref=-30,\n",
    "                       knots=knots_bd,\n",
    "                       spatial_reg=1e-4, temporal_reg=1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.add_data(\"../data/rscha2d/bd_11mayo_naveg_piracy.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inliers = model.ids[model.quality]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(db_path, \"r\")\n",
    "f2 = open(filtered_db_path, \"w\")\n",
    "    \n",
    "for i, line in enumerate(f):\n",
    "    if i in inliers:\n",
    "        f2.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import geofield\n",
    "import xyzfield\n",
    "\n",
    "sha = geofield.SHADIFData(\"../data/sha.dif.14k/coeff_SHA.DIF.14k.dat\",\n",
    "                          \"../data/sha.dif.14k/error_coef_EPSL_tnmgh.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_gcoefs = numpy.loadtxt(\"../data/rscha2d/coef_base.dat\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_years = numpy.array(sha.years)[numpy.array(sha.years) >= -1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "knots = numpy.arange(-1100, 2000, 50)\n",
    "model2 = rscha2d.Model()\n",
    "model2.set_model_params(theta_c, phi_c, theta_0d,\n",
    "                        cap_edge=0, kmax_int=3, kmax_ext=3, m_max=2,\n",
    "                        knots=knots,\n",
    "                        spatial_reg=1e-4, temporal_reg=1e-2,\n",
    "                        refmodel={\"years\" : base_years,\n",
    "                                  \"coefs\" : base_gcoefs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model2.add_data(\"../data/rscha2d/bd_11mayo_naveg_piracy_q.csv\")\n",
    "model2.D_weight = 1./ (~model2.nan_D).sum()\n",
    "model2.I_weight = 1./ (~model2.nan_I).sum()\n",
    "model2.F_weight = 1./ (~model2.nan_F).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "[Memory] Calling rscha_r.rscha_condition_matrix_dif...\n",
      "rscha_condition_matrix_dif(( array([ 0.,  1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 0.,  1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  1.164403e-12,   3.646726e+00,   3.646726e+00,   7.958865e+00,\n",
      "         6.368397e+00,   6.368397e+00,   1.127449e+01,   1.127449e+01,\n",
      "         8.955337e+00,   8.955337e+00])), \n",
      "( array([ 1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  3.646726,   3.646726,   7.958865,   6.368397,   6.368397,\n",
      "        11.274494,  11.274494,   8.955337,   8.955337])), \n",
      "array([ 0,  1, -1,  2, -2]), array([ 6366.087515, ...,  6363.382858]), array([ 0.264736, ...,  0.206587]), array([ 1.289396, ..., -2.113453]), 0.4537856055185257, array([ -3.013645e+05, ...,  -1.250473e+01]), array([ -3.394987e+05, ...,  -1.510790e+01]), array([ -1.114411e+06, ...,   4.201655e+01]), 11182.736964578742)\n",
      "_______________________________________rscha_condition_matrix_dif - 4.1s, 0.1min\n"
     ]
    }
   ],
   "source": [
    "solution = model2.solve(complete=True)\n",
    "g = solution[\"coefs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "name_at = \"Paris\"\n",
    "theta_at, phi_at = numpy.deg2rad((90-48.8, 2.35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "near = (trig.angulardist(theta_at, phi_at, model2.thetav, model2.phiv) \n",
    "        < numpy.arctan(900/constants.a_r))\n",
    "\n",
    "D_reloc, I_reloc, F_reloc = trig.relocate(model2.D_o, model2.I_o, model2.F_o,\n",
    "                                          model2.thetav, model2.phiv, theta_at, phi_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "[Memory] Calling rscha_r.rscha_condition_matrix_dif...\n",
      "rscha_condition_matrix_dif(( array([ 0.,  1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 0.,  1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  1.164403e-12,   3.646726e+00,   3.646726e+00,   7.958865e+00,\n",
      "         6.368397e+00,   6.368397e+00,   1.127449e+01,   1.127449e+01,\n",
      "         8.955337e+00,   8.955337e+00])), \n",
      "( array([ 1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  3.646726,   3.646726,   7.958865,   6.368397,   6.368397,\n",
      "        11.274494,  11.274494,   8.955337,   8.955337])), \n",
      "array([ 0,  1, -1,  2, -2]), array([ 6371.2, ...,  6371.2]), array([ 0.245882, ...,  0.245882]), array([-2.179337, ..., -2.179337]), 0.4537856055185257, array([ -4.093276, ..., -15.032426]), array([-27.218904, ..., -28.819285]), array([ 59.132166, ...,  58.211525]), 11182.736964578742)\n",
      "_______________________________________rscha_condition_matrix_dif - 0.6s, 0.0min\n"
     ]
    }
   ],
   "source": [
    "t_cur = numpy.linspace(-1000, 2000, 400)\n",
    "t_cur, r_cur, theta_cur, phi_cur = coords.curve_at(t_cur, constants.a_r, theta_at, phi_at)\n",
    "D_avg, I_avg, F_avg = model2.synth_data(t_cur, r_cur, theta_cur, phi_cur, g)\n",
    "D_base, I_base, F_base = model2.synth_data(t_cur, r_cur, theta_cur, phi_cur, numpy.zeros_like(g))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model2.set_model_params(theta_c, phi_c, theta_0d,\n",
    "                        cap_edge=0, kmax_int=3, kmax_ext=3, m_max=2,\n",
    "                        knots=knots,\n",
    "                        spatial_reg=1e-4, temporal_reg=1e-2,\n",
    "                        refmodel={\"years\" : sha.years,\n",
    "                                  \"coefs\" : sha.gcomp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = model2.solve(complete=True)\n",
    "g = solution[\"coefs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "[Memory] Calling rscha_r.rscha_condition_matrix_dif...\n",
      "rscha_condition_matrix_dif(( array([ 0.,  1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 0.,  1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  1.164403e-12,   3.646726e+00,   3.646726e+00,   7.958865e+00,\n",
      "         6.368397e+00,   6.368397e+00,   1.127449e+01,   1.127449e+01,\n",
      "         8.955337e+00,   8.955337e+00])), \n",
      "( array([ 1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  3.646726,   3.646726,   7.958865,   6.368397,   6.368397,\n",
      "        11.274494,  11.274494,   8.955337,   8.955337])), \n",
      "array([ 0,  1, -1,  2, -2]), array([ 6371.2, ...,  6371.2]), array([ 0.245882, ...,  0.245882]), array([-2.179337, ..., -2.179337]), 0.4537856055185257, array([-1.128675, ..., -8.276478]), array([-24.458373, ..., -18.351807]), array([ 50.941416, ...,  51.591099]), 11182.736964578742)\n",
      "_______________________________________rscha_condition_matrix_dif - 0.6s, 0.0min\n"
     ]
    }
   ],
   "source": [
    "D_avg_sha, I_avg_sha, F_avg_sha = model2.synth_data(t_cur, r_cur, theta_cur, phi_cur, g)\n",
    "D_base_sha, I_base_sha, F_base_sha = model2.synth_data(t_cur, r_cur, theta_cur, phi_cur, numpy.zeros_like(g))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sai_gc = numpy.loadtxt(\"../data/sai.csv\", delimiter=\",\")[:, 1:]\n",
    "sai_years = numpy.loadtxt(\"../data/sai.csv\", delimiter=\",\")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model2.set_model_params(theta_c, phi_c, theta_0d,\n",
    "                        cap_edge=0, kmax_int=3, kmax_ext=3, m_max=2,\n",
    "                        knots=knots,\n",
    "                        spatial_reg=1e-4, temporal_reg=1e-2,\n",
    "                        refmodel={\"years\" : sai_years,\n",
    "                                  \"coefs\" : sai_gc})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "solution = model2.solve(complete=True)\n",
    "g = solution[\"coefs\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "[Memory] Calling rscha_r.rscha_condition_matrix_dif...\n",
      "rscha_condition_matrix_dif(( array([ 0.,  1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 0.,  1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  1.164403e-12,   3.646726e+00,   3.646726e+00,   7.958865e+00,\n",
      "         6.368397e+00,   6.368397e+00,   1.127449e+01,   1.127449e+01,\n",
      "         8.955337e+00,   8.955337e+00])), \n",
      "( array([ 1.,  1.,  2.,  2.,  2.,  3.,  3.,  3.,  3.]),\n",
      "  array([ 1., -1.,  0.,  2., -2.,  1., -1.,  3., -3.]),\n",
      "  array([  3.646726,   3.646726,   7.958865,   6.368397,   6.368397,\n",
      "        11.274494,  11.274494,   8.955337,   8.955337])), \n",
      "array([ 0,  1, -1,  2, -2]), array([ 6371.2, ...,  6371.2]), array([ 0.245882, ...,  0.245882]), array([-2.179337, ..., -2.179337]), 0.4537856055185257, array([-1.066819, ..., -7.983309]), array([-25.607346, ...,  -9.325517]), array([ 59.726704, ...,  53.768481]), 11182.736964578742)\n",
      "_______________________________________rscha_condition_matrix_dif - 0.6s, 0.0min\n"
     ]
    }
   ],
   "source": [
    "D_avg_sai, I_avg_sai, F_avg_sai = model2.synth_data(t_cur, r_cur, theta_cur, phi_cur, g)\n",
    "D_base_sai, I_base_sai, F_base_sai = model2.synth_data(t_cur, r_cur, theta_cur, phi_cur, numpy.zeros_like(g))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
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       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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",
       "    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=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5faa0b5e48>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.rcParams.update({'font.size': 10})\n",
    "fig, (ax0, ax1, ax2) = pyplot.subplots(3,1, figsize=(8,9))\n",
    "\n",
    "common={\"marker\" : \".\", \"s\" : 1, \"alpha\" : 0.8, \"color\" : \"xkcd:black\"}\n",
    "sha_style = {\"color\" : \"xkcd:dark red\", \"linestyle\" : \"--\", \"linewidth\" : 2}\n",
    "rscha_style = {\"color\" : \"xkcd:light red\", \"alpha\" : 0.005}\n",
    "\n",
    "curve_color = \"xkcd:light red\"\n",
    "curve_style = {\"color\" : curve_color, \"linewidth\" : 1}\n",
    "curve2_style = {\"color\" : \"xkcd:teal\", \"linewidth\" : 1}\n",
    "curve3_style = {\"color\" : \"xkcd:purple\", \"linewidth\" : 1}\n",
    "error_style = {\"color\" : curve_color, \"linestyle\" : \"--\"}\n",
    "fill_style = {\"color\" : \"xkcd:cream\", \"alpha\" : 1.0}\n",
    "\n",
    "ax0.set_ylabel(\"D (°)\")\n",
    "ax1.set_ylabel(\"I (°)\")\n",
    "ax2.set_ylabel(\"F ($\\mu $T)\")\n",
    "ax0.set_title(\"@{}\".format(name_at))\n",
    "\n",
    "#ax0.plot(t_cur, numpy.rad2deg(D_avg+2*D_std), **error_style)\n",
    "#ax0.plot(t_cur, numpy.rad2deg(D_avg-2*D_std), **error_style)\n",
    "#ax0.fill_between(t_cur, numpy.rad2deg(D_avg+2*D_std), numpy.rad2deg(D_avg-2*D_std), **fill_style)\n",
    "ax0.scatter(model2.tv[near], numpy.rad2deg(D_reloc)[near], **common)\n",
    "ax0.plot(t_cur, numpy.rad2deg(D_base), linestyle=\":\", **curve_style, label = \"SCHA.3k\")\n",
    "ax0.plot(t_cur, numpy.rad2deg(D_base_sha), linestyle=\":\", **curve2_style, label = \"SHA.DIF.14k\")\n",
    "ax0.plot(t_cur, numpy.rad2deg(D_base_sai), linestyle=\":\", **curve3_style, label = \"SHAQ\")\n",
    "ax0.plot(t_cur, numpy.rad2deg(D_avg), **curve_style, label = \"RSCHA, based on SCHA.3k\")\n",
    "ax0.plot(t_cur, numpy.rad2deg(D_avg_sha), **curve2_style, label = \"RSCHA, based on SHA.DIF.3k\")\n",
    "ax0.plot(t_cur, numpy.rad2deg(D_avg_sai), **curve3_style, label = \"RSCHA, based on SHAQ\")\n",
    "ax0.set_ybound(-30, 45)\n",
    "\n",
    "#ax1.plot(t_cur, numpy.rad2deg(I_avg+2*I_std), **error_style)\n",
    "#ax1.plot(t_cur, numpy.rad2deg(I_avg-2*I_std), **error_style)\n",
    "#ax1.fill_between(t_cur, numpy.rad2deg(I_avg+2*I_std), numpy.rad2deg(I_avg-2*I_std), **fill_style)\n",
    "ax1.scatter(model2.tv[near], numpy.rad2deg(I_reloc)[near], **common)\n",
    "ax1.plot(t_cur, numpy.rad2deg(I_base), linestyle=\":\", **curve_style)\n",
    "ax1.plot(t_cur, numpy.rad2deg(I_base_sha), linestyle=\":\", **curve2_style)\n",
    "ax1.plot(t_cur, numpy.rad2deg(I_base_sai), linestyle=\":\", **curve3_style)\n",
    "ax1.plot(t_cur, numpy.rad2deg(I_avg), **curve_style)\n",
    "ax1.plot(t_cur, numpy.rad2deg(I_avg_sha), **curve2_style)\n",
    "ax1.plot(t_cur, numpy.rad2deg(I_avg_sai), **curve3_style)\n",
    "ax1.set_ybound(45, 80)\n",
    "\n",
    "#ax2.plot(t_cur, F_avg+2*F_std, **error_style)\n",
    "#ax2.plot(t_cur, F_avg-2*F_std, **error_style)\n",
    "#ax2.fill_between(t_cur, F_avg+2*F_std, F_avg-2*F_std, **fill_style)\n",
    "ax2.plot(t_cur, F_base, linestyle=\":\", **curve_style)\n",
    "ax2.plot(t_cur, F_base_sha, linestyle=\":\", **curve2_style)\n",
    "ax2.plot(t_cur, F_base_sai, linestyle=\":\", **curve3_style)\n",
    "ax2.plot(t_cur, F_avg, **curve_style)\n",
    "ax2.plot(t_cur, F_avg_sha, **curve2_style)\n",
    "ax2.plot(t_cur, F_avg_sai, **curve3_style)\n",
    "ax2.scatter(model2.tv[near], F_reloc[near], **common)\n",
    "ax2.set_ybound(40, 90)\n",
    "ax2.set_xlabel(\"Time (years AD)\")\n",
    "    \n",
    "for ax in (ax0, ax1, ax2):\n",
    "    ax.set_xbound(-1000, 1900)\n",
    "    ax.set_facecolor(\"xkcd:white\")\n",
    "    \n",
    "fig.legend(ncol=2, loc=\"upper center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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