2d_map_plots_interactive.ipynb

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    "# Plot 2D Grid\n",
    "Plot the regridded data.\n",
    "The grid is the average, minimum, maximum, or variance of all datapoints in its cell. \\\n",
    "The only exceptions are \"counts\" which shows the number of datapoints in the cell \\\n",
    "and \"Top_Parameter\" which shows the most common top parameter for all trajectories in the cell. \\\n",
    "Using the button \"Fix colorbar over all levels\" centers the colorbar around zero."
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       " */\n",
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       "  var id = handle.cell.output_area._hv_plot_id;\n",
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       "  events.on('delete.Cell', handle_clear_output);\n",
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google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\n",
    "\n",
    "import panel as pn\n",
    "import xarray as xr\n",
    "\n",
    "\n",
    "from ad_sensitivity_analysis.interactive.project_map import plot_2dmap_interactive\n",
    "\n",
    "pn.extension()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fae128d-43e1-4adc-8b32-a708a66f0413",
   "metadata": {},
   "source": [
    "## Load data\n",
    "You have to regrid the trajectories first. Please compile the CPP code with target `regrid` and execute it. You find more information in the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0549ffda-8b81-44c0-ad43-8560086fa7bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds = xr.open_dataset(\n",
    "    \"/media/mahieron/Austausch/data/Z2_data_stats/grid_100_100_time_flow.nc\",\n",
    "    decode_times=False,\n",
    "    engine=\"netcdf4\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b31a46e-b20a-4deb-93e7-5a071bb66b00",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff7bd032-4426-45b4-9897-cfb6ce843658",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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NrBjjz02jj322OR5lmUxZcqUmDJlyo4LBQAAQENbsWJFTJ8+fZteO378+JgwYcI2vbatrS2+//3vx6ZNm6zmgjdJgQgAAADUjQ0bNsTSpUu36bWHH374G77mscceizPOOCM2b94cvXr1ilmzZsWwYcO6GhMaigIRAAAAqBt9+vTZpmIwImLvvfd+w9cMHjw47rjjjtiwYUPcc8898fnPfz4WLFgQQ4cO7WpUaBgKRAAAAKiILLJozsq5BDH7wxLE4cOHx4IFC7rt5+68886x7777RkTEwQcfHD/72c/iG9/4xjZfkwYimooOAAAAALCjtLe3xyuvvFJ0DCgVE4gAAABAJV111VVx7LHHxp577hkbN26MxYsXx9KlS+OGG24oOhqUigIRAAAAKqSppFeYt4c1a9bE5z73uWhtbY1dd901hg0bFjfddFMcffTRRUeDUlEgAgAAAJV0xRVXFB0BKsEORAAAAAAgSYEIAAAAACS5wgwAAAAVkWURzSUdFbK6EepXSX9bAQAAAAB2BAUiAAAAAJCkQAQAAAAAkuxABAAAgIrIIqKppMsEy5kaGoMJRAAAAAAgSYEIAAAAACQpEAEAAACAJDsQAQAAoEKaS7oDEahfJhABAAAAgCQFIgAAAACQpEAEAAAAAJLsQAQAAICKyCKLppLuQMyinLmhEZhABAAAAACSFIgAAAAAQJICEQAAAABIsgMRAAAAKiLLIppLOipU0tWN0BBK+tsKAAAAALAjKBABAAAAgCRXmAEAAKBCmtwFBrqZCUQAAAAAIEmBCAAAAAAkKRABAAAAgCQ7EAEAAKBCrEAEupsJRAAAAAAgSYEIAAAAACQpEAEAAACAJDsQAQAAoCKyiGiKci5BLGdqaAwmEAEAAACAJAUiAAAAAJCkQAQAAAAAkuxABAAAgKrIIrKyLhMsa25oACYQAQAAAIAkBSIAAAAAkKRABAAAAACS7EAEAACAisgioqmkuwRLGhsagglEAAAAACBJgQgAAAAAJLnCDAAAABWSuQsMdDMTiAAAAABAkgIRAAAAAEhSIAIAAAAASXYgAgAAQIU0hSWIQPcygQgAAAAAJCkQAQAAAIAkBSIAAAAAkGQHIgAAAFREFhFZSVcgljQ2NAQTiAAAAABAkgIRAAAAAEhSIAIAAAAASXYgAgAAQFVkEU1lXSZY1tzQAEwgAgAAAABJCkQAAAAAIEmBCAAAAAAk2YEIAAAAFWKVINDdFIgAAAAAXXDppZe+6e8599xzY/fdd+/+MLAdKBABAAAAuuBb3/pWHHLIIdGjR49tev1Pf/rT+NjHPqZApDQUiAAAAABdNHv27OjXr982vXbUqFHbOQ10LwUiAAAAVEQWWTRl5dyCmJV4e+Pll18eu+666za/ftq0adtcNkI9UCACAAAAdMH48ePf1OtPPfXU7ZQEtg8FIgAAAEA3WbduXURE9O3bNyIiHnvssbjrrrvigAMOiFNOOaXIaNBpTUUHAAAAALpPlpXzqyo+9alPxf333x8Rr5WJH/3oR+Pee++NL37xizFv3ryC00HnKBABAAAAusmvfvWrOOSQQyIi4gc/+EHss88+ceedd8aMGTPitttuKzYcdJICEQAAAKCbvPzyy9GrV6+IiHjooYfi+OOPj4iIAw88MJ599tkio0GnKRABAAAAusm+++4b99xzTzz77LPx4x//OI466qiIiFizZk306dOn4HTQOQpEAAAAqIgsXvuDfhm/qrIG8dxzz42rrroqjj/++DjkkENi5MiRERHx4x//OIYPH15wOugcn8IMAAAA0E3e9773xQ9/+MNYvXp1vPOd7+x4/p73vCfe9773FZgMOk+BCAAAANBFU6ZMiRNOOCGOPfbY6N+/f/Tv33+r8z9OIkIZucIMAAAA0EUHH3xw3HrrrXHUUUfFmWeeGd/4xjdi5cqVRceCbmECEQAAACoky6qyTbBcJk+eHJMnT441a9bE/fffH/fdd19cc801se+++8bYsWPjhBNOiIMOOqjomNApCkQAAACAbtK/f/84/fTT4/TTT49NmzbFv//7v8d9990Xn/jEJ6Jnz55xwgknxIc+9KE44IADio4K20yBCAAAALAd9OrVK8aNGxfjxo2Ltra2WLp0adx///3xyCOPKBApFQUiAAAAwHbW3Nwco0ePjtGjRxcdBd40BSIAAABURRbRVNYViGXN/WfWr18fM2fOjIcffjjWrVsX7e3tW50/9NBDBSWDzlMgAgAAAHSTz3/+8/H444/H+PHjo1+/fj7UhkpQIAIAAAB0k4cffji+9a1vxYEHHlh0FOg2TUUHAAAAAKiKffbZ53XXlqHsFIgAAABQEVlEZFlJv4r+l9dNLrroorjmmmvil7/8ZbS1tRUdB7qFK8wAAAAA3WTfffeNl19+OcaPH/8Xz1esWLGDE0HXKRABAAAAusl5550XL774Ylx88cU+RIXKUCACAAAAdJNHH300vv3tb8fQoUOLjgLdRoEIAAAAFeLDDoq1//77x4svvlh0DOhWfl8BAAAA6Caf/vSn44orroglS5bEmjVr4vnnn9/qC8rIBCIAAABAN5k8eXJERHz84x/fav9hnueRZZkPUaGUFIgAAABQIT60o1jz588vOgJ0OwUiAAAAQDc5/PDDi44A3c4ORAAAAIAuWLt27Zt6/bp16yLP8+2UBrqfAhEAAACgC4466qg3VSKOHTs2Vq1atR0TQfdyhRkAAAAqIouIppKuQCxp7Ih47QNSFi5cGLvssss2vX7Lli3bORF0LwUiAAAAQBcMGDAgvvOd72zz6/v37x877aSSoTz8XysAAABAF9x///1FR4Dtyg5EAAAAACDJBCIAAABUSJl3CQL1yQRiSa1Zsyba29uLjgEAAABAxSkQS+TVV1+NK6+8MkaNGhXvfe974+mnn46IiC996Utx8803F5wOAAAAgCpSIJbIrFmz4v/8n/8TX/rSl2LnnXfueD5ixIj47ne/W2AyAAAAAKpKgVgid955Z1xyySUxduzYyLL/3mpxwAEHxJNPPllcMAAAAOpClkU0lfTrT/6YW2rDhw+PtWvXvu75c889F8OHDy8gEXSdArFEVq9eHQMGDHjd87a2tmhraysgEQAAAPCn8jz/i89feeWV6NGjxw5OA93DpzCXyDve8Y5YtmxZDBw4cKvnd999dxx44IEFpQIAAAC+/vWvR0RElmWxcOHC2GWXXTrO2traYtmyZTFkyJCi4kGXKBBL5Nxzz43Pfe5z8eyzz0Z7e3vcfffd8cQTT8T//t//O2688cai4wEAAEDDWrBgQUS8NoG4aNGiaGr670ufPXr0iIEDB8Yll1xSVDzoEgViiRx//PFx3XXXxQ033BBNTU0xe/bseNe73hVz586N97znPUXHAwAAoA5kVVkmWDL3339/RESceeaZMWvWrNhtt90KTgTdR4FYMkcffXQcffTRRccAAAAA/oI/TiJClSgQAQAAALrRXXfdFQ899FCsXbs22tvbtzqbO3duQamg8xSIde6d73znNo+fr1ixYjunAQAAoN41ucFcqBkzZsT8+fPjiCOOiLe97W2ulFMJCsQ6d91113X8ZrNmzZr4yle+En/9138dhxxySEREPPLII3HvvffGlClTCkwJAAAARER873vfi6uvvjpOPPHEoqNAt1Eg1rk//Q3nnHPOiX/+53+O008/vePZ3/3d38WIESPi3nvvjY985CNFRAQAAAD+oL29PYYPH150DOhWTW/8EurFww8/HEccccTrnh9xxBGxdOnSAhIBAAAAf+qDH/xg/Ou//mvRMaBbmUAskT322CN+8IMfxOTJk7d6/oMf/CD69u1bUCoAAADqRfaHrzIqa+4/t2HDhli8eHE8+OCDMWzYsOjRo8dW5xdffHFByaDzFIgl8k//9E9xwQUXxMMPP7zVDsSHHnooLrvssmLDAQAAAPHrX/863vnOd0ZExG9/+9utznygCmWlQCyR0047LQYPHhzz58+P+++/P/I8j/333z8WLlwYI0eOLDoe0MCadq79Xyftr2zZQUkAAKBYCxYsKDoCdDsFYsmMHDkyrr766qJjAAAAADWsW7cuVq5cGcOHD4+dd9656DjQJQrEEnn++edrnu++++47JAcAAAD1Koum0l6TLWvurb344otx4YUXxj333BNZlsU999wTgwYNii984QvR0tISU6ZMKToivGkKxBI58sgja+5LWLFixQ5MAwAAAPy5q666KlavXh3f/e5348Mf/nDH8+OOOy6uvfZaBSKlpEAskfnz52/16y1btsQvfvGLWLhwYXz6058uJhQAAADQ4f77749Zs2bF8OHDt3q+//77x8qVKwtKBV2jQCyRww8//HXP3vOe98SgQYPi29/+dpx66qkFpAIAAAD+aMOGDbHHHnu87vnGjRujubm5gETQdU1FB6Drhg8fHsuWLSs6BgAAAEXLIrKSflVkBWIcfPDBcd99973u+a233hqjRo0qIBF0nQnEktu4cWN885vfjD333LPoKAAAANDwPvOZz8QnPvGJ+PWvfx1tbW3xjW98Ix5//PH42c9+Ft/61reKjgedokAskVGjRm31ISp5nsfLL78cb33rW+Oqq64qMBkAAAAQEXHooYfGrbfeGvPmzYt99tknHnrooXjXu94Vt956awwbNqzoeNApCsQSmTp16lYFYpZl0bdv3xg5cmTstttuBSYDAAAA/mjYsGExY8aMomNAt1EglsiECROKjgAAAEAdy/I8sjwvOkanlDX3n7v77rujR48eMXbs2K2e33vvvbFly5Y48cQTC0oGnedDVEpk+PDhsXbt2tc9f+6551738fAAAADAjjdr1qx4y1ve8rrnvXr1ilmzZhWQCLpOgVgieeJvY1555ZXo0aPHDk4DAAAA/LmVK1fG4MGDX/d8n332iZUrVxaQCLrOFeYS+PrXvx4Rr+08XLhwYeyyyy4dZ21tbbFs2bIYMmRIUfEAAACAP+jTp0/87ne/i4EDB271/Mknn9zqz/NQJgrEEliwYEFEvDaBuGjRomhq+u/B0R49esTAgQPjkksuKSoeAAAA9SRvLzpB3bjhhhvinnvuiSeeeCJ23nnnOOSQQ+K8886LoUOHbrf3POGEE+Lyyy+PmTNndkwi/va3v40rrrjidXsRoSwUiCVw//33R0TEmWeeGbNmzeqWT1yeOXPm63Yv9O/fPx588MGIeK2snDVrVtx2222xYcOGGDlyZHzhC1+IAw44oMvvDQAAADvC0qVL48Mf/nAcfPDBked5fOUrX4mJEyfGnXfeGbvvvvt2ec/Pfe5z8fd///dx8sknR0tLS0REtLa2xogRI+L888/fLu8J25sCsUT+OInYXQYPHrzVz2xubu7455tuuinmzZsXV1xxRQwePDhmz54dEydOjO9///vRu3fvbs0BAAAA28PXvva1rX595ZVXxrvf/e746U9/Gscff/x2ec/evXvHrbfeGg8++GCsWLEi8jyPAw88MEaPHh1Zlm2X94TtTYFY5y699NI477zzolevXnHppZfWfO3FF1/8pn72Tjvt1PG3IX8qz/OYP39+TJ48OcaNGxcRETNmzIjRo0fH4sWL44wzznhT7wMAAMCOkkdW2ivMf/mDQ7vTxo0bo729Pfr06bPd32vMmDExZsyYiIh49dVXlYeUmgKxzj322GOxZcuWjn/uTitXroyjjz46evToESNHjozzzjsvBg0aFKtWrYrW1taO3+giInr27BmHHXZYLF++XIEIAADAdrNixYqYPn36Nr12/PjxMWHChG3+2ZdddlkMHz48Ro0a1dl4b2j+/Pnx9re/vWMg58ILL4w77rgjBg0aFHPmzPEhqJSSArHO/ekV4+68wjxixIi4/PLLY8iQIbFu3bqYM2dOnHHGGbF48eJobW2NiNd2Iv6pfv36xerVq7stAwAAAPy5DRs2xNKlS7fptYcffvg2/9zLL788/uM//iMWLly41Qqv7rZgwYKOAvQnP/lJ3H333XHVVVfFPffcEzNmzIgbbrhhu703bC8KxBKZNWtWfOITn4i3vvWtWz1/+eWX46tf/Wqce+652/yzjjnmmK1+PXLkyBg7dmzccccdMXLkyIgI49XANmt/ZUvREQAAqIg+ffpsczG49957b9Prpk+fHnfddVd885vfjEGDBnUl3hv6r//6r45c999/f5x44olx0kknxbBhw+LDH/7wdn1v2F4UiCUye/bs+NCHPvS6AvGll16K2bNnv6kC8c/tsssu8Y53vCOefPLJjo+Vb21tjb322qvjNWvXrn3dVCIAAAB1prQ7EF8zfPjwbr2Bd+mll8Zdd90VCxYsiP3337/bfm5K7969Y926dTFgwIBYsmRJfOITn4iI1z6H4JVXXtnu7w/bQ1PRAdh2eZ7/xanAFStWxG677daln7158+Z44oknoqWlJQYOHBgtLS2xZMmSrc6XLVu2XfdEAAAAQHe65JJL4jvf+U5cffXV0adPn2htbY3W1tbYuHHjdnvPMWPGxNSpU+PCCy+Mp556Kt773vdGRMTjjz8eAwcO3G7vC9uTCcQSGDVqVGRZFlmWxQknnLBVidje3h6bN29+0x9sMmPGjDjuuONir732inXr1sX1118fmzZtivHjx0eWZXHWWWfF3LlzY8iQIbHffvvFnDlzolevXnHKKad09388AAAA2C5uueWWiIg4++yzt3p+7rnnxpQpU7bLe37xi1+Ma6+9Np555pn48pe/HLvvvntERPziF7+Ik08+ebu8J2xvCsQS+MIXvhB5nseFF14Yn/nMZ2LXXXftOOvRo0fsvffeb3oy8Pe//32cd9558fzzz8cee+wRhxxySNx+++0dexomTZoUmzdvjmnTpsX69etj5MiRMW/evOjdu3e3/mcDAACA7eWxxx7b4e/Zu3fvmDp16uue/9M//VO8+OKLOzwPdIcsz/O86BBsm6VLl8aoUaOiR48eRUfZLjb96ufx9LVfLDoGAADQQPb+zCXRa+iBRcfoNu1btsSrL5WzpOrx1t7RtFM155yWLVsWixYtih/84AexfPnyouPAm1bN/8+sqD/9FKrW1tZ49dVXtzofMGDAjo4EAAAA/AVr166N7373u7Fo0aJ4+umn48gjj4yLL7646FjQKQrEEnnxxRfjf/2v/xV3333368rDiNc+TAUAAAAoRp7n8cADD8Ttt98eDzzwQBx00EHx1FNPxe233x4HHXRQ0fGg03wKc4lcccUV8ctf/jJmz54db3nLW+Lqq6+O888/P/bcc8+49tpri44HAAAADeu6666LY489Ni677LIYPnx43HXXXbFw4cLIsix69uxZdDzoEhOIJfLAAw/ENddcE+9+97ujqakpDjzwwDjppJOipaUlbrvttjjxxBOLjgh00i57t9Q83/h06w5KAgBAueUReXvRITqp3B/RcOONN8bkyZNjypQp0dzcXHQc6FYmEEvkhRde6NhzuOuuu8bzzz8fERGHHHKIJawAAABQoM997nNx7733xjHHHBPTp0+PX/ziF0VHgm6jQCyRQYMGxcqVKyMiYv/9948777wz8jyPf/u3f4s+ffoUnA4AAAAa18SJE2Px4sUxc+bM2LhxY3z0ox+Nk08+OfI8jzVr1hQdD7pEgVgiEyZMiMceeywiIiZNmhS33XZbHHTQQXHllVfG5MmTC04HAAAAjBo1Ki677LL48Y9/HGeffXYcfPDBcfbZZ8eECRPixhtvLDoedEqW53m5lww0sGeeeSYeffTR2HfffeP666+PL3/5y0VH6pJNv/p5PH3tF4uOAYWwAxEAoBh7f+aS6DX0wKJjdJv2La/Glo3ri47RKTvtsls07dSj6BjbxeOPPx7f/va3Y/HixbFkyZKi48CbZgKxxAYMGBDve9/7Is/zuOeee4qOAwAAAPwFBxxwQFx44YXxox/9qOgo0CkKRAAAAIAdoEePak5YUn0KRAAAAAAgaaeiAwAAAADdJY/I24sO0Uk+ogHqlQKxBM4555ya5xs3btxBSQAAAABoNArEEthjjz3e8HzgwIE7KA1UU/8R76h53t7WVvN80+/Xden9fcoyAABUz4YNG6K9feuJ0N13372YMNAFCsQSuPzyy4uOAAAAQBnkUd4rzBW5wfz000/HF7/4xXj44Ydjy5YtHc/zPI8sy2LFihUFpoPOUSACAAAAdJMLLrggXnjhhZg+fXq87W1viyzLio4EXaZABAAAAOgmP/vZz+K2226LoUOHFh0Fuk1T0QEAAAAAqmLgwIHxyiuvFB0DupUCEQAAACojf20HYhm/KrIE8cILL4xrrrkmfve73xUdBbqNK8wAAAAA3eQf//Ef49VXX40TTzwxdt5552hubt7q/Kc//WlByaDzFIgAAAAA3eQLX/hC0RGg2ykQAQAAALrJ+PHji44A3U6BCAAAAFXS3l50Av6gtbU1Xn311a2eDRgwoKA00HkKRKAh9NqzX83z9ra2mudv3evtNc+f+2XtBcl5W+3/Edejzy41z1/dsLHmOQAAUB9eeOGFuPTSS+Puu+9+XXkYEbFixYoCUkHX+BRmAAAAgG4yY8aM+OUvfxmzZ8+Ot7zlLXH11VfH+eefH3vuuWdce+21RceDTjGBCAAAANBNHnjggbjmmmvi3e9+dzQ1NcWBBx4YJ510UrS0tMRtt90WJ554YtER4U0zgQgAAABVkeeR5e2l/Io8L/rfXrd44YUXOvYc7rrrrvH8889HRMQhhxwSy5cvLzAZdJ4CEQAAAKCbDBo0KFauXBkREfvvv3/ceeedked5/Nu//VvstttuBaeDzlEgAgAAAHSTCRMmxGOPPRYREZMmTYrbbrstDjrooLjyyitj0qRJBaeDzrEDEQAAAKCbnH322R3/PHr06Lj77rvj0UcfjX333TeGDRtWXDDoAgUiAAAAVEneXnQC/sSAAQM6diJCWbnCDAAAANCNbr755jj55JNj5MiRHfsQb7zxxrjrrrsKTgado0AEAAAA6Cbf+MY3Ys6cOXH66adH/iefLP22t70tbr755gKTQee5wgxUQp/Be9U8b2+rfY3jrS19a56/9Ox/1TwfcNzhNc83PvV0zfPnf7Wy5vkbGThuTM3zTaueqXm+7udPdOn938gew/etef7cit9t1/cHAGgcecSflFblUtbcW7v11lvj0ksvjWOPPTauu+66jucHHnhgXH755cUFgy4wgQgAAADQTZ555pk44IADXvd8p512ipdffrmARNB1CkQAAACAbjJo0KD4xS9+8brnP/rRj2L//fcvIBF0nSvMAAAAAN3k4x//eEybNi1eeumliIhYvnx5fO9734uvfvWrMX369ILTQecoEAEAAKBK8tr7v9m+3v/+90dbW1tce+218dJLL8X5558fb3/72+Oiiy6Kk046qeh40CkKRAAAAIBudPrpp8fpp58e69atizzPo1+/fkVHgi5RIAIAAABsB3379i06AnQLBSIAAABAF51zzjnb9Lq5c+du5yTQ/RSIAAAAUBFZnkdW0h2IWZ4XHaFLfvjDH8aAAQPiiCOOKDoKdDsFIlAJm1Y/X/O87/B9a54/98snu/T+PfvtVvP8+V+trHne3HPnLv385x79Vc3zomVNTUVHAACA7erjH/94/Ou//mv85Cc/iQkTJsSECRNizz33LDoWdAt/ogMAAADoovPPPz9+9KMfxQUXXBCPPvpovO9974u///u/j+9///vx6quvFh0PukSBCAAAANANmpub44QTTojrr78+7rvvvjjiiCPiuuuui/e+972xcePGouNBp7nCDAAAAFVS0h2IVfPSSy/FCy+8EJs2bYpevXpFlmVFR4JOUyACAAAAdIOXX3457r777li0aFE8+uijMXbs2JgxY0aMHj266GjQJQpEAAAAgC6aOnVq3HXXXbHvvvvG3/3d38WcOXOiT58+RceCbqFABAAAAOiib3/72zFgwIB429veFg888EA88MADf/F1c+fO3cHJoOsUiAAAAFAZeYl3IOZFB+iS0047zZ5DKkuBCAAAANBFV1xxRdERYLtpKjoAAAAAAFC/TCAClbD5+Rdqnr+8dn3N875/NaLm+bP3PVjz/JkHHql5/kb6DN6r5vmrG1+u/f371f7+N8q35eVXap7v1HPnmudvZN3Pn+jS9wMAAFAcBSIAAABUSWl3IAL1yhVmAAAAACBJgQgAAAAAJLnCDAAAAFWR55GV9QpznhedAEgwgQgAAAAAJCkQAQAAAIAkBSIAAAAAkGQHIgAAAFRJe0l3IAJ1ywQiAAAAAJBkAhGoC7vs3VLzfOPTrTXP937vITXPX9mwseZ52/q1Nc97v0G+9b95uub5G3luxe9qnvfas1/N85dan6t53n/EO2p//9r1Nc/f6N8/AAAA1WUCEQAAAABIMoEIAAAAVZLnRScAKsYEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBV5HpG3F52ic+xuhLplAhEAAAAASFIgAgAAAABJCkQAAAAAIMkORKAubHy6tUvf//uHHq15PujUE2q//29/W/N8/W+eftOZ/tQew/eted7co0ft93/y2ZrneXvtPTe77Nmv5nlX//0DAFA/srLuQATqlglEAAAAACBJgQgAAAAAJLnCDAAAAJWRR5T2CnNedAAgwQQiAAAAAJCkQAQAAAAAkhSIAAAAAECSHYgAAABQJaXdgQjUKxOIAAAAAECSAhEAAAAASHKFGaiEt737nTXPn/t/j9Y8f/Gp/6p53jJqaM3znffYveb5C0+srHn+3Irf1Tzvvc/ba56/Uf7d9t+75jkAAACkKBABAACgKvI8or2t6BSdk+dFJwASXGEGAAAAAJIUiAAAAABAkgIRAAAAAEiyAxEAAAAqI4+8vb3oEJ2ShR2IUK9MIAIAAAAASQpEAAAAACBJgQgAAAAAJNmBCFTC6mW/3K4/P2+rvUdm46pna57vuu+Amudv2X3Xmuety39V8/yN/P6hR7v0/QAAlEQeEe1tRafoHCsQoW6ZQAQAAAAAkhSIAAAAAECSAhEAAAAASLIDEQAAACojL+8OREsQoW6ZQAQAAAAAkhSIAAAAAECSK8wAAABQFXkeeVtJrzDnrjBDvTKBCAAAAAAkKRABAAAAgCRXmAG2wZr//HWXvv/5X63spiQAAACwYykQAQAAoCryiGhvLzpF51iBCHXLFWYAAAAAIEmBCAAAAAAkKRABAAAAgCQ7EAEAAKAy8oj2tqJDdJIliFCvTCACAAAAAEkKRAAAAAAgSYEIAAAAACTZgQgAAAAVked55CXdgZjneWRFhwD+IgUiUAlDPvWpmudta5+tef7cg/9e87y55841z/O29prnz/9qZc1zAAAAqFeuMAMAAAAASQpEAAAAACDJFWYAAACokvba63UA3iwTiAAAAABAkgIRAAAAAEhSIAIAAAAASXYgAgAAQFXkeeTtbUWn6Jw8LzoBkGACEQAAAABIUiACAAAAAEmuMAM7xO5DB9U8323iBTXPf3fBP9Y8b3/huZrnL/3yP2ue99qzX83znfrsVvO89eGu/fxNv19b8xwAALZNHlHWK8zhCjPUKxOIAAAAAECSAhEAAAAASFIgAgAAAABJCkQAAACoijwi2tvL+bUdViD+5Cc/iXPOOSeOPvroGDZsWHznO9/p/jeBBqBABAAAACpp06ZNMXTo0LjooouiZ8+eRceB0vIpzAAAAEAlHXPMMXHMMcdERMQFF1xQcBooLwUiAAAAUDdWrFgR06dP36bXjh8/PiZMmLCdEwEKRAAAAKiMPPK2tqJDdNJrSxA3bNgQS5cu3abvOPzww7dnIOAPFIgAAABA3ejTp882F4N77733dk4DRCgQgYh421ULap73bn2s5vmWXy9/w/dof3lTzfNs88aa5/t84G9rnq9/6Ec1z/O29prnu+xT+394bNmwvuZ5n8F71TxvXf6rmucAAMBrhg8fHgsW1P4zCrBj+RRmAAAAACBJgUhERMydOzeGDRsW06ZN63iW53nMnDkzjjrqqBgxYkSceeaZ8fjjjxeYEgAAgJryPKK9rZxfed7t/zo2btwYK1asiBUrVkR7e3s888wzsWLFinjmmWe6/b2gyhSIxCOPPBK33357DBs2bKvnN910U8ybNy+mTp0aixYtir59+8bEiRPjxRdfLCgpAAAAbLtHH300TjvttDjttNPi5ZdfjpkzZ8Zpp50WX/nKV4qOBqViB2KDe+GFF+Kzn/1sXHbZZXH99dd3PM/zPObPnx+TJ0+OcePGRUTEjBkzYvTo0bF48eI444wziooMAAAA2+SII46Ixx6rvdMdeGMmEBvc1KlTY9y4cTF69Oitnq9atSpaW1tjzJgxHc969uwZhx12WCxf/sYfmAEAAABANZhAbGC33357PPXUU3HllVe+7qy1tTUiIvr377/V8379+sXq1at3SD4AAADerD/sQCyl7t+BCHQPBWKD+u1vfxvXXHNN3HzzzbHzzjsnX5dl2Q5MBQAAAEC9USA2qEceeSSee+65OPXUUzuetbW1xU9+8pO49dZbY/HixRHx2iTiXnvt1fGatWvXvm4qEQAAAIDqUiA2qLFjx8ZBBx201bMLLrgg9ttvv/iHf/iHGDx4cLS0tMSSJUtixIgRERGxefPmWLZsWZx//vlFRGY7evGqT9c8f+vxx9U8z7e8+obvkW9+qeb5psVfq32++rma5+2vbKl5/uqml2ueN/Wo/dvhup8/UfO83vXos0vN81c3bNxBSQAA2K7yiLy9vegUneMGM9QtBWKD6tOnT/Tp02erZ7169Yrddtsthg4dGhERZ511VsydOzeGDBkS++23X8yZMyd69eoVp5xyShGRAQAAACiAApGkSZMmxebNm2PatGmxfv36GDlyZMybNy969+5ddDQAAAAAdhAFIh0WLFiw1a+zLIspU6bElClTCkoEAAAAQNEUiAAAAFAZeUR7W9EhOskSRKhXTUUHAAAAAADqlwIRAAAAAEhSIAIAAAAASXYgAgAAQFXkJd6BmNuBCPXKBCIAAAAAkGQCEYhNv19b8/x3tyyqed4yaugbvscuo46sef70vfPf8GfUstMub615njXX/vuSdT9/okvv/0Z69tut5nnbq1tqnr+6YWOX3r+r3w8AAEDjMoEIAAAAACSZQAQAAICKyCMib28vOkan2IAI9csEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBV5HtHeVnSKzsltQYR6ZQIRAAAAAEhSIAIAAAAASQpEAAAAACDJDkSgy1qX/+oNX7Np9XM1z3vv8/aa5y8+9V81z7dsfOkNMxTp5bXru/T9PfrsUvP81Q0bu/TzAQCoihLvQAw7EKFemUAEAAAAAJIUiAAAAABAkivMAAAAUBV5RN5W0ivMbjBD3TKBCAAAAAAkKRABAAAAgCQFIgAAAACQZAciAAAAVEWeR7S3F52ic3JLEKFemUAEAAAAAJIUiAAAAABAkivMwA6RNdf++4oXn/qvHZSknF7dsLHoCAAAADQoBSIAAABURh7R3lZ0iE6yAxHqlSvMAAAAAECSAhEAAAAASFIgAgAAAABJdiACAABAVeQReVl3IFqBCHXLBCIAAAAAkKRABAAAAACSFIgAAAAAQJIdiMAO8eJT/1V0BAAAaAB55O3tRYfoJEsQoV6ZQAQAAAAAkhSIAAAAAECSAhEAAAAASLIDEQAAACoizyPytnLuQMytQIS6ZQIRAAAAAEhSIAIAAAAASa4wAwAAQFXkeWmvMLvDDPXLBCIAAAAAkKRABAAAAACSFIgAAAAAQJIdiAAAAFAheXtJdyACdcsEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBV5HnlbSXcg5nnRCYAEE4gAAAAAQJICEQAAAABIUiACAAAAAEl2IAIAAEBF5HmUdgeiFYhQv0wgAgAAAABJCkQAAAAAIEmBCAAAAAAk2YEIAAAAlZFHe1tb0SE6yRJEqFcmEAEAAACAJAUiAAAAAJDkCjMAAABURZ5H3t5edIrOyV1hhnplAhEAAAAASFIgAgAAAABJCkQAAAAAIMkORAAAAKiIPI/I28q5A9EKRKhfJhABAAAAgCQFIgAAAACQpEAEAAAAAJLsQAQAAIDKyEu7AzHCEkSoVyYQAQAAAIAkBSIAAAAAkKRABAAAAACS7EAEAACAqsgj8vaS7kC0AhHqlglEAAAAACBJgQgAAAAAJCkQAQAAAIAkOxABAACgIvI8j/a2cu5AzHNLEKFemUAEAAAAAJIUiAAAAABAkgIRAAAAAEiyAxEAAAAqJC/pDkSgfplABAAAAACSFIgAAAAAQJIrzAAAAFAVeV7eK8x5XnQCIMEEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBV5RN5e1h2IRQcAUkwgAgAAAABJCkQAAAAAIEmBCAAAAAAk2YEIAAAAFZFHHnlbOXcg5pYgQt0ygQgAAAAAJCkQAQAAAIAkBSIAAAAAkGQHIgAAAFRFHqXdgWgFItQvE4gAAAAAQJICEQAAAABIUiACAAAAAEl2IAIAAEBV5Hm0t5d1B6IliFCvTCACAAAAAEkKRAAAAAAgyRVmAAAAqIg8IvK2cl5hdoEZ6pcJRAAAAAAgSYEIAAAAACQpEAEAAACAJDsQAQAAoCryPPK2tqJTdE5uCyLUKxOIAAAAAECSAhEAAAAASFIgAgAAAABJdiACAABAVeQReXt70Sk6xwpEqFsmEAEAAACAJAUiAAAAAJCkQAQAAAAAkuxABAAAgMrII28r6Q5ESxChbplABAAAAACSFIgAAABApd18881x/PHHx8EHHxwTJkyIZcuWFR0JSkWBCAAAAFTWXXfdFdOnT49zzjkn7rjjjhg1alRMmjQpnnnmmaKjQWkoEAEAAKAi8jwib2sv59d2WoH49a9/PcaPHx+nn3567L///jF16tRoaWmJhQsXbp83hApSIAIAAACV9Morr8TPf/7zGDNmzFbPx4wZE8uXLy8oFZSPT2EGAAAA6saKFSti+vTp2/Ta8ePHx4QJE5Lnzz33XLS1tUX//v23et6vX79YsmRJl3JCI1EgAgAAAHVjw4YNsXTp0m167eGHH75Nr8uybJueAX+ZAhEAAACqIs+jva296BSd84cliH369NnmYnDvvfeueb7HHntEc3NztLa2bvV87dq1r5tKBNIUiAAAAEDdGD58eCxYsKBbftbOO+8cBx54YCxZsiT+5m/+puP5kiVL4n3ve1+3vAc0AgUiAAAAUFkTJ06M888/P0aMGBGHHnpoLFy4MFavXh1nnHFG0dGgNBSIAAAAUCF5e0mvMG8nJ510Ujz33HMxZ86cWL16dQwdOjRuvPHGN7z+DPw3BSIAAABQaR/5yEfiIx/5SNExoLSaig4AAAAAANQvBSIAAAAAkOQKMwAAAFREnueRt5VzB2Ke50VHABJMIAIAAAAASQpEAAAAACBJgdigbr755jj11FPj0EMPjUMPPTQ++MEPxg9/+MOO8zzPY+bMmXHUUUfFiBEj4swzz4zHH3+8uMAAAAAAFEKB2KDe/va3x2c/+9n47ne/G//yL/8SRx55ZHzyk5+MX/7ylxERcdNNN8W8efNi6tSpsWjRoujbt29MnDgxXnzxxYKTAwAAkJRH5G15Kb/CCkSoWwrEBjV27Ng45phjYt99943BgwfHZz7zmdhll13ikUceiTzPY/78+TF58uQYN25cDB06NGbMmBEbN26MxYsXFx0dAAAAgB1IgUi0tbXFnXfeGZs2bYpRo0bFqlWrorW1NcaMGdPxmp49e8Zhhx0Wy5cvLzApAAAAADvaTkUHoDiPPfZYnHHGGbF58+bo1atXzJo1K4YNGxY//elPIyKif//+W72+X79+sXr16iKiAgAAAFAQBWIDGzx4cNxxxx2xYcOGuOeee+Lzn/98LFiwoOM8y7IC0wEAAPCm5Xm0t7UXnaJzcksQoV4pEBvYzjvvHPvuu29ERBx88MHxs5/9LL7xjW/E//yf/zMiIlpbW2OvvfbqeP3atWtfN5UIAAAAQLXZgUiH9vb2eOWVV2LgwIHR0tISS5Ys6TjbvHlzLFu2LEaNGlVgQgAAAAB2NBOIDeqqq66KY489Nvbcc8+OT1deunRp3HDDDZFlWZx11lkxd+7cGDJkSOy3334xZ86c6NWrV5xyyilFRwcAAABgB1IgNqg1a9bE5z73uWhtbY1dd901hg0bFjfddFMcffTRERExadKk2Lx5c0ybNi3Wr18fI0eOjHnz5kXv3r0LTg4AAEBKHhF5ezl3CZYzNTQGBWKDuuKKK2qeZ1kWU6ZMiSlTpuygRAAAAADUIzsQAQAAAIAkBSIAAAAAkOQKMwAAAFRFHtHeVtJtgiWNDY3ABCIAAAAAkKRABAAAAACSXGEGAACAqsjzyNvai07RObk7zFCvTCACAAAAAEkKRAAAAAAgSYEIAAAAACTZgQgAAAAVkUdE3lbOXYLlTA2NwQQiAAAAAJCkQAQAAAAAkhSIAAAAAECSHYgAAABQFXlEe0l3IFqCCPXLBCIAAAAAkKRABAAAAACSFIgAAAAAQJIdiAAAAFAVeR55W3vRKTontwQR6pUJRAAAAAAgSYEIAAAAACQpEAEAAACAJDsQAQAAoCLyiGhvL+cuwXKmhsZgAhEAAAAASFIgAgAAAABJrjADAABAVeQReVtJLwOXNDY0AhOIAAAAAECSAhEAAAAASFIgAgAAAABJdiACAABAReR5Hu1t7UXH6JQ8twQR6pUJRAAAAAAgSYEIAAAAACQpEAEAAACAJDsQAQAAoCryiLytpLsESxobGoEJRAAAAAAgSYEIAAAAACQpEAEAAACAJDsQAQAAoEJKuwMRqFsmEAEAAACAJAUiAAAAAJCkQAQAAAAAkuxABAAAgIrI8zza29qLjtEpeW53I9QrE4gAAAAAQJICEQAAAABIUiACAAAAAEl2IAIAAEBV5BF5e0l3CZY0NjQCE4gAAAAAQJICEQAAAABIcoUZAAAAKqS9zV1goHuZQAQAAAAAkhSIAAAAAECSAhEAAAAASLIDEQAAACoiz/PI29qLjtEpeW53I9QrE4gAAAAAQJICEQAAAABIUiACAAAAAEl2IAIAAEBV5BF5W0l3CZY0NjQCE4gAAAAAQJICEQAAAABIUiACAAAAAEl2IAIAAEBV5BHtdiAC3cwEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBF55JG3txcdo1NySxChbplABAAAAACSFIgAAAAAQJICEQAAAABIsgMRAAAAqiKPaG8r6S7BksaGRmACEQAAAABIUiACAAAAAEmuMAMAAEBV5BG5K8xANzOBCAAAAAAkKRABAAAAgCQFIgAAAACQZAciAAAAVEQeeeRt7UXH6JTcEkSoWyYQAQAAAIAkBSIAAAAAkKRABAAAAACS7EAEAACAqsgj2ttKukuwpLGhEZhABAAAAACSFIgAAAAAQJICEQAAAABIsgMRAAAAqiKPyO1ABLqZCUQAAAAAIEmBCAAAAAAkKRABAAAAgCQ7EAEAAKAi8ohoz8u5TLCcqaExmEAEAAAAAJIUiAAAAABAkivMAAAAUBF55NFW2ivM5cwNjcAEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBV5RFtZVwmWNTc0ABOIAAAAAECSAhEAAAAASFIgAgAAAABJdiACAABAReQR0ZaXc5lgOVNDYzCBCAAAAAAkKRABAAAAgCQFIgAAAACQZAciAAAAVMRrOxCLTtE5JY0NDcEEIgAAAACQpEAEAAAAAJIUiAAAAABAkh2IAAAAUBGv7UAs5zbBcqaGxmACEQAAAABIUiACAAAAAEkKRAAAAAAgyQ5EAAAAqIo8oq2sywQLzn3bbbfF4sWLY8WKFfHCCy/EfffdFwMHDiw2FNQJE4gAAABAw3vppZfiqKOOinPPPbfoKFB3TCACAAAADe/ss8+OiIif/exnxQaBOqRABAAAgIrII6ItL+cd5nKmhsagQAQAAADqxooVK2L69Onb9Nrx48fHhAkTtnMiQIEIAAAA1I0NGzbE0qVLt+m1hx9+eM3za6+9NubOnVvzNfPnz48jjjhim/NBI1IgAgAAAHWjT58+b1gM/tHee+9d8/xjH/tY/O3f/m3N1wwYMGCbs0GjUiACAABARby2A7HoFJ3zx9jDhw+PBQsWdMvP7Nu3b/Tt27dbfhY0MgUiAAAA0PBaW1tjzZo18eSTT0ZExG9+85t44YUXYq+99ordd9+90GxQNAUiAAAA0PBuvfXWmDVrVsevJ0+eHBERl19+uQ9qoeEpEAEAAICGN2XKlJgyZUrRMaAuKRABAACgIqqwAxGoP01FBwAAAAAA6pcCEQAAAABIUiACAAAAAEl2IAIAAEBV5Hm05SXdJljW3NAATCACAAAAAEkKRAAAAAAgSYEIAAAAACTZgQgAAAAVkUdEW0lXCZY0NjQEE4gAAAAAQJICEQAAAABIcoUZAAAAKuK1K8zlvAxcztTQGEwgNqgbbrgh3v/+98ehhx4aRx55ZJxzzjnxq1/9aqvX5HkeM2fOjKOOOipGjBgRZ555Zjz++OMFJQYAAACgCArEBrV06dL48Ic/HLfeemt885vfjObm5pg4cWI8//zzHa+56aabYt68eTF16tRYtGhR9O3bNyZOnBgvvvhiccEBAAAA2KEUiA3qa1/7Wrz//e+PoUOHxrBhw+LKK6+MdevWxU9/+tOIeG36cP78+TF58uQYN25cDB06NGbMmBEbN26MxYsXF5weAAAAgB1FgUhERGzcuDHa29ujT58+ERGxatWqaG1tjTFjxnS8pmfPnnHYYYfF8uXLi4oJAABADa/tQCznlx2IUL8UiERExGWXXRbDhw+PUaNGRUREa2trRET0799/q9f169cv1qxZs8PzAQAAAFAMn8JMXH755fEf//EfsXDhwmhubt7qLMuyglIBAAAAUA9MIDa46dOnx5133hnf/OY3Y9CgQR3PW1paIuK/JxH/aO3ata+bSgQAAACguhSIDezSSy+NxYsXxze/+c3Yf//9tzobOHBgtLS0xJIlSzqebd68OZYtW9ZxzRkAAIA6k0e05XkpvyxBhPrlCnODuuSSS+J73/tezJ49O/r06dMxadirV6/YZZddIsuyOOuss2Lu3LkxZMiQ2G+//WLOnDnRq1evOOWUUwpODwAAAMCOokBsULfccktERJx99tlbPT/33HNjypQpERExadKk2Lx5c0ybNi3Wr18fI0eOjHnz5kXv3r13dFwAAAAACqJAbFCPPfbYG74my7KYMmVKR6EIAAAAQONRIAIAAEBF5BHRVtJdgiWN/f/bu/tYrev6j+MvRBFxoaFImpbcxFET9ECKimJghRGmS82bAIeMoC01JSDXEPAus4AYqRhioR5R511ZqFs5/3AwFYe4SXfepJgmgiAhmAHf3x/O6+f5wccw9Xe4OI/H5h/n8/2ewwffOxt7Xt/rc0Gr4ENUAAAAAIAiAREAAAAAKBIQAQAAAIAiZyACAADADuKdMxDr8zTB+tw1tA6eQAQAAAAAigREAAAAAKBIQAQAAAAAipyBCAAAADuQzS29AWCH4wlEAAAAAKBIQAQAAAAAiryFGQAAAHYQVapsqqqW3sZ/pUp97htaA08gAgAAAABFAiIAAAAAUCQgAgAAAABFzkAEAACAHURVJZvq9CjBOj26EVoFTyACAAAAAEUCIgAAAABQJCACAAAAAEXOQAQAAIAdRJVkU50eJlifu4bWwROIAAAAAECRgAgAAAAAFAmIAAAAAECRMxABAABgB7LJYYLAR8wTiAAAAABAkYAIAAAAABQJiAAAAABAkTMQAQAAYAdRJdlU1echiPW5a2gdPIEIAAAAABQJiAAAAABAkYAIAAAAABQ5AxEAAAB2EFWVbKrTwwTr9OhGaBU8gQgAAAAAFAmIAAAAAECRtzADAADADqJKsqlO3wtcn7uG1sETiAAAAABAkYAIAAAAABQJiAAAAABAkTMQAQAAYAeyyWGCwEfME4gAAAAAQJGACAAAAAAUtamqOv18d3Y4m9a/mX+99LeW3gYAANCK7Lr/gWnbYfeW3sZHZv2atXnpyWUtvY3/yv6HH5IOe3Zs6W0AWyEgAgAAAABF3sIMAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiAAAAABAkYAIAAAAABQJiPAReeqppzJy5Mg0NjamsbExZ555Zl5//fXa9TfeeCPjx49P375907dv34wfPz5r165twR2zLaqqyqhRo9LQ0JAHHnig2TUzrQ9r1qzJZZddlhNPPDG9e/fO8ccfn8mTJ2f16tXN7jPP+tLU1JRBgwalV69e+cY3vpHFixe39JbYRtdff31OPfXU9OnTJ0cddVTGjh2bv/zlL83uqaoqs2bNyrHHHpvevXtn+PDh+etf/9pCO+aDmD17dhoaGnLppZfW1syzvqxYsSITJ07MUUcdlV69emXIkCF57LHHatfNE6B1EhDhI7B06dKce+65OfLII3P77bfn7rvvzrnnnptddtmlds+4ceOybNmyzJkzJzfccEOWLVuWCRMmtOCu2RY33nhj2rZtu9VrZlofVqxYkVdffTXjx4/Pfffdl5/85CdZvHhxxo0b1+w+86wfCxYsyJVXXpmxY8fm3nvvTWNjY0aPHp2XX365pbfGNnjsscdy9tln57bbbsu8efPStm3bjBw5MmvWrKndM2fOnNx4442ZNGlS7rzzznTq1CkjR47MunXrWm7j/EdPPvlk7rjjjjQ0NDRbN8/6sXbt2px11lmpqiq/+MUvsmDBgkyaNCl77bVX7R7zBGilKuBDO+OMM6rp06cXrz/zzDNVz549q8WLF9fWHn/88apnz57Vs88++/+xRf4LTz31VDVgwIBq5cqVVc+ePav777+/ds1M69vDDz9cNTQ0VP/85z+rqjLPenPaaadVP/zhD5utffnLX65++tOfttCO+DDWrVtXHXTQQdUf/vCHqqqqavPmzVX//v2ra6+9tnbPhg0bqsMPP7yaP39+S22T/2Dt2rXVCSecUC1cuLAaNmxYNXXq1KqqzLPeTJs2rTrjjDOK180ToPXyBCJ8SKtWrcqSJUvSuXPnnHXWWTnmmGNy9tlnZ9GiRbV7lixZkg4dOqRPnz61tb59+6ZDhw5ZsmRJS2yb/2DdunUZN25cLr300mavur/LTOvbunXr0q5du7Rv3z6JedaTt99+O08//XT69+/fbL1///5mVafefPPNbN68OR07dkySvPTSS3nttdeazbh9+/Y54ogjzHg7NmnSpAwePDhHH310s3XzrC+///3vc9hhh+V73/tejj766Jx88sm55ZZbUlVVEvMEaM0ERPiQli9fniSZNWtWTj311Nxwww35whe+kFGjRuVPf/pTkmTlypXp1KlT2rRpU/u+Nm3apFOnTlm5cmWL7Jv3N3ny5Bx33HE5/vjjt3rdTOvX2rVrM3PmzHzzm9/MzjvvnMQ868nq1auzadOm7L333s3W99prr7z22msttCs+jCuuuCIHH3xwGhsbk6Q2x63N2O/j9umOO+7Iiy++mAsuuGCLa+ZZX5YvX55bb701BxxwQObOnZsRI0Zk2rRpaWpqSmKeAK3Zzi29AdhezZgxI7Nnz37fe2666abaOYdnnHFGTjvttCTJIYccksceeyzz58/P1KlTk6RZmHhXVVVbXefjsa0zfeWVV/LnP/85d9111/vea6Yta1vn2a9fv9rX69evz9ixY9OlS5eMHz++2b3mWV+2Nhezqj8/+tGP8sQTT2T+/PlbnDdrnvXhueeey/Tp09PU1JR27doV7zPP+lBVVQ499NDaOcGHHHJIXnjhhTQ1NWXYsGG1+8wToPUREKHgnHPOyde//vX3vWe//farvdravXv3Zte6deuWV155Jck7r9KuWrWqWYyoqiqrV6/e6ttj+Xhs60zvueeePPPMM7WnYd514YUXZt68eZk/f76Zbge2dZ7vevPNN/Ptb387yTufErrrrrvWrpln/fjkJz+Ztm3bbvG04apVq7Z4Iobt25VXXpkFCxZk3rx5OeCAA2rrnTt3TvLOk0777rtvbd2Mt09PPvlkVq9enZNOOqm2tmnTpjz++OO57bbb8tvf/jaJedaLzp07v++/af1+ArReAiIUdOrUKZ06dfqP9+2///7ZZ5998vzzzzdb/9vf/paePXsmSRobG7N+/fosWbKkdsbakiVLsn79+i0iFR+fbZ3phRdemHPPPbfZ2kknnZSJEyfmhBNOSGKm24NtnWfyzpmHo0ePTlVVueGGG7L77rs3u26e9aNdu3b5/Oc/n4ULF+arX/1qbX3hwoX5yle+0oI744O4/PLLs2DBgtx8881bxIr9998/nTt3zsKFC9O7d+8kyb/+9a8sXrzYJ6Nvh770pS/l0EMPbbZ28cUX58ADD8yYMWPStWtX86wjffr02eq/ad99Qc7vJ0DrJSDCh9SmTZuMGjUqs2bNSkNDQw455JDcf//9Wbp0aS655JIk7zydeNxxx2Xy5Mm57LLLUlVVJk+enIEDB6Zbt24t/Dfg/+rSpUu6dOmyxfqnPvWp2lMyZlo/1q1bl1GjRmXdunW55pprsmHDhmzYsCFJsscee6Rdu3bmWWdGjhyZCRMmpHfv3unTp0/mz5+fFStW5Mwzz2zprbENpk6dml//+te55ppr0rFjx9rTpB06dMjuu++eNm3aZMSIEZk9e3a6deuWAw88MNddd106dOiQoUOHtvDu+b86duxY+wCcd3Xo0CF77LFH7YVU86wf55xzTs4666xcd911GTJkSJYtW5abb745F110UZL4/QRoxdpU736kFvChzJkzJ01NTVmzZk169OiRiy66KMccc0zt+po1a3L55ZfnoYceSpIMGjQol1xyyRb/6Gb71NDQkJkzZ+bEE0+srZlpfXj00UczYsSIrV577xmJ5llfmpqaMnfu3KxYsSI9e/bMxRdfnCOOOKKlt8U2aGho2Or6d7/73Zx33nlJ3jlC4Oc//3luv/32vPHGGznssMNyySWX1IIU27fhw4fnc5/7XO2FVPOsLw8//HCmT5+e559/Pvvtt1++9a1vZfjw4c2O+DBPgNZHQAQAAAAAinZq6Q0AAAAAANsvAREAAAAAKBIQAQAAAIAiAREAAAAAKBIQAQAAAIAiAREAAAAAKBIQAQAAAIAiAREAAAAAKBIQAQAAAIAiAREAAAAAKBIQAYC6NmjQoMydO/dj+dlz587NoEGDPpafDQAA9UJABAD+az/4wQ8yZsyYFt3DnXfembPPPrv2dUNDQx544IEW3BEAAOxYdm7pDQAAfBidOnVq6S0AAMAOzROIAMDH4vHHH8/pp5+eXr165ZhjjsmVV16Zt99+u3Z9+PDhmTJlSqZPn55+/frl6KOPzo9//ONs3ry5ds/KlSszduzY9O7dOwMHDsxdd92VoUOHZtasWbV73vsW5nffbnzBBRekoaGh9vWsWbMydOjQZvu7++6709jY2Gxtzpw56d+/fxobGzNhwoSsX79+i7/XXXfdlSFDhqRXr14ZPHhwfvWrXzXbMwAA7GgERADgI/fqq69m9OjROfjgg3PvvffmiiuuyO9+97tMnz692X333Xdf2rZtm9tuuy2TJk3KvHnzsmDBgtr1iRMn5uWXX868efNy7bXX5je/+U3+/ve/F//cO++8M0ly+eWX55FHHql9vS0WLFiQmTNn5rzzzsvdd9+drl275pe//GWze+64447MmDEj559/fhYsWJCJEydmzpw5ufXWW7f5zwEAgHojIAIAH7lbb701nTt3zpQpU9K9e/cMHDgw48aNyy233JINGzbU7uvRo0cuuOCCdO3aNUOGDEm/fv2yaNGiJMlzzz2XRx55JJdeemkaGxtz8MEH56qrrspbb71V/HPffTvzJz7xiXTu3PkDvb35pptuyimnnJIzzzwzXbt2zXe+85307t272T3XXnttvv/97+fEE0/MAQcckEGDBmX06NECIgAAOzRnIAIAH7lnn302hx9+eHba6X9fq+zbt2/+/e9/54UXXshBBx2U5J0PPHmvffbZJ6tWrUryTkDcaaedcuihh9au77vvvtlnn30+tj2fdtppzdYOP/zwvPjii0mS119/Pa+88komT56cqVOn1u7ZuHFjqqr6WPYEAADbAwERAPjIVVWVNm3abPXae9d33nnnLa59HDFuaz9348aNH+hnvHvO4dSpU7c4OxEAAHZk3sIMAHzkevTokSeffLLZh4s88cQT2WWXXfKZz3xmm35Gt27dsnnz5jz99NO1tX/84x9ZsWLF+37fLrvsssWHmnTq1CkrV65sFhH/+Mc/Nrune/fuWbp0abO193699957p0uXLnnxxRfz2c9+dov/AABgR+UJRADgQ1m3bt0WMW7AgAGZN29epkyZknPOOSfLly/PtGnTMmzYsOy2227b9HO7deuWY489NpMnT86UKVOy66675uqrr0779u2LTzcmyac//eksWrQoRxxxRNq1a5c99tgj/fr1yxtvvJHZs2fna1/7Wh599NE8+OCDzb5vxIgRmTBhQnr16pUjjzwyDz74YJYuXZo999yzds95552Xyy67LB07dsyAAQOycePGLFu2LK+++mrGjBmz7f/TAACgjgiIAMCHsnjx4pxyyinN1gYPHpw5c+bk6quvzsknn5yOHTtm6NChueiiiz7Qz77qqqsyadKkDB8+PHvttVfOP//8LF++PO3atSt+z8SJE3PVVVfli1/8Yrp06ZKHHnoo3bt3z5QpU3L99dfn+uuvz8CBAzNmzJj87Gc/q33fkCFDsnz58syYMSNvvfVWBg0alJEjR+aee+6p3XP66adnt912y9y5czNt2rS0b98+PXr0yLBhwz7Q3wsAAOpJm8qp3wBAnXj99dczYMCATJs2LYMHD27p7QAAQKvgCUQAYLu1aNGivPnmm2loaMiqVasyY8aM7LnnnjnuuONaemsAANBqCIgAwHZr48aNmTlzZpYvX5727dvnsMMOS1NTUzp06NDSWwMAgFbDW5gBAAAAgKKdWnoDAAAAAMD2S0AEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIoERAAAAACgSEAEAAAAAIr+B8xnDYXPwIRjAAAAAElFTkSuQmCC&quot; width=&quot;1296px&quot; height=&quot;1296px&quot; alt=&quot;&quot;&gt;&lt;/img&gt;\",\"width\":1296},\"id\":\"1027\",\"type\":\"panel.models.markup.HTML\"},{\"attributes\":{\"button_type\":\"primary\",\"icon\":null,\"label\":\"Save Plot\",\"margin\":[5,10,5,10],\"subscribed_events\":[\"button_click\"]},\"id\":\"1020\",\"type\":\"Button\"},{\"attributes\":{\"children\":[{\"id\":\"1024\"},{\"id\":\"1025\"}],\"margin\":[0,0,0,0],\"name\":\"Row01580\"},\"id\":\"1023\",\"type\":\"Row\"},{\"attributes\":{\"client_comm_id\":\"f67177f65ba24885987ccf09913ee686\",\"comm_id\":\"2eea9ca17c0445f3841430bf60f8077a\",\"plot_id\":\"1002\"},\"id\":\"1028\",\"type\":\"panel.models.comm_manager.CommManager\"}],\"root_ids\":[\"1002\",\"1028\",\"1029\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.2\"}};\n",
       "    var render_items = [{\"docid\":\"4c810f10-e1e9-45d2-85fb-f242dc1e1ecd\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"85a25e61-4ac6-4315-b9ae-494388df7fdb\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] Markdown(str)\n",
       "    [1] RadioButtonGroup(button_type='primary', name='Kind', options=['Mean', 'Min', ...], value='Mean')\n",
       "    [2] RadioButtonGroup(button_type='primary', name='Output Parameter', options=['QV', 'latent_heat', ...], value='QV')\n",
       "    [3] Row\n",
       "        [0] Select(name='Color according to', options=['NCCLOUD', 'NCGRAUPEL', ...], value='w')\n",
       "        [1] Select(name='Colormap', options=['RdBu', 'viridis', ...], value='RdBu')\n",
       "    [4] Row\n",
       "        [0] Toggle(button_type='success', name='Fix colorbar o...)\n",
       "        [1] Toggle(button_type='success', name='Use log colorbar')\n",
       "        [2] Toggle(button_type='success', name='Adjust for Colorblind')\n",
       "    [5] Row\n",
       "        [0] IntSlider(end=15, name='Width in inches', start=3, value=9)\n",
       "        [1] IntSlider(end=15, name='Height in inches', start=3, value=9)\n",
       "        [2] FloatSlider(end=5, name='Scale fontsize', start=0.2, value=0.7)\n",
       "    [6] FloatSlider(end=0, name='Log Threshold (..., start=-25, step=1)\n",
       "    [7] Row\n",
       "        [0] TextInput(value='Path/to/store/plot.png')\n",
       "        [1] Button(button_type='primary', name='Save Plot')\n",
       "        [2] Toggle(button_type='success', name='Latexify')\n",
       "    [8] TextInput(name='Title')\n",
       "    [9] Row\n",
       "        [0] FloatSlider(end=100000.0, name='pressure', step=10000.0, value=70000.0)\n",
       "        [1] IntSlider(end=71, name='timestep')\n",
       "    [10] ParamFunction(function)"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_2dmap_interactive(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c11e42f-d907-4cf1-ab92-fa92828b2175",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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