example1.ipynb

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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "autotime loaded.\n",
      "black loaded.\n",
      "black loaded.\n",
      "time: 878 ms\n"
     ]
    },
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"from init import *\\nfrom hytraj import HyData, HyGen, Receptor  # , MultiReceptor\";\n",
       "                var nbb_formatted_code = \"from init import *\\nfrom hytraj import HyData, HyGen, Receptor  # , MultiReceptor\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from init import *\n",
    "from hytraj import HyData, HyGen, HyReceptor  # , MultiReceptor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.28 ms\n"
     ]
    },
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     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"def surface_ozone():\\n    filenames = glob.glob(\\\"/media/pankaj/Data/reprobus/surf_ozo/*\\\")\\n    kwargs = dict(skiprows=range(30))\\n    data = pd.DataFrame([])\\n    for filename in filenames:\\n        name = filename.split(\\\"/\\\")[-1]\\n        df = pd.read_csv(filename, sep=\\\"\\\\s+\\\", skiprows=range(28), parse_dates=True)[\\n            [\\\"C32\\\", \\\"TIME.1\\\"]\\n        ]\\n        df.columns = [\\\"Date\\\", name]\\n        df.index = pd.to_datetime(df.Date)\\n        df[name][abs(df[name]) > 100] = np.nan\\n        data = pd.concat([data, df], axis=1)\\n    data = data[[\\\"syow\\\", \\\"mcmu\\\", \\\"spol\\\", \\\"neum\\\", \\\"marb\\\", \\\"arht\\\"]]\\n    return data\";\n",
       "                var nbb_formatted_code = \"def surface_ozone():\\n    filenames = glob.glob(\\\"/media/pankaj/Data/reprobus/surf_ozo/*\\\")\\n    kwargs = dict(skiprows=range(30))\\n    data = pd.DataFrame([])\\n    for filename in filenames:\\n        name = filename.split(\\\"/\\\")[-1]\\n        df = pd.read_csv(filename, sep=\\\"\\\\s+\\\", skiprows=range(28), parse_dates=True)[\\n            [\\\"C32\\\", \\\"TIME.1\\\"]\\n        ]\\n        df.columns = [\\\"Date\\\", name]\\n        df.index = pd.to_datetime(df.Date)\\n        df[name][abs(df[name]) > 100] = np.nan\\n        data = pd.concat([data, df], axis=1)\\n    data = data[[\\\"syow\\\", \\\"mcmu\\\", \\\"spol\\\", \\\"neum\\\", \\\"marb\\\", \\\"arht\\\"]]\\n    return data\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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   "source": [
    "def surface_ozone():\n",
    "    filenames = glob.glob(\"/media/pankaj/Data/reprobus/surf_ozo/*\")\n",
    "    kwargs = dict(skiprows=range(30))\n",
    "    data = pd.DataFrame([])\n",
    "    for filename in filenames:\n",
    "        name = filename.split(\"/\")[-1]\n",
    "        df = pd.read_csv(filename, sep=\"\\s+\", skiprows=range(28), parse_dates=True)[\n",
    "            [\"C32\", \"TIME.1\"]\n",
    "        ]\n",
    "        df.columns = [\"Date\", name]\n",
    "        df.index = pd.to_datetime(df.Date)\n",
    "        df[name][abs(df[name]) > 100] = np.nan\n",
    "        data = pd.concat([data, df], axis=1)\n",
    "    data = data[[\"syow\", \"mcmu\", \"spol\", \"neum\", \"marb\", \"arht\"]]\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 531 µs\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"working = \\\"/home/pankaj/phd/code/hytraj/working/\\\"\\nmetdir = \\\"/home/pankaj/Desktop/met/\\\"\\noutdir = \\\"/home/pankaj/Desktop/hysplit/out/\\\"\\nhysplit = \\\"/home/pankaj/phd/code/hytraj/working/hyts_std\\\"\";\n",
       "                var nbb_formatted_code = \"working = \\\"/home/pankaj/phd/code/hytraj/working/\\\"\\nmetdir = \\\"/home/pankaj/Desktop/met/\\\"\\noutdir = \\\"/home/pankaj/Desktop/hysplit/out/\\\"\\nhysplit = \\\"/home/pankaj/phd/code/hytraj/working/hyts_std\\\"\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "working = \"/home/pankaj/phd/code/hytraj/working/\"\n",
    "metdir = \"/home/pankaj/Desktop/met/\"\n",
    "outdir = \"/home/pankaj/Desktop/hysplit/out/\"\n",
    "hysplit = \"/home/pankaj/phd/code/hytraj/working/hyts_std\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-02-01', '2010-02-02', '2010-02-03', '2010-02-04',\n",
       "               '2010-02-05', '2010-02-06', '2010-02-07', '2010-02-08',\n",
       "               '2010-02-09', '2010-02-10', '2010-02-11', '2010-02-12',\n",
       "               '2010-02-13', '2010-02-14', '2010-02-15', '2010-02-16',\n",
       "               '2010-02-17', '2010-02-18', '2010-02-19', '2010-02-20',\n",
       "               '2010-02-21', '2010-02-22', '2010-02-23', '2010-02-24',\n",
       "               '2010-02-25', '2010-02-26', '2010-02-27', '2010-02-28'],\n",
       "              dtype='datetime64[ns]', freq='24H')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.57 ms\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"height = 500\\nrun_time = -120\\ndates = pd.date_range(\\\"2010-02-01\\\", freq=\\\"24H\\\", end=\\\"2010-02-28\\\")\\ndates\";\n",
       "                var nbb_formatted_code = \"height = 500\\nrun_time = -120\\ndates = pd.date_range(\\\"2010-02-01\\\", freq=\\\"24H\\\", end=\\\"2010-02-28\\\")\\ndates\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "metadata": {},
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   "source": [
    "height = 500\n",
    "run_time = -120\n",
    "dates = pd.date_range(\"2010-02-01\", freq=\"24H\", end=\"2010-02-28\")\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 778 µs\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"stations = {\\n    \\\"Davis\\\": (-69, 78),\\n    \\\"South Pole\\\": (-90, 335),\\n    \\\"Neumayer\\\": (-71, 352),\\n    \\\"Marambio\\\": (-64, 303),\\n    \\\"Maitri\\\": (-70, 11),\\n}\";\n",
       "                var nbb_formatted_code = \"stations = {\\n    \\\"Davis\\\": (-69, 78),\\n    \\\"South Pole\\\": (-90, 335),\\n    \\\"Neumayer\\\": (-71, 352),\\n    \\\"Marambio\\\": (-64, 303),\\n    \\\"Maitri\\\": (-70, 11),\\n}\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "stations = {\n",
    "    \"Davis\": (-69, 78),\n",
    "    \"South Pole\": (-90, 335),\n",
    "    \"Neumayer\": (-71, 352),\n",
    "    \"Marambio\": (-64, 303),\n",
    "    \"Maitri\": (-70, 11),\n",
    "}"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28/28 [20:43<00:00, 44.41s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 20min 43s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"hy = HyGen(\\n    stations,\\n    height,\\n    run_time,\\n    working,\\n    metdir,\\n    outdir,\\n    met_type=\\\"ncep\\\",\\n    exe=hysplit,\\n)\\nhy = hy.run(dates, vertical=0, model_top=10000.0)\";\n",
       "                var nbb_formatted_code = \"hy = HyGen(\\n    stations,\\n    height,\\n    run_time,\\n    working,\\n    metdir,\\n    outdir,\\n    met_type=\\\"ncep\\\",\\n    exe=hysplit,\\n)\\nhy = hy.run(dates, vertical=0, model_top=10000.0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   "source": [
    "hy = HyGen(\n",
    "    stations,\n",
    "    height,\n",
    "    run_time,\n",
    "    working,\n",
    "    metdir,\n",
    "    outdir,\n",
    "    met_type=\"ncep\",\n",
    "    exe=hysplit,\n",
    ")\n",
    "hy = hy.run(dates, vertical=0, model_top=10000.0)"
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (geo: 4, step: 121, time: 28)\n",
       "Coordinates:\n",
       "  * step        (step) int64 0 1 2 3 4 5 6 7 ... 113 114 115 116 117 118 119 120\n",
       "  * time        (time) datetime64[ns] 2010-02-01 2010-02-02 ... 2010-02-28\n",
       "  * geo         (geo) &lt;U3 &#x27;lat&#x27; &#x27;lon&#x27; &#x27;alt&#x27; &#x27;pre&#x27;\n",
       "Data variables:\n",
       "    Davis       (geo, step, time) float64 -69.0 -69.0 -69.0 ... 694.1 574.6\n",
       "    South Pole  (geo, step, time) float64 -90.0 -90.0 -90.0 ... 541.1 851.3\n",
       "    Neumayer    (geo, step, time) float64 -71.0 -71.0 -71.0 ... 542.3 774.8\n",
       "    Marambio    (geo, step, time) float64 -64.0 -64.0 -64.0 ... 1.01e+03 995.3\n",
       "    Maitri      (geo, step, time) float64 -70.0 -70.0 -70.0 ... 1.012e+03 598.9</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-ebdf10bf-16be-458a-8d13-b3908564fe61' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebdf10bf-16be-458a-8d13-b3908564fe61' 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'>geo</span>: 4</li><li><span class='xr-has-index'>step</span>: 121</li><li><span class='xr-has-index'>time</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d3000d37-a735-4a46-8f22-f8ca7eccf29d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3000d37-a735-4a46-8f22-f8ca7eccf29d' 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'>step</span></div><div class='xr-var-dims'>(step)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 116 117 118 119 120</div><input id='attrs-56191363-04b4-431d-87bb-e64318390289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56191363-04b4-431d-87bb-e64318390289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87321702-f17e-48d8-84bd-1f5cbdb57c7e' class='xr-var-data-in' type='checkbox'><label for='data-87321702-f17e-48d8-84bd-1f5cbdb57c7e' 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,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120])</pre></div></li><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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-02-01 ... 2010-02-28</div><input id='attrs-272e4d5b-9feb-4577-af7b-dd73dae5e380' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-272e4d5b-9feb-4577-af7b-dd73dae5e380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-072a490a-17c1-43c2-8b12-19c2f95eb48e' class='xr-var-data-in' type='checkbox'><label for='data-072a490a-17c1-43c2-8b12-19c2f95eb48e' 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([&#x27;2010-02-01T00:00:00.000000000&#x27;, &#x27;2010-02-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-03T00:00:00.000000000&#x27;, &#x27;2010-02-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-05T00:00:00.000000000&#x27;, &#x27;2010-02-06T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-07T00:00:00.000000000&#x27;, &#x27;2010-02-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-09T00:00:00.000000000&#x27;, &#x27;2010-02-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-11T00:00:00.000000000&#x27;, &#x27;2010-02-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-13T00:00:00.000000000&#x27;, &#x27;2010-02-14T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-15T00:00:00.000000000&#x27;, &#x27;2010-02-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-17T00:00:00.000000000&#x27;, &#x27;2010-02-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-19T00:00:00.000000000&#x27;, &#x27;2010-02-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-21T00:00:00.000000000&#x27;, &#x27;2010-02-22T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-23T00:00:00.000000000&#x27;, &#x27;2010-02-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-25T00:00:00.000000000&#x27;, &#x27;2010-02-26T00:00:00.000000000&#x27;,\n",
       "       &#x27;2010-02-27T00:00:00.000000000&#x27;, &#x27;2010-02-28T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>geo</span></div><div class='xr-var-dims'>(geo)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;lat&#x27; &#x27;lon&#x27; &#x27;alt&#x27; &#x27;pre&#x27;</div><input id='attrs-a47439bc-826b-4b0b-af18-b9faa88e82dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a47439bc-826b-4b0b-af18-b9faa88e82dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdc6a76d-0299-4297-9b2a-a4fe61ac6571' class='xr-var-data-in' type='checkbox'><label for='data-fdc6a76d-0299-4297-9b2a-a4fe61ac6571' 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([&#x27;lat&#x27;, &#x27;lon&#x27;, &#x27;alt&#x27;, &#x27;pre&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95956a0d-a9fe-4a8c-9fe9-75da6f486db2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-95956a0d-a9fe-4a8c-9fe9-75da6f486db2' 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>Davis</span></div><div class='xr-var-dims'>(geo, step, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-69.0 -69.0 -69.0 ... 694.1 574.6</div><input id='attrs-01753435-7265-4726-a491-ebc5c98954db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01753435-7265-4726-a491-ebc5c98954db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ae65ee8-5df7-4587-8ae9-0cc6c1446dcc' class='xr-var-data-in' type='checkbox'><label for='data-3ae65ee8-5df7-4587-8ae9-0cc6c1446dcc' 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([[[ -69.   ,  -69.   ,  -69.   , ...,  -69.   ,  -69.   ,\n",
       "          -69.   ],\n",
       "        [ -69.073,  -69.074,  -68.975, ...,  -68.952,  -68.848,\n",
       "          -68.944],\n",
       "        [ -69.128,  -69.147,  -68.962, ...,  -68.91 ,  -68.632,\n",
       "          -68.897],\n",
       "        ...,\n",
       "        [ -70.741,  -78.517,  -76.849, ...,  -67.887,  -66.165,\n",
       "          -69.251],\n",
       "        [ -70.669,  -78.441,  -77.044, ...,  -67.894,  -66.205,\n",
       "          -69.161],\n",
       "        [ -70.587,  -78.354,  -77.24 , ...,  -67.904,  -66.26 ,\n",
       "          -69.072]],\n",
       "\n",
       "       [[  78.   ,   78.   ,   78.   , ...,   78.   ,   78.   ,\n",
       "           78.   ],\n",
       "        [  78.009,   78.233,   78.247, ...,   78.351,   79.349,\n",
       "           78.564],\n",
       "        [  78.044,   78.461,   78.433, ...,   78.63 ,   80.648,\n",
       "           79.136],\n",
       "...\n",
       "        [3111.3  , 3350.5  , 3398.8  , ..., 3297.4  , 2309.9  ,\n",
       "         2182.2  ],\n",
       "        [3139.2  , 3386.2  , 3403.1  , ..., 3302.8  , 2314.   ,\n",
       "         2194.6  ],\n",
       "        [3164.3  , 3426.8  , 3403.7  , ..., 3313.2  , 2319.9  ,\n",
       "         2205.6  ]],\n",
       "\n",
       "       [[ 842.2  ,  834.8  ,  831.1  , ...,  831.1  ,  817.6  ,\n",
       "          829.1  ],\n",
       "        [ 832.   ,  824.8  ,  827.6  , ...,  825.7  ,  805.1  ,\n",
       "          820.6  ],\n",
       "        [ 822.8  ,  815.1  ,  823.9  , ...,  820.2  ,  801.4  ,\n",
       "          810.7  ],\n",
       "        ...,\n",
       "        [ 534.5  ,  422.   ,  385.2  , ...,  633.7  ,  698.2  ,\n",
       "          568.5  ],\n",
       "        [ 533.1  ,  422.3  ,  384.3  , ...,  634.4  ,  696.5  ,\n",
       "          571.5  ],\n",
       "        [ 531.5  ,  422.6  ,  383.7  , ...,  635.1  ,  694.1  ,\n",
       "          574.6  ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>South Pole</span></div><div class='xr-var-dims'>(geo, step, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 541.1 851.3</div><input id='attrs-c01f6b07-788e-4fa3-8287-a273dec0106a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c01f6b07-788e-4fa3-8287-a273dec0106a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9825cc9b-e459-411e-b50d-f92005311d0f' class='xr-var-data-in' type='checkbox'><label for='data-9825cc9b-e459-411e-b50d-f92005311d0f' 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([[[ -90.   ,  -90.   ,  -90.   , ...,  -90.   ,  -90.   ,\n",
       "          -90.   ],\n",
       "        [ -89.805,  -89.849,  -89.906, ...,  -89.908,  -89.925,\n",
       "          -89.849],\n",
       "        [ -89.609,  -89.654,  -89.827, ...,  -89.83 ,  -89.858,\n",
       "          -89.702],\n",
       "        ...,\n",
       "        [ -75.598,  -75.523,  -76.693, ...,  -87.085,  -85.789,\n",
       "          -82.081],\n",
       "        [ -75.561,  -75.465,  -76.577, ...,  -86.787,  -85.889,\n",
       "          -82.163],\n",
       "        [ -75.521,  -75.41 ,  -76.483, ...,  -86.504,  -85.985,\n",
       "          -82.25 ]],\n",
       "\n",
       "       [[ 335.   ,  335.   ,  335.   , ...,  335.   ,  335.   ,\n",
       "          335.   ],\n",
       "        [ -30.928,  -39.406,  -27.732, ...,  -12.648,  -13.369,\n",
       "          -13.89 ],\n",
       "        [ -35.639,  -51.347,  -30.125, ...,   -4.111,   -6.325,\n",
       "           -5.717],\n",
       "...\n",
       "        [ 217.4  ,  447.2  ,   78.4  , ..., 1821.5  , 1742.3  ,\n",
       "          442.8  ],\n",
       "        [ 230.3  ,  469.8  ,   80.1  , ..., 1812.8  , 1766.7  ,\n",
       "          480.9  ],\n",
       "        [ 241.5  ,  488.9  ,   83.2  , ..., 1804.4  , 1796.2  ,\n",
       "          521.3  ]],\n",
       "\n",
       "       [[ 636.1  ,  638.8  ,  638.1  , ...,  628.8  ,  626.6  ,\n",
       "          628.2  ],\n",
       "        [ 640.7  ,  643.   ,  638.7  , ...,  627.1  ,  627.   ,\n",
       "          628.1  ],\n",
       "        [ 646.1  ,  649.6  ,  639.7  , ...,  625.3  ,  627.2  ,\n",
       "          627.3  ],\n",
       "        ...,\n",
       "        [ 617.5  ,  659.   ,  767.5  , ...,  537.9  ,  545.6  ,\n",
       "          846.9  ],\n",
       "        [ 612.4  ,  656.   ,  758.9  , ...,  557.   ,  543.6  ,\n",
       "          849.   ],\n",
       "        [ 608.6  ,  653.3  ,  749.7  , ...,  575.5  ,  541.1  ,\n",
       "          851.3  ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Neumayer</span></div><div class='xr-var-dims'>(geo, step, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-71.0 -71.0 -71.0 ... 542.3 774.8</div><input id='attrs-d85a63de-5a9c-4130-b871-7392713e8100' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d85a63de-5a9c-4130-b871-7392713e8100' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd2105d9-a00a-4c88-88a5-c57c9d6a39ee' class='xr-var-data-in' type='checkbox'><label for='data-fd2105d9-a00a-4c88-88a5-c57c9d6a39ee' 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([[[-7.1000e+01, -7.1000e+01, -7.1000e+01, ..., -7.1000e+01,\n",
       "         -7.1000e+01, -7.1000e+01],\n",
       "        [-7.0967e+01, -7.1193e+01, -7.1261e+01, ..., -7.1117e+01,\n",
       "         -7.1062e+01, -7.1113e+01],\n",
       "        [-7.0920e+01, -7.1362e+01, -7.1495e+01, ..., -7.1235e+01,\n",
       "         -7.1126e+01, -7.1205e+01],\n",
       "        ...,\n",
       "        [-6.3348e+01, -4.9123e+01, -4.2665e+01, ..., -7.2603e+01,\n",
       "         -7.4731e+01, -7.3235e+01],\n",
       "        [-6.3437e+01, -4.8987e+01, -4.2527e+01, ..., -7.2486e+01,\n",
       "         -7.4651e+01, -7.3204e+01],\n",
       "        [-6.3526e+01, -4.8847e+01, -4.2398e+01, ..., -7.2358e+01,\n",
       "         -7.4569e+01, -7.3179e+01]],\n",
       "\n",
       "       [[ 3.5200e+02,  3.5200e+02,  3.5200e+02, ...,  3.5200e+02,\n",
       "          3.5200e+02,  3.5200e+02],\n",
       "        [-6.5740e+00, -7.0310e+00, -7.5540e+00, ..., -7.9130e+00,\n",
       "         -8.2580e+00, -8.0430e+00],\n",
       "        [-5.2130e+00, -5.9580e+00, -7.0680e+00, ..., -7.8070e+00,\n",
       "         -8.5310e+00, -8.1000e+00],\n",
       "...\n",
       "        [ 9.0300e+01,  2.3040e+02,  2.5800e+01, ...,  2.1139e+03,\n",
       "          2.5926e+03,  1.9990e+03],\n",
       "        [ 8.8000e+01,  2.3480e+02,  2.4800e+01, ...,  2.1224e+03,\n",
       "          2.6043e+03,  1.9952e+03],\n",
       "        [ 8.5000e+01,  2.3800e+02,  2.3200e+01, ...,  2.1333e+03,\n",
       "          2.6154e+03,  1.9928e+03]],\n",
       "\n",
       "       [[ 8.8440e+02,  8.8590e+02,  8.8060e+02, ...,  8.6900e+02,\n",
       "          8.7920e+02,  8.8780e+02],\n",
       "        [ 8.7710e+02,  8.6650e+02,  8.6070e+02, ...,  8.5620e+02,\n",
       "          8.7480e+02,  8.7900e+02],\n",
       "        [ 8.7310e+02,  8.4760e+02,  8.3980e+02, ...,  8.4320e+02,\n",
       "          8.7070e+02,  8.7240e+02],\n",
       "        ...,\n",
       "        [ 9.8980e+02,  9.8660e+02,  1.0239e+03, ...,  5.9170e+02,\n",
       "          5.3310e+02,  7.7310e+02],\n",
       "        [ 9.9010e+02,  9.8650e+02,  1.0245e+03, ...,  5.9660e+02,\n",
       "          5.3770e+02,  7.7410e+02],\n",
       "        [ 9.9040e+02,  9.8650e+02,  1.0241e+03, ...,  6.0410e+02,\n",
       "          5.4230e+02,  7.7480e+02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Marambio</span></div><div class='xr-var-dims'>(geo, step, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-64.0 -64.0 ... 1.01e+03 995.3</div><input id='attrs-fed995eb-89c5-4c7c-9b75-3ba52331bf85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fed995eb-89c5-4c7c-9b75-3ba52331bf85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-873d7395-5a92-4774-8d82-c97ea0369ac9' class='xr-var-data-in' type='checkbox'><label for='data-873d7395-5a92-4774-8d82-c97ea0369ac9' 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([[[-6.40000e+01, -6.40000e+01, -6.40000e+01, ..., -6.40000e+01,\n",
       "         -6.40000e+01, -6.40000e+01],\n",
       "        [-6.38270e+01, -6.39830e+01, -6.39870e+01, ..., -6.38650e+01,\n",
       "         -6.41200e+01, -6.39670e+01],\n",
       "        [-6.36670e+01, -6.39600e+01, -6.39780e+01, ..., -6.37460e+01,\n",
       "         -6.42190e+01, -6.39640e+01],\n",
       "        ...,\n",
       "        [-6.17340e+01, -6.26680e+01, -6.76260e+01, ..., -4.78180e+01,\n",
       "         -5.22710e+01, -6.35130e+01],\n",
       "        [-6.15060e+01, -6.26360e+01, -6.76680e+01, ..., -4.75070e+01,\n",
       "         -5.18420e+01, -6.35410e+01],\n",
       "        [-6.12520e+01, -6.26020e+01, -6.76990e+01, ..., -4.71910e+01,\n",
       "         -5.14280e+01, -6.35830e+01]],\n",
       "\n",
       "       [[ 3.03000e+02,  3.03000e+02,  3.03000e+02, ...,  3.03000e+02,\n",
       "          3.03000e+02,  3.03000e+02],\n",
       "        [-5.68950e+01, -5.65580e+01, -5.64870e+01, ..., -5.73970e+01,\n",
       "         -5.74530e+01, -5.71560e+01],\n",
       "        [-5.68290e+01, -5.61610e+01, -5.59730e+01, ..., -5.77880e+01,\n",
       "         -5.78910e+01, -5.73590e+01],\n",
       "...\n",
       "        [ 1.33600e+02,  1.44100e+02,  8.44800e+02, ...,  4.68300e+02,\n",
       "          2.07000e+01,  5.06000e+01],\n",
       "        [ 1.27200e+02,  1.42800e+02,  8.74200e+02, ...,  4.63800e+02,\n",
       "          1.75000e+01,  5.16000e+01],\n",
       "        [ 1.20900e+02,  1.40500e+02,  9.02900e+02, ...,  4.57800e+02,\n",
       "          1.70000e+01,  5.25000e+01]],\n",
       "\n",
       "       [[ 9.15700e+02,  9.19900e+02,  9.19200e+02, ...,  9.15200e+02,\n",
       "          9.03500e+02,  9.08500e+02],\n",
       "        [ 9.17500e+02,  9.20800e+02,  9.21000e+02, ...,  9.17000e+02,\n",
       "          9.04100e+02,  9.07700e+02],\n",
       "        [ 9.19200e+02,  9.22100e+02,  9.22500e+02, ...,  9.18500e+02,\n",
       "          9.05800e+02,  9.06700e+02],\n",
       "        ...,\n",
       "        [ 9.52000e+02,  9.58100e+02,  8.86600e+02, ...,  9.67700e+02,\n",
       "          1.00490e+03,  9.96400e+02],\n",
       "        [ 9.54100e+02,  9.57800e+02,  8.84500e+02, ...,  9.69400e+02,\n",
       "          1.00740e+03,  9.95800e+02],\n",
       "        [ 9.56400e+02,  9.57600e+02,  8.80800e+02, ...,  9.70900e+02,\n",
       "          1.00960e+03,  9.95300e+02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Maitri</span></div><div class='xr-var-dims'>(geo, step, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-70.0 -70.0 ... 1.012e+03 598.9</div><input id='attrs-a6905c1b-6c19-4000-b8cc-a2b47f397953' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6905c1b-6c19-4000-b8cc-a2b47f397953' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d847ff98-728c-4944-91b0-d221cbc74752' class='xr-var-data-in' type='checkbox'><label for='data-d847ff98-728c-4944-91b0-d221cbc74752' 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([[[ -70.   ,  -70.   ,  -70.   , ...,  -70.   ,  -70.   ,\n",
       "          -70.   ],\n",
       "        [ -70.052,  -70.016,  -70.117, ...,  -70.165,  -70.195,\n",
       "          -70.173],\n",
       "        [ -70.115,  -69.992,  -70.175, ...,  -70.316,  -70.354,\n",
       "          -70.334],\n",
       "        ...,\n",
       "        [ -60.854,  -51.435,  -42.406, ...,  -66.448,  -47.554,\n",
       "          -74.438],\n",
       "        [ -60.997,  -51.592,  -42.312, ...,  -66.452,  -47.179,\n",
       "          -74.64 ],\n",
       "        [ -61.118,  -51.768,  -42.229, ...,  -66.462,  -46.813,\n",
       "          -74.865]],\n",
       "\n",
       "       [[  11.   ,   11.   ,   11.   , ...,   11.   ,   11.   ,\n",
       "           11.   ],\n",
       "        [  12.467,   12.855,   12.86 , ...,   11.386,   11.558,\n",
       "           11.155],\n",
       "        [  13.931,   14.605,   14.679, ...,   11.832,   12.133,\n",
       "           11.298],\n",
       "...\n",
       "        [ 660.4  ,  590.4  ,  265.4  , ..., 1031.1  ,    2.4  ,\n",
       "          536.8  ],\n",
       "        [ 678.4  ,  598.5  ,  258.7  , ..., 1030.3  ,    2.9  ,\n",
       "          544.3  ],\n",
       "        [ 694.8  ,  609.   ,  248.9  , ..., 1027.3  ,    3.2  ,\n",
       "          555.9  ]],\n",
       "\n",
       "       [[ 888.6  ,  882.2  ,  875.3  , ...,  868.2  ,  876.9  ,\n",
       "          883.7  ],\n",
       "        [ 881.2  ,  881.   ,  855.   , ...,  849.8  ,  850.6  ,\n",
       "          858.   ],\n",
       "        [ 876.1  ,  886.3  ,  846.6  , ...,  834.1  ,  830.4  ,\n",
       "          834.3  ],\n",
       "        ...,\n",
       "        [ 925.8  ,  932.6  ,  994.8  , ...,  873.4  , 1007.3  ,\n",
       "          614.4  ],\n",
       "        [ 923.8  ,  931.7  ,  995.   , ...,  873.5  , 1009.8  ,\n",
       "          607.4  ],\n",
       "        [ 922.3  ,  931.   ,  995.6  , ...,  873.9  , 1011.8  ,\n",
       "          598.9  ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d888fbc1-d038-4157-ab76-05f8d107aeb4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d888fbc1-d038-4157-ab76-05f8d107aeb4' 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:     (geo: 4, step: 121, time: 28)\n",
       "Coordinates:\n",
       "  * step        (step) int64 0 1 2 3 4 5 6 7 ... 113 114 115 116 117 118 119 120\n",
       "  * time        (time) datetime64[ns] 2010-02-01 2010-02-02 ... 2010-02-28\n",
       "  * geo         (geo) <U3 'lat' 'lon' 'alt' 'pre'\n",
       "Data variables:\n",
       "    Davis       (geo, step, time) float64 -69.0 -69.0 -69.0 ... 694.1 574.6\n",
       "    South Pole  (geo, step, time) float64 -90.0 -90.0 -90.0 ... 541.1 851.3\n",
       "    Neumayer    (geo, step, time) float64 -71.0 -71.0 -71.0 ... 542.3 774.8\n",
       "    Marambio    (geo, step, time) float64 -64.0 -64.0 -64.0 ... 1.01e+03 995.3\n",
       "    Maitri      (geo, step, time) float64 -70.0 -70.0 -70.0 ... 1.012e+03 598.9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 277 ms\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"files = sorted(glob.glob(\\\"/home/pankaj/Desktop/hysplit/out/ncep*\\\"))\\nhy = HyData(files, list(stations.keys()))\\ndata = hy.read()\\ndata\";\n",
       "                var nbb_formatted_code = \"files = sorted(glob.glob(\\\"/home/pankaj/Desktop/hysplit/out/ncep*\\\"))\\nhy = HyData(files, list(stations.keys()))\\ndata = hy.read()\\ndata\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "files = sorted(glob.glob(\"/home/pankaj/Desktop/hysplit/out/ncep*\"))\n",
    "hy = HyData(files, list(stations.keys()))\n",
    "data = hy.read()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CQ0Nx7tw5hIeH4+XLl8jIyEBBQQFUVVWhra2NTp06wdDQEP3794eFhYXIv3eEfI3HGO2mTgghhEg6GhInhBBCpAAlbEIIIUQKUMImhBBCpAAlbEIIIUQKUMImhBBCpAAlbEIIIUQKUMImhBBCpAAtnEIIx3JycpCcnIzk5GQkJSWV+TopKQkpKSnIysr65tabysrKUFNTg4KCAmRlZSEjIyP498tqbXw+v9SjuLgYOTk5yM7ORlFRUYXtKygoQFNTE7q6umjWrFmZf798raOjQ9tHEiJmtHAKITWMMYakpCTEx8fj6dOngsfz58+RlJRUZrcuJSWlMsmwadOmaNSoEdTU1KCmpgZVVVXB11+eq6qqVnt964KCAnz69AnZ2dmCx9fPU1NTy3yoeP/+faktQWVkZKCjo4MWLVqgQ4cO6NixI/T19dGxY0d06NBB5BXsCCH/QwmbEDH59OkTHj16VCopx8fH49mzZ8jJyQHweUvQdu3aQV9fH3p6emjevHmZ5NywYcMK1y6XRCUlJfjw4UOZkYHExEQ8e/YMT58+xfv37wX1mzVrho4dOwoe+vr6gu8JbapBiHCUsAmpgk+fPiE2NhbR0dGCx5MnTwT7Nevq6pZJSB07dkTbtm0hJyfHcfTcyMzMLPVh5ssHmqdPnwo+0KioqMDY2Bjdu3cXPPT19SmJEwJK2IR8U3Z2dpnkHB8fD8YYFBUVYWRkJEguRkZG6NixI9TU1LgOW2owxpCcnIzHjx8jJiZG8D1OSEgA8DmJd+vWDd27d4epqSklcVJvUcIm5CtpaWkICQlBcHAwgoOD8eDBg1LJ+UvS6N69Ozp37lxve8w17ePHj4iNjcWdO3eEJvHevXujf//+GDBgAExNTennQOo8Stik3ktPT8eNGzcQHByM69ev4/79+wCAdu3aoX///ujTpw9MTU3RpUsXmvnMsS9J/Pbt2wgJCUFoaCiys7OhoqICCwsL9O/fH/3790f37t0pgZM6hxI2qXcyMzMREhKC69evIzg4GPfu3QNjDG3bthX8we/fvz9atWrFdajkG4qLixETEyMYDQkNDcWnT5+gpKQES0tLfP/994IETkPoRNpRwib1wqtXr3D27FkEBAQgODgYxcXFaN26NQYMGCBI0K1bt+Y6TFJNRUVFWLlyJdavXw8dHR3k5OQgJycH2traGD58OEaMGIFBgwZBRUWF61AJqTQa3yN1Ep/Px507dxAQEICAgADExcVBTk4OAwcOhKenJ4YOHYq2bdtyHSYRMzk5ORQWFgIAJk6ciA0bNiAqKgrnzp1DQEAADh06BAUFBVhZWcHW1ha2trbQ1dXlOGpCREM9bFJn5ObmIigoCAEBATh37hxSUlKgqamJ4cOHw9bWFoMHD6YFO+qB4cOH4/z589i9ezdmzZpV6rWEhATBSEtoaChKSkpgamqKESNGYMSIETA0NJS6e+BJ/UEJm0i1wsJCXLx4ET4+Pjh37hzy8vLQoUMH2NnZYcSIEejVqxdNFKtnOnbsiGfPniEoKAgDBw4st156ejoCAwMREBCAixcvIisrC61bt4aDgwMcHR1hYGBQi1ET8m2UsInUYYzh1q1b8PHxwYkTJ5Ceng4jIyM4ODhg5MiR0NfX5zpEwpHCwkIoKyujpKQEb9++RfPmzUU+7saNGzh58iROnDiBjIwMGBkZwcnJCQ4ODiK3Q0hNooRNpMbjx4/h6+sLX19fvHr1Ci1btoSjoyP1hojA69ev0aZNG8jKyqKwsBAyMpXfkPC/ozYBAQEoLCxE37594erqCnt7e6irq9dA5IR8GyVsItGSk5Ph5+cHHx8fxMTEQF1dHePGjYOjoyMsLS2r9AeZ1F2FhYVQU1NDYWEhEhISoKenV632MjMzcerUKfz8889ITU2FvLw87Ozs4OTkBGtra8jLy4spckK+jf7aEYnDGENQUBBGjx6Nli1bwsPDA61bt8Y///yDlJQU7Nu3D/369aNkTcqQl5eHkZERACA6Orra7amrq8PKygpmZmY4ePAgLCws8PTpU9jZ2aFly5ZYtmwZEhMTq30eQkRBf/GIxPj48SM8PT3RuXNnWFlZ4cmTJ/D09ERKSgpOnToFe3t7KCoqch0mkXDdu3cHANy5c0cs7fn6+sLR0RFOTk6Cldbu37+P8ePHY8eOHWjbti3s7Oxw6dKlUluMEiJulLAJ52JjYzF9+nQ0b94cCxcuhJGREUJCQvDgwQP88MMP0NDQ4DpEIkVMTU0BiKeHzRiDv78/7OzsUFJSIrjjoGvXrti2bRvevXuHXbt24dWrV7C2toa+vj42b96M9PT0ap+bkK9RwiacyM/Ph4+PD3r16gUTExMEBgbCw8MDiYmJOH78OPr27Uv3w5Iq+W/Crm6PNzo6Gp07d4aysjLOnTuH4cOHl/q9VFVVxcyZM3H37l3cvHkTPXv2hIeHB5o3b44pU6aIrZdPCEArnZFalpqaim3btmH37t1ITU2FlZUVTp06BVtbW7pfmohFly5doKCggMzMTDx//hwdOnSoclve3t5wdnYWfO3p6Sm0Ho/HQ58+fdCpUyfo6upCTk4Ovr6+OHToEHr06IFFixbB3t6e1jMn1UI9bFIr3rx5g/nz56N169b4888/4eDggCdPnuDKlSsYNWoUJWsiNnJycujWrRuA6g2LFxUV4datW+jXrx/+/fdf5OTkoE2bNuXWP3fuHIYOHQpNTU0EBQUJVt1TVVXFuHHj0KVLFxw8eFCwdCohlUUJm9So+Ph4TJkyBXp6ejh69CgWLlyI169fY9u2bbTACakx4ph4dunSJQwaNAgyMjI4duwYJkyYILRednY2pk2bhhMnTuDy5ctYsmQJDh48iIkTJ6Jdu3a4du0aIiIi0KVLF0ydOhV6enrYunUrcnJyqhwbqacYITXgzp07bPTo0YzH47FmzZqxTZs2saysLK7DIvXEwYMHGQDWv39/xhhjfD6fXb58mRUXF4vcxrhx49jjx48ZY4xZWVmxrVu3spCQEPb+/XvG5/MZY4yFhoayHj16sH/++afM8S9evGA9e/Zkt2/fFpQ9fPiQubi4MFlZWaapqclWr17N0tLSqvNWST1CCZuIDZ/PZ9euXWODBg1iAFj79u3Zvn37WH5+PtehkXrm3r17DABTU1NjMTEx7Pvvv2cA2JMnT0Q6/uPHj8zS0lLwPD4+nh04cIAtXryY2dnZsV69ejEzMzNma2vLkpOTy23n3bt3zNzcnN28ebNU+atXr5ixsTGTk5NjqqqqzN3dnb17965qb5bUG7TSGRGLmzdvwsPDA2FhYTAyMsKSJUswZswYmmRDOFFcXAw1NTXk5+dj4MCB2LVrF/766y9YWFhg0KBB3zzey8sLeXl5mDNnTrl1CgsLIScn9827GT58+IAxY8agcePGMDY2homJCe7fv4/ExESsWrUK27Ztw86dO5GXl4dZs2Zh2bJl0NHRqfR7JnUfXcMm1RIXFwdbW1tYWloiNzcX58+fR2xsLMaPH0/JmnCmQYMGgolnU6dOhb6+Plq0aIHXr1+LdLyfn1+516y/kJeXF+nWQx0dHYSEhGDHjh0wMTFBTEwM5OTksGPHDjRp0gS//fYbEhMTsXLlShw+fBh6enpYtWoVsrOzRYqV1CNcd/GJdHr58iVzdnZmPB6P6enpsWPHjrGSkhKuwyJE4Mcff2QA2IIFCxhjjD179oyZmpoyPz+/Co97+fIlGzFiRG2EWEZqaipbuHAhU1BQYNra2szT05MuKREB6mGTSvnw4QPmzZuHjh074sqVK9i5cyceP36MCRMm0NreRKJ8veJZ+/btcf36ddy4cQNjx45FTExMmWOKioqwY8cOODk51WqsX2hpaWHjxo149uwZbG1tsWDBAnTq1Ane3t4oKSnhJCYiOegaNhFJdnY2/vzzT/z555+QkZHB4sWLMW/ePKioqHAdGiFCPXjwAF27doWqqio+fvxY6hJNZGSkYElRFxcX8Pl8BAYGIjk5GTY2Nli0aBEUFBQ4jP6zx48fY9myZTh9+jS6du2K33//HTY2NrQKYD1FCZtUqKSkBHv37sWqVauQlZWFH3/8EUuWLIGWlhbXoRFSoeLiYqirqyM3NxePHj1C586dy9RJS0vD0aNHoaysjKFDh6JVq1YcRPptERER8PDwQEhICCwsLLB161bBveak/qAxTFKuW7duwdTUFD/88ANsbGzw7NkzbNq0iZI1kQr/nXhW3opnWlpaWLBgAWbOnCmxyRoAzM3Ncf36dQQGBuLjx4/o0aMHZs2ahbS0NK5DI7WIEjYp48OHD5g8eTL69OkDWVlZRERE4NChQ2jZsiXXoRFSKV+uY9eFTTh4PB6sra0RGxuLLVu24NixY9DX18f+/ftpW896ghI2ESguLsb27dvRsWNHBAQEYM+ePYiMjISZmRnXoRFSJV+GjcWx1aakaNCgAebNm4f4+HjY2NhgxowZMDc3x+3bt7kOjdQwStgEwOeFT7p374558+Zh3LhxiI+Px8yZM+leaiLVvvSwY2Ji6tws66ZNm+LIkSMIDQ1FQUEBzMzMMGPGDKSmpnIdGqkhlLDruZSUFLi4uMDS0hIKCgqIjIzEvn370LhxY65DI6Ta9PX1oaKigtzcXMTHx3MdTo2wsLBAdHQ0PD09ceLECejr62PPnj117gMKoYRdbzHGsG/fPujr6+PChQvYt28fIiIi0KNHD65DI0RsZGVlYWxsDKBuXMcuT4MGDTBnzhzEx8ejW7ducHNzg7m5OR48eMB1aESMKGHXQ69fv8bgwYMxc+ZMjBkzBvHx8Zg+fTotfELqpLo08exr8fHxCAwMFDxXUVFBdnY2AgMDkZubCxMTE/z2228oLi7mMEoiLvQXuh750qvu2rUrnjx5gsDAQBw4cIBu0yJ1Wl2ceAZ8vu3S2dkZu3btgru7O/Ly8vDjjz9i3rx5sLa2RnR0NNzd3bFy5UrqbdcRlLDrif/2qseNG4cHDx7A2tqa67AIqXFfetixsbF15vangIAAeHh44Ny5cwgICECbNm3QtGlTGBsbY/DgwYiPjxes4ubr64uMjAwYGxtTb1vK0UpndRxjDPv378fChQuhrq6O/fv3U6Im9cp/lyjNzMyU+ks/Dx48wKRJkxAcHAxVVVUAn/+fm5ubY8OGDRg0aBCKiorKPd7IyAg+Pj4wMDCorZCJmEj3by6pEPWqCfncswYAY2NjqU/WANCmTRvIy8uXWsf/2rVrMDc3R3p6eqlkrampCS0tLTRu3Bja2trQ0NDAkydP0K1bN+ptS6EGXAdAxI8xhoMHD2LBggVQV1dHYGAgJWpSL7179w6nT58GAJiYmHAczWclJSW4evUqAgMD0bx5c+jr66NTp07Q09MTad0DVVVVdOjQAXfv3oWxsTGSkpKwceNGbNmypdStXO3bt8ezZ8/KHJ+fn4/Vq1dj5cqVOH36NHx8fNCpUyexvkdSM6T/4yYpJSsrCxMnTsS0adMwduxY6lWTemvv3r1o0aKFIGFzvWLfu3fvsHr1apibm+Pq1asYM2YM2rdvj4cPH2LFihVYtWqVyG25urpiy5YtKCoqwsSJE/Hw4UNcunQJy5cvF9T579f/paioiHXr1iE8PBw5OTno3r07jhw5Ut23R2oDR/twkxpw584dpqenx9TU1Jifnx/X4RDCqdGjRzMA7LvvvmNbt25lhYWFnMYzYcIE9vfff7OCgoIyrxUXFzNzc3OR2yopKWFr165ljRs3Zr/++ivLzc1lnTt3ZgCYvLw8u3TpkkjtfPr0iU2ePJkBYM7Oziw7O1vkGEjto4RdB/D5fObp6cnk5ORY9+7d2bNnz7gOiRDO2dnZMQBs7969XIfCGGOsT58+Fb4+bNgw9uHDh0q1mZSUxAoKCpiDgwMDwOTk5Nj58+crHdvRo0eZiooK09fXZ3fv3q308aR20JC4lEtPT8eoUaMwb948/PDDDwgLC0P79u25DosQzn2ZUNWggWRM1WHfuCGnd+/euHXrVqXa1NHRwbRp03Ds2DE0aNAA//zzD4YNG1bp2JydnREdHQ1FRUWYmZlh9+7d34yX1D5K2FLs1q1b6NatG27cuAF/f39s2bIFCgoKXIdFiESQtHHMECkAACAASURBVITdtm3bcvevLigowL179/Dx40eR2+Pz+Zg+fTq8vb0hKyuLEydOwNbWtsrx6evrIyIiAlOnTsXs2bMxduzYSsVDah4lbCnE5/Oxfv169O3bF61atcLdu3cxYsQIrsMiRKJ8SdgFBQUSsRHG1KlTMXfuXOTl5WH+/PmwtLTE1KlT8ccff8DKygrW1tZwdXUVqS0+nw83NzccOnQIsrKyOHbsGEaNGlXtGBUVFbFz506cPHkSV69ehbGxMaKioqrdLhETrsfkSeWkp6cza2trxuPx2NKlS1lRURHXIREikfr168cAMADs+++/Z8XFxVyHxCZNmsR0dXXZ4cOHWUFBAXv48CH7+++/2cOHDys8rrCwkN27d48dPHiQ/fjjj6x79+4MAJORkWF//fVXjcT64sULZmZmxho0aMC2bdvG+Hx+jZyHiI4SthR59OgR69ChA9PQ0BB5Figh9dX48eMFCRsA27hxI9chsezsbPb69WuR6vL5fHb27FlmYWHBFBQUSr0XAExWVpYdPXq0RuMtLCxk8+fPZwDYtGnThM5wJ7WHliaVEhcuXICDgwNatGiBgIAA6OnpcR0SIRLt/fv3iIiIQEJCAhYuXAgFBQXExMSgS5cuXIf2Tbdv38aiRYsQEhIiKGvYsCFMTExgYmKC7t27o0+fPmjdunWtxHP48GHMnDkTPXr0wKlTp6Cjo1Mr5yVf4foTA6kYn89nf/zxB+PxeMzW1pZlZmZyHRIhUoXP57Nhw4YxAMzU1LTc+7H5fD4rKiritBf5/PlzNmHCBEEvWkFBgS1evJg9ffqUlZSUcBYXY4zdunWLNW3alLVs2ZLFxMRwGkt9RQlbguXm5jJHR0cGgC1btozz/7CESKt3794xDQ0NBoBpaGgwTU1Npq6uzlRUVJiCggKTlZUtNdzcu3dvFhUVVWvxpaamsvnz5zM5OTkGgPF4PObq6iry8HltefPmDevevTtTUlJiJ06c4DqceocStoR6+/Yt69GjB1NSUmLHjh3jOhxCpN6xY8fKXAeu6MHj8djUqVMrvZiJqIqKitidO3fYmjVrmLq6uuC8gwcPZrGxsTVyTnHIzc0VLNSyfPly6kjUIrqGLYEiIyMxatQoyMrKwt/fX2I2LSBE2r18+RI5OTmQlZVFgwYNyjxkZWWRlZWFlStXwtvbGwCgrq6ONWvWYPbs2dW6pzsvLw+RkZEIDQ1FaGgowsPD8enTJ8HrRkZG2LBhAwYPHlzt91nTGGPYsGEDlixZghEjRsDb2xtqampch1XnUcKWMH/99RemTJmC7t2749SpU2jSpAnXIRFSL4WFheHHH3/E3bt3AQAGBgbw9PREv379RNpVKz09HWFhYYIEHR0dXWaf6kaNGqFPnz4YP348Jk6cKFK7kuTcuXOYOHEiWrdujXPnztXaJLj6ihK2BPnzzz+xcOFCuLq6Yu/evbRqGSEcKykpwf79+7Fs2TKkp6cDAGRlZdGsWTO0bNkSLVq0KPVvQUEBbt68idDQUDx8+LBMe7q6urC0tBQ8DAwMpH6P7kePHmH48OEoKCjApUuXYGBgwHVIdRYlbAnA5/OxePFibNq0CUuXLsXatWvB4/G4DosQ8v/S0tKwYsUKHDx4EAUFBSIfp6+vXypBt2nTpk7+305JScHQoUPx6tUrBAQEwNLSkuuQ6iRK2BwrKirClClT4OvrC09PT8yZM4frkAgh5SgpKcH79+/x5s0bvH37Fm/evCn1NWMMvXr1gqWlJSwsLKCtrc11yLUmKysLI0eORHh4OPz8/GBnZ8d1SHUOJWwO5eTkYMyYMQgKCoK3tzfGjx/PdUiEEFJlBQUFcHJywqlTp7B3715MmzaN65DqFOm+eCLFUlNTMXDgQNy8eRMXLlygZE0IkXoKCgrw8/ODm5sbpk+fjrVr19I2nWIkGfvO1TOvX7/GkCFDkJGRgZCQELptixBSZ8jKymL79u1o2rQpVqxYgZSUFHh6ekrdDHhJRAm7lsXFxcHa2hqKiooICwtD+/btuQ6JEELEisfjYfny5WjSpAlmzZqFDx8+wNvbm+58qSa6hl2LIiMjYW1tjTZt2iAwMBBNmzblOiRCCKlRZ86cgYODA3r37o2zZ89CWVmZ65CkFiXsWnLr1i1YW1vDyMgI58+fR8OGDbkOiRBCasWNGzcwaNAg9OzZE4GBgVBVVeU6JKlEk85qQWhoKIYMGQITExMEBgZSsiaE1Ct9+/aFra0twsPDMWTIEGRnZ3MdklSihF3DgoODYW1tjZ49e+L8+fP0yZIQUi+5urqipKQEUVFRGDx4MLKysrgOSerQkHgNun79OmxsbGBhYYEzZ87QtRtCSL2Vk5MDLS0tFBQUQE1NDV26dMGlS5egrq7OdWhSg3rYNeTGjRsYPnw4LCws4O/vT8maEFKvqaioYODAgQAAMzMzPHnyBEOHDqXh8UqghF0DwsLCMGzYMJibm8Pf3x9KSkpch0QIIZwbNWoUGjVqhKtXr0JPTw9xcXEYNmxYqW1GSfloSFzMwsPDMXjwYJiamuL8+fPUsyaEkP/HGMPWrVuxbNky5OXlCfYlNzMzw4ULF6CiosJ1iBKNErYYxcTEYMCAATA0NKRbFwghpByvXr3C3LlzcfbsWQCfV0fr27cvAgMDaXGVClDCFpOEhAT06dMHrVu3RlBQENTU1LgOiRBCJJq/vz/mzp2LxMRE8Hg8jB49Gn5+frSMaTnoGrYYpKSkYMiQIWjUqBHOnz9PyZoQQkRgZ2eHR48eYfHixZCRkcHJkycxd+5c2jCkHNTDrqasrCxYWlri33//xa1bt9CmTRuuQyKEEKnz8OFDjBs3Do8ePcLatWuxbNkyrkOSONTDroaCggKMGDECT58+xcWLFylZE0JIFX333Xd4+PAhVq9ejeXLl8PLy4vrkCQO7dZVRSUlJXBycsKNGzfQtWtXGBoach0SIYRIvS9bcs6cORM6OjoYMWIE1yFJDBoSrwLGGObMmYNdu3aBMQYVFRVkZmbSRAlCCBGDkpISjB8/HufPn8eVK1dgYWHBdUgSgYbEq+D333/Hzp07oa+vD+Dzkntv377lOCpCCKkbZGVl4ePjAzMzM9ja2uLBgwdchyQRKGFXkpeXF5YvX45BgwbhyZMngvL/fk0IIaR6FBUV4e/vj9atW2PIkCFITEzkOiTOUcKuhGvXrmHWrFmYNWsWZsyYgQYN/jcF4Pjx4xxGRgghdY+6ujoCAwMhLy8PW1vber+EKV3DFtHz58/Rs2dPdO/eHRcuXBAk6xs3bqBfv35QV1fH+/fvaZUeQggRswcPHqBXr14YNGgQTp48CRmZ+tnXrJ/vupKys7NhZ2cHTU1NHD9+vFTP2sLCAi1atEBmZia6deuGTZs24f379xxGSwghdYuBgQF8fX1x5swZrFmzhutwOEMJ+xv4fD6cnJyQmJiIgIAAaGholHpdRkYGGzduhLKyMp48eYJFixahRYsWcHNzA5/P5yhqQgipW0aMGIFff/0Vq1evxj///MN1OJygIfFvWL58OX7//XecPXsWNjY25dbLysrC8ePHceDAAURGRgIAVq9ejZUrV9ZWqIQQUqcxxuDg4ICzZ8/i1q1bMDIy4jqkWkUJuwLHjx/HhAkT8Mcff+Dnn38W+bhDhw5hypQp4PF4uHDhAqytrWswSkIIqT9yc3NhYWGBtLQ03L59Gzo6OlyHVGsoYZcjJiYGFhYWsLe3h7e3N3g8XqWOd3Nzw549e6ChoYHo6Gi0bdu2hiIlhJD65c2bNzA1NYW+vj6uXr0KeXl5rkOqFZSwhXj//j1MTU3RtGlT3LhxA0pKSpVuo6CgAH379kVUVBRMTEwQFhYGRUXFGoiWEELqn7CwMAwYMACTJ0/Gnj17Kt2pkkY06ewrhYWFsLe3R3FxMc6cOVOlZA0ACgoK+Pvvv6GlpYWYmBi6lk0IIWLUp08f7NmzB/v27cPu3bu5DqdWUML+ioeHB27fvo3Tp0+jefPm1WqrVatW2LhxIwAgNDRUHOERQgj5f1OmTMGcOXMwf/583Llzh+twahwl7P84e/YstmzZgg0bNsDc3FwsbX5J+nl5eWJpjxBCyP9s2rQJRkZGGD9+PDIzM7kOp0ZRwv5/b968waRJkzBixAjMmzdPbO1+GVLPzc0VW5uEEEI+k5eXh5+fH1JTUzFz5kzU5WlZlLABFBcXY+LEiVBWVsbBgwfFOnlBWVkZAPWwCSGkpujp6WH//v04fvw4vLy8uA6nxjT4dpW6b9WqVQgPD0dwcDC0tLTE2vaXhP3x40cUFxeXWtaUEEKIeIwbNw7Xrl3D3LlzYW5ujq5du3IdktjV+9u6rl69isGDB2Pt2rVYunSp2NsvKChA8+bNkZaWhlOnTmHUqFFiPwchhJDPI5lmZmYoLi7G7du3oaKiwnVIYlWvh8Tfv38PJycnWFlZwcPDo0bOoaCggOnTpwMAtm/fXiPnIIQQ8nnO0PHjx/H69WvMmTOH63DErt4mbD6fDwcHBxQWFsLb2xt8Ph8rV65EeHi42M/l5uYGGRkZXL9+HQ8ePBB7+4QQQj7r3Lkzdu7ciUOHDsHHx4frcMSq3ibs9evX4/r162jRogXu3bsHV1dXFBYWYs2aNVi1ahWKi4vFdq5WrVph5MiRAIAdO3aIrV1CCCFlubq6wtnZGbNmzcLTp0+5Dkds6mXCjo2NxS+//IKlS5ciJCQEbm5u0NDQwPr163H+/Hmoq6vDysoKz58/F8v5kpKS0KhRIwDA3r17kZGRIZZ2CSGElMXj8bBr1y7o6urC2dlZrB0wLtW7hF1YWIhJkybhu+++wy+//AINDQ1ERUXh3r17uH79OmRkZLBgwQJs374dzs7OOHjwYJXv64uNjQWPx0Pz5s1x8OBBQfnhw4fF9G4IIYQIo6qqiiNHjuD27dvYvHkz1+GIRb1L2L/99hsePXqEw4cPC3Z40dLSgr+/P1auXIlr164BALp27Ypr167h5s2bgrLKunr1qtDyrVu3gs/nV+0NEEIIEUmvXr3w008/YeXKlXj8+DHX4VRbvUrYsbGx+P3337Fs2TJ069at1Guamprw9/fH4sWLBWWKioro2rVrlYewf/rpJ+zatQvNmjUrVZ6YmIjr169XqU1CCCGi+/XXX9GmTRtMmjRJ6ofG603C/u9QeHn3W2tqakJbWxvp6emCsrS0NGhqalbpnLKysnBzcyv1IeCLhISEKrVJCCFEdEpKSjh06BDu3Lkj9UPj9SZhCxsKF8bY2BixsbGC5xkZGVVO2F8MHTq0TFlqamq12iSEECKaujI0Xi8SdkVD4V8zNjZGTEyM4HlGRgY0NDSqdf4OHTqgXbt2pcpSUlKq1SYhhBDRrVmzRuqHxut8whZlKPy/TE1NER0dLXiemZkpuCWrqng8HoYNG1aq7PLly9VqkxBCiOjqwtB4nU/Yog6Ff/Hq1SvcunVL8DwrKwuqqqrVjuPrYfG6dDM/IYRIA2kfGq/Tm3/ExcXBxMQEy5cvx8KFC7+5EPzHjx8Fw9/37t2DoaEh+vTpg7CwsGrHkpubCy0tLeTn5wvK6vC3nhBCJFJeXh6MjY2hoaGBsLAwyMhIT79VeiKtJMYYZs+eLVh6tGnTphX2ahljpa5Vf7lPWlx7YysrK2PFihWC519f0yaEEFLzlJSUsG/fPkREREjdIlZ1NmH7+vri5s2baNu2LVavXo0bN27A3d293F7tokWLBF+PHz9eMDlNRkYGeXl5YolpyZIlcHFxAQD8+++/tBEIIYRwoG/fvnB0dMTixYulaqnoOjkknpWVhfbt26N79+4IDAwUlM+ePRtDhgyBnZ1dqfpBQUGwsrISPC8sLIScnBwAYMWKFejVq1eZSWNVVVBQgMGDB+PGjRto3bo1IiMj0aRJE7G0TQghRDTJycnQ19eHi4uL1GzKVCd72KtWrUJOTg727dtXqnzt2rX47bffkJubKyh78+ZNqWQdHh4uSNYAMGzYsFJJv6r4fD6ys7ORkZEBLy8vdOjQAa9fvxZp5johhBDxatasGVatWoXdu3fj7t27XIcjkjrXw37w4AG6deuGtWvXwsPDo8zrBw8exIsXL7B27VoUFBSgZ8+euH//PgBg0qRJOHToUKn6JSUl6N27NyIiIip1PTs/Px/BwcE4f/48oqKiIC8vDxUVFaiqqiI8PByHDh3CkCFD0KxZM7x7905s18oJIYSIpqioCMbGxlBXV0doaKjET0CrUwmbMYb+/fsjJSUFcXFxQm/j4vP5sLKywu7du7F161bs2bNH8FpGRobQe65dXFywePFifPfddxWePz09HefOnYO/vz9SUlLQv39/2NjYwMzMDLKysoJ6kyZNwvz589GrVy/k5+fj0aNH6Ny5czXeOSGEkKoIDg7GgAEDcOjQIUyaNInrcCrUgOsAxOnYsWO4ceMGLl26VO491zIyMtiyZQscHBxKLUH6119/lbtAiq2tLQICAspN2AkJCXBzcwNjDMOHD8emTZvQtm3bcuO0tbXFnDlz0LlzZ8TGxiIoKIgSNiGEcKB///5wcHDAzz//jJEjR1Z7oayaVGd62FlZWejUqRN69+6NkydPfrO+kZGRYCh80KBBuHTpUrnD0llZWRgxYgSCg4OFvu7r64uUlBS4u7uLHO+rV68wYcIEREZGws7ODmfOnBH5WEIIIeKTlJQEfX19TJ48Gdu2beM6nHLVmYS9cOFC7N69G48fP0arVq2+Wf+/yfnZs2do3759hfVtbGzg5eWFd+/ewdPTE/n5+SgoKEBBQQHu37+PTZs2wdHRsVIxR0VFwczMDOrq6khNTUWDBnVqwIMQQqTGpk2bsHjxYsTExMDIyIjrcISqEwk7ISEBnTt3xqpVq7Bs2TKRjvlvwr579+43f0D79u1DSUkJTpw4gXXr1qF169ZQUFCAvLw85OXlIScnV+mJYyUlJdDS0kJmZiYiIyPRs2fPSh1PCCFEPIqKimBkZIRmzZrh6tWrEjkRWLKnxIloxYoV0NHRwU8//STyMREREYKvu3XrhqSkpArr29nZ4cCBA/juu+9gbm6OZs2aQVNTE6qqqpCXl6/SD1dWVhb9+/cH8PlecEIIIdyQk5PD+vXrce3aNVy5coXrcISS+oQdGxsLPz8/rFq1CkpKSiIfZ2ZmhlOnTgmeN2/eHJ8+fSq3fpMmTSArK4tBgwZVK96v9enTBwBKbThCCCGk9tna2qJ3795YsmSJYHlqSSL1CXvJkiWCyQKVNWrUqFLbrKmpqVW4T+rZs2exYcMGhISEVClWYc6dOwcA6Nixo9jaJIQQUnk8Hg/r169HTEwM/v77b67DKUOqr2Ffv34dAwcOxMmTJzF69Ogqt+Pm5lbqfmw+n1/uEHdaWhpGjhyJX375pdQKaVVx8+ZNWFpaQk5ODi9evECLFi2q1R4hhJDqGz58OOLj4/Ho0aNSK19yTWp72IwxeHh4oEePHrC3t69WW7t374alpaXgeUW9dS0tLQQEBGDt2rU4f/58tc7722+/Cc5HyZoQQiTD77//jufPn+PAgQNch1KK1PawT58+DXt7ewQFBWHgwIFiafO/vWpNTU08efIE2traQutmZ2dj1KhRcHNzq1LvPjo6GqamppCVlcXTp09pu01CCJEgzs7OuHr1Kp4/fw5lZWWuwwEgpT3s4uJiLF26FIMHDxZbsgY+3x72RXp6OnR0dLB8+XKh17XV1NTg7++PAwcOwMfHp9Ln+tK7njhxIiVrQgiRMGvWrEFaWho8PT25DkVAKnvYBw4cwLRp0xAdHQ0TExOxtp2fn49hw4bh+vXrpcofPHggdGnS/Px8ODg4wNraGjNnzhTpHPv27cPMmTPB4/Hw8OFDWpaUEEIk0Ny5c3H06FG8ePECmpqaXIcjfT3svLw8rFq1CuPHjxd7sgYARUVFXLt2DdHR0aXKDQwMIOyzjaKiIk6cOIF3797ByckJ6enp5bZdWFiIWbNmCRK7m5sbJWtCCJFQy5cvR0lJCdavX891KACkMGF7eXkhOTkZv/76a42ex8TEBEVFRViwYEGpcwsjJyeHNWvWQE9PD7q6upgyZUqZOikpKRg4cCD27t0LHo+HdevWSc2m6YQQUh99WZBr+/bt+PDhA9fhSNeQeFFREdq3bw9LS8sqXTeuqv9ORqvo2zVp0iQcOXJE8Dw8PBwyMjJ4+PAhli9fjqSkJCgqKsLJyQkWFhZwdnaW+P1XCSGkPktPT0fr1q0xd+5cwdwjrkhVwj5y5AgmTZqEuLg4GBgY1Np5f//9d8Ea5RXtXf3mzRuRNh75Yvr06dizZw+ioqJw+/Zt2NjY0AQ0QgiRMAsXLoSXlxcSExPRsGFDzuKQmoTN5/NhYGCA9u3bIyAgoFbPXVhYCAUFBQBAmzZt8PLly3LrMsYwbdo0HDx4sNw6zZs3R3JyMvh8Ppo3b453794B+Dy07ubmhj/++AOKiorifROEEEKqJCkpCW3btsWaNWuwePFizuKQmvFYf39/PH78GEuWLKn1c8vLy8PQ0BDA532sExMTy63L4/Fw4MAB3L9/Hx06dBCUq6urY9q0aQgODkZiYiKOHDkCHo+Hd+/eQVFRUXDNfNu2bfjll19q/D0RQggRja6uLlxdXbFlyxbk5eVxFodU9LAZYzAzM4OysjKCg4M5ieHu3bswNjYuVfbzzz9jzZo1gt7313Jzc7F//360bNkSw4YNK9Nrvnr1KhISEjBu3Dhoamri6NGjcHV1hZKSEl68eIGmTZvW2PshhBAiuoSEBOjr62PHjh1wc3PjJAapSNhBQUGwsrLCxYsXMWTIEM7iGDt2LE6ePFmm3MTEBFFRUZCVla1W+4wx9O7dGxEREZg7d65E3bBPCCH13YQJExAVFYWnT5+iQYMGtX5+qUjYVlZWSE9PR3R0NOebihcUFGDz5s1YunRpqfKKJqNVxpcPJ/Ly8khISEDLli2r3SYh3+L19H9r6U/rGMphJIRIri8jrT4+PnB0dKz180t8wr59+zZ69uyJEydOYOzYsVyHU8qnT5+wYsUK9OrVC2PHjhXLhwnGGAYOHIjg4GBMnz4d+/btE0OkhJRP1GT9pR4ldFKfDRs2DG/evMG9e/dq/bZciU/Y9vb2ePjwIR49elTtIWdpce3aNXz//fdo1KgRMjIyuA6H1HGVTdjfqkdIXRYaGoq+ffsiICAAtra2tXpuiZ4l/uzZM5w5cwaLFi2qN8ka+DwjkZDaQEmYkMqxtLREnz59sHHjxlo/d+1fNa+EPXv2QENDA05OTlyHUqsKCgoAgO7FJjWOkjQhlTdv3jyMGzcOcXFx6Nq1a62dV2J72Lm5uTh06BCmTp1a7xJXTk4OAJR7uxghXJjWMZQSPCEARo4ciaZNm2L37t21el6JTdjHjx/Hx48fMWvWLK5DqXUhISEAgC5dunAcCSFlUdIm9Z2cnBxmzJgBb29vZGVl1dp5JTZh79q1C0OHDq2Xa2ufOXMGwOdPcYQQQiTPjBkzkJeXV7sbUUniLPEvt3KdO3cONjY2XIdTq969e4cWLVqAx+MhKSmJVjsjhBAJNWbMGDx58gRxcXG1skaIRPawd+3ahTZt2sDa2prrUGqdv78/AKBXr16UrAkhRILNnj0bDx8+RGho7VwmkriEnZaWBj8/P8yaNate3cr1xenTpwHQcDghhEi6AQMGQF9fH7t27aqV80lcwj58+DD4fD6mTJnCdSi1LiMjQ7C5CSVsQgiRbDweD7Nnz8Y///yDlJSUGj+fRCVsPp+P7du3o02bNoiMjBTc3lRfXLhwAcXFxfjuu+9Kbc1JCCFEMrm4uEBeXh5eXl41fi6JSthXrlzB69evsWjRIty5cwfDhg2DtbU1Nm/ejCdPnkAC58eJzb///ovVq1cDoN41kU5eTy0FD0Lqi0aNGsHR0RF79+5FSUlJjZ5LomaJT5w4Effv3y814y41NRWXL1/GhQsX8PTpU5iammLMmDEYOHAgx9GKT05ODgYOHIioqCi0atUKUVFRaNKkCddhEVIptMwpqa++3Nl0+fJlDBo0qMbOIzEJOzs7G02aNMHKlSvh4eEhtE5JSQmioqLg5OSE58+f13KENYMxBltbW5w/fx4A8Ouvv8LDw4OTvVYJqS6vp5aUrEm9wxhDp06dYG5ujiNHjtTYeSRmSPzMmTPIy8vDxIkTy60jKysLdXV1WFhY1GJkNevt27eCZA0AK1asgJ+fH4cREUIIqQwejwcnJyecOnUKubm5NXYeiUnYPj4+6NevH1q1alVhvSZNmuDRo0dISkqqpchqVsuWLeHn5wcTExNB2Zw5c3DhwoU6fc2eEELqEkdHR3z69AkBAQE1dg6JSNgpKSm4cuUKHB0dv1lXS0sLO3bsgKOjo2BXK2k3fvx43LlzB3v27IGWlhY+fvwIGxsbWFtb48GDB1yHR4hIaLIZqc/atWuHXr161ehSpRKRsL8MAfv6+or06cTMzAwuLi748ccf60wvlMfjYebMmUhISMDChQshJyeHy5cvw8jICO7u7nXmfRJCSF3l5OSES5cu4d9//62R9iUiYR8+fBjA512q7Ozs4OzsjIyMjAqPmTx5MpSUlGpthZna0qhRI2zcuBGPHz/GyJEjwefzsXnzZqSnp3MdGiEioZ42qa/GjRsHADhx4kSNtM/5LPHHjx+jS5cu8PT0xNu3b/Hnn3+Cz+dDV1cXXl5eGDp0aLnHFhUVwdbWFu7u7jU6lZ4rISEh6N+/P5o1a4Z3797VyuLyhFSFsCRNs8VJfWRra4vU1FSEh4eLvW3Oe9i+vr5o1KgRZsyYgQ0bNiAsLAwdO3ZEUlIShg0bhmnTppW736icnBz++usvrFu3Dt7e3rUcec37sqC8hYUFJWtCCJECTk5OiIiIQEJCgtjb5jRhM8bg6+uLsWPHQlFREQBgbm6O2NhYJPatpgAAIABJREFULFiwADweDwcOHEDXrl0RFBQktA1NTU1cuHABly5dwvLly8Hn82vzLdSoL+uKW1rSECMhhEgDW1tbqKmp4a+//hJ725wm7MjISLx69arM7HBlZWVs3rwZwcHBaNeuHRITE2FlZYUffvgBnz59KtOOoqIivL29oaCgAAsLC8yfPx979+7FjRs3avSeuJpSUFCA6dOnCz6kDBgwACUlJUhNTaXJZ4QQIsGUlZVhb29f9xK2v78/GjduXO5CKH379sW9e/cwe/ZsAJ/3yTYyMhK69yiPx8OKFSsQGBgIBwcHKCgowN/fHyNGjJCq27/evn2Lvn37wsvLCzweDxs2bICBgQHc3Nygra2NFi1awNnZGR8/fuQ6VEIIIULY29sjPj4e8fHxYm2X04QdEBCA4cOHV7jvtaqqKnbu3IkrV66gZcuWePHiBfr16wd3d3fk5eWVqa+urg4zMzNMmjQJf/75J1xcXDB16lSpGSq3sbFBVFQUAGDv3r1YtGgRAAgWiklKSoKPjw9OnjzJWYyEfAtNOCP1mZWVFRQVFXH27FmxtstZwn7+/DkePXqEESNGiFTfysoKcXFxmDJlChhj2Lx5M4yNjREZGVnhcS4uLmjfvj1WrFghjrBrnKGhoeDrH374ATNmzMDjx48hLy9fql5sbGxth0ZIuaZ1DBU8CKnvlJWVMWjQILEnbM5u69q6dSs8PDyQmpoKVVXVSh17/vx5TJ8+HcnJyZCRkYGdnR309PTQqlUrtGzZEiYmJqWWOGWMYcqUKejRowdmzZoFGRnOJ8dXKCQkBGvWrMG1a9cqrEfXswkhRDJ5eXlh5syZ+PDhA7S0tMTSJmcJe+DAgVBUVMSFCxeqdHx6ejrmzp0LX1/fMq81aNAAx48fh729vaCsqKgIc+fORVxcHPh8Pho2bIi2bduiXbt2aNeuHfT09NCuXTs0bNiwyu9J3G7evIlff/0Vly9fLvOasrIyUlNToaSkxEFkhBBCKpKcnAxdXV0cPXoUzs7OYmmTk4SdkZEBbW1t7NixA7NmzapWW6GhoYiOjkZiYiISExPx5MkTPHz4EPLy8rh48SIGDBgg9LjMzEy8fPkSL1++xIsXL/D8+XM8f/4c2dnZaNCgAdq0aQN9fX3MmTOH8ySemZmJvLw8DB06FPr6+ggLC8Pbt28Fk+oI4cqXBVOmdQylrTUJ+YqZmRlatWqFv//+WzwNMg74+voyAOzNmzdib7uoqIiNGjWKAWBqamps5cqV7Pbt26ykpETkNgoLC9nTp0/Z7t272ahRoyp1bG2YO3cuA8Ds7e1ZUVER1+EQCVdSUsLWrl3LnJ2dmb+/P8vNzRVb2/vjLdj+eAvB14SQ/1m7di1TVVVl+fn5YmmPkx72hAkTkJCQgDt37tRI+/n5+Rg6dKhg4REAaNasGYYPH47hw4fDysoKysrKIrW1evVqyMrKYvny5TUSa1WEhYUJboUzNDTEjh07aHEVIlRGRgYmTpyIixcvCspUVVUxZcoUzJw5E3p6evD390dGRgbGjx+Pe/fuISgoCEOHDoWZmVmp+R7Z2dm4du0aBgwYgIYNG9JypIR8Q1xcHAwNDXHx4kUMGTKk2u3VesIuLCyEtrY23N3dsXLlyho7T0FBAY4dO4azZ8/i8uXLpRZcUVRUxPfffw9bW1sMHz4czZs3L7cdPp+PMWPGYPLkybC1ta2xeCvLy8sLP//8s2CTFEdHR2zcuBHNmjXjODIiCXJychAbG4vJkycjISEBCgoKQtcjUFNTQ3Z2dpmvgc97tY8dOxb29va4desW/vjjD6SlpcHBwQEDV70Rel5K2IT8D2MM7dr9H3v3Hdbk9fYB/BumgCCIiyWgggM3FRGqolBXi1Vwb6tV68CqdVe0/dVt3VrrqgO3VcFZXKUUHIiCggqioCCIKKDIJpz3D96kbBLIkycJ9+e6ckmS5znnjuvO2c0wYMAA7Nixo8blyT1hX79+HW5ubrh//z46deoklzpzc3MREBCA8+fP4/z583j58mWJ9zt37gx3d3cMGjQIHTt2LHP/x48f0adPH7Rt2xY2NjawtbWFq6sr72Pb7969w9KlS7Fnzx4wxtCxY0da7lULvXv3DsePH8eTJ0/EmzUkJCSI32/atCl8fX3x4sULREZGonPnzti7dy/Onz8PoVAIMzMzGBgY4MmTJwCKNiy6f/9+ubsKiujoq0OvngYGz7WCw5cNKVETUgEvLy+cO3cOL1++rPmZEDLpWJfC3LlzmZmZGSssLJR31YwxxgoLC9nDhw/ZypUrmaOjIxMIBAyA+DFx4kSWmZlZ5r6cnBwWERHBzp49y5YsWcJGjhzJQ/TlO3bsGAPArK2t+Q6FyFlcXByztrYu8XdY9GjQoAEbMmQIS05OLvfehIQEduvWLZafn8+EQiHz8/NjN27cYIwxlpWVxc6ePctGjRrF9PX1WcOmddiEVTashb1BiTo0tASsd+/ebMKECSw7O1ueH50QpeDv788AsEePHtW4LLm3sO3t7WFnZ4dDhw7Js9oKvX37FpcuXYKfnx98fX1RWFiI9u3b4/Tp07CxsanwvtGjR2Py5MkVzkKXpzNnzsDT0xPdunVDcHAw3+EQOYmLi0OvXr0QFxcHKysrjBgxAi1bthQ/6tevL5N6GGPY96wHAKCwkCH9bR7ysoQ4vvIFIv/9b4tcV1dX+Pr6Qk9PTyb1EqIKsrKyYGhoiI0bN2LmzJk1K6zGKV8KqampTCAQsP3798uzWoldv36dNWrUSDzD/NSpUxVe+/r1a+bo6Mhyc3PlGGH5duzYwQCwQYMGlft+RkYGCw0N5a1Xg8jeixcvmKWlJQPAbGxsZL7iovTs7+IPkTdv3rBx48axefPmsbp16zIAzNnZmaWnp8s0FkKUXffu3ZmHh0eNy5Hrll+BgYFgjMHFxUWe1Uqsd+/eePDgAbp3746MjAwMHToU33//PfLy8spca2pqitGjR8PV1RVLliyBj48PHjx4UO7+5lwTTTz79OlTmd3PMjIy4OTkBHt7e3Tu3BmnTp1Smn3VScXGjh2Lly9fwtbWFjdv3oS5ubnMyi5v9rdI8bHqxo0b4+DBg9iwYQOuXbsGQ0NDBAUFwdXVFe/fv5dZPIQoOxcXFwQEBNT8/94ap3wpzJkzhzVt2lThW3r5+flswYIF4nE6R0dH9urVq3KvffPmDbtx4wbbvn07mzZtGnNxcWGOjo7Mw8ODRUREyCXe0NBQpq6uzgCwffv2iV//+PEjc3d3LzO2+eOPP8olLsIdfX19BoCFhYXJvOzY+CYsNr6J1PeFhYWxhg0bMgBMIBAwLS0tpqOjw/T19ZmRkRFr2LAhMzExYRYWFszKyoq1aNGCtWrVinXq1ImdP39e5p+DEEVx/fp1BoA9fPiwRuXINWF37NiRjR8/Xp5V1oivry8zNDRkAJixsTG7fPmyRPcVFhay8PBw1rVrV/bhwweOoyyyevVqBoDp6uqynj17MhMTE3GC1tbWZpcvX2bz5s1jAFj9+vUVoiufVE9CQoL4z/bdu3c1Lq94N/eeqM+rnbAZY+zx48cVToKr7NG1a9cafw5CFFVWVhbT0tJiW7ZsqVE5cpt0lpqaigYNGmD//v2YMGGCPKqUidjYWAwZMgT379+HQCDAjz/+iOXLl1d6JKiIn58fjhw5guPHj9d8On8VCgsL0bdvX1y7dq3E66ampti2bRs8PDwgFAphYWGBpKQknD17FoMGDeI0JiJ7f/75J6ZMmYLU1FRYW1vj2bNnEv1drEzx7UVloaCgACkpKRAKhSgoKKjwIRQK8f79e3z99ddQU1PD+/fvYWhoKJMYCFE0PXv2hLGxMc6cOVP9QmTy9UEC586dYwBYbGysvKqUmezsbDZt2jRxa8DV1bXCpTKlLVy4kG3cuJHjCIukpqayjRs3Mh8fH3b37l2WlpZW5hpRK9vT01MuMRHZ+PDhA5swYYL472Dnzp3ZkydPpC6n9Pah5U0mkzdbW1sGgJ07d463GAjhmre3N6tfv36NtrqWWwv7+++/h6+vL2JjY+VRHSeOHDmCKVOmICsrC6ampjhx4oR4i9CKFBQUwMPDA8bGxhg+fDhcXV2hqakpp4jLCgsLQ6dOnaClpYU3b97AyMiIt1iIZIKDgzFmzBjExsZCIBBg0aJFWLFiRYkz0otPFKuspSzphDJ5mj59On777TfMmjUL69atQ506dXiJgxAu/f333+jVqxfCwsLQoUOHapUht1nif//9t8LODpfU6NGjERISglatWiExMRGurq548eJFpfdoaGjA19cXXl5eCAgIgLOzM6ZMmYIbN25AKBTKKfL/dOjQAW3btkVeXh6OHj0q9/qJdCIiItCjRw/ExsbC0tISAQEBWLVqVYlkrexcXV0BANu2bYOOjg6+//57TJo0CXZ2dpg6dSouXrzIy+oLQmTJ0dER2traJc64kJZcWtii8es//vgD48eP57o6zn369Ak9evTAgwcPsHz5cnzzzTcwMTGRqOXMGENISAhOnDiBf/75B127dsXw4cPh7Oxc4qAFLq1fvx4LFiyAuro6VqxYgcWLF9d4HJRwY/PmzZgzZw4cHBzg7++PevXqlXtd6ZZzea3l0te46cbgWlYLBKbb4qDDPtkFLaW0tLQqN3nR0dHBF198Id7/v0mTJgCKerA0NDTkESYhNebi4gIjIyOcPXu2WvfLJUMEBweDMYYePXrIozrO1a1bF25ubgCKTvOytLSEtrY2TE1N4eDgAA8PD8yaNQtr165FWFhYiXsFAgEcHBywZs0adOvWDbq6ujh58iQ6deqEx48fyyX+WbNmYfjw4RAKhVi2bBl69uyJlJQUudRNpCPa975Hjx4VJmugbIIef3eS+LE3unuFXeGB6bayC7aajIyM0L59+xKvtWzZEgcPHsT06dNhYWGB7Oxs+Pn54dtvv4WJiQm6du2KYcOGoU6dOti8eTNPkRMinZ49e4r3I6kWWQymV2X58uXM2NhY4ddfS+PZs2fMzc2NWVlZMU1NzQqXq6irq7M1a9aUmWgQFxcnvmbXrl3sq6++Yq6uruzRo0esoKCg3DoTExPZs2fPZBJ/YWEh27VrlziGKVOm1KisY8eOsYsXL8okNvIf0dnu27ZtK/f98nYkG3fnmxKP0juVSbNka9ydb2TyOari6+sr/rs4e/bsEvv5FxYWsrCwMPa///2PdenSpcy/sXr16jF3d3fm5ubGjhw5wrKzs9nt27cV7hx7Qvz8/BgAFhcXV6375dIl7u7ujvz8/BJn8qqSwsJCpKSk4PXr10hISBA/7t+/j7/++gsAMGDAABw8eBANGjQA8N85qaVpamrC3t4eBgYGcHBwQLdu3eDo6AgDAwNoampi0qRJ2Lt3LwoLC2vchd66dWs8ffpU/Dw/P1/q7sWsrCxMmjQJx48fh56eHjIyMjhfwlab2Nvb4/79+/Dz8yv3eFdRy7l0S7m7YXSZa910Y8Q/W5knVVn3+LuTxD8fdNiH8XcncdZ1zhjDiRMnYGVlBUdHx0qvTUpKwsWLF/HgwQPs3LmzzPtmZmZ4/fo1nJyc8Pvvv6Nt27acxEyItBITE2FmZoY///wTHh4eUt8vl4RtamqKiRMnYuXKlVxXpVAYY9i/fz9mzpyJnJwcmJub4/jx43B2dsaqVauwdOnScu9LTEyEjo4O7ty5g1u3buHOnTuIiYlBs2bN0KFDBwgEAmzevBlPnz6FtbV1tWMbOHAgLly4IH5tw4YNmDdvnsRlxMXFYfDgweJu/zp16vA2OSg4OBi+vr7Q1dXF6NGj0aJFC17ikIVPnz7Bw8MDycnJePbsGbKzsxEeHl7uF7ziXd3Fk3ZlCVuSZA2UTNilicrn+1jNpUuXYtWqVRW+r6Ghgfnz52PZsmXQ0dGRY2SElK8m+ZDzhF3TbxSq4OHDhxg6dCiio6Ohrq4OR0dHBAUFid/38vLC1q1bxc+NjIwwf/58pKSkIC0tDZs2bcI///yDd+/eYdKkSThx4gSaNGmC1atX45dffkHHjh1rNGls3759mDx5MnR0dHD79m28efMGHz58QHZ2Nl6/fg0XFxd069atxD03btzAsGHD8P79e2hpaSEvLw8NGzbE27dvqx1HTdja2uLZs2cAgOHDh+P48eO8xFFTjDF4enqWmZSSnp5e7hh26YTd3TBa/Gtp0ibs8sa9Kyqbr8T98eNHrF+/Hnfv3oW/v7/49X379olP4AOA5s2bw9/fH82aNeMlTkJE3N3dkZeXJ+59lQbnCfv8+fMYOHAg4uLiYGlpyWVVCi0jIwNTp07FsWPHyrwn+iPw9/fHvHnzEBERUeJ9CwsLxMfHw8jICC9evBDvBuXr64srV64gLCwMDRo0wKxZs9CnTx+pY2OMwdXVFTdv3iz3/UaNGuG7777Dx48f8eHDB6SmpuL8+fMQCoWwt7fH0KFDsWjRIjRu3Bhv3ryRun5Z0NPTQ1ZWFoCimdWzZ8/mJY6aSkpKgqmpKYCivQtSUlLQoUMHzJ8/v8J7SifW8lrak20DEZdggmtZLcTPK1PZeu3yVDUrXR4J/cSJExg7dixWr14t7ik6d+4cZs6cidevX2PChAn4448/OI+DkMqsWLEC27dvR0pKitTDh5wn7JoEp2oYY7h48SJevXoFFxcXbNu2Dc2bN8cPP/wgvkYoFGL//v04ePAgNDQ0EBAQUKKMMWPGICYmBoMGDcLChQvFrycmJmLkyJHi2efSiomJgb29PT5+/CjxPWPGjMHu3bsxZswYFBQUYOHChXBycpK67prKyckp0d15/fp19O7dW+5xyAJjDP369YO/vz/69u2Ly5cvV/nvprKEDRQlbVHrWpSwSyudUKVJ2JIsIavsWlnKy8srs0Y9ODgYzs7O0NHRQVJSUqWz7QnhWk0asZwn7Jo0/2u7nJwcODo6IiMjA2PGjMHPP/9c4v2QkBB89tln4ufJyckYOHAgDh8+DFtb6ZfrpKamwsfHB7Nnz8Znn32Ge/fuid/r27cv4uLi8OnTJzg4OGDUqFHw9PTEvXv34ODgAIFAgEePHsHOzq76H7iaRMMuIhoaGnj8+DFsbGzkHossPHv2DO3atUNubi5OnTqFIUOGVHr9+LuTSnRTVzQBTbTuWpYqSsCKtKMaYwx2dnZ48uQJjh07hhEjRsi1fkKKq8kwMefrsENDQ2Fvb891NSqpTp06CA0NRUxMDFasWFEiOQNAly5dsHv3bnGXeuPGjXHo0CGMHTsWCxcuxKtXr6Sqr379+vDy8kJaWhpWr15d4r358+cjNjYWzZo1Q//+/bF69Wp07NhRvBHO2LFjeUnWQNEXjeIKCgowZ84cXmKRBRsbG3z55ZcAUGJSoDS6G0aXGWuWdbKuTGC6bYkHnwQCgXhjFtpkhfDN1NQUTZo0QWhoqNT3cpqwExMTkZSUVCbREMmpq6tDIBBAIBBgz549Zd6fOnUqpk6dKk7aLVu2RHBwMD7//HNMmTIFQ4cORWCgdC2aevXqYf369eKd2zQ1NfHTTz8hLy8PgYGB2LJlC+7fv4+HDx/iyZMn0NDQwIoVK2r8Wavr/fv3ZV7r2LEjQkJCeIim5hITE3Hx4kUARV+EqhKeYlrhDPHyJojJSmUt5YMO+xCeYip+Hphui8m2gbxMTouJiUFcXByAovkYhPCtdA+mpDhN2KJvENTClo2OHTuCMQYHB4cSr+/ZswdBQUGIjo5GWFgYTE1NMXDgQKirq2Pp0qXYsGGDVPuGX7x4ES1atBCPr+Tn55dI+pGRkSWunzp1arWXl8lCZmZmmddWrlwJBweHMjvNKYPz588jNzcX6urqKCgokPg+aVuypa+vqFVck9ZyeIopwlNMedv69OzZs7CxscHr168BoFZPfCWKw97eHqGhoVLveMZpwg4LC0P9+vXRtGlTLqupVfLz83H37t0yr3fv3h0tW7ZEp06dxEurLl26hPDwcPj4+GDTpk3IyMgocY9o05MHDx6IXysoKMDKlSvRuXNnxMT8t9GGhoYGRo4cWW5MP/74oyw+WrU5OjpWuMa2OmP5fBs0aBA6dOgAoVBY4XhrXIKJ+NGhYaLEZRffPAUomYwPOuwrkVgrStCi1rKkwr78n8TXytqTJ08AANbW1jh9+jQlbKIQOnfujPfv34u/SEqK04QdHR2NVq1a1frZ4bL06NEjqa6fO3cu8vLy8O2332LKlCklkv2zZ88QHh6OOXPmoHv37jAxMYGxsTEmT54Mb2/vEuUUFBSUuyRt0aJF4oMY+FK/fv1yu45HjBgBR0dHjBgxAkePHkVaWhoP0UmvcePG6N+/PwCgU6dOEt1T3ph1dYkSd+kEXvz94srbq7zjxWUyiaWmRI0FKysreHp68hwNIUVatWoFoChHSoPTWeJdu3ZFmzZtaO2jDMXFxZXb/Txx4kSJf5/NzMzw5s0bCIVCNGjQAO/evSv3OgMDgyqXeaWmpirEmdqRkZFVbkGppaWFbdu2YcqUKXKKqnqys7NhYWGB9+/f4+zZsxg0aFCZa6RdJy1S/ISu4irrsi6941nxa0VxTLYNLDdJ89m6BoDAwED06NEDVlZWiI2N5TUWQkTy8/Oho6OD7du3Y9q0aRLfx1kLmzGGqKgopeySVGRWVlYICgoq859Pr1690LNnT4nKeP36tfgs7oqSNVC0i1Tp1nPxLwWrVq1SiGQNoMwMdQMDA4wbNw7Hjx/H4sWLYWdnh7y8PEydOhVLly6t/mk5cnDs2DG8f/8elpaW5e4fDhQlyJrMwO5uGF1pK1qksmRdnCIma6Do74Wmpibi4uKUcj4DUU2amppo1qwZoqKipLqPs4SdkpKCDx8+oGXLllxVUWs5OTnBysoK2tra4tdatWqFmzdv4vHjx7h+/TrS09MRFhaG58+fV3mYQmVEO5cNGjQIubm5cHV1FR864uXlVbMPIkOld4d7/PgxDh48iOHDh2PVqlV49OgRfvrpJwBFXzTGjBmD3NxcPkKtFGNMvE3tjBkzKtxyNi7BBD+ZXpK6fEl3OitP6WRd3l7jYV/+T/xQBPXr1xf3UOzbx9+Z34SU1rJlS6m7xDlL2KJAqIXNndTUVPz888949eoVunTpAoFAgNatW6N3796oV68eOnTogGbNmuHWrVvIyspCYmIi2rVrV6ac7t0r7l7t378/8vLycPbsWWhpacHCwgIhISGIj4+Hnp4elx9PKk2bNi2xu9mOHTtKvC8QCODt7Y0DBw5AQ0MDR48eRd++fRVuXDswMBDh4eHQ0dHBpEklE2LxiWbyUtXs7uKtfEVJ0qVNnjwZAODj48Pb4TSElGZra6tYCVsgEKB58+ZcVVHr6erqYtmyZbCwsKjyWh0dHZiYmODgwYPYvn078vLykJqairS0NPz999+4f/8+4uPjsWvXLgwePBhNmjRBr169cPbsWfF6bJHOnTvD3Nycq49VLQYGBrh+/bp4w5eKJueNHz8ely9fhoGBAQICAuDs7Cxeo6sI/vzzTwBFB5iINvso7VpWC/FDmolmNelCr6x1fdBhn8ImawBwc3ODpaUl0tPTxb+/hPDN1tYWsbGxyMvLk/geThN206ZN6Ug7BdOpUyfMmDEDmpqaMDIygqGhIdTU1NCpUyeYm5tj6tSpOHPmDJKSknDjxo0S3e7KQNT9HxkZicLCwnKvcXNzw7///gszMzM8efIEjo6O+OeffxRiXNvAwABA0S53pVmZJ2F54oAya6QrUlG3d+mlXZUpb2y6+Gt8ra+WhpqaGr755hsAwN69e3mOhpAitra2EAqFUk2G5CxhR0VF0fg1kbs2bdoAAGJjY9GyZUvcvn273OvatWuH27dvo3379khOTkbPnj3RokULLFq0CPfu3eMteVtZWQFAha1+0Q5i25+5AKj+TmaipVjlLckqLuzL/5VIyoqyXEtaEydOhJqaGgICAqTuhiSEC6L8KM3EM05b2DR+TeStUaNG4sllMTExmDhxYoUtbXNzcwQGBmL8+PHQ0dHBixcvsHbtWnTp0gXNmzfHggULcPfuXbklb6FQKP7H+/LlyxLvFR+/Lm9SlyiBF1deIq4owVeVuIsTbdSiDK1rEQsLC/Tr1w8AsH//fp6jIQQwMTGBnp6eVF8gOUnYQqEQMTExlLAJL7y9vcWndz19+hTt27fHhw8fyr3WwMAABw4cQEpKCk6ePImhQ4dCV1cXsbGxWL9+Pbp27Qpra2v88MMPuH37NmfJ+8GDB+jatSvWr18PAOLtZ0tPMruW1UI8fly8y1vS3c6k6Q4vT9iX/xMvCVM2oslnBw4cQH5+Ps/RkNpOIBBIPfGMk4QdHx+PvLw8pT3ekCi/4ruyRUZGwsHBARMnTsS5c+fKTbp6enoYOnQoTp48iZSUFJw+fRrDhw+Hnp4eXr58iV9//RXdunXDqFGjpJokUpXMzEzMnz8fXbp0QWhoKAwNDbFnz54yrUDRJLOKxqy7G0ZXOaGs9BGc1ZmAVt0NWxTBV199hUaNGiE5OVl8uAohfFKIhJ2QkAAAtIc44Y2dnR2aNWsmfh4dHY0DBw5g8ODB8PT0FK8vL4+uri48PT1x/PhxpKSk4MyZMxg5ciQ0NTVx/PhxuLu7l3vgSFXevn2LZ8+eib8wXLlyBW3btsWGDRsgFAoxfPhwPHnyBJMnTxavdbcyTwJQ1DIOTLcVH6YhUryVLel4dvFjNksn7coScvFdzZSRpqYmJkyYAIAmnxHFYGFhIdV+4pxsTXrq1CkMGzZMYbatJLVTYWEhkpOTERsbi9jYWNy7dw/bt29HQUEBjIyMcPLkSbi5uUlc3l9//QUPDw9kZWXB0dERFy9erHDpVXG5ubnYsWMHFi1ahPz8fFhYWKBFixa4efMmgKIvtjt37hSfgV2aqEtclLxLk6TVG5huK3FCLy8hF69DWRM2UPTFrWXLllBTU8PLly+26EFSAAAgAElEQVQVbnkiqV02btwIb29vfPr0SaLrOWlhJyUlQVtbG4aGhlwUT4hE1NTUYGJiAicnJ4wePRqbNm1CaGgo2rVrh7S0NOzevVuq8vr27Yvr16/DyMgIt2/fRs+ePZGYWHbsWCgUIjQ0FOvWrUPfvn1hZGSEefPmIT8/H+rq6oiPj8fNmzehpqaGOXPmIDIysspkXfpnrqhysgaKuiCdnZ1RWFiIAwcO8B0OqeVMTEyQmZlZ5iTFinCSsBMTE2FqakqndBGF0759e/GOaFevXsXGjRulGpMWrdk2MTFBREQEnJ2dERMTg+joaPz2228YMmQIGjZsiM8++wwLFy6Ev78/srOz0aRJE/z222/4+PEjLl++jNWrV+Pu3bvYuHEj6tatW2W9ojHs8rYDFalsTFoWJ3kpe7JmjGHLli3iE+vK+7JFiDyZmhYNb0n6d5GTLvHx48cjJiYGQUFBsi6akGoRCoX4+eefERwcjGvXrpV4b8GCBVi7dq1U5cXGxqJPnz6IiYmBurq6+DAVEX19fbi4uMDV1RWurq6ws7Or1hfY4q3q5YkDEJ5iWu6uYpJ0i4tO6qqIsifkqowfPx6HDh0CAHh6emLfvn2oV68ez1GR2kw0RHPz5k24uLhUeb0GF0GIWtiEKIp79+7h559/Lve9AQMGSF2etbU1/v33X/Tt2xfh4eHQ0tKCk5OTOEF36dIFGho1/+dlZZ4kTtoVJeviSo9Vi57XdDmXKjh//jyAojPiN2zYQD2AhHcmJkX/tiVtYXOSsJOSktC6dWsuiiakWkTnemtpacHDwwNeXl5wdHSs0X/ajRs3xq1btxAWFoYOHTpAV1dXVuGWIGoVS7Jfd+mub1l0hauKkSNHYufOnYiNjaVkTRSCvr4+6tati6Sk8ieUlsbZGLbomwMhikDU9ZmXl4fjx49j4MCBMilXR0cH3bp14yxZA0W7mLnpxlRr0ploXFs0Bl7aZNtA8UPVjR8/HgDg6+uL9+/f8xwNIUVMTEwkbmFzkrDT0tKoS5wolC5duuDcuXPi5+/evavWWmo+KPJJWMpAKBQiNzcX27ZtA1C0jE5fX5/nqAgpYmpqym8LGwC1sIlCEQgEGDhwoLiVBRQt8Tl58iT27duHFy9e8Bhd1SpqIYuU10IuPp5dW8ewhUIhbGxsUKdOHfj4+EBNTQ1HjhyBlpYW36ERAqAoV0qasDkZwwZALWyicAQCATp27IiDBw8CKJprMXz4cPH727Ztw5gxYzB9+nTk5OSgVatWsLOzw+DBgznt8q5Kx4vL0KGhLX4yvYS4BJMKN1Aprbzx69rQ9V1cYWFhiYNUvL294eTkxGNEhJRkamqK+/fvS3QtZwnb2NiYq6IJqbbLly9X+J63tzdOnjyJwMCSSc3LywtbtmzhOrQKhX35P5ns4V0bW9mampqYPn06tm/fDqBoaIQQRWJsbIzU1FSJruWsS5zGiIgi6t+/PwBAW1sb9vb2Jd5LS0tDYGAgDAwM4O7uLn69R48eco2xPNVtGbvpxtTKRF3cpk2bMHToUACAh4cHrl69ynNEhPxHX1+f353OBAIBr12IhFRk1qxZOH36NGJiYrBx40bx60uWLAFQtOxrx44dCA4OBgBMmzYNnp6evMRaXMeLy6ocxy6PtNerIg0NDRw5cgSDBg1Cbm4uvvzySzg5OWHatGkVnt5GiLzo6+sjNzdXoms52elMmm8MhPBFKBSid+/eKCgowLVr17B69Wp0794dK1asQHBwMDp37oygoCDUqVOH71D/fxw7ET+ZXgJQ/kEgcQkmFSZoUStb0vFvVZSXl4fhw4eXWC0AAIcOHcLYsWN5iorUdqdPn8bQoUMl+uLISQubusOJMlBXV0dAQACCgoKgo6ODn3/+GX/99Ze4da2lpSXed5pvVS3t2hvdXaLWdFyCiVwOEVFEWlpaOHPmDCIiInD06FGMHDkSQFGvi+hIYELkTZp8yUkLu2XLloiKipJ1sYRw6sqVK+IxbpHmzZsjJkZxxoBFh38cdNhX5r2qJqYVH8uuzS1tkYKCAjg5OSEkJASdO3fG0KFD4eHhAVvbig9RIUTWgoOD4ezszF8LW5LThwhRNKKWdXFv3rzhIZKKdTeMlmq70eJJurIW+N7o7jKZia5MNDQ0cOjQIdSpUwf379/H4sWL0bt3b6lObyOkpqTJl9QlTsj/W7JkCdasWVPitSFDhvAUTfkm2wZKvU0pl7PERYleWRN+q1atcP/+faxcuRKNGjXC69evceLECb7DIrWINPmSk3XYlLCJMqpTpw4WLlyIe/fu4fTp0wCAzz//HIwxhTosQnSClyhp/3eiV9Vj2OJWdnR38VIxUaKtyaYqyrwhS+vWrcWHFS1duhTjxo3D48eP8eOPP0JPT4/n6IiqkyZfUgubkFJ27tyJvn37AgC+/fZbLF++nOeIyirevV1Za7uqiWg1TdaqdHDI1KlTxT+vWbMGK1as4C8YUmvwnrBpDJsos4YNG+LChQto3rw5gKKztBXNZNvAGq2xnmwbKJOWtSoxNjbGTz/9JH6+ZcsWhZpwSFSTlpYWNDQk6+zmJGFTNxJRdps3b8bz58+ho6ODDRs28B1OuYon2qqSt5tujPioTaCoZe2mGyOTZK3s49jFeXt7Izc3F/369UN+fr7C/tkT1SEQCCRu5HKSsCX9tkCIIgoJCcHixYsBFLWy2rRpw3NEFRMl6somlomuKT27nHZBK5+WlhbMzMwAQKHmLhDVpa6uLtF1nCRsNTXOtignhFMZGRkYOXIkCgoKMGTIEEyePJnvkCo12TZQvKa6qtngpd+nrvDy5ebm4s8//wSAEqe5EcIVSXMmJ01hSthEWc2YMQPPnz9H06ZNsXv3bpVsYdX2w0CqcuXKFaSnp8PU1BTduyt3Fz9RDpSwCZHS4cOHcfjwYaipqeHo0aMwMjLiOySpiZLxtawWZTZN4erkLlVrqR87dgwAMGLECIm7KgmpCUrYhEghIyMD06dPB1B0QpezszPPEVVP8XFpvsaoS088U7aE/s8//wAA+vXrx3MkpLaQNGdSZiUEQGFhIXR0dAAAERERPEfDLSvzJLnsJa6sa7Q7d+4MAPj33395joTUFpIOvXGSsOl8WaJs6tWrh1WrVgEoSt7KRpSERQmyvAlm8krUIsq6zGvUqFEAgKNHj9L/ZUQuJP0/h5MucaFQyEWxhHBKtElGu3bteI6kZkTJuqLWbVyCCaeJu/SWp8pm4MCB0NbWRkxMDKKiotCqVSu+QyIqTtKcyUnCVsYWCiGhoaEAgLZt2/IcSc1UloxF25juLbaXuCwp+/g1UNRDKDqxy9DQkOdoSG0gac7kpEucEjZRNps2bcK1a9cAFB34oepEJ35Jc+qXJETj1srcyr537x4YY2jatCmaNGnCdzikFqAucUIkdPr0acydOxdA0aEP7du35zki+eKqi1wZW9fAf5MOTU1NFe6kNqKaeE3YOTk5XBRLCCfWrl0LoGjTlAULFvAcjXyIErSohc31uLYycXJygrq6Om7fvg0vLy907doVDRo0QLdu3VCvXj2+wyMqKDs7W6LrOOkSz8jI4KJYQjjx4cMHAEUbZah6a6r0TPHia7VF3ddcdJUrE3t7e/GhH9u3b8fYsWPRv39/tGnTBm/fvuU5OqJqCgoKJG7kctLCpoRNlElubq5E14WEhODhw4dwdHSEnZ0dx1HJh6jbOi7BBG66Mf9/ihfEr4kSurJ2b1fX7Nmzoauri5s3b+Ldu3cICgpCYmIili1bht9//53v8IgKkSZfctLC/vTpExfFEsIJ0WlcmzZtKvH6p0+f8OOPP2LChAmoW7cuHBwcMHnyZLRt2xbXr1/nI1TOWJkniZNz8Va3aImYtGuqlXUNtohAIMCUKVOwY8cONG7cWNxlKWnXJSGSkiZfUgub1Hrr1q3D1atXcebMGVy5ckW8JeXo0aPh5+dX7j2vXr2SZ4hyMdk2sNyucDfdGFiZJ1UrAYvuUcYWulAohIuLCx49egQ1NTUsWLAAy5cv5zssomJ4b2FTwibKpF27dvDy8gIAzJw5E+/fv8fZs2dLJOshQ4bgyy+/FD+vU6eO3OPkU+l128regpZEXFwcHj16BC0tLQQHB2P16tW17s+dcI/3hE1d4kTZrFixAiYmJnj+/DkaNGgADw8P8Xvfffcdjh8/jgsXLqBbt24AIN53XBlIk1grmikuGt8u3WIWvVa8DlVJ5I8fPwYAtG7dGl27duU5GqKqpMmX1MImBICBgQG2bNkifm5lZYXvvvsOycnJ2Llzp/iYRdEY5smTJ/Hy5UteYuVKebPDyzvxq6Ju7opa3crYHQ4AT58+BQCYm5vzHAlRZby3sDMyMmjTfKJ0hg4dinv37iE6OhovXrzAzp070ahRoxLXODk5ASg6M7lNmzZ4/vw5H6HKXEXLuNx0Y8RJu/iBIqLkrKwncklCNBnx4sWLCAgI4Dkaoqp4T9hCoZA2TyFKyd7eHjY2NhWux96+fTv69OkDoOjvuba2tjzD40xlm6YUT9TltapFr5eXuJU1mTPGcOvWLfFzVZxkSBRDRkaGuAevKpzMEgeK+uWVaZyPEEmsX78e/v7+EAgE8PHxUYruUlknzcm2gRWOUytrgi5t69atWLlyJQDA3d0dY8aM4Tkioqo+ffoEfX19ia7lpIUNAGlpaVwVTQgv4uPjsXDhQgDAxo0bMWTIEJ4jkq3SrexrWS1KjGEXf7/0jHFVUlhYiJCQEPHz8+fPY/78+TxGRFRZWlqaxFvecpawk5JoX2KiWlJSUgAATZo0wffff89zNDU3/u4k8aO04ola9HPpcW5VaU0XxxjDnDlzcOTIkRKvFxQU8BQRUXVJSUkwNTWV6FrOusQpYRNVIxriEZ2VrAq6G0ZXeU3xMezSVC1p//LLL9i6dSsAQFNTE5qamlizZg1mzJjBc2REVSUlJcHERLK9+zlpYevq6lLCJipHtGlGamoqIiMjeY6m5g467CvzWukZ4aWTdVyCicp1gYvs3LkT3t7eAIrGsFNTU5GamopZs2ZBTY2zzkhSy/GesE1NTZGYmMhF0YTwpmHDhuJWdvfu3cWnfKmS4i3mylrWqpa0jx07hpkzZwIAli9fjlmzZqFu3boqswqAKK7ExESJu8Q5SdgmJibUwiYqp27duggJCYGlpSXS0tLg7+/Pd0g1Vt5yrPI2S5HkPWV16dIljBs3DowxzJw5k/YLJ3KTm5uL9+/fUwubEC7Y2dmJZ4dfuHCB52i4cy2rRZlzs4sftakqY9dv3rzBkCFDUFBQgJEjR2LLli0qfyY6URxv3rwBAGphE8IV0SEgly9fRmFhIc/RyF7xhFz82E3Re6okJydHvN3s7t27aayayJUoT0rawuZkljglbKLKPv/8cxgYGCAlJQUhISEqfzCEqiXp4iwtLVG/fn2kpqbi6dOn+Oyzz/gOidQi0iZszrrEP3z4gKysLC6KJ4RXmpqa6Nu3LwDV7havDQQCgThJL1myhIbyiFwlJiZCU1MTxsbGEl3PWZc4QGuxier66quvAACHDh1SiSVetdnMmTOhqamJq1evYvHixXyHQ2qRpKQkNGnSROKhGM5a2ACQkJDARfGE8M7d3R2mpqZ49eoVunXrJt4FjSgfd3d3caKW9BAGQmTh9evXEk84AzhK2FZWVhAIBCpz9CAhpRkZGSE0NBSmpqbIyMjAo0ePAICOlVVSorMPmjRpwnMkpDaJiYlB8+bNJb6ek4Sto6MDS0tLREdXve0hIcqqSZMmMDMzAwBcuXIFgwcPhp6eHhYuXKiSs8dVTXx8PKZOnQo7Ozts374dANC4cWOeoyK1SXR0NFq2bCnx9ZztJW5ra4uoqCiuiidEIejq6gIoOnZTZN26dXj+/DkOHz5MR8wqqLt372LgwIFITk4Wv9ayZUvxkj1CuJaeno63b9/C1tZW4ns4W3Roa2tLLWyi8iwsLAAUdZF///332Lp1K7S0tPDnn3+id+/eePv2Lc8RktJOnjyJnj17Ijk5Ge3bt4efnx/evHmDp0+fokUL1dvJjSgmUX5UmIQdExMDoVDIVRWE8G779u3466+/kJiYiE2bNmHWrFm4evUqjIyMcPv2bTg6OuLp06ec1Z+ZmYlHjx4hPz+fszpUyebNmzF8+HDk5OTgq6++wr///gt3d3fqCidyJ0rYNjY2Et8jYBzNkvnrr7/Qr18/vHjxAtbW1lxUQYjCioqKwoABA/DixQvo6emhU6dOsLa2Fj+aNWuGbt26QVNTs8IycnNz8fvvv6Nx48YYNmyYeMvMzMxMXLp0CSdPnsTFixeRnZ2NevXqoV+/fvjqq6/Qv39/idd11iZJSUmwsrJCXl4evv/+e2zYsIFmhRPeeHt7Y+/evdKt/WcciY2NZQDY5cuXuaqCEIX29u1b1q1bNwag3EfHjh1ZSkpKuffev3+ftWvXTnzt+PHjWWZmJouJiWGNGzcuUY62tnaJ52pqaszZ2ZmtXr2aRUREsMLCQqnivnnzJrt9+7YsfgsUyoIFCxgA5uTkxHcohLBhw4YxFxcXqe7hLGEXFBQwbW1ttmXLFq6qIEThFRQUsLt377Ljx4+z1atXs2+//Za5ubmxevXqMQCsXbt2LDk5WXx9Xl4eW7FiBdPQ0GAAWP369ZmamhoDwNq3b8+++OILBoCZmZmxBQsWsHv37rGCggIWHBzMlixZwtq3b1/mi4G1tTU7e/ZslbFmZmayMWPGMABMXV2d3bhxg8vfGrlKT09nBgYGDADz9fXlOxxCWMeOHdmUKVOkuoezhM0YY3Z2dmzGjBlcVkGIUnry5AkzMTFhAFibNm3Y5s2b2YEDB1jnzp3FidbT05O9ffuW3bx5kzVq1Ej8ukAgYA8fPqyw7JcvX7KdO3eyAQMGiFvfAoGA/fLLLywqKop9+vSp3Pt27dpVItEbGxuz2NhYjn4H5Gvr1q3i32uhUMh3OKSWKywsZLq6umzDhg1S3cdpwh48eDD74osvuKyCEKUVFRXFTE1Ny7SI69evz44dO1aiK/v169fM2dmZAWDffPONxHV8+vSJTZs2rUwdhoaGrG3btqxv375s0qRJzNvbm02ZMoUBYPXq1RN/cejQoUOFCV6Z/PjjjwwAmz59Ot+hEMISEhIYAObn5yfVfZwm7IULFzILCwsuqyBEqcXFxbF58+ax4cOHMxcXFzZx4kSWmJhY7rV5eXksODiY5efnS1VHYWEh2759O2vfvj2rW7duhWPqxR/+/v7iVv26detk8VF5ExQUJP5cI0aM4DscQtj169cZAPb06VOp7uNs4xQAaNeuHeLj45Gamor69etzWRUhSsnS0hIbNmyQ6FpNTU1069ZN6joEAgFmzJiBGTNmAAA+fvyIhIQEJCQk4PXr12V+NTAwgIODA7y9vTFz5kxcunQJ8+fPl7peRbF7927xz9LsKkUIV8LCwqCjoyPVtqQAh8u6AODp06do3bo1rl69Cjc3N66qIYRwICYmBjY2NtDU1ERqairq1q3Ld0hSy8zMhLGxMXJzc7FmzRosWLBAvDyOEL6MHj0aL168wK1bt6S6j7ONU4CizVPq1q2L0NBQLqshhHCgRYsWaNasGfLz83Hz5k2+w6kWNzc35ObmAgA8PT0pWROFEBoaCnt7e6nv4zRhq6mpoXPnzrh37x6X1RBCZKygoACnT58WJzt/f3+eI6qe+Ph4AMCAAQNo21GiED5+/IioqCh89tlnUt/LacIGAHt7e2phE6JkhgwZgqFDh+L169cAID7NSlm8evUKPj4+yMvLAwDMnDmT54gIKfLgwQMAULwWNlAUVGxsLFJTU7muihAiI0ZGRiWet2/fnqdIpHP8+HFYW1vD0tISY8eORUpKCoD/TlUjhG+hoaHQ0dFB69atpb5XLgkbAO7fv891VYQQGfnjjz/g5+cnft6vXz8eo5Hcvn37EBcXB3V1dTg4OOCHH37AjRs30LNnT75DIwRAUcLu0KEDNDSkX6TFecKmiWeEKKe5c+eKf/b09OQxEsmJlr15enrizp07WL9+PXr16sVzVIT8p7oTzgA5JGzRxDNK2IQoD6FQiJiYGPHzLl268BiN5EQ9Af7+/igoKOA5GkJKysjIQHR0dLUmnAFySNgATTwjRNlERUWJf27RooXSnGvfrl07qKurIz09HWFhYXyHQ0gJDx48AGNMcVvYQFHCfvHiBU08I0QJxMXFYceOHeLnYWFh1RpvkzfGGL777jsIhUI0atQIVlZWfIdESAn37t2r9oQzAJDLv0LRuFJQUBDc3d3lUSUhpBoiIiJgb28vXg7l5uYGPT09nqOSzLZt23DkyBGoq6vj5MmTaNCgAd8hEVLCv//+i88++6zaX4Dl0sK2traGhYUF/v77b3lURwippufPnyMvLw+NGjWCr68vrly5wndIEvnnn38wb948AMCGDRtoVjhROIWFhQgICKjRJEi5JGyBQAAXFxdK2IQoONF+4Y0aNcLAgQOhrq7Oc0SVEwqF8PX1xbBhw1BQUICRI0di9uzZfIdFSBkRERFITU2Fi4tLtcuQS8IGgF69euHBgwdIS0uTV5WEECmJur8/ffrEcySVS01Nxfr169GiRQsMGjQIycnJaNeuHfbs2UP7hROFdPPmTWhra1frxD0RuSVsFxcXMMYQGBgoryoJIdWUmZnJdwjlevjwIb799luYm5tjwYIFiIuLQ/369bFgwQLcuHFDacbbSe3z999/w9HREXXq1Kl2GXJL2FZWVmjatKnSnvpDiKpjjGHx4sUAgGbNmvEczX/y8/Nx6tQp9OjRAx06dMDevXuRnZ0t/jkhIQFr166lSWZEYYnGr2vSHQ7IMWELBAL06tWLxrEJUVAfPnwQ//u8c+cO9u7di8LCQk7rjIuLQ0REBLKzs8u8JxQKsW7dOlhbW2PYsGEIDAyEurq6+OcHDx5g0qRJ0NHR4TRGQmrq4cOHSEtLq/Gue3JdXOni4oJDhw4hNTUV9evXl2fVhJAqGBoaws7ODpGRkQCAb7/9FgcPHsSuXbtgZ2cnvi4jIwMvX77Eq1evUK9ePTg5OUk9bpycnIxFixbhwIEDAIq+0Jubm8PGxga9e/dGly5dcO3aNaxfvx5A0SS4KVOmYNq0aTAzM5PNByZETv7++29oa2uja9euNSpHwBhjMoqpSnFxcbC2tsbZs2cxaNAgeVVLCJFQUlISevTogZiYGOjp6SEzMxMaGhpwc3PDmzdv8PLlyzITR7/77jts3bq1yrWlN27cwIoVK6ClpYWQkBB8/PgRAGBgYCD+uTy//PILfvjhB2hra9f8AxLCg6+//hofP36s8ZCwXBM2ULQm++uvv8bmzZvlWS0hREKMMQgEAsTHx2PWrFnw9fUtc42hoSEsLCwQEREBxhj69++PL774Ap8+fRI/srKy0KlTJ3zzzTd48eIFnJ2dS8w+/+yzz7Bt2zZ07doV7969w7Nnz+Ds7Cx+39zcHI6Ojjhx4gTU1OQ2ekeITAmFQjRo0ABz5syBt7d3jcqSe8KeOHEi7t+/j/DwcHlWSwippuvXryMqKgpNmzaFpaUlLC0tYWBgAAA4c+YMRo8ejZycnArvNzIygpaWFpKTkwEA3bt3x7hx4/DNN9+UScTFu9bl/F8TIZy4f/8+7O3tERAQgB49etSoLLlvENynTx8cOHAACQkJMDc3l3f1hBApubq6wtXVtdz3PDw8EBAQgK1bt4Ixhrp164ofampqOHr0KKKjowEArVq1QnBwMIyMjCqsa/z48Th48CB++eUXTj4LIfJ26dIl6Ovr13j8GuChhZ2WloZGjRph69at+O677+RZNSFEzoRCIfz8/HDz5k3MmzcPlpaWlV6fmZmJwMBA9OnTh7rBiUpwcHCAlZUVTp48WeOy5J6wgaJv7FpaWrh8+bK8qyaEEELkIjExEWZmZjh8+DDGjBlT4/J4+Qo7cOBA3LhxQ+G3PySEEEKq6+LFi1BXV8eAAQNkUh4vCdvd3R15eXnw9/fno3pCCCGEc35+fnB2dpbZviO8JOxmzZrBzs4Ofn5+fFRPCCGEcCozMxPXrl3DwIEDZVYmb7M6Bg4ciAsXLkAoFPIVAiGEEMKJa9euIScnR3US9vv373Hr1i2+QiCEEEI44efnh1atWsHGxkZmZfKWsB0cHNCoUSOcP3+erxAIIYQQmSssLMSFCxdk2roGeEzYampqcHd3p3FsQgghKuXu3bt4+/at6iRsoKhb/OnTp4iKiuIzDEIIIURmzp07hwYNGsDR0VGm5fKasPv06QMDAwMcPXqUzzAIIYQQmSgsLMSxY8fg6ekJdXV1mZbNa8KuU6cOhgwZgiNHjtBG/4QQQpReUFAQXr16JZOdzUrjfbPeMWPG4Pnz57hz5w7foRBCCCE14uPjAysrKzg5Ocm8bN4Tds+ePWFmZgYfHx++QyGEEEKqLTc3FydPnsSoUaM4ObyG94StpqaGUaNG4cSJE8jPz+c7HEIIIaRaLl++jPT0dIwePZqT8nlP2EBRt/i7d+9ob3FCCCFKy8fHB507d0abNm04KV8hEnb79u3Rtm1b6hYnhBCilNLT03H+/HnOWteAgiRsoKiV7evri4yMDL5DIYQQwrOcnBysW7cO8+fPR0BAgMKfO/Hnn3+ioKAAI0aM4KwOAVOQ9VTx8fFo2rQpDh48iHHjxvEdDiGEEJ6Eh4dj9OjRiIyMFL/WsGFDjBw5EmvWrIGOjg6P0ZWvV69e0NTU5HRoV2Fa2BYWFnBxcaFucUIIqaWEQiHWrVuHLl26IDIyEo0bN8bo0aNhaGiIlJQUbN26FQEBAXyHWUZ8fDz+/vtvTtZeF6cwCRsAxo4di2vXriEuLo7vUAghhMjR69ev4erqioULFyI/Px9ff/01Hj16BB8fH7x9+xbNmjUDULThlqI5cOAAdHV1MXjwYOvhAQoAACAASURBVE7rUaiEPXz4cOjr62P37t18h0IIIUROhEIhBg8ejICAAOjp6WHv3r04e/YsGjZsCADQ1NREZmYmACA0NFShdsYsKCjA77//jtGjR0NfX5/TuhQqYevp6WHChAnYu3cvcnNz+Q6HEEKIHOzbtw8hISEwMDDAgwcPMGnSJAgEghLXdOnSBQDwww8/wM3NDTExMXyEWsb58+fx+vVrTJ8+nfO6FGbSmUhUVBRatWqFI0eOYNSoUXyHQwghhEPv3r1Dy5YtkZqaii1btsDLy6vc6/Lz87Fp0yYsX74cOTk5qFOnDpYvX46xY8eisLAQQqGwxK/q6upo1qwZJzuOFffFF18gKysLQUFBnNYDKGDCBgA3Nzfk5OTg33//5TsUQgghHJo6dSp2796N9u3bIzQ0FBoaGpVe//z5c0ybNg3Xrl2rsmwzMzN4enpi6NChcHJyqnbyzsvLQ0FBAXR1dUu8Lmpg+vj4cLr+WkQhE/aZM2fg6emJsLAwdOjQge9wCCGEcMTExARv3rzBxYsXMWDAAInuYYzh8OHDWLJkCZKTk6Gurg41NbUSv2ZnZyMnJ0d8j6mpKTw9PdG9e3dkZ2fj48ePEj1SUlLEZZw6dQpDhgwRP58zZw58fHyQkJAAbW1t2f2mVEAhE3ZBQQEsLS3h7u6OXbt28R0OIYQQjnTs2BHh4eG4cOECvvzyS5mVm5ubC39/f5w6dQq+vr74+PFjjcv09fXFwIEDAQCZmZkwMzPDd999h9WrV9e4bElU3vfAEw0NDUydOhXr1q3D2rVrUa9ePb5DIoQQwoHmzZsjPDwcL168kGm52tracHd3h7u7O3Jzc3H16lWcOnUKMTEx0NfXh4GBQaWPhw8fYuHChSXK/PrrrwEAT548QVBQED5+/IipU6fKNO7KKGQLGwASExNhaWmJTZs2YebMmXyHQwghhAPz58/Hhg0bMHv2bGzevJnvcMTS09Ph4uKC8PDwMu85ODggPT0dNjY2uHDhgtxiUqhlXcWZmppi8ODB2Llzp0KtuSOEECI7zZs3BwCZt7BrytDQsMIJcHfv3kV0dDRmzJgh15gUNmEDwPTp0/HkyRPcuHGD71AIIYRwQLSD2fPnz3mOpKzVq1fDzMwMpqamGDBgANzc3ODk5IT69evDwsICffv2lWs8CtslDhTNBLS3t4exsTGuXr3KdziEEEJk7Pnz52jRogXq1KmDrKysMhumKJrY2FjY2Nhg48aNFa4Z54pCt7AFAgEWLVqEa9eu4d69e3yHQwghRMaaNm0KdXV15OTkICkpie9wqrRhwwYYGRlh8uTJcq9boRM2AHh6esLGxgZr1qzhOxRCCCEyVnzsOisri8dIqpacnIz9+/dj9uzZZTZRkQeFT9jq6upYsGABzpw5g6dPn/IdDiGEEBlhjGHatGkQCoXo37+/eAKaotqyZQs0NDTkPtlMRKHHsEVyc3PRrFkz9O3bF/v37+c7HEIIITJw+PBhjBs3Djo6OoiMjIS1tTXfIVXow4cPaNq0qXiPED4ofAsbKFoAP2/ePBw+fBjx8fF8h0MIIaSGUlNTMW/ePACAt7e3QidrAPjtt9+Qm5uLOXPm8BaDUiRsAJgyZQoMDAzw66+/8h0KIYSQGlq4cCFSUlLQpk0bzJ07l+9wKpWdnY1NmzZhwoQJMDEx4S0OpUnYdevWxaxZs7Bnzx68e/eO73AIIYRUU1BQEPbu3QsA+P3336GlpcVzRJX7448/8O7dO8yfP5/XOJQmYQPArFmzIBAIsHXrVr5DIYQQUg35+fmYNm0aAGDSpEn4/PPPeY6ocgUFBVi/fj2GDx/O+6Q4pUrYxsbGmDJlCrZt2yaTk1cIIYTI16ZNmxAREYEGDRpg7dq1fIdTpWPHjiEuLg6LFi3iOxTlmCVeXGJiIpo3b4758+fj559/5jscQgghEoqLi0ObNm2QnZ2NAwcOYPz48XyHVKm8vDy0bt0a7dq1w7lz5/gOR7la2EDRoSCzZ8/Gxo0bkZyczHc4hBBCJMAYw8yZM5GdnY2ePXti3LhxfIdUpd27dyMuLg4rV67kOxQAStjCBoC0tDQ0a9YMY8aMwbZt2/gOhxBCSBVCQkLg4OAANTU1REREoHXr1nyHVKlPnz6hefPmGDBgAP744w++wwGghC1sADAyMsKiRYvw+++/K9yRbIQQQsoyNzeHhoYGCgsLkZ2dzXc4Vdq0aRPS09Px008/8R2KmFImbKBoxnjDhg2xbNkyvkMhhBBSBRMTEwwbNgxA0RafiiwlJQXr16/HjBkz0LRpU77DEVPahK2rq4vly5fj6NGjCAsL4zscQgghVRAdR3n8+HGFnoO0evVqAMCSJUt4jqQkpU3YADBx4kTY2Ngo3G8qIYSQsrp27YquXbsiLy8PPj4+fIdTrpcvX2LHjh2YP38+GjRowHc4JSh1wtbU1MTKlStx+fJlBAQE8B0OIYSQKrRo0QJA0ZIpRbR8+XIYGRnxumd4RZQ6YQPAkCFDYG9vj0WLFkEJJ7wTQkit8uzZMwBAy5YteY6krIiICBw6dAjLli1D3bp1+Q6nDKVP2AKBAGvWrMHt27dx9uxZvsMhhBBSAcYYoqOjAQA2NjY8R1PW4sWLYW1tjW+//ZbvUMqllOuwyzNgwAA8fvwYjx8/hq6uLt/hEEIIKSUlJQWNGjUCAGRlZUFHR4fniP5z6dIlfPnllzhx4oR4NruiUfoWtsiWLVuQlJSENWvW8B0KIYSQcoi6w5s2bapQyTonJwdeXl5wdXXF0KFD+Q6nQiqTsG1sbDB//nysW7cOz58/5zscQgghpYi6w21tbXmOpKQNGzbg5cuX2LZtGwQCAd/hVEhlEjZQtGaucePGmD17Nt+hEEIIKeXixYsAgFatWvEcyX9evnyJVatWYc6cOQq/XarKjGGLnDlzBp6envDz84O7uzvf4RBCCAFw7949dOnSBQKBAOHh4WjXrh3fIQEAPDw8cOfOHTx9+hT6+vp8h1MplUvYjDH069cPz549Q2RkpEKNkxBCSG3Vp08fXL16FWPHjsWhQ4f4DgcA8Ndff6Ffv344duwYRowYwXc4VVK5hA0UjZO0bdsWS5cuxfLly/kOhxBCarXr16/Dzc0NmpqaiIqKgrW1Nd8hITc3F+3atYO5uTmuX7+u0GPXIio1hi1ia2uLH374AWvWrKHTvAghhEeMMSxevBgAMG3aNIVI1gCwceNGxMbGYvv27UqRrAEVbWEDQGZmJlq3bo2OHTvCz8+P73AIIaRWEs0r0tPTw/Pnz9G4cWO+Q8KrV6/QqlUrTJ8+HRs2bOA7HImpZAsbAPT09LBx40acP3+edkAjhBAeFBQUYOnSpQCAuXPnKkSyZoxh1qxZMDQ0VLohU5VtYQNFfzCDBg3CnTt3EBkZCWNjY75DIoSQWmP//v2YNGkSjI2N8fz5c9SrV4/vkODj44OxY8fi7NmzGDRoEN/hSEWlEzYAJCUlwc7ODv3798eRI0f4DocQQmqFnJwc2NjYICEhAb/++ivmzp3Ld0hKnw9UtktcxMTEBNu2bcPRo0dx7tw5vsMhhJBaYefOnUhISIC5uTmmT5/OdzhgjGHatGnQ0tLC1q1b+Q6nWlS+hQ1Q1zghhMjThw8f0Lx5c7x//x779u3DN998w3dISt0VLqLyLWyg6AjOXbt2IS8vD15eXnyHQwhRIfn5+YiJicG7d+9QC9o/Evn111/x/v17tGrVCuPGjeM7HCQlJcHLywujRo1S2mQN1JKEDVDXOCFEtuLj42Fvb486derAxsYGs2bNgpWVFebMmYOgoKBam7yTk5OxceNGAMDKlSuhoaHBazyq0BUuUiu6xEWoa5wQIivXrl3DF198UeH7Xl5e2Lx5s9JsyiErXl5e2LZtG7p06YI7d+7w/vlVoStcpFYlbED5ZwkSQhRDdnY2GjZsiMzMTNy+fRvt27eHv78/xo4di4yMDACAt7c3fvrpJ54jlZ/Y2Fi0bNkS+fn5uH79Onr37s1rPKr2/32t6RIXKd41fubMGb7DIYQomZycHPTu3RsdOnQQtx7//PNP6Ojo4Ouvv8a8efPE1/78888YPHgwbt68yVe4crV8+XLk5+fjiy++4D1ZM8YwdepUlegKF2O1UGFhIfPw8GCGhoYsLi6O73AIIUrk48ePTFNTkwEQP3r06CF+Pz8/n23evJm1bt26xDW7d+/mMWpuhYWFsWHDhok/a0hICN8hsa1btzIAzNfXl+9QZKZWJmzGGEtNTWWWlpasW7duLC8vj+9wCCFKZOLEiQwAMzc3Zz/++CO7fft2uddFRkaycePGMQBMU1OTBQUFyTnS8iUlJbE9e/awr7/+mgFgbdu2ZRkZGVKXc+vWLfbVV1+V+GLyww8/cBCxdEJDQ5mWlhabPXs236HIVK1N2IwxFhwczNTV1dmiRYv4DoUQokSePn0qTlCPHj0q95qUlBR28eJFtmzZMqalpcUAMDMzs2o1EPLz89nNmzfZ+fPn2Zs3b2oUe2FhITMzMyuRZAGwXbt2saysLInuv3HjBuvdu7f4XoFAwIYPH87Cw8NrFJssfPjwgbVo0YJ17tyZ5eTk8B2OTNXqhM0YY2vXrmUA2JUrV/6vvXuPy/n+/wf+uDqXShHCHCIiTIooUiYlGmMMMWeGNr85q5k2m9OM7ePQMsfPRKwxhyRaJ4cZiwhRLTmkFDUqOlyH1+8P396foqJcXa/rffW8327Xzbt69X49rper63m9T6837yiEEBEpX+zK27lzJ2vfvv0rBREA69ChwxsXbKlUyiIjI9nMmTOZhYVFhfW0bt2ajR49mm3bto3J5fI3WpeXlxezsbFhubm5bMSIEZXmMzU1ZVOnTmXR0dHs3r177Pvvv2cjRoxgFy9eZAqFgoWFhTEnJyehvY6ODpsyZQpLTk6u1Rgqm0KhYOPGjWMmJiYsNTWVdxylq/cFWy6XM09PT9akSROWmZnJOw4hRCTKF7pHjx4xxhi7ePEi09LSEr5vY2PDJk6cyLZs2cLi4+NfW6yrK9IWFhbM1taWSSSSCt+fMmUKk8lkr6zryZMnLCkpiTHG2IoVK4T2q1evZsXFxczBwYEBYL6+vszf35+1bt260iIOgLVq1YrZ2dkJX+vr6zNfX1+1Owdo+/btDAALCQnhHaVO1PuCzRhj2dnZrHnz5mzAgAGVvvAJIeRlWVlZQgFzcXFh+fn5rEuXLgwAGzVqFMvLy3vjdZ0/f77KIj1z5kwWGRnJpFIpY+zFSW/R0dFs2bJlTFtbmwFg48aNq/BhIDs7m1lZWTEtLS22e/fuCuvs168fY4yx999/nwFg27ZtY4y92HiJi4tj06dPZw0bNhTalt99bmxszBYtWsSysrKUOJLKcf36dWZoaMhmzJjBO0qdoYL9f6Kjo5lEImErVqzgHYUQIhI3btxgpqamwm5qAKxp06bCFvebuHPnToWt5sqKdFV+++23CsX45MmTLDw8nDk7O1e5tbxy5UrGGGODBg1iANiePXteWW9xcbHwgeP06dOsT58+LCAggOXm5tZgdFTn2bNnzNbWlnXp0oU9e/aMd5w6QwW7nICAAKalpcViY2N5RyFE1C5evMhWrVrFLly4UOEYq0Kh0LirMk6ePCls6QJgoaGhNfr9kJAQBoC1a9fujYr0y6o6Hl3Vo0mTJiw5OZn169ePAWC//fZbjfpTR9OmTWOGhobsxo0bvKPUKSrY5chkMubq6spatGjBsrOzecchRJQOHTrE9PX1hQJhaWnJZsyYwWbPns1atWrFtLW12ZkzZ3jHVKqgoCCmra3NJk2aVOPfXbx4MQPAPvnkk1r1rVAo2OzZs5mRkRHr3r07c3BwYAMHDmShoaHC/0H5DxQAmKurK+vZsycDwMLCwmrVr7oIDg5mANiOHTt4R6lzVLBfkpGRwZo2bcpcXFxYSUkJ7ziEiMoPP/wgnHTVrVs3ZmJiUulWnr+/P++oSpeXl8cUCkWVP5dKpSw3N5c9fPiQ3b9/nz1//pydP3++wmVVylbVrvHGjRsLx9ujoqKU3q+qXLp0iRkaGrIJEyZUO/aagu9tVNRQy5YtcejQIQwYMACfffYZgoKCuE9eT4gY7Ny5E/PmzYOVlRUGDhyIn376CXK5HHFxcTh+/DgUCgWysrJw8OBBpKen846rdObm5lX+7MmTJ+jSpQsyMzOrbGNvb6/0TCdOnEBoaCj279+P6OhoKBQKAEBubi6MjIwAAAYGBkrvVxWys7MxfPhwdOnSBT///HP9eJ/m/YlBXe3YsYMBYFu2bOEdhRC1FxERIZw4tXTp0kq3dh48eCBs4fXo0YNDSn6ePn362mPLRUVFdZohOzubpaWlVThcAYBdvny5TvutC8XFxczZ2ZlZWlqyjIwM3nFUpt7d/ONNTZ06FXPnzsXcuXMRHR3NOw4ham3fvn1gjMHHxwerVq2qdGunbIsOABISElQZj7vyz/3QoUMIDAys8POePXvW+ZZu06ZN0a5dO/j5+VX4fnJycp32q2yMMcyePRvx8fH4/fff0bJlS96RVIf3JwZ1JpVKmbu7O2vUqBFLS0vjHYcQtVU2J/XWrVurbFNQUMCaNm3KALA1a9aoMB1/u3btEi7ZKpuzOy8vj5WWlrKDBw+yf/75R6V5/vvf/75yOZhY/PjjjwwA2717N+8oKkdb2NXQ0dHBgQMHYG5ujmHDhgn3uCWEVPT06VMAgKmpaZVt1q1bh5ycHFhbW2PevHmqisadTCbDV199BQBYvHgxjI2NAbw45q2rq4uRI0eiffv2Ks00ceJEnD59Wvj6wIEDKu2/tiIjIzF//nwsWLAAkyZN4h1H5ahgv0ajRo1w9OhR3Lt3Dx9//LFw0gYh5H/y8/MBAA0bNqz05w8ePMC6desAAGvWrIGenp7KsvE2b9483L17FwAwZ84czmn+x8XFBXfv3sXKlSuxaNEi3nFeKzU1FWPGjIGHhwfWrl3LOw4XEsYY4x1CDMLCwjBs2DB88cUX+Oabb3jHIUStWFtbIy0tDWfPnkXfvn0r/IwxhmnTpmHXrl1wdnbG2bNn68cZvQCkUmmFDyf0dls7T58+hZOTE+RyOS5cuAAzMzPekbigLew35O3tjVWrVuHbb7/Fvn37eMchRK1UtoX96NEjzJo1C23btsWuXbsAAOvXr683xRoAvvvuO2F5yJAhHJOIl0wmg4+PDzIzM3H06NF6W6wBgK7DroElS5bg5s2bmDx5Mpo2bQp3d3fekQjh6unTpzhx4gT+/fdfAP87hl1YWIghQ4YgPj4eAKCrq4slS5agT58+3LKqEmMM69evx7JlywAA7777Lg4dOsQ5lfgwxjBz5kycOnUKx48fh42NDe9IfPE8402MSktLmZeXFzM2Nmbx8fG84xCicnfv3mUfffTRK9cRN2jQgD1//pyVlpYyT09P4azosLAwVlhYyDu2ysjlcvb5558L4zJ//vw3umc1eZWfn1+VNyipj6hg10JhYSFzdHRkTZo0YSkpKbzjEKJSZW+iLz9OnDjBFAoFmzhxIgPAjIyM2IULF3jHVani4mI2ZswYYUy+//573pFEq+zyrfXr1/OOojaoYNfSo0ePmI2NDbOyslLLe8MSUlcePnzILC0tXynYkZGRbPny5cLNJo4fP847qsrNmjWLAWC6urps7969vOOIVtkdzBYuXMg7ilqhk85qycLCAidPnkRJSQm8vLyEk24I0XTNmjXD559/DgAYP348SktLkZ6eDkdHR6xevRoA8PPPP9fLk6zkcjkAYODAgfDx8eGcRpz++OMPTJw4ER9//HG9vXyrKlSw30KbNm0QERGB9PR0fPDBBygpKeEdiZA6V1BQgM2bNwMAunbtCl1dXbRt2xZRUVGQSqWwtrbG1KlTOafkY968eZBIJIiIiEBSUhLvOKJz6dIljBgxAu7u7tixYwe0tKhElUej8Za6deuGY8eO4c8//8SECROET9iEaKovv/wSGRkZsLKywty5c4XvnzhxAkD9vnypc+fOGDlyJAAIexvIm0lNTYWXlxdsbW0RGhoKXV1d3pHUDk2coiSHDx/Ghx9+iNmzZ2PTpk316lpTUn/Ex8ejd+/eUCgUOHnyJDw8PAC8uPymVatWePDgAU6cOIHBgwdzTsrPpUuX0LNnT2hrayMlJQXt2rXjHUntPXz4EM7OztDT08PZs2dhYWHBO5Jaoi1sJfnggw8QFBSELVu2wM/Pj2Y0IhpHJpNh5syZUCgU8PHxEYo1AFy/fh0PHjyAoaEhXF1dOabkz8HBAZ6enpDL5cJ0rKRqjx49gru7O0pKSnDy5Ekq1tWggq1EM2bMwIYNG7B27Vp8+eWXVLSJRtm4cSMSEhJgZmaGDRs2VPhZeHg4AGDAgAEwNDTkEU+t+Pv7AwB27tyJzMxMzmnU1+PHjzFw4EA8fvwYUVFRaNOmDe9Iao0KtpLNmzcP69atw8qVK7FixQrecQh5K1euXMHatWuRkZEhvJ7XrVuHZs2aQSqVCu3o+HVFLi4u6Ny5M0pLS3H27FnecdRSXl4eBg0ahIcPHyI6OhqdOnXiHUnt0THsOrJmzRr4+fnhm2++EaYnJERMtm/fDl9fX5SWlsLIyAjPnz8HALRq1Qqmpqa4f/8+wsPD0bVrVzRu3BhyuRxpaWl0zBaAQqGAubk58vPzcfnyZfTo0YN3JLXy5MkTuLu74+7du4iJiUHXrl15RxIFmku8jixduhRSqRRffvklAFDRJqKyYcMGLFiwQPi6rFgDwP3794XlUaNGYd68eZDL5bCxsaFi/X+Sk5OFuRm6devGOY16ycvLg4eHB9LT0xEdHU3Fuga0vyq7szpROldXV2hra2P58uXC13T2OFF3jDGMGzcOT58+hb+/P+zs7HDx4sUKbRYvXozMzExkZGTgjz/+AABMmzYNgwYN4hFZ7cTFxSE0NBQAoK2tDTc3N76B1ERubi4GDRqEu3fvIioqCnZ2drwjiQu/Sdbqj1WrVjEAbNmyZUyhUPCOQ0i1/vrrLwaAGRsbs+fPnwtzY8+aNYv5+/uz9PR0xhhj1tbWwrSk3t7eLD8/n29wNfLgwQPWsmVLBoB16NCBdxy1kJOTw959913WpEkTlpiYyDuOKNEucRXw8/ODjo4OFi9eDKlUitWrV9OWNlFbv/76KwDg/fffr3DGd+fOnStMlDJx4kQsX74cEyZMwJ49e1SeU521aNECf//9N1q0aIG0tDSUlJRAX1+fdyxucnJyMHDgQOTk5CAmJgZdunThHUmUqGCryKJFi6Cjo4P58+ejoKAAGzduhLa2Nu9YhFTAGBN25Y4ePRoAhA+X7KXzU7/88kvhHA3yKktLS5iamiI/Px+pqan19ljtnTt34Onpifz8fMTGxqJz5868I4kWXdalQvPmzcO2bdsQFBSEsWPHori4mHckQiq4cOEC7t+/D2NjY2G2srL5nHfv3o24uDie8URFIpEIxenWrVuc0/CRmJgIZ2dnyGQynDlzhor1W6KCrWLTp0/HoUOHEBYWBi8vLzx9+pR3JEIEZVvX5XeHjx49GoaGhrhy5Qrc3NwwePBgXL58mWdM0SgrUDdv3uScRPVOnz6N/v37w9LSEn/++Sesra15RxI9KtgcDB8+HJGRkcIb4MOHD3lHIqTS3eHAi2l309LSMGfOHOjo6ODkyZNwcHDAb7/9xiuqaJQVqZSUFM5JVOv333+Hh4cHevbsidjYWDRr1ox3JI1ABZuTfv364cyZM8jJyYGzszNSU1N5RyL1EGMM27Ztw8SJE9GzZ89XdoeXad68OTZs2ABPT0/hewqFQtVxRadsT0SrVq04J1GdrVu3YtSoURg+fDiOHz8OU1NT3pE0B9+T1MmdO3eYjY0Na9KkCYuPj+cdh9Qzp0+fFi7NKnv4+vpW2tbHx4cBYDo6OmzHjh0qTio+BQUFzMDAgAFgly9f5h2nzikUCvb1118zAOzTTz9lcrmcdySNQ1vYnLVp0wZnz55Fu3bt4ObmhsjISN6RSD0SEREB4MUen4MHDyIpKQmbNm16pZ1UKsXBgwcBvNjdOXXqVJXmFKPjx4+juLgY7du31/gJQuRyOXx9fREQEICVK1di48aNwsmKRHloRNWAhYUFoqKi0L9/fwwdOhT79u3jHYnUE2UfEKdPn46RI0eic+fOlc4RcPPmTZSUlKBhw4YYOnSoqmOKUtn17B999JFGz7tQVFSEMWPGYOvWrdi+fTv8/f01+vnyRAVbTTRo0ACHDx/G+PHjMX78eHzxxRd0jJDUqby8PMTHxwMA3N3d8ezZM+zatQuXL19+5ZrrsmOxPXr0oDfjN1BYWCjccrT8CXyaJiMjAy4uLggPD8fvv/+OadOm8Y6k0WjiFDWiq6uLnTt3wtbWFkuWLMG1a9cQHBxMJ22QOhEdHQ3GGGxtbaFQKNCvXz9cuXIFANCxY0eMHTsWkydPhpWVlVCwNX3XrrLExMQI8yxo6gRJ58+fx4gRI6Cnp4dz587RHclUgLaw1YxEIsGiRYsQFhaGuLg4ODk5IS0tjXcsooHKbtqhpaUFR0dHXLlyBWZmZjAwMEBKSgpWrFgBOzs73LlzByYmJgBe3NRCLpfzjC0KnTp1QuPGjQEA/v7+nNMo3+7du+Hm5gZra2vEx8dTsVYRKthqasiQIbhw4QKkUil69eqFqKgo3pGIhjEyMgIAXL9+HQ8fPkS3bt1w5coV5OTkIDg4GLa2tsjPz4eVlRXMzc0BAAkJCfjxxx95xhaFDh06YMOGDQCA0tJSzmmURyaTYf78+ZgyZQomTpyI6OhoNG3alHes+oP3aeqkenl5eczDw4Npa2uzjRs30t2+iNLIZDIWFhbGZs2axebMmfPK3bYiIyNfueSr7JGcnMwptXjs2rWLAWBeXl68oyhF+feiTZs20XsRB3QMW82Zm5vj+PHjWLJkgRC5hwAAIABJREFUCebOnYvExERs2bIFenp6vKMRkdPW1sbQoUOrPOt74MCBVf7uhx9+iGvXrtVVNI1QduhAE45h37x5E8OHD0dubi5OnjxZ7WuD1B3aJS4COjo6WL9+PXbt2oVffvkF7733HrKzs3nHIhpOIpFg+/btwte9e/cWlq9fv44ZM2bwiCUamlKww8PD0adPH+jq6uLixYtUrDmigi0ikydPRmxsLNLS0mBnZ4fo6GjekYiGmzRpkrB8//79Cj/bvn27cOkSeVVZwWYvXSInFlKpFH5+fhg6dChcXV1x/vx5tG/fnneseo0Ktsg4OTkhISEBXbp0gbu7O5YvXw6ZTMY7FtFQOjo6ePDgAS5fvowlS5a88vOhQ4ciNzeXQzL1V3bjj0uXLomuaN+7dw9ubm5Yt24d1q5di8OHD9PlpWpAwsT2SiIAXnx6X7NmDZYvX45+/fph3759aNmyJe9YRMOtW7cOixcvrvC94cOH4/fff6cJVV5SVFQEc3NzlJSUICkpSTT3gj5y5AimTJkCExMT7N+/H05OTrwjkf9DW9gipa2tjS+++AIxMTFIS0tD9+7dcfz4cd6xiIZbtGgRunfvDgDo0qULdHV1ceTIEWzZsoVzMvVjaGiIfv36AfjfNe/qrKSkBP/v//0/fPDBB3B1dUVCQgIVazVDBVvk+vfvjytXrqBPnz7w9vbGwoULNeq6T6J+SkpKAAA3btyAVCoFAHz22We4evUqz1hqadCgQQCg9jf1+eeff+Ds7IygoCBs3LgRhw4dQqNGjXjHIi+hgq0BLCwscOzYMaxfvx4bN26Ei4sL0tPTecciGsrLy6vS79vb2+PZs2cqTqPeygp2bGys8OFG3YSEhMDe3h75+fk4f/48PvvsMzq8oaaoYGsIiUSC+fPn4+zZs3j06BF69OiBAwcO8I5FNNDKlSsRHx+PCxcuwNLSUvi+QqHA3LlzOSZTP3Z2dmjcuDEKCgpw8eJF3nEqKCwsxPTp0+Hj4wNvb29cvnwZ9vb2vGORalDB1jCOjo64fPkyPD09MXbsWIwePRo5OTm8YxENYmhoCAcHBzg6OiIxMRFubm7Cz3bu3Il79+7xC6dmtLS0hOuW1Wm3eHR0NLp164aQkBBs27YNe/fuFeaLJ+qLCrYGMjMzw/79+7F//37ExMSgS5cuCA0N5R2LaKAmTZrg1KlTGDFiBNq3b4/PPvuMrlZ4iTodxy4sLMScOXMwcOBAtGnTBteuXcP06dNpF7hI0GVdGi4nJwdz5szBwYMHMWrUKGzZsoUm6ydEhe7cuQMrKytoa2sjLy+P2/XM0dHRmDZtGnJycvDdd99h9uzZ0NKibTYxof8tDde0aVOEhobS1jYhnLRt2xbW1taQy+VYv369yidRqWyr2tfXl4q1CNH/WD0gkUgwZswYJCUlwdXVFR999BEd2yZEhXx9fQEAK1asgK+vr8ruKV52rPq///0vNm/ejOjoaLRr104lfRPlo4Jdj1S2tf3rr7+KbtpEQsTm888/xw8//ACJRIKffvoJI0eOxPPnz+usv4KCAtqq1kD0v1fPvLy1PWbMGHh5eSElJYV3NEI02ueff47Q0FAYGBjg6NGjGDBggNL3cjHGEBISgk6dOtFWtQaigl1PlW1tHzlyBMnJyejWrRv8/f1p4gtC6tCHH36IqKgoNGrUCBcvXoSrq6vSZia8ceMG3nvvPfj4+KB3795ISkqirWoNQ/+T9ZhEIsGwYcOQlJSEpUuXYsOGDejcuTMOHjxIu8kJqSPOzs44f/48LCwscOvWrbe+B0B+fj4WLFiA7t2748GDB4iIiMChQ4fQpk0bJSUm6oIKNoGhoSG+/vpr3LhxA927d8eoUaPg6emJ5ORk3tEI0UgdO3bE1KlTAQC7d++u1ToYY9i7dy9sbGwQFBSEb775BteuXYOnp6cSkxJ1QgWbCNq3b49jx47h2LFjSEtLQ7du3bB06VIUFhbyjkaIxpk0aRIA4Pjx48jOzq7R7167dg1ubm6YMGEC+vXrh5s3b8LPzw/6+vp1EZWoCSrY5BXe3t64ceMGli1bhv/85z/o3Lkz9u/fD4VCwTsaIRrD1tYWjo6OkMvl2Ldv3xv9Tl5eHj7//HP06NED2dnZOHXqFEJDQ9G6des6TkvUARVsUikDAwMsX74cSUlJcHBwwLhx49CrVy+cOnWKjm8ToiSTJ08GAOzatavaD8TPnj3D6tWr0a5dO2zfvh0rV65EYmKiMO0pqR+oYJNqWVlZ4fDhw4iLi4O+vj48PT3h7u6Ov//+m3c0QkRv7Nix0NfXx7Vr19CzZ89X5huXSqUICgqCtbU1AgICMHHiRNy+fRtLliyBnp4ep9SEFyrY5I30798f586dw5EjR5CdnQ1HR0eMHj2aTkwj5C2Ym5tj69atMDExQUJCAjw8PODh4YH4+HgcOHAAtra2mDNnDtzd3ZGcnIyNGzfSvQDqMbr5B6kxuVyOPXv2YPny5cjMzMTUqVMREBBAd2kipJYeP36Mb7/9FoGBgZBKpdDW1oZcLoeXlxfWrFmDd999l3dEogZoC5vUmLa2NiZPnoyUlBSsW7cOhw4dgrW1NZYsWYK8vDze8QgRHQsLC4wfPx69evUCANjb2yMuLg7h4eFUrImACjapNQMDA8ybNw+3b9/GokWLsGXLFrRt2xaLFy9GVlYW73iEqD3GGOLi4jB48GA4Ojri6dOnOHLkCC5cuID+/fvzjkfUDBVs8tZMTU2xYsUKpKWlwdfXF1u3boWVlRVmzZqFtLQ03vEIUTsKhQLHjh1D37594ebmhszMTOzduxdXr17FsGHDIJFIeEckaogKNlGaZs2aYfXq1bh79y4CAgJw6NAhdOzYET4+PkhMTOQdjxDuZDIZ9u7di+7duwuF+dixY7h69Sp8fHygra3NOyJRY1SwidKZmZnBz88Pd+/excaNG/Hnn3+ie/fu8Pb2xtmzZ3nHI0TliouL8dNPP6Fjx46YMGECWrVqhdOnT+PcuXPw9vamLWryRqhgkzpjaGgIX19fpKam4pdffkF6ejpcXFzg4uKC48eP08xpROP9+++/WLt2Ldq2bYtPP/0Ujo6OSEhIQHh4OFxcXHjHIyJDl3URlSk7brd69WpcuHAB7dq1w6xZszBlyhRYWFjwjkeI0ly6dAmBgYEICQmBTCbDpEmTsHjxYnTo0IF3NCJiVLCJyjHG8NdffyEwMBC//vorJBIJxowZgzlz5sDR0ZF2DxJRKioqwq+//orAwEBcvHgRrVq1wieffIJp06bB0tKSdzyiAahgE64ePXqEnTt3IigoCHfu3IG9vT3mzJmDcePGwcjIiHc8Ql4rLS0NQUFB2LlzJ/Ly8uDp6YnZs2dj6NCh0NHR4R2PaBAq2EQtyOVyREREIDAwECdOnEDDhg0xefJkzJ49Gx07duQdj5AK5HI5wsPDERgYiIiICJibm2Pq1Kn45JNPaLc3qTNUsInauX37NrZu3YodO3YgNzcX/fr1w/jx4zF69Gg0btyYdzxSjyUmJiI4OBj79u3DgwcP0LNnT/j6+mLMmDEwNDTkHY9oOCrYRG0VFxfj4MGDCA4OxqlTp6CtrY0hQ4ZgwoQJ8Pb2hoGBAe+IpB64f/8+9u3bh+DgYFy/fh2NGzfG2LFjMWnSJGEqUUJUgQo2EYXs7GwcOHAAwcHB+Pvvv2FqaopRo0ZhwoQJcHV1hZYWXaFIlOfJkyf47bffEBwcjLi4OBgYGOCDDz7A+PHj4enpCV1dXd4RST1EBZuITkpKCvbu3Yvg4GDcvn0bLVu2hI+PD8aNGwc7Ozs6y5zUSlFRESIiIhAcHIywsDDIZDIMHDgQ48ePx4gRI2Bqaso7IqnnqGAT0Sq7PGzv3r3Yv38/cnNz0bp1awwbNgzDhg2Dq6sr9PT0eMckaiw7OxthYWE4duwYTp06haKiItjb22P8+PEYO3YsWrRowTsiIQIq2EQjSKVSxMTE4NixYzh69Cju3bsHExMTeHl5YdiwYfDy8kKjRo14xyScMcaQlJSEo0eP4ujRo7hw4QIkEgmcnZ3x/vvvY9iwYejUqRPvmIRUigo20TiMMSQmJgrF+++//4a2tjb69esnbH1bW1vzjklURCqV4uzZs0KRvn37Nho0aABPT08MGzYMQ4YMQZMmTXjHJOS1qGATjZeZmYmwsDAcPXoUUVFRKC4uhrW1NQYMGIABAwbAzc0NzZs35x2TKIlcLkdiYiJiYmIQGxuL06dP4+nTp2jZsqXwgc3NzY2uMiCiQwWb1CvPnj3DH3/8gcjISMTExCApKQkAYGNjAzc3N+FBU0mKh0KhQGJiImJjYxETE4PTp0/jyZMnMDAwEO43PWTIEPTo0YNOSCSiRgWb1GvZ2dmIi4tDbGwsYmNjcfPmTQBAp06dhOLdr18/tGjRgt7s1YRMJsP169eF/7e4uDj8+++/0NfXh7OzM9zc3DBgwAA4OjpCX1+fd1xClIYKNiHlPHz4EHFxccLu1OTkZABAs2bN4ODgAAcHB/Ts2RMODg5UxFVAKpUiKSkJly5dEh5Xr15FcXEx9PT04OTkJBzW6N27N+3mJhqNCjYh1cjMzMTFixcRHx8vFIxHjx4BqFjEywo5FfHaq644SyQSdOrU6ZXxpulASX1CBZuQGmCMISMjo0JRuXTpEnJycgAAjRs3RseOHWFjY4OOHTsKD2trayoueDF+jx8/RkpKivBITk5GSkoK/vnnH5SUlFRanO3s7GBiYsI7PiFcUcEm5C0xxvDgwQNcunQJ165dq1CInjx5AgCQSCRo3bq1UMBtbGzQvn17tGzZEs2bN4eFhYXGTK9aUlKChw8fIjMzE/fv33+lOFc2JmUfcHr06AE7OzsYGxtzfhaEqB8q2ITUkZe3Jsu2JFNSUpCamorS0lKhrY6ODiwtLdGiRQs0b94czZs3F5bL/m3YsCFMTExgYmICfX19le16VygUePbsGQoKClBQUIDHjx8jKysLWVlZyMzMrPBvVlYWcnNzK/w+7XUgRDmoYBPCgVwux4MHD14pfC8XwUePHqGyP1EdHR0YGxsLBbz8somJCQwMDKClpQVtbW1oaWkJD8YYFApFhYdMJqtQkAsLC4XlgoICPHv2rNLnoK+vX+WHi7Llli1b0gxzhCgJFWxCCCFEBDTjoBkhhBCi4ahgE0IIISJABZsQQggRASrYhBBCiAjo8A5Qpri4uMJlLoQQQggPenp6ajnNrVoU7OLiYjQ0aoRSVsQ7CiGEkHrO0tIS6enpale01aJgl5aWopQVwdVsHHQkerzjVIu1Fcd9k5+904B3hDfyrLk27whv5HlT3gneTGkzKe8Ib8SsWSHvCG+ka+Ms3hHeiLPZP7wjvBE3w9u8I7xWYSGDs+NDlJaWUsGujo5EDzpaal6wtdXrP7AqOrriyKmtJ46CLZL/dmgZimQ8jcTxwULPWL3fj8oYGqvVW3mVTIzEcNqUgneAKolh9AghhJB6jwo2IYQQIgJUsAkhhBARoIJNCCGEiAAVbEIIIUQEqGATQgghIkAFmxBCCBEBtSjYJSUlAAAF5JyTaAaFQoZ7SaegkMt4R9EICrkMuVERUMhoPJWBSWV4uDcOCimNpzLIS+U4uukupKXqe/2wmJSWMAD/q0vqRL0KNqOCrQwKJsP9W5FQKOgNURmYXIa8mFNgVLCVgslkyA45Ayalv3dlkJfKcWzzfcioYCtFqZQKNiGEEELeAhVsQgghRASoYBNCCCEiQAWbEEIIEQEq2IQQQogIUMEmhBBCRIAKNiGEECICVLAJIYQQEaCCTQghhIgAFWxCCCFEBKhgE0IIISKgwztAeTImhUxRyjtGtZi8mHeE15LJX8yBK5Oqf1Z5qTbvCK8lL30xjooS9R9PRZGUd4TXUhS9GEf5c/Wbq/llpYXq/X4EANJnLzIWFar/XPcFcvWf77ywkPGOUCUJY4x7uqdPn+Kdd95BYWEh7yiEEELqOWNjY2RkZKBhw4a8o1SgFgUbAIqLi1Faqv6fZgkhhGg2PT09GBgY8I7xCrUp2IQQQgipGp10RgghhIgAFWxCCCFEBKhgE0IIISLw1gX7yJEjGDVqFNq0aQMDAwM0bdoUTk5O+O677/D06VNlZHxFbGwsJk6ciHbt2sHQ0BCNGjWCvb09vvrqK2RlZdVJn6qiqvEsLi5GeHg4Fi5cCFdXV1haWkJPTw/GxsZo164dPvroI4SGhkIqVf/LhKrD4/X5soCAAEgkEuExefJklfRbF3iNZ2pqKgICAtC7d2/htWppaYnu3btj+vTpCA4OxvPnz+us/7qi6vHMycnBmjVr8N5776FZs2bQ19eHkZERWrdujaFDhyIoKEh0V+vI5XJcv34du3fvxmeffQYnJycYGRmp5O9N5bWI1VJ+fj7z9vZmAKp8vPPOO+zcuXO17eIVUqmUTZs2rdo+zc3N2eHDh5XWp6qocjxDQkKYiYlJtX2VPbp27coSExOV8AxVi8frszJXr15lurq6FfqdNGlSnfZZF3iNZ0lJCVu4cOErY1jZIyEhQal91yUe47l3717WsGHD145jixYtWExMjNL6rWsjR46s9vnUxd8br1pUq4ItlUrZoEGDhGDNmjVjy5YtY/v27WObN29mffv2FX5mZmbGrl+/rpSwU6ZMEdbbsGFDNn/+fBYcHMy2bt3Khg4dKvxMT0+PRUdHK6VPVVD1eK5evVpYn4WFBRs7dizbsGEDCwkJYXv27GHz589nFhYWQpvGjRuzW7duKenZ1j1er8+XyWQy5uDgwACwBg0aiLZg8xrPoqIiNmTIEGHdpqambPLkyeynn35iBw4cYIGBgczf35+5ubkxHR0d0RRsHuN59OhRJpFIKnwQ//bbb9mePXvYtm3b2KJFiyr8zRsYGLBr164p4dnWveHDh1colI0aNWIdOnSo0783XrWoVgU7MDBQCGRra8sePnz4SpsFCxYIbfr27fvWQcPDw4X1NW/enKWkpLzSZuPGjUIbKysrVlJS8tb9qoKqx3P16tWsd+/e7ODBg6y0tLTSNo8fP67wxjFgwIC36lOVeLw+K7Nq1SoGgJmYmLAVK1aItmDzGs+ZM2cK6/T29maPHj2qsm1ubi4rLCxUSr91jcd4WltbC+tbtmwZUygUr7QpLCxk7733ntBuxIgRb92vKqxcuZItXbqUhYaGstu3bzPGGNu1a1ed/b3xrEU1LtgymYxZWloKYS5dulRlOzs7O6FdRETEWwXt2bOnsK6DBw9W2e79998X2gUFBb1Vn6rAYzxzc3PfqF1WVhYzNDQU+kxPT691n6rC6/X5sps3bzJ9fX0GgG3atKlO30DqEq/xjI6OFtbl5OTEpFLpW61PXfAYz9TU1Apb8zKZrMq2165dq7BnTazq8u+NZy2qccGOiooSQri6ulbbdufOnUoZtLS0tAqfVir7dFim/B/66/KpAx7jWRODBw8W+jxy5IhK+nwb6jCecrmcOTk5MQCsT58+TC6Xi7Zg8xpPT09PYV3x8fFvtS51wmM8z507J6zH2dm52rYlJSVCW319/Vr3yVtd/b3xrkU1Pks8IiJCWB4yZEi1bcv//MSJEzXtqtI+Bw8eDIlEUmVbFxcXGBsbAwDOnDmj9mc88hjPmjAxMRGWi4qKVNLn21CH8fzPf/6D8+fPQ09PD9u3b4eWlnivnuQxnvfv38epU6cAAD169ICDg0Ot16VueIxns2bNhOXbt29Doaj6BhypqanCcteuXWvdp6biXYtq/E5y7do1YblXr17Vtm3WrBlatWoF4MXlBI8ePappdzXuU0dHBz169AAAKBQK3Lx5s1Z9qgqP8ayJ8vnatGlT5/29Ld7jmZaWhmXLlgEAli5dii5durz1OnniMZ5nzpwB+78ZkwcOHAgACAsLw/Dhw9GiRQvo6+vD0tISHh4eCAwMREmJ+t/1qwyP8Wzfvj26desGAHj48CG++eabStsVFRVh3rx5wtcLFy6sVX+ajHctqnHBTk5OFpatrKxe2758m/K/q+59qoo6P7fY2FjcunULANCkSZPXvkDVAc/xZIxh+vTpeP78OTp37owvvvjirdanDniMZ3x8vLDcoUMHjBs3Du+//z6OHj2KrKwslJaWIjs7G5GRkfD19YWtrW2FN1J1xuv1+fPPPwt7y7766ivY2dlh1apV2Lt3L3bu3ImlS5eibdu2iIyMhI6ODn744QeMHTu21v1pKt7v1zW+H/aTJ0+EZQsLi9e2b9y4caW/q+59qoq6PreioiLMnj1b+NrPzw/a2up/72qe4/nTTz8hNjYWEokE27Ztg56e3lutTx3wGM/yE058//33SE1NhZaWFj766CO4u7ujQYMGuHXrFnbs2IGMjAzcvn0bAwYMwOXLl9G6deta9akqvF6fffr0wV9//YWZM2fi3LlzuHr1Kq5evVqhjUQiwZw5czB37lzY2NjUui9Nxvv9usZb2OX3w7/J7ccMDQ2F5YKCgpp2x61PVVHX5zZlyhRh67pXr1749NNP66wvZeI1nvfu3cPSpUsBALNnz0bfvn1rvS51wmM8//33X2E5NTUV+vr6OHnyJEJCQjBt2jSMHTsWX331FW7evAlXV1cAQG5uLubMmVOr/lSJ59+7ra0tfvzxR3h4eFT6c8YYdu/ejTVr1iA3N/et+tJUvN+vRXc2THUH+Yly+Pv748CBAwBefEI8cOAAdHV1OadSbzNmzEBBQQFatmyJ1atX844jai+fFPXFF1/A3d39lXbGxsbYv38/jIyMAADHjx+vcNIU+Z/nz5/Dx8cHvXr1wunTp4UPPMXFxSgoKMCff/6Jjz/+GM+fP8fu3bvRp08fpKen846t1njUohoX7LKz3oAX81G/Tvkzi8ufcVzbPt/kTGVl9KkqPMazOitXrhQKjpmZGU6dOvVGx2rUBY/x3Llzp3BWc2BgIExNTWu1HnXEYzxf/r1PPvmkyraWlpYYPny48HVUVFSt+lQVHuOpUCgwdOhQhISEQE9PD1FRUQgICECnTp2gr68PY2NjODk54ZdffsG6desAAP/88w/Gjx9fq/40Ge9aVOOCbWZmJiw/fvz4te3L71op/7vq3qeqqNNzW7NmjXCGc8OGDXHy5EnY29srtY+6purxzMzMxIIFCwAAo0ePxrBhw2q8DnXG4/Vpbm4uLLdq1QpNmzattn35y77++eefWvWpKjzG89ChQ4iNjQUATJ48Gc7OzlW2XbBggXD8+vz587h48WKt+tRUvN+va3zSmY2NjbCrJD09HW3btq22ffndKrU9kcHGxgYxMTHC+tzc3Oq8T1XhMZ6VWbt2Lfz8/AAApqamOHnyJBwdHZW2flVR9XiGhoYKJ5M0a9YM3377baXtEhIShOXExEShnaWlJaZPn17jflWFx+uzU6dOwvKb7K1o2LChsJyfn1+rPlWFx3geO3ZMWK7q+HUZiUSCgQMHCmc0X7hwQZTvA3WFdy2qccHu1q2bcPH433//jQEDBlTZNjs7G/fv3wcANG3aFE2aNKlVyLJrCMv6nDJlSpVtZTKZ8OaopaUFW1vbWvWpKjzG82Vr1qwRirWJiQkiIiLQu3dvpaxb1VQ9nmXXCwPA5s2b3+h3EhIShNdo2e0h1RWP12f37t2F5Te5xWT5s2/LF291xGM8MzMzheU3+QBUfktQ3SeeUjXetajGu8QHDx4sLL9u9p3w8HBh+XWz+rxpnxERERXeJF9WfkaZ/v37o0GDBrXuVxV4jGd55besjY2NERERAScnJ6Wsmwfe46lpeIyni4uLcLwvIyMD2dnZ1ba/dOmSsKzue9R4jGf5In3v3r3Xtr97966wXP6yJKIGtaimc5nyuhlAr1696OYfSr5Zxdq1a4X1GRsbs7Nnz771OnlTl5t/vEysc4nzGs/JkycL6/r666+rbFf+BjVaWlrs7t27b9VvXeMxngEBAcJ63nvvvWrb/vvvv8zMzExoL5Zblr6sLv/eeNait769ZpcuXVh2dvYrbRYuXCi0qe72cDExMUK7Nm3aVNnu5VuapaamvtJm06ZNFSZmF+PtNVU1nuvWratQrM+cOaOMp6IWeIzn64i1YDPGZzzT09OFu53p6+uzP/7445U2BQUFzM3NTVjf+PHja/X8VE3V45mUlMS0tLSEdsuXL6/0phX5+fnMw8NDaPfuu+9We3MLdVabvzcx1KIaH8MGXlxz+vvvvyMyMhI3btxA9+7dMWPGDNja2iIvLw8hISE4e/YsgBfHlLZu3Vqbbirw8vLClClTsGvXLmRlZaFnz56YPn067O3t8ezZMxw9ehRhYWEAAD09PezYsUM0M02pejy3b9+ORYsWCV9/8sknePz4MQ4fPlzt73Xq1KnCCUHqisfrU5PxGM+2bdvixx9/xOzZs1FSUgIPDw+MGTMGgwYNgpGREW7duoXt27cjIyMDwIt57n/88ce37lcVVD2enTt3xvz58/H9998DAFasWIFjx45hzJgxsLKyglQqRWJiIvbs2SPMMqevr4/AwEBRzHuRnp6OHTt2VPheYmKisJyQkCBc/VLG3t4eI0eOrFV/XGtRbSt9fn4+8/b2Fj5FVPZ455132Llz56pdT022YKRSKZs6dWq1fZqbm7PDhw/X9mlxo8rxnDRpUrX9VPUICAhQ/hOvIzxen9UR8xY2Y/zGc/PmzRXuyV7Zw8HBgd27d09Jz1Q1VD2eCoWC+fv7M21t7df+nVtaWrJTp04p+RnXnfJj8KaPyv4GxVCLal2wyxw+fJiNHDmStWrViunr6zMLCwvWu3dvtnbtWvbkyZPX/n5t3hBjYmLYhAkTmJWVFTMwMGBmZmbMzs6OLV++nGVmZr7lM+JLFeNZHwp2GR6vz8qIvWCX4TGeaWlpbMmSJezdd99lZmZmTE9Pj7Vo0YJ98MEHLCQkhMnl8rd8VvyoejxTU1OZn58f69uWdSsQAAAArElEQVS3L7OwsGC6urrMwMCAvfPOO2zo0KFsy5YtLD8/XwnPTHV4FOzyv6PKWiRhrJrT3AghhBCiFkQ3lzghhBBSH1HBJoQQQkSACjYhhBAiAlSwCSGEEBGggk0IIYSIABVsQgghRASoYBNCCCEiQAWbEEIIEQEq2IQQQogIUMEmhBBCRIAKNiGEECICVLAJIYQQEaCCTQghhIgAFWxCCCFEBKhgE0IIISLw/wFQkPIHZv0UbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 571 ms\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"ozone = surface_ozone()[\\\"spol\\\"].loc[\\\"2010-02-01\\\":\\\"2010-02-28\\\"]\\nmodel = Receptor(ozone, data, station_name=\\\"South Pole\\\")\\ncwt = model.calculate_cwt(weighted=False)\\npscf = model.calculate_pscf(thresh=0.95)\\nrtwc = model.calculate_rtwc(normalise=True)\\nmodel.plot_map(rtwc, boundinglat=-25)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"ozone = surface_ozone()[\\\"spol\\\"].loc[\\\"2010-02-01\\\":\\\"2010-02-28\\\"]\\nmodel = Receptor(ozone, data, station_name=\\\"South Pole\\\")\\ncwt = model.calculate_cwt(weighted=False)\\npscf = model.calculate_pscf(thresh=0.95)\\nrtwc = model.calculate_rtwc(normalise=True)\\nmodel.plot_map(rtwc, boundinglat=-25)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "ozone = surface_ozone()[\"spol\"].loc[\"2010-02-01\":\"2010-02-28\"]\n",
    "model = HyReceptor(ozone, data, station_name=\"South Pole\")\n",
    "cwt = model.calculate_cwt(weighted=False)\n",
    "pscf = model.calculate_pscf(thresh=0.95)\n",
    "rtwc = model.calculate_rtwc(normalise=True)\n",
    "model.plot_map(rtwc, boundinglat=-25)\n",
    "plt.show()"
   ]
  }
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