Laure_maps_and_implementation-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fifth-annual",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: GOOGLE_APPLICATION_CREDENTIALS=/home/ag7531/access_key.json\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "from analysis.utils import GlobalPlotter\n",
    "import os.path\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cmocean\n",
    "from cartopy.crs import PlateCarree, EqualEarth\n",
    "%env GOOGLE_APPLICATION_CREDENTIALS /home/ag7531/access_key.json\n",
    "%matplotlib notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "editorial-formula",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ext3/miniconda3/envs/analysis/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: 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": [
    "path_to_data = '/scratch/ag7531/paper_plots_data'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dutch-platform",
   "metadata": {},
   "source": [
    "This is the class used for map plots. If you just want to change args passed to plots, you don't need to modify this directly, but rather modify the arg lists (see further on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "grand-highlight",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mlflow\n",
    "from mlflow.tracking import client\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib.patches import Rectangle\n",
    "import pandas as pd\n",
    "from analysis.analysis import TimeSeriesForPoint\n",
    "import xarray as xr\n",
    "from scipy.ndimage import gaussian_filter\n",
    "from data.pangeo_catalog import get_patch, get_whole_data\n",
    "from cartopy.crs import PlateCarree, EqualEarth\n",
    "\n",
    "\n",
    "from enum import Enum\n",
    "\n",
    "CATALOG_URL = 'https://raw.githubusercontent.com/pangeo-data/pangeo-datastore\\\n",
    "/master/intake-catalogs/master.yaml'\n",
    "\n",
    "class GlobalPlotter:\n",
    "    \"\"\"General class to make plots for global data. Handles masking of\n",
    "    continental data + showing a band near coastlines.\"\"\"\n",
    "\n",
    "    def __init__(self, margin: int = 2, cbar: bool = True, ice: bool = True):\n",
    "        self.mask = self._get_global_u_mask()\n",
    "        self.margin = margin\n",
    "        self.cbar = cbar\n",
    "        self.ticks = dict(x=None, y=None)\n",
    "        self.ice = ice\n",
    "\n",
    "    @property\n",
    "    def mask(self):\n",
    "        return self._mask\n",
    "\n",
    "    @mask.setter\n",
    "    def mask(self, value):\n",
    "        self._mask = value\n",
    "\n",
    "    @property\n",
    "    def borders(self):\n",
    "        return self._borders\n",
    "\n",
    "    @borders.setter\n",
    "    def borders(self, value):\n",
    "        self._borders = value\n",
    "\n",
    "    @property\n",
    "    def margin(self):\n",
    "        return self._margin\n",
    "\n",
    "    @margin.setter\n",
    "    def margin(self, margin):\n",
    "        self._margin = margin\n",
    "        self.borders = self._get_continent_borders(self.mask, self.margin)\n",
    "\n",
    "    @property\n",
    "    def x_ticks(self):\n",
    "        return self.ticks['x']\n",
    "\n",
    "    @x_ticks.setter\n",
    "    def x_ticks(self, value):\n",
    "        self.ticks['x'] = value\n",
    "\n",
    "    @property\n",
    "    def y_ticks(self):\n",
    "        return self.ticks['y']\n",
    "\n",
    "    @y_ticks.setter\n",
    "    def y_ticks(self, value):\n",
    "        self.ticks['y'] = value\n",
    "\n",
    "    def plot(self, u: xr.DataArray = None, projection_cls=PlateCarree,\n",
    "             lon: float = -100.0, lat: float = None, ax=None, animated=False,\n",
    "             borders_color='grey', borders_alpha=1.,\n",
    "             colorbar_label='', **plot_func_kw):\n",
    "        \"\"\"\n",
    "        Plots the passed velocity component on a map, using the specified\n",
    "        projection. Uses the instance's mask to set as nan some values.\n",
    "        Parameters\n",
    "        ----------\n",
    "        u : xr.DataArray\n",
    "            Velocity array. The default is None.\n",
    "        projection : Projection\n",
    "            Projection used for the 2D plot.\n",
    "        lon : float, optional\n",
    "            Central longitude. The default is -100.0.\n",
    "        lat : float, optional\n",
    "            Central latitude. The default is None.\n",
    "        Returns\n",
    "        -------\n",
    "        None.\n",
    "        \"\"\"\n",
    "        fig = plt.figure()\n",
    "        projection = projection_cls(lon)\n",
    "        if ax is None:\n",
    "            ax = plt.axes(projection=projection)\n",
    "            plt.rc('axes',facecolor='black')\n",
    "            plt.rc('axes',edgecolor='k')\n",
    "        mesh_x, mesh_y = np.meshgrid(u['longitude'], u['latitude'])\n",
    "        if u is not None:\n",
    "            extra = self.mask.isel(longitude=slice(0, 10))\n",
    "            extra['longitude'] = extra['longitude'] + 360\n",
    "            mask = xr.concat((self.mask, extra), dim='longitude')\n",
    "            mask = mask.interp({k: u.coords[k] for k in ('longitude',\n",
    "                                                         'latitude')})\n",
    "            u = u * mask\n",
    "            im = ax.pcolormesh(mesh_x, mesh_y, u.values,\n",
    "                               transform=PlateCarree(),\n",
    "                               animated=animated, **plot_func_kw)\n",
    "        if self.x_ticks is not None:\n",
    "            ax.set_xticks(self.x_ticks)\n",
    "        if self.y_ticks is not None:\n",
    "            ax.set_yticks(self.y_ticks)\n",
    "        ax.set_global()\n",
    "        ax.coastlines()\n",
    "        # \"Gray-out\" near continental locations\n",
    "        if self.margin > 0:\n",
    "            extra = self.borders.isel(longitude=slice(0, 10))\n",
    "            extra['longitude'] = extra['longitude'] + 360\n",
    "            borders = xr.concat((self.borders, extra), dim='longitude')\n",
    "            borders = borders.interp({k: u.coords[k]\n",
    "                                     for k in ('longitude', 'latitude')})\n",
    "            borders_cmap = colors.ListedColormap([borders_color, ])\n",
    "            ax.pcolormesh(mesh_x, mesh_y, borders, animated=animated,\n",
    "                          transform=PlateCarree(), alpha=borders_alpha,\n",
    "                          cmap=borders_cmap)\n",
    "\n",
    "        # Add locations of ice\n",
    "        if self.ice:\n",
    "            ice = self._get_ice_border()\n",
    "            ice = xr.where(ice, 1., 0.)\n",
    "            ice = ice.interp({k: u.coords[k] for k in ('longitude',\n",
    "                                                       'latitude')})\n",
    "            ice = xr.where(ice != 0, 1., 0.)\n",
    "            ice = abs(ice.diff(dim='longitude')) + abs(ice.diff(dim='latitude'))\n",
    "            ice = xr.where(ice != 0., 1, np.nan)\n",
    "            ice_cmap = colors.ListedColormap(['black', ])\n",
    "            ax.pcolormesh(mesh_x, mesh_y, ice, animated=animated,\n",
    "                          transform=PlateCarree(), alpha=0.5,\n",
    "                          cmap=ice_cmap)\n",
    "        if u is not None and self.cbar:\n",
    "            cbar = plt.colorbar(im, shrink=0.6)\n",
    "            cbar.ax.tick_params(length=0)\n",
    "            if colorbar_label:\n",
    "                cbar.set_label(colorbar_label)\n",
    "            cbar.ax.tick_params(length=0, which='both')\n",
    "        return ax\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_global_u_mask(factor: int = 4, base_mask: xr.DataArray = None):\n",
    "        \"\"\"\n",
    "        Return the global mask of the low-resolution surface velocities for\n",
    "        plots. While the coarse-grained velocities might be defined on\n",
    "        continental points due to the coarse-graining procedures, these are\n",
    "        not shown as we do not use them -- the mask for the forcing is even\n",
    "        more restrictive, as it removes any point within some margin of the\n",
    "        velocities mask.\n",
    "        Parameters\n",
    "        ----------\n",
    "        factor : int, optional\n",
    "            Coarse-graining factor. The default is 4.\n",
    "        base_mask: xr.DataArray, optional\n",
    "            # TODO\n",
    "            Not implemented for now.\n",
    "        Returns\n",
    "        -------\n",
    "        None.\n",
    "        \"\"\"\n",
    "        if base_mask is not None:\n",
    "            mask = base_mask\n",
    "        else:\n",
    "            _, grid_info = get_whole_data(CATALOG_URL, 0)\n",
    "            mask = grid_info['wet']\n",
    "            mask = mask.coarsen(dict(xt_ocean=factor, yt_ocean=factor))\n",
    "        mask_ = mask.max()\n",
    "        mask_ = mask_.where(mask_ > 0.1)\n",
    "        mask_ = mask_.rename(dict(xt_ocean='longitude', yt_ocean='latitude'))\n",
    "        return mask_.compute()\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_ice_border():\n",
    "        \"\"\"Return an xarray.DataArray that indicates the locations of ice\n",
    "        in the oceans. \"\"\"\n",
    "        temperature, _ = get_patch(CATALOG_URL, 1, None, 0,\n",
    "                                        'surface_temp')\n",
    "        temperature = temperature.rename(dict(xt_ocean='longitude',\n",
    "                                              yt_ocean='latitude'))\n",
    "        temperature = temperature['surface_temp'].isel(time=0)\n",
    "        ice = xr.where(temperature <= 0., True, False)\n",
    "        return ice\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_continent_borders(base_mask: xr.DataArray, margin: int):\n",
    "        \"\"\"\n",
    "        Returns a boolean xarray DataArray corresponding to a mask of the\n",
    "        continents' coasts, which we do not process.\n",
    "        Hence margin should be set according to the model.\n",
    "        Parameters\n",
    "        ----------\n",
    "        mask : xr.DataArray\n",
    "            Mask taking value 1 where coarse velocities are defined and used\n",
    "            as input and nan elsewhere.\n",
    "        margin : int\n",
    "            Margin imposed by the model used, i.e. number of points lost on\n",
    "            one side of a square.\n",
    "        Returns\n",
    "        -------\n",
    "        mask : xr.DataArray\n",
    "            Boolean DataArray taking value True for continents.\n",
    "        \"\"\"\n",
    "        assert margin >= 0, 'The margin parameter should be a non-negative' \\\n",
    "                            ' integer'\n",
    "        assert base_mask.ndim <= 2, 'Velocity array should have two'\\\n",
    "                                    ' dims'\n",
    "        # Small trick using the guassian filter function\n",
    "        mask = xr.apply_ufunc(lambda x: gaussian_filter(x, 1., truncate=margin),\n",
    "                              base_mask)\n",
    "        mask = np.logical_and(np.isnan(mask),  ~np.isnan(base_mask))\n",
    "        mask = mask.where(mask)\n",
    "        return mask.compute()\n",
    "\n",
    "\n",
    "uv_plotter = GlobalPlotter()\n",
    "uv_plotter.margin = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "photographic-pickup",
   "metadata": {},
   "source": [
    "These functions are used for the implementation plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "quality-terrorism",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ext3/miniconda3/envs/analysis/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: 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 plot_implementation_1(ds):\n",
    "    \"\"\"Plots the figure showing the std of eta for the high-rez, low-rez no param,\n",
    "    low-rez with param\"\"\"\n",
    "    func = 'std'\n",
    "    cmaps = dict(mean=cmocean.cm.delta, std=cmocean.cm.matter)\n",
    "    args = dict(mean=dict(), std=dict(norm=matplotlib.colors.LogNorm()))\n",
    "    vmins=dict(mean=-1.96, std=0.5)\n",
    "    vmaxs=dict(mean=1.96, std=3)\n",
    "    vmins2=dict(mean=0, std=0.0001)\n",
    "    extent = (0, 3840, 0, 3840)\n",
    "\n",
    "    fig = plt.figure()\n",
    "    # Determine limits.\n",
    "    std_h = getattr(ds['high_rez'], func)(dim='time').std()\n",
    "    for i, var in enumerate(ds):\n",
    "        if i > 2:\n",
    "            break\n",
    "        plt.subplot(1, 3, i + 1)\n",
    "        if i > 0:\n",
    "            im = plt.imshow(getattr(ds[var], func)(dim='time'), cmap=cmaps[func], **args[func], \n",
    "                                                              vmin=std_h*vmins[func] + vmins2[func],\n",
    "                                                              vmax=std_h*vmaxs[func], origin='lower',\n",
    "                                                              extent=extent)\n",
    "        else:\n",
    "            im = plt.imshow(getattr(ds[var], func)(dim='time'), cmap=cmaps[func], **args[func],\n",
    "                                                  vmin=std_h*vmins[func] + vmins2[func],\n",
    "                                                              vmax=std_h*vmaxs[func], origin='lower',\n",
    "                                                                extent=extent)\n",
    "        if i > 0:\n",
    "            im.axes.set_yticks([])\n",
    "        if i == 0:\n",
    "            im.axes.set_xlabel('km')\n",
    "            im.axes.set_ylabel('km')\n",
    "    fig.subplots_adjust(right=0.8)\n",
    "    cbar_ax = fig.add_axes([0.85, 0.3, 0.025, 0.4])\n",
    "    fig.colorbar(im, cax=cbar_ax, label='m')\n",
    "\n",
    "def plot_implementation_2(ds):\n",
    "    \"\"\"Plot the kE time series\"\"\"\n",
    "    plt.figure()\n",
    "    for var_name, var_values in ds.items():\n",
    "        plt.plot(var_values.mean(dim=('x', 'y')))\n",
    "    plt.ylabel(r'$m^2/s^2$')\n",
    "    plt.xlabel('day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "critical-filter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['corr_X_control', 'eta_std_l', 'mse1pct', 'mse_control', 'online_kE', 'r2_1pctC02_month', 'r2_control_month', 'rel_diff_forcing', 'rel_diff_uvnorm', 'variance_forcing_control', 'variance_forcing_control_pred', 'velocities_std_control', 'z_r2_control_month_x', 'z_r2_control_month_y']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(os.listdir(path_to_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "refined-maine",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot n°0: corr_X_control\n",
      "Plot n°1: eta_std_l\n",
      "Plot n°2: mse1pct\n",
      "Plot n°3: mse_control\n",
      "Plot n°4: online_kE\n",
      "Plot n°5: r2_1pctC02_month\n",
      "Plot n°6: r2_control_month\n",
      "Plot n°7: rel_diff_forcing\n",
      "Plot n°8: rel_diff_uvnorm\n",
      "Plot n°9: variance_forcing_control\n",
      "Plot n°10: variance_forcing_control_pred\n",
      "Plot n°11: velocities_std_control\n",
      "Plot n°12: z_r2_control_month_x\n",
      "Plot n°13: z_r2_control_month_y\n"
     ]
    }
   ],
   "source": [
    "# These define the plot args used for each plot in the list above, in the same order\n",
    "plot_args=[\n",
    "    dict(vmin=0.7, vmax=1., lon=0., cmap=cmocean.cm.diff),\n",
    "    dict(),\n",
    "    dict(vmin=0.01, vmax=10, lon=0., cmap=cmocean.cm.thermal,\n",
    "        colorbar_label=r'$1e^{-14}m^2/s^4$', norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=0.01, vmax=10, cmap=cmocean.cm.thermal, lon=0., \n",
    "         colorbar_label=r'$1e^{-14}m^2s^{-4}$', norm=matplotlib.colors.LogNorm()),\n",
    "    dict(),\n",
    "    dict(vmin=0.5, vmax=1, cmap=cmocean.cm.diff, lon=0., norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=0.5, vmax=1, cmap=cmocean.cm.diff, lon=0., norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=-50, vmax=50, lon=0., cmap=cmocean.cm.diff, colorbar_label='%'),\n",
    "    dict(vmin=-50, vmax=50, lon=0., cmap=cmocean.cm.diff, colorbar_label='%'),\n",
    "    dict(vmin=0.01, vmax=10, cmap=cmocean.cm.thermal, lon=0., colorbar_label=r'$1e^{-14}m^2/s^4$',\n",
    "         norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=0.01, vmax=10, lon=0., cmap=cmocean.cm.thermal, colorbar_label=r'$1e^{-14}m^2/s^4$', \n",
    "         norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=0.01, vmax=1, lon=0., cmap=cmocean.cm.thermal,\n",
    "        colorbar_label=r'$m/s$', norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=0.5, vmax=1, cmap=cmocean.cm.diff, lon=0., norm=matplotlib.colors.LogNorm()),\n",
    "    dict(vmin=0.5, vmax=1, cmap=cmocean.cm.diff, lon=0., norm=matplotlib.colors.LogNorm())\n",
    "]\n",
    "\n",
    "# These define the functions called for plotting\n",
    "plot_funcs = [\n",
    "    uv_plotter.plot,\n",
    "    plot_implementation_1,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    plot_implementation_2,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot,\n",
    "    uv_plotter.plot\n",
    "]\n",
    "\n",
    "list_of_plots = list(zip(sorted(os.listdir(path_to_data)), plot_funcs, plot_args))\n",
    "\n",
    "# If you want to change the projection cls for all plots use this\n",
    "projection_cls = EqualEarth\n",
    "for i, args in enumerate(plot_args):\n",
    "    # This only applies to map plots\n",
    "    if list_of_plots[i][1] == uv_plotter.plot:\n",
    "        args['projection_cls'] = projection_cls\n",
    "\n",
    "\n",
    "for i, plot in enumerate(list_of_plots):\n",
    "    print(f'Plot n°{i}: {plot[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bright-manchester",
   "metadata": {},
   "source": [
    "Specify which plot to do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "constitutional-quantum",
   "metadata": {},
   "outputs": [],
   "source": [
    "choice = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "raising-costa",
   "metadata": {},
   "outputs": [
    {
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       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    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'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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 / fig.ratio,\n",
       "        fig.canvas.height / fig.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 * this.ratio;\n",
       "    var y = canvas_pos.y * this.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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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 / this.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 (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\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>"
      ]
     },
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     "output_type": "display_data"
    },
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-d66fb066e4c4>:25: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n",
      "  im = plt.imshow(getattr(ds[var], func)(dim='time'), cmap=cmaps[func], **args[func],\n",
      "<ipython-input-4-d66fb066e4c4>:20: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n",
      "  im = plt.imshow(getattr(ds[var], func)(dim='time'), cmap=cmaps[func], **args[func],\n",
      "<ipython-input-4-d66fb066e4c4>:20: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.\n",
      "  im = plt.imshow(getattr(ds[var], func)(dim='time'), cmap=cmaps[func], **args[func],\n"
     ]
    }
   ],
   "source": [
    "if choice == 14:\n",
    "    ds1 = xr.open_zarr(os.path.join(path_to_data, list_of_plots[5][0]))\n",
    "    ds2 = xr.open_zarr(os.path.join(path_to_data, list_of_plots[6][0]))\n",
    "    ds1 = ds1.rename(dict(r2_1pctC02_month='r2_control_month'))\n",
    "    ds = ds1 - ds2\n",
    "    func = list_of_plots[7][1]\n",
    "    plot_args = list_of_plots[7][2]\n",
    "    plot_args['vmin'] = -0.1\n",
    "    plot_args['vmax'] = 0.1\n",
    "else:\n",
    "    ds = xr.open_zarr(os.path.join(path_to_data, list_of_plots[choice][0]))\n",
    "    func = list_of_plots[choice][1]\n",
    "    plot_args = list_of_plots[choice][2]\n",
    "\n",
    "# For the kE time series re-order as in paper\n",
    "if choice == 4:\n",
    "    ds = ds[['low_rez_0', 'low_rez_1', 'low_rez_2', 'low_rez_3', 'high_rez']]\n",
    "\n",
    "# Change the sign for dataset 6, as I changed the sign by mistake when saving...\n",
    "if choice == 7:\n",
    "    ds = -ds\n",
    "\n",
    "#plt.style.use(\"seaborn-dark\")\n",
    "uv_plotter.margin = 10\n",
    "if func == uv_plotter.plot:\n",
    "    print('ok')\n",
    "    for key in ds:\n",
    "        print(key)\n",
    "        ax = func(ds[key], borders_color='black', **plot_args)\n",
    "        ax.coastlines(color='white')\n",
    "else:\n",
    "    func(ds.load())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "brave-zambia",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ext3/miniconda3/envs/analysis/lib/python3.8/site-packages/ipykernel/ipkernel.py:283: 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(list_of_plots[choice][0] + '_Laure.jpg', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "joint-failing",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.savefig('r2_control_month_y.jpg', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "funded-lunch",
   "metadata": {},
   "outputs": [
    {
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       "\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,\n",
       ".xr-attrs 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.Dataset&gt;\n",
       "Dimensions:    (time: 3599, x: 96, y: 96)\n",
       "Coordinates:\n",
       "  * time       (time) float64 8.584e+04 1.722e+05 ... 3.107e+08 3.108e+08\n",
       "  * x          (x) float64 2e+04 6e+04 1e+05 ... 3.74e+06 3.78e+06 3.82e+06\n",
       "  * y          (y) float64 2e+04 6e+04 1e+05 ... 3.74e+06 3.78e+06 3.82e+06\n",
       "Data variables:\n",
       "    high_rez   (y, x, time) float64 -0.02526 -0.0269 -0.02708 ... 0.1493 0.2022\n",
       "    low_rez_0  (time, y, x) float64 0.004493 0.002024 ... 0.1798 0.1783\n",
       "    low_rez_1  (time, y, x) float64 0.002879 0.0006572 ... 0.01557 0.01781\n",
       "    low_rez_2  (time, y, x) float64 0.003269 -0.0004145 ... -0.2851 -0.2859\n",
       "    low_rez_3  (time, y, x) float64 0.005626 0.002861 ... -0.4476 -0.4495</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-97f7cdbf-4382-469a-bc80-56c69ac50a56' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97f7cdbf-4382-469a-bc80-56c69ac50a56' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3599</li><li><span class='xr-has-index'>x</span>: 96</li><li><span class='xr-has-index'>y</span>: 96</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-de2d04d3-bbc8-4ee6-9da0-d345ba9f5b68' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de2d04d3-bbc8-4ee6-9da0-d345ba9f5b68' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.584e+04 1.722e+05 ... 3.108e+08</div><input id='attrs-de2cabf7-503b-4108-86d7-e4b5d957af37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de2cabf7-503b-4108-86d7-e4b5d957af37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92862221-6705-4f30-862c-8ce874a19d9a' class='xr-var-data-in' type='checkbox'><label for='data-92862221-6705-4f30-862c-8ce874a19d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([8.583800e+04, 1.721900e+05, 2.585420e+05, ..., 3.106076e+08,\n",
       "       3.106940e+08, 3.107803e+08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2e+04 6e+04 ... 3.78e+06 3.82e+06</div><input id='attrs-5bf4d2f6-142a-43be-97bb-82318ab4a713' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bf4d2f6-142a-43be-97bb-82318ab4a713' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d2ce02f-5f26-4f74-9d6c-dc6721dd61bb' class='xr-var-data-in' type='checkbox'><label for='data-2d2ce02f-5f26-4f74-9d6c-dc6721dd61bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  20000.,   60000.,  100000.,  140000.,  180000.,  220000.,  260000.,\n",
       "        300000.,  340000.,  380000.,  420000.,  460000.,  500000.,  540000.,\n",
       "        580000.,  620000.,  660000.,  700000.,  740000.,  780000.,  820000.,\n",
       "        860000.,  900000.,  940000.,  980000., 1020000., 1060000., 1100000.,\n",
       "       1140000., 1180000., 1220000., 1260000., 1300000., 1340000., 1380000.,\n",
       "       1420000., 1460000., 1500000., 1540000., 1580000., 1620000., 1660000.,\n",
       "       1700000., 1740000., 1780000., 1820000., 1860000., 1900000., 1940000.,\n",
       "       1980000., 2020000., 2060000., 2100000., 2140000., 2180000., 2220000.,\n",
       "       2260000., 2300000., 2340000., 2380000., 2420000., 2460000., 2500000.,\n",
       "       2540000., 2580000., 2620000., 2660000., 2700000., 2740000., 2780000.,\n",
       "       2820000., 2860000., 2900000., 2940000., 2980000., 3020000., 3060000.,\n",
       "       3100000., 3140000., 3180000., 3220000., 3260000., 3300000., 3340000.,\n",
       "       3380000., 3420000., 3460000., 3500000., 3540000., 3580000., 3620000.,\n",
       "       3660000., 3700000., 3740000., 3780000., 3820000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2e+04 6e+04 ... 3.78e+06 3.82e+06</div><input id='attrs-3b3d48da-8a94-4181-8441-58a3bf479381' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b3d48da-8a94-4181-8441-58a3bf479381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b3211f9-1040-4375-a6d2-623c94f0777e' class='xr-var-data-in' type='checkbox'><label for='data-5b3211f9-1040-4375-a6d2-623c94f0777e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  20000.,   60000.,  100000.,  140000.,  180000.,  220000.,  260000.,\n",
       "        300000.,  340000.,  380000.,  420000.,  460000.,  500000.,  540000.,\n",
       "        580000.,  620000.,  660000.,  700000.,  740000.,  780000.,  820000.,\n",
       "        860000.,  900000.,  940000.,  980000., 1020000., 1060000., 1100000.,\n",
       "       1140000., 1180000., 1220000., 1260000., 1300000., 1340000., 1380000.,\n",
       "       1420000., 1460000., 1500000., 1540000., 1580000., 1620000., 1660000.,\n",
       "       1700000., 1740000., 1780000., 1820000., 1860000., 1900000., 1940000.,\n",
       "       1980000., 2020000., 2060000., 2100000., 2140000., 2180000., 2220000.,\n",
       "       2260000., 2300000., 2340000., 2380000., 2420000., 2460000., 2500000.,\n",
       "       2540000., 2580000., 2620000., 2660000., 2700000., 2740000., 2780000.,\n",
       "       2820000., 2860000., 2900000., 2940000., 2980000., 3020000., 3060000.,\n",
       "       3100000., 3140000., 3180000., 3220000., 3260000., 3300000., 3340000.,\n",
       "       3380000., 3420000., 3460000., 3500000., 3540000., 3580000., 3620000.,\n",
       "       3660000., 3700000., 3740000., 3780000., 3820000.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2cd0539-fa23-49eb-9960-f0f7abc45ca4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2cd0539-fa23-49eb-9960-f0f7abc45ca4' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>high_rez</span></div><div class='xr-var-dims'>(y, x, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.02526 -0.0269 ... 0.1493 0.2022</div><input id='attrs-0907f5c2-d1a0-4f8a-8633-0809f5324c38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0907f5c2-d1a0-4f8a-8633-0809f5324c38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37b0e592-4a03-46c4-920d-deb779a78e0f' class='xr-var-data-in' type='checkbox'><label for='data-37b0e592-4a03-46c4-920d-deb779a78e0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.02526089, -0.02689899, -0.02708424, ...,  0.19758125,\n",
       "          0.25976356,  0.29674091],\n",
       "        [-0.02689791, -0.02838132, -0.02828623, ...,  0.18843534,\n",
       "          0.25144221,  0.28886293],\n",
       "        [-0.0286482 , -0.02996805, -0.02958079, ...,  0.17209837,\n",
       "          0.23612237,  0.27410983],\n",
       "        ...,\n",
       "        [-0.12705104, -0.13538537, -0.14892144, ..., -0.13350183,\n",
       "         -0.0972648 , -0.07411279],\n",
       "        [-0.13018614, -0.13850594, -0.15201918, ..., -0.13633794,\n",
       "         -0.10018724, -0.07709941],\n",
       "        [-0.13293739, -0.14125405, -0.1547618 , ..., -0.13916734,\n",
       "         -0.10311076, -0.0800882 ]],\n",
       "\n",
       "       [[-0.02469087, -0.02617606, -0.0260716 , ...,  0.19355791,\n",
       "          0.25608876,  0.29325528],\n",
       "        [-0.02507107, -0.0260903 , -0.02513389, ...,  0.17145524,\n",
       "          0.23522575,  0.27308677],\n",
       "        [-0.02551863, -0.02606437, -0.0242517 , ...,  0.13278423,\n",
       "          0.19820638,  0.23702401],\n",
       "...\n",
       "        [-0.20376474, -0.21747464, -0.23798302, ...,  0.05330116,\n",
       "          0.14197509,  0.19438444],\n",
       "        [-0.20380541, -0.21745076, -0.23783114, ...,  0.06025355,\n",
       "          0.14916442,  0.20173842],\n",
       "        [-0.20406165, -0.21765963, -0.23793757, ...,  0.06127894,\n",
       "          0.15049813,  0.20327413]],\n",
       "\n",
       "       [[-0.20169425, -0.22541348, -0.26192094, ..., -0.09284307,\n",
       "         -0.0031371 ,  0.05095093],\n",
       "        [-0.20015855, -0.2237489 , -0.26004716, ..., -0.09098906,\n",
       "         -0.00130566,  0.05270138],\n",
       "        [-0.19855013, -0.22201545, -0.25811344, ..., -0.10214958,\n",
       "         -0.01151153,  0.04306451],\n",
       "        ...,\n",
       "        [-0.20561505, -0.21928186, -0.23967389, ...,  0.05613169,\n",
       "          0.14529125,  0.19803058],\n",
       "        [-0.20504398, -0.21866034, -0.23896386, ...,  0.05901359,\n",
       "          0.14827783,  0.20108295],\n",
       "        [-0.20464734, -0.21822882, -0.23846709, ...,  0.05989293,\n",
       "          0.1493073 ,  0.20220358]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_rez_0</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.004493 0.002024 ... 0.1798 0.1783</div><input id='attrs-7f532af6-73c5-4089-b534-5bca3769a719' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f532af6-73c5-4089-b534-5bca3769a719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf64f62b-bea5-43d5-b7b9-d5c2d804941b' class='xr-var-data-in' type='checkbox'><label for='data-bf64f62b-bea5-43d5-b7b9-d5c2d804941b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 4.49276784e-03,  2.02397108e-03, -2.14576935e-04, ...,\n",
       "         -1.02889559e-01, -1.05999273e-01, -1.09137746e-01],\n",
       "        [ 4.66612167e-03,  2.71132251e-03,  9.66638888e-04, ...,\n",
       "         -1.01952870e-01, -1.05456883e-01, -1.08998953e-01],\n",
       "        [ 4.73998721e-03,  3.33230064e-03,  2.11322154e-03, ...,\n",
       "         -1.01003811e-01, -1.04919352e-01, -1.08875816e-01],\n",
       "        ...,\n",
       "        [-9.08349715e-02, -8.87651003e-02, -8.69132740e-02, ...,\n",
       "         -1.42885110e-01, -1.43680894e-01, -1.44712588e-01],\n",
       "        [-9.11115142e-02, -8.94400446e-02, -8.79516716e-02, ...,\n",
       "         -1.44574959e-01, -1.44798940e-01, -1.45193444e-01],\n",
       "        [-9.14018172e-02, -9.02169925e-02, -8.91359829e-02, ...,\n",
       "         -1.46626729e-01, -1.46174263e-01, -1.45735045e-01]],\n",
       "\n",
       "       [[-5.53406513e-02, -5.80098051e-02, -6.08263945e-02, ...,\n",
       "         -1.16999171e-01, -1.21030177e-01, -1.24699391e-01],\n",
       "        [-5.47788458e-02, -5.61594386e-02, -5.77397125e-02, ...,\n",
       "         -1.15693648e-01, -1.20254567e-01, -1.24420036e-01],\n",
       "        [-5.43110238e-02, -5.42915710e-02, -5.45251025e-02, ...,\n",
       "         -1.14266937e-01, -1.19389768e-01, -1.24078608e-01],\n",
       "...\n",
       "        [ 2.81276654e-02, -7.65930241e-03, -2.90899368e-02, ...,\n",
       "          2.45820469e-01,  2.12013691e-01,  1.88134808e-01],\n",
       "        [ 4.10367271e-02,  2.93063345e-02,  2.46170197e-02, ...,\n",
       "          2.11320426e-01,  1.94644470e-01,  1.83551978e-01],\n",
       "        [ 4.69990675e-02,  4.61742652e-02,  4.71532051e-02, ...,\n",
       "          1.85762013e-01,  1.82216417e-01,  1.80324073e-01]],\n",
       "\n",
       "       [[ 3.59669784e-01,  3.57860255e-01,  3.54984152e-01, ...,\n",
       "          5.64361010e-02,  5.36779646e-02,  5.13594180e-02],\n",
       "        [ 3.65127402e-01,  3.72146711e-01,  3.75997081e-01, ...,\n",
       "          5.39209145e-02,  5.16107510e-02,  5.06218010e-02],\n",
       "        [ 3.74805326e-01,  4.03347476e-01,  4.21939782e-01, ...,\n",
       "          5.17860574e-02,  4.94750730e-02,  4.97300055e-02],\n",
       "        ...,\n",
       "        [ 6.47851078e-02,  3.17761466e-02,  1.32364565e-02, ...,\n",
       "          2.43531953e-01,  2.08943853e-01,  1.85806660e-01],\n",
       "        [ 7.69561197e-02,  6.66205144e-02,  6.30517463e-02, ...,\n",
       "          2.07829125e-01,  1.91483696e-01,  1.81269316e-01],\n",
       "        [ 8.23437415e-02,  8.18449949e-02,  8.30320933e-02, ...,\n",
       "          1.82956718e-01,  1.79803552e-01,  1.78324587e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_rez_1</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.002879 0.0006572 ... 0.01781</div><input id='attrs-0447bac7-2071-4b5d-bcfa-f827e7b1b0bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0447bac7-2071-4b5d-bcfa-f827e7b1b0bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18495803-9a93-4c85-9255-cd4956baa643' class='xr-var-data-in' type='checkbox'><label for='data-18495803-9a93-4c85-9255-cd4956baa643' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 2.87858425e-03,  6.57211824e-04, -2.62881692e-04, ...,\n",
       "         -1.00799694e-01, -1.04977158e-01, -1.08665736e-01],\n",
       "        [ 2.11059770e-03,  1.58253473e-03,  1.06738269e-03, ...,\n",
       "         -1.01209464e-01, -1.04994698e-01, -1.08263421e-01],\n",
       "        [ 3.14162185e-03,  2.89191659e-03,  2.52470156e-03, ...,\n",
       "         -1.01520188e-01, -1.04792582e-01, -1.07333841e-01],\n",
       "        ...,\n",
       "        [-9.00069201e-02, -8.81647942e-02, -8.73314630e-02, ...,\n",
       "         -1.43589494e-01, -1.43351856e-01, -1.44311832e-01],\n",
       "        [-8.95577157e-02, -8.83732411e-02, -8.83566389e-02, ...,\n",
       "         -1.44529768e-01, -1.43517275e-01, -1.43319393e-01],\n",
       "        [-8.93056296e-02, -8.88349673e-02, -8.95043579e-02, ...,\n",
       "         -1.46290428e-01, -1.44183054e-01, -1.42071647e-01]],\n",
       "\n",
       "       [[-5.87811643e-02, -6.10404149e-02, -6.32909943e-02, ...,\n",
       "         -1.15782748e-01, -1.18592725e-01, -1.20578744e-01],\n",
       "        [-5.83267311e-02, -5.92766072e-02, -6.03471952e-02, ...,\n",
       "         -1.14222871e-01, -1.17904204e-01, -1.21466540e-01],\n",
       "        [-5.75178017e-02, -5.75475590e-02, -5.74403835e-02, ...,\n",
       "         -1.12347356e-01, -1.17507853e-01, -1.22257936e-01],\n",
       "...\n",
       "        [-5.84415286e-01, -5.18507519e-01, -4.37392264e-01, ...,\n",
       "         -3.03518885e-01, -2.95206745e-01, -2.82420075e-01],\n",
       "        [-5.94409314e-01, -5.44201949e-01, -4.86007415e-01, ...,\n",
       "         -2.97064453e-01, -2.89703195e-01, -2.80636798e-01],\n",
       "        [-6.07487104e-01, -5.87503228e-01, -5.65385161e-01, ...,\n",
       "         -2.86706961e-01, -2.82775326e-01, -2.78467495e-01]],\n",
       "\n",
       "       [[ 2.05340724e-01,  1.97604184e-01,  1.89059918e-01, ...,\n",
       "         -2.19844509e-01, -2.23233680e-01, -2.25588801e-01],\n",
       "        [ 2.03230122e-01,  1.93825927e-01,  1.87317792e-01, ...,\n",
       "         -2.39233471e-01, -2.33751683e-01, -2.26114147e-01],\n",
       "        [ 2.06478967e-01,  2.04667109e-01,  2.06682840e-01, ...,\n",
       "         -2.62038740e-01, -2.46685202e-01, -2.28301631e-01],\n",
       "        ...,\n",
       "        [-2.79977595e-01, -2.12894648e-01, -1.25632845e-01, ...,\n",
       "         -8.09729952e-03, -1.35358685e-03,  1.02822398e-02],\n",
       "        [-2.87340301e-01, -2.38128074e-01, -1.77788273e-01, ...,\n",
       "          1.26391601e-03,  6.49287316e-03,  1.45224105e-02],\n",
       "        [-2.98306990e-01, -2.80361569e-01, -2.59446609e-01, ...,\n",
       "          1.48009778e-02,  1.55657610e-02,  1.78132632e-02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_rez_2</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003269 -0.0004145 ... -0.2859</div><input id='attrs-83823f78-5e99-497a-9dad-d517a70702ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83823f78-5e99-497a-9dad-d517a70702ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e258e345-2283-4fce-bb38-52c8061786d7' class='xr-var-data-in' type='checkbox'><label for='data-e258e345-2283-4fce-bb38-52c8061786d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00326893, -0.00041445, -0.00404315, ..., -0.1008537 ,\n",
       "         -0.10506267, -0.10953704],\n",
       "        [ 0.00405707,  0.00127119, -0.00175127, ..., -0.10137921,\n",
       "         -0.10570456, -0.11038272],\n",
       "        [ 0.00415657,  0.00204918,  0.00044856, ..., -0.10139606,\n",
       "         -0.10620594, -0.11073358],\n",
       "        ...,\n",
       "        [-0.09042119, -0.08866535, -0.08632667, ..., -0.14262379,\n",
       "         -0.14387736, -0.14526133],\n",
       "        [-0.09081446, -0.09013358, -0.08866231, ..., -0.14433723,\n",
       "         -0.14465719, -0.14499693],\n",
       "        [-0.09127634, -0.0912849 , -0.09071892, ..., -0.14587042,\n",
       "         -0.14530117, -0.14467444]],\n",
       "\n",
       "       [[-0.05138775, -0.05569317, -0.06065495, ..., -0.1136036 ,\n",
       "         -0.12010139, -0.12542111],\n",
       "        [-0.05304778, -0.05539764, -0.05891588, ..., -0.1137995 ,\n",
       "         -0.12085718, -0.12657243],\n",
       "        [-0.05556192, -0.05619745, -0.05701739, ..., -0.11413159,\n",
       "         -0.12150664, -0.12760215],\n",
       "...\n",
       "        [ 0.06062495,  0.02671402,  0.00318131, ..., -0.02197819,\n",
       "         -0.02985783, -0.03801809],\n",
       "        [ 0.06989829,  0.06741627,  0.06738197, ..., -0.02779846,\n",
       "         -0.03338163, -0.0384501 ],\n",
       "        [ 0.06823115,  0.07342062,  0.07997084, ..., -0.03512006,\n",
       "         -0.03770035, -0.03974214]],\n",
       "\n",
       "       [[-0.01616545, -0.01667546, -0.0172723 , ..., -0.12995045,\n",
       "         -0.14015145, -0.14674735],\n",
       "        [-0.01270497, -0.00854282, -0.00642893, ..., -0.11647475,\n",
       "         -0.13288818, -0.14418605],\n",
       "        [-0.00835416,  0.00773134,  0.01816141, ..., -0.09896354,\n",
       "         -0.12269008, -0.13933722],\n",
       "        ...,\n",
       "        [-0.17152757, -0.18763706, -0.18895119, ..., -0.26897272,\n",
       "         -0.27619019, -0.28350539],\n",
       "        [-0.16122103, -0.15485118, -0.14545299, ..., -0.27667326,\n",
       "         -0.28014013, -0.28431453],\n",
       "        [-0.16294751, -0.15755277, -0.15304925, ..., -0.28493087,\n",
       "         -0.28507658, -0.28592083]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_rez_3</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.005626 0.002861 ... -0.4495</div><input id='attrs-25dd9214-c5ef-432f-ac23-01bb3cdf2e36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25dd9214-c5ef-432f-ac23-01bb3cdf2e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de074ea8-1ce6-4104-8541-59ce6ef0e519' class='xr-var-data-in' type='checkbox'><label for='data-de074ea8-1ce6-4104-8541-59ce6ef0e519' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.62570511e-03,  2.86110723e-03,  3.64400040e-05, ...,\n",
       "         -1.04766317e-01, -1.07753521e-01, -1.10059279e-01],\n",
       "        [ 5.32794663e-03,  3.08188192e-03,  1.27407771e-03, ...,\n",
       "         -1.04164531e-01, -1.06993745e-01, -1.09908662e-01],\n",
       "        [ 5.42584368e-03,  3.85572895e-03,  2.50798515e-03, ...,\n",
       "         -1.02702410e-01, -1.05724602e-01, -1.09191363e-01],\n",
       "        ...,\n",
       "        [-8.75712369e-02, -8.63545891e-02, -8.53862796e-02, ...,\n",
       "         -1.43136370e-01, -1.43937851e-01, -1.44665945e-01],\n",
       "        [-8.95355210e-02, -8.80158607e-02, -8.69940842e-02, ...,\n",
       "         -1.44227774e-01, -1.44619520e-01, -1.45077131e-01],\n",
       "        [-8.98263990e-02, -8.94132147e-02, -8.89890521e-02, ...,\n",
       "         -1.45371274e-01, -1.45592587e-01, -1.46206972e-01]],\n",
       "\n",
       "       [[-5.46041530e-02, -5.67383162e-02, -5.89845274e-02, ...,\n",
       "         -1.18721638e-01, -1.21726146e-01, -1.25198627e-01],\n",
       "        [-5.44897481e-02, -5.57714998e-02, -5.65907866e-02, ...,\n",
       "         -1.17114026e-01, -1.20840699e-01, -1.24576819e-01],\n",
       "        [-5.42393586e-02, -5.43486842e-02, -5.48243500e-02, ...,\n",
       "         -1.15711419e-01, -1.19826797e-01, -1.23864557e-01],\n",
       "...\n",
       "        [-1.87790902e-01, -2.57675009e-01, -3.10758297e-01, ...,\n",
       "         -2.07574612e-01, -2.20011388e-01, -2.27720093e-01],\n",
       "        [-1.60094225e-01, -1.89748321e-01, -2.09958966e-01, ...,\n",
       "         -2.20661188e-01, -2.26407932e-01, -2.30280802e-01],\n",
       "        [-1.43845215e-01, -1.49794982e-01, -1.53429439e-01, ...,\n",
       "         -2.29985644e-01, -2.30955867e-01, -2.31247943e-01]],\n",
       "\n",
       "       [[-8.20148291e-02, -8.51614265e-02, -8.91565461e-02, ...,\n",
       "         -1.58043279e-01, -1.60461953e-01, -1.63059531e-01],\n",
       "        [-8.06198430e-02, -7.58459690e-02, -7.27002076e-02, ...,\n",
       "         -1.50761110e-01, -1.57922556e-01, -1.61864586e-01],\n",
       "        [-7.64076538e-02, -5.77406410e-02, -4.27569796e-02, ...,\n",
       "         -1.42904940e-01, -1.55481762e-01, -1.60474640e-01],\n",
       "        ...,\n",
       "        [-3.82423316e-01, -4.47536692e-01, -4.99477677e-01, ...,\n",
       "         -4.25870790e-01, -4.39768366e-01, -4.48011011e-01],\n",
       "        [-3.57277571e-01, -3.82996713e-01, -4.01585212e-01, ...,\n",
       "         -4.36704852e-01, -4.44920620e-01, -4.49341289e-01],\n",
       "        [-3.45112220e-01, -3.50508183e-01, -3.54258435e-01, ...,\n",
       "         -4.43546976e-01, -4.47567872e-01, -4.49539047e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a83644d-be71-49c5-8557-7b793a108c70' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a83644d-be71-49c5-8557-7b793a108c70' 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.Dataset>\n",
       "Dimensions:    (time: 3599, x: 96, y: 96)\n",
       "Coordinates:\n",
       "  * time       (time) float64 8.584e+04 1.722e+05 ... 3.107e+08 3.108e+08\n",
       "  * x          (x) float64 2e+04 6e+04 1e+05 ... 3.74e+06 3.78e+06 3.82e+06\n",
       "  * y          (y) float64 2e+04 6e+04 1e+05 ... 3.74e+06 3.78e+06 3.82e+06\n",
       "Data variables:\n",
       "    high_rez   (y, x, time) float64 -0.02526 -0.0269 -0.02708 ... 0.1493 0.2022\n",
       "    low_rez_0  (time, y, x) float64 0.004493 0.002024 ... 0.1798 0.1783\n",
       "    low_rez_1  (time, y, x) float64 0.002879 0.0006572 ... 0.01557 0.01781\n",
       "    low_rez_2  (time, y, x) float64 0.003269 -0.0004145 ... -0.2851 -0.2859\n",
       "    low_rez_3  (time, y, x) float64 0.005626 0.002861 ... -0.4476 -0.4495"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "protecting-radius",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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