Figure1.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code to generate Figure 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analysis.utils import select_experiment, select_run, GlobalPlotter\n",
    "import mlflow\n",
    "from mlflow.tracking import MlflowClient\n",
    "import xarray as xr\n",
    "from dask.diagnostics import ProgressBar\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.linalg import norm\n",
    "\n",
    "import cmocean\n",
    "cmap_solar = cmocean.cm.solar\n",
    "cmap_balance = cmocean.cm.balance\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (4, 4 / 1.618)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run_control_id = 'a06e42d44402481eb3a8f9e705770950'\n",
    "# run_1pct_id = '32ebeb9bdec9441ca761e91b8777acd1'\n",
    "run_control_id = None\n",
    "run_1pct_id = None\n",
    "var_name = 'vsurf'\n",
    "cmap = cmocean.cm.amp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ag7531/miniconda3/envs/analysis/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "def select_run_id():\n",
    "    exp_name = select_experiment()\n",
    "    experiment_id = mlflow.get_experiment_by_name(exp_name).experiment_id\n",
    "    cols = ['params.CO2', 'params.factor']\n",
    "    run = select_run(cols=cols, experiment_ids=(experiment_id,))\n",
    "    return run.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 :  multiregion\n",
      "17 :  meeting22july\n",
      "15 :  datacm21\n",
      "3 :  multiregions\n",
      "19 :  data-global\n",
      "14 :  forcingdatav3\n",
      "2 :  training\n",
      "4 :  default\n",
      "7 :  Unet\n",
      "18 :  forcing-data-global\n",
      "12 :  test\n",
      "9 :  forcingdata1pct\n",
      "5 :  regionsfortraining\n",
      "20 :  test_global\n",
      "8 :  arctan\n",
      "16 :  meeting15july\n",
      "13 :  forcingdatav2\n",
      "Select the id of an experiment: 19\n",
      "                              run_id experiment_id params.CO2 params.factor\n",
      "0   359a13e63c9744d18336a7b04380569f            19          1             8\n",
      "1   7a60369c9d5b48058366adef1df59728            19          1             7\n",
      "2   b85d663b7a1f4e59b09b0d3813cbcab8            19          1             6\n",
      "3   0c1de3ebefec40748311812a482a8024            19          1             5\n",
      "4   a5049bd09c7e409791944731e5631a10            19          0             8\n",
      "5   8f038ef65bdb4667a815029ccf8be3c4            19          0             7\n",
      "6   2ebecb60c35642fbb1b60d1f6d33df2d            19          0             6\n",
      "7   1dad89889fae43e798d8fbb171627ad0            19          0             5\n",
      "8   743777154cb745cf8a4272dcbd3fa189            19          0             4\n",
      "9   e5e4964d11aa4be1b70143f2289ccfb0            19          1            10\n",
      "10  f4df0dbb52924d649224c4507cd160aa            19          0            10\n",
      "11  29399585cd654ffbbac18531738ce984            19          1            10\n",
      "12  4feb4b3a4ca340ed9e87b75e9e0706f6            19          0            10\n",
      "13  b681d3b8e4e149a7a9ff3193a62be3d4            19          1             4\n",
      "14  324a91cc5d64485da4109288a6222090            19          1             4\n",
      "15  233450fe93354d86bdae6268cc284832            19          1             4\n",
      "16  dfa9c1bbc76249c994beacea5fa70b90            19          1             4\n",
      "17  0cc065fcdcea4a2c83c30c8ed66d0b81            19          1             4\n",
      "18  5a86a3dbda6c4208949f0a0edbce5356            19          1             4\n",
      "19  ffe4682876504d89892f2ae7571a675c            19          1             4\n",
      "20  52162444c2b94caa828ef3ccc12693ba            19          1             4\n",
      "21  45cf9545dfe24adfb235105781bfe852            19          1             4\n",
      "22  6930f1d7501447f7aac4719474b87896            19          1             4\n",
      "23  34008ec087d840e6a81407c5d95d12e2            19          1             4\n",
      "24  11170a3a29684216ac32a0f54db62ccd            19          0             4\n",
      "25  143e8895afd7453ba0aa7b0b02858702            19          1             4\n",
      "26  72b9f00c3bfa405e865f3fd6d07edad1            19          0             4\n",
      "Run id?8\n",
      "6 :  multiregion\n",
      "17 :  meeting22july\n",
      "15 :  datacm21\n",
      "3 :  multiregions\n",
      "19 :  data-global\n",
      "14 :  forcingdatav3\n",
      "2 :  training\n",
      "4 :  default\n",
      "7 :  Unet\n",
      "18 :  forcing-data-global\n",
      "12 :  test\n",
      "9 :  forcingdata1pct\n",
      "5 :  regionsfortraining\n",
      "20 :  test_global\n",
      "8 :  arctan\n",
      "16 :  meeting15july\n",
      "13 :  forcingdatav2\n",
      "Select the id of an experiment: 19\n",
      "                              run_id experiment_id params.CO2 params.factor\n",
      "0   359a13e63c9744d18336a7b04380569f            19          1             8\n",
      "1   7a60369c9d5b48058366adef1df59728            19          1             7\n",
      "2   b85d663b7a1f4e59b09b0d3813cbcab8            19          1             6\n",
      "3   0c1de3ebefec40748311812a482a8024            19          1             5\n",
      "4   a5049bd09c7e409791944731e5631a10            19          0             8\n",
      "5   8f038ef65bdb4667a815029ccf8be3c4            19          0             7\n",
      "6   2ebecb60c35642fbb1b60d1f6d33df2d            19          0             6\n",
      "7   1dad89889fae43e798d8fbb171627ad0            19          0             5\n",
      "8   743777154cb745cf8a4272dcbd3fa189            19          0             4\n",
      "9   e5e4964d11aa4be1b70143f2289ccfb0            19          1            10\n",
      "10  f4df0dbb52924d649224c4507cd160aa            19          0            10\n",
      "11  29399585cd654ffbbac18531738ce984            19          1            10\n",
      "12  4feb4b3a4ca340ed9e87b75e9e0706f6            19          0            10\n",
      "13  b681d3b8e4e149a7a9ff3193a62be3d4            19          1             4\n",
      "14  324a91cc5d64485da4109288a6222090            19          1             4\n",
      "15  233450fe93354d86bdae6268cc284832            19          1             4\n",
      "16  dfa9c1bbc76249c994beacea5fa70b90            19          1             4\n",
      "17  0cc065fcdcea4a2c83c30c8ed66d0b81            19          1             4\n",
      "18  5a86a3dbda6c4208949f0a0edbce5356            19          1             4\n",
      "19  ffe4682876504d89892f2ae7571a675c            19          1             4\n",
      "20  52162444c2b94caa828ef3ccc12693ba            19          1             4\n",
      "21  45cf9545dfe24adfb235105781bfe852            19          1             4\n",
      "22  6930f1d7501447f7aac4719474b87896            19          1             4\n",
      "23  34008ec087d840e6a81407c5d95d12e2            19          1             4\n",
      "24  11170a3a29684216ac32a0f54db62ccd            19          0             4\n",
      "25  143e8895afd7453ba0aa7b0b02858702            19          1             4\n",
      "26  72b9f00c3bfa405e865f3fd6d07edad1            19          0             4\n",
      "Run id?13\n"
     ]
    }
   ],
   "source": [
    "ml_client = MlflowClient()\n",
    "if not run_control_id:\n",
    "    run_control_id = select_run_id()\n",
    "if not run_1pct_id:\n",
    "    run_1pct_id = select_run_id()\n",
    "\n",
    "run_control = mlflow.get_run(run_control_id)\n",
    "run_1pct = mlflow.get_run(run_1pct_id)\n",
    "\n",
    "data_control = xr.open_zarr(ml_client.download_artifacts(run_control_id, 'forcing'))\n",
    "data_1pct = xr.open_zarr(ml_client.download_artifacts(run_1pct_id, 'forcing'))\n",
    "data_control = data_control.rename(dict(xu_ocean='longitude', yu_ocean='latitude'))\n",
    "data_1pct = data_1pct.rename(dict(xu_ocean='longitude', yu_ocean='latitude'))\n",
    "\n",
    "# Rescale the forcing\n",
    "for var in ('S_x', 'S_y'):\n",
    "    data_control[var] /= 1e-7\n",
    "    data_1pct[var] /= 1e-7\n",
    "\n",
    "# Remove the seasonal means\n",
    "seasonal_groupby_control = data_control.groupby('time.month').mean()\n",
    "seasonal_groupby_1pct = data_1pct.groupby('time.month').mean()\n",
    "\n",
    "data_control = data_control.groupby('time.month') - seasonal_groupby_control\n",
    "data_1pct = data_1pct.groupby('time.month') - seasonal_groupby_1pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[###                                     ] | 8% Completed | 30.9s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ag7531/miniconda3/envs/analysis/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  6min  1.1s\n",
      "[###                                     ] | 8% Completed | 25.5s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ag7531/miniconda3/envs/analysis/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  5min 21.6s\n"
     ]
    }
   ],
   "source": [
    "var_control = data_control[var_name].std(dim='time')\n",
    "var_1pct = data_1pct[var_name].std(dim='time')\n",
    "with ProgressBar():\n",
    "    var_control = var_control.compute()\n",
    "    var_1pct = var_1pct.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ag7531/miniconda3/envs/analysis/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "diff =  var_1pct - var_control\n",
    "r_diff = diff / var_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cached version!\n",
      "Using cached version!\n"
     ]
    },
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       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\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,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        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, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\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.innerHTML =\n",
       "        '<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'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cached version!\n",
      "Using cached version!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "from cartopy.crs import PlateCarree\n",
    "from data.pangeo_catalog import get_patch, get_whole_data\n",
    "from scipy.ndimage import gaussian_filter\n",
    "from matplotlib import colors\n",
    "\n",
    "\n",
    "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n",
    "/master/intake-catalogs/master.yaml'\n",
    "\n",
    "\n",
    "plotter = GlobalPlotter(cbar=True, margin=4)\n",
    "plotter.x_ticks = np.arange(-150., 151., 50)\n",
    "plotter.y_ticks = np.arange(-80., 81., 20)\n",
    "\n",
    "ax=plotter.plot(diff, vmin=-0.05, vmax=0.05, cmap=cmocean.cm.delta, lon=0., colorbar_label='m/s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ag7531/miniconda3/envs/analysis/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "plt.savefig('r_diff_' + var_name + '.jpg', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ag7531/miniconda3/envs/analysis/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;usurf&#x27; ()&gt;\n",
       "array(0.04863561)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'usurf'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e887f3f3-9ed8-44c1-b2c0-f583dc55b3b6' class='xr-array-in' type='checkbox' checked><label for='section-e887f3f3-9ed8-44c1-b2c0-f583dc55b3b6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.04864</span></div><div class='xr-array-data'><pre>array(0.04863561)</pre></div></div></li><li class='xr-section-item'><input id='section-418941e3-33b0-427a-ae0f-3e610e283fbf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-418941e3-33b0-427a-ae0f-3e610e283fbf' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-67aee8e7-d690-4df8-b030-e2045271b3f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67aee8e7-d690-4df8-b030-e2045271b3f5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'usurf' ()>\n",
       "array(0.04863561)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv_plotter = plotter\n",
    "def apply_complete_mask(array):\n",
    "    mask = uv_plotter.borders\n",
    "    mask2 = uv_plotter.mask\n",
    "    mask = mask.interp({k: array.coords[k] for k in ['longitude', 'latitude']})\n",
    "    mask2 = mask2.interp({k: array.coords[k] for k in ['longitude', 'latitude']})\n",
    "    array = array.where(np.isnan(mask) & (~np.isnan(mask2)))\n",
    "    #array = array.sel(latitude=slice(pred['latitude'][0], pred['latitude'][-1]))\n",
    "    return array\n",
    "apply_complete_mask(r_diff).sel(latitude=slice(-60, 60)).mean().compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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