diff --git a/environment.yml b/environment.yml
index 4577c09..96f0dac 100644
--- a/environment.yml
+++ b/environment.yml
@@ -15,10 +15,11 @@ dependencies:
   - pandas
   - geopandas
   - fiona
+  - hvplot
+  - panel
   - cfgrib
   - pip
   - pip:
-    - -r doc-requirements.txt
     - arm_pyart
     - act-atmos
     - wradlib
diff --git a/notebooks/bnf-cmac-radclss.ipynb b/notebooks/bnf-cmac-radclss.ipynb
index 32a35f4..dfe8990 100644
--- a/notebooks/bnf-cmac-radclss.ipynb
+++ b/notebooks/bnf-cmac-radclss.ipynb
@@ -70,19 +70,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 1,
    "id": "e22ab264-3cec-4824-b8ee-712c2e7a664c",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR 1: PROJ: proj_create_from_database: Open of /autofs/nccsopen-svm1_home/jrobrien/miniforge3/envs/sail-xprecip-dev/share/proj failed\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<dask.config.set at 0x7f025450c2d0>"
+       "<dask.config.set at 0x7fbf7dd37920>"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "0c7b71a6",
    "metadata": {},
    "outputs": [],
@@ -139,9 +163,9 @@
     "# Define the desired processing date for the BNF CSAPR-2 in YYYY-MM-DD format.\n",
     "DATE = \"2025-03-05\"\n",
     "# Define the directory where the BNF CSAPR-2 CMAC files are located.\n",
-    "RADAR_DIR = os.getenv(\"BNF_RADAR_R5_DIR\") + \"202503/\"\n",
+    "RADAR_DIR = \"/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/\"\n",
     "# Define an output directory for downloaded ground instrumentation\n",
-    "INSITU_DIR = os.getenv(\"BNF_INSITU_DIR\")"
+    "INSITU_DIR = \"/gpfs/wolf2/arm/atm124/proj-shared/bnf/in_situ/\""
    ]
   },
   {
@@ -152,7 +176,30 @@
    "outputs": [],
    "source": [
     "# Define the RadCLss file\n",
-    "RADCLSS = sorted(glob.glob(\"data/bnf-csapr2-radclss.c2.\" + \"*\" + DATE.replace('-', '') + '*.nc'))"
+    "RADCLSS = sorted(glob.glob(\"/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2radclssS3.c2/bnfcsapr2radclssS3.c2.\" + \"*\" + DATE.replace('-', '') + '*.nc'))\n",
+    "RADCLSS.extend(sorted(glob.glob(\"/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2radclssS3.c2/bnfcsapr2radclssS3.c2.\" + \"*20250304*.nc\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "751a0258-2e73-4b0a-afd0-3c68dcd20c6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2radclssS3.c2/bnfcsapr2radclssS3.c2.20250305.000000.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2radclssS3.c2/bnfcsapr2radclssS3.c2.20250304.000000.nc']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RADCLSS"
    ]
   },
   {
@@ -165,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 5,
    "id": "4604862a",
    "metadata": {},
    "outputs": [],
@@ -194,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "9c8adcc2",
    "metadata": {},
    "outputs": [],
@@ -285,23 +332,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "0ae0a15f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = xr.open_dataset(RADCLSS[0])"
+    "ds = xr.open_mfdataset(RADCLSS)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "cf6bca3f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x1000 with 11 Axes>"
       ]
@@ -316,11 +363,11 @@
     "\n",
     "fig.suptitle(\"BNF RadCLss - \" + DATE)\n",
     "\n",
-    "ds.sel(station=\"M1\").sel(time=slice(\"2025-03-05T00:00:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[0, 0], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
-    "ds.sel(station=\"S4\").sel(time=slice(\"2025-03-05T00:00:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[0, 1], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
-    "ds.sel(station=\"S20\").sel(time=slice(\"2025-03-05T00:00:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[1, 0], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
-    "ds.sel(station=\"S30\").sel(time=slice(\"2025-03-05T00:00:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[1, 1], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
-    "ds.sel(station=\"S40\").sel(time=slice(\"2025-03-05T00:00:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[2, 0], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
+    "ds.sel(station=\"M1\").sel(time=slice(\"2025-03-04T08:30:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[0, 0], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
+    "ds.sel(station=\"S4\").sel(time=slice(\"2025-03-04T08:30:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[0, 1], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
+    "ds.sel(station=\"S20\").sel(time=slice(\"2025-03-04T08:30:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[1, 0], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
+    "ds.sel(station=\"S30\").sel(time=slice(\"2025-03-04T08:30:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[1, 1], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
+    "ds.sel(station=\"S40\").sel(time=slice(\"2025-03-04T08:30:00\", \"2025-03-05T08:30:00\")).corrected_reflectivity.plot(y=\"height\", ax=axarr[2, 0], vmin=-30, vmax=65, cmap=\"ChaseSpectral\")\n",
     "\n",
     "axarr[2, 1].set_visible(False)"
    ]
@@ -335,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 9,
    "id": "033e4ca3",
    "metadata": {},
    "outputs": [],
@@ -392,7 +439,7 @@
     "    except:\n",
     "        return\n",
     "    \n",
-    "    ds = xr.open_dataset(radclss)\n",
+    "    ds = xr.open_mfdataset(radclss)\n",
     "\n",
     "    # define the sites of interest\n",
     "    nsite = {\"M1\" : [34.34525, -87.33842],\n",
@@ -544,7 +591,7 @@
     "         time=slice(radar_time.strftime(\"%Y-%m-%dT00:00:00\"), radar_time.strftime(\"%Y-%m-%dT%H:%M:%S\"))\n",
     "         ).plot(x=\"time\", \n",
     "                ax=ax3,\n",
-    "                label=\"CMAC\"\n",
+    "                label=\"CMAC - R(A)\"\n",
     "    )\n",
     "\n",
     "    # Pluvio2 Weighing Bucket Rain Gauge\n",
@@ -558,7 +605,7 @@
     "\n",
     "    # LDQUANTS derived rain rate\n",
     "    if dis_site == \"M1\" or dis_site == \"S30\":\n",
-    "        ds[\"rain_rate\"].sel(station=dis_site).sel(\n",
+    "        ds[\"ldquants_rain_rate\"].sel(station=dis_site).sel(\n",
     "            time=slice(radar_time.strftime(\"%Y-%m-%dT00:00:00\"), radar_time.strftime(\"%Y-%m-%dT%H:%M:%S\"))\n",
     "            ).plot(x=\"time\",\n",
     "                   ax=ax3,\n",
@@ -588,7 +635,7 @@
     "         time=slice(radar_time.strftime(\"%Y-%m-%dT00:00:00\"), radar_time.strftime(\"%Y-%m-%dT%H:%M:%S\"))\n",
     "         ).plot(x=\"time\", \n",
     "                ax=ax4,\n",
-    "                label=\"CMAC\"\n",
+    "                label=\"CMAC - R(A)\"\n",
     "    )\n",
     "\n",
     "    # PLUVIO2 Accumulation \n",
@@ -603,7 +650,7 @@
     "\n",
     "    # LDQUANTS Accumulation\n",
     "    if dis_site == \"M1\" or dis_site == \"S30\":\n",
-    "         ld_precip_accum = act.utils.accumulate_precip(ds.sel(station=dis_site), 'rain_rate').rain_rate_accumulated.compute()\n",
+    "         ld_precip_accum = act.utils.accumulate_precip(ds.sel(station=dis_site), 'ldquants_rain_rate').ldquants_rain_rate_accumulated.compute()\n",
     "         ld_precip_accum.sel(\n",
     "            time=slice(radar_time.strftime(\"%Y-%m-%dT00:00:00\"), radar_time.strftime(\"%Y-%m-%dT%H:%M:%S\"))\n",
     "            ).plot(x=\"time\", \n",
@@ -615,7 +662,7 @@
     "    ax4.set_ylabel(\"Accumulated Precipitation [mm]\")\n",
     "    ax4.set_xlabel(\"Time [UTC]\")\n",
     "    ax4.set_xlim([radar_time.strftime(\"%Y-%m-%dT00:00:00\"), final_time.strftime(\"%Y-%m-%dT00:00:00\")])\n",
-    "    ax4.legend(loc=\"upper left\")\n",
+    "    ax4.legend(loc=\"upper right\")\n",
     "    ax4.grid(True)\n",
     "    ax4.set_ylim(0, radar_accum[\"rain_rate_A_accumulated\"].max()+0.25)\n",
     "    # Add a blank space next to the subplot to shape it as the above plot\n",
@@ -657,7 +704,316 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 10,
+   "id": "29332533-7bb1-4136-a59e-10aba948a3b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the RadCLss file\n",
+    "file_list = sorted(glob.glob(RADAR_DIR + \"*\" + DATE.replace('-', '') + '*.0?????*.nc'))\n",
+    "file_list.extend(sorted(glob.glob(RADAR_DIR + \"*20250304*\" + \"1?????*.nc\")))\n",
+    "file_list.extend(sorted(glob.glob(RADAR_DIR + \"*20250304*\" + \"2?????*.nc\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e9b3ede0-96bf-4249-b7df-908abd8eca26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.064047.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.064105.nc',\n",
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+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.070005.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.070950.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.071008.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.071952.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.072010.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.072955.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.073013.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.073957.nc',\n",
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+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.080002.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.080020.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.081005.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.081023.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.082007.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.082025.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.083010.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.083028.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.084012.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.084030.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.085015.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.085033.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.090017.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.090035.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.091020.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.091038.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.092022.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.092040.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.093025.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.093043.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.094027.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.094045.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.095030.nc',\n",
+       " '/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250305.095048.nc']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(file_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "fbfd1bf3",
    "metadata": {},
    "outputs": [
@@ -665,292 +1021,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.212139.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.213001.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.214003.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.214942.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.215002.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.215947.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.220005.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.221014.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.221032.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.222016.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.222034.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.223016.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.223034.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.223956.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.224014.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.224958.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.225016.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.230003.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.230939.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.231002.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.232004.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.233002.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.233935.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.234002.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.234947.nc\n",
-      "/nfs/gce/globalscratch/obrienj/bnf-cmac-r4/bnfcsapr2cmacS3.c1.20250305.235005.nc\n"
+      "/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250304.100139.nc\n",
+      "/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250304.100157.nc\n",
+      "/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250304.101157.nc\n",
+      "/gpfs/wolf2/arm/atm124/proj-shared/bnf/bnfcsapr2cmacS3.c1/bnfcsapr2cmacS3.c1.20250304.101215.nc\n"
      ]
     }
    ],
    "source": [
     "templocation = tempfile.mkdtemp() + \"/\"\n",
-    "for cmac in file_list[2:]:\n",
+    "for cmac in sorted(file_list[2:]):\n",
     "    print(cmac)\n",
     "    mosaic = bnf_mosasic_display(cmac, \n",
-    "                                 RADCLSS[0],\n",
+    "                                 RADCLSS,\n",
     "                                 out_dir=templocation)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "id": "c6ef5b8f",
    "metadata": {},
    "outputs": [],
@@ -1035,9 +1124,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "amf3-radar-examples-dev",
+   "display_name": "sail-mamba",
    "language": "python",
-   "name": "python3"
+   "name": "sail-xprecip-dev"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1049,7 +1138,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/bnf-radclss-dod.ipynb b/notebooks/bnf-radclss-dod.ipynb
index 0adcf85..85ef4b5 100644
--- a/notebooks/bnf-radclss-dod.ipynb
+++ b/notebooks/bnf-radclss-dod.ipynb
@@ -60,20 +60,20 @@
     "            Dataset containing all required parameters and attributes for RadCLss\n",
     "    \"\"\"\n",
     "    cmac_mvc = {'attenuation_corrected_differential_reflectivity' : {'dtype' : np.single, '_FillValue': -9999.0},\n",
-    "            'attenuation_corrected_differential_reflectivity_lag_1' : {'dtype' : np.single, '_FillValue': -9999.0},\n",
-    "            'attenuation_corrected_reflectivity_h' : {'dtype' : np.single, '_FillValue': -9999.0},\n",
-    "            'corrected_velocity' : {'dtype' : np.double, '_FillValue' : -9999.0},\n",
-    "            'corrected_differential_phase' : {'dtype' : np.double, '_FillValue' : -9999.0},\n",
-    "            'filtered_corrected_differential_phase' : {'dtype' : np.double, '_FillValue' : -9999.0},\n",
-    "            'rain_rate_A' : {\"dtype\" : np.double, \"_FillValue\": -9999.0},\n",
-    "            'rain_rate_Z' : {'dtype' : np.double, \"_FillValue\": -9999.0},\n",
-    "            \"rain_rate_Kdp\" : {\"dtype\": np.double, \"_FillValue\": -9999.0}\n",
+    "                'attenuation_corrected_differential_reflectivity_lag_1' : {'dtype' : np.single, '_FillValue': -9999.0},\n",
+    "                'attenuation_corrected_reflectivity_h' : {'dtype' : np.single, '_FillValue': -9999.0},\n",
+    "                'corrected_velocity' : {'dtype' : np.double, '_FillValue' : -9999.0},\n",
+    "                'corrected_differential_phase' : {'dtype' : np.double, '_FillValue' : -9999.0},\n",
+    "                'filtered_corrected_differential_phase' : {'dtype' : np.double, '_FillValue' : -9999.0},\n",
+    "                'rain_rate_A' : {\"dtype\" : np.double, \"_FillValue\": -9999.0},\n",
+    "                'rain_rate_Z' : {'dtype' : np.double, \"_FillValue\": -9999.0},\n",
+    "                'rain_rate_Kdp' : {\"dtype\": np.double, \"_FillValue\": -9999.0}\n",
     "    }\n",
     "\n",
     "    dims = {'time': 1, 'height': 32, 'station': 6}\n",
     "    out_ds = act.io.arm.create_ds_from_arm_dod('csapr2radclss.c2', \n",
     "                                               dims, \n",
-    "                                               version='1.1')\n",
+    "                                               version='1.2')\n",
     "    \n",
     "    for key in cmac_mvc:\n",
     "        out_ds[key].data[out_ds[key].data == -9999] = cmac_mvc[key]['_FillValue']\n",
@@ -520,7 +520,7 @@
        "  * time                                                   (time) int64 8B 0\n",
        "  * height                                                 (height) int64 256B ...\n",
        "  * station                                                (station) int64 48B ...\n",
-       "Data variables: (12/65)\n",
+       "Data variables: (12/63)\n",
        "    base_time                                              float64 8B -9.999e+03\n",
        "    time_offset                                            (time) float64 8B ...\n",
        "    gate_time                                              (time, height, station) float64 2kB ...\n",
@@ -547,8 +547,8 @@
        "    known_issues:          False phidp jumps in insect regions. Still uses ol...\n",
        "    field_names:           base_time, time_offset, time, height, station, gat...\n",
        "    source:                Atmospheric Radiation Measurement (ARM) program C-...\n",
-       "    history:               </pre><div class='xr-wrap' style='display:none'><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-a0d46d67-4a37-4ffa-9563-f8b4db14e6f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0d46d67-4a37-4ffa-9563-f8b4db14e6f1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>height</span>: 32</li><li><span class='xr-has-index'>station</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0fda604a-e6d6-4fa0-b29d-38ee188bf287' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0fda604a-e6d6-4fa0-b29d-38ee188bf287' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-3dca82d8-24a8-40a8-968d-87bf8fe6e7d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dca82d8-24a8-40a8-968d-87bf8fe6e7d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94c4e2c1-9774-4323-a5a6-cf73ee57c5ca' class='xr-var-data-in' type='checkbox'><label for='data-94c4e2c1-9774-4323-a5a6-cf73ee57c5ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 26 27 28 29 30 31</div><input id='attrs-81850096-7a4f-4cca-b26c-18e6ce73654d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81850096-7a4f-4cca-b26c-18e6ce73654d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cd20f01-e49c-4047-a9f8-b85491925459' class='xr-var-data-in' type='checkbox'><label for='data-8cd20f01-e49c-4047-a9f8-b85491925459' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Height Above Ground Level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>station</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-93f6be44-bd58-48cf-9fbc-4329b993650c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93f6be44-bd58-48cf-9fbc-4329b993650c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-398afa2e-f525-45a3-bf14-dd8d59005471' class='xr-var-data-in' type='checkbox'><label for='data-398afa2e-f525-45a3-bf14-dd8d59005471' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bankhead National Forest AMF-3 In-Situ Ground Observation Station Identifiers</dd><dt><span>units :</span></dt><dd>location of in-situ sensors</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6658ab0a-dd9f-4b2e-94a9-4c530111e570' class='xr-section-summary-in' type='checkbox'  ><label for='section-6658ab0a-dd9f-4b2e-94a9-4c530111e570' class='xr-section-summary' >Data variables: <span>(65)</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>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03</div><input id='attrs-22de9602-cb22-4977-88d2-8c9b32883eb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22de9602-cb22-4977-88d2-8c9b32883eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31f08209-c9df-436d-a05d-66b91c4fb67e' class='xr-var-data-in' type='checkbox'><label for='data-31f08209-c9df-436d-a05d-66b91c4fb67e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(-9999.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03</div><input id='attrs-5b210c62-7072-4020-94a8-253a18653ac5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b210c62-7072-4020-94a8-253a18653ac5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-354eb1c9-3e5a-4034-8fd0-e15be5ddb6ff' class='xr-var-data-in' type='checkbox'><label for='data-354eb1c9-3e5a-4034-8fd0-e15be5ddb6ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([-9999.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gate_time</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-94d847f9-8c31-4b48-935f-32c8df240b8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94d847f9-8c31-4b48-935f-32c8df240b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-557a127f-f03c-4152-9332-c4003a1f4c80' class='xr-var-data-in' type='checkbox'><label for='data-557a127f-f03c-4152-9332-c4003a1f4c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time in Seconds Since Midnight that corresponds to the Center of Each Height GateAbove the Target Location</dd><dt><span>units :</span></dt><dd>seconds since 1970-1-1 0:00:00 0:00</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "    history:               </pre><div class='xr-wrap' style='display:none'><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-d906b1a5-5f3b-42c4-9df6-8f1d66961244' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d906b1a5-5f3b-42c4-9df6-8f1d66961244' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>height</span>: 32</li><li><span class='xr-has-index'>station</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-98c4a5ad-adf9-4f34-8ac2-0b1594b78eaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98c4a5ad-adf9-4f34-8ac2-0b1594b78eaf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-91fb2329-e1e7-41c6-932b-2c46d3672f41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91fb2329-e1e7-41c6-932b-2c46d3672f41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b1b55b1-cf9e-401d-85b1-9b0996637794' class='xr-var-data-in' type='checkbox'><label for='data-2b1b55b1-cf9e-401d-85b1-9b0996637794' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 26 27 28 29 30 31</div><input id='attrs-ec00e235-8a5d-407c-ab33-c166bfc9bd41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec00e235-8a5d-407c-ab33-c166bfc9bd41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03700ea1-99c9-4280-a435-8fee71a26520' class='xr-var-data-in' type='checkbox'><label for='data-03700ea1-99c9-4280-a435-8fee71a26520' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Height Above Ground Level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>station</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5</div><input id='attrs-f943ceb3-0e4e-4b27-a7ab-480957bf238a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f943ceb3-0e4e-4b27-a7ab-480957bf238a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2abf0c2a-3793-45da-8228-1349d6292afa' class='xr-var-data-in' type='checkbox'><label for='data-2abf0c2a-3793-45da-8228-1349d6292afa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bankhead National Forest AMF-3 In-Situ Ground Observation Station Identifiers</dd><dt><span>units :</span></dt><dd>location of in-situ sensors</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c5f2337-77d2-4f15-9447-1e4c48704b22' class='xr-section-summary-in' type='checkbox'  ><label for='section-5c5f2337-77d2-4f15-9447-1e4c48704b22' class='xr-section-summary' >Data variables: <span>(63)</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>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03</div><input id='attrs-eac86f7c-26e2-440b-889f-e035cdbb5a12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eac86f7c-26e2-440b-889f-e035cdbb5a12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a1f8cd-379d-4964-96cc-97acb484df57' class='xr-var-data-in' type='checkbox'><label for='data-f4a1f8cd-379d-4964-96cc-97acb484df57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(-9999.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03</div><input id='attrs-5ce415b8-7230-41ea-b2a2-df1567c6f2cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce415b8-7230-41ea-b2a2-df1567c6f2cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f98cd484-36f1-4903-8b27-379d6db89232' class='xr-var-data-in' type='checkbox'><label for='data-f98cd484-36f1-4903-8b27-379d6db89232' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([-9999.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gate_time</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-d497367a-e194-4438-be9d-3efcc83fe560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d497367a-e194-4438-be9d-3efcc83fe560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96137371-88a9-461b-a0e1-c7d009b85b7f' class='xr-var-data-in' type='checkbox'><label for='data-96137371-88a9-461b-a0e1-c7d009b85b7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time in Seconds Since Midnight that corresponds to the Center of Each Height GateAbove the Target Location</dd><dt><span>units :</span></dt><dd>seconds since 1970-1-1 0:00:00 0:00</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -579,7 +579,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-2fa2d219-ad96-433c-b9d2-97aec2df1091' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fa2d219-ad96-433c-b9d2-97aec2df1091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cc242fa-0f87-4dff-8b2b-40ee3a180a38' class='xr-var-data-in' type='checkbox'><label for='data-9cc242fa-0f87-4dff-8b2b-40ee3a180a38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected differential reflectivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>applied_bias_correction :</span></dt><dd>-0.4732</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-e6e9f097-6880-4822-a105-0e856935ae62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6e9f097-6880-4822-a105-0e856935ae62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33214351-5896-4114-9acc-ef26d85d1f00' class='xr-var-data-in' type='checkbox'><label for='data-33214351-5896-4114-9acc-ef26d85d1f00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected differential reflectivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>applied_bias_correction :</span></dt><dd>-0.4732</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -610,7 +610,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity_lag_1</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-cd6787fc-af56-48e1-a139-10b0823c5d40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd6787fc-af56-48e1-a139-10b0823c5d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09d0c22a-119f-4c5e-b7a7-ecb221ce2fe0' class='xr-var-data-in' type='checkbox'><label for='data-09d0c22a-119f-4c5e-b7a7-ecb221ce2fe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Differential reflectivity estimated at lag 1 corrected for rainfall attenuation</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>applied_bias_correction :</span></dt><dd>-0.4732</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity_lag_1</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-78d4bc80-ff0a-46b7-8f3e-e968f07d1297' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78d4bc80-ff0a-46b7-8f3e-e968f07d1297' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0436bff3-143c-41c6-bd6b-4abcdce09ac1' class='xr-var-data-in' type='checkbox'><label for='data-0436bff3-143c-41c6-bd6b-4abcdce09ac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Differential reflectivity estimated at lag 1 corrected for rainfall attenuation</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>applied_bias_correction :</span></dt><dd>-0.4732</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -641,7 +641,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_reflectivity_h</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-64c5da64-8966-481f-b89c-e30eccfc3db6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64c5da64-8966-481f-b89c-e30eccfc3db6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dedff711-1634-44f4-8e06-3beffc5a60cf' class='xr-var-data-in' type='checkbox'><label for='data-dedff711-1634-44f4-8e06-3beffc5a60cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected reflectivity, horizontal channel</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>applied_bias_correction :</span></dt><dd>2.2</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_reflectivity_h</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-162d8d37-6492-4622-bf73-63a7611fc828' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-162d8d37-6492-4622-bf73-63a7611fc828' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a07636cb-fda9-41a4-8530-0c8cff04bd8b' class='xr-var-data-in' type='checkbox'><label for='data-a07636cb-fda9-41a4-8530-0c8cff04bd8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected reflectivity, horizontal channel</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>applied_bias_correction :</span></dt><dd>2.2</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -672,7 +672,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_velocity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-3e2685dc-2124-4a70-8e88-1aba0d9cde06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e2685dc-2124-4a70-8e88-1aba0d9cde06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38542d4d-64cf-4ce5-b514-1f6332a30100' class='xr-var-data-in' type='checkbox'><label for='data-38542d4d-64cf-4ce5-b514-1f6332a30100' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected mean doppler velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>corrected_radial_velocity_of_scatterers_away_from_instrument</dd><dt><span>valid_min :</span></dt><dd>-49.476</dd><dt><span>valid_max :</span></dt><dd>49.476</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_velocity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f671061e-4a34-426e-94cb-51f9dd59bcbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f671061e-4a34-426e-94cb-51f9dd59bcbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b54d78c-932e-406e-9fe7-bd7582ebb18d' class='xr-var-data-in' type='checkbox'><label for='data-8b54d78c-932e-406e-9fe7-bd7582ebb18d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected mean doppler velocity</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>corrected_radial_velocity_of_scatterers_away_from_instrument</dd><dt><span>valid_min :</span></dt><dd>-49.476</dd><dt><span>valid_max :</span></dt><dd>49.476</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -703,7 +703,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-25289954-c8e8-44c5-8985-c40f997c03a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25289954-c8e8-44c5-8985-c40f997c03a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9cca34b-bb00-4e37-bc85-5f4b183e1e52' class='xr-var-data-in' type='checkbox'><label for='data-a9cca34b-bb00-4e37-bc85-5f4b183e1e52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected Differential Phase</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-40fed792-5713-4891-8da5-3f06597236ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40fed792-5713-4891-8da5-3f06597236ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3673b86b-05a9-4c3c-b884-e33485126d0c' class='xr-var-data-in' type='checkbox'><label for='data-3673b86b-05a9-4c3c-b884-e33485126d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected Differential Phase</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -734,7 +734,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_differential_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-b67e88c3-9a39-431a-aad4-7422fa6a584a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b67e88c3-9a39-431a-aad4-7422fa6a584a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cc03c64-c4b8-44d5-9203-f4eb20cb432b' class='xr-var-data-in' type='checkbox'><label for='data-8cc03c64-c4b8-44d5-9203-f4eb20cb432b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Filtered Corrected Differential Phase</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_differential_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-b2d2fe37-eab1-40ea-b762-1aed85f5cca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2d2fe37-eab1-40ea-b762-1aed85f5cca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1ae6b34-82e4-4581-8e45-a43a912b4ec7' class='xr-var-data-in' type='checkbox'><label for='data-c1ae6b34-82e4-4581-8e45-a43a912b4ec7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Filtered Corrected Differential Phase</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -765,7 +765,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-8b2ec285-2755-42e3-a62f-231c8c6d8e09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b2ec285-2755-42e3-a62f-231c8c6d8e09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0db0b2f7-cdd3-40c8-8481-bce8e6769307' class='xr-var-data-in' type='checkbox'><label for='data-0db0b2f7-cdd3-40c8-8481-bce8e6769307' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected Specific differential phase (KDP)</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-e9db450b-0043-4f15-86fd-9b8800be38cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9db450b-0043-4f15-86fd-9b8800be38cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81fc0667-ba0c-48af-82cc-2ae424ddcb44' class='xr-var-data-in' type='checkbox'><label for='data-81fc0667-ba0c-48af-82cc-2ae424ddcb44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected Specific differential phase (KDP)</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -796,7 +796,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-0a2ceb6c-3d50-4735-b364-90a483dad8f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a2ceb6c-3d50-4735-b364-90a483dad8f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38d65a13-a1c9-40ea-b9d0-ee97c8ebcd08' class='xr-var-data-in' type='checkbox'><label for='data-38d65a13-a1c9-40ea-b9d0-ee97c8ebcd08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Filtered Corrected Specific differential phase (KDP)</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-92fbb3c7-114b-49e2-8812-71374e750c3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92fbb3c7-114b-49e2-8812-71374e750c3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd5f0ce3-de44-438c-b4d5-43e81c7b519e' class='xr-var-data-in' type='checkbox'><label for='data-dd5f0ce3-de44-438c-b4d5-43e81c7b519e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Filtered Corrected Specific differential phase (KDP)</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -827,7 +827,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-42e59f5e-71b5-49ec-8629-1ad02a83e382' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42e59f5e-71b5-49ec-8629-1ad02a83e382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1ee2197-a0b9-441c-9989-88d077b72596' class='xr-var-data-in' type='checkbox'><label for='data-d1ee2197-a0b9-441c-9989-88d077b72596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected differential reflectivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>corrected_log_differential_reflectivity_hv</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-ef69c178-7f09-4165-8e98-80f7856d9c6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef69c178-7f09-4165-8e98-80f7856d9c6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72fa14a4-e9df-428c-908f-38e70f5bca04' class='xr-var-data-in' type='checkbox'><label for='data-72fa14a4-e9df-428c-908f-38e70f5bca04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected differential reflectivity</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>corrected_log_differential_reflectivity_hv</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -858,7 +858,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-935d7547-dbb4-481d-8e7e-e4a7f3d23f15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-935d7547-dbb4-481d-8e7e-e4a7f3d23f15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d081995-0c42-4416-88bd-42961029ee77' class='xr-var-data-in' type='checkbox'><label for='data-5d081995-0c42-4416-88bd-42961029ee77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected reflectivity</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-5e252422-7c02-47f0-8d45-6ea5a33f5220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e252422-7c02-47f0-8d45-6ea5a33f5220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6c53a5a-b681-40d3-bd3c-ea3ba84d11cd' class='xr-var-data-in' type='checkbox'><label for='data-e6c53a5a-b681-40d3-bd3c-ea3ba84d11cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Corrected reflectivity</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -889,7 +889,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-6333c30a-d83a-4443-a172-76b0d0fa25c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6333c30a-d83a-4443-a172-76b0d0fa25c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6653186c-f181-407a-ad5b-4d4ca366c400' class='xr-var-data-in' type='checkbox'><label for='data-6653186c-f181-407a-ad5b-4d4ca366c400' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall rate from specific attenuation</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from specific_attenuation, R=51.3*specific_attenuation**0.81, note R=0.0 where norm coherent power &lt; 0.4 or rhohv &lt; 0.8</dd><dt><span>A_coefficient :</span></dt><dd>51.3</dd><dt><span>B_exponent :</span></dt><dd>0.81</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-0389c998-d936-47df-8b7d-05ee4f7e1288' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0389c998-d936-47df-8b7d-05ee4f7e1288' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc29ee97-5ba6-474d-af12-9770d1546388' class='xr-var-data-in' type='checkbox'><label for='data-dc29ee97-5ba6-474d-af12-9770d1546388' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall rate from specific attenuation</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from specific_attenuation, R=51.3*specific_attenuation**0.81, note R=0.0 where norm coherent power &lt; 0.4 or rhohv &lt; 0.8</dd><dt><span>A_coefficient :</span></dt><dd>51.3</dd><dt><span>B_exponent :</span></dt><dd>0.81</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -920,7 +920,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Z</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-587b64ac-99e1-4524-8072-f6f1c6e0dac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-587b64ac-99e1-4524-8072-f6f1c6e0dac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06c63389-e50b-4411-828e-652129f7c039' class='xr-var-data-in' type='checkbox'><label for='data-06c63389-e50b-4411-828e-652129f7c039' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall rate from reflectivity</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from R = AZe^B</dd><dt><span>A_coefficient :</span></dt><dd>0.017</dd><dt><span>B_exponent :</span></dt><dd>0.714</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Z</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-c9825efd-d403-4ddb-8a9c-6e680262e8f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9825efd-d403-4ddb-8a9c-6e680262e8f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da74b3b-13e8-4aea-9704-ab74aac08ccd' class='xr-var-data-in' type='checkbox'><label for='data-8da74b3b-13e8-4aea-9704-ab74aac08ccd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall rate from reflectivity</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from R = AZe^B</dd><dt><span>A_coefficient :</span></dt><dd>0.017</dd><dt><span>B_exponent :</span></dt><dd>0.714</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -951,7 +951,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Kdp</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-8e66f7f5-b658-4285-9fb3-54cd3f145a06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e66f7f5-b658-4285-9fb3-54cd3f145a06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-788a7e63-efed-41bb-8060-71bfc8649de7' class='xr-var-data-in' type='checkbox'><label for='data-788a7e63-efed-41bb-8060-71bfc8649de7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall rate from specific differential phase</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from R = AKdp^B</dd><dt><span>A_coefficient :</span></dt><dd>294</dd><dt><span>B_exponent :</span></dt><dd>0.89</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Kdp</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f419aeaa-5e66-4938-9363-6057f24bef3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f419aeaa-5e66-4938-9363-6057f24bef3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-412d2a74-787a-4cdf-ac2c-efc028f81443' class='xr-var-data-in' type='checkbox'><label for='data-412d2a74-787a-4cdf-ac2c-efc028f81443' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rainfall rate from specific differential phase</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>least_significant_digit :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from R = AKdp^B</dd><dt><span>A_coefficient :</span></dt><dd>294</dd><dt><span>B_exponent :</span></dt><dd>0.89</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -982,7 +982,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atmos_pressure</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-0afd7c6c-7f5a-4aa7-bf24-e78088d3e0b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0afd7c6c-7f5a-4aa7-bf24-e78088d3e0b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0175527-d1a5-4b38-8a52-0015955b66b5' class='xr-var-data-in' type='checkbox'><label for='data-d0175527-d1a5-4b38-8a52-0015955b66b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Atmospheric Pressure</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-4f82c2b4-4c0e-4f3d-8148-9583563ab9e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f82c2b4-4c0e-4f3d-8148-9583563ab9e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a10f987-6e80-4cfd-8fd9-0ba0908e079e' class='xr-var-data-in' type='checkbox'><label for='data-9a10f987-6e80-4cfd-8fd9-0ba0908e079e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-40</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-a6876890-5b5c-497c-87f1-013881f48858' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6876890-5b5c-497c-87f1-013881f48858' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c4031a-7829-47b9-9df1-5c73c5d19bf2' class='xr-var-data-in' type='checkbox'><label for='data-a4c4031a-7829-47b9-9df1-5c73c5d19bf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative humidity mean</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>-2</dd><dt><span>valid_max :</span></dt><dd>104</dd><dt><span>valid_delta :</span></dt><dd>30</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vapor_pressure_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-2b6af64c-f366-4030-975f-47424bad37fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b6af64c-f366-4030-975f-47424bad37fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2996e28-f66f-4d06-8c2f-ee8033397a0d' class='xr-var-data-in' type='checkbox'><label for='data-f2996e28-f66f-4d06-8c2f-ee8033397a0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vapor pressure mean, calculated</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10</dd><dt><span>valid_delta :</span></dt><dd>1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>water_vapor_partial_pressure_in_air</dd><dt><span>comment :</span></dt><dd>The calculation is done with respect to ice or water, depending on the measured temperature being below or above 0 degC, respectively</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_arith_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-b9908dca-f4a3-4784-9a77-4ddb9f0fda72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9908dca-f4a3-4784-9a77-4ddb9f0fda72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae1330c8-834b-4c93-9be6-b73282bba950' class='xr-var-data-in' type='checkbox'><label for='data-ae1330c8-834b-4c93-9be6-b73282bba950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed arithmetic mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>60</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_vec_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-90ac73db-57ca-4a1c-aa36-7c12d9ed82a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90ac73db-57ca-4a1c-aa36-7c12d9ed82a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0d0c472-65c6-4668-b254-c238cf5141ff' class='xr-var-data-in' type='checkbox'><label for='data-f0d0c472-65c6-4668-b254-c238cf5141ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed vector mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>60</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdir_vec_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-c9288bb6-d253-4a4d-90ad-4ef3192320cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9288bb6-d253-4a4d-90ad-4ef3192320cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31318d22-7c47-4126-8651-1e2fb21fbb2e' class='xr-var-data-in' type='checkbox'><label for='data-31318d22-7c47-4126-8651-1e2fb21fbb2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction vector mean</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>360</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tbrg_precip_total_corr</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-feb71285-e23c-4e83-be55-aa9fc6bf7359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feb71285-e23c-4e83-be55-aa9fc6bf7359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b29c0c6-a193-4100-b64a-e64e23bd5084' class='xr-var-data-in' type='checkbox'><label for='data-4b29c0c6-a193-4100-b64a-e64e23bd5084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>TBRG precipitation total, corrected</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_pres</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-eb70fad3-6e75-4a8c-899c-7c2caa96b9a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb70fad3-6e75-4a8c-899c-7c2caa96b9a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8866792-9a2d-4e3b-8a22-26ecadb3fd4f' class='xr-var-data-in' type='checkbox'><label for='data-f8866792-9a2d-4e3b-8a22-26ecadb3fd4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1100</dd><dt><span>valid_delta :</span></dt><dd>10</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atmos_pressure</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-6531a117-d106-435b-932b-1ce66b4c7df5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6531a117-d106-435b-932b-1ce66b4c7df5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43b89f46-8e8a-4b63-8527-93a07f7db3ab' class='xr-var-data-in' type='checkbox'><label for='data-43b89f46-8e8a-4b63-8527-93a07f7db3ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Atmospheric Pressure</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-81ad389f-8a8e-4224-97db-64e1fc0af156' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81ad389f-8a8e-4224-97db-64e1fc0af156' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c6cf016-107b-48ad-9930-a3d0ae127ffd' class='xr-var-data-in' type='checkbox'><label for='data-3c6cf016-107b-48ad-9930-a3d0ae127ffd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-40</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-ec8ab150-1ded-4e83-ac57-4f5650807ef3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec8ab150-1ded-4e83-ac57-4f5650807ef3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-063ce8fe-5a15-42db-9e3e-564bf90b875b' class='xr-var-data-in' type='checkbox'><label for='data-063ce8fe-5a15-42db-9e3e-564bf90b875b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative humidity mean</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>-2</dd><dt><span>valid_max :</span></dt><dd>104</dd><dt><span>valid_delta :</span></dt><dd>30</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vapor_pressure_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-c0f97964-5720-496a-9f39-956c95df9417' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0f97964-5720-496a-9f39-956c95df9417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1603c08f-a8c0-425d-a02f-3ad1d6a18eb5' class='xr-var-data-in' type='checkbox'><label for='data-1603c08f-a8c0-425d-a02f-3ad1d6a18eb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vapor pressure mean, calculated</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10</dd><dt><span>valid_delta :</span></dt><dd>1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>water_vapor_partial_pressure_in_air</dd><dt><span>comment :</span></dt><dd>The calculation is done with respect to ice or water, depending on the measured temperature being below or above 0 degC, respectively</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_arith_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-efc22b30-9d14-4db6-bae2-a22ed85b7b0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc22b30-9d14-4db6-bae2-a22ed85b7b0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0c54c4-82df-4cf9-8393-68b4605203b9' class='xr-var-data-in' type='checkbox'><label for='data-8d0c54c4-82df-4cf9-8393-68b4605203b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed arithmetic mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>60</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_vec_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-a68ea122-5564-4ae4-b481-381f41701866' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a68ea122-5564-4ae4-b481-381f41701866' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43199cd3-c654-4339-97f4-e132149c1057' class='xr-var-data-in' type='checkbox'><label for='data-43199cd3-c654-4339-97f4-e132149c1057' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed vector mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>60</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdir_vec_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-6823e655-53c7-4776-98d3-0fa2596e56bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6823e655-53c7-4776-98d3-0fa2596e56bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47db8e47-0b5e-4ebf-a22a-289f033ac3a8' class='xr-var-data-in' type='checkbox'><label for='data-47db8e47-0b5e-4ebf-a22a-289f033ac3a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction vector mean</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>360</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tbrg_precip_total_corr</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-60980b93-e18a-401e-a845-2e021d690878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60980b93-e18a-401e-a845-2e021d690878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287f62f7-1edc-4dbc-897d-249152f2150d' class='xr-var-data-in' type='checkbox'><label for='data-287f62f7-1edc-4dbc-897d-249152f2150d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>TBRG precipitation total, corrected</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>10</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_pres</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-5f8d0830-4292-4a8e-a2c9-e1ba7b65d1d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f8d0830-4292-4a8e-a2c9-e1ba7b65d1d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdb175ae-4635-42a4-b198-4baa1174afa0' class='xr-var-data-in' type='checkbox'><label for='data-bdb175ae-4635-42a4-b198-4baa1174afa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>1100</dd><dt><span>valid_delta :</span></dt><dd>10</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1013,7 +1013,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_tdry</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-9d839c17-32c5-46b7-9cec-ae5190beeb30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d839c17-32c5-46b7-9cec-ae5190beeb30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eec163b6-1429-4199-9b6c-fa49d296cf32' class='xr-var-data-in' type='checkbox'><label for='data-eec163b6-1429-4199-9b6c-fa49d296cf32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dry Bulb Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>10</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_tdry</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f539763e-cd96-4fba-8066-bd9e1180a15b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f539763e-cd96-4fba-8066-bd9e1180a15b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-129c870f-9743-4766-b365-7122943829c8' class='xr-var-data-in' type='checkbox'><label for='data-129c870f-9743-4766-b365-7122943829c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dry Bulb Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>10</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1044,7 +1044,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_dp</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-dd28edf8-e083-49f7-b684-a6483b83c07b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd28edf8-e083-49f7-b684-a6483b83c07b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0e229df-7194-4fa0-9a46-a5bbe6c90531' class='xr-var-data-in' type='checkbox'><label for='data-f0e229df-7194-4fa0-9a46-a5bbe6c90531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dewpoint Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-110</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_dp</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-7c4dcb4a-87c8-4c6e-8119-910b9e7383e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c4dcb4a-87c8-4c6e-8119-910b9e7383e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b1caf5b-f240-4cbb-a9d7-4d1306dcc129' class='xr-var-data-in' type='checkbox'><label for='data-9b1caf5b-f240-4cbb-a9d7-4d1306dcc129' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dewpoint Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-110</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1075,7 +1075,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_wspd</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-b75100d6-ec20-4ab6-b1fe-c7a81034fb16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b75100d6-ec20-4ab6-b1fe-c7a81034fb16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4338107a-3d45-4a14-933d-8115f0d6066e' class='xr-var-data-in' type='checkbox'><label for='data-4338107a-3d45-4a14-933d-8115f0d6066e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_wspd</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-5bf5e8b4-9428-426c-9a03-464018577aff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf5e8b4-9428-426c-9a03-464018577aff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cbefb76-f11f-4a89-bf16-bf98d354e492' class='xr-var-data-in' type='checkbox'><label for='data-7cbefb76-f11f-4a89-bf16-bf98d354e492' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1106,7 +1106,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_deg</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-c61bcae5-6f6a-4c1b-a8a3-bacfeb14ed8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c61bcae5-6f6a-4c1b-a8a3-bacfeb14ed8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f906d9c-2af1-48e4-8cae-5de6d97c6808' class='xr-var-data-in' type='checkbox'><label for='data-5f906d9c-2af1-48e4-8cae-5de6d97c6808' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind Direction</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>360</dd><dt><span>resolution :</span></dt><dd>1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_deg</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-681f3970-4ea7-4d2c-bbbe-14af0141bb8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-681f3970-4ea7-4d2c-bbbe-14af0141bb8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-724debc2-eb39-4de8-b129-3516b12d5f61' class='xr-var-data-in' type='checkbox'><label for='data-724debc2-eb39-4de8-b129-3516b12d5f61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind Direction</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>360</dd><dt><span>resolution :</span></dt><dd>1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1137,7 +1137,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_rh</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-e0cb8ec1-0820-4b4b-81bd-b6e9e413f859' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0cb8ec1-0820-4b4b-81bd-b6e9e413f859' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2467817-ec1e-4875-8417-60f33aedc275' class='xr-var-data-in' type='checkbox'><label for='data-e2467817-ec1e-4875-8417-60f33aedc275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>resolution :</span></dt><dd>1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_rh</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-98cf0fd1-a75b-48a1-b030-79ff4bad3d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98cf0fd1-a75b-48a1-b030-79ff4bad3d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-387a257c-a814-44e5-80e8-7f1fe5078a85' class='xr-var-data-in' type='checkbox'><label for='data-387a257c-a814-44e5-80e8-7f1fe5078a85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>resolution :</span></dt><dd>1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1168,7 +1168,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_u_wind</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f79fd381-771f-472d-bac1-e54f59e71480' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f79fd381-771f-472d-bac1-e54f59e71480' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f33a75fb-79ac-4ded-84ab-8146bdb739bf' class='xr-var-data-in' type='checkbox'><label for='data-f33a75fb-79ac-4ded-84ab-8146bdb739bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Eastward Wind Component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>-75</dd><dt><span>valid_max :</span></dt><dd>75</dd><dt><span>valid_delta :</span></dt><dd>5</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>calculation :</span></dt><dd>(-1.0 * sin(wind direction) * wind speed)</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_u_wind</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-844c2ddd-2391-420e-be7b-9a331630f5a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-844c2ddd-2391-420e-be7b-9a331630f5a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07035f48-8f16-4ecc-906f-2a498fc34a5e' class='xr-var-data-in' type='checkbox'><label for='data-07035f48-8f16-4ecc-906f-2a498fc34a5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Eastward Wind Component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>-75</dd><dt><span>valid_max :</span></dt><dd>75</dd><dt><span>valid_delta :</span></dt><dd>5</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>calculation :</span></dt><dd>(-1.0 * sin(wind direction) * wind speed)</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1199,7 +1199,7 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_v_wind</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-73cea45c-9ff1-4c2a-9b09-f1322db97bfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73cea45c-9ff1-4c2a-9b09-f1322db97bfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-626b33dc-4324-42df-8ff2-d9c25a4503ed' class='xr-var-data-in' type='checkbox'><label for='data-626b33dc-4324-42df-8ff2-d9c25a4503ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Northward Wind Component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>-75</dd><dt><span>valid_max :</span></dt><dd>75</dd><dt><span>valid_delta :</span></dt><dd>5</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>calculation :</span></dt><dd>(-1.0 * cos(wind direction) * wind speed)</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_v_wind</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-3d05c87f-3715-403c-bc5e-a6e9fc3e4853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d05c87f-3715-403c-bc5e-a6e9fc3e4853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc30471-1e59-49f8-a612-705143db9862' class='xr-var-data-in' type='checkbox'><label for='data-acc30471-1e59-49f8-a612-705143db9862' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Northward Wind Component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>-75</dd><dt><span>valid_max :</span></dt><dd>75</dd><dt><span>valid_delta :</span></dt><dd>5</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>calculation :</span></dt><dd>(-1.0 * cos(wind direction) * wind speed)</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd></dl></div><div class='xr-var-data'><pre>array([[[-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
@@ -1230,9 +1230,9 @@
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
        "        [-9999., -9999., -9999., -9999., -9999., -9999.],\n",
-       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-1c858e3b-6024-4291-bf70-2996bf01c8df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c858e3b-6024-4291-bf70-2996bf01c8df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92e19000-b74d-40d5-a0a1-d58bd4a32328' class='xr-var-data-in' type='checkbox'><label for='data-92e19000-b74d-40d5-a0a1-d58bd4a32328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Heavy precipitation alarm</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3000</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>6 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>comment_1 :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd><dt><span>comment_2 :</span></dt><dd>The time bounds for the sum is [-60, 0]</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_rtnrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-83b78d4a-371d-4a57-b564-4130302aea96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83b78d4a-371d-4a57-b564-4130302aea96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a613f67-579b-41ec-94e7-697ce26f325d' class='xr-var-data-in' type='checkbox'><label for='data-0a613f67-579b-41ec-94e7-697ce26f325d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Accumulated amounts of precipitation over the sampling interval exceeding a threshold of 0.05mm or the accumulated amount of fine precipitation observed over the last hour</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_rtnrt variable is calculated by first measuring the accumulated amount of rain in the last minute. If this measurement exceeds the threshold, it reports this real time value. If the real time measurement does not reach the threshold, it reports the non real time measurement using the same equation as the accum_nrt variable.</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_nrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-8d0e8554-70e8-4377-8324-c631008e6b1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d0e8554-70e8-4377-8324-c631008e6b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac8366be-0502-4f6f-b5cc-6101d8cf4bcf' class='xr-var-data-in' type='checkbox'><label for='data-ac8366be-0502-4f6f-b5cc-6101d8cf4bcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Accumulated precipitation over the sampling interval filtered and delayed by 5 minute</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_nrt variable is calculated by measuring the amount of rain accumulate in a sampling interval at most 1 hour long, with the end of the interval at the given time. The start of the sampling interval occurs within the past hour, but is unknown. The start of the interval is determined once the accumulated sum either exceeds 0.05 or the interval length reaches an hour</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bucket_nrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-40dea18b-0a0f-4b11-882d-a91c6e4fad54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40dea18b-0a0f-4b11-882d-a91c6e4fad54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3ac5f0b-0e81-4055-b78a-498d62212f26' class='xr-var-data-in' type='checkbox'><label for='data-f3ac5f0b-0e81-4055-b78a-498d62212f26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The currently measured, filtered bucket contents since last reset</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>20</dd><dt><span>valid_max :</span></dt><dd>1800</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.01 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>comment :</span></dt><dd>Only increases that exceed the threshold are recorded. Any increase less than threshold is reported as no increase</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rtnrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f48d965a-517d-46f7-bd74-52e051f67926' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f48d965a-517d-46f7-bd74-52e051f67926' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebb9817e-5b5b-463d-9eee-8ffe747190b4' class='xr-var-data-in' type='checkbox'><label for='data-ebb9817e-5b5b-463d-9eee-8ffe747190b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rain intensity based upon accum_rtnrt</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>30000</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.3 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>equation :</span></dt><dd>Calculated by accum_rtnrt * 60</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_rain_rate</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-4c63fef9-080e-4db6-8232-e22d2533d402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c63fef9-080e-4db6-8232-e22d2533d402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ef46129-3e49-4b3d-9f5d-5a08c1056d64' class='xr-var-data-in' type='checkbox'><label for='data-8ef46129-3e49-4b3d-9f5d-5a08c1056d64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Instantaneous Rainfall Rate of Water Flux</dd><dt><span>units :</span></dt><dd>mm/hour</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_reflectivity_factor_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-4a2416da-b2b2-498a-9f55-ee523d33ecf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a2416da-b2b2-498a-9f55-ee523d33ecf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74f32140-eb9e-429a-bd49-86da5f14ec29' class='xr-var-data-in' type='checkbox'><label for='data-74f32140-eb9e-429a-bd49-86da5f14ec29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Estimated Horizontal Radar Reflectivity from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_differential_reflectivity_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f70377b6-591b-4978-9d3a-faa0ac54e937' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f70377b6-591b-4978-9d3a-faa0ac54e937' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0532acd2-3970-4221-b007-80f1adc9ab1a' class='xr-var-data-in' type='checkbox'><label for='data-0532acd2-3970-4221-b007-80f1adc9ab1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Estimated Differential Radar Reflectivity (H, V) from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_differential_phase_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-2643c075-900c-4718-931a-a96a6276599e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2643c075-900c-4718-931a-a96a6276599e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde71b12-e3ae-4661-8478-c1895da807ff' class='xr-var-data-in' type='checkbox'><label for='data-dde71b12-e3ae-4661-8478-c1895da807ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Specific Differential Phase from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-e1c7f417-3fd7-490b-a93a-94c2d5d6e1f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1c7f417-3fd7-490b-a93a-94c2d5d6e1f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8e1f326-158f-4b62-b798-67d5c533fc44' class='xr-var-data-in' type='checkbox'><label for='data-c8e1f326-158f-4b62-b798-67d5c533fc44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Specific Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_med_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-539d5511-6844-4142-b491-3749bdec0bc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-539d5511-6844-4142-b491-3749bdec0bc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0b35885-c8cc-4478-9407-773098b02cc9' class='xr-var-data-in' type='checkbox'><label for='data-a0b35885-c8cc-4478-9407-773098b02cc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Median Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_mass_weighted_mean_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-de304535-7979-4815-b48f-b0fde69895e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de304535-7979-4815-b48f-b0fde69895e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b5422b6-fb7d-41c8-ada9-e5735a29e597' class='xr-var-data-in' type='checkbox'><label for='data-3b5422b6-fb7d-41c8-ada9-e5735a29e597' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Mean Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_lwc</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-7782766f-59cb-4e16-8e1a-6b58e7fde5ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7782766f-59cb-4e16-8e1a-6b58e7fde5ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c783b2a4-c4e8-490b-802d-fbbeb48b6b27' class='xr-var-data-in' type='checkbox'><label for='data-c783b2a4-c4e8-490b-802d-fbbeb48b6b27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Liquid Water Content</dd><dt><span>units :</span></dt><dd>g/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_total_droplet_concentration</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-f2fa5057-895d-4990-83c9-a02378fba4b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2fa5057-895d-4990-83c9-a02378fba4b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f88d0693-a38e-4012-b64b-f5de6577d9cb' class='xr-var-data-in' type='checkbox'><label for='data-f88d0693-a38e-4012-b64b-f5de6577d9cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Total Droplet Concentration</dd><dt><span>units :</span></dt><dd>1/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_mean_doppler_vel_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-1b0ad8a8-a49c-4b82-a979-7f0c12fbc902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b0ad8a8-a49c-4b82-a979-7f0c12fbc902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35fbe877-f8a7-40ad-9edb-523be6f93bbd' class='xr-var-data-in' type='checkbox'><label for='data-35fbe877-f8a7-40ad-9edb-523be6f93bbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Mean Doppler Velocity C-band when temperature is 20C</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_differential_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-a92a470f-2675-4a46-9e6c-57d3e5e71821' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a92a470f-2675-4a46-9e6c-57d3e5e71821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8713834-8e56-4e63-9e84-11b3b601f8c8' class='xr-var-data-in' type='checkbox'><label for='data-d8713834-8e56-4e63-9e84-11b3b601f8c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Specific Differential Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_rain_rate</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-cf8aad33-16ed-46cd-a31e-4d75dda9df97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf8aad33-16ed-46cd-a31e-4d75dda9df97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42e40d37-cb0e-4572-a3a5-47d13fe6d5cb' class='xr-var-data-in' type='checkbox'><label for='data-42e40d37-cb0e-4572-a3a5-47d13fe6d5cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Instantaneous Rainfall Rate of Water Flux</dd><dt><span>units :</span></dt><dd>mm/hour</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_reflectivity_factor_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-d6475893-b0b5-438c-b8ee-dcd0c1ea1a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6475893-b0b5-438c-b8ee-dcd0c1ea1a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c88f92e9-d077-4039-8cf8-691024380291' class='xr-var-data-in' type='checkbox'><label for='data-c88f92e9-d077-4039-8cf8-691024380291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Horizontal Radar Reflectivity from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_differential_reflectivity_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-52053a9d-a957-40ce-88a6-982edc61afa7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52053a9d-a957-40ce-88a6-982edc61afa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6192074d-c20d-4eae-9dd5-7674f4789a79' class='xr-var-data-in' type='checkbox'><label for='data-6192074d-c20d-4eae-9dd5-7674f4789a79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Differential Radar Reflectivity (H, V) from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_differential_phase_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-562531c7-51ca-4ee4-b54a-8f42e1d5d10d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-562531c7-51ca-4ee4-b54a-8f42e1d5d10d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e14bf646-5a92-41d4-b644-a40d2b20d7b6' class='xr-var-data-in' type='checkbox'><label for='data-e14bf646-5a92-41d4-b644-a40d2b20d7b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Phase from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>deg/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-64b2ee51-960e-4c33-8b9b-1ceaffb51669' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64b2ee51-960e-4c33-8b9b-1ceaffb51669' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d5f18c0-ed82-43d0-9448-d2f97fc849bd' class='xr-var-data-in' type='checkbox'><label for='data-5d5f18c0-ed82-43d0-9448-d2f97fc849bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_med_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-40e5a4a5-ba7a-4f51-b05e-3e69103ebc9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40e5a4a5-ba7a-4f51-b05e-3e69103ebc9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf10def3-6af4-4b5c-92d5-f99bcac15efd' class='xr-var-data-in' type='checkbox'><label for='data-cf10def3-6af4-4b5c-92d5-f99bcac15efd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Median Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_mass_weighted_mean_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-d555beda-351c-4bf3-9338-d7b50539b7cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d555beda-351c-4bf3-9338-d7b50539b7cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77307752-7165-486a-9414-20ab7edc321a' class='xr-var-data-in' type='checkbox'><label for='data-77307752-7165-486a-9414-20ab7edc321a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_lwc</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-525ee256-13af-40c1-9521-10291df249f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-525ee256-13af-40c1-9521-10291df249f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2bec8b3-f9a3-4c90-acfb-198ff89f1fe7' class='xr-var-data-in' type='checkbox'><label for='data-a2bec8b3-f9a3-4c90-acfb-198ff89f1fe7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Liquid Water Content</dd><dt><span>units :</span></dt><dd>g/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_total_droplet_concentration</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-57c19467-6e57-45e4-a31f-1e78b0905e1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57c19467-6e57-45e4-a31f-1e78b0905e1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-888bb74e-3f46-4cdf-956c-cfcd4bbbb0df' class='xr-var-data-in' type='checkbox'><label for='data-888bb74e-3f46-4cdf-956c-cfcd4bbbb0df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total Droplet Concentration</dd><dt><span>units :</span></dt><dd>1/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_mean_doppler_vel_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-295078e5-a1d4-4bb4-aa2b-1140f8f5b5cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-295078e5-a1d4-4bb4-aa2b-1140f8f5b5cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-892a45f4-1415-44b7-8ccb-c301b78fab00' class='xr-var-data-in' type='checkbox'><label for='data-892a45f4-1415-44b7-8ccb-c301b78fab00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Doppler Velocity C-band when temperature is 20C</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_differential_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-c7cf1cad-bb29-40d7-9557-ad461e02a8d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7cf1cad-bb29-40d7-9557-ad461e02a8d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e8b809d-5d68-415f-8248-e1e727fc14b5' class='xr-var-data-in' type='checkbox'><label for='data-9e8b809d-5d68-415f-8248-e1e727fc14b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_temp_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-3c8aaf98-cceb-41d6-a4cf-489b34f9600e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c8aaf98-cceb-41d6-a4cf-489b34f9600e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-566dbb4f-6398-45c6-97fb-7b58abf75d86' class='xr-var-data-in' type='checkbox'><label for='data-566dbb4f-6398-45c6-97fb-7b58abf75d86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vaisala Weather Transmitter (WXT) Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>valid_min :</span></dt><dd>-40</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_precip_rate_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-d0b3346c-0dfe-462c-9273-a8d3f8627a66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0b3346c-0dfe-462c-9273-a8d3f8627a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72c5b66a-e7fe-49d9-aead-386848b36630' class='xr-var-data-in' type='checkbox'><label for='data-72c5b66a-e7fe-49d9-aead-386848b36630' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WXT mean precipitation rate</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>200</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_cumul_precip</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-0f6867dc-d3fe-4f8d-9c7a-c6378bce103b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f6867dc-d3fe-4f8d-9c7a-c6378bce103b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-295796da-4006-4e80-89b8-fcea1b26f0dd' class='xr-var-data-in' type='checkbox'><label for='data-295796da-4006-4e80-89b8-fcea1b26f0dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WXT cumulative precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-7391d49c-b3cb-4f53-a62e-245401db031a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7391d49c-b3cb-4f53-a62e-245401db031a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28dea877-0d5e-4431-8794-960af7e90785' class='xr-var-data-in' type='checkbox'><label for='data-28dea877-0d5e-4431-8794-960af7e90785' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>90</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-9999., -9999., -9999., -9999., -9999., -9999.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-6702820f-e40e-4ad9-a5c6-8622a924d81f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6702820f-e40e-4ad9-a5c6-8622a924d81f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ec3c25-012c-40af-8bd5-10a9b0f1808a' class='xr-var-data-in' type='checkbox'><label for='data-57ec3c25-012c-40af-8bd5-10a9b0f1808a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180</dd><dt><span>valid_max :</span></dt><dd>180</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-9999., -9999., -9999., -9999., -9999., -9999.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-b322416c-857e-4e89-9cbc-d66050f1d4f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b322416c-857e-4e89-9cbc-d66050f1d4f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123a3233-090c-46fe-92bf-c023e4c8b9f4' class='xr-var-data-in' type='checkbox'><label for='data-123a3233-090c-46fe-92bf-c023e4c8b9f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>array([-9999., -9999., -9999., -9999., -9999., -9999.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa23ce20-8b75-4f63-93e7-fbceb46af381' class='xr-section-summary-in' type='checkbox'  ><label for='section-fa23ce20-8b75-4f63-93e7-fbceb46af381' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-22a5c5a2-8bc0-4b1d-8c13-e00465c3518f' class='xr-index-data-in' type='checkbox'/><label for='index-22a5c5a2-8bc0-4b1d-8c13-e00465c3518f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3c832403-db81-4a7d-895b-b90ba1c243e5' class='xr-index-data-in' type='checkbox'/><label for='index-3c832403-db81-4a7d-895b-b90ba1c243e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "        [-9999., -9999., -9999., -9999., -9999., -9999.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-39637ca3-4a25-4e99-9d17-3ccd49c2690d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39637ca3-4a25-4e99-9d17-3ccd49c2690d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5ef082c-935e-47fb-b758-6422bd88800f' class='xr-var-data-in' type='checkbox'><label for='data-b5ef082c-935e-47fb-b758-6422bd88800f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Heavy precipitation alarm</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>3000</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>6 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>comment_1 :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd><dt><span>comment_2 :</span></dt><dd>The time bounds for the sum is [-60, 0]</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_rtnrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-ed12d8de-04e1-4df1-8a2d-49762b45f3e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed12d8de-04e1-4df1-8a2d-49762b45f3e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-951af400-36b1-4687-992f-2b895c12ed33' class='xr-var-data-in' type='checkbox'><label for='data-951af400-36b1-4687-992f-2b895c12ed33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Accumulated amounts of precipitation over the sampling interval exceeding a threshold of 0.05mm or the accumulated amount of fine precipitation observed over the last hour</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_rtnrt variable is calculated by first measuring the accumulated amount of rain in the last minute. If this measurement exceeds the threshold, it reports this real time value. If the real time measurement does not reach the threshold, it reports the non real time measurement using the same equation as the accum_nrt variable.</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_nrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-351fcd87-d82c-4f2f-abf2-f12fffb08fda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-351fcd87-d82c-4f2f-abf2-f12fffb08fda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cef12f33-cc49-47ee-b494-a1033833510a' class='xr-var-data-in' type='checkbox'><label for='data-cef12f33-cc49-47ee-b494-a1033833510a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Accumulated precipitation over the sampling interval filtered and delayed by 5 minute</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_nrt variable is calculated by measuring the amount of rain accumulate in a sampling interval at most 1 hour long, with the end of the interval at the given time. The start of the sampling interval occurs within the past hour, but is unknown. The start of the interval is determined once the accumulated sum either exceeds 0.05 or the interval length reaches an hour</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bucket_nrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-06370554-c39d-4b2b-89bb-afecd280f30d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06370554-c39d-4b2b-89bb-afecd280f30d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adc24a0a-1580-409c-b147-d48936117ab2' class='xr-var-data-in' type='checkbox'><label for='data-adc24a0a-1580-409c-b147-d48936117ab2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The currently measured, filtered bucket contents since last reset</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>20</dd><dt><span>valid_max :</span></dt><dd>1800</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.01 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>comment :</span></dt><dd>Only increases that exceed the threshold are recorded. Any increase less than threshold is reported as no increase</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rtnrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-cc3212b1-eee4-40dc-af46-24dc1162d422' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc3212b1-eee4-40dc-af46-24dc1162d422' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3c5eb1f-d65b-4846-8658-e2241fe98fe9' class='xr-var-data-in' type='checkbox'><label for='data-d3c5eb1f-d65b-4846-8658-e2241fe98fe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rain intensity based upon accum_rtnrt</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>30000</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>threshold :</span></dt><dd>0.3 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>equation :</span></dt><dd>Calculated by accum_rtnrt * 60</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_rain_rate</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-2e92fa65-d264-48ec-8976-168bbf45b0a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e92fa65-d264-48ec-8976-168bbf45b0a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3985f160-a866-40c5-8155-73df882a79b8' class='xr-var-data-in' type='checkbox'><label for='data-3985f160-a866-40c5-8155-73df882a79b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Instantaneous Rainfall Rate of Water Flux</dd><dt><span>units :</span></dt><dd>mm/hour</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_reflectivity_factor_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-770a5e8d-4ae8-4bd8-bc8d-84ea3725b9fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-770a5e8d-4ae8-4bd8-bc8d-84ea3725b9fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86afcc1e-50a9-4219-a2b6-f0aadf685bea' class='xr-var-data-in' type='checkbox'><label for='data-86afcc1e-50a9-4219-a2b6-f0aadf685bea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Estimated Horizontal Radar Reflectivity from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_differential_reflectivity_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-453255ba-ef80-4b8e-be7a-cff7d71f24ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-453255ba-ef80-4b8e-be7a-cff7d71f24ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81506b2a-d2c5-4b32-828c-b2bf1ebe2f36' class='xr-var-data-in' type='checkbox'><label for='data-81506b2a-d2c5-4b32-828c-b2bf1ebe2f36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Estimated Differential Radar Reflectivity (H, V) from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_differential_phase_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-0981f4ec-2085-4405-8fa9-045c4b7b1a4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0981f4ec-2085-4405-8fa9-045c4b7b1a4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9ed2cde-b834-4b26-a373-c6d48610613b' class='xr-var-data-in' type='checkbox'><label for='data-b9ed2cde-b834-4b26-a373-c6d48610613b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Specific Differential Phase from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-23667462-c31b-4c3b-9722-4833cfb86d81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23667462-c31b-4c3b-9722-4833cfb86d81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe834790-8ff1-41d9-b3a1-e045dcef21de' class='xr-var-data-in' type='checkbox'><label for='data-fe834790-8ff1-41d9-b3a1-e045dcef21de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Specific Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_med_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-7cdf37b7-7c83-4a0e-8937-3996506f36f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cdf37b7-7c83-4a0e-8937-3996506f36f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0257a520-0b9c-46a9-ad3d-eb8eb0723bf5' class='xr-var-data-in' type='checkbox'><label for='data-0257a520-0b9c-46a9-ad3d-eb8eb0723bf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Median Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_mass_weighted_mean_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-190ae339-c854-4c80-a976-7d60568998ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-190ae339-c854-4c80-a976-7d60568998ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90f890e6-99f4-4c9c-8fd9-4b864f2f4c3e' class='xr-var-data-in' type='checkbox'><label for='data-90f890e6-99f4-4c9c-8fd9-4b864f2f4c3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Mean Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_lwc</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-1b43f4fa-7d93-4119-81af-4c379903a918' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b43f4fa-7d93-4119-81af-4c379903a918' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e823d488-4385-4211-9d61-b603681ffbef' class='xr-var-data-in' type='checkbox'><label for='data-e823d488-4385-4211-9d61-b603681ffbef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Liquid Water Content</dd><dt><span>units :</span></dt><dd>g/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_total_droplet_concentration</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-9b484529-783a-4157-9952-ef9bc5ad9683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b484529-783a-4157-9952-ef9bc5ad9683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d983d7a-7543-4673-b825-bd743561e839' class='xr-var-data-in' type='checkbox'><label for='data-8d983d7a-7543-4673-b825-bd743561e839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Total Droplet Concentration</dd><dt><span>units :</span></dt><dd>1/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_differential_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-70300f96-c5b3-4584-8348-2ae7c9981c12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70300f96-c5b3-4584-8348-2ae7c9981c12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-745ef9a3-96c8-449c-af33-cc3a228e7cc0' class='xr-var-data-in' type='checkbox'><label for='data-745ef9a3-96c8-449c-af33-cc3a228e7cc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>LDQUANTS Specific Differential Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_rain_rate</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-1e2b0f6b-00e3-4256-b4a1-cab5782c610b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e2b0f6b-00e3-4256-b4a1-cab5782c610b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e33fba0-f2da-48ab-8031-a20b5bbdfb96' class='xr-var-data-in' type='checkbox'><label for='data-3e33fba0-f2da-48ab-8031-a20b5bbdfb96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Instantaneous Rainfall Rate of Water Flux</dd><dt><span>units :</span></dt><dd>mm/hour</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_reflectivity_factor_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-6e954d2f-b95b-423b-b489-45e45eb025e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e954d2f-b95b-423b-b489-45e45eb025e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd14701a-4110-4856-b80a-d059b0980405' class='xr-var-data-in' type='checkbox'><label for='data-dd14701a-4110-4856-b80a-d059b0980405' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Horizontal Radar Reflectivity from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_differential_reflectivity_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-4f61fcbd-8e32-43f8-af94-b69e06ab9c7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f61fcbd-8e32-43f8-af94-b69e06ab9c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8f251ea-e3e6-4b28-b277-4f2288af846c' class='xr-var-data-in' type='checkbox'><label for='data-d8f251ea-e3e6-4b28-b277-4f2288af846c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Differential Radar Reflectivity (H, V) from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_differential_phase_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-46ca1c30-fda2-4464-970d-20db2fd1f362' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46ca1c30-fda2-4464-970d-20db2fd1f362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97d8f204-1ba3-4d0f-99c2-854ec91dd59b' class='xr-var-data-in' type='checkbox'><label for='data-97d8f204-1ba3-4d0f-99c2-854ec91dd59b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Phase from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>deg/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-e1a75dd2-a4e6-4bcb-87b6-bbd6be5a8415' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1a75dd2-a4e6-4bcb-87b6-bbd6be5a8415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d79cfbe-1e25-4039-a8b2-b502acf7b080' class='xr-var-data-in' type='checkbox'><label for='data-7d79cfbe-1e25-4039-a8b2-b502acf7b080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_med_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-ebe671e2-93bf-4f83-8ea1-b285dc46682e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebe671e2-93bf-4f83-8ea1-b285dc46682e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a31ac39-80b1-4f40-a4d0-83c073e187d4' class='xr-var-data-in' type='checkbox'><label for='data-7a31ac39-80b1-4f40-a4d0-83c073e187d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Median Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_mass_weighted_mean_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-322de428-ee2c-41b4-9f30-9b30c93d33b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-322de428-ee2c-41b4-9f30-9b30c93d33b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3f2ef9c-f9dc-4f39-a786-782dbe0f8c1c' class='xr-var-data-in' type='checkbox'><label for='data-b3f2ef9c-f9dc-4f39-a786-782dbe0f8c1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_lwc</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-5d4270a7-8bed-4887-859b-1244d6252151' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d4270a7-8bed-4887-859b-1244d6252151' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2abd6354-3294-46a1-b8a6-17ea80009b29' class='xr-var-data-in' type='checkbox'><label for='data-2abd6354-3294-46a1-b8a6-17ea80009b29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Liquid Water Content</dd><dt><span>units :</span></dt><dd>g/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_total_droplet_concentration</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-33de9f11-07b7-4ce4-8dc8-a0207ae92fe0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33de9f11-07b7-4ce4-8dc8-a0207ae92fe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786c440b-114f-407b-a5bc-63e77b813721' class='xr-var-data-in' type='checkbox'><label for='data-786c440b-114f-407b-a5bc-63e77b813721' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total Droplet Concentration</dd><dt><span>units :</span></dt><dd>1/m^3</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_differential_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-8f0855fc-19d6-4cef-8ce7-51223436b302' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f0855fc-19d6-4cef-8ce7-51223436b302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2eb75b9-0d83-47c0-b8da-4aaa5e6a6cfa' class='xr-var-data-in' type='checkbox'><label for='data-d2eb75b9-0d83-47c0-b8da-4aaa5e6a6cfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_temp_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-830e5bc3-249a-45b7-896b-dbaef3d428b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-830e5bc3-249a-45b7-896b-dbaef3d428b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f66b20c8-2755-4c44-ac90-0d70e99148c8' class='xr-var-data-in' type='checkbox'><label for='data-f66b20c8-2755-4c44-ac90-0d70e99148c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vaisala Weather Transmitter (WXT) Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>valid_min :</span></dt><dd>-40</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_precip_rate_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-9176b848-25bf-4465-9e7f-266808fc5ed1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9176b848-25bf-4465-9e7f-266808fc5ed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eba46408-a1d1-4282-8dfd-8831e9988ed3' class='xr-var-data-in' type='checkbox'><label for='data-eba46408-a1d1-4282-8dfd-8831e9988ed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WXT mean precipitation rate</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>200</dd><dt><span>missing_value :</span></dt><dd>-9999</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_cumul_precip</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-d5154436-e332-4fd3-b916-3f97089135cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5154436-e332-4fd3-b916-3f97089135cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b000e43e-3472-4c11-8cec-4b67b369e50d' class='xr-var-data-in' type='checkbox'><label for='data-b000e43e-3472-4c11-8cec-4b67b369e50d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WXT cumulative precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([[-9999., -9999., -9999., -9999., -9999., -9999.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-d1558c24-b5fb-4ffb-9c3b-da6aadf2f77b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1558c24-b5fb-4ffb-9c3b-da6aadf2f77b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8589f655-7352-482e-9937-17e9f893b6f5' class='xr-var-data-in' type='checkbox'><label for='data-8589f655-7352-482e-9937-17e9f893b6f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>90</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-9999., -9999., -9999., -9999., -9999., -9999.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-3b716471-7b1b-498a-a5a8-e4a3a9251edf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b716471-7b1b-498a-a5a8-e4a3a9251edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63fa72cc-dd69-4335-af38-2fd98c58d51a' class='xr-var-data-in' type='checkbox'><label for='data-63fa72cc-dd69-4335-af38-2fd98c58d51a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180</dd><dt><span>valid_max :</span></dt><dd>180</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-9999., -9999., -9999., -9999., -9999., -9999.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-9.999e+03 ... -9.999e+03</div><input id='attrs-99ffbb24-c9f6-494d-95dd-6caebb023e69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99ffbb24-c9f6-494d-95dd-6caebb023e69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1e7e569-ae03-4eb9-9df8-733131d6c8db' class='xr-var-data-in' type='checkbox'><label for='data-d1e7e569-ae03-4eb9-9df8-733131d6c8db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>array([-9999., -9999., -9999., -9999., -9999., -9999.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-173ea6fc-4bb5-4dc5-bba6-d70477592d26' class='xr-section-summary-in' type='checkbox'  ><label for='section-173ea6fc-4bb5-4dc5-bba6-d70477592d26' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-00289479-8d80-4308-ae2e-9301733ff5e1' class='xr-index-data-in' type='checkbox'/><label for='index-00289479-8d80-4308-ae2e-9301733ff5e1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1b27e248-048d-483c-880e-3a3aecb9b554' class='xr-index-data-in' type='checkbox'/><label for='index-1b27e248-048d-483c-880e-3a3aecb9b554' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>station</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b3d5a8b7-b6be-4436-9c6b-ef238ef13681' class='xr-index-data-in' type='checkbox'/><label for='index-b3d5a8b7-b6be-4436-9c6b-ef238ef13681' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;station&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34fd2cb6-c8b7-4f84-a345-fa167acdf9c1' class='xr-section-summary-in' type='checkbox'  ><label for='section-34fd2cb6-c8b7-4f84-a345-fa167acdf9c1' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF/Radial instrument_parameters ARM-1.3</dd><dt><span>command_line :</span></dt><dd></dd><dt><span>process_version :</span></dt><dd></dd><dt><span>dod_version :</span></dt><dd></dd><dt><span>input_datastreams :</span></dt><dd></dd><dt><span>site_id :</span></dt><dd></dd><dt><span>platform_id :</span></dt><dd></dd><dt><span>facility_id :</span></dt><dd></dd><dt><span>data_level :</span></dt><dd></dd><dt><span>location_description :</span></dt><dd></dd><dt><span>datastream :</span></dt><dd></dd><dt><span>institution :</span></dt><dd>U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility</dd><dt><span>references :</span></dt><dd>See CSAPR2 Instrument Handbook</dd><dt><span>doi :</span></dt><dd>10.5439/2586707</dd><dt><span>comment :</span></dt><dd>This is highly experimental and initial data. There are many known and unknown issues. Please do not use before contacting the Translator responsible scollis@anl.gov</dd><dt><span>attributions :</span></dt><dd>This data is collected by the ARM User Facility. Radar system is operated by the radar engineering team radar@arm.gov and the data is processed by the precipitation radar products team. LP code courtesy of Scott Giangrande, BNL.</dd><dt><span>version :</span></dt><dd>1.0</dd><dt><span>vap_name :</span></dt><dd>radclss</dd><dt><span>known_issues :</span></dt><dd>False phidp jumps in insect regions. Still uses old Giangrande code.</dd><dt><span>field_names :</span></dt><dd>base_time, time_offset, time, height, station, gate_time, attenuation_corrected_differential_reflectivity, attenuation_corrected_differential_reflectivity_lag_1, attenuation_corrected_reflectivity_h, corrected_velocity, corrected_differential_phase, filtered_corrected_differential_phase, corrected_specific_diff_phase, filtered_corrected_specific_diff_phase, corrected_differential_reflectivity, corrected_reflectivity, rain_rate_A, rain_rate_Z, rain_rate_Kdp, atmos_pressure, temp_mean, rh_mean, vapor_pressure_mean, wspd_arith_mean, wspd_vec_mean, wdir_vec_mean, tbrg_precip_total_corr, sonde_pres, sonde_tdry, sonde_dp, sonde_wspd, sonde_deg, sonde_rh, sonde_u_wind, sonde_v_wind, intensity_rt, accum_rtnrt, accum_nrt, bucket_nrt, intensity_rtnrt, ldquants_rain_rate, ldquants_reflectivity_factor_cband20c, ldquants_differential_reflectivity_cband20c, ldquants_specific_differential_phase_cband20c, ldquants_specific_attenuation_cband20c, ldquants_med_diameter, ldquants_mass_weighted_mean_diameter, ldquants_lwc, ldquants_total_droplet_concentration, ldquants_mean_doppler_vel_cband20c, ldquants_specific_differential_attenuation_cband20c, vdisquants_specific_differential_phase_cband20c, vdisquants_specific_attenuation_cband20c, vdisquants_med_diameter, vdisquants_mass_weighted_mean_diameter, vdisquants_lwc, vdisquants_total_droplet_concentration, vdisquants_specific_differential_attenuation_cband20c, wxt_temp_mean, wxt_precip_rate_mean, wxt_cumul_precip, lat, lon, alt</dd><dt><span>source :</span></dt><dd>Atmospheric Radiation Measurement (ARM) program C-band Scanning ARM Precipitation Radar 2 (CSAPR2) Corrected Moments in Antenna Coordinates (CMAC)</dd><dt><span>history :</span></dt><dd></dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;int64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>station</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ba90e2a9-b845-4bc8-8820-a78686a8b235' class='xr-index-data-in' type='checkbox'/><label for='index-ba90e2a9-b845-4bc8-8820-a78686a8b235' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5], dtype=&#x27;int64&#x27;, name=&#x27;station&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d431e16a-618b-4756-b95e-b989dd2943d0' class='xr-section-summary-in' type='checkbox'  ><label for='section-d431e16a-618b-4756-b95e-b989dd2943d0' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF/Radial instrument_parameters ARM-1.3</dd><dt><span>command_line :</span></dt><dd></dd><dt><span>process_version :</span></dt><dd></dd><dt><span>dod_version :</span></dt><dd></dd><dt><span>input_datastreams :</span></dt><dd></dd><dt><span>site_id :</span></dt><dd></dd><dt><span>platform_id :</span></dt><dd></dd><dt><span>facility_id :</span></dt><dd></dd><dt><span>data_level :</span></dt><dd></dd><dt><span>location_description :</span></dt><dd></dd><dt><span>datastream :</span></dt><dd></dd><dt><span>institution :</span></dt><dd>U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility</dd><dt><span>references :</span></dt><dd>See CSAPR2 Instrument Handbook</dd><dt><span>doi :</span></dt><dd>10.5439/2586707</dd><dt><span>comment :</span></dt><dd>This is highly experimental and initial data. There are many known and unknown issues. Please do not use before contacting the Translator responsible scollis@anl.gov</dd><dt><span>attributions :</span></dt><dd>This data is collected by the ARM User Facility. Radar system is operated by the radar engineering team radar@arm.gov and the data is processed by the precipitation radar products team. LP code courtesy of Scott Giangrande, BNL.</dd><dt><span>version :</span></dt><dd>1.0</dd><dt><span>vap_name :</span></dt><dd>radclss</dd><dt><span>known_issues :</span></dt><dd>False phidp jumps in insect regions. Still uses old Giangrande code.</dd><dt><span>field_names :</span></dt><dd>base_time, time_offset, time, height, station, gate_time, attenuation_corrected_differential_reflectivity, attenuation_corrected_differential_reflectivity_lag_1, attenuation_corrected_reflectivity_h, corrected_velocity, corrected_differential_phase, filtered_corrected_differential_phase, corrected_specific_diff_phase, filtered_corrected_specific_diff_phase, corrected_differential_reflectivity, corrected_reflectivity, rain_rate_A, rain_rate_Z, rain_rate_Kdp, atmos_pressure, temp_mean, rh_mean, vapor_pressure_mean, wspd_arith_mean, wspd_vec_mean, wdir_vec_mean, tbrg_precip_total_corr, sonde_pres, sonde_tdry, sonde_dp, sonde_wspd, sonde_deg, sonde_rh, sonde_u_wind, sonde_v_wind, intensity_rt, accum_rtnrt, accum_nrt, bucket_nrt, intensity_rtnrt, ldquants_rain_rate, ldquants_reflectivity_factor_cband20c, ldquants_differential_reflectivity_cband20c, ldquants_specific_differential_phase_cband20c, ldquants_specific_attenuation_cband20c, ldquants_med_diameter, ldquants_mass_weighted_mean_diameter, ldquants_lwc, ldquants_total_droplet_concentration, ldquants_mean_doppler_vel_cband20c, ldquants_specific_differential_attenuation_cband20c, vdisquants_specific_differential_phase_cband20c, vdisquants_specific_attenuation_cband20c, vdisquants_med_diameter, vdisquants_mass_weighted_mean_diameter, vdisquants_lwc, vdisquants_total_droplet_concentration, vdisquants_specific_differential_attenuation_cband20c, wxt_temp_mean, wxt_precip_rate_mean, wxt_cumul_precip, lat, lon, alt</dd><dt><span>source :</span></dt><dd>Atmospheric Radiation Measurement (ARM) program C-band Scanning ARM Precipitation Radar 2 (CSAPR2) Corrected Moments in Antenna Coordinates (CMAC)</dd><dt><span>history :</span></dt><dd></dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 36kB\n",
@@ -1243,7 +1243,7 @@
        "  * time                                                   (time) int64 8B 0\n",
        "  * height                                                 (height) int64 256B ...\n",
        "  * station                                                (station) int64 48B ...\n",
-       "Data variables: (12/65)\n",
+       "Data variables: (12/63)\n",
        "    base_time                                              float64 8B -9.999e+03\n",
        "    time_offset                                            (time) float64 8B ...\n",
        "    gate_time                                              (time, height, station) float64 2kB ...\n",
@@ -1284,12 +1284,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "476842a9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "dod.to_netcdf(\"data/radclss/bnf-csapr2-radclss.dod.v1.1.nc\")"
+    "dod.to_netcdf(\"data/radclss/bnf-csapr2-radclss.dod.v1.2.nc\")"
    ]
   },
   {
@@ -5051,7 +5051,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "amf3-radar-examples-dev",
+   "display_name": "crocus",
    "language": "python",
    "name": "python3"
   },
@@ -5065,7 +5065,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.12"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/bnf-radclss.ipynb b/notebooks/bnf-radclss.ipynb
index b880960..8e51431 100644
--- a/notebooks/bnf-radclss.ipynb
+++ b/notebooks/bnf-radclss.ipynb
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 65,
    "id": "e22ab264-3cec-4824-b8ee-712c2e7a664c",
    "metadata": {
     "scrolled": true
@@ -83,10 +83,10 @@
     {
      "data": {
       "text/plain": [
-       "<dask.config.set at 0x7fa9bc1ade10>"
+       "<dask.config.set at 0x7fd0cf416450>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 66,
    "id": "0c7b71a6",
    "metadata": {},
    "outputs": [],
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 67,
    "id": "4f0b9b5c",
    "metadata": {},
    "outputs": [],
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 68,
    "id": "855ec645",
    "metadata": {},
    "outputs": [],
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 69,
    "id": "a521b48c",
    "metadata": {},
    "outputs": [],
@@ -316,7 +316,6 @@
     "                'wxt' : ['base_time',\n",
     "                         'time_offset',\n",
     "                         'time_bounds',\n",
-    "                         'temp_mean',\n",
     "                         'qc_temp_mean',\n",
     "                         'temp_std',\n",
     "                         'rh_mean',\n",
@@ -354,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 70,
    "id": "2de88caa",
    "metadata": {},
    "outputs": [],
@@ -523,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 71,
    "id": "db8c86e6",
    "metadata": {},
    "outputs": [],
@@ -532,7 +531,6 @@
     "                       ground, \n",
     "                       site, \n",
     "                       discard, \n",
-    "                       resample='sum', \n",
     "                       DataSet=False,\n",
     "                       prefix=None):\n",
     "    \"\"\"\n",
@@ -557,11 +555,6 @@
     "        List containing the desired input ground instrumentation variables to be \n",
     "        removed from the xarray DataSet. \n",
     "    \n",
-    "    resample : str\n",
-    "        Mathematical operational for resampling ground instrumentation to the radar time.\n",
-    "        Default is to sum the data across the resampling period. Checks for 'mean' or \n",
-    "        to 'skip' altogether. \n",
-    "    \n",
     "    DataSet : boolean\n",
     "        Boolean flag to determine if ground input is an Xarray Dataset.\n",
     "        Set to True if ground input is Xarray DataSet. \n",
@@ -596,20 +589,61 @@
     "    # If so, first interpolate heights to match radar, before interpolating time.\n",
     "    if 'height' in grd_ds.dims:\n",
     "        grd_ds = grd_ds.interp(height=np.arange(500, 8500, 250), method='linear')\n",
-    "        \n",
-    "    # Resample the ground data to 5 min and interpolate to the CSAPR-2 base time. \n",
-    "    # Keep data variable attributes to help distingish between instruments/locations\n",
-    "    if resample.split('=')[-1] == 'mean':\n",
-    "        matched = grd_ds.resample(time='5Min', \n",
-    "                                  closed='right').mean(keep_attrs=True).interp(time=column.time, \n",
-    "                                                                               method='linear')\n",
-    "    elif resample.split('=')[-1] == 'skip':\n",
-    "        matched = grd_ds.interp(time=column.time, method='linear')\n",
-    "    else:\n",
-    "        matched = grd_ds.resample(time='5Min', \n",
-    "                                  closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
-    "                                                                              method='linear')\n",
+    "\n",
+    "    # Create blank DataSet to store individual interpolation results\n",
+    "    matched = xr.Dataset()\n",
     "    \n",
+    "    for var_name, da in grd_ds.data_vars.items():\n",
+    "        if var_name == \"tbrg_precip_total_corr\":\n",
+    "            interpol_da = da.resample(time='5Min', \n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"intensity_rt\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"accum_rtnrt\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"accum_nrt\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"intensity_rtnrt\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"wxt_cumul_precip\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"ldquants_rain_rate\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"ldquants_lwc\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"vdisquants_rain_rate\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        elif var_name == \"vdisquants_lwc\":\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').sum(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                  method='linear')\n",
+    "        else:\n",
+    "            interpol_da = da.resample(time=\"5min\",\n",
+    "                                      closed='right').mean(keep_attrs=True).interp(time=column.time, \n",
+    "                                                                                   method='linear')\n",
+    "        # Check for ancillary variables\n",
+    "        if hasattr(interpol_da, \"ancillary_variables\"):\n",
+    "            del interpol_da.attrs['ancillary_variables']\n",
+    "        # assign to the matched dataset\n",
+    "        matched[var_name] = interpol_da  \n",
+    "\n",
     "    # Add SAIL site location as a dimension for the Pluvio data\n",
     "    matched = matched.assign_coords(coords=dict(station=site))\n",
     "    matched = matched.expand_dims('station')\n",
@@ -626,7 +660,7 @@
     "    # global attributes will be lost on merging into the matched dataset.\n",
     "    # Need to keep as many references and descriptors as possible\n",
     "    for var in matched.data_vars:\n",
-    "        matched[var].attrs.update(source=matched.datastream)\n",
+    "        matched[var].attrs.update(source=grd_ds.datastream)\n",
     "        \n",
     "    # Merge the two DataSets\n",
     "    column = xr.merge([column, matched])\n",
@@ -636,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 72,
    "id": "d8c80ac1",
    "metadata": {},
    "outputs": [],
@@ -674,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 73,
    "id": "56e05cfa",
    "metadata": {},
    "outputs": [],
@@ -773,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 74,
    "id": "5cc35db1-9ef4-4838-8f81-2b93d798540f",
    "metadata": {},
    "outputs": [
@@ -861,9 +895,11 @@
       "https://doi.org/10.5439/1432694\n",
       "\n",
       "No files returned or url status error.\n",
-      "Check datastream name, start, and end date.\n",
+      "Check datastream name, start, and end date. \n",
+      "\n",
       "No files returned or url status error.\n",
-      "Check datastream name, start, and end date.\n"
+      "Check datastream name, start, and end date. \n",
+      "\n"
      ]
     }
    ],
@@ -891,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 75,
    "id": "fd81b915-b600-4b18-ab78-37a63de18b4b",
    "metadata": {
     "scrolled": true
@@ -904,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 76,
    "id": "f78763f9-c320-469b-aed1-a4b50477a878",
    "metadata": {},
    "outputs": [
@@ -955,100 +991,9 @@
       "## If you use this software to prepare a publication, please cite:\n",
       "##\n",
       "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-10-27 11:30:19,082 - distributed.nanny - WARNING - Restarting worker\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
-      "## library for working with weather radar data. Py-ART is partly\n",
-      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
-      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
-      "## Science user facility.\n",
-      "##\n",
-      "## If you use this software to prepare a publication, please cite:\n",
-      "##\n",
-      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-10-27 11:31:52,122 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:40555' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {'subset_points-baa70e32844c0e096cf787e1858c2d5f', 'subset_points-5b3b25dcfd250be18c9d0d89c5cc08eb', 'subset_points-261038330ee183afc71edf901c3ec209', 'subset_points-d8c32662bc6cb6c89b9fa53e19d5bc5f', 'subset_points-c2e6a05df6fd8f89d216b6bfe240e44b', 'subset_points-b1aabfbfccba84d10e2d1adc5fbfb4a7', 'subset_points-5a633e7d81fc71178a8baaf033eb665a', 'subset_points-9fb89c833083809e53e50d2a0b594820', 'subset_points-0665c3bd3a5e718864b4e3be8c115f50', 'subset_points-05aaa43e2049ac431568e2c136b6cf2f', 'subset_points-60668851b5f274ebdd52308ad4f13bfe', 'subset_points-2f677ab976e0b595515b5fcb24dbdc3d', 'subset_points-8759c7574f946ff0ddafcdb1dcdcad11', 'subset_points-63cf72d3ee44ddff4dc2af575bdef3d8', 'subset_points-5516a819e9045c49c260d192c4d24d15', 'subset_points-3afc5205b8688dfc920bc392e5d98df7', 'subset_points-f26f40dceb49084383a1dfc97d73a25a', 'subset_points-5d7476df343f0a425117cde109e02f09', 'subset_points-63b29b77e5e4ec6ff5566b6227ea3e6d', 'subset_points-78078764ce24b8624c4525a99d770eed', 'subset_points-d757a6e5d10477453c74430a02f01bd6', 'subset_points-6edf715095e70d426801d6df9dece1c5', 'subset_points-214eda69e12ab1b6585c0424444361f3', 'subset_points-2714f963ded84e29182732819f96883c', 'subset_points-9a163967df62ae9c96a826d759cf568c', 'subset_points-b2db7102f537a8ab4d2f31bf8aaa2816', 'subset_points-3100825e06380a74c00c053c0a72a144', 'subset_points-a69babf92e97ba3ee61c4bd09d7f29ac', 'subset_points-7d15aef1467f20b8957cf835685b4491', 'subset_points-a262cb3d46ee7a94dafbf5235775969e', 'subset_points-4886e4ec8d4fbddf7f0852ca01bdfd6c', 'subset_points-ec6546e1d9b6093bfc6a136cc79e545e', 'subset_points-b8d15367bfc6df1fef4d7e2e175df772', 'subset_points-2d6b96f11480501b8773b3e32ee2bb55', 'subset_points-4c5ad14a5296b8fce0ff71c3486bdcb6'} (stimulus_id='handle-worker-cleanup-1761582712.1206381')\n",
-      "2025-10-27 11:31:52,144 - distributed.nanny - WARNING - Restarting worker\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
-      "## library for working with weather radar data. Py-ART is partly\n",
-      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
-      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
-      "## Science user facility.\n",
-      "##\n",
-      "## If you use this software to prepare a publication, please cite:\n",
-      "##\n",
-      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-10-27 11:32:00,937 - distributed.nanny - WARNING - Restarting worker\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
       "\n",
-      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
-      "## library for working with weather radar data. Py-ART is partly\n",
-      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
-      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
-      "## Science user facility.\n",
-      "##\n",
-      "## If you use this software to prepare a publication, please cite:\n",
-      "##\n",
-      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-10-27 11:34:21,594 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 33.75 GiB -- Worker memory limit: 46.89 GiB\n",
-      "2025-10-27 11:34:43,194 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 33.52 GiB -- Worker memory limit: 46.89 GiB\n",
-      "2025-10-27 11:34:44,320 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 37.57 GiB -- Worker memory limit: 46.89 GiB\n",
-      "2025-10-27 11:34:44,648 - distributed.worker.memory - WARNING - Worker is at 52% memory usage. Resuming worker. Process memory: 24.43 GiB -- Worker memory limit: 46.89 GiB\n",
-      "2025-10-27 11:34:56,898 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker.  Process memory: 37.58 GiB -- Worker memory limit: 46.89 GiB\n",
-      "2025-10-27 11:34:57,074 - distributed.worker.memory - WARNING - Worker is at 35% memory usage. Resuming worker. Process memory: 16.69 GiB -- Worker memory limit: 46.89 GiB\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 1min 20s, sys: 17 s, total: 1min 37s\n",
-      "Wall time: 9min 36s\n"
+      "CPU times: user 1min 36s, sys: 1min 1s, total: 2min 37s\n",
+      "Wall time: 22min 9s\n"
      ]
     }
    ],
@@ -1063,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 77,
    "id": "43aae040",
    "metadata": {},
    "outputs": [],
@@ -1083,7 +1028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 78,
    "id": "3cf06b09",
    "metadata": {},
    "outputs": [
@@ -1091,10 +1036,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2025-10-27 11:39:46,840 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n",
-      "2025-10-27 11:39:46,842 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n",
-      "2025-10-27 11:39:46,853 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n",
-      "2025-10-27 11:39:46,854 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n"
+      "2025-12-02 20:50:58,697 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n",
+      "2025-12-02 20:50:58,699 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n",
+      "2025-12-02 20:50:58,707 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n",
+      "2025-12-02 20:50:58,708 - distributed.nanny - WARNING - Worker process still alive after 4.0 seconds, killing\n"
      ]
     }
    ],
@@ -1104,7 +1049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 79,
    "id": "edd98940",
    "metadata": {},
    "outputs": [],
@@ -1114,7 +1059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 80,
    "id": "63cf50da",
    "metadata": {},
    "outputs": [
@@ -1136,39 +1081,86 @@
        "</symbol>\n",
        "</defs>\n",
        "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        "html[theme=\"dark\"],\n",
        "html[data-theme=\"dark\"],\n",
        "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1f1f1f;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
        "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
        "}\n",
        "\n",
        ".xr-text-repr-fallback {\n",
@@ -1191,11 +1183,18 @@
        "}\n",
        "\n",
        ".xr-obj-type,\n",
-       ".xr-array-name {\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
        "  margin-left: 2px;\n",
        "  margin-right: 10px;\n",
        "}\n",
        "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
        ".xr-obj-type {\n",
        "  color: var(--xr-font-color2);\n",
        "}\n",
@@ -1204,6 +1203,8 @@
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
        "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -1214,10 +1215,12 @@
        "  display: inline-block;\n",
        "  opacity: 0;\n",
        "  height: 0;\n",
+       "  margin: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -1226,7 +1229,7 @@
        "}\n",
        "\n",
        ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0);\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label:hover {\n",
@@ -1271,7 +1274,6 @@
        ".xr-section-summary,\n",
        ".xr-section-inline-details {\n",
        "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
        "}\n",
        "\n",
        ".xr-section-inline-details {\n",
@@ -1281,6 +1283,7 @@
        ".xr-section-details {\n",
        "  display: none;\n",
        "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
        "  margin-bottom: 5px;\n",
        "}\n",
        "\n",
@@ -1288,6 +1291,32 @@
        "  display: contents;\n",
        "}\n",
        "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
        ".xr-array-wrap {\n",
        "  grid-column: 1 / -1;\n",
        "  display: grid;\n",
@@ -1358,7 +1387,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -1369,6 +1400,7 @@
        ".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",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -1418,8 +1450,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -1432,6 +1471,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -1491,14 +1536,22 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
        "Dimensions:                                                (time: 142,\n",
        "                                                            station: 6,\n",
        "                                                            height: 32)\n",
        "Coordinates:\n",
-       "  * height                                                 (height) int64 256B ...\n",
-       "  * station                                                (station) &lt;U3 72B ...\n",
        "  * time                                                   (time) datetime64[s] 1kB ...\n",
+       "  * station                                                (station) &lt;U3 72B ...\n",
+       "  * height                                                 (height) int64 256B ...\n",
        "Data variables: (12/20)\n",
        "    attenuation_corrected_differential_reflectivity        (time, station, height) float64 218kB ...\n",
        "    attenuation_corrected_differential_reflectivity_lag_1  (time, station, height) float64 218kB ...\n",
@@ -1517,9 +1570,7 @@
        "    distance_from_radar:    36.909 km\n",
        "    azimuth:                210.709 degrees\n",
        "    latitude_of_location:   34.34525 degrees\n",
-       "    longitude_of_location:  -87.33842 degrees</pre><div class='xr-wrap' style='display:none'><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-cbc51d2c-ca5b-4ddd-91e1-7ba73da56584' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cbc51d2c-ca5b-4ddd-91e1-7ba73da56584' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 142</li><li><span class='xr-has-index'>station</span>: 6</li><li><span class='xr-has-index'>height</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5a69cf7a-3bfb-4cc1-8a59-3dd90264f5e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a69cf7a-3bfb-4cc1-8a59-3dd90264f5e1' 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'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>500 750 1000 ... 7750 8000 8250</div><input id='attrs-a8efcad1-e646-4b77-a61c-c743cf7ac352' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8efcad1-e646-4b77-a61c-c743cf7ac352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66a24c87-8e42-4658-b0cf-43e225bf65bf' class='xr-var-data-in' type='checkbox'><label for='data-66a24c87-8e42-4658-b0cf-43e225bf65bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Height of Radar Beam</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>Height Above Sea Level [in meters] for the Center of Each Radar Gate Above the Target Location</dd></dl></div><div class='xr-var-data'><pre>array([ 500,  750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250,\n",
-       "       3500, 3750, 4000, 4250, 4500, 4750, 5000, 5250, 5500, 5750, 6000, 6250,\n",
-       "       6500, 6750, 7000, 7250, 7500, 7750, 8000, 8250])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>station</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;M1&#x27; &#x27;S4&#x27; &#x27;S20&#x27; &#x27;S30&#x27; &#x27;S40&#x27; &#x27;S13&#x27;</div><input id='attrs-122849ef-f1a1-4d25-b791-dc9eaa881932' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-122849ef-f1a1-4d25-b791-dc9eaa881932' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1b56e23-8c43-4f6e-8737-2d25854d2499' class='xr-var-data-in' type='checkbox'><label for='data-b1b56e23-8c43-4f6e-8737-2d25854d2499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bankhead National Forest AMF-3 In-Situ Ground Observation Station Identifers</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;M1&#x27;, &#x27;S4&#x27;, &#x27;S20&#x27;, &#x27;S30&#x27;, &#x27;S40&#x27;, &#x27;S13&#x27;], dtype=&#x27;&lt;U3&#x27;)</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[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11 ... 2025-03-...</div><input id='attrs-a0dbad92-8bc3-4070-b96b-2b414c155e6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0dbad92-8bc3-4070-b96b-2b414c155e6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ec5d3c-84b9-40ab-b57e-5ec893377871' class='xr-var-data-in' type='checkbox'><label for='data-88ec5d3c-84b9-40ab-b57e-5ec893377871' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd><dt><span>units :</span></dt><dd>UTC Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-03-05T00:03:11&#x27;, &#x27;2025-03-05T00:13:13&#x27;, &#x27;2025-03-05T00:23:16&#x27;,\n",
+       "    longitude_of_location:  -87.33842 degrees</pre><div class='xr-wrap' style='display:none'><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-c9fc8eff-cdd5-41e5-8ede-422f95ad30ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9fc8eff-cdd5-41e5-8ede-422f95ad30ab' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 142</li><li><span class='xr-has-index'>station</span>: 6</li><li><span class='xr-has-index'>height</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c8485ff2-686b-40bc-8dc1-041d4918d3d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8485ff2-686b-40bc-8dc1-041d4918d3d4' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11 ... 2025-03-...</div><input id='attrs-b8853334-ffc8-4717-a92b-2c22eeb6519c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8853334-ffc8-4717-a92b-2c22eeb6519c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5f398d0-f69a-4ad1-b135-641d8dcc4a41' class='xr-var-data-in' type='checkbox'><label for='data-f5f398d0-f69a-4ad1-b135-641d8dcc4a41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd><dt><span>units :</span></dt><dd>UTC Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-03-05T00:03:11&#x27;, &#x27;2025-03-05T00:13:13&#x27;, &#x27;2025-03-05T00:23:16&#x27;,\n",
        "       &#x27;2025-03-05T00:33:19&#x27;, &#x27;2025-03-05T00:43:21&#x27;, &#x27;2025-03-05T00:53:24&#x27;,\n",
        "       &#x27;2025-03-05T01:10:01&#x27;, &#x27;2025-03-05T01:20:04&#x27;, &#x27;2025-03-05T01:30:07&#x27;,\n",
        "       &#x27;2025-03-05T01:40:10&#x27;, &#x27;2025-03-05T01:50:03&#x27;, &#x27;2025-03-05T02:00:02&#x27;,\n",
@@ -1566,7 +1617,9 @@
        "       &#x27;2025-03-05T22:20:34&#x27;, &#x27;2025-03-05T22:30:34&#x27;, &#x27;2025-03-05T22:40:14&#x27;,\n",
        "       &#x27;2025-03-05T22:50:16&#x27;, &#x27;2025-03-05T23:00:03&#x27;, &#x27;2025-03-05T23:10:02&#x27;,\n",
        "       &#x27;2025-03-05T23:20:04&#x27;, &#x27;2025-03-05T23:30:02&#x27;, &#x27;2025-03-05T23:40:02&#x27;,\n",
-       "       &#x27;2025-03-05T23:50:05&#x27;], dtype=&#x27;datetime64[s]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4642cf88-56a2-4622-a0ce-bca88ff860a3' class='xr-section-summary-in' type='checkbox'  ><label for='section-4642cf88-56a2-4622-a0ce-bca88ff860a3' class='xr-section-summary' >Data variables: <span>(20)</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>attenuation_corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.8108 -0.7111</div><input id='attrs-6980fcdc-77f5-444e-bd2f-a644a509ef62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6980fcdc-77f5-444e-bd2f-a644a509ef62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdbc0719-5bb3-474e-bc87-d4d320cbf3b3' class='xr-var-data-in' type='checkbox'><label for='data-fdbc0719-5bb3-474e-bc87-d4d320cbf3b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected differential reflectivity</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
+       "       &#x27;2025-03-05T23:50:05&#x27;], dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>station</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;M1&#x27; &#x27;S4&#x27; &#x27;S20&#x27; &#x27;S30&#x27; &#x27;S40&#x27; &#x27;S13&#x27;</div><input id='attrs-26c67818-d2a2-4f46-b6e0-792b0a471d75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26c67818-d2a2-4f46-b6e0-792b0a471d75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77c7b545-4cb5-4ed3-807b-65b0371a3c13' class='xr-var-data-in' type='checkbox'><label for='data-77c7b545-4cb5-4ed3-807b-65b0371a3c13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bankhead National Forest AMF-3 In-Situ Ground Observation Station Identifers</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;M1&#x27;, &#x27;S4&#x27;, &#x27;S20&#x27;, &#x27;S30&#x27;, &#x27;S40&#x27;, &#x27;S13&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>500 750 1000 ... 7750 8000 8250</div><input id='attrs-8d1bd863-c9ea-4469-8ffd-dd493be2c8da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d1bd863-c9ea-4469-8ffd-dd493be2c8da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-435a39fe-21ad-4db6-aa1f-aff4f26884de' class='xr-var-data-in' type='checkbox'><label for='data-435a39fe-21ad-4db6-aa1f-aff4f26884de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Height of Radar Beam</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>Height Above Sea Level [in meters] for the Center of Each Radar Gate Above the Target Location</dd></dl></div><div class='xr-var-data'><pre>array([ 500,  750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250,\n",
+       "       3500, 3750, 4000, 4250, 4500, 4750, 5000, 5250, 5500, 5750, 6000, 6250,\n",
+       "       6500, 6750, 7000, 7250, 7500, 7750, 8000, 8250])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6035709d-7d02-4ca0-b070-a3d7cd37e04f' class='xr-section-summary-in' type='checkbox'  ><label for='section-6035709d-7d02-4ca0-b070-a3d7cd37e04f' class='xr-section-summary' >Data variables: <span>(20)</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>attenuation_corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.8108 -0.7111</div><input id='attrs-8a3d9acc-cd56-4c5a-aa06-9460e5e79257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a3d9acc-cd56-4c5a-aa06-9460e5e79257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41d3c1a9-8800-495c-bb9a-301f0727ec88' class='xr-var-data-in' type='checkbox'><label for='data-41d3c1a9-8800-495c-bb9a-301f0727ec88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected differential reflectivity</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
        "          3.56824895e-01,  3.57210429e-01,  2.25953399e-01],\n",
        "        [            nan,  5.95300614e-01,  3.83962409e-01, ...,\n",
        "                     nan,             nan,             nan],\n",
@@ -1606,7 +1659,7 @@
        "         -1.15153202e+00, -2.53407987e+00, -3.91662771e+00],\n",
        "        [            nan,             nan,             nan, ...,\n",
        "         -1.17357042e+00, -8.10761441e-01, -7.11141491e-01]]],\n",
-       "      shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity_lag_1</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.918 1.638 1.536</div><input id='attrs-e24f5f2c-0a1b-4c97-a9f9-d68f524480c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e24f5f2c-0a1b-4c97-a9f9-d68f524480c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eea1dc9b-23e5-448f-b716-aa96b47e558c' class='xr-var-data-in' type='checkbox'><label for='data-eea1dc9b-23e5-448f-b716-aa96b47e558c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Differential reflectivity estimated at lag 1 corrected for rainfall attenuation</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
+       "      shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity_lag_1</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.918 1.638 1.536</div><input id='attrs-8c340b7f-6d01-4b4f-bc4a-8e47b72415c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c340b7f-6d01-4b4f-bc4a-8e47b72415c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecdb67c0-1f8f-447f-9719-a8be5400d98d' class='xr-var-data-in' type='checkbox'><label for='data-ecdb67c0-1f8f-447f-9719-a8be5400d98d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Differential reflectivity estimated at lag 1 corrected for rainfall attenuation</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
        "          1.54088580e+00,  1.56154729e+00,  1.45289154e+00],\n",
        "        [            nan,  1.66184232e+00,  1.44807196e+00, ...,\n",
        "                     nan,             nan,             nan],\n",
@@ -1646,7 +1699,7 @@
        "          1.39624329e+00,  1.12960987e+00,  8.62976445e-01],\n",
        "        [            nan,             nan,             nan, ...,\n",
        "          1.91793342e+00,  1.63802579e+00,  1.53636175e+00]]],\n",
-       "      shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_reflectivity_h</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... -14.83 -15.11 -15.1</div><input id='attrs-756254f9-2cac-4563-8a2f-09c5c3fbab53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-756254f9-2cac-4563-8a2f-09c5c3fbab53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3d31095-c169-4b67-8b3e-fa61c2e9f955' class='xr-var-data-in' type='checkbox'><label for='data-e3d31095-c169-4b67-8b3e-fa61c2e9f955' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected reflectivity, horizontal channel</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,  -2.81274341,\n",
+       "      shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_reflectivity_h</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... -14.83 -15.11 -15.1</div><input id='attrs-c466b5c3-aa09-43bb-9782-54fd62820e2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c466b5c3-aa09-43bb-9782-54fd62820e2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868eb923-c7d2-4bf7-8f86-808cfdf07a64' class='xr-var-data-in' type='checkbox'><label for='data-868eb923-c7d2-4bf7-8f86-808cfdf07a64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected reflectivity, horizontal channel</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,  -2.81274341,\n",
        "          -3.43382221,  -4.50334579],\n",
        "        [         nan,  35.30548986,  35.06246771, ...,          nan,\n",
        "                  nan,          nan],\n",
@@ -1686,7 +1739,7 @@
        "        [         nan,          nan,          nan, ..., -12.18537768,\n",
        "         -12.11202445, -12.03867122],\n",
        "        [         nan,          nan,          nan, ..., -14.83411182,\n",
-       "         -15.10773064, -15.09986046]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>copol_correlation_coeff</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.07276 0.07256</div><input id='attrs-76086b72-8605-4a3e-8b11-6ab0e0808834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76086b72-8605-4a3e-8b11-6ab0e0808834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac617bda-df33-428f-8596-f44760791957' class='xr-var-data-in' type='checkbox'><label for='data-ac617bda-df33-428f-8596-f44760791957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>radar_correlation_coefficient_hv</dd><dt><span>long_name :</span></dt><dd>Copolar correlation coefficient (also known as rhohv)</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[       nan,        nan,        nan, ..., 0.6372836 ,\n",
+       "         -15.10773064, -15.09986046]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>copol_correlation_coeff</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.07276 0.07256</div><input id='attrs-0c1e6f6c-b8b3-4504-a260-582bca00f80d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c1e6f6c-b8b3-4504-a260-582bca00f80d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fba71488-2962-4885-8e0f-033aaaaf5680' class='xr-var-data-in' type='checkbox'><label for='data-fba71488-2962-4885-8e0f-033aaaaf5680' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>radar_correlation_coefficient_hv</dd><dt><span>long_name :</span></dt><dd>Copolar correlation coefficient (also known as rhohv)</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[       nan,        nan,        nan, ..., 0.6372836 ,\n",
        "         0.60337995, 0.54466455],\n",
        "        [       nan, 0.99013957, 0.99038146, ...,        nan,\n",
        "                nan,        nan],\n",
@@ -1726,7 +1779,7 @@
        "        [       nan,        nan,        nan, ..., 0.08129748,\n",
        "         0.08076512, 0.08023276],\n",
        "        [       nan,        nan,        nan, ..., 0.07564546,\n",
-       "         0.07276001, 0.07256295]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_velocity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan -22.41 ... nan nan nan</div><input id='attrs-27ee4a70-cf69-4ff4-9209-42c14f518f37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27ee4a70-cf69-4ff4-9209-42c14f518f37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce6fc3ad-4cf1-4c36-a29e-9bd48867ee3a' class='xr-var-data-in' type='checkbox'><label for='data-ce6fc3ad-4cf1-4c36-a29e-9bd48867ee3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>corrected_radial_velocity_of_scatterers_away_from_instrument</dd><dt><span>long_name :</span></dt><dd>Corrected mean doppler velocity</dd><dt><span>valid_max :</span></dt><dd>49.476</dd><dt><span>valid_min :</span></dt><dd>-49.476</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
+       "         0.07276001, 0.07256295]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_velocity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan -22.41 ... nan nan nan</div><input id='attrs-f280b411-33ee-480c-b0d3-4f3074d9a739' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f280b411-33ee-480c-b0d3-4f3074d9a739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c716a638-e4be-49db-b592-63b4030fb8d4' class='xr-var-data-in' type='checkbox'><label for='data-c716a638-e4be-49db-b592-63b4030fb8d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>corrected_radial_velocity_of_scatterers_away_from_instrument</dd><dt><span>long_name :</span></dt><dd>Corrected mean doppler velocity</dd><dt><span>valid_max :</span></dt><dd>49.476</dd><dt><span>valid_min :</span></dt><dd>-49.476</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan, -18.22945234, -20.64301495, ...,          nan,\n",
        "                  nan,          nan],\n",
@@ -1766,7 +1819,7 @@
        "        [         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan,          nan,          nan, ...,          nan,\n",
-       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-e52f74cf-41bc-44ed-93a8-e921e90127fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e52f74cf-41bc-44ed-93a8-e921e90127fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e11beda-09d8-4a3d-90b2-f502cee1d76b' class='xr-var-data-in' type='checkbox'><label for='data-1e11beda-09d8-4a3d-90b2-f502cee1d76b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Corrected Differential Phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
+       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-b2b77009-0d77-4bfc-8c4c-1d4bbe1925b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b77009-0d77-4bfc-8c4c-1d4bbe1925b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7955ca8e-9675-4d43-b10c-c8001dd13985' class='xr-var-data-in' type='checkbox'><label for='data-7955ca8e-9675-4d43-b10c-c8001dd13985' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Corrected Differential Phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan, 232.86263161, 232.94866818, ...,          nan,\n",
        "                  nan,          nan],\n",
@@ -1806,7 +1859,7 @@
        "        [         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan,          nan,          nan, ...,          nan,\n",
-       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_differential_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-b4e5be7a-7ed8-4f53-93f1-383399e41bbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4e5be7a-7ed8-4f53-93f1-383399e41bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eebbde62-dc0e-4f8a-9186-61ae1f3337bb' class='xr-var-data-in' type='checkbox'><label for='data-eebbde62-dc0e-4f8a-9186-61ae1f3337bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Filtered Corrected Differential Phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
+       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_differential_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-c845d25a-5366-4b7d-84bf-49073109c695' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c845d25a-5366-4b7d-84bf-49073109c695' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b4cc47-3c9c-4ae7-9cc0-9cb03679e879' class='xr-var-data-in' type='checkbox'><label for='data-e9b4cc47-3c9c-4ae7-9cc0-9cb03679e879' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Filtered Corrected Differential Phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan, 232.86263161, 232.94866818, ...,          nan,\n",
        "                  nan,          nan],\n",
@@ -1846,7 +1899,7 @@
        "        [         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan,          nan,          nan, ...,          nan,\n",
-       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-55281e77-2c0d-4863-96a1-f83fc2207781' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55281e77-2c0d-4863-96a1-f83fc2207781' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee9c2ba-0c06-4e03-9eab-7bf1d161addd' class='xr-var-data-in' type='checkbox'><label for='data-bee9c2ba-0c06-4e03-9eab-7bf1d161addd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Corrected Specific differential phase (KDP)</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
+       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-822a026a-ed1a-406d-ba27-f3e7d95aa2ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-822a026a-ed1a-406d-ba27-f3e7d95aa2ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95d8cb1c-8193-404c-9514-173822c0bfa0' class='xr-var-data-in' type='checkbox'><label for='data-95d8cb1c-8193-404c-9514-173822c0bfa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Corrected Specific differential phase (KDP)</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan,  0.27605289,  0.20183932, ...,         nan,\n",
        "                 nan,         nan],\n",
@@ -1886,7 +1939,7 @@
        "        [        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.1171 ... 0.0 0.0 0.0</div><input id='attrs-deeaa208-5dff-4846-8a38-003e87dce26f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deeaa208-5dff-4846-8a38-003e87dce26f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd2195a-4055-48aa-8dc8-09ecaa0f7d56' class='xr-var-data-in' type='checkbox'><label for='data-6bd2195a-4055-48aa-8dc8-09ecaa0f7d56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Filtered Corrected Specific differential phase (KDP)</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,  0.        ,\n",
+       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 0.1171 ... 0.0 0.0 0.0</div><input id='attrs-866824bb-b2e4-46ee-be21-6aef52c30b9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-866824bb-b2e4-46ee-be21-6aef52c30b9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbe32cde-40ad-4305-9c5f-d31515a7a74b' class='xr-var-data-in' type='checkbox'><label for='data-fbe32cde-40ad-4305-9c5f-d31515a7a74b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Filtered Corrected Specific differential phase (KDP)</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [        nan,  0.27605289,  0.20183932, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
@@ -1926,7 +1979,7 @@
        "        [        nan,         nan,         nan, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [        nan,         nan,         nan, ...,  0.        ,\n",
-       "          0.        ,  0.        ]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 3.163 ... nan nan nan</div><input id='attrs-a1326264-f89e-4ad6-aa97-2aadd5d5bfab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1326264-f89e-4ad6-aa97-2aadd5d5bfab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0a32d4b-ecd6-49d9-8b1a-3ed644dd294f' class='xr-var-data-in' type='checkbox'><label for='data-d0a32d4b-ecd6-49d9-8b1a-3ed644dd294f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>corrected_log_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Corrected differential reflectivity</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,  5.07029742,\n",
+       "          0.        ,  0.        ]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 3.163 ... nan nan nan</div><input id='attrs-b986a25c-9fce-4863-959e-13aa5c550421' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b986a25c-9fce-4863-959e-13aa5c550421' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5719e5ed-6557-481b-bb47-a425aa3eec5e' class='xr-var-data-in' type='checkbox'><label for='data-5719e5ed-6557-481b-bb47-a425aa3eec5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>corrected_log_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Corrected differential reflectivity</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,  5.07029742,\n",
        "                 nan,         nan],\n",
        "        [        nan,  2.18288176,  2.46656055, ...,         nan,\n",
        "                 nan,         nan],\n",
@@ -1966,7 +2019,7 @@
        "        [        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 36.05 ... nan nan nan</div><input id='attrs-b822af3c-6bfb-4c26-a3fa-bc386f2a0d13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b822af3c-6bfb-4c26-a3fa-bc386f2a0d13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e95b59-cbce-43b7-b283-10a33daacd6e' class='xr-var-data-in' type='checkbox'><label for='data-b2e95b59-cbce-43b7-b283-10a33daacd6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd>Corrected reflectivity</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ..., 12.09665154,\n",
+       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 36.05 ... nan nan nan</div><input id='attrs-8c5ca799-47d5-48dd-b0fe-a53611d3d0d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c5ca799-47d5-48dd-b0fe-a53611d3d0d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89804cdf-43db-484f-811d-b3b10449427b' class='xr-var-data-in' type='checkbox'><label for='data-89804cdf-43db-484f-811d-b3b10449427b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd>Corrected reflectivity</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ..., 12.09665154,\n",
        "                 nan,         nan],\n",
        "        [        nan, 39.65196447, 41.23227214, ...,         nan,\n",
        "                 nan,         nan],\n",
@@ -2006,7 +2059,7 @@
        "        [        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 33.44 ... nan nan nan</div><input id='attrs-09eb9cb2-27fc-4fc6-ad91-bef65d72c96e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09eb9cb2-27fc-4fc6-ad91-bef65d72c96e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41bdabef-d7b2-4de7-a81d-6eb14c14fed5' class='xr-var-data-in' type='checkbox'><label for='data-41bdabef-d7b2-4de7-a81d-6eb14c14fed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from specific attenuation</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,   0.        ,\n",
+       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 33.44 ... nan nan nan</div><input id='attrs-1a18cecc-3f6f-46a3-ac47-47efdf1b3202' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a18cecc-3f6f-46a3-ac47-47efdf1b3202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2aa0e05-fca1-44b7-aedf-cfedec650ff3' class='xr-var-data-in' type='checkbox'><label for='data-b2aa0e05-fca1-44b7-aedf-cfedec650ff3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from specific attenuation</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan, ...,   0.        ,\n",
        "                  nan,          nan],\n",
        "        [         nan,  47.36101983,  74.09102172, ...,          nan,\n",
        "                  nan,          nan],\n",
@@ -2046,7 +2099,7 @@
        "        [         nan,          nan,          nan, ...,          nan,\n",
        "                  nan,          nan],\n",
        "        [         nan,          nan,          nan, ...,          nan,\n",
-       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Z</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 7.045 ... nan nan nan</div><input id='attrs-12ef978b-399d-4537-ba08-c4a4b29a0913' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12ef978b-399d-4537-ba08-c4a4b29a0913' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1e47f80-f6ca-4ed5-8adb-25cd420686f8' class='xr-var-data-in' type='checkbox'><label for='data-b1e47f80-f6ca-4ed5-8adb-25cd420686f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from reflectivity</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,  0.12874039,\n",
+       "                  nan,          nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Z</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan 7.045 ... nan nan nan</div><input id='attrs-8510e412-e15b-4554-92ed-8797331d524b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8510e412-e15b-4554-92ed-8797331d524b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e480b94c-eac6-4005-9af9-e7825b0aea72' class='xr-var-data-in' type='checkbox'><label for='data-e480b94c-eac6-4005-9af9-e7825b0aea72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from reflectivity</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,  0.12874039,\n",
        "                 nan,         nan],\n",
        "        [        nan, 12.19670677, 15.46920764, ...,         nan,\n",
        "                 nan,         nan],\n",
@@ -2086,7 +2139,7 @@
        "        [        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Kdp</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-0a7f5411-9037-4a0e-b68f-7c3f61d5a739' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a7f5411-9037-4a0e-b68f-7c3f61d5a739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12b86f13-24c6-4997-a958-cee905d35804' class='xr-var-data-in' type='checkbox'><label for='data-12b86f13-24c6-4997-a958-cee905d35804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from specific differential phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
+       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Kdp</span></div><div class='xr-var-dims'>(time, station, height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a42ad46a-ee43-463b-b245-28022458966c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a42ad46a-ee43-463b-b245-28022458966c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0637c442-34b6-4cbf-9cb9-95afea8011dc' class='xr-var-data-in' type='checkbox'><label for='data-0637c442-34b6-4cbf-9cb9-95afea8011dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from specific differential phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan, 10.25591826,  7.77949627, ...,         nan,\n",
        "                 nan,         nan],\n",
@@ -2126,7 +2179,7 @@
        "        [        nan,         nan,         nan, ...,         nan,\n",
        "                 nan,         nan],\n",
        "        [        nan,         nan,         nan, ...,         nan,\n",
-       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11 ... 2025-03-...</div><input id='attrs-df145209-7618-4598-b1ff-34b116ab0d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df145209-7618-4598-b1ff-34b116ab0d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f35d5d4-dc75-41e4-a639-f2fdc8ca93a5' class='xr-var-data-in' type='checkbox'><label for='data-1f35d5d4-dc75-41e4-a639-f2fdc8ca93a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd><dt><span>units :</span></dt><dd>UTC Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-03-05T00:03:11&#x27;, &#x27;2025-03-05T00:13:13&#x27;,\n",
+       "                 nan,         nan]]], shape=(142, 6, 32))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11 ... 2025-03-...</div><input id='attrs-e11b5886-308f-490a-82a6-d5518f693f3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e11b5886-308f-490a-82a6-d5518f693f3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39e201e9-e946-4ad0-8cbf-d10d1d61b339' class='xr-var-data-in' type='checkbox'><label for='data-39e201e9-e946-4ad0-8cbf-d10d1d61b339' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd><dt><span>units :</span></dt><dd>UTC Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-03-05T00:03:11&#x27;, &#x27;2025-03-05T00:13:13&#x27;,\n",
        "       &#x27;2025-03-05T00:23:16&#x27;, &#x27;2025-03-05T00:33:19&#x27;,\n",
        "       &#x27;2025-03-05T00:43:21&#x27;, &#x27;2025-03-05T00:53:24&#x27;,\n",
        "       &#x27;2025-03-05T01:10:01&#x27;, &#x27;2025-03-05T01:20:04&#x27;,\n",
@@ -2166,8 +2219,8 @@
        "       &#x27;2025-03-05T23:00:03&#x27;, &#x27;2025-03-05T23:10:02&#x27;,\n",
        "       &#x27;2025-03-05T23:20:04&#x27;, &#x27;2025-03-05T23:30:02&#x27;,\n",
        "       &#x27;2025-03-05T23:40:02&#x27;, &#x27;2025-03-05T23:50:05&#x27;],\n",
-       "      dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11</div><input id='attrs-caa3e085-d2f3-4d7e-8e74-1e82a82c1eaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-caa3e085-d2f3-4d7e-8e74-1e82a82c1eaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a2f2df9-d20c-4180-b62e-d69f46d0ba73' class='xr-var-data-in' type='checkbox'><label for='data-3a2f2df9-d20c-4180-b62e-d69f46d0ba73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd><dt><span>units :</span></dt><dd>UTC Time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2025-03-05T00:03:11&#x27;, dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.35 34.46 34.65 34.39 34.18 34.34</div><input id='attrs-fd8ccf97-c771-4ed7-a048-ddcdeee93796' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd8ccf97-c771-4ed7-a048-ddcdeee93796' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee089fe8-db2b-4782-9f6f-33bec7f6ec6b' class='xr-var-data-in' type='checkbox'><label for='data-ee089fe8-db2b-4782-9f6f-33bec7f6ec6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of BNF AMF-3 Ground Observation Site</dd><dt><span>units :</span></dt><dd>Degrees North</dd></dl></div><div class='xr-var-data'><pre>array([34.34525 , 34.46451 , 34.65401 , 34.38501 , 34.17932 , 34.343889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.34 -87.24 ... -87.45 -87.35</div><input id='attrs-e8dbbae4-815f-46c7-9553-da21cdb4c472' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8dbbae4-815f-46c7-9553-da21cdb4c472' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-555f08c5-c457-48f6-bd4f-6276ef135441' class='xr-var-data-in' type='checkbox'><label for='data-555f08c5-c457-48f6-bd4f-6276ef135441' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of BNF AMF-3 Ground Observation Site</dd><dt><span>units :</span></dt><dd>Degrees East</dd></dl></div><div class='xr-var-data'><pre>array([-87.33842 , -87.23598 , -87.29264 , -86.92757 , -87.45349 ,\n",
-       "       -87.350556])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gate_time</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:07:06 ... 2025-03-...</div><input id='attrs-e75459cb-167b-4579-970d-c62419ae11a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e75459cb-167b-4579-970d-c62419ae11a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0c77c4a-1e28-4f93-a163-fdf1ea3192cb' class='xr-var-data-in' type='checkbox'><label for='data-b0c77c4a-1e28-4f93-a163-fdf1ea3192cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time in Seconds that Cooresponds to the Start of each Individual Radar Volume Scan before Concatenation</dd><dt><span>description :</span></dt><dd>Time in Seconds that Cooresponds to the Minimum Height Gate</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2025-03-05T00:07:06&#x27;, &#x27;2025-03-05T00:07:12&#x27;,\n",
+       "      dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11</div><input id='attrs-5ed4ad61-1893-42ff-a65b-5f85b61938df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ed4ad61-1893-42ff-a65b-5f85b61938df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66c91df8-4451-4dfc-8e26-2551cb533a32' class='xr-var-data-in' type='checkbox'><label for='data-66c91df8-4451-4dfc-8e26-2551cb533a32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd><dt><span>units :</span></dt><dd>UTC Time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2025-03-05T00:03:11&#x27;, dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>34.35 34.46 34.65 34.39 34.18 34.34</div><input id='attrs-84501e82-37a1-4ef9-9300-9ac10f462d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84501e82-37a1-4ef9-9300-9ac10f462d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46324c88-9dda-4d20-ac8e-7c689bd837fb' class='xr-var-data-in' type='checkbox'><label for='data-46324c88-9dda-4d20-ac8e-7c689bd837fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of BNF AMF-3 Ground Observation Site</dd><dt><span>units :</span></dt><dd>Degrees North</dd></dl></div><div class='xr-var-data'><pre>array([34.34525 , 34.46451 , 34.65401 , 34.38501 , 34.17932 , 34.343889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.34 -87.24 ... -87.45 -87.35</div><input id='attrs-0b5ff47b-64e0-4b8d-9d01-c5f09ec299b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b5ff47b-64e0-4b8d-9d01-c5f09ec299b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c24cc8ee-6b67-4ddb-8db7-3743b4c76c15' class='xr-var-data-in' type='checkbox'><label for='data-c24cc8ee-6b67-4ddb-8db7-3743b4c76c15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of BNF AMF-3 Ground Observation Site</dd><dt><span>units :</span></dt><dd>Degrees East</dd></dl></div><div class='xr-var-data'><pre>array([-87.33842 , -87.23598 , -87.29264 , -86.92757 , -87.45349 ,\n",
+       "       -87.350556])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gate_time</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:07:06 ... 2025-03-...</div><input id='attrs-6288fced-8253-4529-b3fc-e13a1cdc8011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6288fced-8253-4529-b3fc-e13a1cdc8011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9554c62-9ea2-4893-9ba7-61a4ee105134' class='xr-var-data-in' type='checkbox'><label for='data-a9554c62-9ea2-4893-9ba7-61a4ee105134' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time in Seconds that Cooresponds to the Start of each Individual Radar Volume Scan before Concatenation</dd><dt><span>description :</span></dt><dd>Time in Seconds that Cooresponds to the Minimum Height Gate</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2025-03-05T00:07:06&#x27;, &#x27;2025-03-05T00:07:12&#x27;,\n",
        "        &#x27;2025-03-05T00:06:39&#x27;, &#x27;2025-03-05T00:06:53&#x27;,\n",
        "        &#x27;2025-03-05T00:08:52&#x27;, &#x27;2025-03-05T00:07:05&#x27;],\n",
        "       [&#x27;2025-03-05T00:31:17&#x27;, &#x27;2025-03-05T00:27:18&#x27;,\n",
@@ -2207,21 +2260,7 @@
        "       [&#x27;2025-03-06T23:40:56&#x27;, &#x27;2025-03-06T23:41:01&#x27;,\n",
        "        &#x27;2025-03-06T23:42:10&#x27;, &#x27;2025-03-06T23:41:29&#x27;,\n",
        "        &#x27;2025-03-05T23:50:05&#x27;, &#x27;2025-03-05T23:50:05&#x27;]],\n",
-       "      dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>293 197 178 183 236 286</div><input id='attrs-d5233e73-f2cc-4ae8-a093-d9f4dbd4e0b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5233e73-f2cc-4ae8-a093-d9f4dbd4e0b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a10e012c-68f8-439f-97ca-0967ddb3ff1b' class='xr-var-data-in' type='checkbox'><label for='data-a10e012c-68f8-439f-97ca-0967ddb3ff1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level for each station</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([293, 197, 178, 183, 236, 286])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c45b330b-71fe-427d-a152-837771adbb23' class='xr-section-summary-in' type='checkbox'  ><label for='section-c45b330b-71fe-427d-a152-837771adbb23' class='xr-section-summary' >Indexes: <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-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b7fdb541-cabc-4127-8308-c606616477ba' class='xr-index-data-in' type='checkbox'/><label for='index-b7fdb541-cabc-4127-8308-c606616477ba' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 500,  750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250,\n",
-       "       3500, 3750, 4000, 4250, 4500, 4750, 5000, 5250, 5500, 5750, 6000, 6250,\n",
-       "       6500, 6750, 7000, 7250, 7500, 7750, 8000, 8250],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>station</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7b9a8be1-042f-4854-95f7-727ac9161f78' class='xr-index-data-in' type='checkbox'/><label for='index-7b9a8be1-042f-4854-95f7-727ac9161f78' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;M1&#x27;, &#x27;S4&#x27;, &#x27;S20&#x27;, &#x27;S30&#x27;, &#x27;S40&#x27;, &#x27;S13&#x27;], dtype=&#x27;object&#x27;, name=&#x27;station&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c6e4530-e4de-4ab7-9a15-acfb8add3089' class='xr-index-data-in' type='checkbox'/><label for='index-8c6e4530-e4de-4ab7-9a15-acfb8add3089' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2025-03-05 00:03:11&#x27;, &#x27;2025-03-05 00:13:13&#x27;,\n",
-       "               &#x27;2025-03-05 00:23:16&#x27;, &#x27;2025-03-05 00:33:19&#x27;,\n",
-       "               &#x27;2025-03-05 00:43:21&#x27;, &#x27;2025-03-05 00:53:24&#x27;,\n",
-       "               &#x27;2025-03-05 01:10:01&#x27;, &#x27;2025-03-05 01:20:04&#x27;,\n",
-       "               &#x27;2025-03-05 01:30:07&#x27;, &#x27;2025-03-05 01:40:10&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2025-03-05 22:20:34&#x27;, &#x27;2025-03-05 22:30:34&#x27;,\n",
-       "               &#x27;2025-03-05 22:40:14&#x27;, &#x27;2025-03-05 22:50:16&#x27;,\n",
-       "               &#x27;2025-03-05 23:00:03&#x27;, &#x27;2025-03-05 23:10:02&#x27;,\n",
-       "               &#x27;2025-03-05 23:20:04&#x27;, &#x27;2025-03-05 23:30:02&#x27;,\n",
-       "               &#x27;2025-03-05 23:40:02&#x27;, &#x27;2025-03-05 23:50:05&#x27;],\n",
-       "              dtype=&#x27;datetime64[s]&#x27;, name=&#x27;time&#x27;, length=142, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbf1f6fb-70c2-4930-8aa3-8c881310c9bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dbf1f6fb-70c2-4930-8aa3-8c881310c9bc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>distance_from_radar :</span></dt><dd>36.909 km</dd><dt><span>azimuth :</span></dt><dd>210.709 degrees</dd><dt><span>latitude_of_location :</span></dt><dd>34.34525 degrees</dd><dt><span>longitude_of_location :</span></dt><dd>-87.33842 degrees</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>293 197 178 183 236 286</div><input id='attrs-a82d31a5-a9c4-4c6a-b3d3-ee30916d4532' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a82d31a5-a9c4-4c6a-b3d3-ee30916d4532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca75117e-dbb9-46aa-bc27-66cda403f265' class='xr-var-data-in' type='checkbox'><label for='data-ca75117e-dbb9-46aa-bc27-66cda403f265' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level for each station</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([293, 197, 178, 183, 236, 286])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1cb8cbe-82da-4494-a89f-5408434cb409' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1cb8cbe-82da-4494-a89f-5408434cb409' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>distance_from_radar :</span></dt><dd>36.909 km</dd><dt><span>azimuth :</span></dt><dd>210.709 degrees</dd><dt><span>latitude_of_location :</span></dt><dd>34.34525 degrees</dd><dt><span>longitude_of_location :</span></dt><dd>-87.33842 degrees</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 3MB\n",
@@ -2229,9 +2268,9 @@
        "                                                            station: 6,\n",
        "                                                            height: 32)\n",
        "Coordinates:\n",
-       "  * height                                                 (height) int64 256B ...\n",
-       "  * station                                                (station) <U3 72B ...\n",
        "  * time                                                   (time) datetime64[s] 1kB ...\n",
+       "  * station                                                (station) <U3 72B ...\n",
+       "  * height                                                 (height) int64 256B ...\n",
        "Data variables: (12/20)\n",
        "    attenuation_corrected_differential_reflectivity        (time, station, height) float64 218kB ...\n",
        "    attenuation_corrected_differential_reflectivity_lag_1  (time, station, height) float64 218kB ...\n",
@@ -2253,7 +2292,7 @@
        "    longitude_of_location:  -87.33842 degrees"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2272,23 +2311,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 81,
    "id": "cf6bca3f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7faa0c31e090>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0cfe2c710>"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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5c2fExMSgYcOGKF26NC5duoQFCxagQoUKqFatmpTPLzMzE+3atcPRo0exYMEC6HQ6k0eXS5cujSpVqgDInQBl4sSJeOeddxASEoK4uDgcOnQI8fHxeOWVV1C7dm3j+4YNG2acDKVu3bomNdVqtbFD8/fff8f06dPRrVs3VK5cGVlZWfj555+xZMkSPPXUU8a7F/NjT7ueeeYZ1K9fH4888ghCQ0Nx/fp1rFixArt27cLHH38Mb++CY/2UKVPQqFEjdOrUCePGjUNWVhYmTZqEsLAwkxmt33vvPeOM5+XKlTNOELN161bEx8cjLCyswH0RERFRMSaIiIiIPNzy5csFAJOXRqMRjzzyiJg3b57Iysoy2R6AGDJkiFmdrVu3Gt9/6NAh4/LJkycLAOLWrVt2t61FixaiTp06Ftf9+OOPAoCYMmWKEEKIq1eviv/85z+iVKlSIigoSLRv316cPHlSVKhQQfTr18/kvXv27BGPP/64UKvVIjIyUrz55ptiyZIlAoBISEgw2Van04mVK1eKp556SoSEhAhvb29RunRp8fTTT4s1a9YIvV4vhBAiISHB7HP892v58uVi06ZN4umnnxZly5YVvr6+Ijw8XHTo0EH8/vvvxv29//77omnTpiIsLEz4+vqK8uXLiwEDBoiLFy/a/flZU1BbH/y8hBDigw8+ENWrVze2afLkyUKr1ZpsU6FCBas1K1SoYNzu77//Fh06dBBly5YVarVa+Pn5ibp164rp06ebfb0VxJZ2zZ49WzRq1EiUKlVKqFQqERoaKtq1ayc2bdpk174OHz4sWrduLQICAkRwcLDo2rWr+Oeff0y22bBhg3jiiSdE6dKlhbe3twgKChJPPvmk+Oqrr+zaFxERERVPihAOGJiIiIiIiIiIiIiIih2O2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkaiYm7NmjVYsGDBQ9X45JNPsGLFCrPlFy9ehKIoFtd5khUrVkBRFCiKgp07d5qtF0KgatWqUBQFLVu2NFn3xRdf4Pnnn0eNGjXg5eWFihUrOqXNRERERJ6GudQ2Bw4cQLdu3VC+fHmo1WpERESgSZMmGD16tNX3CCHQvHlzKIqCN954w4mtJaLiiJ2NRMWcI0NdmTJlsG/fPnTs2PGh6ruLoKAgLFu2zGz5rl27cP78eQQFBZmtW7VqFU6dOoXHHnsMVapUcUYziYiIiDwSc2nBfvzxRzRt2hRpaWmYM2cOtm7dig8++ADNmjXDunXrrL7v448/xj///OPElhJRcebt6gYQUdGlVqvx+OOPu7oZ0vTs2RNffvklPv74YwQHBxuXL1u2DE2aNEFaWprZe7Zs2QIvr9zrOp06dcLJkyed1l4iIiIiylVUcumcOXNQqVIlbNmyBd7e//tz/vnnn8ecOXMsvufixYsYP348vvjiC3Tv3t1ZTSWiYox3NhIVYbdu3cKgQYMQHR0NtVqN0qVLo1mzZvjll18AAC1btsSPP/6IS5cuGR8TVhTF+P4pU6agcePGCAkJQXBwMB599FEsW7YMQgjjNhUrVsSpU6ewa9cu4/vzHhW29rjK7t270bp1awQFBSEgIABNmzbFjz/+aLJN3qPLO3bswOuvv46wsDCEhoaie/fuuH79umM+sAL06tULAPDVV18Zl6WmpuLbb7/Fyy+/bPE9eR2NRERERMUZc6kcd+7cQVhYmElHYx5ruXPQoEFo27YtunXr5ujmEREB4J2NREVa37598ccff2D69OmoXr067t69iz/++AN37twBkPuYyaBBg3D+/Hl89913Zu+/ePEiXn31VZQvXx4AsH//fgwdOhTXrl3DpEmTAADfffcdnn32WWg0GnzyyScAcq8cW7Nr1y60bdsW9erVw7Jly6BWq/HJJ5+gc+fO+Oqrr9CzZ0+T7V955RV07NgRa9aswZUrV/Dmm2/ihRdewK+//prvsQshoNfrbfqcLIU1S4KDg/Hss8/i888/x6uvvgogt+PRy8sLPXv2fOjHfoiIiIiKKuZSObm0SZMm+OyzzzBs2DD06dMHjz76KHx8fKxu/9lnn+HgwYM4ffq0TfsnIpJCEFGRVaJECTFixIh8t+nYsaOoUKFCgbX0er3IyckRU6dOFaGhocJgMBjX1alTR7Ro0cLsPQkJCQKAWL58uXHZ448/LsLDw8W9e/eMy3Q6nYiJiRHlypUz1l2+fLkAIAYPHmxSc86cOQKAuHHjRr7tzXu/La+C5NU6dOiQ2LFjhwAgTp48KYQQolGjRqJ///75fg55bP2siYiIiIoa5lI5ufT27dviiSeeMG7v4+MjmjZtKmbOnGlyHEIIcfXqVaHRaMSnn35qXAZADBkypMD9EBE9DN7ZSFSEPfbYY1ixYgVCQ0PRpk0bxMbG5nvl80G//vorZsyYgUOHDpmNR5iUlISIiAi72pOeno4DBw7g9ddfR4kSJYzLVSoV+vbti7feegtnz55FzZo1jeu6dOliUqNevXoAgEuXLiEyMtLqvjp37oxDhw7Z1T5btGjRAlWqVMHnn3+O/v3749ChQ3j//fel74eIiIioKGEulZNLQ0ND8fvvv+Pw4cPYvn07Dh8+jJ07d2L8+PH49NNPcejQIYSFhQEAXnvtNdSvXx8DBw6Usm8iIluxs5GoCFu3bh2mTZuGzz77DO+88w5KlCiBbt26Yc6cOfkGIgA4ePAg4uLi0LJlSyxduhTlypWDr68vvv/+e0yfPh2ZmZl2tyclJQVCCJQpU8ZsXVRUFAAYH6XJExoaavLvvEdhCtp/SEgINBqN3W0siKIoeOmll/Dhhx8iKysL1atXx5NPPil9P0RERERFCXOp3FzasGFDNGzYEACQk5ODt956C/Pnz8ecOXMwZ84c/Pe//8XmzZuxe/dupKammrxXq9Xi7t27CAwMtKvDl4jIVpy5gKgICwsLw4IFC3Dx4kVcunQJM2fOxPr169G/f/8C37t27Vr4+Phg06ZN6NGjB5o2bWoMNIVVqlQpeHl54caNG2br8gbXzrsS+7BWrlwJHx8fm1726t+/P27fvo3FixfjpZdektJeIiIioqKMudQxuRQAfHx8MHnyZADAyZMnjf/V6XR4/PHHUapUKeMLAJYuXYpSpUqZTYRDRCQL72wkKibKly+PN954A9u3b8eePXuMy9VqtcWrsYqiwNvbGyqVyrgsMzMTq1atMtvWWo0HBQYGonHjxli/fj3mzp0Lf39/AIDBYMDq1atRrlw5VK9evTCHZ8ZRj1EDQNmyZfHmm2/ir7/+Qr9+/RyyDyIiIqKiirm08G7cuGHxbswzZ84A+N9dmf3790fLli3NtmvVqhW6du2K4cOHIyYmRkqbiIgexM5GoiIqNTUVrVq1Qu/evVGzZk0EBQXh0KFD2Lx5M7p3727crm7duli/fj0WLVqE2NhYeHl5oWHDhujYsSPmzZuH3r17Y9CgQbhz5w7mzp1rcUa/unXrYu3atVi3bh0qV64MPz8/1K1b12K7Zs6cibZt26JVq1YYM2YMfH198cknn+DkyZP46quvoCiKlOMPDQ01e9RFplmzZtm03enTp42z/yUmJiIjIwP//e9/AQC1a9dG7dq1HdZGInI/D44zZovg4GAHtISIyHmYS+Xl0nbt2qFcuXLo3LkzatasCYPBgGPHjuH9999HiRIlMHz4cABAxYoVUbFiRYs1ypYta7EjkoiKB2fkUXY2EhVRfn5+aNy4MVatWoWLFy8iJycH5cuXx1tvvYWxY8catxs+fDhOnTqFt99+G6mpqRBCQAiBp556Cp9//jlmz56Nzp07o2zZshg4cCDCw8MxYMAAk31NmTIFN27cwMCBA3Hv3j1UqFABFy9etNiuFi1a4Ndff8XkyZPRv39/GAwG1K9fHxs2bECnTp0c+ZG4xNdff40pU6aYLHvuuecAAJMnT0Z8fLwLWkVErlKyZEm7/nhVFAXnzp1D5cqVHdgqIiLHYi6VZ+LEifjhhx8wf/583LhxA9nZ2ShTpgzatGmD8ePHo1atWq5uIhG5OWfkUUUIIQrTOCIiIiKyj5eXF7799luEhIQUuK0QAh06dMDJkyfZ2UhEREREUjgjj/LORiIiIiInqVChApo3b27z43SVK1fmTKFEREREJI0z8qhNdzY++uij9hVVFGzYsAFly5a1631ERERERJYwjxIRERF5BpvubDx27BhGjx6NEiVKFLitEAKzZs1Cdnb2QzeOiIiIqLgxGAy4evUqypcv7+qmuBXmUSIiIiLneNg8atOdjV5eXkhMTER4eLhNRYOCgvDnn39yfCEiIiKiB6hUKrzxxhuYP38+vLy8zNbfvHkTUVFR0Ov1Lmid+2IeJSIiIpLD0XnUvKIFCQkJKF26tM1FT58+jQoVKhSqQURERERFmRACy5cvR1xcHJKTk61uQ6aYR4mIiIjkcHQe5WzUNjIYDLh+/TqCgoLsmiKciIioqBBC4N69e4iKirJ4BdQRsrKyoNVqra739fWFn5+fU9oii0qlwt69e/Hqq68iLS0NGzZsQExMjHE972wka5hHiYiouHPHPAp4XiZ1dB4t1GzUWVlZOH78OJKSkmAwGEzWdenSpVANcXfXr19HdHS0q5tBRETkcleuXEG5cuUcvp+srCxo/EtCC+vj7kVGRiIhIcGjwp0QApUqVcK+ffvQv39/NGnSBCtXrkT37t1d3TSPwjxKRERUfLlTHgU8L5M6Oo/a3dm4efNmvPjii7h9+7bZOkVRiuxV+KCgIAC5X9DBwcEPXe9S8mcPXSNPhZBXpNUikuHu72Ol1TKcvSqtll+ndtJqBUT2k1ZLtuRJcv7IDpm6QUqdPCkbXpNWq1SXxdJqpewYI60WbqdIKyXS5U1sEdJ/tZQ6aWlpiI6ONv5OdDStVgststHarzO84WO2XoccbE/cCK1W6zHB7t/8/f2xbt06zJw5Ez179sTbb7+NKVOmuLpZHoF5VE4eJSKiwkte8YK0WrKyGgAkz+gmrZbSpIq0WqVazZVSx93yKODZmdRRedTuzsY33ngDzz33HCZNmoSIiIiHboCnyHtUJTg4WEq4C8rxf+gaeRg2yd0YAtXyavlb/oFeGH5B8r7vAtz4+y5HXaib1s3I/tmiD/CVVktm2/SB8tqFDHlfr+KBO7Uehuxz6ezHNwN8A+CjmJ+nHKEFspzaFIcYP3486tevjz59+uD48eN47733XN0kt8c8KiePEhFR4ekk/p0i82e6rL8FAMBLYk4uqnkUKBqZVHYetfurMCkpCaNGjSpWwY6IiIhcJ9C/BHy9zMOd1qAF0lzQoIdkKRx36NABBw4cQNeuXdGmTRsXtMqzMI8SERGRM1nLo4BnZlJH51G7R9N89tlnsXPnzofaaR6dToeJEyeiUqVK8Pf3R+XKlTF16lSTcXeEEIiPj0dUVBT8/f3RsmVLnDp1yqROdnY2hg4dirCwMAQGBqJLly64etX00cuUlBT07dsXGo0GGo0Gffv2xd27d6UcBxERETmOr48vfH3UFl4S70p1Imtz81WvXh0HDhxA3bp1ndwiz8M8SkRERM5kPY96ZiZ1dB61+87Gjz76CM899xx+//131K1bFz4+prcODxs2zOZas2fPxuLFi7Fy5UrUqVMHhw8fxksvvQSNRoPhw4cDAObMmYN58+ZhxYoVqF69OqZNm4a2bdvi7Nmzxmf0R4wYgY0bN2Lt2rUIDQ3F6NGj0alTJxw5cgQqlQoA0Lt3b1y9ehWbN28GAAwaNAh9+/bFxo0b7f0IpPD1lveYKZG7EemZ8ordzX/WL3vkJF2RVgtl5JWSLeeWztVNsEikZri6CZZly/saE4n3pdVCCXmP5ni6wIASUKvMf29m6+3/jK5du4a33noLP//8MzIzM1G9enUsW7YMsbGxAHKD15QpU7BkyRKkpKSgcePG+Pjjj1GnTp2HPo48D05m8m9BQUHYuHEjLl++LG1/RRHzKBERFcaVlCXSagXq5A15I5XOcidSYXhFhkurdfbmh1Lq3L/nmueVreVRwP5MWhzyqN2djWvWrMGWLVvg7++PnTt3mtx6qSiKXeFu3759eOaZZ9CxY0cAQMWKFfHVV1/h8OHDAHI/4AULFmDChAnGGXFWrlyJiIgIrFmzBq+++ipSU1OxbNkyrFq1ynib5+rVqxEdHY1ffvkF7dq1w5kzZ7B582bs378fjRs3BgAsXboUTZo0wdmzZ1GjRg17PwYiIiJyEl8fH/iqzK8YCy/7Qn5KSgqaNWuGVq1a4eeff0Z4eDjOnz+PkiVLGrexpVPJUS5cuIDMzEzUqlUL5cuXd+i+PB3zKBERETmTtTwK2JdJi0setfsx6okTJ2Lq1KlITU3FxYsXkZCQYHxduHDBrlpPPPEEtm/fjnPnzgEA/vzzT+zevRsdOnQAACQkJCAxMRFxcXHG96jVarRo0QJ79+4FABw5cgQ5OTkm20RFRSEmJsa4zb59+6DRaIzBDgAef/xxaDQa4zYPys7ORlpamsmLiIiInC8gIBCBgeavgIBAu+rMnj0b0dHRWL58OR577DFUrFgRrVu3RpUquTMtPtipFBMTg5UrVyIjIwNr1qyRdjxarRaTJ09G586dMX36dOj1evTq1QvVqlVDvXr1EBMTg4sXL0rbX1HEPEpERETOZC2P2ptJi0setbuzUavVomfPnvDysvutZt566y306tULNWvWhI+PDxo0aIARI0agV69eAIDExEQAMBv8OyIiwrguMTERvr6+KFWqVL7bhIeb3/4bHh5u3OZBM2fONI6no9FoEB0d/XAHS0RERIXirfaBt9rXwiv3kZUHO2Oys7Mt1tmwYQMaNmyI5557DuHh4WjQoAGWLl1qXG9Lp5IM48ePx6JFixAREYHPP/8c3bt3x9GjR7FmzRqsXbsW3t7emDBhgrT9FUXMo0RERORM1vOofZm0uORRux+j7tevH9atW4e333670DvNs27dOqxevRpr1qxBnTp1cOzYMYwYMQJRUVHo16+fcbsHZ8kRQhQ4zfmD21jaPr8648ePx6hRo4z/TktLY8AjIiJygYASAfDz8Tdb7pWT+zv8wd/PkydPRnx8vNn2Fy5cwKJFizBq1Ci8/fbbOHjwIIYNGwa1Wo0XX3wx306lS5cuSToa4L///S9WrFiBDh064Ny5c6hZsyZ+/PFHPP300wByO5/69OkjbX9FEfMoEREROZO1PArYl0mLSx61u7NRr9djzpw52LJlC+rVq2c2IPe8efNsrvXmm29i3LhxeP755wEAdevWxaVLlzBz5kz069cPkZGRAHKvBJcp87/ZGJKSkowffGRkJLRaLVJSUkyuJiclJaFp06bGbW7evGm2/1u3bpmdwDxqtRpqNSdxISIicjWVrze8fcwji0rJXXblyhUEBwcbl1v7/W0wGNCwYUPMmDEDANCgQQOcOnUKixYtwosvvmjcrjCdSva4fv066tevDyB3xj+1Wo2qVasa11evXt3qnW6Ui3mUiIiInMlaHgXsy6TFJY/a3dl44sQJNGjQAABw8uRJk3X2HnhGRobZ4y8qlco4K06lSpUQGRmJbdu2Gfep1Wqxa9cuzJ49GwAQGxsLHx8fbNu2DT169AAA3LhxAydPnsScOXMAAE2aNEFqaioOHjyIxx57DABw4MABpKamGgOgrc4kLkKJdD+73mNJlCbyoWsUJzJnDctPdKlBDt/HH1fmS6tVJyhKWi11yZ7Sakmd3TdL3szKIkPebMgpq56XVgsA/Nq1l1ZLsfsnu2V3D46TUyhPVo7cerLo9PJqZUms5f3wj4cWFX5BfvDzNb+SLLS5sy0GBwebBDtrypQpg9q1a5ssq1WrFr799lsAsKlTSQa9Xm/SOebt7W2crRgAvLy8IIS8mSSLouKeR4mIqHA0/qHSauncNatJnCVbXaaKvFqQM+6w1lve34f2sJZHAfsyaXHJo3b/Sbpjx45C7+xBeQNRli9fHnXq1MHRo0cxb948vPzyywByw+KIESMwY8YMVKtWDdWqVcOMGTMQEBCA3r17AwA0Gg0GDBiA0aNHIzQ0FCEhIRgzZgzq1q1rnA2wVq1aaN++PQYOHIhPP/0UADBo0CB06tSJM/8RERG5OZXaGypf63c22qpZs2Y4e/asybJz586hQoUKAGzrVJJly5Yt0Gg0AHKvcG/fvt3YaXb37l2p+yqKmEeJiIjImazlUcC+TFpc8qik+18KZ+HChXjnnXcwePBgJCUlISoqCq+++iomTZpk3Gbs2LHIzMzE4MGDkZKSgsaNG2Pr1q0m033Pnz8f3t7e6NGjBzIzM9G6dWusWLHCpFf2yy+/xLBhw4yDbHbp0gUfffSR8w6WiIiICkVdwg9+avMryYZs+67cjxw5Ek2bNsWMGTPQo0cPHDx4EEuWLMGSJbl3z9vSqSTLv8cCBIBXX33V5N8yH5Oh/DGPEhERUUGs5VHAvkxaXPKoTZ2N3bt3x4oVK2x6RAkA+vTpg/nz51ucce/fgoKCsGDBAixYsMDqNoqiID4+3uJA73n8/PywcOFCLFy40Oo2ISEhWL16dUFNJyIiIjfjo/aGj9o8svjYec20UaNG+O677zB+/HhMnToVlSpVwoIFC0wGv7alU+lh5T2eS/ZhHiUiIiJXsZZHAfsyaXHJozZ9Ij/88ANu3bplU0EhBDZu3Ih33323wHBHREREVJCAQH8E+Fm4kuxtf0jq1KkTOnXqZHW9LZ1K5BrMo0REROQqVvMoYHcmLQ551KbORiEEqlev7ui2eARvLwXeXg//aJOv98NPMlOc6A0SJ11wsZAAeec+526StFrqktJKAelZEotJJHXiGrmTnejvpUirpfKT9PilxM8LAKAr+hNeyJzQSPHmY7R5ggN8EehvPpuft5ebTjqUjw0bNti8bZcuXRzYEs/DPEpERA+rhHegtFp3pVWSS+RIzNz/P56fDL7p2XLqqFzzhIi1PAp4XiZ1Rh61qbOxMINwly1b1u73EBERET3Iz0cFfx+V2XK9znyZu+vatavJvxVFMZnp799j4+j1RedCmwzMo0REROQq1vIo4HmZ1Bl51KbOxhYtWhSqOBEREdHD0qiAIAsZTuVZuQ6A6fg4v/zyC9566y3MmDEDTZo0gaIo2Lt3LyZOnIgZM2a4sJXuiXmUiIiIXMVaHgU8L5M6I4+6dDZqIiIiooKodDqodOaPqFta5klGjBiBxYsX44knnjAua9euHQICAjBo0CCcOXPGha0jIiIiojzW8mjeOk/lqDzKzkYiIiJya1730+FlIcN5ZaU7vzESnT9/HhoLYyFpNBpcvHjR+Q0iIiIiIous5VHAszOpo/IoOxvtpPJS4O3l9dB11N6WBxYly4rUBDGB8mbF1N84Ja2WTOK+nMF/AUidVESkZ0qrhSy5X5P6+3el1VKCfKTUEbIniCkOZE6CI/lrzJMp2hwoFgbeVrSeNRj3gxo1aoQRI0Zg9erVKFOmDAAgMTERo0ePxmOPPebi1hERERU92cmJ8op5e9izs4VwLeWitFoa/1JS6viqXJORreXRvHWeylF5lJ2NRERE5NZEejaEhYG3RbbECxsu8Pnnn6Nbt26oUKECypcvDwC4fPkyqlevju+//961jSMiIiIiI2t5FPDsTOqoPMrORiIiInJrIlsPAfOr2CLbs+/+rFq1Ko4fP45t27bhr7/+ghACtWvXRps2bUxmASQiIiIi17KWR/PWeSpH5VG7Oxvv3LmDSZMmYceOHUhKSjKZxQYAkpOTC90YIiIiogcZ0rNhyDG/kmzQeu5V5DyKoiAuLg5xcXGubopHYR4lIiIiZ7KWRwHPz6SOyKN2dza+8MILOH/+PAYMGICIiAheeSciIiKHEtl6CGHhzkat511F/vDDDzFo0CD4+fnZtP3ixYvRp08fBAUFObhlnoV5lIiIiJzJWh4FPC+TOiOP2t3ZuHv3buzevRv169e3961FgspLBZXXww8Eq1IefpKZ4kT3wB0LnqyEd6C0Wmkpd6XVkuq+xAFydRLPvcwJT7KsTEVWSCLtnrRaSgk5E8RA5oQ6gNxz6aZEpmcFDU+hv5cNvY/57019juddRR45ciR69eplc7gbO3Ys4uLi2Nn4gOKeR4mIqHBy7lyXVkvxdtO/6SVm7oup8v7maRRkW/YpiI+3a/6msJZHAc/LpM7Io3Z3NtasWROZmZL/ACUiIiKyQmh1MAjzsCty5Hb6O4MQAq1bt4a3t20RjJnLMuZRIiIiciZreRTwvEzqjDxqd2fjJ598gnHjxmHSpEmIiYmBj4/pHTTBwcF2N4KIiIjImpz72cjxNn9MNkfnWVeRAWDy5Ml2bf/MM88gJCTEQa3xXMyjRERE5EzW8ijgeZnUGXnU7s7GkiVLIjU1FU899ZTJciEEFEWBXs9HyIiIiEgevTYHeoP5ECZ6ncQhG5zE3nBHljGPEhERkTNZy6OA52VSZ+RRuzsb+/TpA19fX6xZs4YDchMREZHDZWVlwlslzJfrs1zQGnIHzKNERETkTNbyKMBMaondnY0nT57E0aNHUaNGDUe0h4iIiMiETqdHjoUxcnS8e63YYh4lIiIiZ7KWRwFmUkvs7mxs2LAhrly5UmzDnUqRMxu1QStvVlyVWlopt5VdhGax1abekVZLJKdKqyWTkDgbtcixfPWoUKTORi33F4pIuy+vmKzZqGV+XkCxmI1a6jHqeKdWngxtJhQv8882w+BZ4+OQPMU9jxIRUeEY7qa4ugkOZ6U/rFD+SvGVVqtZRTkdF77erunYs5ZHAWZSS+zubBw6dCiGDx+ON998E3Xr1jUbkLtevXrSGkdERESUoc0CLHY2Su4QJ4/BPEpERETOZC2PAsykltjd2dizZ08AwMsvv2xcpigKB+QmIiIih8jRa2HpJuccDw92O3fuRMuWLV3dDI/EPEpERETOZC2PAp6dSR2VR+3ubExISJDeCCIiIiJrsrIyoCjmwzNkCc+a+e9B7du3R9myZfHSSy+hX79+iI6OdnWTPAbzKBERETmTtTwKeHYmdVQetbuzsUKFClJ2TERERGQLfVY29Ir5Yyt6Dw52AHD9+nWsXr0aK1asQHx8PFq3bo0BAwaga9eu8PWVN0ZSUcQ8SkRERM5kLY8Cnp1JHZVH7e5szHP69GlcvnwZ2gcmOunSpUuhG+MJypV6CcHBwa5uhsOka3+QVivQ9xmJtQr9pWoXnWGzw/ehz0iTV0ziBB63h7WSVkvxf/hJlIwkTrghsuSNliwkTxCj6CTW85Pz+Yt0yQMd6+RN9nPw0nxptar5yevU0WfJO0Zvf2mlkJm0Qk6de5lS6thLaDMgFPOJj4QHBzsACAkJwbBhwzBs2DAcO3YMn3/+OYYMGYLXX38dffr0wYABA1C/fn1XN9OtFdc8WtRtOfeB1HotQsOk1fIL7SOtljW7Lixw+D58vCROwAegacWRUuvJkjyzo7Raqg4NpdXSfb1PWi2fd16VVsuw/zdpta6+e0ZarXIzH5VWy10n2HRXmRL/RFGr5GRutco1+c9aHgU8O5M6Ko/a3YNz4cIFdOvWDSdOnDCOjQPkjpMDgGPkEBERkVQqlYDKwpVklRBAEZnk/JFHHsG4ceMQEhKCWbNm4fPPP8cnn3yCJk2aYPHixahTp46rm+hWmEeJiIjImazlUaDoZFKZedTL3p0PHz4clSpVws2bNxEQEIBTp07ht99+Q8OGDbFz5057yxERERHlS+1ngJ+Fl9rP81NdTk4O/vvf/6JDhw6oUKECtmzZgo8++gg3b95EQkICoqOj8dxzz7m6mW6HeZSIiIicyVoeLQqZ1BF51O47G/ft24dff/0VpUuXhpeXF7y8vPDEE09g5syZGDZsGI4ePWpvSSIiIiKrVCpApVhYLvcpQKcbOnQovvrqKwDACy+8gDlz5iAmJsa4PjAwELNmzULFihVd1EL3xTxKREREzmQtjwKenUkdlUftvrNRr9ejRIkSAICwsDBcv34dQO5A3WfPnrW3HBEREVG+1L6AWm3h9ZBD/8ycOROKomDEiBHGZUIIxMfHIyoqCv7+/mjZsiVOnTr1cDuy4vTp01i4cCGuX7+OBQsWmAS7PFFRUdixY4dD9u/JmEeJiIjImazm0YfMpEU1j9p9Z2NMTAyOHz+OypUro3HjxpgzZw58fX2xZMkSVK5c2d5y5GbU3mpXN8GiymHVnbIfbWqKw/ehCpA4wZCmhLRSQt7cKRD35I2VZZA4qYjdV1ecyVvipDqyJq/xdt/HASqHhkur5VX2nrRaQidvMHSlpLyfx7q7SXLq3M+SUsdeKhWgsvANbO3qsi0OHTqEJUuWoF69eibL58yZg3nz5mHFihWoXr06pk2bhrZt2+Ls2bMICgoq/A4tmDx5Mpo2bQpvb9M4ptPpsHfvXjRv3hze3t5o0aKF1P0WBcyjRds9rdzf2NqkS9Jq+YVKK+VSoTIn83NjUicHTJOXF2ROgKiWeIwZqfel1VIHSMyREifFRLq8ye6ExPMok8y/n5qXkTdhpKy/tbVpGVLq2MtaHgUKn0mLch61+zf5xIkTYTDkflNNmzYNly5dwpNPPomffvoJH374ob3liIiIiPLlowZ8Lbx8/r8/Ni0tzeSVnZ1/ML5//z769OmDpUuXolSpUsblQggsWLAAEyZMQPfu3RETE4OVK1ciIyMDa9askX5crVq1QnJystny1NRUtGrVSvr+ihLmUSIiInIma3m0sJm0qOdRuzsb27Vrh+7duwMAKleujNOnT+P27dtISkrCU089VeiGEBEREVmiUilQeVt4/f9l5OjoaGg0GuNr5syZ+dYbMmQIOnbsiDZt2pgsT0hIQGJiIuLi4ozL1Go1WrRogb1790o/LiGEcfbkf7tz5w4CAwOl768oYR4lIiIiZ7KaRwuZSYt6HrX7Meo8//zzD86fP4/mzZsjJCQEQnjwiJhERETktnx9AV8LT/zl/P+IAVeuXEFw8P+GqFCrrT+CvnbtWvzxxx84dOiQ2brExEQAQEREhMnyiIgIXLok7zHMvE4yRVHQv39/k/bq9XocP34cTZs2lba/oox5lIiIiJzBWh4F7M+kxSGP2n1n4507d9C6dWtUr14dHTp0wI0bNwAAr7zyCkaPHm13A65du4YXXngBoaGhCAgIwCOPPIIjR44Y19syMGZ2djaGDh2KsLAwBAYGokuXLrh69arJNikpKejbt6+xh7lv3764e/eu3e0lIiIi51J5W38BQHBwsMnLWrC7cuUKhg8fjtWrV8PPz8/q/h68umvtim9h5WURIQSCgoJMroBHRkZi0KBBWL16tbT9FUXMo0RERORM+eVRezJpccmjdt/ZOHLkSPj4+ODy5cuoVauWcXnPnj0xcuRIvP/++zbXSklJQbNmzdCqVSv8/PPPCA8Px/nz51GyZEnjNrYMjDlixAhs3LgRa9euRWhoKEaPHo1OnTrhyJEjUKlyu5579+6Nq1evYvPmzQCAQYMGoW/fvti4caO9H0GR5qW45xQa9/8+5pT9CL3EWVKsCKxcr+CNbJQV4C+tlj7LPe8G0WXL+4Hqo5M3cY3Mgb0BSJ4gxvFfx64WLKz/YraXoUwVabXS9b9Kq4Uweceov2s+Dkuh6tyXN0i4Pbx9BXy8zX9Geds5APqRI0eQlJSE2NhY4zK9Xo/ffvsNH330kXEW48TERJQpU8a4TVJSktnV5YexfPlyAEDFihUxZswYPjJdCMyjRdu9HLl51HD7prxitQre5GE5Y86Jkv4ax+/EDYh7EieIyZA4sUiOvNyddem0tFpIS5dWSh0oL3cLiRPEiHSJWUbm3xZuql7ZatJq5VxNkFPnnrzvRXtYy6OAfZm0uORRuzsbt27dii1btqBcuXImy6tVq2b3LZ2zZ89GdHS08SCB3APN8+DAmACwcuVKREREYM2aNXj11VeRmpqKZcuWYdWqVcZn3VevXo3o6Gj88ssvaNeuHc6cOYPNmzdj//79aNy4MQBg6dKlaNKkCc6ePYsaNWrY+zEQERGRk/z7irHJcjvrtG7dGidOnDBZ9tJLL6FmzZp46623ULlyZURGRmLbtm1o0KABAECr1WLXrl2YPXt2IVtv3eTJk6XXLC6YR4mIiMiZrOVRwL5MWlzyqN2djenp6QgICDBbfvv27XzHSLJkw4YNaNeuHZ577jns2rULZcuWxeDBgzFw4EAABQ+M+eqrr+LIkSPIyckx2SYqKgoxMTHYu3cv2rVrh3379kGj0RiDHQA8/vjj0Gg02Lt3r8Vwl52dbTJzUFpaml3HRkRERHJ4qwW8Ld3ZqLLvzpCgoCDExMSYLAsMDERoaKhx+YgRIzBjxgxUq1YN1apVw4wZMxAQEIDevXsX/gD+5dFHH8X27dtRqlQpNGjQIN/HYf744w8p+yyKmEeJiIjImazlUcC+TFpc8qjdnY3NmzfHF198gXfffRdA7nPkBoMB7733nt3TYl+4cAGLFi3CqFGj8Pbbb+PgwYMYNmwY1Go1XnzxRZsGxkxMTISvr6/JVOF52+S9PzExEeHh4Wb7Dw8PN27zoJkzZ2LKlCl2HQ8RERHJ5+NrgI+P+XOFPl7ynzUcO3YsMjMzMXjwYKSkpKBx48bYunWr8VHZh/XMM88YO8OeeeYZqWPvFCfMo0RERORM1vIoID+TFoU8andn43vvvYeWLVvi8OHD0Gq1GDt2LE6dOoXk5GTs2bPHrloGgwENGzbEjBkzAAANGjTAqVOnsGjRIrz44ovG7QozMOaD21jaPr8648ePx6hRo4z/TktLQ3R0dMEHRURERFJ5qXJfZssl5LqdO3ea/FtRFMTHxyM+Pv7hi1vw70dVHLWP4oB5lIiIiJzJWh4FHj6TFsU8andnY+3atXH8+HEsWrQIKpUK6enp6N69O4YMGWIyeKUtypQpg9q1a5ssq1WrFr799lsAQGRkJID8B8aMjIyEVqtFSkqKydXkpKQk4zTdkZGRuHnTfGDoW7duWR1gU61W2/0YTpGgl9gjb/dXl3W6M2flFcuHoinh8H2IGvI+GCVYzpUNADBIHN/YoJN3ZUTmBDEyyRzYGwCgkjlBjJyTKSSPUK/4yfvaz7xwXFqtwLpNpNWS+bWvaMwfES0skZIqp066vAHa7eGttnwl2dsBdzY600svvYQXXngBTz31FO9wtBPzaNGWppX7/SBu3JJaryjQBIS6uglOIbIk/p5IvS+vlkSGsxek1RJ3s6TV8g2QmJX1Ev9QuS8xy3gX/d/dukt/S6ulv39XUh0XTVhoJY8Cnp1JHZVHCzXVW2RkJKZMmYJNmzbhp59+wrRp0+wOdgDQrFkz40w7ec6dO4cKFSoAACpVqmQcGDNP3sCYecEtNjYWPj4+JtvcuHEDJ0+eNG7TpEkTpKam4uDBg8ZtDhw4gNTUVOM2RERE5J68vIXVlye7c+cOOnbsiHLlymH06NE4duyYq5vkUZhHiYiIyFnyy6OenEkdlUcf6jaT9PR0rFu3DpmZmYiLi0O1avZNiz5y5Eg0bdoUM2bMQI8ePXDw4EEsWbIES5YsAZB762hBA2NqNBoMGDAAo0ePRmhoKEJCQjBmzBjUrVvXOBtgrVq10L59ewwcOBCffvopAGDQoEHo1KkTZ/4jIiJyc96+At6+FiaIUTw32AG5E5PcvXsXX3/9NdasWYMFCxagRo0aeOGFF9C7d2+TGZHJOuZRIiIicjRreRTw7EzqqDxq852Nly9fRosWLRAUFIS2bdvi8uXLePTRR/HKK69g6NCheOSRR/Dbb7/ZtfNGjRrhu+++w1dffYWYmBi8++67WLBgAfr06WPcZuzYsRgxYgQGDx6Mhg0b4tq1a2YDY86fPx9du3ZFjx490KxZMwQEBGDjxo1Q/euxxC+//BJ169ZFXFwc4uLiUK9ePaxatcqu9hIREZHz5Y2RY+nl6UqWLIlBgwZh586duHTpEl566SWsWrUKVatWdXXT3BLzKBEREblCfnnU0zOpI/KozXc2jhkzBlqtFosWLcI333yDdu3aoVq1avjtt9/g5eWFwYMHIz4+Hr/++qtdDejUqRM6depkdb0tA2P6+flh4cKFWLhwodVtQkJCsHr1arvaRkRERK6n8lOg8jUfQ0blVXTGSsrJycHhw4dx4MABXLx40eoYfsUd8ygRERG5grU8ChSdTCozj9rc2fjbb79hw4YNeOyxx9ChQweEhYXh888/N+584sSJaN26daEbQkRERGSJ4p37MlvuuWNxG+3YsQNr1qzBt99+C71ej+7du2Pjxo146qmnXN00t8Q8SkRERK5gLY8Cnp9JHZFHbe5svHXrlnGg7JCQEAQEBJj0ckZGRiIlJaXQDSH3IPQ6ecUkzkYtjl+XVyw/FTUO30WGNl1aLVWJktJqyZxFV5tRqLmnLNJLbBckz64sk+It7957g6zZqDMlzvwHubNRG85fklbrRvmy0moJmR9ZiMSfR7eS5dTJyJFTx06KnwqK2vx7RPHy3PFxAKBcuXK4c+cO2rVrh08//RSdO3eGn5+fq5vl1phHi4c0yT9qxMVUuQUdTGdw/B0yvjKDuhvLSZf3e0KVmiGtlsxMKs7ellYL3vIyvHcJiV/HWRJnkL4v8QdMCR95tSQSEv+kz/nzT2m1FEnZVqS7ZjZqa3kU8OxM6qg8avNvGSGEyTTYMqfEJiIiIrLK28vyH0B6eX8UucKkSZPw3HPPoVSpUq5uisdgHiUiIiKXsJZHAY/OpI7Ko3Zd0po0aRICAgIAAFqtFtOnT4dGk9s7nZEh8UoPERERUR61CoqfpSvJnnsVGcidiZjsxzxKRERETmc1jwKenEkdlUdt7mxs3rw5zp49a/x306ZNceHCBbNtiIiIiKTyVixfSfb2vLvaunfvjhUrViA4OBjdu3fPd9v169c7qVWeg3mUiIiIXMJaHs1b50GckUdt7mzcuXNnoXZARERE9FD8VLkvM+47Dqs1Go3G+OhvcHAwHwO2E/MoERERuYTVPAp4WiZ1Rh4tHiMDk830WnmDrarU0krh/qksecXyUULioMjWZGrvS6sVFBAkrZYuW94PmByJtWROXCOVG082I2tiF5Hlxsd46qa0Wjdi5U3aFC7x61UJLiGtluHMVSl1RKZrJoiBysoYOSrPGx9n+fLlxv9fsWKF6xpC5MZStXJ/9+uveNbj9c6YIEabekdqPb9QqeWkkZlvcVfe30kyJ/DI+Eve30n+5eRNWKgESZw8JVveBDFC4gQxiofdzVYYhgNyMiQAeDWWU0dkSJwwyB7W8mjeOg/ijDzqWZ8IERERFT95V5ItvTzYU089hbt375otT0tLw1NPPeX8BhERERGRZfnlUQ/OpI7Ko7yzkYiIiNybtyr3Zbbcfe++tcXOnTuh1Zpfnc/KysLvv//ughYRERERkUXW8ijg0ZnUUXmUnY1ERETk1hS1NxQ/88iiGDxz5r/jx48b///06dNITEw0/luv12Pz5s0oW7asK5pGRERERBZYy6OAZ2ZSR+dRuzobdTodpk+fjpdffhnR0dGF3ikRERGRzdQ+gNrXfLmc4Umd7pFHHoGiKFAUxeLjKf7+/li4cKELWuYZmEeJiIjI6azlUcAjM6mj86hdnY3e3t5477330K9fv0LvkNxb5oXjBW9kI9/6/5FWS5ftpOFFS0gcyNjaLvxKSqvlrcgbXTo7Q95nLHNSl9Rb8sa/iNDJu+LkVdpPWi0AEEnJ8mpJmtjFIPHzAgAviQNyyxQaIO/73stb3mcmUlKl1UIJK8HIXioXDYSuUuW+LC33QAkJCRBCoHLlyjh48CBKly5tXOfr64vw8HCoPPTYnIF5tOib+9TrUutdXfa1tFopPwyQVsuahq2aO3wfOYnXpNb79J9PpNUa3niwtFoy/4Yw3MiUVkufJS8v+FeU9DsegO6WvIk3vEvLa5fVDp5CEPckThAj8W/HXRcWSKtV209eXhM5ErPtnXty6rhswkIreTRvnYdxdB61+zHqNm3aYOfOnejfv3+hd0pERERkM7Uv4Fd07mysUKECAMBg8NzxfVyNeZSIiIicyloeBTwykzo6j9rd2fj0009j/PjxOHnyJGJjYxEYGGiyvkuXLtIaR0RERFTU7mzMM3PmTERERODll182Wf7555/j1q1beOutt1zUMvfHPEpEREROVcTubMzjqDxqd2fj66/nPtYwb948s3WKokCv98AuXSIiInJbitoHioVHqBTJj/o726effoo1a9aYLa9Tpw6ef/55djbmg3mUiIiInMlaHgU8O5M6Ko/a3dnIR36IiIjIqbxVuS9Lyz1YYmIiypQpY7a8dOnSuHHjhgta5DmYR4mIiMiprOXRvHUeylF51O7ORiraDOcvyStWX16pnGznTEqglJQ76Ycl/l5qicXk1cqROIC2SiXvyk7yLTe9ShQmeYKYm3IGTAbkDTquSP4NITLd806jkMDSBW9kI3lnEUCyvAliFE2AnDo+8gaOt4uPL+Br4Uqy1k1/PtgoOjoae/bsQaVKlUyW79mzB1FRUS5qFVHRk5km74/AwEB/abWsCZY4eYg12XflTUwHAG3KS8y3EuklTlqYdVNmjpHXLnVMmLRa2T/Iu9DlLS9eQdGUkFZLd1/e95dPlrzJOhuXryqt1n0/eX/XKUES/yBIzJBTR+LnbhdreRTw6EzqqDxaqK+c9PR07Nq1C5cvX4ZWa/qHx7BhwwrdGCIiIqIHKd7eULzNI4vi7Z4d2LZ65ZVXMGLECOTk5OCpp54CAGzfvh1jx47F6NGjXdw698c8SkRERM5iLY/mrvPcTOqoPGp3Z+PRo0fRoUMHZGRkID09HSEhIbh9+zYCAgIQHh7OcEdERERSKb6+UHzN75pRfD33KjIAjB07FsnJyRg8eLCxs8zPzw9vvfUWxo8f7+LWuTfmUSIiInIma3k0d53nZlJH5VG7768dOXIkOnfujOTkZPj7+2P//v24dOkSYmNjMXfu3EI3hIiIiMgSReVt9WWPmTNnolGjRggKCkJ4eDi6du2Ks2fPmmwjhEB8fDyioqLg7++Pli1b4tSpUzIPx0hRFMyePRu3bt3C/v378eeffyI5ORmTJk1yyP6KEuZRIiIicqb88qg9mbS45FG7OxuPHTuG0aNHQ6VSQaVSITs7G9HR0ZgzZw7efvvth2oMERER0YMUHz8ovv7mLx/7xk7dtWsXhgwZgv3792Pbtm3Q6XSIi4tDenq6cZs5c+Zg3rx5+Oijj3Do0CFERkaibdu2uHdP6oicJhITE5GcnIwqVapArVZDCM+9Ou4szKNERETkTFbzqJ2ZtLjkUbsfo/bx8YGi5A5oGxERgcuXL6NWrVrQaDS4fPnyQzWGiIiI6EHWrhjnLUtLSzNZrlaroVabP+ayefNmk38vX74c4eHhOHLkCJo3bw4hBBYsWIAJEyage/fuAICVK1ciIiICa9aswauvvirrkAAAd+7cQY8ePbBjxw4oioK///4blStXxiuvvIKSJUvi/fffl7q/ooR5lIiIiJwpvzsY7cmkxSWP2t3Z2KBBAxw+fBjVq1dHq1atMGnSJNy+fRurVq1C3bp1C9UIch/i1E15xbrLKyVzpuR8BTp+Nmpt6h15xTQaaaWy0+XNihcQLO+unMREiTOg6QzSSillQ6TVAgDDkevSaukkzd7u4y337ir9ffccODnUv5S0WunyJjyFuC1pxj4ASi1JX68q18xG7aX2h5fafAZYr/9vTnR0tMnyyZMnIz4+vsC6qam5M36HhOR+PgkJCUhMTERcXJxxG7VajRYtWmDv3r3Sw93IkSPh4+Nj7CjL07NnT4wcOZKdjflgHiV7ZGdInJlVbWUmUomyDu11+D5kq1n/CVc3wSKDxOhxN9FHWq2AYHkNK1G9UsEb2Sg7PUlarUCJuRsB8maBz0qT9/PA+568WZG1fx+XVkvxlxdIFT95f4vpr8jJtgata2ajtpZHgYfLpEU1j9r9lTNjxgzjrZvvvvsu+vXrh9dffx1Vq1bF8uXLC9UIIiIiImtyrySb/5GnqHIAAFeuXEFwcLBxuaW7Gh8khMCoUaPwxBNPICYmBkDu4yNA7p1y/xYREYFLly4Vuv3WbN26FVu2bEG5cuVMllerVs0h+ytKmEeJiIjImazl0dx1hcukRTmP2t3Z2LBhQ+P/ly5dGj/99FOhd05ERERUEC8fP3j5mt957uWTe9dEcHCwSbCzxRtvvIHjx49j9+7dZuvyHs/NI4QwWyZDeno6AgICzJbfvn3bpg7T4ox5lIiIiJzJWh7NXVe4TFqU86iTnk0lIiIiKhxZs1HnGTp0KDZs2IAdO3aYXMWNjIwE8L8rynmSkpLMri7L0Lx5c3zxxRfGfyuKAoPBgPfeew+tWrWSvj8iIiIiKhxZs1HnKep51KZPpEGDBjb3oP7xxx+FbgwRERHRg1S+vlD5ml9ZVfnaN2aPEAJDhw7Fd999h507d6JSJdNxripVqoTIyEhs27YNDRo0AABotVrs2rULs2fPLvwBWPHee++hZcuWOHz4MLRaLcaOHYtTp04hOTkZe/bskb4/T8c8SkRERK5iLY/mrrM9kxaXPGpTZ2PXrl2N/5+VlYVPPvkEtWvXRpMmTQAA+/fvx6lTpzB48OBCN4TcQ9Y/mdJq3XmrjbRaumyJsy7kxwmDfuvuyht4WQkqIa1WlsQJYtSB8moFRZQreCOb3ZBXKlDeQNUAoLslb+INnaQJlbzVcieIkTVxDQB4SZy8Jjv5lrRaMgeiF7fl/Tz2kvSzTdHJ/Zqwmcor92VpuR2GDBmCNWvW4IcffkBQUJDxirFGo4G/vz8URcGIESMwY8YMVKtWDdWqVcOMGTMQEBCA3r17yzgSE7Vr18bx48exaNEiqFQqpKeno3v37hgyZAjKlCkjfX+ejnn0f5YfXQL/Eg//e2hQ7BAJrSlm9I6f7EycSyx4o4dVUu6kiFlJV6TVCowueBtb+UjMMtnp0krh9lV5k82E3LsvrVZgiLyJN3Qp0krB6+I1abVkThjld1fe56U6d15aLZl/V6gy5f3My0mX8/2Yo3WzPJq3zkbFJY/a1Nk4efJk4/+/8sorGDZsGN59912zba5ckfdLhoiIiAgADMIAgzCf1dLSsvwsWrQIANCyZUuT5cuXL0f//v0BAGPHjkVmZiYGDx6MlJQUNG7cGFu3bkVQUFCh2l6QyMhITJkyxSG1ixrmUSIiInIVa3k0b52tiksetfvB8m+++QaHDx82W/7CCy+gYcOG+Pzzz6U0jIiIiAgAsnVaZOvMI0u2zr4r90IUfCVcURTEx8cjPj7ertq2On78uM3b1qtXzyFtKAqYR4mIiMiZrOXRvHW2Ki551O7ORn9/f+zevRvVqlUzWb579274+cm9HZ+IiIhIb9BBbzB/VMnSMnf3yCOPQFGUAoOmoijQO+FxTU/FPEpERETOZC2P5q3zJM7Io3YPWDBixAi8/vrreOONN7B69WqsXr0ab7zxBoYMGYKRI0cWqhEAMHPmTOOz6XmEEIiPj0dUVBT8/f3RsmVLnDp1yuR92dnZGDp0KMLCwhAYGIguXbrg6tWrJtukpKSgb9++0Gg00Gg06Nu3L+7evVvothIREZHz5OiyoLXwytFlubppdktISMCFCxeQkJCQ7+vChQuubqpbc1QeBZhJiYiIyJy1POqJmdQZedTuOxvHjRuHypUr44MPPsCaNWsAALVq1cKKFSvQo0ePQjXi0KFDWLJkidntmXPmzMG8efOwYsUKVK9eHdOmTUPbtm1x9uxZ47PqI0aMwMaNG7F27VqEhoZi9OjR6NSpE44cOQKVKndSkd69e+Pq1avYvHkzAGDQoEHo27cvNm7cWKj2FmW+GnkTOGRelXdHhjbb/qnkC8MZA3LrK0ic8OTcn/JqSSRzIpBA3wBptWQSf1yWWi/9lpMmQbKDNkPeeQQAg05ePT+JA75nHt0nr1aavPPoL3Fwb6+rcn62iYwcKXXspTfordzZ6Hl3/nXr1g3bt29HqVKlMHXqVIwZMwYBAe75c86dOSKPAp6TSf29BfwlTpRV1KlU8j4rkS3vZ7M1hisSZyKxwitL7s9P/X2Js4FI5Btg39i++UqW9zs+4Zy8r8mqf18teCMbqcvJmyzz3ll5d3p5H5E3yWN2urwJYjJS5H1N+J67Ka1W2g15fzurA+X9rDDo5Hz22hx559Ae1vJo3jpP4ow8Wqiz1KNHD+zZswfJycnG6bALG+zu37+PPn36YOnSpShVqpRxuRACCxYswIQJE9C9e3fExMRg5cqVyMjIMIbK1NRULFu2DO+//z7atGmDBg0aYPXq1Thx4gR++eUXAMCZM2ewefNmfPbZZ2jSpAmaNGmCpUuXYtOmTTh79myh2kxERETOo9VnW315mjNnziA9PbcjYcqUKbh/X94sosWNzDwKMJMSERGRdfnlUU/LpM7Io865XSwfQ4YMQceOHdGmTRtMmzbNuDwhIQGJiYmIi4szLlOr1WjRogX27t2LV199FUeOHEFOTo7JNlFRUYiJicHevXvRrl077Nu3DxqNBo0bNzZu8/jjj0Oj0WDv3r2oUaOGxXZlZ2cjO/t/XzBpaWkyD5uIiIhspBd6i1eM9cKzriIDuWPkvPTSS3jiiScghMDcuXNRokQJi9tOmjTJya0r3twxkzKPEhERuQdreTRvnSdxRh61u7NRr9dj/vz5+Prrr3H58mVotaaPEiQnJ9tca+3atfjjjz9w6NAhs3WJibmPfEVERJgsj4iIwKVLl4zb+Pr6mlx9ztsm7/2JiYkIDw83qx8eHm7cxpKZM2dKn/qbiIiI7KfV6ZCtM3+EW6vzrMG4AWDFihWYPHkyNm3aBEVR8PPPP8Pb2zyOKYrCzsZ8yMyjgPtmUuZRIiIi92Atj+at8yTOyKN2dzZOmTIFn332GUaNGoV33nkHEyZMwMWLF/H999/b1YgrV65g+PDh2Lp1a76zBiqK6RhfQgizZQ96cBtL2xdUZ/z48Rg1apTx32lpaYiOjs53v0RERCSfziCgM5iPb2VpmburUaMG1q5dCwDw8vLC9u3bLXZAUf5k5VHAvTMp8ygREZF7sJZH89Z5EmfkUbs7G7/88kssXboUHTt2xJQpU9CrVy9UqVIF9erVw/79+zFs2DCb6hw5cgRJSUmIjY01LtPr9fjtt9/w0UcfGceuSUxMRJkyZYzbJCUlGa8sR0ZGQqvVIiUlxeRKclJSEpo2bWrc5uZN88FWb926ZXaF+t/UajXUarVNx1KUKKXkDQp895S8bzhnzSSvPeH4x5P8mmVIq2U48Je0WjJlpcubCMRH5SOtlkz3/5R3HgEg7Za8773g0nK+YbLT5U5aI3PcZP8QeQO+i0MJ0mrJnCAmM1neB1biHzkD94ss11y1zdYZ4KMzP+fZFpZ5EoPBs9vvSrLyKODemdRaHvVTCfhLnPSkqPOWOKkY0jPl1bLi/mXHn9sSOrk5xnDPPR/x9wl0z++ToJDa0mqJf05Iq6VUDJJW6/5+eRMdZZ+Q9/syK8M1k4sUJPBveWPmpSRav3BmL78AeZ+XOlDOeXTVBDHW8mjeOk/lqDxq91lKTExE3bp1AQAlSpRAamoqAKBTp0748ccfba7TunVrnDhxAseOHTO+GjZsiD59+uDYsWOoXLkyIiMjsW3bNuN7tFotdu3aZQxtsbGx8PHxMdnmxo0bOHnypHGbJk2aIDU1FQcPHjRuc+DAAaSmphq3ISIiIvelM1h/ebpVq1ahWbNmiIqKMj6SO3/+fPzwww8ubpl7k5VHAWZSIiIiKlh+edTTM6kj8qjdnY3lypXDjRu5U89XrVoVW7duBQAcOnTIrjsBg4KCEBMTY/IKDAxEaGgoYmJioCgKRowYgRkzZuC7777DyZMn0b9/fwQEBKB3794AAI1GgwEDBmD06NHYvn07jh49ihdeeAF169ZFmzZtAAC1atVC+/btMXDgQOzfvx/79+/HwIED0alTJ6uTwxAREZH7yNIryLTwytLLu4vaFRYtWoRRo0ahQ4cOuHv3LvT63LtZS5UqhQULFri2cW5OVh4FmEmJiIioYNbyqKdnUkflUbs7G7t164bt27cDAIYPH4533nkH1apVw4svvoiXX3650A2xZOzYsRgxYgQGDx6Mhg0b4tq1a9i6dSuCgv53e/f8+fPRtWtX9OjRA82aNUNAQAA2btwIlep/j7J9+eWXqFu3LuLi4hAXF4d69eph1apVUttKREREjlFUryIvXLgQS5cuxYQJE0xyS8OGDXHihLzH4ooiZ+ZRgJmUiIiouCuqdzY6Ko/aPWbjrFmzjP//7LPPIjo6Gnv27EHVqlXRpUuXQjcEAHbu3Gnyb0VREB8fj/j4eKvv8fPzw8KFC7Fw4UKr24SEhGD16tUP1TYiIiJyjWy9F1Q68+uj2Xr3HHfJVgkJCWjQoIHZcrVajfR0eWNdFUWOzKMAMykRERGZspZH89Z5KkflUbs7Gx/UuHFjNG7cGEDuoyuNGjV62JJERERERjmG3Jel5Z6sUqVKOHbsGCpUqGCy/Oeff0bt2vImDygOmEeJiIjIkazl0bx1nspRedTuzsb79+9DpVLB39/fuOzYsWN455138NNPPxmf7ybPFDpli7xa0ioBP5VrJ7GadY9/vtMp+5HmMXmlmg2VV0um44/PlVdMd1paqbs35M6S/chued97x56Q8/3i5cYznWanyRsXRfwt7w6yR3bvlFbr785tpNUKuJUlpY7QumY26ky9AsXCFeNMDx4fBwDefPNNDBkyBFlZWRBC4ODBg/jqq68wc+ZMfPbZZ65unlsr7nm0a+3XEBwc7OpmeIyg0vJ+dpXqvkJaLev7cPgupLt7cJyrm2BRxJKd8mpJqwT4tvlYXjE/efkW93OklTJIjAw3bzz0PVJGmRny8m2p0vLuZrt7Vd4xptySd4wqlbysFRwi5xjvu+hXvLU8mrfOUzkqj9r83XH16lU0a9YMGo0GGo0Go0aNQkZGBl588UU0atQIarUau3fvLnRDiIiIiCzJ1inIsvDK1nlusAOAl156CZMnT8bYsWORkZGB3r17Y/Hixfjggw/w/PPPu7p5bol5lIiIiFzBWh719EzqqDxqc9fyuHHjcP/+fXzwwQf49ttv8cEHH2DXrl2oX78+zp07h0qVKhW6EURERETW5BgA7yL2GLVOp8OXX36Jzp07Y+DAgbh9+zYMBgPCw8Nd3TS3xjxKRERErmAtj+at80SOzKM2dzbu2LEDX3/9NZo1a4Znn30WUVFReO655zBunHveMk9ERERFQ5ZeASxcMc7y4EdWvL298frrr+PMmTMAgLCwMBe3yDMwjxIREZErWMujxnUeyJF51ObHqBMTE1GlShUAQGRkJPz9/fHMM89IawgRERGRJTnC+suTNW7cGEePHnV1MzwK8ygRERG5Qn551JMzqaPyqF0jdKpUKuP/e3l5wc/PT3qDiCxJFxVd3QRykeB65eQV08n7LeAlbwxn6a6cl1Mnopy8Qa8BQCVxwplbF32l1QotJ28wdJkMEge/FpIGaZdVx16ZOsv7znJRe2QZPHgwRo8ejatXryI2NhaBgYEm6+vVq+eilrk35lGyl0+02tVNKPIUNT9jewTUi5JWSylXQlot8U+qtFoGvargjWyUlCgvQ6any8u3JYLktev2FXl/XNy+Ja0UsrPlncfIbDnBLcPgmp49a3kU8OxM6qg8avNXtBACrVu3hrd37lsyMzPRuXNn+Pqa/sH3xx9/FKohRERERJboDApyDOaPp+gsLPMkPXv2BAAMGzbMuExRFAghoChKkZ9RuTCYR4mIiMgVrOXRvHWeylF51ObOxsmTJ5v8m4+sEBERkTNk6AC9hSvGki6Qu0xCQoKrm+BxmEeJiIjIFazlUcCzM6mj8mihOxuJiIiInCHHAHgVsdmoAaBChQquboLHYR4lIiIiV7CWR/PWeSpH5VE3HnWMiIiICMjSKTBYmP1Pa2VGQCIiIiIimazlUYCZ1BJ2NpJHaPDYY65uArmIKkbeBDHijrz722VOdiJblldFOXXSL0mpk0elkvdLOPm2vM8/IFjewNcyWQszhaHPkvN56bWu+brXCQVelsZsFAx2RFQwpaLG1U0o8hRfThBjD13titJqKWVCpdXK2n1HWi29Tl6+yvEpK62WJiKw4I1slJlxTlqtO7ellYJXCXmfV+VykdJqXTj/p5Q6mcK98mjeOjIld6pRIiIiIsmydV7IsvDK1hUuxnzyySeoVKkS/Pz8EBsbi99//11yi4mIiIioKLGWRwubSYt6HmVnIxEREbk1vcHL6ste69atw4gRIzBhwgQcPXoUTz75JJ5++mlcvnzZAS0nIiIioqIgvzxqbyYtDnnUpk8kJCQEt2/n3tf78ssv4969ew5tFBEREVEebY6P1Ze95s2bhwEDBuCVV15BrVq1sGDBAkRHR2PRokUOaDnJxDxKRERErpJfHrU3kxaHPGrTmI1arRZpaWkICwvDypUrMXv2bAQFBTm6bUREREQw6L1g0JuP/WTQ514zTUtLM1muVquhVpuPH6bVanHkyBGMGzfOZHlcXBz27t0rscXWlSpVCopi27g+ycnJDm6NZ2EeJSIiIlexlkfz1gG2ZdLikkdt6mxs0qQJunbtitjYWAghMGzYMPj7+1vc9vPPPy9UQ4jyc/LIMafsp6pT9kL2yAmQN4hz8ja9tFrqQPedIOa5a5+6ugke5Xq/lq5ugkV6vbyBpu/dkjMf3P0cKWXslqPzBXJ8LSzPnfQpOjraZPnkyZMRHx9vtv3t27eh1+sRERFhsjwiIgKJiYnyGpyPBQsWGP//zp07mDZtGtq1a4cmTZoAAPbt24ctW7bgnXfecUp7PAnzqHvTZ2+SVivzRoK0WgCghLBT2tH8ytVwdRM8yo0seZm05HV5k7p4ueececi8c1NaLYNa3mffNX2LtFoby7STVkuluiat1qWLN6TVKlvOIKVOukEPXJFSyi7W8mjuOtszaXHJozb99bF69WrMnz8f58+fh6IoSE1NRVZWVqF3SkRERGQroVdZvJIs/n/ZlStXEBwcbFxu6a7Gf3vwSq4Qwuaruw+rX79+xv//z3/+g6lTp+KNN94wLhs2bBg++ugj/PLLLxg5cqRT2uQpmEeJiIjIVazl0bx1gH2ZtKjnUZs6GyMiIjBr1iwAQKVKlbBq1SqEhoYWaodERERE9tDl+ALe5leSdTm5V5GDg4NNgp01YWFhUKlUZleNk5KSzK4uO8OWLVswe/Zss+Xt2rUze7SGmEeJiIjIdazl0dx1tmfS4pJH7Z7GMSEhgcGOiIiInEav9YZe62PhZd/j4b6+voiNjcW2bdtMlm/btg1NmzaV2WSbhIaG4rvvvjNb/v333zNrFYB5lIiIiJzJeh61L5MWlzxaqEGcdu3ahblz5+LMmTNQFAW1atXCm2++iSeffLLQDSEiIiKyyOAF6C1cHzXYfc0Uo0aNQt++fdGwYUM0adIES5YsweXLl/Haa69JaKh9pkyZggEDBmDnzp3GMXL279+PzZs347PPPnN6ezwN8ygRERE5jbU8mrfODsUhj9rd2bh69Wq89NJL6N69O4YNGwYhBPbu3YvWrVtjxYoV6N27d6EbQ2SNr+Gcq5tARcCFE/JGva7bIltaLXItdTnLj0O4mkEnr1baLTlf+/d1rpkYSdF6Q/EyjyyKnXc2AkDPnj1x584dTJ06FTdu3EBMTAx++uknVKhQQUZT7dK/f3/UqlULH374IdavXw8hBGrXro09e/agcePGTm+PJ2EedT8GvbxJFwyZ96TVAgBFU0JqPTKXIu5Lq+X8hwidLytH3i95w7nCzRRriSJnPjkAcie6K1tO3udVqrRzxsSzV+lIee2KrKKVVutuoo+0WoGl5PyeuJ/jmglirOXRvHX2KA551O4fJ9OnT8ecOXNMBokcPnw45s2bh3fffZfhjoiIiKTy0ue+LC0vjMGDB2Pw4MEP1yhJGjdujC+//NLVzfA4zKNERETkTNbyaN46exX1PGr380cXLlxA586dzZZ36dIFCQkJUhpFRERElMdHa/3l6c6fP4+JEyeid+/eSEpKAgBs3rwZp06dcnHL3BvzKBERETlTfnnU0zOpI/Ko3Z2N0dHR2L59u9ny7du3Izo6utANISIiIrJEZQC89eYvlcHVLXs4u3btQt26dXHgwAF8++23uH8/9zHE48ePY/LkyS5unXtjHiUiIiJnspZHPT2TOiqP2v0Y9ejRozFs2DAcO3YMTZs2haIo2L17N1asWIEPPvig0A0hIiIiskStNcBHMU9xXloPTnYAxo0bh2nTpmHUqFEICgoyLm/VqhUzVQGYR4mIiMiZrOVRwLMzqaPyqN2dja+//joiIyPx/vvv4+uvvwYA1KpVC+vWrcMzzzxT6IYQERERWeKtE/BWmU9OI1w0YY0sJ06cwJo1a8yWly5dGnfu3HFBizwH8ygRERE5k7U8Cnh2JnVUHi3UfFPdunVDt27dCr1TT5ayZRj0AQ8/c2mpjksktCZXlm6TtFp+3p2k1bo9qrW0WlEVJU6Nlo+zHdo4fB/hvcKk1SrVd620WreHtZJWy6tSUMEb2chHN7LgjWyUeMPukSOsqi9vYmsAwNU+8j7/cl/ukFarOFAiA6TVkvkzxMtb3tdrylU5ddJddNHWV2uAr4UryYoHX0UGgJIlS+LGjRuoVKmSyfKjR4+ibNmyLmqV5yjOefSrCgPhrzz8DKHdD8VKaE2uElXk/b7O0P8prRYAQCXvl/bBRu2l1bKm6uBgh+/DcELuBY3bb3aRVit7kLxMlJkm79yHVJQ343qp+vOk1UpJkHeMoY/Jm3nY76zEz6u0vExUKlLezNYyaUrL+7yCqsj7vLzV8gYj9FZL6pDTuuYcWsujgGdnUkflUXlfhUREREQOoNIboNJZeOk9N9gBQO/evfHWW28hMTERiqLAYDBgz549GDNmDF588UVXN4+IiIiI/p/VPOrhmdRReZSdjUREROTWvLUGeGv1Fl6eG+wAYPr06ShfvjzKli2L+/fvo3bt2mjevDmaNm2KiRMnurp5RERERPT/rOdRz86kjsqjznk2lYiIiKiQvPQGeFm4YmxpmSfx8fHBl19+iXfffRd//PEHDAYDGjRogGrVqrm6aURERET0L9byaN46T+WoPOrSOxtnzpyJRo0aISgoCOHh4ejatSvOnj1rso0QAvHx8YiKioK/vz9atmyJU6dOmWyTnZ2NoUOHIiwsDIGBgejSpQuuXjUdoColJQV9+/aFRqOBRqNB3759cffuXUcfIhERET0kRauz+vJkU6dORUZGBipXroxnn30WPXr0QLVq1ZCZmYmpU6e6unnFBvMoERERFSS/POrJmdRRedTuOxunTp2KMWPGICDAdED9zMxMvPfee5g0aZLNtXbt2oUhQ4agUaNG0Ol0mDBhAuLi4nD69GkEBgYCAObMmYN58+ZhxYoVqF69OqZNm4a2bdvi7Nmzxmm5R4wYgY0bN2Lt2rUIDQ3F6NGj0alTJxw5cgSq/x8Qunfv3rh69So2b94MABg0aBD69u2LjRs32nX84p+bEH4SBs7t+PAl8qRmyBvg2U/ieNSKjyKtlrTBZAsQWErewLxWSZz0QSYlSN6A0MiS9zlmH7wgrVZIiLwrTllpcs+jb6DnXg3zeH7yBlb3kfizykct7/vofpqcBxkyXPRlKoQewmD+eQjhhJ/ZDjRlyhS89tprZpkqIyMDU6ZMsStTFTfFPY/Kort+RV6xKvJKKSq5D1/JTJG+Tsikvs2bOHwfd3/ZILVetk7eL4jQAHmfceJ5eV9LpZ8uIa1W+O1b0mrdvCUvx4RV1EirVSJE3jF6SfyRoA6Ulx3OxLWVViuotLzOKiUsUFotn5T70mp5ecvpH1B5yetnsIe1PJq3zlM5Ko/a/dfylClTcP+++RdcXkPssXnzZvTv3x916tRB/fr1sXz5cly+fBlHjhwBkHsVecGCBZgwYQK6d++OmJgYrFy5EhkZGcapuVNTU7Fs2TK8//77aNOmDRo0aIDVq1fjxIkT+OWXXwAAZ86cwebNm/HZZ5+hSZMmaNKkCZYuXYpNmzaZXbkmIiIi92LQaa2+PJkQAopiHpj//PNPhISEuKBFnoN5lIiIiJwpvzzqyZnUUXnU7s5GRwbj1NRUADDWSUhIQGJiIuLi4ozbqNVqtGjRAnv37gUAHDlyBDk5OSbbREVFISYmxrjNvn37oNFo0LhxY+M2jz/+ODQajXGbB2VnZyMtLc3kRURERM5nMBhgMOgtvDzzjuBSpUohJCQEiqKgevXqCAkJMb40Gg3atm2LHj16uLqZbo15lIiIiJzJeh71zEzq6Dxq8w3JpUqVgqIoxob8O+Dp9Xrcv38fr732WqEbIoTAqFGj8MQTTyAmJgYAkJiYCACIiIgw2TYiIgKXLl0ybuPr64tSpUqZbZP3/sTERISHh5vtMzw83LjNg2bOnGn3lXEiIiKST6fPhpeX+fVRnT7bBa15eAsWLIAQAi+//DKmTJkCjeZ/j635+vqiYsWKaNLE8Y9ReiLmUSIiInIFa3k0b52ncXQetbmz0dENeeONN3D8+HHs3r3bbN2DV66tXc3ObxtL2+dXZ/z48Rg1apTx32lpaYiOjs53n0RERCSfXq+F3st8TCq93jMfWenXrx8AoFKlSmjWrBm8veWOT1eUMY8yjxIREbmCtTyat87TODqP2lzt3w1p2rQpfHzkTSYxdOhQbNiwAb/99hvKlStnXB4ZGQkg90pwmTJljMuTkpKMV5cjIyOh1WqRkpJicjU5KSkJTZs2NW5z8+ZNs/3eunXL7Cp1HrVaDbVabbZcd/Yecnzd64+C+9n3pNWy/GkUksSJUNQBzrkt2S/UCTtRyRvEWSalhLzvaZElb4Djc8dOSqsVEiZvMOHsDLkTxPgFe96t90WFImPSr//nrZZ3HmVOjJWa5iulTqZwzQRXwqCHwWD+c8XaIN2eIj09Hdu3b0e7du1Mlm/ZsgUGgwFPP/20i1rmvphHc+nSUqBTJBy7ulzB27iAb3gFqfVyJE44E1n9D2m1rNFduejwfdy75V5/z/ybbyV/abWisrOk1VIiSkurpd1zQ1qtrHR5+VZk5UirJTMTZafL+3oNqC8vy2Sny8tqfhHuOYmoqoT7/e2qKO6VR/PWeSpH5VG7z1KLFi2gUqlw7tw57N69G7/99pvJyx5CCLzxxhtYv349fv31V1SqVMlkfaVKlRAZGYlt27YZl2m1WuzatcsY3GJjY+Hj42OyzY0bN3Dy5EnjNk2aNEFqaioOHjxo3ObAgQNITU01bkNERETuSafXWn15snHjxkGvtzTLtsC4ceNc0CLPwTxKREREzpRfHvXkTOqoPGr3JYL9+/ejd+/euHTpEoQw7clXFMViI60ZMmQI1qxZgx9++AFBQUHG8Wo0Gg38/f2hKApGjBiBGTNmoFq1aqhWrRpmzJiBgIAA9O7d27jtgAEDMHr0aISGhiIkJARjxoxB3bp10aZNGwBArVq10L59ewwcOBCffvopAGDQoEHo1KkTatSoYe9HQERERE5kEDqLV5INQt5d1K7w999/o3bt2mbLa9asiX/++ccFLfIczKNERETkTNbyaN46T+WoPGp3Z+Nrr72Ghg0b4scff0SZMmUKHKsmP4sWLQIAtGzZ0mT58uXL0b9/fwDA2LFjkZmZicGDByMlJQWNGzfG1q1bERQUZNx+/vz58Pb2Ro8ePZCZmYnWrVtjxYoVUP3rcdUvv/wSw4YNM84S2KVLF3z00UeFbjsRERE5h06vtfjIjCdfRQZyO6guXLiAihUrmiz/559/EBgY6JpGeQjmUSIiInIma3k0b52nclQetbuz8e+//8Z///tfVK1atdA7zfPglWhLFEVBfHw84uPjrW7j5+eHhQsXYuHChVa3CQkJwerVqwvTTCIiInIhIQwQwtLjHZ491mmXLl0wYsQIfPfdd6hSpQqA3GA3evRodOnSxcWtc2/Mo0RERORM1vJo3jpP5ag8andnY+PGjfHPP/9ICXeeKDPFC94+7jl4q7tRysm7K8PnbKq0Wi4XKG/Qa6n85A3+K27JG4w78co5abVC6korBR+Jk3cAgCJxnPYtFdsVvJENGr0qd/D4jJMZ0molX5U3KUS5F4IK3shGViaoc3ktjaQJiHwMBiBZSim76HTZsHTfmk6X7fS2yPTee++hffv2qFmzpnFCkqtXr+LJJ5/E3LlzXdw691bc86iv2gC1ImEw+mx5d2LsvThfWq36Kj9ptQCgRLVHpNXKqbZLWi1rDIfOO3wfMichAwCNxMnWZGZSqbLlTZ5y+8xlabVkEv/I+5vLoJM3cY1Mio+8dqkD5U0KovhI/Lr3k5fhFX/3+35UvOT+/LKVtTyat85TOSqP2vRVePz4ceP/Dx06FKNHj0ZiYiLq1q1rNgtgvXr1Ct0YIiIiogcZhB4GC1eSLS3zJBqNBnv37sW2bdvw559/wt/fH/Xq1UPz5s1d3TS3xDxKRERErmItj+at81SOyqM2dTY+8sgjUBTF5DGTl19+2fj/eevsHZCbiIiIqCB6vRaWhuTTe/D4OHkURUFcXByaN28OtVr9UGMPFnXMo0REROQq1vJo3jpP5og8alNnY0JCwkPviIiIiKgwhEEPYbAwZqOFZZ7EYDBg+vTpWLx4MW7evIlz586hcuXKeOedd1CxYkUMGDDA1U10K8yjRERE5CrW8mjeOk/lqDxqU2djhQoVClWciIiI6GHpDFrAQobTGTz7KvK0adOwcuVKzJkzBwMHDjQur1u3LubPn8/OxgcwjxIREZGrWMujxnUeylF51O6RQzds2GBxuaIo8PPzQ9WqVVGpUqVCNYaIiIjoQc4es/HixYt499138euvvyIxMRFRUVF44YUXMGHCBPj6+hq3u3z5MoYMGYJff/0V/v7+6N27N+bOnWuyTX6++OILLFmyBK1bt8Zrr71mXF6vXj389ddf0o+rKGEeJSIiImdyxZiNzsikjsqjdnc2du3a1Wy8HMB0nJwnnngC33//PUqVKlXohrkrXbYXcgzuNRu1v2+Aq5tgkVK+tLRa3uq70mrlR3ff8TNbeQeXcPg+CkXizH/6+/J+2IaU0kmrBavzh9nPL1juLxQvb3lt08v8yCTSZcuc/U/O7MoAgPvyZpeUSeYM5aUj5Xz26XrFJbNR6/U5Fr979XrHnLu//voLBoMBn376KapWrYqTJ09i4MCBSE9PN87Kp9fr0bFjR5QuXRq7d+/GnTt30K9fPwghsHDhQpv2c+3aNYuzKRsMBuTkuOfXpbso7nk00F8gQMJsnCLtvoTW5IoIlDcbse7mLWm1AOCEl7xZQqOrhEirZc3tVTccvo9S1SRnXn+J32fe8v7Wkvlko0iU9/3y15Wz0mpVl3jTd+ZVeR+YQS/vPKokZiLDPXnH6Bsg7/tI5Mir5VXST1otqTlZJyu/u2Y2amt5NG+dIzgjkzoqj9r9E2Dbtm1o1KgRtm3bhtTUVKSmpmLbtm147LHHsGnTJvz222+4c+cOxowZU+hGEREREeUxGPRWX47Qvn17LF++HHFxcahcuTK6dOmCMWPGYP369cZttm7ditOnT2P16tVo0KAB2rRpg/fffx9Lly5FWlqaTfupU6cOfv/9d7Pl33zzDRo0aCDteIoi5lEiIiJypvzyqCdnUkflUbuvEQwfPhxLlixB06ZNjctat24NPz8/DBo0CKdOncKCBQtMZgckIiIiKqzcGf7Mr2LnXUV+MEip1Wqo1WqpbUhNTUVIyP/uaNq3bx9iYmIQFRVlXNauXTtkZ2fjyJEjaNWqVYE1J0+ejL59++LatWswGAxYv349zp49iy+++AKbNm2S2v6ihnmUiIiInMlaHs1d57mZ1FF51O47G8+fP4/g4GCz5cHBwbhw4QIAoFq1arh9+3ahG0VERESURwi91RcAREdHQ6PRGF8zZ86Uuv/z589j4cKFJuPYJCYmIiIiwmS7UqVKwdfXF4mJiTbV7dy5M9atW4effvoJiqJg0qRJOHPmDDZu3Ii2bdtKPYaihnmUiIiInCm/POrJmdRRedTuOxtjY2Px5ptv4osvvkDp0rlj8t26dQtjx45Fo0aNAAB///03ypUrV+hGEREREeXR6bUQ+dzZeOXKFZOOJ2tXkOPj4zFlypR893Xo0CE0bNjQ+O/r16+jffv2eO655/DKK6+YbKso5iP35I0XWBCdTofp06fj5Zdfxq5duwrcnkwxjxIREZEzWcujgOdmUkfmUbs7G5ctW4ZnnnkG5cqVQ3R0NBRFweXLl1G5cmX88MMPAID79+/jnXfekdpQd1Hl6w0Wr6S7UpTGPR8RKtVxicRa0kqRFSGvf+fqJljUVd6XUbHR4RNXt8Ayxw+p73rueowRBW9ik7S0NECjkVTNdrkBzlJnY+5sSMHBwTb9bn7jjTfw/PPP57tNxYoVjf9//fp1tGrVCk2aNMGSJaY/jCIjI3HgwAGTZSkpKcjJyTG7umyJt7c33nvvPfTr16/Abclccc+jvmrAV8bcC+mZEor8fymtvNnJypapIq0WACw4IG9ij3kVy0qrZc2dq3InyLGk9Nt1pdYrERAqrdb9huYTFRRWiSx5E7Hk/C3v6yi6tLzvF5kTAwbESJx49GSGtFLqAHkTA969Im9SzJDa0kpBKSFvki2larS0WuLqaXm1ZE0Qo5c4UaQ9u7WSR3PXeWYmdWQetbuzsUaNGjhz5gy2bNmCc+fOQQiBmjVrom3btvDyyk09Xbt2ld1OIiIiKqYMQg9FmF+ZNQj7BuMOCwtDWFiYTdteu3YNrVq1QmxsLJYvX27MOHmaNGmC6dOn48aNGyhTpgyA3AG61Wo1YmNjbdpHmzZtsHPnTvTv39+u4yDmUSIiInIua3k0b5093CmTOiqPFmoSeUVR0L59e7Rv315qY4iIiIgepDdoAZhfxdYbJN7O8S/Xr19Hy5YtUb58ecydOxe3bv3vLqPIyEgAQFxcHGrXro2+ffvivffeQ3JyMsaMGYOBAwfa/ATE008/jfHjx+PkyZOIjY1FYGCgyfouXbrIO6giiHmUiIiInMVaHs1d57mZ1FF51KbOxg8//BCDBg2Cn58fPvzww3y3HTZsWKEaQkRERGSJwaCHAgt3Nhrsu4psq61bt+Kff/7BP//8YzbmnxC5j8+oVCr8+OOPGDx4MJo1awZ/f3/07t0bc+fOtXk/r7/+OgBg3rx5ZusURYFe75jj81TMo0REROQq1vJo3jpHcEYmdVQetamzcf78+ejTpw/8/Pwwf/58q9spisJwR0RERFLpDTqLA3I7Ktj179/fpkdJypcvj02bNhV6PwaDa8Yc8lTMo0REROQq1vIo4NmZ1FF51KbOxoSEBIv/T0RERORowqC3GO2Eg4KdM+h0Ovj5+eHYsWOIiYlxdXM8AvPo/wQGKQhUFTzruTOFlZA4gaLkp9FiS8v7WSH+uSytlit5h0dJradPS5VWSylVUlotcT9HXi2JX5e+vvJqabXyaim1I6XVMvx5QVotmVTeljuLCiPnlryfLb5hftJqKUFB0mrBT96EOvJ+a8k7h3bt1UoezVvniRyZRws9j51Wq8XZs2eh0znm2XQiIiIiANDptVZfnsrb2xsVKlTgo9IPiXmUiIiInCG/POqpmdSRedTuzsaMjAwMGDAAAQEBqFOnDi5fzr26N2zYMMyaNUt6A4mIiKh4E8Jg9eXJJk6ciPHjxyM5OdnVTfE4zKNERETkTPnlUU/OpI7Ko3Z3No4fPx5//vkndu7cCT+//93q26ZNG6xbt05q44iIiIj0+hzo9VoLL3mPx7nChx9+iN9//x1RUVGoUaMGHn30UZMXWcc8SkRERM5kPY96diZ1VB61aczGf/v++++xbt06PP7441CU/z11X7t2bZw/f77QDSEiIiKyxCAMFofnMXjwVWQA6Nq1q6ub4LGYR4mIiMiZrOVR4zoP5ag8andn461btxAeHm62PD093STsEREREcmgN+RACPOHMTw52AHA5MmTXd0Ej1Xc82izQ+sRHCxxQhYJSrm6AfkYEiqxWKPBEotZ9ngvh+/CralLSiw2XWItieRNw+K+Sj3j6haQFG44h513WhowVeP0/VrLo4BnZ1JH5VG7OxsbNWqEH3/8EUOHDgUAY6BbunQpmjRpIrd1REREVOwJgx4GxfxSsiePj/NvR44cwZkzZ6AoCmrXro0GDRq4ukluj3mUiIiInMlaHgWKRiaVnUft7mycOXMm2rdvj9OnT0On0+GDDz7AqVOnsG/fPuzateuhGkNERET0oJycHIt3qwlh5VkWD5GUlITnn38eO3fuRMmSJSGEQGpqKlq1aoW1a9eidOnSrm6i22IeJSIiImeylkcBz86kjsqjdk8Q07RpU+zZswcZGRmoUqUKtm7dioiICOzbtw+xsbGFagQRERGRNXqD3urLkw0dOhRpaWk4deoUkpOTkZKSgpMnTyItLQ3Dhg1zdfPcGvMoEREROVN+edSTM6mj8qgiPLkL1onS0tKg0WiQmprqdmPkEBEROYOzfxfm7U+l+Fq9s1EvtB77u1mj0eCXX35Bo0aNTJYfPHgQcXFxuHv3rmsaRm6LeZSIiIo7d8ujgGdnUkflUZsfo05LS7NpO0/7YG2V1ydr6+dARERU1OT9DnT2dUq90Fmd/c+TGQwG+Pj4mC338fGBweD5Y/84AvMo8ygRERVvzKNyOSqP2tzZWLJkyXxn9xNCQFEU6PWee/tofu7duwcAiI6OdnFLiIiIXOvevXvQaBw/C6Cvry8iIyORmJhodZvIyEj4+vo6vC2O8NRTT2H48OH46quvEBUVBQC4du0aRo4cidatW7u4de6JeZR5lIiICHCvPAp4biZ1VB61+THqfw+2LYRAhw4d8Nlnn6Fs2bIm27Vo0aLQjXFnBoMB169fR1BQEO7du4fo6GhcuXKlyF45T0tL4zEWEcXhOHmMRQOP0f0JIXDv3j1ERUXBy8vuYZ8LJSsrC1qt1up6X19f+Pn5OaUtsl25cgXPPPMMTp48iejoaCiKgsuXL6Nu3br44YcfUK5cOVc30e0wj/4vjyqK4vE/U2zBYywaeIxFA4+xaPD0Y3THPAp4biZ1VB61+c7GB0ObSqXC448/jsqVKxdqx57Gy8vL+CHnXVEPDg72yG9Oe/AYi47icJw8xqKBx+jenHEF+d/8/Pw8MrjZIjo6Gn/88Qe2bduGv/76C0II1K5dG23atHF109wW86iXxdDvyT9TbMVjLBp4jEUDj7Fo8ORjZB6Vx1F51ObORiIiIiKSr23btmjbtq2rm0FERERExZTsPOqce06JiIiICADw66+/onbt2hYn+UhNTUWdOnXw+++/u6BlRERERFQcODqPPlRnY34DdBdlarUakydPhlqtdnVTHIbHWHQUh+PkMRYNPEYqLhYsWICBAwdafHRJo9Hg1Vdfxbx581zQMs9UXPMoUDx+pvAYiwYeY9HAYywaisMxUsEcnUdtniCme/fuJv/euHEjnnrqKQQGBposX79+faEbQ0RERFTUVahQAZs3b0atWrUsrv/rr78QFxeHy5cvO7ll7o95lIiIiOjhOTqP2jxm44MDcL7wwguF2iERERFRcXbz5k34+PhYXe/t7Y1bt245sUWeg3mUiIiI6OE5Oo/a3Nm4fPnyQu+EiIiIiHKVLVsWJ06cQNWqVS2uP378OMqUKePkVnkG5lEiIiKih+foPMoJYoiIiIicqEOHDpg0aRKysrLM1mVmZmLy5Mno1KmTC1pGRERERMWBo/OozWM2EhEREdHDu3nzJh599FGoVCq88cYbqFGjBhRFwZkzZ/Dxxx9Dr9fjjz/+QEREhKubSkRERERFkKPzaLG8s/GTTz5BpUqV4Ofnh9jYWJPpvIUQiI+PR1RUFPz9/dGyZUucOnWqwJonTpxAixYt4O/vj7Jly2Lq1Kl4sB93165diI2NhZ+fHypXrozFixdLP7Y8so/x7NmzaNWqFSIiIoztnzhxInJycky2c5djXL9+Pdq1a4ewsDAoioJjx47ZVNOTzmNhjtEdzyNg/ThzcnLw1ltvoW7duggMDERUVBRefPFFXL9+vcCannIuC3uM7ngu8/t6jY+PR82aNREYGIhSpUqhTZs2OHDgQIE1PeU8AoU7Rnc8j+R4ERER2Lt3L2JiYjB+/Hh069YNXbt2xdtvv42YmBjs2bOHHY3FQHHIowAzKTOpZZ50HplHrfOk8wgwj1rjjueRHM/heVQUM2vXrhU+Pj5i6dKl4vTp02L48OEiMDBQXLp0SQghxKxZs0RQUJD49ttvxYkTJ0TPnj1FmTJlRFpamtWaqampIiIiQjz//PPixIkT4ttvvxVBQUFi7ty5xm0uXLggAgICxPDhw8Xp06fF0qVLhY+Pj/jvf//rEcd4/vx58fnnn4tjx46Jixcvih9++EGEh4eL8ePHu+UxfvHFF2LKlCli6dKlAoA4evRogTU97TwW5hjd7TwWdJx3794Vbdq0EevWrRN//fWX2Ldvn2jcuLGIjY3Nt6YnncvCHqO7ncuCvl6//PJLsW3bNnH+/Hlx8uRJMWDAABEcHCySkpKs1vSk81jYY3S380jOl5ycLA4ePCgOHDggkpOTXd0ccpLikEcddZzu9nOTmbRoZFLmUeZRazzpPBb2GN3tPJLzOSKPFrvOxscee0y89tprJstq1qwpxo0bJwwGg4iMjBSzZs0yrsvKyhIajUYsXrzYas1PPvlEaDQakZWVZVw2c+ZMERUVJQwGgxBCiLFjx4qaNWuavO/VV18Vjz/+uIzDMuGIY7Rk5MiR4oknnjD+212O8d8SEhJsDj2edB7/zZ5jtMSV51EI248zz8GDBwUA4y9USzz1XOax5Rgt8YTvyTypqakCgPjll1+s1vT082jLMVri6u9JInK84pBHhWAm/Tdm0oJ5wnnMwzxqnSedR+ZR65hH6WEVq8eotVotjhw5gri4OJPlcXFx2Lt3LxISEpCYmGiyXq1Wo0WLFti7d69xWf/+/dGyZUvjv/ft24cWLVpArVYbl7Vr1w7Xr1/HxYsXjds8uN927drh8OHDZrcnu+MxPuiff/7B5s2b0aJFC+MydzlGW3nyebSVO59HoHDHmZqaCkVRULJkSeOyonYubTnGB3nS96RWq8WSJUug0WhQv3594/KidB5tPcYHufp7kogcrzjkUYCZlJnUVFE7j8yjlnnSeWQebWl1P8yjJEOx6my8ffs29Hq92XPnERERSExMRGJiovHfltbnKVOmDMqXL2/8d2JiosX35K3LbxudTofbt28/5JH9j6OOMU/Tpk3h5+eHatWq4cknn8TUqVON69zlGG3lyefRVu58HgH7jzMrKwvjxo1D7969ERwcbFxelM6lrceYxx3Opa3HuGnTJpQoUQJ+fn6YP38+tm3bhrCwMOP6onAe7T3GPO5wHonIOYpDHgWYSZlJTRWl88g86tnnkXnUvc8jFR3erm6AKyiKYvJvIYTJsoLWz5w506aaDy63ZRtZHHGMALBu3Trcu3cPf/75J958803MnTsXY8eOzbeupeUyFHQMBSkK57EgnnAere3vwWU5OTl4/vnnYTAY8Mknn5isKyrn0t5jBNzrXBZ0jK1atcKxY8dw+/ZtLF26FD169MCBAwcQHh4OoGicx8IcI+Be55GInKM45FFr+2MmNeUJ57I4ZFLm0VzMo55/HplHyR0Uq87GsLAwqFQqs6s3SUlJiIiIQGRkJIDcXvsyZcqYrbcmMjLSYk3gf1c9rG3j7e2N0NDQwh/UAxx1jHmio6MBALVr14Zer8egQYMwevRoqFQqtznGwvKk8/iw3OE8ArYfZ05ODnr06IGEhAT8+uuvJldYLfHEc2nvMeZxh3Np6zEGBgaiatWqqFq1Kh5//HFUq1YNy5Ytw/jx4y3W9cTzaO8x5nGH80hEzlEc8ijATMpMahtPOo/Mo9Z50nlkHrXOHc4jFR3F6jFqX19fxMbGYtu2bSbLt23bhqZNm6JSpUqIjIw0Wa/VarFr1y40bdrUat0mTZrgt99+g1arNS7bunUroqKiULFiReM2D+5369ataNiwIXx8fCQcXS5HHaMlQgjk5OQYr2i4yzEWliedR5lcdR4B244zL/T8/fff+OWXX2z6ZeZp57Iwx2iJp31PCiGQnZ1tdb2nnUdLCjpGa+9x1fckETleccijADMpM6n93Pk8Mo/azp3Po7X2Mo9afg/zKD2Uh59jxrPkTRW/bNkycfr0aTFixAgRGBgoLl68KIQQYtasWUKj0Yj169eLEydOiF69eokyZcqItLQ0Y41x48aJvn37Gv999+5dERERIXr16iVOnDgh1q9fL4KDg8XcuXON2+RNFT9y5Ehx+vRpsWzZModNFe+IY1y9erVYt26dOH36tDh//rz4+uuvRdmyZUWfPn3c8hjv3Lkjjh49Kn788UcBQKxdu1YcPXpU3Lhxw+oxetp5LMwxutt5LOg4c3JyRJcuXUS5cuXEsWPHxI0bN4yv7Oxsq8fpSeeysMfobucyv2O8f/++GD9+vNi3b5+4ePGiOHLkiBgwYIBQq9Xi5MmTVo/Rk85jYY/R3c4jETlHccijjjpOd/u5yUxaNDIp8yjzqLVj9KTzyDxK7qTYdTYKIcTHH38sKlSoIHx9fcWjjz4qdu3aZVxnMBjE5MmTRWRkpFCr1aJ58+bixIkTJu/v16+faNGihcmy48ePiyeffFKo1WoRGRkp4uPjhcFgMNlm586dokGDBsLX11dUrFhRLFq0yGOOce3ateLRRx8VJUqUEIGBgaJ27dpixowZIjMz0y2Pcfny5QKA2Wvy5MlWj1EIzzqPhTlGdzyP+R1nQkKCxWMEIHbs2GH1OIXwnHNZ2GN0x3Np7RgzMzNFt27dRFRUlPD19RVlypQRXbp0EQcPHjR5vyefx8IeozueRyJyjuKQR4VgJmUmtXyMnnQemUetH6MnnUfmUevH6I7nkTyfIsT/3xdLRERERERERERE9BCK1ZiNRERERERERERE5DjsbCQiIiIiIiIiIiIp2NlIREREREREREREUrCzkYiIiIiIiIiIiKRgZyMRERERERERERFJwc5GIiIiIiIiIiIikoKdjURERERERERERCQFOxuJiKhImTlzJho1aoSgoCCEh4eja9euOHv2rMk2QgjEx8cjKioK/v7+aNmyJU6dOmVcn5ycjKFDh6JGjRoICAhA+fLlMWzYMKSmpprUqVixIhRFMXmNGzcu3/bt3LkTiqKgVKlSyMrKMll38OBBYx1HyM7OxtChQxEWFobAwEB06dIFV69eNdmmMMdERERERKaYSa1jJi362NlIVIS0bNkSI0aMcHUzLEpMTETbtm0RGBiIkiVLWtymf//+6Nq160PvS1EUfP/99wCAixcvQlEUHDt2rFDvt6QwNW1RsWJFLFiwQGpNa/LCxd27d52yP2fatWsXhgwZgv3792Pbtm3Q6XSIi4tDenq6cZs5c+Zg3rx5+Oijj3Do0CFERkaibdu2uHfvHgDg+vXruH79OubOnYsTJ05gxYoV2Lx5MwYMGGC2v6lTp+LGjRvG18SJE21qZ1BQEL777juTZZ9//jnKly//EEefS6vVWlw+YsQIfPfdd1i7di12796N+/fvo1OnTtDr9SbbFfaYiIiIAGbSPMykBWMmZSZlJi2iBBFZdfPmTTFo0CARHR0tfH19RUREhIiLixN79+41bgNAfPfdd1L2l5CQIACIo0eP5rvdjh07BACRkpJisvzOnTsiLS1NSltkGzt2rKhTp444d+6cuHnzpsVt7t69a3ZMhfHvc6LT6cSNGzdETk6Oze+/ceOGyMrKsrre1vNkrwoVKoj58+dLrWmNta+hoigpKUkAELt27RJCCGEwGERkZKSYNWuWcZusrCyh0WjE4sWLrdb5+uuvha+vr8nXUmHOWd5nP3HiRNGmTRvj8oyMDKHRaMQ777wj/v3r+fbt2+L5558XZcuWFf7+/iImJkasWbPGpGaLFi3EkCFDxMiRI0VoaKho3ry52X7v3r0rfHx8xNq1a43Lrl27Jry8vMTmzZsf6piIiMixmEnlYSYtGDOpYzCT5mImLR54ZyNRPv7zn//gzz//xMqVK3Hu3Dls2LABLVu2RHJysvR9WbvqY4+QkBAEBQVJaI1858+fR2xsLKpVq4bw8HCL22g0GqtXmAtLpVIhMjIS3t7eNr8nMjISarVaajscQcbXjCfv31Z5j5mEhIQAABISEpCYmIi4uDjjNmq1Gi1atMDevXvzrRMcHGz2tTR79myEhobikUcewfTp023+XPr27Yvff/8dly9fBgB8++23qFixIh599FGT7bKyshAbG4tNmzbh5MmTGDRoEPr27YsDBw6YbLdy5Up4e3tjz549+PTTT832d+TIEeTk5Jgcd1RUFGJiYsyOu7DHREREjsFMKg8zqXyuzgmu3r+tmElzMZMWE67u7SRyVykpKQKA2Llzp9VtKlSoIAAYXxUqVBBCCPHPP/+ILl26iPDwcBEYGCgaNmwotm3bZvbed999V/Tr108EBweLF1980aQWANGiRQuzfeZdwfz3q1+/fkKI3CtJw4cPN9tH3759RWBgoChfvrz4/vvvRVJSkujSpYsIDAwUMTEx4tChQyb72LNnj3jyySeFn5+fKFeunBg6dKi4f/9+vp/XJ598IipXrix8fHxE9erVxRdffGH1c8pr74P69esnnnnmGeO/W7RoIYYOHSrefPNNUapUKRERESEmT55s8p5z586JJ598UqjValGrVi2xdetWk6vI/77iq9frRdmyZcWiRYtMahw5ckQAEOfPnxdCmN8ZcODAAfHII48ItVotYmNjxfr1602uIi9fvlxoNBqTmt99953JlUBbvybyu3qX9/nMmDFDlClTxvj1tmrVKhEbGytKlCghIiIiRK9evcyu1P/444+iWrVqws/PT7Rs2VIsX77c5CqyrCuV7sZgMIjOnTuLJ554wrhsz549AoC4du2aybYDBw4UcXFxFuvcvn1blC9fXkyYMMFk+bx588TOnTvFn3/+KZYuXSrCwsLEgAED8m3Tv6/gd+3aVUyZMkUIIUSrVq3EBx98YPa1Y0mHDh3E6NGjjf9u0aKFeOSRR/J9z5dffil8fX3Nlrdt21YMGjTooY6JiIgch5mUmTQPM+n/zgczKTMpuTd2NhJZkZOTI0qUKCFGjBhh9fGFvFvhly9fLm7cuCGSkpKEEEIcO3ZMLF68WBw/flycO3dOTJgwQfj5+YlLly4Z31uhQgURHBws3nvvPfH33//H3p3HRVX1fwD/XAYYdmRRcMF9Q0FzKbdSzAUzU/OXWipqrqW5pLnlbi5puZSVWamYZtlTVtqikqW57+aSqSnuECoIyjbMzPn9wcM8jjODDJyZYeDzfr3mpdx7+d5zh2Hmw7n3nnNBXLhwQRw6dEgAEL/++qtISEgQd+7cMdmnVqsV3377rQAgzp07JxISEsTdu3eFEOaDXWBgoPj444/F+fPnxauvvip8fX1Fp06dxNdffy3OnTsnunfvLsLDw4VerxdCCHHy5Enh4+Mjli5dKs6fPy/27t0rGjVqJAYOHGjxudq0aZNwc3MTH374oTh37pxYvHixUKlU4rfffjM8T506dRK9evUyau/DzAU7Pz8/MWvWLHH+/Hmxdu1aoSiK2L59uxBCCJ1OJyIiIkRUVJQ4fvy42LVrl2jUqJHFYCeEEOPHjzf6kM9b1qJFC8PXD37//fv3RdmyZUXv3r3F6dOnxZYtW0T16tWtDnYFfU08Ktj5+PiImJgYcfr0aXHq1CkhhBCrVq0SP//8s7h48aLYv3+/aN68uXjmmWcM33f16lWhVqvFmDFjxN9//y3Wr18vQkJCjILd9evXxTvvvCOOHz8uLl68KN5//32hUqnEgQMHjH4ePj4+YsKECeLvv/8WZ8+etdjW4mLEiBGiSpUq4tq1a4ZlecHu5s2bRtsOGTJEREdHm9RITU0VzZo1E506dRIajSbf/X3zzTcCgLh9+7YQQoh69eoJb29v4e3tLTp16iSEMA52mzdvFtWqVRMXL14UHh4e4vbt2yavHa1WK+bOnSsiIyNFYGCg8Pb2Fq6urqJnz56Gbdq0aSOGDBli+HrevHmG/Xp7e4srV65YDHbt27cXw4cPL/AxERGRfTGTMpMKwUzKTMpMykzqXNjZSJSPb775RgQEBAgPDw/RsmVLMWXKFPHnn38abfPwGUdL6tWrJ5YvX274ukqVKqJ79+5G2xR1fBxzwa5fv36GrxMSEgQAMX36dMOy/fv3CwAiISFBCCFETEyM0RklIYTYvXu3cHFxEZmZmWbb07JlSzF06FCjZT179hSdO3c2fN2tWzeLZ4/zmAt2D4ewxx9/XEyaNEkIIcS2bduESqUy+tD+5Zdf8g12x44dE4qiiMuXLwshhOHM8ocffmio8eD3r1y5UgQGBor09HTD+hUrVlgd7Mwx95p4VLALCQkR2dnZ+dbN+wPh3r17QgghpkyZYhTehRBi0qRJjxwfpzBnKouT1157TVSqVElcunTJaPnFixcFAHHs2DGj5V27dhX9+/c3WpaWliZatGgh2rVrZ/H1/6Dr168LAIZAfPnyZcMfbtevXxdCGP/+arVaUb58eREVFWUIag+/dhYuXCiCgoLEunXrxIkTJ8SFCxfEs88+a/K78uDv/p07dwz7vXDhgsjJyRE7duwQAERycrJRmxs0aCBmzJhR4GMiIiL7YybNxUzKTCoEMykzKTOpM+CYjUT5+L//+z/cvHkTmzdvRnR0NHbu3InGjRsjNjY23+9LT0/HxIkTUa9ePZQpUwY+Pj74+++/DeNg5GnatKkNW5+rQYMGhv+HhIQAACIjI02WJSUlAcgdQyM2NhY+Pj6GR3R0NPR6PeLj483u4+zZs2jVqpXRslatWuHs2bNS2w8A5cuXN7T17NmzqFy5MipVqmRY36JFi3zrNWrUCHXr1sWXX34JIHeWuKSkJPTq1cvs9mfPnkXDhg3h5eVV4H2YU9DXxKNERkbC3d3daNnx48fRrVs3VKlSBb6+voiKigIAQ+2zZ8+iefPmUBTF4jHodDrMmzcPDRo0QFBQEHx8fLB9+3aHvGaLSgiB1157DZs2bcJvv/2GatWqGa2vVq0aQkNDERcXZ1im0Wiwa9cutGzZ0rAsLS0NHTt2hLu7OzZv3gwPD49H7vv48eMAcl+nAFClShXUrFkTNWvWRMWKFU22V6lUiImJwc6dOzFo0CCzNXfv3o1u3bqhX79+aNiwIapXr44LFy7k247AwEDDfmvWrAlXV1c0adIEbm5uRsedkJCA06dPGx33o46JiIjsj5mUmZSZlJmUmZSZ1JkUfHRaolLKw8MDHTp0QIcOHTBjxgwMGTIEM2fOxMCBAy1+z4QJE7Bt2za8++67qFmzJjw9PfHCCy+YDGjr7e1t49YDbm5uhv/nfbCbW6bX6w3/Dh8+HKNHjzapVblyZYv7eTA0ALkfrg8vK4wH25q3n7y2CiEe2Q5z+vbtiw0bNmDy5MnYsGEDoqOjERwcbHZbc/t4mIuLi8l2OTk5Rl8X9DXxKA+/ZtLT09GxY0d07NgR69evR9myZXH16lVER0cbahfkGBYvXoylS5di2bJliIyMhLe3N8aOHeuQ12xRjRw5Ehs2bMAPP/wAX19fJCYmAsgd7N3T0xOKomDs2LGYP38+atWqhVq1amH+/Pnw8vJCnz59AAD37t1Dx44dkZGRgfXr1yMtLQ1paWkAgLJly0KlUmH//v04cOAA2rZtC39/fxw+fBivv/46unbtmu/vysPeeustTJgwAUFBQWbX16xZE99++y327duHgIAALFmyBImJiQgPD7fqefH398fgwYMxfvx4BAUFITAwEG+88QYiIyPRvn17AJB2TEREJB8z6f8wk5rHTFq8MJOax0xaOrCzkchK9erVw/fff2/42s3NDTqdzmib3bt3Y+DAgXj++ecBAPfv38fly5cfWTvv7ODD9Qq7XWE0btwYZ86cQc2aNQv8PeHh4dizZw/69+9vWLZv3z6rP3isVa9ePVy9ehU3b95EhQoVAOR+MD1Knz59MG3aNBw9ehTffPMNVqxYke8+1q1bh8zMTHh6egIADhw4YLRN2bJlce/ePaSnpxuCz4kTJ4y2Kexr4lH+/vtv3L59G2+//TbCwsIAAEeOHDE5hgdfs+aO4cEzlUBuwL9w4YLNf4a2kPfzzDubnmfNmjWGP8gmTpyIzMxMjBgxAikpKWjWrBm2b99umDnz6NGjhpn1Hv5diI+PR9WqVaFWq7Fx40bMnj0b2dnZqFKlCoYOHYqJEyda1V53d3eLf1gAwPTp0xEfH4/o6Gh4eXlh2LBh6N69u2FGQ2ssXboUrq6u6NWrFzIzM9GuXTvExsZCpVIBgLRjIiIi22MmNcVMykxanDCTWsZMWvKxs5HIgjt37qBnz54YNGgQGjRoAF9fXxw5cgSLFi1Ct27dDNtVrVoVO3bsQKtWraBWqxEQEICaNWti06ZNeO6556AoCqZPn24485mfcuXKwdPTE1u3bkWlSpXg4eEBf39/k+2qVKkCRVHw448/onPnzvD09ISPj4+U4540aRKaN2+OkSNHYujQofD29sbZs2cRFxeH5cuXm/2eCRMmoFevXmjcuDHatWuHLVu2YNOmTfj111+ltMmS9u3bo06dOujfvz8WL16MtLQ0TJ069ZHfV61aNbRs2RKDBw+GVqs1+nk+rE+fPpg6dSoGDx6MadOm4fLly3j33XeNtmnWrBm8vLzw5ptvYtSoUTh06JDJbU2FfU08SuXKleHu7o7ly5fjlVdewenTp/HWW28ZbfPKK69g8eLFGDduHIYPH264Lenh9sk4U1kcFOSsuaIomDVrFmbNmmV2fVRU1CPrNG7c2CQgF8Sjanfv3t1ofWBgoEkwf9jOnTsLtG8PDw8sX77c4u9yYY+JiIhsh5mUmRRgJmUmtYyZlIojjtlIZIGPjw+aNWuGpUuXonXr1oiIiMD06dMxdOhQfPDBB4btFi9ejLi4OISFhaFRo0YAcs/UBAQEoGXLlnjuuecQHR2Nxo0bP3Kfrq6ueP/997Fy5UpUqFDBYuCoWLEiZs+ejcmTJyMkJASvvfaanING7ng0u3btwoULF/DUU0+hUaNGmD59er5jY3Tv3h3vvfce3nnnHdSvXx8rV67EmjVrTM7iyebi4oLvvvsO2dnZeOKJJzBkyBDMmzevQN/bt29f/Pnnn+jRo4fh7LA5Pj4+2LJlC/766y80atQIU6dOxcKFC422CQwMxPr16/Hzzz8jMjISX375pUlgKOxr4lHKli2L2NhY/Oc//0G9evXw9ttvmwTPypUr49tvv8WWLVvQsGFDfPzxx5g/f77RNtOnT0fjxo0RHR2NqKgohIaGonv37kVuHxERERUNMykzKcBMSkTORREF6W4nIiIiIiIiIiIiegRe2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NRGTRrFmz8Nhjjzm6GVIkJiaiQ4cO8Pb2RpkyZRzdHKtFRUVh7Nix+W6jKAq+//77AtUrST9bIiIiKrlKUmZhHjVWkn62RGTM1dENICKyh6VLlyIhIQEnTpyAv7+/o5tjEwkJCQgICHB0M4iIiIjIDOZRIiot2NlIRKXCxYsX0aRJE9SqVcviNjk5OXBzc7Njq+QKDQ11dBOIiIiIyALmUSIqLXgbNZEk33zzDSIjI+Hp6YmgoCC0b98e6enphvWrV69G/fr1oVarUb58ebz22muGdUuWLEFkZCS8vb0RFhaGESNG4P79+4b1sbGxKFOmDLZt24bw8HD4+PigU6dOSEhIMNsWvV6PSpUq4eOPPzZafuzYMSiKgkuXLgEArl69im7dusHHxwd+fn7o1asX/v33X4vHaO7Wie7du2PgwIGGr6tWrYq5c+eif//+8PHxQZUqVfDDDz/g1q1bhn1FRkbiyJEjRnX27duH1q1bw9PTE2FhYRg9erTR8/fRRx+hVq1a8PDwQEhICF544QWL7XxY1apV8e233+Lzzz+HoiiG9iqKgo8//hjdunWDt7c35s6dCwBYsWIFatSoAXd3d9SpUwfr1q0zqqcoClauXIkuXbrAy8sL4eHh2L9/P/755x9ERUXB29sbLVq0wMWLFy226f/+7/8watQow9djx46Foig4c+YMAECr1cLX1xfbtm0zbKPX6zFx4kQEBgYiNDQUs2bNMmnXg7etXL9+HS+++CICAwPh7e2Npk2b4uDBg0bfs27dOlStWhX+/v548cUXce/evQI/r0RERFS8MI/mYh5lHiUix2JnI5EECQkJeOmllzBo0CCcPXsWO3fuRI8ePSCEAJAbFkaOHIlhw4bh1KlT2Lx5M2rWrGn4fhcXF7z//vs4ffo01q5di99++w0TJ0402kdGRgbeffddrFu3Dn/88QeuXr2KN954w2x7XFxc8OKLL+KLL74wWr5hwwa0aNEC1atXhxAC3bt3R3JyMnbt2oW4uDhcvHgRvXv3LvLzsXTpUrRq1QrHjx/Hs88+i5iYGPTv3x/9+vXDsWPHULNmTfTv39/w/Jw6dQrR0dHo0aMHTp48iY0bN2LPnj2GAHzkyBGMHj0ac+bMwblz57B161a0bt26wO05fPgwOnXqhF69eiEhIQHvvfeeYd3MmTPRrVs3nDp1CoMGDcJ3332HMWPGYPz48Th9+jSGDx+Ol19+Gb///rtRzbfeegv9+/fHiRMnULduXfTp0wfDhw/HlClTDMH1wQD/sKioKOzcudPw9a5duxAcHIxdu3YZ2pyVlYVWrVoZtlm7di28vb1x8OBBLFq0CHPmzEFcXJzZ+vfv30ebNm1w8+ZNbN68GX/++ScmTpwIvV5v2ObixYv4/vvv8eOPP+LHH3/Erl278Pbbbxf4eSUiIqLig3nUGPMo8ygROZAgoiI7evSoACAuX75sdn2FChXE1KlTC1zv66+/FkFBQYav16xZIwCIf/75x7Dsww8/FCEhIRZrHDt2TCiKYmiTTqcTFStWFB9++KEQQojt27cLlUolrl69avieM2fOCADi0KFDQgghZs6cKRo2bGhY36ZNGzFmzBij/XTr1k0MGDDA8HWVKlVEv379DF8nJCQIAGL69OmGZfv37xcAREJCghBCiJiYGDFs2DCjurt37xYuLi4iMzNTfPvtt8LPz0+kpaVZPN5HebidQggBQIwdO9ZoWcuWLcXQoUONlvXs2VN07tzZ6PumTZtmcjyrVq0yLPvyyy+Fh4eHxfacPHlSKIoibt26JZKTk4Wbm5uYO3eu6NmzpxBCiPnz54tmzZoZtm/Tpo148sknjWo8/vjjYtKkSUbt+u6774QQQqxcuVL4+vqKO3fumN3/zJkzhZeXl9FzOmHCBKN9EhERkfNgHh1g+Jp5NBfzKBE5Cq9sJJKgYcOGaNeuHSIjI9GzZ098+umnSElJAQAkJSXh5s2baNeuncXv//3339GhQwdUrFgRvr6+6N+/P+7cuWN024aXlxdq1Khh+Lp8+fJISkqyWLNRo0aoW7cuvvzySwC5ZyqTkpLQq1cvAMDZs2cRFhaGsLAww/fUq1cPZcqUwdmzZwv3RPxXgwYNDP8PCQkBAERGRposy2v/0aNHERsbCx8fH8MjOjoaer0e8fHx6NChA6pUqYLq1asjJiYGX3zxBTIyMorUxjxNmzY1+vrs2bNGZ28BoFWrVibPSUGOMSsrC2lpaWb3GxERgaCgIOzatQu7d+9Gw4YN0bVrV8OZ5J07d6JNmzYW9wnk/xo4ceIEGjVqhMDAQLPrgdzbeXx9fQtUj4iIiIo35lFjzKPMo0TkOOxsJJJApVIhLi4Ov/zyC+rVq4fly5ejTp06iI+Ph6enZ77fe+XKFXTu3BkRERH49ttvcfToUXz44YcAcgeIzvPwQNGKohhu+7Ckb9++2LBhA4DcW1aio6MRHBwMABBCQFEUk++xtBzIvR3m4X0+2EZzbc2rZW5Z3i0Uer0ew4cPx4kTJwyPP//8ExcuXECNGjXg6+uLY8eO4csvv0T58uUxY8YMNGzYEHfv3s33+AvC29vbZNnDx2/uObH2GM3to3Xr1ti5cyd27dqFqKgoREREQKfT4dSpU9i3bx+ioqIs7jOvhqX6j3rdWVuPiIiIijfmUWPMo8yjROQ47GwkkkRRFLRq1QqzZ8/G8ePH4e7uju+++w6+vr6oWrUqduzYYfb7jhw5Aq1Wi8WLF6N58+aoXbs2bt68KaVNffr0walTp3D06FF888036Nu3r2FdvXr1cPXqVVy7ds2w7K+//kJqairCw8PN1itbtqzRIOA6nQ6nT58ucjsbN26MM2fOoGbNmiYPd3d3AICrqyvat2+PRYsW4eTJk7h8+TJ+++23Iu/7YeHh4dizZ4/Rsn379ll8Tooib5ycnTt3IioqCoqi4KmnnsK7776LzMxMkzPa1mjQoAFOnDiB5ORkiS0mIiKi4ox5tPCYR5lHiUgeV0c3gKgkOHjwIHbs2IGOHTuiXLlyOHjwIG7dumUIBLNmzcIrr7yCcuXK4ZlnnsG9e/ewd+9ejBo1CjVq1IBWq8Xy5cvx3HPPYe/evSaz9hVWtWrV0LJlSwwePBharRbdunUzrGvfvj0aNGiAvn37YtmyZdBqtRgxYgTatGljcitHnqeffhrjxo3DTz/9hBo1amDp0qVSzuZOmjQJzZs3x8iRIzF06FB4e3vj7NmziIuLw/Lly/Hjjz/i0qVLaN26NQICAvDzzz9Dr9ejTp06Rd73wyZMmIBevXqhcePGaNeuHbZs2YJNmzbh119/lb6vqKgojBkzBq6urnjqqacMy8aPH4/GjRvDz8+v0LVfeuklzJ8/H927d8eCBQtQvnx5HD9+HBUqVECLFi1kHQIREREVE8yjRcM8yjxKRPLwykYiCfz8/PDHH3+gc+fOqF27NqZNm4bFixfjmWeeAQAMGDAAy5Ytw0cffYT69eujS5cuuHDhAgDgsccew5IlS7Bw4UJERETgiy++wIIFC6S1rW/fvvjzzz/Ro0cPo1sZFEXB999/j4CAALRu3Rrt27dH9erVsXHjRou1Bg0ahAEDBqB///5o06YNqlWrhrZt2xa5jQ0aNMCuXbtw4cIFPPXUU2jUqBGmT5+O8uXLAwDKlCmDTZs24emnn0Z4eDg+/vhjfPnll6hfv36R9/2w7t2747333sM777yD+vXrY+XKlVizZo3JLSQyREREIDg4GA0bNjQEuTZt2kCn05mMj2Mtd3d3bN++HeXKlUPnzp0RGRmJt99+GyqVSkbTiYiIqJhhHi0a5lHmUSKSRxGPGmSDiIiIiIiIiIiIqAB4ZSMRERERERERERFJwc5GIiIiIiIiIiIikoKdjURERERERERERCQFOxuJiIiIiIiIiIhICnY2EjnAwIED0b17d0c3gxwoNjYWZcqUsep7oqKioCgKFEXBiRMnbNIuIiIiKh2YR4l5lIhshZ2NROTUqlatimXLltllX4UJZLINHToUCQkJiIiIAADs3LkTiqLg7t27Jts+9thjmDVrlmGb/B6xsbEAgG+//RZRUVHw9/eHj48PGjRogDlz5iA5OdmOR0lERETkPJhHmUeJyBg7G4moyDQajaObkC+dTge9Xu/oZkjh5eWF0NBQuLq6Fvh7WrZsiYSEBMOjV69e6NSpk9Gy3r17Y+rUqejduzcef/xx/PLLLzh9+jQWL16MP//8E+vWrbPhUREREREVDfOo/TCPEtGjsLORqBD0ej0WLlyImjVrQq1Wo3Llypg3b55h/alTp/D000/D09MTQUFBGDZsGO7fv2+xnrmzoXlnAfMoioKVK1eiS5cu8PLyQnh4OPbv349//vkHUVFR8Pb2RosWLXDx4kXD98yaNQuPPfYY1q1bh6pVq8Lf3x8vvvgi7t27Z7Etd+7cwUsvvYRKlSrBy8sLkZGR+PLLL422iYqKwmuvvYZx48YhODgYHTp0AAD89ddf6Ny5M3x8fBASEoKYmBjcvn073+dy3759aN26NTw9PREWFobRo0cjPT0dAPD555/Dx8cHFy5cMGw/atQo1K5dG+np6YiKisKVK1fw+uuvG86IAv874/vjjz+iXr16UKvVuHLlCg4fPowOHTogODgY/v7+aNOmDY4dO2bUnrt372LYsGEICQmBh4cHIiIi8OOPP2Lnzp14+eWXkZqaathX3s9Ho9Fg4sSJqFixIry9vdGsWTPs3LnTqG5sbCwqV64MLy8vPP/887hz506+z4tM7u7uCA0NNTw8PT2hVquNlp06dQrz58/H4sWL8c4776Bly5aoWrUqOnTogG+//RYDBgywW3uJiIjo0ZhHmUeZR4mouGJnI1EhTJkyBQsXLsT06dPx119/YcOGDQgJCQEAZGRkoFOnTggICMDhw4fxn//8B7/++itee+21Iu/3rbfeQv/+/XHixAnUrVsXffr0wfDhwzFlyhQcOXIEAEz2c/HiRXz//ff48ccf8eOPP2LXrl14++23Le4jKysLTZo0wY8//ojTp09j2LBhiImJwcGDB422W7t2LVxdXbF3716sXLkSCQkJaNOmDR577DEcOXIEW7duxb///otevXpZ3NepU6cQHR2NHj164OTJk9i4cSP27NljOIb+/fujc+fO6Nu3L7RaLbZu3YqVK1fiiy++gLe3NzZt2oRKlSphzpw5hjOieTIyMrBgwQJ89tlnOHPmDMqVK4d79+5hwIAB2L17Nw4cOIBatWqhc+fOhrCr1+vxzDPPYN++fVi/fj3++usvvP3221CpVGjZsiWWLVsGPz8/w77eeOMNAMDLL7+MvXv34quvvsLJkyfRs2dPdOrUyRBKDx48iEGDBmHEiBE4ceIE2rZti7lz5xb0x24XX3zxBXx8fDBixAiz6x19uw4REREZYx5lHmUeJaJiSxCRVdLS0oRarRaffvqp2fWffPKJCAgIEPfv3zcs++mnn4SLi4tITEwUQggxYMAA0a1bN8P6KlWqiKVLlxrVadiwoZg5c6bhawBi2rRphq/3798vAIhVq1YZln355ZfCw8PD8PXMmTOFl5eXSEtLMyybMGGCaNasmVXH3LlzZzF+/HjD123atBGPPfaY0TbTp08XHTt2NFp27do1AUCcO3fObN2YmBgxbNgwo2W7d+8WLi4uIjMzUwghRHJysqhUqZJ49dVXRUhIiJg7d67R9uaeuzVr1ggA4sSJE/kel1arFb6+vmLLli1CCCG2bdsmXFxcLLZ3zZo1wt/f32jZP//8IxRFETdu3DBa3q5dOzFlyhQhhBAvvfSS6NSpk9H63r17m9R6lDZt2ogxY8YYLfv9998FAJGSkmKy/cOvoTwPv/6EEOKZZ54RDRo0sKo9RERE5BjMo8yjD2IeJaLipuCDLBARAODs2bPIzs5Gu3btLK5v2LAhvL29DctatWoFvV6Pc+fOGc44F0aDBg0M/8+rExkZabQsKysLaWlp8PPzA5B7S4yvr69hm/LlyyMpKcniPnQ6Hd5++21s3LgRN27cQHZ2NrKzs42OBwCaNm1q9PXRo0fx+++/w8fHx6TmxYsXUbt2bZPlR48exT///IMvvvjCsEwIAb1ej/j4eISHhyMgIACrVq1CdHQ0WrZsicmTJ1ts+4Pc3d2Nni8ASEpKwowZM/Dbb7/h33//hU6nQ0ZGBq5evQoAOHHiBCpVqmS2rZYcO3YMQgiT78nOzkZQUBCA3NfE888/b7S+RYsW2Lp1a4H3Y2tCCMNtP0RERFS8MY/mYh7NxTxKRMUNOxuJrOTp6Znv+vw+JC0td3FxgRDCaFlOTo7Jdm5ubia1zC17cPDpB9fnbZPf4NSLFy/G0qVLsWzZMkRGRsLb2xtjx441GXT74bCn1+vx3HPPYeHChSY1y5cvb3Zfer0ew4cPx+jRo03WVa5c2fD/P/74AyqVCjdv3kR6erohuObH09PT5PkeOHAgbt26hWXLlqFKlSpQq9Vo0aKF4dge9bO1dAwqlQpHjx6FSqUyWpcXdB/+2cqU91ykpqaa3Fpy9+5d+Pv7F6hO7dq1sWfPHuTk5Ji8ZoiIiKh4YR7NxTz6v2NgHiWi4oRjNhJZqVatWvD09MSOHTvMrq9Xrx5OnDhhGFQaAPbu3QsXFxeLZyjLli1rNL5LWloa4uPj5Ta8gHbv3o1u3bqhX79+aNiwIapXr240ILYljRs3xpkzZ1C1alXUrFnT6PFwEHz4ex7evmbNmnB3dweQO2D3okWLsGXLFvj5+WHUqFFGNdzd3aHT6Qp8bKNHj0bnzp1Rv359qNVqowHDGzRogOvXr+P8+fNmv9/cvho1agSdToekpCSTYwgNDQWQ+5o4cOCA0fc9/HVh1apVCy4uLjh8+LDR8oSEBNy4cQN16tQpUJ0+ffrg/v37+Oijj8yuv3v3blGbSkRERJIwj5rHPMo8SkTFAzsbiazk4eGBSZMmYeLEifj8889x8eJFHDhwAKtWrQIA9O3bFx4eHhgwYABOnz6N33//HaNGjUJMTIzFW1aefvpprFu3Drt378bp06cxYMAAk7OS9lKzZk3ExcVh3759OHv2LIYPH47ExMRHft/IkSORnJyMl156CYcOHcKlS5ewfft2DBo0yGL4mjRpEvbv34+RI0fixIkTuHDhAjZv3mwIcPfu3UNMTAxGjRqFZ555Bhs2bMDXX3+N//znP4YaVatWxR9//IEbN248cqbBmjVrYt26dTh79iwOHjyIvn37Gp09btOmDVq3bo3/+7//Q1xcHOLj4/HLL78Ybi+pWrUq7t+/jx07duD27dvIyMhA7dq10bdvX/Tv3x+bNm1CfHw8Dh8+jIULF+Lnn38GAIwePRpbt27FokWLcP78eXzwwQfSblnx9fXF8OHDMX78eHz//feIj4/H3r178dJLLyE8PBwdO3YsUJ1mzZph4sSJGD9+PCZOnIj9+/fjypUr2LFjB3r27Im1a9dKaS8REREVHfOoecyjzKNEVDyws5GoEKZPn47x48djxowZCA8PR+/evQ3jznh5eWHbtm1ITk7G448/jhdeeAHt2rXDBx98YLHelClT0Lp1a3Tp0gWdO3dG9+7dUaNGDXsdjpHp06ejcePGiI6ORlRUFEJDQ9G9e/dHfl+FChWwd+9e6HQ6REdHIyIiAmPGjIG/vz9cXMy/1TRo0AC7du3ChQsX8NRTT6FRo0aYPn264TaXMWPGwNvbG/PnzwcA1K9fHwsXLsQrr7yCGzduAADmzJmDy5cvo0aNGihbtmy+bVy9ejVSUlLQqFEjxMTEYPTo0ShXrpzRNt9++y0ef/xxvPTSS6hXrx4mTpxoCKctW7bEK6+8gt69e6Ns2bJYtGgRAGDNmjXo378/xo8fjzp16qBr1644ePAgwsLCAADNmzfHZ599huXLl+Oxxx7D9u3bMW3atEc+pwW1dOlSDBkyBG+++Sbq16+Pvn37olq1ati+fTtcXQs+WsbChQuxYcMGHDx4ENHR0ahfvz7GjRuHBg0aYMCAAdLaS0REREXHPGqKeZR5lIiKB0XYcvAGIiKSJioqCo899hiWLVvm6KYQERERUSnEPEpEBcErG4mInMhHH30EHx8fnDp1ytFNISIiIqJSiHmUiB6FVzYSETmJGzduIDMzE0Du7Ih5g5YTEREREdkD8ygRFQQ7G4mIiIiIiIiIiEgK3kZNREREREREREREUrCzkYgcatasWXjsscfssh9FUaAoCge0JiIiIiIjzKRERPKws5GIbCYvSFl6DBw4EG+88QZ27Nhh87a88cYbSEhIQKVKlWy+LyIiIiIqPphJiYjsy9XRDSCikishIcHw/40bN2LGjBk4d+6cYZmnpyd8fHzg4+Nj87bk7UelUtl8X0RERERUfDCTEhHZF69sJCKbCQ0NNTz8/f2hKIrJsodvWRk4cCC6d++O+fPnIyQkBGXKlMHs2bOh1WoxYcIEBAYGolKlSli9erXRvm7cuIHevXsjICAAQUFB6NatGy5fvmzfAyYiIiKiYoeZlIjIvtjZSETFzm+//YabN2/ijz/+wJIlSzBr1ix06dIFAQEBOHjwIF555RW88soruHbtGgAgIyMDbdu2hY+PD/744w/s2bMHPj4+6NSpEzQajYOPhoiIiIicETMpEVHhsLORiIqdwMBAvP/++6hTpw4GDRqEOnXqICMjA2+++SZq1aqFKVOmwN3dHXv37gUAfPXVV3BxccFnn32GyMhIhIeHY82aNbh69Sp27tzp2IMhIiIiIqfETEpEVDgcs5GIip369evDxeV/50JCQkIQERFh+FqlUiEoKAhJSUkAgKNHj+Kff/6Br6+vUZ2srCxcvHjRPo0mIiIiohKFmZSIqHDY2UhExY6bm5vR14qimF2m1+sBAHq9Hk2aNMEXX3xhUqts2bK2aygRERERlVjMpEREhcPORiJyeo0bN8bGjRtRrlw5+Pn5Obo5RERERFQKMZMSEeXimI1E5PT69u2L4OBgdOvWDbt370Z8fDx27dqFMWPG4Pr1645uHhERERGVAsykRES52NlIRE7Py8sLf/zxBypXrowePXogPDwcgwYNQmZmJs8qExEREZFdMJMSEeVShBDC0Y0gIrKXqlWrYuzYsRg7dqyjm0JEREREpRQzKRGVZLyykYhKhfnz58PHxwdXr151dFOIiIiIqJRiJiWi0oBXNhJRqZCcnIzk5GQAubMB+vv7O7hFRERERFTaMJMSUWnAzkYiIiIiIiIiIiKSgrdRExERERERERERkRTsbCQiIiIiIiIiIiIp2NlIREREREREREREUrCzkYiIiIiIiIiIiKRgZyMRERERERERERFJwc5GIiIiIiIiIiIikoKdjURERERERERERCQFOxuJiIiIiIiIiIhICnY2EhERERERERERkRTsbCQiIiIiIiIiIiIp2NlIREREREREREREUrCzkYiIiIiIiIiIiKRgZyMRERERERERERFJwc5GIiIiIiIiIiIikoKdjURERERERERERCQFOxuJiIiIiIiIiIhICnY2EhERERERERERkRTsbCQiIiIiIiIiIiIp2NlIREREREREREREUrCzkYiIiIiIiIiIiKRgZyMRERERERERERFJwc5GolJmw4YNWLZsWZFqfPTRR4iNjTVZfvnyZSiKYnadszl48CCef/55VK5cGWq1GiEhIWjRogXGjx9v2Ean02HJkiXo1KkTKlWqBC8vL4SHh2Py5Mm4e/eu2brLly9H3bp1oVarUa1aNcyePRs5OTl2OioiIiKi4oGZtGAKkkkB4P3330fz5s0RHBwMtVqNypUr48UXX8SZM2fM1mUmJSJbUoQQwtGNICL76dKlC06fPo3Lly8XukZERASCg4Oxc+dOo+XZ2dk4fvw4atSogbJlyxatoQ70008/oWvXroiKisLQoUNRvnx5JCQk4MiRI/jqq69w/fp1AMD9+/dRoUIFvPTSS+jQoQOCg4Nx7NgxzJ07F+XLl8eRI0fg6elpqDtv3jxMnz4dkydPRseOHXH48GFMmzYNAwYMwCeffOKowyUiIiKyO2bSRytoJgWAmTNnwsXFBQ0bNkRAQAAuXbqEt99+Gzdu3MDRo0dRp04dw7bMpERka+xsJCplbBnsSoo2bdrgxo0b+Pvvv+Hq6mq0Tq/Xw8Ul96JwnU6Hu3fvIigoyGibb775Bj179sS6devQr18/AMCdO3dQqVIl9O/fHytXrjRsO3/+fEybNg2nT59GvXr1bHxkRERERMUDM+mjFTSTWnL27FnUq1cP06dPx5w5cwAwkxKRffA2aqIS5NatWxg2bBjCwsKgVqtRtmxZtGrVCr/++isAICoqCj/99BOuXLkCRVEMjzyzZ89Gs2bNEBgYCD8/PzRu3BirVq3Cg+ckqlatijNnzmDXrl2G769atSoAy7es7NmzB+3atYOvry+8vLzQsmVL/PTTT0bbxMbGQlEU/P7773j11VcRHByMoKAg9OjRAzdv3rTNE2bBnTt3EBwcbBLqABiFOpVKZdLRCABPPPEEAODatWuGZVu3bkVWVhZefvllo21ffvllCCHw/fffS2o9ERERkWMxk8pR0ExqSd5VnQ9+PzMpEdmD6bsWETmtmJgYHDt2DPPmzUPt2rVx9+5dHDt2DHfu3AGQO67NsGHDcPHiRXz33Xcm33/58mUMHz4clStXBgAcOHAAo0aNwo0bNzBjxgwAwHfffYcXXngB/v7++OijjwAAarXaYpt27dqFDh06oEGDBli1ahXUajU++ugjPPfcc/jyyy/Ru3dvo+2HDBmCZ599Fhs2bMC1a9cwYcIE9OvXD7/99lu+xy6EgE6nK9DzZC6wPahFixb47LPPMHr0aPTt2xeNGzeGm5tbgWoDMLS1fv36hmWnT58GAERGRhptW758eQQHBxvWExERETk7ZlLHZVKdTgetVov4+HhMnjwZ5cqVM+pYZCYlIrsQRFRi+Pj4iLFjx+a7zbPPPiuqVKnyyFo6nU7k5OSIOXPmiKCgIKHX6w3r6tevL9q0aWPyPfHx8QKAWLNmjWFZ8+bNRbly5cS9e/cMy7RarYiIiBCVKlUy1F2zZo0AIEaMGGFUc9GiRQKASEhIyLe9ed9fkMej3L59Wzz55JOG7d3c3ETLli3FggULjI7DnOvXr4uQkBDRtGlTodPpDMuHDh0q1Gq12e+pXbu26Nix4yPbRUREROQMmEkdl0nVarVh+9q1a4u//vrLaD0zKRHZA69sJCpBnnjiCcTGxiIoKAjt27dHkyZNrL4ib/78+Th8+DDS0tKM1iUlJSEkJMSq9qSnp+PgwYN49dVX4ePjY1iuUqkQExODSZMm4dy5c6hbt65hXdeuXY1qNGjQAABw5coVhIaGWtzXc889h8OHD1vVPkuCgoKwe/duHDlyBDt27MCRI0ewc+dOTJkyBStXrsThw4cRHBxs8n3Jycno3LkzhBDYuHGjye0tD94e9LD81hERERE5E2ZSx2XSffv2QaPR4OLFi1i6dCnatm2LHTt2GN1xw0xKRLbGzkaiEmTjxo2YO3cuPvvsM0yfPh0+Pj54/vnnsWjRonxDEQAcOnQIHTt2RFRUFD799FNUqlQJ7u7u+P777zFv3jxkZmZa3Z6UlBQIIVC+fHmTdRUqVAAAw+00eR4eAzHvdphH7T8wMBD+/v5WtzE/TZs2RdOmTQEAOTk5mDRpEpYuXYpFixZh0aJFRtumpKSgQ4cOuHHjBn777TdUr17daH1QUBCysrKQkZEBLy8vo3XJyclo0qSJ1LYTEREROQozqeMyaePGjQEAzZs3R9euXVGzZk28+eab+OGHHwAwkxKRfXCCGKISJDg4GMuWLcPly5dx5coVLFiwAJs2bcLAgQMf+b1fffUV3Nzc8OOPP6JXr15o2bKlIdQUVkBAAFxcXJCQkGCyLm+AbXNXCBbG2rVr4ebmVqBHYbi5uWHmzJkAYDKWTUpKCtq3b4/4+HjExcUZznw/KG9cnFOnThktT0xMxO3btxEREVGodhEREREVN8ykjsmkD/P19UXdunVx/vx5wzJmUiKyB17ZSFRCVa5cGa+99hp27NiBvXv3Gpar1WqzZ2QVRYGrqytUKpVhWWZmJtatW2eyraUaD/P29kazZs2wadMmvPvuu/D09AQA6PV6rF+/HpUqVULt2rULc3gmZN6ykpCQYPbM99mzZwH87ww48L+OxkuXLiEuLg6NGjUyW7NTp07w8PBAbGwsmjVrZlieN+Nh9+7dpbSdiIiIqDhhJi08azKpObdv38apU6fQqlUrwzJmUiKyB3Y2EpUQqampaNu2Lfr06YO6devC19cXhw8fxtatW9GjRw/DdpGRkdi0aRNWrFiBJk2awMXFBU2bNsWzzz6LJUuWoE+fPhg2bBju3LmDd9991+ysfpGRkfjqq6+wceNGVK9eHR4eHiYz2uVZsGABOnTogLZt2+KNN96Au7s7PvroI5w+fRpffvmltHFhgoKCTG53Kazo6GhUqlQJzz33HOrWrQu9Xo8TJ05g8eLF8PHxwZgxYwDkBt/o6GgcP34cy5Ytg1arxYEDBwx1ypYtixo1agDIvaVm2rRpmD59OgIDA9GxY0ccPnwYs2bNwpAhQ1CvXj0pbSei4u3hsccKws/PzwYtISKyDWZS+2fS1NRUdOjQAX369EGtWrXg6emJ8+fP47333kN2drbhSkiAmZSI7JRHHTxBDRFJkpWVJV555RXRoEED4efnJzw9PUWdOnXEzJkzRXp6umG75ORk8cILL4gyZcoIRVGMZsJbvXq1qFOnjlCr1aJ69epiwYIFYtWqVQKAiI+PN2x3+fJl0bFjR+Hr6ysAGGYSNDfznxBC7N69Wzz99NPC29tbeHp6iubNm4stW7YYbZM3c9/hw4eNlv/+++8CgPj999+lPE8FsXHjRtGnTx9Rq1Yt4ePjI9zc3ETlypVFTEyM0Yx+ecdr6TFgwACT2u+9956oXbu2cHd3F5UrVxYzZ84UGo3GbsdGRI6lKIpwcXEp8EOlUomLFy86utlERAXGTCpPQTNpVlaWGDJkiAgPDxc+Pj7C1dVVVKpUSfTr10+cOXPGbG1mUqLSyx55VBFCCKu7NImIiIjIai4uLvj2228RGBj4yG2FEOjcuTNOnz5tMukUEREREVFh2COP8jZqIiIiIjupUqUKWrduXeBb7KpXr17oSQSIiIiIiB5mjzzKKxuJiIiIiIiIiIhIigJd2di4cWOriiqKgs2bN6NixYqFahQRERFRaaXX63H9+nVUrlzZ0U0pVphHiYiIiOyjqHm0QJ2NJ06cwPjx4+Hj4/PIbYUQePvtt5GdnV2oBhERERGVZCqVCq+99hqWLl0KFxcXk/W3bt1CtWrVoNPpHNC64ot5lIiIiEgOW+fRAt1G7eLigsTERJQrV65ARX19ffHnn39yMHMiIiKih7i4uMDHxwdPPPEEvv76a5PBuf/991+UL18eer3eQS0snphHiYiIiOSwdR4t0JWN8fHxKFu2bIGL/vXXX6hQoUKhGlRc6fV63Lx5E76+vlAUxdHNISIisjshBO7du4cKFSqYPQNqC1lZWdBoNBbXu7u7w8PDwy5tkUVRFMTFxWH48OFo2rQpNm/ejIiICJNtyBjzKPMoERFRccyjgPNlUlvnUU4QU0DXr19HWFiYo5tBRETkcNeuXUOlSpVsvp+srCz4e5aBBpZvhQ0NDUV8fLxThbu8K/R8fX0xcOBA/Pzzz1i7di169OgBIPdMcoUKFXgbNZlgHiUiIspVnPIo4HyZ1NZ5tEBXNj4sKysLJ0+eRFJSkskllV27di1UQ4o7X19fALkvaD8/Pwe3xjlcT1kjrValgJel1crP/Usf2nwfPtVHSquVkbhWWi2v0AHSaqVkfCutlnL0oLRa8PSUVsqtwRPSagGAJu5nabUCnpXzOs6+K+/nCADa+ynSanlXGiKtlkwp/xksrVZAz1XSat09PkdKnXvp2Yh49gPDZ6KtaTQaaJCN1qpOcIWbyXotcvBH4lZoNBqnCXYP8vT0xMaNG7FgwQL07t0bb775JmbPnu3oZjmN0pxHD556Gz6+RX/NVywjbxKiHRfPS6v1ZNVq0moBwO17CdJq2SuT2lpa1g9S6/l5dJNaTxaZWUZd5v+k1bq7b4q0WnBVSSulrhYurdbBtFRptZq6yrt6TfP7Hmm1Ageul1ZLpuT5z0ur9fOzz0ir1UWfKKVOccujgHNnUlvlUas7G7du3Yr+/fvj9u3bJusURSmxZ+HzLh/18/NjZ2MB+erkdezY6zl3kRDcH8VH4rG4pst7jr0ktkvn6iWtluKtllYLXvJqufnJO0YA0Hi5S6sl6/clWy/3GLVKlrRa3sX0fVjnZT6AFIbM9z29j8TfI9j/Fl9/b3+4Kaa/IzlCA6TZtSk2MWXKFDRs2BB9+/bFyZMn8c477zi6ScVeac+jPr4e8PUregbw8/Muco083j7yMpSf5M/YbMX5MqnNuct9jv08iufzIjPLqGV+LsvMtzI7G33l/a54C3kTdPlJPEaNZ/HMajLlqAt1TZlZnj4S3z/1JTOPAiUjk8rOo1afInjttdfQs2dPJCQkQK/XGz1KarAjIiIix3F3dYe7m5mHq7xOensyF447d+6MgwcP4ty5c2jfvr0DWuVcmEeJiIjInizmUSfNpLbOo1Z3NiYlJWHcuHEICQkp0o6JiIiICsLbywc+Xr4mD28vH0c3rVAsDZddu3ZtHDx4EJGRkXZukfNhHiUiIiJ7spRHnTWT2jqPWt3Z+MILL2Dnzp1F2mkerVaLadOmoVq1avD09ET16tUxZ84co3F3hBCYNWsWKlSoAE9PT0RFReHMmTNGdbKzszFq1CgEBwfD29sbXbt2xfXr1422SUlJQUxMDPz9/eHv74+YmBjcvXtXynEQERGR7Vg8i+zmfGeRgdwZhcuVK2d2na+vL7Zs2YL4+Hg7t8q5MI8SERGRPeWXR50xk9o6j1p9M/8HH3yAnj17Yvfu3YiMjISbm/GYB6NHjy5wrYULF+Ljjz/G2rVrUb9+fRw5cgQvv/wy/P39MWbMGADAokWLsGTJEsTGxqJ27dqYO3cuOnTogHPnzhkGBB07diy2bNmCr776CkFBQRg/fjy6dOmCo0ePQqXKHd+hT58+uH79OrZu3QoAGDZsGGJiYrBlyxZrnwIqIH/PIEc3wWrau6ZjPxVnunvJ8oqVl1fK18NfWq171/6VVgvlAqSVctdKKwUAyE6+J7egBDl3k6TW06VK/P3iZKxWEYm35NTJ0EipYy1vL2+oVabjwWXrrB+T6MaNG5g0aRJ++eUXZGZmonbt2li1ahWaNGkCILdTafbs2fjkk0+QkpKCZs2a4cMPP0T9+vWLfByPcunSJWRmZiI8PByVK8ubuKMkKu151FvtBx910ceiy7kv77OnvLf5KyQKw9VF3nhjAOCjlpdLSgqNVt44ysWZzCyjLiOtFERGprRaipe8MfVk0urljacnNUNmcagNa3i4yntvh2NipDSW8ihgfSYtDXnU6k/yDRs2YNu2bfD09MTOnTuN7vNWFMWqcLd//35069YNzz77LACgatWq+PLLL3HkyBEAuU/wsmXLMHXqVMP022vXrkVISAg2bNiA4cOHIzU1FatWrcK6desM95SvX78eYWFh+PXXXxEdHY2zZ89i69atOHDgAJo1awYA+PTTT9GiRQucO3cOderUMWlbdnY2srP/N6htWpqTj/ZJRETkpNzc3eGmMj1jrNfpzWxtWUpKClq1aoW2bdvil19+Qbly5XDx4kWUKVPGsE1BOpWKSqPRYN68eTh27BiaN2+OyZMno1+/fvj6668BAHXq1MHPP/+MqlWrStlfScQ8SkRERPZkKY8C1mXS0pJHrb6Netq0aZgzZw5SU1Nx+fJlxMfHGx6XLl2yqtaTTz6JHTt24Pz58wCAP//8E3v27EHnzp0BAPHx8UhMTETHjh0N36NWq9GmTRvs27cPAHD06FHk5OQYbVOhQgVEREQYttm/fz/8/f0NwQ4AmjdvDn9/f8M2D1uwYIHhFhd/f3+EhfEyGiIiIkfw9PaCl4+3ycPTO/fKrrS0NKPHg50zD1q4cCHCwsKwZs0aPPHEE6hatSratWuHGjVqADDtVIqIiMDatWuRkZGBDRs2SDueKVOmYMWKFQgJCcHq1avRo0cPHD9+HBs2bMBXX30FV1dXTJ06Vdr+SiLmUSIiIrInS3nU2kxaWvKo1Vc2ajQa9O7dGy4uVvdTmpg0aRJSU1NRt25dqFQq6HQ6zJs3Dy+99BIAIDExEQBMBv8OCQnBlStXDNu4u7sjICDAZJu8709MTDR7L3q5cuUM2zxsypQpGDdunOHrtLQ0BjwiIiIHcHN3g5urm8lynUvueAYPfz7PnDkTs2bNMtl+8+bNiI6ORs+ePbFr1y5UrFgRI0aMwNChQwE8ulNp+PDhUo7nm2++QWxsLDp37ozz58+jbt26+Omnn/DMM88AyM0nffv2lbKvkop5lIiIiOzJUh4FrMukpSWPWt3ZOGDAAGzcuBFvvvlmoXeaZ+PGjVi/fj02bNiA+vXr48SJExg7diwqVKiAAQMGGLZ7eEpuIYTZabrz28bc9vnVUavVUKvV1hwOERER2YCHjyc83cyMSZWT+8+1a9fg5+dnWGzp8/vSpUtYsWIFxo0bhzfffBOHDh3C6NGjoVar0b9//wJ1Kslw8+ZNNGzYEEDujH9qtRo1a9Y0rK9du7bFzifKxTxKRERE9mQxjwJWZdLSkket7mzU6XRYtGgRtm3bhgYNGpgMyL1kyZIC15owYQImT56MF198EQAQGRmJK1euYMGCBRgwYABCQ0MB5J4JLl/+f7NXJCUlGZ740NBQaDQapKSkGJ1NTkpKQsuWLQ3b/Puv6SQTt27dMvkBkjw+rt6OboLVREqqo5tgFd294tlekWX+FsZC1bos7xgV16JfAZNHc+emtFoAgLvFb6B2qYNxAxApd6XWK5a01o0haC8i6a6cOpk5UupYy1XtClc30zPJri657fHz8zMKdpbo9Xo0bdoU8+fPBwA0atQIZ86cwYoVK9C/f3/DdoXpVLKGTqczyk+urq6GCUQAwMXFBUJIHJC9BCrtedTXww++HkXPWdk35M16HuBT9Alr8qgUeZ/XAODp7nyZ1NYyc9Id3QS70N6ReOKmqrxSSJc3QYx44POjqBSV+au2CkPmBDFSM+R9x2QZZ+WmMI/ksZRHAesyaWnJo1Z3Np46dQqNGjUCAJw+fdponbUHnpGRYXL7i0qlgl6f+8datWrVEBoairi4OMM+NRoNdu3ahYULFwIAmjRpAjc3N8TFxaFXr14AgISEBJw+fRqLFi0CALRo0QKpqak4dOgQnnjiCQDAwYMHkZqaagiAREREVDypfT2gdjed/U+vsa5zt3z58qhXr57RsvDwcHz77bcAUKBOJVm2bdsGf//cGXL1ej127NhhyFV3796Vuq+SiHmUiIiI7MlSHgWsy6SlJY9a3dn4+++/F2mHD3ruuecwb948VK5cGfXr18fx48exZMkSDBo0CEBuWBw7dizmz5+PWrVqoVatWpg/fz68vLzQp08fAIC/vz8GDx6M8ePHIygoCIGBgXjjjTcQGRlpmA0wPDwcnTp1wtChQ7Fy5UoAwLBhw9ClSxezM/8RERFR8eHq7gZXtZkrG2HdFRitWrXCuXPnjJadP38eVapUAVCwTiVZHrw9F4DJ+Dsyz1yXRMyjREREZE+W8ihgXSYtLXnU6s5GmZYvX47p06djxIgRSEpKQoUKFTB8+HDMmDHDsM3EiRORmZmJESNGICUlBc2aNcP27duNpvteunQpXF1d0atXL2RmZqJdu3aIjY01ugT0iy++wOjRow2DbHbt2hUffPCB/Q6WiIiICsXDVw1PtemZZOGus6rO66+/jpYtW2L+/Pno1asXDh06hE8++QSffPIJgIJ1KsmQd8UcFQ/Mo0RERPQolvIoYF0mLS15tECdjT169EBsbGyBxkMCgL59+2Lp0qVmZ9x7kK+vL5YtW4Zly5ZZ3EZRFMyaNcvsrJJ5PDw8sHz5cixfvtziNoGBgVi/fv2jmk5ERETFjJe3J7w8zAzI7WpdSHr88cfx3XffYcqUKZgzZw6qVauGZcuWGc20V5BOJXIM5lEiIiJyFIt5FLAqk5aWPFqgzsYffvgBt27dKlBBIQS2bNmCt95665Hhjkq27GR5AzJ72uullFw8J1yx6N59R7fALO39FGm1NPHyBtB2LyNvRk+txNc3AIjbxW+CGP2tO3ILOtvvV0mSKGkigCzHDKru7q6C2t10AHyt3vpB8bt06YIuXbpYXF+QTqWi2Lx5c4G37dq1q03a4KyYR/9HZGkg3It+g5I2tWDPZ0EEhjSUVkuv0UirBQDuKofezFUsZWiKX+6whWI7+WO6xOdf7S6tlCLxdyVTK3GCGIkZUmRppdUqDTxdJU4QI28OUYewlEcB6zNpacijBXo3EUKgdu3ahdoBERERUVGU8XSHj6fpyQI3xflmlOzevbvR14qiGM309+DYODqddbeJl3TMo0REROQolvIo4HyZ1B55tECdjYUZhLtixYpWfw8RERHRw7zcVPByMz1jrNdaf2Wjoz04Ps6vv/6KSZMmYf78+WjRogUURcG+ffswbdo0zJ8/34GtLJ6YR4mIiMhRLOVRwPkyqT3yaIE6G9u0aVPoHRAREREVhYcmG54q01n+tBrnvh9n7Nix+Pjjj/Hkk08alkVHR8PLywvDhg3D2bNnHdi64od5lIiIiBzFUh4FnDuT2iqPcgATIiIiKtZccjRwUZmGOJccueO62dvFixfh7+9vstzf3x+XL1+2f4OIiIiIyCxLeTRvnbOyVR5lZyPZjGe5gY5ugtXEv/cc3QSrlIl6z9FNMEvmz95ztbxaxZq8cfWlCWj/gdR6ycu7S6v1ydEPpdWKCJIXDsKlVQL02q3yaiVkSKkjNI4ZVF25lwElx3SgeSVLznE5yuOPP46xY8di/fr1KF++PAAgMTER48ePxxNPPOHg1lFxpk2/C61L0a+iEPfk5R4PIe8WMp30K0Tk1XN3nolA85WuKSVjwt5KdnQLzBL35b0mFW95OUbmBDE5QuIEMXfkvVeJeyV/ghjF1UVaLVcXiRPEODlLeRRw7kxqqzzKzkYiIiIq1kSODsLF9I8DkePcfyyvXr0azz//PKpUqYLKlSsDAK5evYratWvj+++/d2zjiIiIiMjAUh7NW+esbJVH2dlIRERExZq4lw2hMb1qSmQ77/g4AFCzZk2cPHkScXFx+PvvvyGEQL169dC+fXujWQCJiIiIyLEs5VHAuTOprfIoOxuJiIioWBM5WgjF3JWNzn8rlKIo6NixIzp27OjophARERGRBZbyaN46Z2aLPGr1zfx37tzByJEjUa9ePQQHByMwMNDoQURERCST/p7G4sPZvP/++8jKyirw9h9//DHuSRxXr6RgHiUiIiJ7yi+POlsmtUcetfrKxn79+uHixYsYPHgwQkJCeJsPERER2ZTQ6CBEyRiz8fXXX8dLL70EDw+PAm0/ceJEdOzYEb6+JWRWCkmYR4mIiMieLOVRwPkyqT3yqNWdjXv27MGePXvQsGExnDqVqIgCR33v6CYQlVxavbRSNfxz5NUqpldBZd26Lq2WLkvOTII6jWNmJNTdy4LWzbQzSZdT8DOyxYUQAu3atYOra8EiWGZmpo1b5JxKex7V3bsLHYr++hfJqRJakys7OVFaLZkz4gKA0Mm7ve3vtGXSalnSoOJYm++jcdjrUutlJsVKq+VZbqC0WuKuvPfQc/++L61W2SyJHRPZxfOKqgreEm8rvS8v94kseXlUpm9OL5dWq63EzF1J4rlOJVPOe7vi6piOPUt5FHC+TGqPPGr1T7tu3boMvkRERGQ3eo0Wer3pHy16rfONjzNz5kyrtu/WrRtvCzaDeZSIiIjsyVIeBZwvk9ojj1rd2fjRRx9h8uTJmDFjBiIiIuDm5ma03s/Pz9qSRERERBZp7mdBYyaxaLTOdRYZsD7ckXnMo0RERGRPlvIo4HyZ1B551OrOxjJlyiA1NRVPP/200XIhBBRFgU7nXPeqExERUfGm1WqhNTNGjlbirZHkXJhHiYiIyJ4s5VGAmdQcqzsb+/btC3d3d2zYsIEDchMREZHNZWVlQaUyHS8yS5ftgNZQccA8SkRERPZkKY8CzKTmWN3ZePr0aRw/fhx16tSxRXuIiKikkjgYelV/eZMHBLqWkVbrvrRKQE7SNWm1ZOUfnYPGoM/QZEJxMR3sPEPPYFdalfY8qktPhU6RcMtWmrx3LZnvWSqfMtJqAYBeI298T79yAdJqlSSahHhptTzLSSsF3JX3wVUpoIq0WtkSM5HMSXBkala5prRaGdpfpdWSNWmebL7uEieukfj6qlG2hrRa2gw5x6gSjrll2VIeBZhJzXGx9huaNm2Ka9fkhQkiIiKi/Gh1Ocgx89Dq5M1OSc6FeZSIiIjsyVIeZSY1z+pLQ0aNGoUxY8ZgwoQJiIyMNBmQu0GDBtIaR0RERJSuyYRwMT1Ln6F30KWWkuzcuRNRUVGOboZTYh4lIiIie7KURwHnzqS2yqNWdzb27t0bADBo0CDDMkVROCA3ERER2YRWmwOti+mYfFq9c59F7tSpEypWrIiXX34ZAwYMQFhYmKOb5DSYR4mIiMieLOVRwLkzqa3yqNWdjfHx8sbjICIiInqU7Pv34aK4mS4XzhvsAODmzZtYv349YmNjMWvWLLRr1w6DBw9G9+7d4e7u7ujmFWvMo0RERGRPlvIo4NyZ1FZ51OrOxipV5A2MS0REpYfI0kqrFeov7wqwjPN/Sqslk/7WHWm1tNlyZurV5jhmxl99jgZ6xXRAd70TBzsACAwMxOjRozF69GicOHECq1evxsiRI/Hqq6+ib9++GDx4MBo2bOjoZhZLpT2Pinv3ICTcsiVzYgl9YpK0WgiVVwoARLa8gfs9K1aUVqskkfrzl/i2J+7K+9m7ZsiblCJbK3EykPvyPguFTl5W01w4J62WTLIykWwBanmvCZEp7+p+XYK88ZFdvHzl1NHJmyjSGpbyKODcmdRWebTQP6W//voLV69ehUZjHHS6du1a2JJEREREJlxdcuBqJty5Ci1QQu6WfeyxxzB58mQEBgbi7bffxurVq/HRRx+hRYsW+Pjjj1G/fn1HN7FYYh4lIiIie7CUR4GSk0ll5lGrOxsvXbqE559/HqdOnTKMjQPkjpMDgGPkEBERkVQqV2E23KmEAJz3RDIAICcnBz/88ANWr16NuLg4NG3aFB988AFeeuklJCcnY9KkSejZsyf++usvRze1WGEeJSIiInuylEcB58+ktsijLtY2YsyYMahWrRr+/fdfeHl54cyZM/jjjz/QtGlT7Ny509pyRERERPlSq4XFhzMbNWoUypcvj1deeQW1a9fG8ePHsX//fgwZMgTe3t4ICwvD22+/jb///tvRTS12mEeJiIjInvLLo86cSW2VR63ubNy/fz/mzJmDsmXLwsXFBS4uLnjyySexYMECjB492tpyRERERPlydQFcVWYeVqcYYwsWLICiKBg7dqxhmRACs2bNQoUKFeDp6YmoqCicOXOmaDuy4K+//sLy5ctx8+ZNLFu2DBERESbbVKhQAb///rtN9u/MmEeJiIjInizm0SJm0pKaR62+jVqn08HHxwcAEBwcjJs3b6JOnTqoUqUKzp0rnoPAEhFRMZAl77ZGt2yJA5ifvyStllQJt6WV0mYXsVcur06OnDrWclcD7mZ2XZTx9Q8fPoxPPvkEDRo0MFq+aNEiLFmyBLGxsahduzbmzp2LDh064Ny5c/D1lTOweZ6ZM2eiZcuWcHU1jmNarRb79u1D69at4erqijZt2kjdb0lQ2vOoyMiCUCRMJnBb3oQXwkPepFbCy1NaLQAQ2UWfTCePj0cZabVKEnFV4gQxEunvycse2QkXpdUSEjNRkT4MbUh74ITEYvKuGiuuE8R4mws6haTLkvd8ac7J6+DybtZWSh1XvVpKHWtZyqNA4X8NS3IetfoVHRERgZMnTwIAmjVrhkWLFmHv3r2YM2cOqlevbm05IiIiony5uAIqMw+X/2aitLQ0o0f2I2aevX//Pvr27YtPP/0UAQEBhuVCCCxbtgxTp05Fjx49EBERgbVr1yIjIwMbNmyQflxt27ZFcnKyyfLU1FS0bSsnkJdUzKNERERkT5byaGEzaUnPo1Z3Nk6bNg16fW637dy5c3HlyhU89dRT+Pnnn/H+++8XuiFERERE5ri7K3BXm3m4516dEBYWBn9/f8NjwYIF+dYbOXIknn32WbRv395oeXx8PBITE9GxY0fDMrVajTZt2mDfvn3Sj0sIYZjQ5EF37tyBt7e39P2VJMyjREREZE8W82ghM2lJz6NW30YdHR1t+H/16tXx119/ITk5GQEBAWYbSERERFQUKlXuw2T5f/+9du0a/Pz8DMvVasu313z11Vc4duwYDh8+bLIuMTERABASEmK0PCQkBFeuXLG+4Rb06NEDQO7MyQMHDjRqr06nw8mTJ9GyZUtp+yuJmEeJiIjInizlUcD6TFoa8mihBwb4559/sG3bNmRmZiIwMLDQDbhx4wb69euHoKAgeHl54bHHHsPRo0cN6wsyMGZ2djZGjRqF4OBgeHt7o2vXrrh+/brRNikpKYiJiTH0MMfExODu3buFbjcRERHZh7va8gMA/Pz8jB6Wgt21a9cwZswYrF+/Hh4eHhb393BnlaUzvoWVl0WEEPD19TU6Ax4aGophw4Zh/fr10vZXkjGPEhERkT3kl0etyaSlJY9afWXjnTt30KtXL/z+++9QFAUXLlxA9erVMWTIEJQpUwaLFy8ucK2UlBS0atUKbdu2xS+//IJy5crh4sWLKFOmjGGbggyMOXbsWGzZsgVfffUVgoKCMH78eHTp0gVHjx6F6r9dz3369MH169exdetWAMCwYcMQExODLVu2WPsUEBERkR0proCLmTPJ1uato0ePIikpCU2aNDEs0+l0+OOPP/DBBx8YJhZJTExE+fLlDdskJSWZnF0uijVr1gAAqlatijfeeIO3TBcC8ygRERHZk6U8CliXSUtLHrW6s/H111+Hm5sbrl69ivDwcMPy3r174/XXX7cq3C1cuBBhYWGGgwRyDzTPwwNjAsDatWsREhKCDRs2YPjw4UhNTcWqVauwbt06w73u69evR1hYGH799VdER0fj7Nmz2Lp1Kw4cOIBmzZoBAD799FO0aNEC586dQ506dax9GoiIyEoiR97MeFnX5c02K878K60WKkmcHS4xXVqpHEkzL2pzHHN7qru7gLur6evH3crZKdu1a4dTp04ZLXv55ZdRt25dTJo0CdWrV0doaCji4uLQqFEjAIBGo8GuXbuwcOHCwh+ABTNnzpRes7Qo9Xk07T6gdS/49hboU+TN0uyCe9JqiXL3pdUCAKRnSivl5SZ3puySQlyX/DOTJDtVXi3Xq9fkFZM4g7TIlJevhE4rrVbWTnkzlHvUlPd7l5Mtb9Znmbzdi/6enkeX/zx5VhGnrkqr5RolpzPLNUdKGatZyqOAdZm0tORRqzsbt2/fjm3btqFSpUpGy2vVqmX1/eObN29GdHQ0evbsiV27dqFixYoYMWIEhg4dCuDRA2MOHz4cR48eRU5OjtE2FSpUQEREBPbt24fo6Gjs378f/v7+hmAHAM2bN4e/vz/27dtnNtxlZ2cbzRyUlpZm1bERERGRHK5qAVcz4c5VZd0fWL6+voiIiDBa5u3tjaCgIMPysWPHYv78+ahVqxZq1aqF+fPnw8vLC3369Cn8ATygcePG2LFjBwICAtCoUaN8b4c5duyYlH2WRMyjREREZE+W8ihgXSYtLXnU6s7G9PR0eHl5mSy/fft2vgOym3Pp0iWsWLEC48aNw5tvvolDhw5h9OjRUKvV6N+/f4EGxkxMTIS7u7vRVOF52+R9f2JiIsqVK2ey/3Llyhm2ediCBQswe/Zsq46HiIiI5FOpBFRmQpxKyLuaI8/EiRORmZmJESNGICUlBc2aNcP27dsNt8oWVbdu3Qx5qVu3bpzMpJCYR4mIiMieLOVRQH4mLQl51OrOxtatW+Pzzz/HW2+9BSB30Eq9Xo933nkHbdu2taqWXq9H06ZNMX/+fABAo0aNcObMGaxYsQL9+/c3bFeYgTEf3sbc9vnVmTJlCsaNG2f4Oi0tDWFhYY8+KCIiIpLK1V3A1c3MlY0uRQ92O3fuNPpaURTMmjULs2bNKnJtcx68VcVW+ygNmEeJiIjInizlUaDombQk5lGrOxvfeecdREVF4ciRI9BoNJg4cSLOnDmD5ORk7N2716pa5cuXR7169YyWhYeH49tvvwUAhIaGAsh/YMzQ0FBoNBqkpKQYnU1OSkoyTNMdGhqKf/81HZPr1q1bFgfYVKvVVp8ZJyIiIvlcXAVUZm5bcbHBlY329PLLL6Nfv354+umneYWjlZhHiYiIyJ4s5VHAuTOprfKo1Z2N9erVw8mTJ7FixQqoVCqkp6ejR48eGDlypFEAK4hWrVoZZtrJc/78eVSpUgUAUK1atUcOjNmkSRO4ubkhLi4OvXr1AgAkJCTg9OnTWLRoEQCgRYsWSE1NxaFDh/DEE08AAA4ePIjU1FRDACQiIhuTOBi6/vZtabV0CVnSarlW9ZdWS39X3ujeeq2c4KDXOaZDzFWth6ub6evH1UXea8oR7ty5g2effRZBQUF48cUXERMTg8cee8zRzXIKpT2PirRMiJyiT+Sgu68rco3/kTfZjErihC4AIFIzpNXSZsptmznZCUttvg+fGq9LrSckTjaUdecLabX0Ml/iyfJmm5E5aZ7M32NNknVj3ubn3i2ruxos8qhbPCd1SX6ni7Ralca/Jq1WoqTcBwAiUd77p7OzlEcB586ktsqjhXoHCA0NlTJ+zOuvv46WLVti/vz56NWrFw4dOoRPPvkEn3zyCYDcS0cfNTCmv78/Bg8ejPHjxyMoKAiBgYF44403EBkZaZgNMDw8HJ06dcLQoUOxcuVKAMCwYcPQpUsXzkRNRERUzLmoch8my5031wHInZjk7t27+Prrr7FhwwYsW7YMderUQb9+/dCnTx+jGZHJFPMoERER2YulPAo4dya1VR4t0umG9PR0bNy4EZmZmejYsSNq1apl1fc//vjj+O677zBlyhTMmTMH1apVw7Jly9C3b1/DNgUZGHPp0qVwdXVFr169kJmZiXbt2iE2NhYq1f9eCV988QVGjx5tmCWwa9eu+OCDD4py+ERERGQHru4Cru5mxmxUnPeWlTxlypTBsGHDMGzYMFy/fh1ffvklVq9ejRkzZkCrLfqVa6UB8ygRERHZmqU8Cjh/JrVFHi1wZ+PVq1cRExODY8eOoXnz5li1ahU6dOiACxcuAAA8PT3xyy+/oHXr1lY1oEuXLujSxfLlxwUZGNPDwwPLly/H8uXLLW4TGBiI9evXW9U2IiIicjzFNfdhstyJzyI/LCcnB0eOHMHBgwdx+fJli2P4lXbMo0REROQIlvIoUHIyqcw8WuDBD9544w1oNBqsWLECXl5eiI6ORq1atZCQkIB///0XnTt35qyKREREJJ3KQ7H4cHa///47hg4dipCQEAwYMAC+vr7YsmULrl275uimFUvMo0REROQI+eVRZ8+ktsijBb6y8Y8//sDmzZvxxBNPoHPnzggODsbq1asNPZ3Tpk1Du3btCt0QIiKighISB2nXayXe9uAqbwBzkSlzVHt5g7Q7hMrF/HOrK54DxhdUpUqVcOfOHURHR2PlypV47rnn4OHh4ehmFWvMo/+VngNIeIsQEu/UF1nyLuuQOaELAEDihFva+ynSalncx7XLNt8HasgtJ/O1pNfYfhKewhBZEg9S4qR5Onkvb+hu3JRWKztd4me0a/HsyBF35U2MpPn3urRaMsnMozqNnBerTiPvebeKpTwKOHUmtVUeLfBfH7du3TLMyhcYGAgvLy+jSypDQ0ORkmL7D18iIiIqXRRPFRS16Yjciotzj48zY8YM9OzZEwEBAY5uitNgHiUiIiJHsJRHAefOpLbKowXubBRCQFH+d0bhwf8TERER2YyrYv6qBp1zZ5Fhw4Y5uglOh3mUiIiIHMJSHgWcOpPaKo9adV/VjBkz4OXlBQDQaDSYN28e/P39AQAZGZJvdyAiIiICALUKioe5yOJ8Z5F79OiB2NhY+Pn5oUePHvluu2nTJju1yrkwjxIREZHdWcyjgLNlUnvk0QJ3NrZu3Rrnzp0zfN2yZUtcunTJZBsiIiIiqSydSS6mYzjlx9/f33A1np+fH6/MsxLzKBERETlEflc2OlkmtUceLXBn486dO6XvnIiIqDBE8j15tSSO9w6V+XFcCkNqu5yd2hUwdyZZONdZZABYs2aN4f+xsbGOa4iTYh79r1SNlAlZtNky/7iQ9/uoup8jrRYAiNtZ0mrpMuR9/liij0+w+T4QJbecLkvez19o5f38XeR9LAP35U1KIfMzXiNxIhYPia+9rAyZE8TIq6VylfhavSfvtar55y9ptfQS5xjU3pf3fOk1ct6LZdWxmqU8CjhdJrVHHnXeKXOIiIiodMg7k2zu4cSefvpp3L1712R5Wloann76afs3iIiIiIjMyy+POnEmtVUetWrMRiIiIiK783DLfTzMuU4im9i5cyc0GtMrZbKysrB7924HtIiIiIiIzLKURwGnzqS2yqPsbCQiIqJiTVG7mh2QW9E7Z7I7efKk4f9//fUXEhMTDV/rdDps3boVFStWdETTiIiIiMgMS3kUcM5Maus8ys5GIiIiKt5ULubHw1RJHJTIjh577DEoigJFUczenuLp6Ynly5c7oGVEREREZJalPAo4ZSa1dR61qrNRq9Vi3rx5GDRoEMLCwgq9UyIioiK5neHoFpjnKm8kepmD7Ts9tXvu42HOl+sAAPHx8RBCoHr16jh06BDKli1rWOfu7o5y5cpBJXGyoZKGeRQQdzUQ7sVtghh53CVPEKNNkTcbh+v9FGm1LBH/3LX5PmSTOSGFcNTkD4+SJe91pNfK+4zPyZY4Od0/qdJq5WRJvK5J4gQxMmlS5f0cXf6+Iq2WTJoMeZ8Tek2mnDo5jpogxkIeBZwyk9o6j1r1DuDq6op33nkHAwYMKPQOiYiIiKziqjLfkSuxc9eeqlSpAgDQ64veWVQaMY8SERGR3VnKo3nrnIyt86jVpwjat2+PnTt32qApRERERGa4u//vbPKDD3cLZ5edxIIFC7B69WqT5atXr8bChQsd0CLnwTxKREREdmUpjzp5JrVVHrX62uZnnnkGU6ZMwenTp9GkSRN4e3sbre/atWuhG0NERET0MMXVBYqZM8ZKMb2tqqBWrlyJDRs2mCyvX78+XnzxRUyaNMkBrXIOzKNERERkT5byaN46Z2WrPGp1Z+Orr74KAFiyZInJOkVRoNM54c3qREREVHxZGiNH4phXjpCYmIjy5cubLC9btiwSEhIc0CLnwTxKREREdpXfmI1OnEltlUet7n7V6/UWHwx2REREJJ2LKnf2v4cfLs43Ps6DwsLCsHfvXpPle/fuRYUKFRzQIufBPEpERER2ZSmPOnkmtVUelThFFBERkX2IuxpptWTOoAmJMwjLnCVWp5NTS1YdaylqNRS12nS53Alr7W7IkCEYO3YscnJy8PTTTwMAduzYgYkTJ2L8+PEObh0VZ5pUAY170a+i0Ovk3falzZZWCvoUee/xAJCdJu+9S52RIa2WJekXbf/mFiS5nszPLKErnm/uQvIs6bJkZ8j7Pb53Ud6M25kZEq/0cpX3+nJRyWuXJl3ec+9+9q60WjJlp8vLtvpsSbNRS6pjLUt5FHDuTGqrPFqozsb09HTs2rULV69ehUZjHAZGjx5d6MYQERERmVC5mO/IVTnv+DgAMHHiRCQnJ2PEiBGGPOXh4YFJkyZhypQpDm5d8cc8SkRERHZjKY/mrXNStsqjVnc2Hj9+HJ07d0ZGRgbS09MRGBiI27dvw8vLC+XKlWO4IyIiIqlc3D3g4u5hZrl1dRYsWIBNmzbh77//hqenJ1q2bImFCxeiTp06hm2EEJg9ezY++eQTpKSkoFmzZvjwww9Rv379oh6GCUVRsHDhQkyfPh1nz56Fp6cnatWqBbWFs+b0P8yjREREZE+W8mjuuoLXKS151Oru19dffx3PPfcckpOT4enpiQMHDuDKlSto0qQJ3n333SI1hoiIiOhhiosbFJWZh4ubVXV27dqFkSNH4sCBA4iLi4NWq0XHjh2Rnp5u2GbRokVYsmQJPvjgAxw+fBihoaHo0KED7t27J/uwDBITE5GcnIwaNWpArVZDCOcdZNxemEeJiIjInizmUSszaWnJo1Zf2XjixAmsXLkSKpUKKpUK2dnZqF69OhYtWoQBAwagR48eRWoQERER0YMUdzUUM2eSFSvHrNu6davR12vWrEG5cuVw9OhRtG7dGkIILFu2DFOnTjXkmbVr1yIkJAQbNmzA8OHDC38QZty5cwe9evXC77//DkVRcOHCBVSvXh1DhgxBmTJlsHjxYqn7K0mYR4mIiMieLOXR3HUFz6SlJY9a3dno5uYGRckdoDUkJARXr15FeHg4/P39cfXq1UI1gkqm5AXPSqsVOOUnabXyc7pdR5vvo+qq56TVujRgi7RaDXZtl1ZLpgONOkmrVaujvEGvE47IHZcjsJK8wfBvX7by3lILwkaGSKmTR5cl72otF628UZilDmqfdFdaLY3EAd/1kl76supYS3F1g+JqesZYcc1tUFpamtFytVpdoFs/UlNTAQCBgYEAgPj4eCQmJqJjx/99FqjVarRp0wb79u2THu5ef/11uLm5GbJUnt69e+P1119nZ2M+Snse1WoUaEXR37tyJL7/uUm8+19k6eUVg9xJQsX1RHnFLEhPtv0cnnd3jpFaz1Ut7zNen5n+6I0KSOZnvP6WvKyWky6vXTKPUeZkMxqJk0YhS95sfjKPUSbXfyUeo8RJXWQ+XzmXL8ipky7zxVVwlvJo7rrCZ9KSmket/iRr1KgRjhw5gtq1a6Nt27aYMWMGbt++jXXr1iEyMrJQjSAiIiKyJHeMHE8zy3P/uA0LCzNaPnPmTMyaNSvfmkIIjBs3Dk8++SQiIiIA5N4+AuR2Xj0oJCQEV65cKWzzLdq+fTu2bduGSpUqGS2vVauWTfZXkjCPEhERkT1ZyqO56wqXSUtyHrW6s3H+/PmG+8TfeustDBgwAK+++ipq1qyJNWvWFLohREREROYoLq5QVKaRRXHJXXbt2jX4+fkZlhfkqsbXXnsNJ0+exJ49e0zrKsZXiQghTJbJkJ6eDi8vL5Plt2/f5iQxj8A8SkRERPZkKY/mrQOsz6QlOY9a3dnYtGlTw//Lli2Ln3/+udA7JyIiInoUy7NR595q6efnZxTsHmXUqFHYvHkz/vjjD6OzuKGhoQByzyiXL1/esDwpKcnk7LIMrVu3xueff4633noLQG6o1Ov1eOedd9C2bVvp+ytJmEeJiIjInvKfjdr6TFrS86jtBwQhIiIiKgKVuztU7qZnVlXu1g0iKYTAqFGj8N1332Hnzp2oVq2a0fpq1aohNDQUcXFxaNSoEQBAo9Fg165dWLhwYeEPwIJ33nkHUVFROHLkCDQaDSZOnIgzZ84gOTkZe/fulb4/IiIiIiocS3k0d13BM2lpyaMF6mxs1KhRgS/XPHbsWKEbQyWMVu7g3iVFzplTjm6CU5E5uLT+XvEceBmQO3i9i6TTSOJKipxC/yVzMHSpA9Fr5Q18LS7fk1YrO13iBDE6Oc+9rDrWUlQuUFSmz4e5ZfkZOXIkNmzYgB9++AG+vr6GMXH8/f3h6ekJRVEwduxYzJ8/H7Vq1UKtWrUwf/58eHl5oU+fPlKO5UH16tXDyZMnsWLFCqhUKqSnp6NHjx4YOXKk0ZlsysU8+j+adBWy3Yr+wZGTLXOiBHm5T+aEYgDg5i2vlrh4W14xCzLSbD+BhUi9L7We1El4MjKk1ZL5Gs9OlZcjM9PkPWE6iZO3ycweEn+McieIkXiMxZXU173E/K7/57qcOpnyJoq0hqU8mreuoEpLHi3Qn6Tdu3c3/D8rKwsfffQR6tWrhxYtWgAADhw4gDNnzmDEiBGFbggRERGROTqhh06YdmSYW5afFStWAACioqKMlq9ZswYDBw4EAEycOBGZmZkYMWIEUlJS0KxZM2zfvh2+vr6FavujhIaGYvbs2TapXdIwjxIREZGjWMqjeesKqrTk0QJ1Ns6cOdPw/yFDhmD06NGG+7kf3ObatWtSG0dERESk1eug1ZteuqHVW3elgxCPvlpKURTMmjXrkbNZF9bJkycLvG2DBg1s0gZnxTxKREREjmIpj+atK6jSkketvtnuP//5D44cOWKyvF+/fmjatClWr15dqIYQERERmaPRZkOjNb3lTKOVOM6CnTz22GNQFOWRQVNRFOh08m4bK2mYR4mIiMieLOXRvHXOxB551Oqb+T09Pc1Oy71nzx54eJifmacgFixYYLg3PY8QArNmzUKFChXg6emJqKgonDlzxuj7srOzMWrUKAQHB8Pb2xtdu3bF9evGYwGkpKQgJiYG/v7+8Pf3R0xMDO7evVvothIREZH96PRaiw9nEx8fj0uXLiE+Pj7fx6VLlxzd1GLNVnkUYCYlIiIiU/nlUWfLpPbIo1Zf2Th27Fi8+uqrOHr0KJo3bw4gd4yc1atXY8aMGYVqxOHDh/HJJ5+YXJ65aNEiLFmyBLGxsahduzbmzp2LDh064Ny5c4Z71ceOHYstW7bgq6++QlBQEMaPH48uXbrg6NGjUKlye5379OmD69evY+vWrQCAYcOGISYmBlu2bClUe6lgBCeIMUsck3d7l6Mma7AnTba8QeKzU6WVQk623Ode5VH8fpa6+HSp9bQSnzMr757Nl8xBtDMvyxuwOjvDXVotjaTnXuOg95wcbTY0ZibyyXGys8gA8Pzzz2PHjh0ICAjAnDlz8MYbb8DLy8vRzXI6tsijgPNkUq1GgVZf9N9HmQP/F+IaBotkfl4AgIePvFo5F+ROrGJOelrROswLJE3uZ7zUHJOtkVZK5mspPUXepC4ys4fsTCpLusSJWESWvI6crIziOUGMzJ+jStJkkQCQJXOCmFN3pNQR2Y7p2LOUR/PWORN75FGrf9MmT56Mzz//HMePH8fo0aMxevRoHD9+HLGxsZg8ebLVDbh//z769u2LTz/9FAEBAYblQggsW7YMU6dORY8ePRAREYG1a9ciIyMDGzZsAACkpqZi1apVWLx4Mdq3b49GjRph/fr1OHXqFH799VcAwNmzZ7F161Z89tlnaNGiBVq0aIFPP/0UP/74I86dO2d1e4mIiMi+dEJn8eFszp49i/T03D/yZ8+ejfv3bd9xURLJzqMAMykRERFZll8edbZMao88Wqg+7169eqFXr15SGjBy5Eg8++yzaN++PebOnWtYHh8fj8TERHTs2NGwTK1Wo02bNti3bx+GDx+Oo0ePIicnx2ibChUqICIiAvv27UN0dDT2798Pf39/NGvWzLBN8+bN4e/vj3379qFOnTpm25WdnY3s7P/1TqelpUk5XiIiIrKORmf+TLJG51xnkYHcMXJefvllPPnkkxBC4N1334WPj/nLropyhV5pIDOPAsUzkzKPEhERFQ+W8mjeOmdijzwq8QJb63311Vc4duwYDh8+bLIuMTERABASEmK0PCQkBFeuXDFs4+7ubnT2OW+bvO9PTExEuXLlTOqXK1fOsI05CxYskD71NxEREVlPp9dBZ+beeXPLirvY2FjMnDkTP/74IxRFwS+//AJXV9M4pigKOxvtqLhmUuZRIiKi4sFSHs1b50zskUet7mzU6XRYunQpvv76a1y9ehUajfGYGsnJyQWqc+3aNYwZMwbbt2/PdyBvRTEeI0AIYbLsYQ9vY277R9WZMmUKxo0bZ/g6LS0NYWFh+e6XiIiI5MvW6uGmNQ1x2U44NnCdOnXw1VdfAQBcXFywY8cOsx1QlD9ZeRQo3pmUeZSIiKh4sJRH89Y5E3vkUavHbJw9ezaWLFmCXr16ITU1FePGjUOPHj3g4uKCWbNmFbjO0aNHkZSUhCZNmsDV1RWurq7YtWsX3n//fbi6uhrOHj98pjcpKcmwLjQ0FBqNBikpKflu8++//5rs/9atWyZnqB+kVqvh5+dn9CAiIiL70+mFxYcz0+v17GgsJFl5FCjemZR5lIiIqHjIL486cya1VR61+srGL774Ap9++imeffZZzJ49Gy+99BJq1KiBBg0a4MCBAxg9enSB6rRr1w6nTp0yWvbyyy+jbt26mDRpEqpXr47Q0FDExcWhUaNGAACNRoNdu3Zh4cKFAIAmTZrAzc0NcXFxhjF7EhIScPr0aSxatAgA0KJFC6SmpuLQoUN44oknAAAHDx5EamoqWrZsae3hkzW0zvsLZ0t3j8mbYc/BIyHYhUbi8BcaibPPZWZIKwUAUIrhj/LuNXmzLgJyZ5B2kdg0me1Kvu4mrZbM2f9kcdTY11laAVcznylZJeBzZt26dfj4448RHx+P/fv3o0qVKli6dCmqV6+Obt26Obp5xZasPAo4ZyZNSVRBoyr6G6FO4u+0zJlU1V5yP39c1fJmLk2R+D5vSWqK7d/bAmK+klswRm45WS5OjpZWS+Zr3E0t72csM5O6uMo7Rp1O4qzPFq4mK4ycrOI563NxbZdG4p+u96/Ked3fz3FM/rOUR/PWOTNb5FGr3wESExMRGRkJAPDx8UFqaioAoEuXLvjpp58KXMfX1xcRERFGD29vbwQFBSEiIgKKomDs2LGYP38+vvvuO5w+fRoDBw6El5cX+vTpAwDw9/fH4MGDMX78eOzYsQPHjx9Hv379EBkZifbt2wMAwsPD0alTJwwdOhQHDhzAgQMHMHToUHTp0sXi5DBERERUfOhE7vmrhx865851WLFiBcaNG4fOnTvj7t270P235ycgIADLli1zbOOKOVl5FGAmJSIiokezlEedPZPaKo9a3dlYqVIlJCQkAABq1qyJ7du3AwAOHz4MtVpd6IaYM3HiRIwdOxYjRoxA06ZNcePGDWzfvh2+vr6GbZYuXYru3bujV69eaNWqFby8vLBlyxaoHjjb+8UXXyAyMhIdO3ZEx44d0aBBA6xbt05qW4mIiMg2MrWKxYczW758OT799FNMnTrVKLc0bdrU5Eo7MmbPPAowkxIREZV2+eVRZ86ktsqjVl9g+/zzz2PHjh1o1qwZxowZg5deegmrVq3C1atX8frrrxe6IQCwc+dOo68VRcGsWbPyHXvHw8MDy5cvx/Llyy1uExgYiPXr1xepbUREROQYWqFAqzcNcVrhvMEOAOLj4w235T5IrVYjPT3dAS1yHrbMowAzKRERERmzlEfz1jkrW+VRqzsb3377bcP/X3jhBYSFhWHv3r2oWbMmunbtWuiGEBEREZmTpVXgYuaMcZYTn0UGgGrVquHEiROoUqWK0fJffvkF9erVc1CrnAPzKBEREdmTpTyat85Z2SqPFnno0GbNmqFZs2YAcm9defzxx4takshEyrZX7bKf+8kSBzJ24D5KEk9veW/cKQnyRkv2ljwhqC6r+A30cee63Flr/MvKnF1E3vOVnS7vd1Lm4PEuEp9+lUrO82VpUGxby9QpUMwMNJ+pc95gBwATJkzAyJEjkZWVBSEEDh06hC+//BILFizAZ5995ujmOZXSlkcv/aPAUyn669/DQy+hNbm0Ev/QcnGV+/kj8/Psfprt3weTbnvYfB+lRXqavNdlUqK8n325UHntkvma9PJzmtMAAQAASURBVAuQVgpqtcTcp5X3XqXRyMt93sX0V1Unb04suLvLq3U/Rc57cbrE47OGpTyat85Z2SqPWv3Tvn//PlQqFTw9PQ3LTpw4genTp+Pnn382DCZJREREJINOb/7vDJ28vz0c4uWXX4ZWq8XEiRORkZGBPn36oGLFinjvvffw4osvOrp5xRrzKBEREdmTpTyat85Z2SqPFrhb//r162jVqhX8/f3h7++PcePGISMjA/3798fjjz8OtVqNPXv2FLohREREROaUxMG4tVot1q5di+eeew5XrlxBUlISEhMTce3aNQwePNjRzSu2mEeJiIjIEUriBDG2zKMFvrJx8uTJuH//Pt577z18++23eO+997Br1y40bNgQ58+fR7Vq1YrUECIiIiJzcvQKXM0MyJ1jYZBuZ+Dq6opXX30VZ8+eBQAEBwc7uEXOgXmUiIiIHMFSHs1b54xsmUcL3Nn4+++/4+uvv0arVq3wwgsvoEKFCujZsycmT54srTFERERED8vSAzBzV2yWE9+yAuSOM3j8+HGTAbnJMuZRIiIicgRLedSwzknZKo8WuLMxMTERNWrUAACEhobC09MT3bp1k9oYIkvEyWt22U9qiu0H/c4s/OzxJnwkT1JSHMmciOXf6/Jq1Wggd2TinPTidzbslsSB0AHAJ7D4HSMAZKTJGyhc5qQu7mp5z7/aS04C0kscoN0aOXpAZWbXOU4c7ABgxIgRGD9+PK5fv44mTZrA29vbaH2DBg0c1LLii3k0V5/b38LPr+SGgG1Vo6XWi768TWo9ch4yJ095IVne60jma/y5hOL5+v7HX94xihx5P8e257ZKq1UaHGjUSVqtiB3bpdRJS0sD/P2l1LKGpTyat85Z2SqPWvVnkUqlMvzfxcUFHh7FdPolIiIiKjGytAqEmbFwsp10fJw8vXv3BgCMHj3asExRFAghoCgKJzmxgHmUiIiI7M1SHgWcO5PaKo8WuLNRCIF27drB1TX3WzIzM/Hcc8/B/aG50I8dO1aohhARERGZk6MHXErglY3x8fGOboLTYR4lIiIiR7CUR/PWOStb5dECdzbOnDnT6OvSeMsKERER2V+GFtCZGbkgW+5oBnbHsRqtxzxKREREjmApjwLOnUltlUcL3dlIREREZA86PWBuuEidE59FpsJhHiUiIiJHsJRH89aRMYlD2RMZU2qWkVfsbpa8Wvmo1cj2pySun3OTViukavE8hXJ39zhptWROknE7o6y0WpXSk6TVAgD/UHk/S1mDL0dIqWIbMgerbn68eA4Ufun5dtJquajk/B5pcxwzhmCmzgU6relEPhqdvMl9iKj4CA51dAuopKhQ1dEtMC+kkqNbYHtq/9rSaoms09JqkXVkTn7o7CzlUYCZ1Bw+I0RERFSsafWKxUdhfPTRR6hWrRo8PDzQpEkT7N69W3KLiYiIiKgkyS+PFiaTlvQ8ys5GIiIiKtY0WhWyzTw0WtWjv/khGzduxNixYzF16lQcP34cTz31FJ555hlcvXrVBi0nIiIiopLAUh4tTCYtDXmUnY1ERERUrOl0KosPay1ZsgSDBw/GkCFDEB4ejmXLliEsLAwrVqywQcuJiIiIqCTIL49am0lLQx4t0B34gYGBOH/+PIKDgzFo0CC899578PX1tXXbiIiIiJCT4wrkmI53m5OTAwBIS0szWq5Wq6FWq02212g0OHr0KCZPnmy0vGPHjti3b5/EFlsWEBAARSnYrTbJyck2bo1zYR4lIiIiR7GUR3PXFTyTlpY8WqDORo1Gg7S0NAQHB2Pt2rVYuHAhwx0RERHZhU6ngouZM8Z5Z5HDwsKMls+cOROzZs0y2f727dvQ6XQICQkxWh4SEoLExER5Dc7HsmXLDP+/c+cO5s6di+joaLRo0QIAsH//fmzbtg3Tp0+3S3ucCfMoEREROYqlPJq3DihYJi0tebRAnY0tWrRA9+7d0aRJEwghMHr0aHh6eprddvXq1YVuDJUsqsflzWWrP/W3tFr5CYgJtPk+XL++Iq2WT7Pi+UeWuHxTWi1XibNRN4l4XFqthOs/S6sFABUaFm6ii9Lq+hV5r4viyre83tFNMKHXOOZ512ndoeS4m1meO4v7tWvX4OfnZ1hu7qrGBz18JlcIUeCzu0U1YMAAw///7//+D3PmzMFrr71mWDZ69Gh88MEH+PXXX/H666/bpU3Ognm09Agor3V0E6iEKFcj29FNMCu4isbRTbC5urXrSqul156SVous4y7xbzFnZymP5q6zPpOW9DxaoDEb169fj86dO+P+/ftQFAWpqalISUkx+yAiIiKSSZvjbvEBAH5+fkYPS8EuODgYKpXK5KxxUlKSydlle9i2bRs6depksjw6Ohq//vqr3dtT3DGPEhERkaPkl0etyaSlJY8W6MrGkJAQvP322wCAatWqYd26dQgKCir0TomIiIgKSuhUEGZuWzG3LD/u7u5o0qQJ4uLi8PzzzxuWx8XFoVu3bkVup7WCgoLw3XffYcKECUbLv//+e+YsM5hHiYiIyFEs5dG8dQVVWvJogTobHxQfH1/onRERERFZS5+jgl5lGln0OdbPRj1u3DjExMSgadOmaNGiBT755BNcvXoVr7zyioymWmX27NkYPHgwdu7caRgj58CBA9i6dSs+++wzu7fHmTCPEhERkT1ZyqN566xRGvKo1Z2NALBr1y68++67OHv2LBRFQXh4OCZMmICnnnqq0A0hIiIiMkvnkvswt9xKvXv3xp07dzBnzhwkJCQgIiICP//8M6pUqSKhodYZOHAgwsPD8f7772PTpk0QQqBevXrYu3cvmjVrZvf2OBvmUSIiIrIbS3k0b50VSkMetbqzcf369Xj55ZfRo0cPjB49GkII7Nu3D+3atUNsbCz69OlT6MY4gztTnoNGXag+WiPBS3ZIaE3x5hZUQVqtbL/r0mrlx61uuM334X1U3uQpSkV/abUSB0VJq+XeqIy0Wl5+Omm1FL9gabV0V6WVAgAIjoVvlTIBjm6B7anKe8grVknOZFIumTnAKimlrKLSuEDlYhrihMb6zkYAGDFiBEaMGFHUZknRrFkzfPHFF45uhtMp7Xm0pHP3Kn4TZJU0KT8Nk1swPVNaKf2f/0qr5R5k/RXw9qCWF+GLLRcJfzMbarlyIkVHkTlZp7OzlEeBwmXSkp5HrX4HmDdvHhYtWmQ0I82YMWOwZMkSvPXWWwx3REREJJWLLvdhbrmzu3jxItasWYNLly5h2bJlKFeuHLZu3YqwsDDUr1/f0c0rtphHiYiIyJ4s5dG8dc7MFnnU6u7XS5cu4bnnnjNZ3rVrV46fQ0RERNK55wBqjenDPcfRLSuaXbt2ITIyEgcPHsS3336L+/fvAwBOnjyJmTNnOrh1xRvzKBEREdmTpTzq7JnUVnnU6s7GsLAw7Nhhegvwjh07EBYWVuiGEBEREZmj0gmLD2c2efJkzJ07F3FxcXB3dzcsb9u2Lfbv3+/AlhV/zKNERERkT/nlUWfOpLbKo1bfRj1+/HiMHj0aJ06cQMuWLaEoCvbs2YPY2Fi89957hW4IERERkTke2Xq4wXQMN1W2c4/rdurUKWzYsMFkedmyZXHnzh0HtMh5MI8SERGRPVnKo4BzZ1Jb5VGrOxtfffVVhIaGYvHixfj6668BAOHh4di4cSO6detW6IY4C02yDhp3DlBrd2r3R28jgXu5yjbfhyZY4qQPOnmDQ7hX85RWSybvsvKOMSNbWin4B8h9H8hMllquxKv9uMbRTbA9Hzdppdzbypmd1/1eJoDvpdSyhqUzxnonPosMAGXKlEFCQgKqVatmtPz48eOoWLGig1rlHEp7HiUqsuQ0ufXKy5uEL/2ivIkhfepLzN0SKfLmTim2cu5lSavl4ls8J/opDdzUztuJJlt+VzA6cya1VR4t1Nvc888/j+eff77QOyUiIiIqKHeNDu4wPfGgaJx7NO4+ffpg0qRJ+M9//gNFUaDX67F371688cYb6N+/v6ObV+wxjxIREZG9WMqjgHNnUlvlUevn5yYiIiKyo9zZ/4SZh6NbVjTz5s1D5cqVUbFiRdy/fx/16tVD69at0bJlS0ybNs3RzSMiIiKi/7KcR507k9oqj5aCC7iJiIjImblqtHCF1mS5XmO6zJm4ubnhiy++wFtvvYVjx45Br9ejUaNGqFWrlqObRkREREQPsJRHAefOpLbKow69snHBggV4/PHH4evri3LlyqF79+44d+6c0TZCCMyaNQsVKlSAp6cnoqKicObMGaNtsrOzMWrUKAQHB8Pb2xtdu3bF9evGY32kpKQgJiYG/v7+8Pf3R0xMDO7evWvrQyQiIqIiUnR6iw9nNmfOHGRkZKB69ep44YUX0KtXL9SqVQuZmZmYM2eOo5tXajCPEhER0aPkl0edOZPaKo8qQgiHjWTZqVMnvPjii3j88ceh1WoxdepUnDp1Cn/99Re8vb0BAAsXLsS8efMQGxuL2rVrY+7cufjjjz9w7tw5+Pr6AsgdJHzLli2IjY1FUFAQxo8fj+TkZBw9ehQqVe5gss888wyuX7+OTz75BAAwbNgwVK1aFVu2bClQW9PS0uDv749DbaPg41r0C0LDt8cVuUZxl6X9UV6tw7uk1cqP3+PtbL6Pu2s+lVZLKVdGWi2RdFdaLWTJu45cJGZIq/X39y2k1XLzOiitFgAEV8qRVqv6dzuk1NlWNVpKnTwVa8j7uCnfXFop3Dsn70xk1W9+k1Yr5esYabU8e/SWUictLQMhQb2RmpoKPz8/KTXz31/uZ+/LPdfC3c3LZL0mJwNr/jPAbu2RTaVSISEhAeXKlTNafufOHZQrVw46iZOAkWXOmEf3tmwrJY/mZMub7Ewl8X6p4CpyJwHTO9mv0uWTapvvI2KC3PdMl7pVpNW6OvlPabUqvegtrVbajlRptXxryLvmJ+m4vN9jme8JWXflTE4HADVf2Cet1vVfJU4+mSbv56iTeGGczPhQvoa8hqUkyvmguK/Tot3xHcUmjwLOnUltlUet/u3I6/V8WGF6Pbdu3YqBAweifv36aNiwIdasWYOrV6/i6NGjAHLPIi9btgxTp05Fjx49EBERgbVr1yIjI8MwNXdqaipWrVqFxYsXo3379mjUqBHWr1+PU6dO4ddffwUAnD17Flu3bsVnn32GFi1aoEWLFvj000/x448/mpy5JiIiouJF6HUWH85MCAFFMf3D7s8//0RgYKADWuQ8mEeJiIjInvLLo86cSW2VR63ubJw9ezbu379vsjwjIwOzZ88udEOA3KAGwHBA8fHxSExMRMeOHQ3bqNVqtGnTBvv25Z7dOHr0KHJycoy2qVChAiIiIgzb7N+/H/7+/mjWrJlhm+bNm8Pf39+wzcOys7ORlpZm9CAiIiL702lzoNNqzDzkXRFsTwEBAQgMDISiKKhduzYCAwMND39/f3To0AG9evVydDOLNeZRIiIisifLedQ5M6mt86jV17HaqtdTCIFx48bhySefREREBAAgMTERABASEmK0bUhICK5cuWLYxt3dHQEBASbb5H1/YmKiySWhAFCuXDnDNg9bsGBBkcMqERERFZ1Wlw0XF9Pzo1pdtgNaU3TLli2DEAKDBg3C7Nmz4e/vb1jn7u6OqlWrokULecM+lETMo0RERGRPlvJo3jpnY+s8WuDOxoCAACiKYuj1fDDg6XQ63L9/H6+88kqhG/Laa6/h5MmT2LNnj8m6h8OkpYCZ3zbmts+vzpQpUzBu3DjD12lpaQgLC8t3n0RERCSfpdtTnPWWlQEDBgAAqlWrhlatWsFVwth7pQXzKPMoERGRI+R3u7QzZlJb59ECV7Nlr+eoUaOwefNm/PHHH6hUqZJheWhoKIDcM8Hly5c3LE9KSjKcXQ4NDYVGo0FKSorR2eSkpCS0bNnSsM2///5rst9bt26ZnKXOo1aroVabDs58+4YKmf8d5LsowotcofhbdfyqtFoxZQIevZEMKjtM0K6VN1OVa3gdabW0GSek1RJmbm0rLMWj6L9veXRC3qQuGsl3slUp63wfUNbyCZA3wHT2dZkzvtnh974QXOpUl1br1r2bUurcu5cppY61dDoNtGbOJOt0cieRsLf09HTs2LED0dHGkzFt27YNer0ezzzzjINaVnwxj+ZyUwu4uRZ90i21t7z30vvJ8j6vZdNmy3ufd1XbfsZRd9vPDyN1kkEAgIS/j/LodfImKVHKeEqr5aq+K60WJOZbF1d5GdJTLW8yv0vxSdJq1ZT4fKm95d3uKnOCGE0xjTQqCZ81efSS/hQQDvqzyVIezVvnrGyVRwvc2fhgr2fLli3h5uZWqB0+SAiBUaNG4bvvvsPOnTtRrVo1o/XVqlVDaGgo4uLi0KhRIwCARqPBrl27sHDhQgBAkyZN4Obmhri4OMP95AkJCTh9+jQWLVoEAGjRogVSU1Nx6NAhPPHEEwCAgwcPIjU11RAAiYiIqHjS63XQmzljbG6ZM5k8eTLefvttk+VCCEyePJmdjWYwjxIREZEjWMqjeeucla3yqNXXSbZp0wZ6vR7nz59HUlIS9Hrjs3utW7cucK2RI0diw4YN+OGHH+Dr62sYr8bf3x+enp5QFAVjx47F/PnzUatWLdSqVQvz58+Hl5cX+vTpY9h28ODBGD9+PIKCghAYGIg33ngDkZGRaN++PQAgPDwcnTp1wtChQ7Fy5UoAwLBhw9ClSxfUqSPvyjAiIiKST6vPhmLmShet3vnGx3nQhQsXUK9ePZPldevWxT///OOAFjkP5lEiIiKyJ0t5NG+ds7JVHrW6s/HAgQPo06cPrly5AiGML6lVFAU6XcF7dFesWAEAiIqKMlq+Zs0aDBw4EAAwceJEZGZmYsSIEUhJSUGzZs2wfft2+Pr6GrZfunQpXF1d0atXL2RmZqJdu3aIjY2F6oHL+b/44guMHj3aMEtg165d8cEHH1hz6EREROQAeqGD3sw9M+aWORN/f39cunQJVatWNVr+zz//wNvb2zGNchLMo0RERGRPlvJo3jpnZas8anVn4yuvvIKmTZvip59+Qvny5R85MHZ+Hg6H5iiKglmzZmHWrFkWt/Hw8MDy5cuxfPlyi9sEBgZi/fr1hWkmEREROZBOlwMXxdyYjfLGXXKErl27YuzYsfjuu+9Qo0YNALnBbvz48ejatauDW1e8MY8SERGRPVnKo3nrnJWt8qjVnY0XLlzAN998g5o1axZ6p0REREQFpddrodebDg6vlzXSuIO888476NSpE+rWrWuYkOT69et46qmn8O677zq4dcUb8ygRERHZk6U8mrfOWdkqj1rd2disWTP8888/pTbcXYlX4FmEs+d5Cj6SkPM6vrraozcqoMFzk6XVyo+8ue/ycV/eWQ/dzRvSahVXSk15M5HXeVLOjLwAcG6Ph7RaAJB8ueiTHOTJ6dtWSh13MzOgFkWOxNlAPfzkzQYqc9ZLmbxqNZZWa8Y+Oa/97HTH3CKi1WnMXrmmdeKZ/4Dc21b27duHuLg4/Pnnn/D09ESDBg2sGm+wtCrtedTdSw+1W9HfB2XOrJySYPWfFRbJHmc/J1ve+7yrHWaK9vCWN/urJUqg/6M3chCNxJ8X/HyklXKVOFOz4iHv98VNLa+Tw91L3ntC6r8Sxx72kPeL5+kn7w3m9jV5P8fMdHmvL09vme958l4TOkkvVStGSpHKUh7NW+esbJVHC/TbcfLkScP/R40ahfHjxyMxMRGRkZEmswA2aNCgSA0iIiIiepCwMPufcOKZ//IoioKOHTuidevWUKvVRboduKRjHiUiIiJHsZRH89Y5M1vk0QJ1Nj722GNQFMVoTJtBgwYZNUwIYfWA3ERERESPotNpYC7z6Jz4LDIA6PV6zJs3Dx9//DH+/fdfnD9/HtWrV8f06dNRtWpVDB482NFNLFaYR4mIiMhRLOXRvHXOylZ5tECdjfHx8YUqTkRERFRUJXU26rlz52Lt2rVYtGgRhg4dalgeGRmJpUuXsrPxIcyjRERE5CgldTZqW+XRAnU2VqlSpVDFiYiIiIpKq9cAZjKcVu+8Z5EB4PPPP8cnn3yCdu3a4ZVXXjEsb9CgAf7++28Htqx4Yh4lIiIiR7GURw3rnJSt8qjVI5pu3rzZ7HJFUeDh4YGaNWuiWjV5E4MUN4p3IBRF3kQOJdlnK56RVuvu0anSauUn67zt3ySUKvImPNHHJ0irpVQIklYL19Pk1ZI4gLlr2dvSaoVUlTvjmIv5ic0KxUPSj9IvQO4A9TKHMnErK29Abi/I+1leer6dtFrVv9shrdZ77eXUSUtLw0qMl1PMCnq9DnrFzJWNNhof5/Lly3jrrbfw22+/ITExERUqVEC/fv0wdepUuLu7G7a7evUqRo4cid9++w2enp7o06cP3n33XaNt8nPjxg2zE5zo9Xrk5MibTKwkKu15NPOeCirXon9wBFeR9zvkIu9tGXqt3LFLZX7G2kPFBnb4o1Ur+f1TJe9J9pA4SQmy5T2XWokT17j7yPx7MktaJbXEeYMaNZf4GvMp2OdqQaj95OVbmZM5aTTyXl+eXtJKSSXr+dJqbT+JljmW8mjeOluwRya1VR61OhZ0797dZLwcwHicnCeffBLff/89AgLkdaoQERFR6aTT5cBcBNfpbNMh9/fff0Ov12PlypWoWbMmTp8+jaFDhyI9PR3vvvvuf/etw7PPPouyZctiz549uHPnDgYMGAAhBJYvX16g/dSvXx+7d+82uWLvP//5Dxo1aiT9uEoS5lEiIiKyJ0t5NG+dLdgjk9oqj1rd2RgXF4epU6di3rx5eOKJJwAAhw4dwrRp0zB9+nT4+/tj+PDheOONN7Bq1apCN4yIiIgIyBsjx8XsciD3issHqdVqqNXqQu+vU6dO6NSpk+Hr6tWr49y5c1ixYoUh2G3fvh1//fUXrl27hgoVKgAAFi9ejIEDB2LevHnw8/N75H5mzpyJmJgY3LhxA3q9Hps2bcK5c+fw+eef48cffyx0+0sD5lEiIiKyJ0t5NG8d4JyZ1FZ51PwzlY8xY8ZgyZIlaNeuHXx9feHr64t27drh3XffxYQJE9CqVSssW7YMcXFxhW4UERERUR69TgOdmYf+vzP/hYWFwd/f3/BYsGCB9DakpqYiMDDQ8PX+/fsRERFhCHUAEB0djezsbBw9erRANZ977jls3LgRP//8MxRFwYwZM3D27Fls2bIFHTp0kH4MJQnzKBEREdmTpTzq7JnUVnnU6isbL168aLZn1M/PD5cuXQIA1KpVC7dvyxsbjYiIiEovrU4DAdPxefJuWbl27ZpRNinKGWRzLl68iOXLl2Px4sWGZYmJiQgJCTHaLiAgAO7u7khMTHxkTa1Wi3nz5mHQoEHYtWuX1PaWBsyjREREZE+W8ijgvJnUlnnU6s7GJk2aYMKECfj8889RtmxZAMCtW7cwceJEPP744wCACxcuoFKlSlIbWlzosrKhUyQOWkwFov3igF32o+ocbpf9yKIE+kqr5VannrRaOUkp0mqJq0nSainBHtJqBUdmSKsFADm35E1SonjKGaU/pIbcAerv3ZI4e4CFD3pHc1EVz3Y5O73QQxFmJogRuZ/Hfn5+BbptedasWZg9e3a+2xw+fBhNmzY1fH3z5k106tQJPXv2xJAhQ4y2VRTTkXvyxgt8FFdXV7zzzjsYMGDAI7clU6U9j2bcc4GLyuoblEy4lnGymVMKSeXqXO/NqkaBj96oBHNTS/x5SZwgRpNR9N+5PF4exfN3z8VXXrsqRsmb8ETxlzfjicpH3jGqJU4Q4ylxAiKpv0MSyWqXm4PytqU8mrcOcL5Mass8avVffqtWrUK3bt1QqVIlhIWFQVEUXL16FdWrV8cPP/wAALh//z6mT58uvbFERERU+uh0GgCmJ/p0Ous66V977TW8+OKL+W5TtWpVw/9v3ryJtm3bokWLFvjkk0+MtgsNDcXBgweNlqWkpCAnJ8fk7LIl7du3x86dOzFw4MACbU//wzxKRERE9mQpj+auc95Maqs8anVnY506dXD27Fls27YN58+fhxACdevWRYcOHeDiknu2p3v37lIbSURERKWXXuig6E3PzOotnF22JDg4GMHBwQXa9saNG2jbti2aNGmCNWvWGDJOnhYtWmDevHlISEhA+fLlAeQO0K1Wq9GkSZMC7eOZZ57BlClTcPr0aTRp0gTe3t5G67t27VqgOqUR8ygRERHZk6U8mrfOGsUpk9oqjxbqnjZFUUxmxSEiIiKyBZ1OAwgzVzbq5Q0/8KCbN28iKioKlStXxrvvvotbt24Z1oWGhgIAOnbsiHr16iEmJgbvvPMOkpOT8cYbb2Do0KEFun0GAF599VUAwJIlS0zWKYoCnc664FraMI8SERGRvVjKo4BzZ1Jb5dECdTa+//77GDZsGDw8PPD+++/nu+3o0aML1RAiIiIic4TQmz1jLCwEvqLavn07/vnnH/zzzz8mY/4JkTtOkEqlwk8//YQRI0agVatW8PT0RJ8+ffDuu+8WeD96PceAtgbzKBERETmKpTyat84W7JFJbZVHFZHXwnxUq1YNR44cQVBQEKpVq2a5mKIYZgAsadLS0uDv748Vbu3hqRR9koMX/x1Y9Eb9l7pMb2m1Oq/7j7RaP8f0lFbr8gtPS6uVH///Z+++45uqGjeAP+lKd+mAlkIpe7ZsZSktMoqIgLwvICBDUVBGmTKULUNQAV8QRGQJMt5XhoCKDKHsTWWKiGVJa5ktdKVNzu+P/hoJSdqkPVnt8/188oHce3PuOblp+vTce8/5V4jld5Ij8Yc5wLSrZ0zh2aK1tLLSd+2QVhaysuUVta/gGWJN5VbLW1pZAKBOSJNXVqacQZNlTxxw57S88kLryftcCIknIrNS5JXluqS/tLIG/ihncp7s9HT8NGAgUlJSTL56ryjyfveGh0bAyUn/86PRqHHjzgWr1UemnJwcuLu7Iz4+HhEREbaujkNgHv3nZ8IRP/NEREQyWPt3YUF5FHDcTGrJPGrSXx8JCQkG/09ERERkaRqhNjgBubnj49gTFxcXhIeH81ZpMzCPEhERka0Yy6PadQ7IknnUqeBNDFOpVLhy5QpycixzbzoRERERAKjV2UYfjmzixImYMGECHjx4YOuqOCzmUSIiIrKG/PKoI2dSS+VRs++rSk9Px7Bhw7B69WoAwO+//47KlSsjNjYWoaGhGD9+vNQKEhERUcmmERojVzY69piH//nPf/DHH38gNDQU4eHherP/nTlzxkY1s3/Mo0RERGRNxvKodp2DslQeNbuzccKECfj111+xf/9+ndn/2rRpgylTpjDcERERkVRqdTaE0L8Zw9EnWOnSpYutq+CwmEeJiIjImozlUcCxM6ml8qjZnY1bt27Fxo0b0bRpUygUCu3y2rVr49q1a1IrR0RERKQRakCjfyrZkc8iA8CUKVNsXQWHxTxKRERE1mQsj+auc9xMaqk8anZn4927d1GmTBm95WlpaTphr7gKCtLA06noH6Ts+3ck1CaXspS0oqTOIC2Ti1LO7LoFEU+yLL4P16gm0srKOS7vFrt0N2lFAX7yZmoWvydKK+vXX5TSynquktxBdJ38XaWVlX1bJaUchavc7/TsLInluRR6yGE9Tv5yZmoGgJxkeeO1XLv3UFpZr1ST8/nKeJKBn6SUZB6NOhtCoX/MhQMHu6edPn0aly9fhkKhQO3atdGgQQNbV8nulfQ8SkRERNZlLI8CxSOTys6jZv+F9dxzz+GHH37AsGHDAEAb6JYtW4ZmzZoVqTJEREREz1Kr1VAo9E86OXqwS05Oxuuvv479+/ejVKlSEEIgJSUFrVq1woYNG1C6dGlbV9FuMY8SERGRNRnLo4BjZ1JL5VGzOxtnz56N9u3b49KlS8jJycHnn3+Oixcv4ujRo4iLiytUJYiIiIiMyc7JNni1mhDWuerdUoYNG4bU1FRcvHgRtWrVAgBcunQJ/fr1Q2xsLNavX2/jGtov5lEiIiKyJmN5FHDsTGqpPGr2fWjNmzfH4cOHkZ6ejipVqmDXrl0IDg7G0aNH0ahRo0JVgoiIiMgYtTrH6MOR7dy5E0uWLNEGOyB3zMEvvvgCP/1kixvWHQfzKBEREVlTfnnUkTOppfJooQaqioyMxOrVqwu9U0eU11OdrpHzIUp9nCmlHADQpKZKK8tePc62zg9vToa88daMcX0i79hLrW9quryy0uSMGQgAQmIb0yT9/AJAaqa8MQMBAFnyxoDMUslpp0ryEKZPcuSd8XucJe9YKiSOVyrzuypN4u+JjCdy6pWZllsna5+9VeVYfjxdW9BoNHB11R9P09XV1aFnNbSWkpxHU0tA9iMiIjIk73cg86gclsqjCmHiETI11Pj6+ha6Mvbs9u3bCAsLs3U1iIiIbO7WrVsoX768xfeTmZmJSpUqISkpyeg2ISEhSEhIgLu7u8XrI1vnzp3x6NEjrF+/HqGhoQCAv/76C71794a/vz+2bNli4xraH+ZR5lEiIiLAvvIo4LiZ1FJ51OTORicnp3xn9xNCQKFQQK2WO0OrvdBoNLhz5w58fHzw+PFjhIWF4datW8U2zKamprKNxURJaCfbWDywjfZPCIHHjx8jNDQUTk6Sr+41IjMzEyqV8aul3dzcHC7U5bl16xY6d+6MCxcuICwsDAqFAjdv3kRkZCS+//57qwRoR8M8+k8eVSgUDv+dYgq2sXhgG4sHtrF4cPQ22mMeBRw3k1oqj5p8G/W+ffu0/xdCoEOHDvj6669Rrly5Qu3Y0Tg5OWnf5LyQ6+vr65A/nOZgG4uPktBOtrF4YBvtm5+fn1X35+7u7pDBzRRhYWE4c+YMdu/ejd9++w1CCNSuXRtt2rSxddXsFvOok8HQ78jfKaZiG4sHtrF4YBuLB0duI/OoPJbKoyZ3NkZFRek8d3Z2RtOmTVG5cuUiVYCIiIioJGvbti3atm1r62o4BOZRIiIiIvlk51HrXHNKRERERACAX375BbVr1zY4/mBKSgrq1KmDgwcP2qBmRERERFQSWDqPsrOxEJRKJaZMmQKlUmnrqlgM21h8lIR2so3FA9tIJcWCBQvwzjvvGLx1yc/PD4MGDcK8efNsUDNyNCXhO4VtLB7YxuKBbSweSkIbqWCWzqMmTxDzLB8fH5w7dw6VKlUq9M6JiIiISprw8HDs3LkTtWrVMrj+t99+Q7t27XDz5k0r18zxMI8SERERmc/SedTkMRu7du2q8zwzMxPvvvsuvLy8dJZv3ry5UBUhIiIiKgn+/vtvuLq6Gl3v4uKCu3fvWrFGjoN5lIiIiKjoLJ1HTe5sfHa2nzfeeKPQOyUiIiIqqcqVK4fz58+jatWqBtefO3cOZcuWtXKtHAPzKBEREVHRWTqPFvo2aiIiIiIy37Bhw7B//36cPHkS7u7uOusyMjLw/PPPo1WrVvjPf/5joxoSERERUXFm6TzKzkYiIiIiK/r777/RsGFDODs7Y+jQoahRowYUCgUuX76ML774Amq1GmfOnEFwcLCtq0pERERExZCl82iJnI168eLFqFSpEtzd3dGoUSOd6byFEJg6dSpCQ0Ph4eGB6OhoXLx4scAyz58/j6ioKHh4eKBcuXKYPn06nu3HjYuLQ6NGjeDu7o7KlSvjyy+/lN62PLLbeOXKFbRq1QrBwcHa+k+cOBHZ2dk629lLGzdv3oyYmBgEBQVBoVAgPj7epDId6TgWpo32eBwB4+3Mzs7GuHHjEBkZCS8vL4SGhqJv3764c+dOgWU6yrEsbBvt8Vjm93mdOnUqatasCS8vL/j7+6NNmzY4fvx4gWU6ynEECtdGezyOZHnBwcE4cuQIIiIiMGHCBLz22mvo0qULPvjgA0RERODw4cPsaCwBSkIeBZhJmUkNc6TjyDxqnCMdR4B51Bh7PI5keRbPo6KE2bBhg3B1dRXLli0Tly5dEsOHDxdeXl7ixo0bQgghPv74Y+Hj4yM2bdokzp8/L3r06CHKli0rUlNTjZaZkpIigoODxeuvvy7Onz8vNm3aJHx8fMSnn36q3ebPP/8Unp6eYvjw4eLSpUti2bJlwtXVVXz33XcO0cZr166JFStWiPj4eHH9+nXx/fffizJlyogJEybYZRu/+eYbMW3aNLFs2TIBQJw9e7bAMh3tOBamjfZ2HAtq56NHj0SbNm3Exo0bxW+//SaOHj0qmjRpIho1apRvmY50LAvbRns7lgV9Xr/99luxe/duce3aNXHhwgUxYMAA4evrK5KTk42W6UjHsbBttLfjSNb34MEDceLECXH8+HHx4MEDW1eHrKQk5FFLtdPevjeZSYtHJmUeZR41xpGOY2HbaG/HkazPEnm0xHU2Pv/88+Ldd9/VWVazZk0xfvx4odFoREhIiPj444+16zIzM4Wfn5/48ssvjZa5ePFi4efnJzIzM7XLZs+eLUJDQ4VGoxFCCDF27FhRs2ZNndcNGjRING3aVEazdFiijYaMHDlSvPDCC9rn9tLGpyUkJJgcehzpOD7NnDYaYsvjKITp7cxz4sQJAUD7C9UQRz2WeUxpoyGO8DOZJyUlRQAQe/bsMVqmox9HU9poiK1/JonI8kpCHhWCmfRpzKQFc4TjmId51DhHOo7Mo8Yxj1JRlajbqFUqFU6fPo127drpLG/Xrh2OHDmChIQEJCUl6axXKpWIiorCkSNHtMv69++P6Oho7fOjR48iKioKSqVSuywmJgZ37tzB9evXtds8u9+YmBicOnVK7/Jke2zjs/744w/s3LkTUVFR2mX20kZTOfJxNJU9H0egcO1MSUmBQqFAqVKltMuK27E0pY3PcqSfSZVKha+++gp+fn6oV6+ednlxOo6mtvFZtv6ZJCLLKwl5FGAmZSbVVdyOI/OoYY50HJlHo43uh3mUZChRnY337t2DWq3Wu+88ODgYSUlJSEpK0j43tD5P2bJlUaFCBe3zpKQkg6/JW5ffNjk5Obh3714RW/YPS7UxT/PmzeHu7o5q1arhxRdfxPTp07Xr7KWNpnLk42gqez6OgPntzMzMxPjx49GrVy/4+vpqlxenY2lqG/PYw7E0tY07duyAt7c33N3dMX/+fOzevRtBQUHa9cXhOJrbxjz2cByJyDpKQh4FmEmZSXUVp+PIPOrYx5F51L6PIxUfLraugC0oFAqd50IInWUFrZ89e7ZJZT673JRtZLFEGwFg48aNePz4MX799Ve8//77+PTTTzF27Nh8yzW0XIaC2lCQ4nAcC+IIx9HY/p5dlp2djddffx0ajQaLFy/WWVdcjqW5bQTs61gW1MZWrVohPj4e9+7dw7Jly9C9e3ccP34cZcqUAVA8jmNh2gjY13EkIusoCXnU2P6YSXU5wrEsCZmUeTQX86jjH0fmUbIHJaqzMSgoCM7Oznpnb5KTkxEcHIyQkBAAub32ZcuW1VtvTEhIiMEygX/OehjbxsXFBYGBgYVv1DMs1cY8YWFhAIDatWtDrVZj4MCBGD16NJydne2mjYXlSMexqOzhOAKmtzM7Oxvdu3dHQkICfvnlF50zrIY44rE0t4157OFYmtpGLy8vVK1aFVWrVkXTpk1RrVo1LF++HBMmTDBYriMeR3PbmMcejiMRWUdJyKMAMykzqWkc6TgyjxrnSMeRedQ4eziOVHyUqNuo3dzc0KhRI+zevVtn+e7du9G8eXNUqlQJISEhOutVKhXi4uLQvHlzo+U2a9YMBw4cgEql0i7btWsXQkNDUbFiRe02z+53165daNy4MVxdXSW0Lpel2miIEALZ2dnaMxr20sbCcqTjKJOtjiNgWjvzQs/Vq1exZ88ek36ZOdqxLEwbDXG0n0khBLKysoyud7TjaEhBbTT2Glv9TBKR5ZWEPAowkzKTms+ejyPzqOns+Tgaqy/zqOHXMI9SkRR9jhnHkjdV/PLly8WlS5fEiBEjhJeXl7h+/boQQoiPP/5Y+Pn5ic2bN4vz58+Lnj17irJly4rU1FRtGePHjxd9+vTRPn/06JEIDg4WPXv2FOfPnxebN28Wvr6+4tNPP9VukzdV/MiRI8WlS5fE8uXLLTZVvCXauHbtWrFx40Zx6dIlce3aNfHf//5XlCtXTvTu3dsu23j//n1x9uxZ8cMPPwgAYsOGDeLs2bMiMTHRaBsd7TgWpo32dhwLamd2drbo1KmTKF++vIiPjxeJiYnaR1ZWltF2OtKxLGwb7e1Y5tfGJ0+eiAkTJoijR4+K69evi9OnT4sBAwYIpVIpLly4YLSNjnQcC9tGezuORGQdJSGPWqqd9va9yUxaPDIp8yjzqLE2OtJxZB4le1LiOhuFEOKLL74Q4eHhws3NTTRs2FDExcVp12k0GjFlyhQREhIilEqlaNmypTh//rzO6/v16yeioqJ0lp07d068+OKLQqlUipCQEDF16lSh0Wh0ttm/f79o0KCBcHNzExUrVhRLlixxmDZu2LBBNGzYUHh7ewsvLy9Ru3ZtMWvWLJGRkWGXbVy5cqUAoPeYMmWK0TYK4VjHsTBttMfjmF87ExISDLYRgNi3b5/RdgrhOMeysG20x2NprI0ZGRnitddeE6GhocLNzU2ULVtWdOrUSZw4cULn9Y58HAvbRns8jkRkHSUhjwrBTMpMariNjnQcmUeNt9GRjiPzqPE22uNxJMenEOL/r4slIiIiIiIiIiIiKoISNWYjERERERERERERWQ47G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NRMVIdHQ0RowYYetqGJSUlIS2bdvCy8sLpUqVMrhN//790aVLlyLvS6FQYOvWrQCA69evQ6FQID4+vlCvN6QwZZqiYsWKWLBggdQyjdm/fz8UCgUePXpklf1Z0+zZs/Hcc8/Bx8cHZcqUQZcuXXDlyhWdbYQQmDp1KkJDQ+Hh4YHo6GhcvHhRu/7BgwcYNmwYatSoAU9PT1SoUAGxsbFISUnRKadixYpQKBQ6j/Hjx+dbv7z33t/fH5mZmTrrTpw4oS3HErKysjBs2DAEBQXBy8sLnTp1wu3bt3W2KUybiIiInsZMmouZtGDMpMykzKTFEzsbifKRnJyMQYMGoUKFClAqlQgJCUFMTAyOHj2q3aagEGAOUwODsV/KmzdvxkcffSSlLrLNnz8fiYmJiI+Px++//25wm88//xyrVq2Sut+wsDAkJiYiIiLC5NckJibi5ZdflloPsp64uDgMGTIEx44dw+7du5GTk4N27dohLS1Nu83cuXMxb948LFq0CCdPnkRISAjatm2Lx48fAwDu3LmDO3fu4NNPP8X58+exatUq7Ny5EwMGDNDb3/Tp05GYmKh9TJw40aR6+vj4YMuWLTrLVqxYgQoVKhSh9blUKpXB5SNGjMCWLVuwYcMGHDp0CE+ePEHHjh2hVqt1titsm4iIyDKYSeVhJiVrYSZlJi3JXGxdASJ79q9//QvZ2dlYvXo1KleujL///ht79+7FgwcPpO/L2BexOQICAiTUxDKuXbuGRo0aoVq1aka38fPzk75fZ2dnhISEmPUac7e3FZVKBTc3txK7f2N27typ83zlypUoU6YMTp8+jZYtW0IIgQULFuDDDz9E165dAQCrV69GcHAw1q1bh0GDBiEiIgKbNm3SllGlShXMnDkTb7zxBnJycuDi8s+vTx8fn0J9Zvr164cVK1agZ8+eAICMjAxs2LABsbGxOn+g3b9/H0OHDsXBgwfx4MEDVKlSBR988IH2dUDuFSQRERFwc3PDN998gzp16iAuLk5nfykpKVi+fDnWrFmDNm3aAADWrl2LsLAw7NmzBzExMUVuExERWQYzqTzMpPLZOhPaev/GMJMyk5ZkvLKRyIhHjx7h0KFDmDNnDlq1aoXw8HA8//zzmDBhAl555RUAuZd2A8Brr70GhUKhfX7t2jV07twZwcHB8Pb2xnPPPYc9e/bolF+xYkXMmDED/fv3h5+fH9555x1UqlQJANCgQQMoFApER0fr1ev69eto1aoVAMDf3x8KhQL9+/cHoH/LSt4++vbtC29vb4SHh+P777/H3bt30blzZ3h7eyMyMhKnTp3S2ceRI0fQsmVLeHh4ICwsDLGxsTpn4AxZsmQJqlSpAjc3N9SoUQNr1qzRqcemTZvwzTff6NT3Wc/eshIdHY3Y2FiMHTsWAQEBCAkJwdSpU3Vec/XqVbRs2RLu7u6oXbs2du/erfd+5Z2Z12g0KF++PL788kudbc6cOQOFQoE///wTgP6VASdOnECDBg3g7u6Oxo0b4+zZszqvX7Vqld5tOFu3btW57cCUz0RB8t6f2bNnIzQ0FNWrVweQ+8u5cePG2l/GvXr1QnJyss5rf/zxR1SvXh0eHh5o1aoVrl+/rrP+/v376NmzJ8qXLw9PT09ERkZi/fr1OttER0dj6NChGDVqFIKCgtC2bVuz6m8rebeZ5P3hk5CQgKSkJLRr1067jVKpRFRUFI4cOZJvOb6+vjqhDgDmzJmDwMBA1K9fHzNnzjT5j7Q+ffrg4MGDuHnzJgBg06ZNqFixIho2bKizXWZmJho1aoQdO3bgwoULGDhwIPr06YPjx4/rbLd69Wq4uLjg8OHDWLp0qd7+Tp8+jezsbJ12h4aGIiIiQq/dhW0TERHJx0zKTJqHmTQXMykzKdk/djYSGeHt7Q1vb29s3boVWVlZBrc5efIkgNyzVImJidrnT548QYcOHbBnzx6cPXsWMTExePXVV7Vf4Hk++eQTRERE4PTp05g0aRJOnDgBANizZw8SExOxefNmvX2GhYVpz25duXIFiYmJ+Pzzz422Y/78+WjRogXOnj2LV155BX369EHfvn3xxhtv4MyZM6hatSr69u0LIQQA4Pz584iJiUHXrl1x7tw5bNy4EYcOHcLQoUON7mPLli0YPnw4Ro8ejQsXLmDQoEF48803sW/fPu371L59e3Tv3r3A+j5r9erV8PLywvHjxzF37lxMnz5dG940Gg26du0KZ2dnHDt2DF9++SXGjRtntCwnJye8/vrr+Pbbb3WWr1u3Ds2aNUPlypX1XpOWloaOHTuiRo0aOH36NKZOnYoxY8aYXP88pn4mCrJ3715cvnwZu3fvxo4dOwDkns396KOP8Ouvv2Lr1q1ISEjQCc+3bt1C165d0aFDB8THx+Ptt9/WG+9EVniwN0IIjBo1Ci+88IL2tqWkpCQAQHBwsM62wcHB2nXPun//Pj766CMMGjRIZ/nw4cOxYcMG7Nu3D0OHDsWCBQswePBgk+pWpkwZvPzyy9rbtFasWIG33npLb7ty5cphzJgxqF+/PipXroxhw4YhJiYG//vf/3S2q1q1KubOnYsaNWqgZs2aeuUkJSXBzc0N/v7++ba7KG0iIiL5mEmZSQFmUmbSXMykzKQOQxCRUd99953w9/cX7u7uonnz5mLChAni119/1dkGgNiyZUuBZdWuXVssXLhQ+zw8PFx06dJFZ5uEhAQBQJw9ezbfsvbt2ycAiIcPH+osj4qKEsOHD9fZxxtvvKF9npiYKACISZMmaZcdPXpUABCJiYlCCCH69OkjBg4cqFPuwYMHhZOTk8jIyDBYn+bNm4t33nlHZ1m3bt1Ehw4dtM87d+4s+vXrl2+7+vXrJzp37qzTnhdeeEFnm+eee06MGzdOCCHEzz//LJydncWtW7e063/66SedY/Lse3rmzBmhUCjE9evXhRBCqNVqUa5cOfHFF19oy3j69UuXLhUBAQEiLS1Nu37JkiU6Za5cuVL4+fnp1HPLli2ioK9YQ5+J+fPnG92+X79+Ijg4WGRlZeVb7okTJwQA8fjxYyGEEBMmTBC1atUSGo1Gu824ceMMfoae1qFDBzF69Gjt86ioKFG/fv18921vBg8eLMLDw3U+I4cPHxYAxJ07d3S2ffvtt0VMTIxeGSkpKaJJkyaiffv2QqVS5bu/7777TgAQ9+7dE0LkHmMvLy/h5eUl2rdvL4TQ/fndtm2bqFSpkrh27Zpwd3cX9+7d0/vs5OTkiBkzZojIyEgREBAgvLy8hIuLi+jWrZt2m6ioKPH2229rn8+cOVO7Xy8vL3Hjxg3x7bffCjc3N706t2nTRgwaNMjkNhERkfUxk+ZiJmUmFYKZlJmUmdQR8MpGonz861//wp07d7Bt2zbExMRg//79aNiwYYEDRqelpWHs2LGoXbs2SpUqBW9vb/z22296ZwwbN25swdrnqlu3rvb/eWfNIiMj9Zbl3eJw+vRprFq1SnsW3dvbGzExMdBoNEhISDC4j8uXL6NFixY6y1q0aIHLly9LrT8AlC1bVlvXy5cvo0KFCihfvrx2fbNmzfItr0GDBqhZs6b2doy4uDgkJyeje/fuBre/fPky6tWrB09PT5P3YYipn4mCREZG6o1Jc/bsWXTu3Bnh4eHw8fHR3uqUV/bly5fRtGlTnVtonm2DWq3GzJkzUbduXQQGBsLb2xu7du2yyWdWlmHDhmHbtm3Yt2+fzmckb9yXZ88YJycn651Zfvz4Mdq3bw9vb29s2bIFrq6u+e6zadOmAIA//vgDQO6tQvHx8YiPj8fXX3+tt32HDh2QmZmJAQMG4NVXX0VgYKDeNp999hnmz5+PsWPH4pdffkF8fDxiYmL0biPx8vLS/v/dd9/V7jc+Ph6hoaEICQmBSqXCw4cPC2x3fm0iIiLrYyZlJmUmZSZlJmUmdSTsbCQqgLu7O9q2bYvJkyfjyJEj6N+/P6ZMmZLva95//31s2rQJM2fOxMGDBxEfH4/IyMh8v4gt5elfRHm/2A0t02g02n8HDRqk80vh119/xdWrV1GlShWj+3k6NAC5two8u6yo9c/bT15dxf/fZpNfPQzp3bs31q1bByD3dpWYmBgEBQUZ3NbQPp7l5OSkt112drbOc1M/EwV59jOTlpaGdu3awdvbG2vXrsXJkye1s8nllW1KGwoTHuyVEAJDhw7F5s2b8csvv2jHncpTqVIlhISE6IylpFKpEBcXh+bNm2uXpaamol27dnBzc8O2bdvg7u5e4L7zxk4qW7YsACA8PBxVq1ZF1apVUa5cOb3tnZ2d0adPH+zfv9/g7SoAcPDgQXTu3BlvvPEG6tWrh8qVK+Pq1av51iMgIEC736pVq8LFxQWNGjWCq6urTrsTExNx4cIFnXYX1CYiIrINZlJm0oIwk9oXZlJm0pKMs1ETmal27do6AzW7urpCrVbrbHPw4EH0798fr732GoDcsVGeHfzYkLyzg8+WV9jtCqNhw4a4ePEiqlatavJratWqhUOHDqFv377aZUeOHEGtWrWk1+9ptWvXxs2bN3Hnzh2EhoYCAI4ePVrg63r16oWJEyfi9OnT+O6777BkyZJ897FmzRpkZGTAw8MDAHDs2DGdbUqXLo3Hjx8jLS1NG3zi4+N1tinsZ6Igv/32G+7du4ePP/4YYWFhAKA3uPqzn1lDbXg6PAC5Af/q1asWP4aWMGTIEKxbtw7ff/89fHx8tGeL/fz84OHhAYVCgREjRmDWrFmoVq0aqlWrhlmzZsHT0xO9evUCkHv2uF27dkhPT8fatWuRmpqK1NRUALnH29nZGUePHsWxY8fQqlUr+Pn54eTJkxg5ciQ6deqEChUqmFzfjz76CO+//77BM8hA7rg3mzZtwpEjR+Dv74958+YhKSnJ7GPj5+eHAQMGYPTo0QgMDERAQADGjBmDyMhI7UyAstpERESWx0yqj5mUmdSeMJMaxkxaMvDKRiIj7t+/j5deeglr167FuXPnkJCQgP/973+YO3cuOnfurN2uYsWK2Lt3L5KSkrSXgletWhWbN2/WnoHt1auX9sxnfsqUKQMPDw/s3LkTf//9t3bGsmeFh4dDoVBgx44duHv3Lp48eSKn0QDGjRuHo0ePYsiQIYiPj8fVq1exbds2DBs2zOhr3n//faxatQpffvklrl69innz5mHz5s2FGrTaHG3atEGNGjXQt29f/Prrrzh48CA+/PDDAl9XqVIlNG/eHAMGDEBOTo7O8XxWr1694OTkhAEDBuDSpUv48ccf8emnn+ps06RJE3h6euKDDz7AH3/8gXXr1und1lTYz0RBKlSoADc3NyxcuBB//vkntm3bho8++khnm3fffRfXrl3DqFGjcOXKFaP12717N44cOYLLly9j0KBBRgemtndLlixBSkoKoqOjUbZsWe1j48aN2m3Gjh2LESNGYPDgwWjcuDH++usv7Nq1Cz4+PgByb906fvw4zp8/j6pVq+qUc+vWLQC5swVu3LgR0dHRqF27NiZPnox33nlHb8bEgri5uSEoKMjoFRCTJk1Cw4YNERMTg+joaISEhOjMkGmO+fPno0uXLujevTtatGgBT09PbN++Hc7OzlLbRERE8jCTMpMCzKSOiJnUOGbSEsA2Q0US2b/MzEwxfvx40bBhQ+Hn5yc8PT1FjRo1xMSJE0V6erp2u23btomqVasKFxcXER4eLoTIHQC6VatWwsPDQ4SFhYlFixYZHCjb0MDLy5YtE2FhYcLJyUlERUUZrd/06dNFSEiIUCgU2kGuTdkHnhk83NAA4CdOnBBt27YV3t7ewsvLS9StW1fMnDkz3/dr8eLFonLlysLV1VVUr15dfPPNNzrrCzsY99PtMVTOlStXxAsvvCDc3NxE9erVxc6dO/MdjDvPF198IQCIvn376tXj2ffo6NGjol69esLNzU3Ur19fbNq0Sa/MLVu2iKpVqwp3d3fRsWNH8dVXX+kMqFyUz4Sx9yfPunXrRMWKFYVSqRTNmjUT27Zt06vf9u3bRdWqVYVSqRQvvviiWLFihc5g3Pfv3xedO3cW3t7eokyZMmLixImib9++BR4PIiIisixmUmbSPMykuZhJieyfQggTBk4gIiIiIiIiIiIiKgBvoyYiIiIiIiIiIiIp2NlIREREREREREREUrCzkYiIiIiIiIiIiKRgZyMRERERERERERFJwc5GIjJq6tSpqF+/vq2rIUVSUhLatm0LLy8vlCpVytbVMVt0dDRGjBiR7zYKhQJbt241qbzidGyJiIio+CpOmYV5VFdxOrZEpMvF1hUgIrKG+fPnIzExEfHx8fDz87N1dSwiMTER/v7+tq4GERERERnAPEpEJQU7G4moRLh27RoaNWqEatWqGd0mOzsbrq6uVqyVXCEhIbauAhEREREZwTxKRCUFb6MmkuS7775DZGQkPDw8EBgYiDZt2iAtLU27fsWKFahTpw6USiXKli2LoUOHatfNmzcPkZGR8PLyQlhYGAYPHownT55o169atQqlSpXCzz//jFq1asHb2xvt27dHYmKiwbpoNBqUL18eX375pc7yM2fOQKFQ4M8//wQA3Lx5E507d4a3tzd8fX3RvXt3/P3330bbaOjWiS5duqB///7a5xUrVsSMGTPQt29feHt7Izw8HN9//z3u3r2r3VdkZCROnTqlU86RI0fQsmVLeHh4ICwsDLGxsTrv3+LFi1GtWjW4u7sjODgY//73v43W81kVK1bEpk2b8M0330ChUGjrq1Ao8OWXX6Jz587w8vLCjBkzAABLlixBlSpV4Obmhho1amDNmjU65SkUCixduhQdO3aEp6cnatWqhaNHj+KPP/5AdHQ0vLy80KxZM1y7ds1onf71r39h2LBh2ucjRoyAQqHAxYsXAQA5OTnw8fHBzz//rN1Go9Fg7NixCAgIQEhICKZOnapXr6dvW7l9+zZef/11BAQEwMvLC40bN8bx48d1XrNmzRpUrFgRfn5+eP311/H48WOT31ciIiKyL8yjuZhHmUeJyLbY2UgkQWJiInr27Im33noLly9fxv79+9G1a1cIIQDkhoUhQ4Zg4MCBOH/+PLZt24aqVatqX+/k5IT//Oc/uHDhAlavXo1ffvkFY8eO1dlHeno6Pv30U6xZswYHDhzAzZs3MWbMGIP1cXJywuuvv45vv/1WZ/m6devQrFkzVK5cGUIIdOnSBQ8ePEBcXBx2796Na9euoUePHkV+P+bPn48WLVrg7NmzeOWVV9CnTx/07dsXb7zxBs6cOYOqVauib9++2vfn/PnziImJQdeuXXHu3Dls3LgRhw4d0gbgU6dOITY2FtOnT8eVK1ewc+dOtGzZ0uT6nDx5Eu3bt0f37t2RmJiIzz//XLtuypQp6Ny5M86fP4+33noLW7ZswfDhwzF69GhcuHABgwYNwptvvol9+/bplPnRRx+hb9++iI+PR82aNdGrVy8MGjQIEyZM0AbXpwP8s6Kjo7F//37t87i4OAQFBSEuLk5b58zMTLRo0UK7zerVq+Hl5YXjx49j7ty5mD59Onbv3m2w/CdPniAqKgp37tzBtm3b8Ouvv2Ls2LHQaDTaba5du4atW7dix44d2LFjB+Li4vDxxx+b/L4SERGR/WAe1cU8yjxKRDYkiKjITp8+LQCI69evG1wfGhoqPvzwQ5PL++9//ysCAwO1z1euXCkAiD/++EO77IsvvhDBwcFGyzhz5oxQKBTaOqnValGuXDnxxRdfCCGE2LVrl3B2dhY3b97UvubixYsCgDhx4oQQQogpU6aIevXqaddHRUWJ4cOH6+ync+fOol+/ftrn4eHh4o033tA+T0xMFADEpEmTtMuOHj0qAIjExEQhhBB9+vQRAwcO1Cn34MGDwsnJSWRkZIhNmzYJX19fkZqaarS9BXm2nkIIAUCMGDFCZ1nz5s3FO++8o7OsW7duokOHDjqvmzhxol57li9frl22fv164e7ubrQ+586dEwqFQty9e1c8ePBAuLq6ihkzZohu3boJIYSYNWuWaNKkiXb7qKgo8cILL+iU8dxzz4lx48bp1GvLli1CCCGWLl0qfHx8xP379w3uf8qUKcLT01PnPX3//fd19klERESOg3m0n/Y582gu5lEishVe2UgkQb169dC6dWtERkaiW7duWLZsGR4+fAgASE5Oxp07d9C6dWujr9+3bx/atm2LcuXKwcfHB3379sX9+/d1btvw9PRElSpVtM/Lli2L5ORko2U2aNAANWvWxPr16wHknqlMTk5G9+7dAQCXL19GWFgYwsLCtK+pXbs2SpUqhcuXLxfujfh/devW1f4/ODgYABAZGam3LK/+p0+fxqpVq+Dt7a19xMTEQKPRICEhAW3btkV4eDgqV66MPn364Ntvv0V6enqR6pincePGOs8vX76sc/YWAFq0aKH3npjSxszMTKSmphrcb0REBAIDAxEXF4eDBw+iXr166NSpk/ZM8v79+xEVFWV0n0D+n4H4+Hg0aNAAAQEBBtcDubfz+Pj4mFQeERER2TfmUV3Mo8yjRGQ77GwkksDZ2Rm7d+/GTz/9hNq1a2PhwoWoUaMGEhIS4OHhke9rb9y4gQ4dOiAiIgKbNm3C6dOn8cUXXwDIHSA6z7MDRSsUCu1tH8b07t0b69atA5B7y0pMTAyCgoIAAEIIKBQKvdcYWw7k3g7z7D6frqOhuuaVZWhZ3i0UGo0GgwYNQnx8vPbx66+/4urVq6hSpQp8fHxw5swZrF+/HmXLlsXkyZNRr149PHr0KN/2m8LLy0tv2bPtN/SemNtGQ/to2bIl9u/fj7i4OERHRyMiIgJqtRrnz5/HkSNHEB0dbXSfeWUYK7+gz5255REREZF9Yx7VxTzKPEpEtsPORiJJFAoFWrRogWnTpuHs2bNwc3PDli1b4OPjg4oVK2Lv3r0GX3fq1Cnk5OTgs88+Q9OmTVG9enXcuXNHSp169eqF8+fP4/Tp0/juu+/Qu3dv7bratWvj5s2buHXrlnbZpUuXkJKSglq1ahksr3Tp0jqDgKvValy4cKHI9WzYsCEuXryIqlWr6j3c3NwAAC4uLmjTpg3mzp2Lc+fO4fr16/jll1+KvO9n1apVC4cOHdJZduTIEaPvSVHkjZOzf/9+REdHQ6FQ4MUXX8Snn36KjIwMvTPa5qhbty7i4+Px4MEDiTUmIiIie8Y8WnjMo8yjRCSPi60rQFQcHD9+HHv37kW7du1QpkwZHD9+HHfv3tUGgqlTp+Ldd99FmTJl8PLLL+Px48c4fPgwhg0bhipVqiAnJwcLFy7Eq6++isOHD+vN2ldYlSpVQvPmzTFgwADk5OSgc+fO2nVt2rRB3bp10bt3byxYsAA5OTkYPHgwoqKi9G7lyPPSSy9h1KhR+OGHH1ClShXMnz9fytnccePGoWnTphgyZAjeeecdeHl54fLly9i9ezcWLlyIHTt24M8//0TLli3h7++PH3/8ERqNBjVq1Cjyvp/1/vvvo3v37mjYsCFat26N7du3Y/PmzdizZ4/0fUVHR2P48OFwcXHBiy++qF02evRoNGzYEL6+voUuu2fPnpg1axa6dOmC2bNno2zZsjh79ixCQ0PRrFkzWU0gIiIiO8E8WjTMo8yjRCQPr2wkksDX1xcHDhxAhw4dUL16dUycOBGfffYZXn75ZQBAv379sGDBAixevBh16tRBx44dcfXqVQBA/fr1MW/ePMyZMwcRERH49ttvMXv2bGl16927N3799Vd07dpV51YGhUKBrVu3wt/fHy1btkSbNm1QuXJlbNy40WhZb731Fvr164e+ffsiKioKlSpVQqtWrYpcx7p16yIuLg5Xr17Fiy++iAYNGmDSpEkoW7YsAKBUqVLYvHkzXnrpJdSqVQtffvkl1q9fjzp16hR538/q0qULPv/8c3zyySeoU6cOli5dipUrV+rdQiJDREQEgoKCUK9ePW2Qi4qKglqt1hsfx1xubm7YtWsXypQpgw4dOiAyMhIff/wxnJ2dZVSdiIiI7AzzaNEwjzKPEpE8ClHQIBtEREREREREREREJuCVjURERERERERERCQFOxuJiIiIiIiIiIhICnY2EhERERERERERkRTsbCQiIiIiIiIiIiIp2NlIREREREREREREUrCzkcgG+vfvjy5duti6GmRDq1atQqlSpcx6TXR0NBQKBRQKBeLj4y1SLyIiIioZmEeJeZSILIWdjUTk0CpWrIgFCxZYZV+FCWSyvfPOO0hMTERERAQAYP/+/VAoFHj06JHetvXr18fUqVO12+T3WLVqFQBg06ZNiI6Ohp+fH7y9vVG3bl1Mnz4dDx48sGIriYiIiBwH8yjzKBHpYmcjERWZSqWydRXypVarodFobF0NKTw9PRESEgIXFxeTX9O8eXMkJiZqH927d0f79u11lvXo0QMffvghevTogeeeew4//fQTLly4gM8++wy//vor1qxZY8FWERERERUN86j1MI8SUUHY2UhUCBqNBnPmzEHVqlWhVCpRoUIFzJw5U7v+/PnzeOmll+Dh4YHAwEAMHDgQT548MVqeobOheWcB8ygUCixduhQdO3aEp6cnatWqhaNHj+KPP/5AdHQ0vLy80KxZM1y7dk37mqlTp6J+/fpYs2YNKlasCD8/P7z++ut4/Pix0brcv38fPXv2RPny5eHp6YnIyEisX79eZ5vo6GgMHToUo0aNQlBQENq2bQsAuHTpEjp06ABvb28EBwejT58+uHfvXr7v5ZEjR9CyZUt4eHggLCwMsbGxSEtLAwB888038Pb2xtWrV7XbDxs2DNWrV0daWhqio6Nx48YNjBw5UntGFPjnjO+OHTtQu3ZtKJVK3LhxAydPnkTbtm0RFBQEPz8/REVF4cyZMzr1efToEQYOHIjg4GC4u7sjIiICO3bswP79+/Hmm28iJSVFu6+846NSqTB27FiUK1cOXl5eaNKkCfbv369T7qpVq1ChQgV4enritddew/379/N9X2Ryc3NDSEiI9uHh4QGlUqmz7Pz585g1axY+++wzfPLJJ2jevDkqVqyItm3bYtOmTejXr5/V6ktEREQFYx5lHmUeJSJ7xc5GokKYMGEC5syZg0mTJuHSpUtYt24dgoODAQDp6elo3749/P39cfLkSfzvf//Dnj17MHTo0CLv96OPPkLfvn0RHx+PmjVrolevXhg0aBAmTJiAU6dOAYDefq5du4atW7dix44d2LFjB+Li4vDxxx8b3UdmZiYaNWqEHTt24MKFCxg4cCD69OmD48eP62y3evVquLi44PDhw1i6dCkSExMRFRWF+vXr49SpU9i5cyf+/vtvdO/e3ei+zp8/j5iYGHTt2hXnzp3Dxo0bcejQIW0b+vbtiw4dOqB3797IycnBzp07sXTpUnz77bfw8vLC5s2bUb58eUyfPl17RjRPeno6Zs+eja+//hoXL15EmTJl8PjxY/Tr1w8HDx7EsWPHUK1aNXTo0EEbdjUaDV5++WUcOXIEa9euxaVLl/Dxxx/D2dkZzZs3x4IFC+Dr66vd15gxYwAAb775Jg4fPowNGzbg3Llz6NatG9q3b68NpcePH8dbb72FwYMHIz4+Hq1atcKMGTNMPexW8e2338Lb2xuDBw82uN7Wt+sQERGRLuZR5lHmUSKyW4KIzJKamiqUSqVYtmyZwfVfffWV8Pf3F0+ePNEu++GHH4STk5NISkoSQgjRr18/0blzZ+368PBwMX/+fJ1y6tWrJ6ZMmaJ9DkBMnDhR+/zo0aMCgFi+fLl22fr164W7u7v2+ZQpU4Snp6dITU3VLnv//fdFkyZNzGpzhw4dxOjRo7XPo6KiRP369XW2mTRpkmjXrp3Oslu3bgkA4sqVKwbL7dOnjxg4cKDOsoMHDwonJyeRkZEhhBDiwYMHonz58uK9994TwcHBYsaMGTrbG3rvVq5cKQCI+Pj4fNuVk5MjfHx8xPbt24UQQvz888/CycnJaH1Xrlwp/Pz8dJb98ccfQqFQiL/++ktneevWrcWECROEEEL07NlTtG/fXmd9jx499MoqSFRUlBg+fLjOsn379gkA4uHDh3rbP/sZyvPs508IIV5++WVRt25ds+pDREREtsE8yjz6NOZRIrI3pg+yQEQAgMuXLyMrKwutW7c2ur5evXrw8vLSLmvRogU0Gg2uXLmiPeNcGHXr1tX+P6+cyMhInWWZmZlITU2Fr68vgNxbYnx8fLTblC1bFsnJyUb3oVar8fHHH2Pjxo3466+/kJWVhaysLJ32AEDjxo11np8+fRr79u2Dt7e3XpnXrl1D9erV9ZafPn0af/zxB7799lvtMiEENBoNEhISUKtWLfj7+2P58uWIiYlB8+bNMX78eKN1f5qbm5vO+wUAycnJmDx5Mn755Rf8/fffUKvVSE9Px82bNwEA8fHxKF++vMG6GnPmzBkIIfRek5WVhcDAQAC5n4nXXntNZ32zZs2wc+dOk/djaUII7W0/REREZN+YR3Mxj+ZiHiUie8PORiIzeXh45Ls+v1+SxpY7OTlBCKGzLDs7W287V1dXvbIMLXt68Omn1+dtk9/g1J999hnmz5+PBQsWIDIyEl5eXhgxYoTeoNvPhj2NRoNXX30Vc+bM0SuzbNmyBvel0WgwaNAgxMbG6q2rUKGC9v8HDhyAs7Mz7ty5g7S0NG1wzY+Hh4fe+92/f3/cvXsXCxYsQHh4OJRKJZo1a6ZtW0HH1lgbnJ2dcfr0aTg7O+usywu6zx5bmfLei5SUFL1bSx49egQ/Pz+TyqlevToOHTqE7Oxsvc8MERER2Rfm0VzMo/+0gXmUiOwJx2wkMlO1atXg4eGBvXv3Glxfu3ZtxMfHaweVBoDDhw/DycnJ6BnK0qVL64zvkpqaioSEBLkVN9HBgwfRuXNnvPHGG6hXrx4qV66sMyC2MQ0bNsTFixdRsWJFVK1aVefxbBB89jXPbl+1alW4ubkByB2we+7cudi+fTt8fX0xbNgwnTLc3NygVqtNbltsbCw6dOiAOnXqQKlU6gwYXrduXdy+fRu///67wdcb2leDBg2gVquRnJys14aQkBAAuZ+JY8eO6bzu2eeFVa1aNTg5OeHkyZM6yxMTE/HXX3+hRo0aJpXTq1cvPHnyBIsXLza4/tGjR0WtKhEREUnCPGoY8yjzKBHZB3Y2EpnJ3d0d48aNw9ixY/HNN9/g2rVrOHbsGJYvXw4A6N27N9zd3dGvXz9cuHAB+/btw7Bhw9CnTx+jt6y89NJLWLNmDQ4ePIgLFy6gX79+emclraVq1arYvXs3jhw5gsuXL2PQoEFISkoq8HVDhgzBgwcP0LNnT5w4cQJ//vkndu3ahbfeesto+Bo3bhyOHj2KIUOGID4+HlevXsW2bdu0Ae7x48fo06cPhg0bhpdffhnr1q3Df//7X/zvf//TllGxYkUcOHAAf/31V4EzDVatWhVr1qzB5cuXcfz4cfTu3Vvn7HFUVBRatmyJf/3rX9i9ezcSEhLw008/aW8vqVixIp48eYK9e/fi3r17SE9PR/Xq1dG7d2/07dsXmzdvRkJCAk6ePIk5c+bgxx9/BADExsZi586dmDt3Ln7//XcsWrRI2i0rPj4+GDRoEEaPHo2tW7ciISEBhw8fRs+ePVGrVi20a9fOpHKaNGmCsWPHYvTo0Rg7diyOHj2KGzduYO/evejWrRtWr14tpb5ERERUdMyjhjGPMo8SkX1gZyNRIUyaNAmjR4/G5MmTUatWLfTo0UM77oynpyd+/vlnPHjwAM899xz+/e9/o3Xr1li0aJHR8iZMmICWLVuiY8eO6NChA7p06YIqVapYqzk6Jk2ahIYNGyImJgbR0dEICQlBly5dCnxdaGgoDh8+DLVajZiYGERERGD48OHw8/ODk5Phr5q6desiLi4OV69exYsvvogGDRpg0qRJ2ttchg8fDi8vL8yaNQsAUKdOHcyZMwfvvvsu/vrrLwDA9OnTcf36dVSpUgWlS5fOt44rVqzAw4cP0aBBA/Tp0wexsbEoU6aMzjabNm3Cc889h549e6J27doYO3asNpw2b94c7777Lnr06IHSpUtj7ty5AICVK1eib9++GD16NGrUqIFOnTrh+PHjCAsLAwA0bdoUX3/9NRYuXIj69etj165dmDhxYoHvqanmz5+Pt99+Gx988AHq1KmD3r17o1KlSti1axdcXEwfLWPOnDlYt24djh8/jpiYGNSpUwejRo1C3bp10a9fP2n1JSIioqJjHtXHPMo8SkT2QSEsOXgDERFJEx0djfr162PBggW2rgoRERERlUDMo0RkCl7ZSETkQBYvXgxvb2+cP3/e1lUhIiIiohKIeZSICsIrG4mIHMRff/2FjIwMALmzI+YNWk5EREREZA3Mo0RkCnY2EhERERERERERkRS8jZqIiIiIiIiIiIikYGcjEdnU1KlTUb9+favsR6FQQKFQcEBrIiIiItLBTEpEJA87G4nIYvKClLFH//79MWbMGOzdu9fidRkzZgwSExNRvnx5i++LiIiIiOwHMykRkXW52LoCRFR8JSYmav+/ceNGTJ48GVeuXNEu8/DwgLe3N7y9vS1el7z9ODs7W3xfRERERGQ/mEmJiKyLVzYSkcWEhIRoH35+flAoFHrLnr1lpX///ujSpQtmzZqF4OBglCpVCtOmTUNOTg7ef/99BAQEoHz58lixYoXOvv766y/06NED/v7+CAwMROfOnXH9+nXrNpiIiIiI7A4zKRGRdbGzkYjszi+//II7d+7gwIEDmDdvHqZOnYqOHTvC398fx48fx7vvvot3330Xt27dAgCkp6ejVatW8Pb2xoEDB3Do0CF4e3ujffv2UKlUNm4NERERETkiZlIiosJhZyMR2Z2AgAD85z//QY0aNfDWW2+hRo0aSE9PxwcffIBq1aphwoQJcHNzw+HDhwEAGzZsgJOTE77++mtERkaiVq1aWLlyJW7evIn9+/fbtjFERERE5JCYSYmICodjNhKR3alTpw6cnP45FxIcHIyIiAjtc2dnZwQGBiI5ORkAcPr0afzxxx/w8fHRKSczMxPXrl2zTqWJiIiIqFhhJiUiKhx2NhKR3XF1ddV5rlAoDC7TaDQAAI1Gg0aNGuHbb7/VK6t06dKWqygRERERFVvMpEREhcPORiJyeA0bNsTGjRtRpkwZ+Pr62ro6RERERFQCMZMSEeXimI1E5PB69+6NoKAgdO7cGQcPHkRCQgLi4uIwfPhw3L5929bVIyIiIqISgJmUiCgXOxuJyOF5enriwIEDqFChArp27YpatWrhrbfeQkZGBs8qExEREZFVMJMSEeVSCCGErStBRGQtFStWxIgRIzBixAhbV4WIiIiISihmUiIqznhlIxGVCLNmzYK3tzdu3rxp66oQERERUQnFTEpEJQGvbCSiEuHBgwd48OABgNzZAP38/GxcIyIiIiIqaZhJiagkYGcjERERERERERERScHbqImIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORiIiIiIiIiIiIpGBnIxEREREREREREUnBzkYiIiIiIiIiIiKSgp2NREREREREREREJAU7G4mIiIiIiIiIiEgKdjYSERERERERERGRFOxsJCIiIiIiIiIiIinY2UhERERERERERERSsLORqIRZt24dFixYUKQyFi9ejFWrVuktv379OhQKhcF1jub48eN47bXXUKFCBSiVSgQHB6NZs2YYPXq00dcIIdCyZUsoFAoMHTrU4DYLFy5EzZo1oVQqUalSJUybNg3Z2dmWagYRERGRXWImNY2pmfTQoUN4++230ahRIyiVSigUCly/fl2vvLS0NLz++uuoUaMGfHx84OXlhTp16mDGjBlIS0uzUquIqLhjZyNRCWPJYFe2bFkcPXoUr7zySpHKt7UffvgBzZs3R2pqKubOnYtdu3bh888/R4sWLbBx40ajr/viiy/wxx9/GF0/c+ZMDB8+HF27dsXPP/+MwYMHY9asWRgyZIglmkFERERkt5hJC2ZOJt27dy/27NmDChUqoHnz5kbLzM7OhhACo0aNwqZNm/D999/jX//6F6ZPn47OnTtbuklEVEIohBDC1pUgIuvp2LEjLly4YPBMp6kiIiIQFBSE/fv3S6uXPYmKisJff/2F3377DS4uLjrrNBoNnJz0z9Ncv34dkZGR+Oabb9C1a1cMGTIEixYt0q6/f/8+ypcvj759+2Lp0qXa5bNmzcLEiRNx4cIF1K5d23KNIiIiIrIjzKQFMyeTPv38008/xfvvv4+EhARUrFjRpH2NGzcOc+fOxbVr11C5cmVpbSCikolXNhIVI3fv3sXAgQMRFhYGpVKJ0qVLo0WLFtizZw8AIDo6Gj/88ANu3LgBhUKhfeSZNm0amjRpgoCAAPj6+qJhw4ZYvnw5nj4nUbFiRVy8eBFxcXHa1+eFGGO3rBw6dAitW7eGj48PPD090bx5c/zwww8626xatQoKhQL79u3De++9h6CgIAQGBqJr1664c+eOZd4wI+7fv4+goCC9UAfAYEcjAAwcOBBt27bFa6+9ZnD9zp07kZmZiTfffFNn+ZtvvgkhBLZu3VrkehMRERHZA2ZSOczJpMYyqqlKly4NAAb3RURkLn6TEBUjffr0wZkzZzBz5kxUr14djx49wpkzZ3D//n0AubeaDBw4ENeuXcOWLVv0Xn/9+nUMGjQIFSpUAAAcO3YMw4YNw19//YXJkycDALZs2YJ///vf8PPzw+LFiwEASqXSaJ3i4uLQtm1b1K1bF8uXL4dSqcTixYvx6quvYv369ejRo4fO9m+//TZeeeUVrFu3Drdu3cL777+PN954A7/88ku+bRdCQK1Wm/Q+FRSimjVrhq+//hqxsbHo3bs3GjZsCFdXV6Pbf/311zhx4gQuXbpkdJsLFy4AACIjI3WWly1bFkFBQdr1RERERI6OmdQ2mdQcefVMT0/HkSNH8Nlnn6Fnz57a95yIqEgEERUb3t7eYsSIEflu88orr4jw8PACy1Kr1SI7O1tMnz5dBAYGCo1Go11Xp04dERUVpfeahIQEAUCsXLlSu6xp06aiTJky4vHjx9plOTk5IiIiQpQvX15b7sqVKwUAMXjwYJ0y586dKwCIxMTEfOub93pTHgW5d++eeOGFF7Tbu7q6iubNm4vZs2frtEMIIW7fvi38/PzE0qVLtcsAiCFDhuhs98477wilUmlwf9WrVxft2rUrsF5EREREjoCZ1PqZ9GmffPKJACASEhKMbrN+/Xqdurz55psiOzu7wDoREZmCVzYSFSPPP/88Vq1ahcDAQLRp0waNGjUy6+znL7/8glmzZuHkyZNITU3VWZecnIzg4GCz6pOWlobjx4/jvffeg7e3t3a5s7Mz+vTpg3HjxuHKlSuoWbOmdl2nTp10yqhbty4A4MaNGwgJCTG6r1dffRUnT540q37GBAYG4uDBgzh16hT27t2LU6dOYf/+/ZgwYQKWLl2KkydPIigoCADw7rvvol69enjnnXcKLPfp24PMWUdERETkSJhJrZ9JzRUTE4OTJ0/i8ePHOHr0KObMmYP79+9jy5YtRb4lm4iInY1ExcjGjRsxY8YMfP3115g0aRK8vb3x2muvYe7cufmGIgA4ceIE2rVrh+joaCxbtgzly5eHm5sbtm7dipkzZyIjI8Ps+jx8+BBCCJQtW1ZvXWhoKABob6fJExgYqPM873aYgvYfEBAAPz8/s+uYn8aNG6Nx48YAcmfuGzduHObPn4+5c+di7ty5+O6777Bz504cOnQIKSkpOq9VqVR49OgRvLy84OrqisDAQGRmZiI9PR2enp462z548ACNGjWSWnciIiIiW2EmtW4mLQx/f39tma1atUKVKlXw+uuv4/vvvzc6BjkRkal4yoKoGAkKCsKCBQtw/fp13LhxA7Nnz8bmzZvRv3//Al+7YcMGuLq6YseOHejevTuaN2+uDSCF5e/vDycnJyQmJuqtyxtgu7BnY5+1evVquLq6mvQoDFdXV0yZMgXAP+MvXrhwATk5OWjatCn8/f21DwBYtmwZ/P39tYOO543VeP78eZ1yk5KScO/ePURERBSqXkRERET2hpnUuplUhueffx4A8Pvvv0srk4hKLl7ZSFRMVahQAUOHDsXevXtx+PBh7XKlUmnwjKxCoYCLiwucnZ21yzIyMrBmzRq9bY2V8SwvLy80adIEmzdvxqeffgoPDw8AgEajwdq1a1G+fHlUr169MM3TI/OWlcTERINnvi9fvgzgnzPg/fv3R3R0tN52rVq1QpcuXTB8+HBtJ2L79u3h7u6OVatWoUmTJtpt82Y87NKli5S6ExEREdkTZtLCMzWTyrBv3z4AQNWqVaWVSUQlFzsbiYqJlJQUtGrVCr169ULNmjXh4+ODkydPYufOnejatat2u8jISGzevBlLlixBo0aN4OTkhMaNG+OVV17BvHnz0KtXLwwcOBD379/Hp59+anBWv8jISGzYsAEbN25E5cqV4e7urjfLcp7Zs2ejbdu2aNWqFcaMGQM3NzcsXrwYFy5cwPr166WNVRgYGKh3u0thxcTEoHz58nj11VdRs2ZNaDQaxMfH47PPPoO3tzeGDx8OAKhYsSIqVqxosIxy5crpdEQGBARg4sSJmDRpEgICAtCuXTucPHkSU6dOxdtvv43atWtLqTsR2bdnxx4zha+vrwVqQkRkGcyk1s+kAHD37l3ExcUB+OdOmp9++gmlS5dG6dKlERUVBQBYunQpDh48iHbt2iEsLAxpaWk4ePAgFi5ciObNm6Nz585S6k5E9ssqedTWM9QQkRyZmZni3XffFXXr1hW+vr7Cw8ND1KhRQ0yZMkWkpaVpt3vw4IH497//LUqVKiUUCoXOTHgrVqwQNWrUEEqlUlSuXFnMnj1bLF++XG82u+vXr4t27doJHx8fAUA7k6Chmf+EEOLgwYPipZdeEl5eXsLDw0M0bdpUbN++XWebvJn7Tp48qbN83759AoDYt2+flPfJFBs3bhS9evUS1apVE97e3sLV1VVUqFBB9OnTR1y6dKnA18PAbNR5Pv/8c1G9enXh5uYmKlSoIKZMmSJUKpXsJhCRnVIoFMLJycnkh7Ozs7h27Zqtq01EZDJmUnnMyaR59TP0eHrG7sOHD4uOHTuK0NBQ4ebmJjw9PUW9evXERx99pHN8iKj4skYeVQghhNldmkRERERkNicnJ2zatAkBAQEFbiuEQIcOHXDhwgVUrlzZCrUjIiIiouLOGnmUt1ETERERWUl4eDhatmxp8i12lStXLvQkAkREREREz7JGHuWVjURERERERERERCSFSVc2NmzY0KxCFQoFtm3bhnLlyhWqUkREREQllUajwe3bt1GhQgVbV8WuMI8SERERWUdR86hJnY3x8fEYPXo0vL29C9xWCIGPP/4YWVlZhaoQERERUXHm7OyMoUOHYv78+XByctJbf/fuXVSqVAlqtdoGtbNfzKNEREREclg6j5p0G7WTkxOSkpJQpkwZkwr18fHBr7/+ysHMiYiIiJ7h5OQEb29vPP/88/jvf/+rNzj333//jbJly0Kj0diohvaJeZSIiIhIDkvnUf3uSwMSEhJQunRpkwu9dOkSwsPDC1UhIiIiouJMoVBg9+7duHfvHho3bowLFy4Y3IZ0MY8SERERyWHpPMoJYkyk0Whw584d+Pj48A8AIiIqkYQQePz4MUJDQw3ebmEJmZmZUKlURte7ubnB3d3dKnWRJe8KPR8fH/Tv3x8//vgjVq9eja5duwLIPZMcGhrK26hJD/MoERGVdPaYRwHHy6SWzqMmjdn4rMzMTJw7dw7Jycl6l1R26tSpUBWxd3fu3EFYWJitq0FERGRzt27dQvny5S2+n8zMTPh5lIIKxsfdCwkJQUJCgkOFuzweHh7YuHEjZs+ejR49euCDDz7AtGnTbF0th8E8SkREVHLZUx4FHDeTWiqPmt3ZuHPnTvTt2xf37t3TW6dQKIrtWXgfHx8AwPnfv4CPj0fRy3P3LXIZeVyc2korS6b7TzZKKyvQu4e0sooTddZOaWWlXde/bLqwnCvVklaWaud2aWW51K4prSxnb39pZQGAZ0g/qeXJkKb6QWp5Wf/9r7SyAt5YLa2sB6vekFaWovtr0sry9/yXtLJkSU1NRVhYmPZ3oqWpVCqokIXW7q/CBa5663OQjb1J26FSqRwu2D1twoQJqFevHnr37o1z587hk08+sXWV7F5Jz6O3bt2Cr2/Rs+TthyuLXEae8v5vSiuLSJaH+8bIK+zeQ2lFKV9sKa0se8yQAPBgjrwcEzBuk7SyZH4m/Ft9Kq0smfUSN5KklYUUeROtBQz/n5Ry7C2PAsUjk8rOo2Z3Ng4dOhTdunXD5MmTERwcXKSdO5K8W1V8fDzg6+tZ5PJ83L2KXEYeFyd5HZcyqZyK/j7l8fW2zzbamjpL3nvs7C3vS9FZws9IHpWnm7SyXGS2UcJJh6d5SvijUTZnlbzjCABZHoZ/OReGjD+y8+RIrJdC4mff19P+PhN5rH37ZinPUnB10v8uyNaogEyrVkUKQ+9fhw4dcPz4cXTp0gVt2rSxQa0cS0nPo76+vlK+B33U8n6XyfxeJpJF7SUvRyJdXl5QSsyR9pghASDHvVA3URok8/tF5mfCXuslJGZbZMmbrE727wl7yaOAY2ZSS+dRs29wT05OxqhRo0pUsCMiIiLbcXV1g5uBh6urxD8ircjYcNnVq1fH8ePHERkZaeUaOR7mUSIiIrImY3nUUTOppfOo2Z2N//73v7F///4i7TRPTk4OJk6ciEqVKsHDwwOVK1fG9OnTdcbdEUJg6tSpCA0NhYeHB6Kjo3Hx4kWdcrKysjBs2DAEBQXBy8sLnTp1wu3bt3W2efjwIfr06QM/Pz/4+fmhT58+ePTokZR2EBERkeV4e3rD29PHwMPb1lUrFI1GgzJlyhhc5+Pjg+3btyMhIcHKtXIszKNERERkTcbzqGNmUkvnUbOvbV60aBG6deuGgwcPIjIyEq6uupfoxsbGmlzWnDlz8OWXX2L16tWoU6cOTp06hTfffBN+fn4YPnw4AGDu3LmYN28eVq1aherVq2PGjBlo27Ytrly5or1Hf8SIEdi+fTs2bNiAwMBAjB49Gh07dsTp06fh7OwMAOjVqxdu376NnTtzx7gbOHAg+vTpg+3bzRsPzs3FHW4uRb8V00lhnVmTbEnG+0T5y36SIq0sjYFxrwrLI1xaUVDlyBt3S+Es75YC9eMH0soCAJSVW5wM7sJZannyRnyRLEfe7SH83rMMV1c3uDnrnzEWasNnZPPz119/Ydy4cfjpp5+QkZGB6tWrY/ny5WjUqFFumUJg2rRp+Oqrr/Dw4UM0adIEX3zxBerUqVPkdhTkzz//REZGBmrVqoUKFSpYfH+OrKTn0VM3v4C3T9G/b8L9A4pcRklzJ2WFxfcR6veWxfex948FUsurF1JaWllB3r2llYXUJ9KKEo8ypJWVff+OtLIenhggrSz3Zi9KK0vzMP+Ze82RenmWtLJwV3KGlyVL3vuFJ9nyysosnmMgF4axPAqYn0lLQh41u7Nx3bp1+Pnnn+Hh4YH9+/fr3OetUCjMCndHjx5F586d8corrwAAKlasiPXr1+PUqVMAct/gBQsW4MMPP9ROv7169WoEBwdj3bp1GDRoEFJSUrB8+XKsWbNGe0/52rVrERYWhj179iAmJgaXL1/Gzp07cezYMTRp0gQAsGzZMjRr1gxXrlxBjRo1zH0biIiIyEq8vLygdNbvWHFRmxdjHj58iBYtWqBVq1b46aefUKZMGVy7dg2lSpXSbmNKp1JRqVQqzJw5E2fOnEHTpk0xfvx4vPHGG/jv/0+iVKNGDfz444+oWLGilP0VR8yjREREZE3G8ihgXiYtKXnU7MvrJk6ciOnTpyMlJQXXr19HQkKC9vHnn3+aVdYLL7yAvXv34vfffwcA/Prrrzh06BA6dOgAAEhISEBSUhLatWunfY1SqURUVBSOHDkCADh9+jSys7N1tgkNDUVERIR2m6NHj8LPz08b7ACgadOm8PPz027zrKysLKSmpuo8iIiIyPpcXVzh5qr/cHUx72rlOXPmICwsDCtXrsTzzz+PihUronXr1qhSpQoA/U6liIgIrF69Gunp6Vi3bp209kyYMAFLlixBcHAwVqxYga5du+Ls2bNYt24dNmzYABcXF3z44YfS9lccMY8SERGRNRnLo+Zm0pKSR82+slGlUqFHjx5wcir6bcDjxo1DSkoKatasCWdnZ6jVasycORM9e/YEACQl5U7Z/uzg38HBwbhx44Z2Gzc3N/j7++ttk/f6pKQkg/eilylTRrvNs2bPno1p06YVrYFERERUZB4+nnB30Z+5U5GTm0We7YBRKpVQKpV622/btg0xMTHo1q0b4uLiUK5cOQwePBjvvPMOgII7lQYNGiSlPd999x1WrVqFDh064Pfff0fNmjXxww8/4OWXXwaQm09695Z4G2ExxDxKRERE1mQsjwLmZdKSkkfNTmj9+vXDxo0bC73Dp23cuBFr167FunXrcObMGaxevRqffvopVq9erbPds1NyCyEKnOb82W0MbZ9fORMmTEBKSor2cevWLVObRURERBK5urnCVWng4ZZ7FjksLEw74Yafnx9mz55tsJw///wTS5YsQbVq1fDzzz/j3XffRWxsLL755hsA+XcqGesMKow7d+6gXr16AHJn/FMqlahatap2ffXq1aXurzhiHiUiIiJrMppHzcykJSWPmn1lo1qtxty5c/Hzzz+jbt26egNyz5s3z+Sy3n//fYwfPx6vv/46ACAyMhI3btzA7Nmz0a9fP4SEhADIfbPLlv1n9oTk5GTtGx8SEgKVSoWHDx/qnE1OTk5G8+bNtdv8/fffevu/e/eu3gHMY+yqCA9Xd3i4Gu7NJl1uLvrvH8mVI3GSEvFA3mQzWYnm3cLmiNSP7HRwaYlUKfImDbJrEge+VvJ7zyKUPu5wN/C7V2TnDsZ969Yt+Pr6/rO9gd/fQO6se40bN8asWbkDzTdo0AAXL17EkiVL0LdvX+12helUModardbJTy4uLtoJRADAyckJQpg/+U1JUtLzqCwebo43e6atqXLsdrozs1T0M/vPwHwFesmbIEYqmZNuSMwLIj1dWllIlzdxjfrxQ2llKTzkTTSoeSxvCAmRmSOtLKkkToopc/JDIbEsR2csjwLmZdKSkkfN/i1z/vx5NGjQAABw4cIFnXXmNjw9PV3v9hdnZ2doNLkf6EqVKiEkJAS7d+/W7lOlUiEuLg5z5swBADRq1Aiurq7YvXs3unfvDgBITEzEhQsXMHfuXABAs2bNkJKSghMnTuD5558HABw/fhwpKSnaAEhERET2ycXNBS5u+pHFRZG7zNfXVyfYGVO2bFnUrl1bZ1mtWrWwadMmADCpU0mWn3/+GX5+fgByQ+fevXu1uerRo0dS91UcMY8SERGRNRnLo4B5mbSk5FGzOxv37dtXpB0+7dVXX8XMmTNRoUIF1KlTB2fPnsW8efPw1ltvAcgNiyNGjMCsWbNQrVo1VKtWDbNmzYKnpyd69eoFAPDz88OAAQMwevRoBAYGIiAgAGPGjEFkZKR2NsBatWqhffv2eOedd7B06VIAwMCBA9GxY0fO/EdERGTnlD4eUCr1zyRrssw729qiRQtcuXJFZ9nvv/+O8PBwAKZ1KsnSr18/nefPjr8j88x1ccQ8SkRERNZkLI8C5mXSkpJH5V4/b6aFCxdi0qRJGDx4MJKTkxEaGopBgwZh8uTJ2m3Gjh2LjIwMDB48GA8fPkSTJk2wa9cunem+58+fDxcXF3Tv3h0ZGRlo3bo1Vq1apXMJ6LfffovY2FjtIJudOnXCokWLrNdYIiIiKhR3HyU8lO56y4WbebccjRw5Es2bN8esWbPQvXt3nDhxAl999RW++uorAKZ1KsmQd8Uc2QfmUSIiIiqIsTwKmJdJS0oeNamzsWvXrli1apVJtygBQO/evTF//nyDM+49zcfHBwsWLMCCBQuMbqNQKDB16lRMnTrV6Dbu7u5YuHAhFi5caHSbgIAArF27tqCqExERkZ1xdXWBq4HbVlw15p0zfe6557BlyxZMmDAB06dPR6VKlbBgwQKdmfZM6VQi22AeJSIiIlsxlkcB8zJpScmjJr0j33//Pe7evWtSgUIIbN++HR999FGB4c4hqTW5jyJSOJs9EbjDcXN2s3UVij31E3mDOIvkR9LKykm+I60sqCUOliyReChvQh17lfMo2dZVsA6JA747yTxBWPx/TZjMy9sdXu76Z5IVLuYfu44dO6Jjx45G15vSqVQU27ZtM3nbTp06WaQOjop59B8uTrmPovJ08yp6ISVMmkrihCM2VNqnbMEbmSEj6aa0sjwlVk08kTihj8xJN1KfyCtL4oQn6iePpJWl8HYteCMTyXy/kJktryyZZP7NkyNxkjmZZTk4Y3kUMD+TloQ8alJnoxAC1atXL9QOiIiIiIrC3cUZ7q76kUWdI2+mS2vp0qWLznOFQqEz09/TY+Oo7fRki60wjxIREZGtGMujgONlUmvkUZM6GwszCHe5cuXMfg0RERHRswI83eDtoX+1vFLheFfQPz0+zp49ezBu3DjMmjULzZo1g0KhwJEjRzBx4kTMmjXLhrW0T8yjREREZCvG8ijgeJnUGnnUpM7GqKioQu+AiIiIqChcNWq4afTPqroaWOZIRowYgS+//BIvvPCCdllMTAw8PT0xcOBAXL582Ya1sz/Mo0RERGQrxvJo3jpHZak8atPZqImIiIgK4pqRDleh0F+emW6D2shz7do1+Pn56S338/PD9evXrV8hIiIiIjLIWB4FHDuTWiqPcvh5IiIism9Z2cYfDuy5557DiBEjkJiYqF2WlJSE0aNH4/nnn7dhzYiIiIhIR3551IEzqaXyKK9sNFNORjpyJEys5eIhb/Y/hb0eRQmzdmvZaxttTKRLPIOSJK8s4ZckrSypnCV+kFIkzopnp9SPHti6CtYhcXZJtUrerJdO/N77R1omkGPg/GhWpvXrItGKFSvw2muvITw8HBUqVAAA3Lx5E9WrV8fWrVttWzmyay5OCrg4Gb66whxOEqNaSbmEIU0l802zHQ+N3F8yaXcSpJUlczZqZEq8tVFmWTJnV34ib4Z08fCRtLLgLnHCDKmzUdvp7a45Mmejljhzeoadvl+2YCyPAg6dSS2VR/mnDBEREdk3VQ6gyDG83IFVrVoV586dw+7du/Hbb79BCIHatWujTZs2OrMAEhEREZGNGcujeesclKXyKDsbiYiIyK5pnmRBo9K/QkIj8UpSW1EoFGjXrh3atWtn66oQERERkRHG8ijg+JnUEnnU7Bse7t+/jyFDhqB27doICgpCQECAzoOIiIhIJqFSG304mv/85z/IzDT9Vpsvv/wSjx8/tmCNHBPzKBEREVlTfnnU0TKpNfKo2Vc2vvHGG7h27RoGDBiA4OBg3uZDREREFqV5rILG1cCVjdnyxqmylpEjR6Jnz55wd3c3afuxY8eiXbt28PHxsXDNHAvzKBEREVmTsTwKOF4mtUYeNbuz8dChQzh06BDq1atn7kuLBU12JjQqCSNgS5wgxl5xogTLExIHS1YnyhvU1tk7RVpZiiBPaWXJJO4X/wlixEN5xxGA3IGvJRISBwrXqCQODm2fH32b0GTlQKPRHwtHk+144+MIIdC6dWu4uJj2iy0jI8PCNXJMJT2PysKsZr7H2cWjYzsr+abU8sRtiZMDNpJXFDLl/Z4QEifdUKRJzAsy2yhzAkSJE8SIFImTYtrrBDFqefWS+VmVOdmMozOWRwHHy6TWyKNmx4KaNWsy+BIREZHVqJ9kIsdF/w98dY7jzfw3ZcoUs7bv3Lkzbws2gHmUiIiIrMlYHgUcL5NaI4+a3dm4ePFijB8/HpMnT0ZERARcXV111vv6+ppbJBEREZFRapUaagNnktV2erVsfswNd2QY8ygRERFZk7E8CjheJrVGHjW7s7FUqVJISUnBSy+9pLNcCAGFQgG1xMt/iYiIiLLSM+DqLPSXqx3rLDLJwzxKRERE1mQsjwLMpIaY3dnYu3dvuLm5Yd26dRyQm4iIiCwuMzMTzgbCXaZa3nhz5FiYR4mIiMiajOVRgJnUELM7Gy9cuICzZ8+iRo0alqiP3VOnP4baueiDfzoF9pZQm1xpqu+llaW5fkVaWS6lykgrS6g3SSsrP/c0kifEMCDU7y15hUmcICZLYtM97kk8s1PKtBmyTKFwljh6/SO5M449uTZfWlkalaRxzGQOEm7PJA58LdTyBodOvTxLWlm+tT6QVpYtZOdkI1voT86Wrc62QW3IHpT0POrspICzU9E7WNUZ8r7nXUvIpFaPZUwUaQdyEiVPEHPzvtTyZBFPJP6eyDHcyVAY4pG8rCyzjYrUNHlleSullSXuyZsgRkicUEcqqZO6SPys2unbZQvG8ijATGqI2X95N27cGLdu3Sqx4Y6IiIisKyM7Awq1fgjP0PAscknFPEpERETWZCyPAsykhpjd2Ths2DAMHz4c77//PiIjI/UG5K5bt660yhERERGp1Gq4GDi1rtI49rh8+/fvR3R0tK2r4ZCYR4mIiMiajOVRwLEzqaXyqNmdjT169AAAvPXWP7eCKhQKDshNREREFpGRlQaFk/7tKRkaucMZWFv79u1Rrlw5vPnmm+jXrx/CwsJsXSWHwTxKRERE1mQsjwKOnUktlUfN7mxMSEiQsmMiIiIiU+TkZCPbwPB0OcKxx8e5c+cO1q5di1WrVmHq1Klo3bo1BgwYgC5dusDNzc3W1bNrzKNERERkTcbyKODYmdRSedTszsbw8PBC76w4EKosCJV9zXjo7OQsrazsv5OklSVzogQEhsorKx9lgqyzH1lEirzBkqVe+S1zgOMn8s4SuUqctChLZhsBqDMeSy1PiizJvzQzi/+VRk5u8iY0cnLzkFaWo8t+/BguClf95Q4c7AAgICAAsbGxiI2NRXx8PFasWIEhQ4bgvffeQ+/evTFgwADUq1fP1tW0SyU9j9YKeQ++vr62robFXPn7P1LLqxEcK62syNKWnyDm79TVFt+HUvYJDW955d0b1VpaWQoPeX8niccSJ2IJkVaU1MlAoNT/XVtYMv9OkUnhYqeTPJXxl1aUOHtXWlkKd3nvlxC7JJUjbyIjcxjLo4BjZ1JL5dFCT8166dIl3Lx5EyqVbkdAp06dClskERERkR6FyIEC+if6FMVoisT69etj/PjxCAgIwMcff4wVK1Zg8eLFaNasGb788kvUqVPH1lW0S8yjREREZA3G8mjeuuJAZh41u7Pxzz//xGuvvYbz589rx8YBcsfJAcAxcoiIiEgqN6UGSoV+vlALDZBhgwpJlJ2dje+//x4rVqzA7t270bhxYyxatAg9e/bEgwcPMG7cOHTr1g2XLl2ydVXtCvMoERERWZOxPAo4fia1RB41+5rY4cOHo1KlSvj777/h6emJixcv4sCBA2jcuDH2799vbnFERERE+XJxEUYfjmzYsGEoW7Ys3n33XVSvXh1nz57F0aNH8fbbb8PLywthYWH4+OOP8dtvv9m6qnaHeZSIiIisKb886siZ1FJ51OzOxqNHj2L69OkoXbo0nJyc4OTkhBdeeAGzZ89GbKy88VCIiIiIgNwhpJRuBh5FHFpq9uzZUCgUGDFihHaZEAJTp05FaGgoPDw8EB0djYsXLxZtR0ZcunQJCxcuxJ07d7BgwQJERETobRMaGop9+/ZZZP+OjHmUiIiIrMloHi1iJi2uedTszka1Wg1vb28AQFBQEO7cuQMgd6DuK1eumFscERERUb6cXYw/CuvkyZP46quvULduXZ3lc+fOxbx587Bo0SKcPHkSISEhaNu2LR4/lj+J05QpU9CtWze9mf5ycnJw4MABAICLiwuioqKk79vRMY8SERGRNeWXRwubSYtzHjX7LYmIiMC5c+dQuXJlNGnSBHPnzoWbmxu++uorVK5c2dziHI4mOwMalX1dIuvmLG/2t4zb8maj1kgcL0njLW92rvyoU1Msvg+XUhILe5QpsTA79UTezF6u3j7SyhKSZ1YW6fJm7FMolVLKEZmyZ6MuHgMn50dRlN6vZ8uSOLO1o3NVAm4GTo9mF3JS+CdPnqB3795YtmwZZsyYoV0uhMCCBQvw4YcfomvXrgCA1atXIzg4GOvWrcOgQYMKt0MjWrVqhcTERJQpU0ZneUpKClq1asVxB/NR0vNocefj7m3rKhhVyb+qxfehcLb8bLmPPf+SWp7w85RXWI68mXSRY3gyh8JQZ8r7G9BO50POvURLlkyJnSI5hfyF70CcypWVVlZOprzhV5xKy/tMZD2Q87Od9dg2M50by6NA4TJpcc+jZn/PTZw4ERpN7js5Y8YM3LhxAy+++CJ+/PFH/Oc//ylUJYiIiIiMcXY2/gCA1NRUnUdWVla+5Q0ZMgSvvPIK2rRpo7M8ISEBSUlJaNeunXaZUqlEVFQUjhw5Ir1dQgjthCZPu3//Pry8vKTvrzhhHiUiIiJryi+PFiaTFvc8avYlGDExMdr/V65cGZcuXcKDBw/g7+9vsIJEREREReHmpoCbs37GyFbnLgsLC9NZPmXKFEydOtVgWRs2bMCZM2dw8uRJvXVJSblX9wcHB+ssDw4Oxo0bNwpTdYPyzlIrFAr0798fyqeuRlar1Th37hyaN28ubX/FEfMoERERWZOxPAqYn0lLQh4t9P1ef/zxB65du4aWLVsiICAAQtjXrcVERERUPDx9xlhn+f//e+vWLfj6+mqXK40MJXDr1i0MHz4cu3btgru78dvUn+2sMnbGt7D8/Py05fr4+MDDw0O7zs3NDU2bNsU777wjbX/FGfMoERERWYOxPAqYl0lLSh41u7Px/v376N69O/bt2weFQoGrV6+icuXKePvtt1GqVCl89tlnZpX3119/Ydy4cfjpp5+QkZGB6tWrY/ny5WjUqBGA3IZPmzYNX331FR4+fIgmTZrgiy++QJ06dbRlZGVlYcyYMVi/fj0yMjLQunVrLF68GOXLl9du8/DhQ8TGxmLbtm0AgE6dOmHhwoUoVaqUuW8BERERWZGLO+BqINy5/P8QMr6+vjrBzpjTp08jOTlZmzGA3DO3Bw4cwKJFi7QTiyQlJaFs2X/GTkpOTtY7u1wUK1euBABUrFgRY8aM4S3ThcA8SkRERNZkLI8C5mXSkpJHze5sHDlyJFxdXXHz5k3UqlVLu7xHjx4YOXKkWeHu4cOHaNGiBVq1aoWffvoJZcqUwbVr13QCV94sPKtWrUL16tUxY8YMtG3bFleuXIGPT+5kDyNGjMD27duxYcMGBAYGYvTo0ejYsSNOnz4N5//veu7Vqxdu376NnTt3AgAGDhyIPn36YPv27Wa1X6hUECr7uj1HZm3E7VSJpcmjCS1nlf2oMuTP7vQsZSl5ZYlH+Y9LZg6NxAG0RbbEK0skThDzOMt+B6oWqU/kFeZv5LeguSRPgiO9PDvk7CZnch4AULi4SivL0bm5Cbi56H+vuOWY913TunVrnD9/XmfZm2++iZo1a2LcuHGoXLkyQkJCsHv3bjRo0AAAoFKpEBcXhzlz5hS+AUZMmTJFepklRUnPo8Wdt9LP1lUw6vFvJyy+D7cyFSy+D2fJky+qfeVN6iNzIhZnyMseanmxG64uEv+Ck5lJvTwK3sZU9joxoIt9Ts+jLFtFWlkZmb9IK8vZW14ezXmULKecx7aZJNVYHgXMy6QlJY+a3dm4a9cu/PzzzzpnaQGgWrVqZt8/PmfOHISFhWl7VIHcXtU8pszCk5KSguXLl2PNmjXagTXXrl2LsLAw7NmzBzExMbh8+TJ27tyJY8eOoUmTJgCAZcuWoVmzZrhy5Qpq1KihV7esrCydwTxTU+2zE46IiKi4c3IWcHLWD3FOZt4y6+Pjg4iICJ1lXl5eCAwM1C4fMWIEZs2ahWrVqqFatWqYNWsWPD090atXr8I34CkNGzbE3r174e/vjwYNGuR7O8yZM2ek7LM4Yh4lIiIiazKWRwHzMmlJyaNmdzampaXB09NTb/m9e/eMjpFkzLZt2xATE4Nu3bohLi4O5cqVw+DBg7X3hRc0C8+gQYNw+vRpZGdn62wTGhqKiIgIHDlyBDExMTh69Cj8/Py0wQ4AmjZtCj8/Pxw5csRguJs9ezamTZtmVnuIiIhIPlelgKuBM8muRgJfUYwdOxYZGRkYPHiw9nbZXbt2aa9eK6rOnTtr81Lnzp05mUkhMY8SERGRNRnLo4D8TFoc8qjZnY0tW7bEN998g48++ghA7qCVGo0Gn3zyCVq1amVWWX/++SeWLFmCUaNG4YMPPsCJEycQGxsLpVKJvn37mjQLT1JSEtzc3ODv76+3Td7rk5KSUKZMGb39lylTRrvNsyZMmIBRo0Zpn6empurNLERERESW5+Qs4GQg3Jl7ZaMh+/fv13muUCgwdepUo7NZF9XTt6pYah8lAfMoERERWZOxPAoUPZMWxzxqdmfjJ598gujoaJw6dQoqlQpjx47FxYsX8eDBAxw+fNissjQaDRo3boxZs2YBABo0aICLFy9iyZIl6Nu3r3a7wszC8+w2hrbPrxylUmn2mXEiIiKSz0Up4OqqH+JcnBx75uE333wTb7zxBl566SVe4Wgm5lEiIiKyJmN5FHDsTGqpPGp2Z2Pt2rVx7tw5LFmyBM7OzkhLS0PXrl0xZMgQnZlyTFG2bFnUrl1bZ1mtWrWwadMmAEBISAiA/GfhCQkJgUqlwsOHD3XOJicnJ6N58+babf7++2+9/d+9e9fs2XxEVhaEnY3ZL9TyBgXW3EqTVpaTu9kfL6M0KusMAiseW36CGJnEQ5XE0uxzoGrxWF5ZGSp5k7A4y5wEBwCyZB5LSSQP7C1KwAQxMjm5SRyk3cEZHbNR47jBDsidUfmVV15BYGAgXn/9dfTp0wf169e3dbUcQknPo8Wdt1LObWKWoDn/m8X3oapt+d+XyvLVpZan8Jc3qY+QGD9klqVKkzexiLu0kuROzKhQukkrS2ruc5c0+SEAyJycRyK3gNLSylKly2ujm8wJYu7r/w4sVDlPJM7WZIZ8x2x04ExqqTxaqG/MkJAQTJs2DTt27MCPP/6IGTNmmB3sAKBFixbaab3z/P777wgPDwcAVKpUSTsLT568WXjyglujRo3g6uqqs01iYiIuXLig3aZZs2ZISUnBiRP/zB53/PhxpKSkaLchIiIi++SiFEYfjmzbtm1ISkrClClTcPr0aTRq1Ai1a9fGrFmzcP36dVtXz+4xjxIREZG15JdHHTmTWiqPFunSs7S0NGzcuBEZGRlo164dqlWrZtbrR44ciebNm2PWrFno3r07Tpw4ga+++gpfffUVgNxbTQqahcfPzw8DBgzA6NGjERgYiICAAIwZMwaRkZHa2QBr1aqF9u3b45133sHSpUsBAAMHDkTHjh0NDsZNRERE9sPomI0OfBY5T6lSpTBw4EAMHDgQt2/fxvr167FixQpMnjwZOTlyry4urphHiYiIyNLyHbPRwTOpJfKoyZ2NN2/eRJ8+fXDmzBk0bdoUy5cvR9u2bXH16lUAgIeHB3766Se0bNnS5J0/99xz2LJlCyZMmIDp06ejUqVKWLBgAXr37q3dxpRZeObPnw8XFxd0794dGRkZaN26NVatWgVn538ut/72228RGxurnSWwU6dOWLRokcl1JSIiIttwVgLOBu7scrbPO6EKJTs7G6dOncLx48dx/fp13lZrBPMoERER2YKxPAoUn0wqM4+a3Nk4ZswYqFQqLFmyBP/73/8QExODatWq4cCBA3BycsLgwYMxdepU/PLLL2ZVoGPHjujYsaPR9abMwuPu7o6FCxdi4cKFRrcJCAjA2rVrzaobERER2Z6TiwJOBsZYctI4frLbt28f1q1bh02bNkGtVqNr167Yvn07XnrpJVtXzS4xjxIREZEtGMujgONnUkvkUZM7Gw8cOIBt27bh+eefR4cOHRAUFIQVK1ZoezonTpyI1q1bF7oijsIloAxcfGQO6Wtf1JnyLv91LiXvfRLp6dLKypc9TtKRD5mDXhu7JLwwcp7IK8u1tLyJhvw8A6WV9UTiJDgAgBx5g2i7BIZIKUeVeUZKOVqy3zNZXOQN+J5++6q0soQqQ1pZ7vI++jahUDpBodQ/TgqFvGNnC+XLl8f9+/cRExODpUuX4tVXX4W7e/HNGDIwj5YMOU/kTtjnJnG+GfVJORMc5CtA3mQrxrjWfE5qedk+8uqckyXvj3aFxHybnSVzkhI7/f1lpxPEKGROEGOnklJvSysrJ0ve50vhrZRWlniYIqecNNv8zW4sjwKOnUktlUdN/iv+7t272oGyAwIC4OnpqXNJZUhICB4+fFjkChERERE9TeGqgMJV/49PhYOfRZ48eTK6deumM3sx5Y95lIiIiGzBWB4FHDuTWiqPmtzZKISAQvHPG/j0/4mIiIgsxt0ZUBq4qkHh2INxDxw40NZVcDjMo0RERGQTxvIo4NCZ1FJ51Kz7EydPngxPT08AgEqlwsyZM+Hnl3vJfLq1bnMlIiKiksXZCQpDt5zlON4tK127dsWqVavg6+uLrl275rvt5s2brVQrx8I8SkRERFZnLI8CDpdJrZFHTe5sbNmyJa5cuaJ93rx5c/z555962xARERFJ5e6UezZZj52OA5oPPz8/7dV4vr6+vDLPTMyjREREZBNG8yjgaJnUGnnU5M7G/fv3S985ERERUYFcnAwPpm+vA+znY+XKldr/r1q1ynYVcVDMo0RERGQTxvJo3joHYo08Km+a1xLCNSAErr6etq6GDoWzvA+2OktaUUCAr7yyrDRLtJA4I7CjcZI4yZsqXd6ZEVeJs8+5lZCvPLfAUCnlZNnr7NF2LOdWgrzCXIr/zIsmUzobPpMsHPsz+tJLL2Hz5s0oVaqUzvLU1FR06dIFv/zyi20qRmRjquSbUsuTORt1+h3Lf+/4PJAzY2t+FM5yM5GzT4C0smTORu0kcTbqjFSJnQkSM5axCSsKJT1DWlEiQ+Zs1BL/RpP4t8WD2a9IK0s1sKO0sqSS2YmW8kROOem2mY3aaB4FHDqTWiqPloy/vImIiMhxubvmPp7luGNxA8i9Sk+l0g/MmZmZOHjwoA1qREREREQGGcujgENnUkvlUXY2EhERkV1TuBgekNvoIN127ty5c9r/X7p0CUlJSdrnarUaO3fuRLly5WxRNSIiIiIywFgezVvnaCydR9nZSERERPbNzQVQGjiTrHbM08j169eHQqGAQqHASy+9pLfew8MDCxcutEHNiIiIiMggY3kUcMhMauk8alZnY05ODmbOnIm33noLYWFhhd4pERERkclcnA2PYemg41omJCRACIHKlSvjxIkTKF26tHadm5sbypQpA2dnx2ybNTCPEhERkdUZy6N56xyMpfOoWZ2NLi4u+OSTT9CvX79C79DRufqVgquvl62roUNmH7oqTd7lv56+3tLKEhIHK86Xg00Qo8mRd/SdJF7nnPZQXmHe3kbOHhVC9hPLD7heaGp5nz0nL0nfUZlyfx5Etp2e8XORN7C65o/b0spSBEic0cDRKd1yH89yrK9srfDwcACARuO4g4nbEvNo8ZedcE1ugVXkFZWTZYVb5awwQVtORprU8pQBIdLKypb4HsucICZd5gQxEilKKaWVJZIfyCsrU97nWDzJllaW1J8viX+LeSvlTa6arpTZRjssS5UjpxxzGcujgENmUkvnUbO/Mdu0aYP9+/dboCpEREREBjg7/3M2+emHg1/9N3v2bKxYsUJv+YoVKzBnzhwb1MhxMI8SERGRVRnLow6eSS2VR82+/Ojll1/GhAkTcOHCBTRq1Ahez1xB06lTp0JXhoiIiOhZCqUrFAbOJCskXlFgC0uXLsW6dev0ltepUwevv/46xo0bZ4NaOQbmUSIiIrImY3kUcOxMaqk8anZn43vvvQcAmDdvnt46hUIBtcRbAYmIiIjgbOSMsQOfRQaApKQklC1bVm956dKlkZiYaIMaOQ7mUSIiIrIqY3k0b52DslQeNbuzkeMLERERkVW5GRkjx17HATVRWFgYDh8+jEqVKuksP3z4MEJDQ21UK8fAPEpERERWZSyPAg6dSS2VRyVOCVEyOClaw0khb/BWGTRCXuCWOSAzPD3klfXAShN7lOArIZwknozJkjjRUMD7O6SVJVNqzpdyC5Q4OdGj9PtyCpL9S1PiANPqLPv8XIjrEr+rHGzCKosqplc2vv322xgxYgSys7Px0ksvAQD27t2LsWPHYvTo0TauHZHtiMvyJtsCALSRV1R2lrxJxYwRVpggRqPKlFueh7xJSnKkvsfyfk+kPJSYi2TecilxMkXxl7wco86U10aFi8RMZKedQt7uftLKypQYj6R+H0nKtiJT4oRB5iimVzZaKo8WqrMxLS0NcXFxuHnzJlQqlc662NjYQleGiIiI6FkKpRsUSv0/ZBV2+geDqcaOHYsHDx5g8ODB2jzl7u6OcePGYcKECTaunf1jHiUiIiJrMZZHAcfOpJbKo2Z3Np49exYdOnRAeno60tLSEBAQgHv37sHT0xNlypRhuCMiIiKpFM4uUDjrRxZDy/Ize/ZsbN68Gb/99hs8PDzQvHlzzJkzBzVq1NBuI4TAtGnT8NVXX+Hhw4do0qQJvvjiC9SpU6fI7XiWQqHAnDlzMGnSJFy+fBkeHh6oVq0alEaCLP2DeZSIiIisyVgezVtnqpKSR82+13HkyJF49dVX8eDBA3h4eODYsWO4ceMGGjVqhE8//bRIlSEiIiJ6lpOrB5zcDDxczRuuIy4uDkOGDMGxY8ewe/du5OTkoF27dkhLS9NuM3fuXMybNw+LFi3CyZMnERISgrZt2+Lx48eym6WVlJSEBw8eoEqVKlAqlRDCcc+OWwvzKBEREVmT0TxqZiYtKXnU7M7G+Ph4jB49Gs7OznB2dkZWVhbCwsIwd+5cfPDBB0WqDBEREZEeZ2fA2cXAw7zxcXbu3In+/fujTp06qFevHlauXImbN2/i9OnTAHLPIi9YsAAffvghunbtioiICKxevRrp6elYt26d9Gbdv38frVu3RvXq1dGhQwftjH9vv/02x2wsAPMoERERWZXRPGpeJi0pedTs26hdXV2hUOQO2hscHIybN2+iVq1a8PPzw82bNwtdESq8rJwsaWXJHJBZ3PhLXlnX7kkrK19BntbZjyTO7hKPV460ovDorsSJhuyURubA3gDEjYfSyvJz8pZSTorkAepznsh7zzISE6SVJR5L/PBLfM8ULunSynJ0TkoPOCn1zxg7/f8wfampqTrLlUqlSbd+pKTkDoQfEBAAAEhISEBSUhLatWunU1ZUVBSOHDmCQYMGFbYJBo0cORKurq7aLJWnR48eGDlyJD777DOp+ytOmEeLN/VlK00MWAhSJ1M0xgoThGlUGVLLe5wu73epNSbhKYzHqQVvYzKZeUHi3y/qs3ellSV3oh95GdLZXd57L/PvATeJc/e6KCX+nWKryVjyY4VJtAwxlkeBomXS4ppHzf5EN2jQAKdOnUL16tXRqlUrTJ48Gffu3cOaNWsQGRlZqEoQERERGVPQmI1hYWE6y6dMmYKpU6fmW6YQAqNGjcILL7yAiIgIALm3jwC5nVdPCw4Oxo0bNwpbfaN27dqFn3/+GeXLl9dZXq1aNYvsrzhhHiUiIiJrMmXMRnMzaXHOo2Z3Ns6aNUt7n/hHH32Efv364b333kPVqlWxcuXKQleEiIiIyBAnV3c4uRm4stE198z9rVu34Ovrq11uylWNQ4cOxblz53Do0CG9dXlXzOURQugtkyEtLQ2envpXpNy7d4+TxBSAeZSIiIisyVgezV1XuExanPOo2Z2NjRs31v6/dOnS+PHHHwu9cyIiIqKCOLm5w8nN3cDy3NtofH19dYJdQYYNG4Zt27bhwIEDOmdxQ0JCAOSeUS5btqx2eXJyst7ZZRlatmyJb775Bh999BGA3FCp0WjwySefoFWrVtL3V5wwjxIREZE1GcujuevMz6TFPY/KGxiAiIiIyAKcnJ3hZGDgbUPL8iOEwLBhw7Blyxbs378flSpV0llfqVIlhISEYPfu3WjQoAEAQKVSIS4uDnPmzCl8A4z45JNPEB0djVOnTkGlUmHs2LG4ePEiHjx4gMOHD0vfHxEREREVjrE8mrfOVCUlj5rU2digQQOTL9c8c+ZMoStDhaPKyZRWlswBmcUVeQMMq66mFbyRBG61Ct7GrrjIG6RcIfHUw4O7cidPsUdqefMyAQCcEuR9xtP//FVaWTKp0uV9v+TckjdBTM4jyw/EXxjOEr/bHZ2Tmyuc3NwMLDdv0PIhQ4Zg3bp1+P777+Hj46MdE8fPzw8eHh5QKBQYMWIEZs2ahWrVqqFatWqYNWsWPD090atXLylteVrt2rVx7tw5LFmyBM7OzkhLS0PXrl0xZMgQnTPZlIt5tOTIkjw/zIPZr0grSyNxTjGjnlh+QgZNltwJYvxKh0gr67adThCTmioxLEuc4EJInMAj82+Jk6dIjFcyJ5vRqO3z75T021ellSXzvZf6fSTrb9cs22R3Y3k0d53p71NJyaMmfWN26dJF+//MzEwsXrwYtWvXRrNmzQAAx44dw8WLFzF48OBCV4SIiIjIEAHD81Ca++fCkiVLAADR0dE6y1euXIn+/fsDAMaOHYuMjAwMHjwYDx8+RJMmTbBr1y74+PiYW22ThISEYNq0aRYpu7hhHiUiIiJbMZZHkc9yQ0pKHjWps3HKlCna/7/99tuIjY3V3s/99Da3bt2SWjkiIiIilTobKrXK4HJzCFFwFFQoFJg6dWqBs1kX1rlz50zetm7duhapg6NiHiUiIiJbMZZH89aZqqTkUbOvBf/f//6HU6dO6S1/44030LhxY6xYsaJQFSEiIiIyRK1RQ23gniBDy+xd/fr1oVAoCgyaCoUCarXjtc9amEeJiIjImozl0bx1jsQaedTszkYPDw8cOnQI1apV01l+6NAhuLsbnpmHiIiIqLBUOZlQ5eiP8yNzzGJrSUiQN95oScY8SkRERNZkLI/mrXMk1sijZo/QOWLECLz33nsYOnQo1q5di7Vr12Lo0KEYMmQIRo4cWeiKzJ49WzsQZh4hBKZOnYrQ0FB4eHggOjoaFy9e1HldVlYWhg0bhqCgIHh5eaFTp064ffu2zjYPHz5Enz594OfnBz8/P/Tp0wePHj0qdF2JiIjIejSaHKgNPDRWmalBrtdeew2+vr4IDw/H6tWrUbp0aYSHhxt8kHGWyqMAMykRERHpM5ZHHTGTWiOPmn1l4/jx41G5cmV8/vnnWLduHQCgVq1aWLVqFbp3716oSpw8eRJfffWV3r3gc+fOxbx587Bq1SpUr14dM2bMQNu2bXHlyhXtwJgjRozA9u3bsWHDBgQGBmL06NHo2LEjTp8+Def/n368V69euH37Nnbu3AkAGDhwIPr06YPt27cXqr72JiNb3iy2MmejzvxNXr3uXTc865Ns5Uo71hkJhYezratg0P1Hxf+qEk2O3JkSH92SdyyDrt2QUo6Q/DszJ0ve7Oni+h1pZcmcJVvq5yJVXlGl5RVlEyq1Ciq1/s+IsXFz7Nnly5eRlpYGf39/TJs2De+++y48PT1tXS2HY4k8CjCT2gv3QLnlZSfIy6QatVJaWcaIpHSL70P95KHc8i7ek1ZWZpq836XOEieQdvH1l1dYjrz3X9x+Iq2sjFR5edRVKW9ma/OnhDNOlS6vjZ7+8m6dzf41XlpZqjR5mVt5T97fx4pSkr4/M21zy7KxPJq3zpFYI48W6uu3e/fuRQpyT3vy5Al69+6NZcuWYcaMGdrlQggsWLAAH374Ibp27QoAWL16NYKDg7Fu3ToMGjQIKSkpWL58OdasWYM2bdoAANauXYuwsDDs2bMHMTExuHz5Mnbu3Iljx46hSZMmAIBly5ahWbNmuHLlCmrUqGGwXllZWcjKytI+T02V+FcfERERmSzvrLGh5Y6mfv36ePPNN/HCCy9ACIFPP/0U3t7eBredPHmylWvnWGTmUcA+MynzKBERkX0wlkfz1jkSa+RReV3ehTRkyBC88sor2mCWJyEhAUlJSWjXrp12mVKpRFRUFI4cOQIAOH36NLKzs3W2CQ0NRUREhHabo0ePws/PTxvqAKBp06bw8/PTbmPI7Nmztbe4+Pn5ISwsTEp7iYiIyDwqtQqqHAMPBzuLDACrVq1CYGAgduzYAYVCgZ9++glbtmzRe2zdutXWVS1x7DGTMo8SERHZB6N51AEzqTXyqNlXNqrVasyfPx///e9/cfPmTahUum/qgwcPTC5rw4YNOHPmDE6ePKm3LikpCQAQHBysszw4OBg3btzQbuPm5gZ/f3+9bfJen5SUhDJlyuiVX6ZMGe02hkyYMAGjRo3SPk9NTWXAIyIisgG1RiBHo387lloj77Yqa6lRowY2bNgAAHBycsLevXsN5hTKn8w8CthvJmUeJSIisg/G8mjeOkdijTxq9pWN06ZNw7x589C9e3ekpKRg1KhR6Nq1K5ycnDB16lSTy7l16xaGDx+OtWvX5jtroEKhO2aHEEJv2bOe3cbQ9gWVo1Qq4evrq/MgIiIi68vK0Rh9ODKNRsOOxkKSlUcB+86kzKNERET2Ib886siZ1FJ51OwrG7/99lssW7YMr7zyCqZNm4aePXuiSpUqqFu3Lo4dO4bY2FiTyjl9+jSSk5PRqFEj7TK1Wo0DBw5g0aJFuHLlCoDcs8Bly5bVbpOcnKw9sxwSEgKVSoWHDx/qnElOTk5G8+bNtdv8/fffevu/e/eu3hlqRxXq95a8so7JK0umUCvt5++B0VbakyQu8gbQVrjIG1WhbPnK0sqyV+W/3Se1vGMN2ksrq1rn5XIK6iynmDz2OkmJfx9b18Cwc1HtCt6ohMjRCOQYOGNsaJmjWbNmDb788kskJCTg6NGjCA8Px/z581G5cmV07iz5h7AYkZVHAWZSe6Twlzsx4KOTtplMoLAyblu+vt6PH0stT3PsirSyVBLvRnSXOIyah4eXtLJE5l1pZWXfyip4IxOlPZQ3AZJvafv8HZ0lcfIUr9LyflY1J25JKysrXV4b3ZLk/RAp3SVNzpNlm+90Y3k0b50js0QeNftTmJSUhMjISACAt7c3UlJSAAAdO3bEDz/8YHI5rVu3xvnz5xEfH699NG7cGL1790Z8fDwqV66MkJAQ7N69W/salUqFuLg4bWhr1KgRXF1ddbZJTEzEhQsXtNs0a9YMKSkpOHHihHab48ePIyUlRbsNERER2a8sde7Eg88+bJQ1pVmyZAlGjRqFDh064NGjR1Crcxvk7++PBQsW2LZydk5WHgWYSYmIiKhgxvKoo2dSS+VRs69sLF++PBITE1GhQgVUrVoVu3btQsOGDXHy5EkolaafCfHx8UFERITOMi8vLwQGBmqXjxgxArNmzUK1atVQrVo1zJo1C56enujVqxcAwM/PDwMGDMDo0aMRGBiIgIAAjBkzBpGRkdrBvWvVqoX27dvjnXfewdKlSwEAAwcORMeOHY3ORE1ERET2I1ujQLZG/0puQ8scycKFC7Fs2TJ06dIFH3/8sXZ548aNMWbMGBvWzP7JyqMAMykREREVzFgezVvnqCyVR83ubHzttdewd+9eNGnSBMOHD0fPnj2xfPly3Lx5EyNHjix0RQwZO3YsMjIyMHjwYDx8+BBNmjTBrl274OPjo91m/vz5cHFxQffu3ZGRkYHWrVtj1apVcHb+5xLdb7/9FrGxsdoZAjt16oRFixZJrSsRERFZRqZaAecc/RCXqXbcYAfkznLcoEEDveVKpRJpaWk2qJHjsGYeBZhJiYiISjpjeTRvnaOyVB41u7Px6Z7Of//73wgLC8Phw4dRtWpVdOrUqdAVAYD9+/frPFcoFJg6dWq+A327u7tj4cKFWLhwodFtAgICsHbt2iLVjYiIiGwjM0cBJ0OdjUYCn6OoVKkS4uPjER4errP8p59+Qu3atW1UK8dgyTwKMJMSERGRLmN5NG+do7JUHjW7s/FZTZo0QZMmTQAAJ0+exHPPPVfUIolsxsW/yD8SViVzUheZgnwDbF0Fh+PkWB89IqvK0QA5Bm5PceCJ/wAA77//PoYMGYLMzEwIIXDixAmsX78es2fPxtdff23r6jkU5tHiJXDaz3LLk1jW9rIxEkszrP4huZPQWUUzeUW1kn9xshSZdabIKywnQVpR6Q8lTboBoP4heT97J56TN/mhX2l5k5RkZ8nrFEpNlBfgfXLkzYxU48f90sq6/u+XpJXl9ljOcRRZEmd+MoOxPJq3zlFZKo+a/dPx5MkTODs7w8PDQ7ssPj4ekyZNwo8//qgdTJKIiIhIhky1AgoDt6c48i0rAPDmm28iJycHY8eORXp6Onr16oVy5crh888/x+uvv27r6tk15lEiIiKyJmN5NG+do7JUHjX5sqjbt2+jRYsW8PPzg5+fH0aNGoX09HT07dsXzz33HJRKJQ4dOlToihAREREZkqNRGH04qpycHKxevRqvvvoqbty4geTkZCQlJeHWrVsYMGCAratnt5hHiYiIyBbyy6OOmkktmUdNvrJx/PjxePLkCT7//HNs2rQJn3/+OeLi4lCvXj38/vvvqFSpUpEqQkRERGRIeo4CwsBYOBkOPD6Oi4sL3nvvPVy+fBkAEBQUZOMaOQbmUSIiIrIFY3kUcNxMask8anJn4759+/Df//4XLVq0wL///W+EhoaiW7duGD9+vLTKEBERET1LLQyPhaMW1q+LTE2aNMHZs2f1BuQm45hHiYiIyBaM5dG8dY7KUnnU5M7GpKQkVKlSBQAQEhICDw8PdO7cWWpliGxNUUpp6yqYR+YEMRJHtfXy85VWVknh7OzAv6HIIjhp0D/S1YDGwBB8mQ4+LN/gwYMxevRo3L59G40aNYKXl5fO+rp169qoZvaLeZRsTe1a3dZVIBspXTFUWlkiQ94vsPRUeRPEyHTzmrxsW1Upr41qiXOLpN+WF9ZclfY5w0h2lry/N4Wk4CZUtnmvjOVRwLEzqaXyqFk/Hc7O//yQOzk5wd3dvVA7JSIiIjJV7ux/hpc7sh49egAAYmNjtcsUCgWEEFAoFJzkxAjmUSIiIrI2Y3k0b52jslQeNbmzUQiB1q1bw8Ul9yUZGRl49dVX4ebmprPdmTNnClURIiIiIkMychTQGBgLJ8tBx8fJk5CQYOsqOBzmUSIiIrIFY3kUcOxMaqk8anJn45QpU3Se85YVIiIisoZsDeBk4IxxtgOfRQbAsRoLgXmUiIiIbMFYHs1b56gslUcL3dlIREREZA2ZOYDGwBhLKonjLpFjYB4lIiIiWzCWRwFmUkM4/DwRERHZtWyNAgqN/u0p2QaWERERERHJZiyP5q0jXexsJHpaEAeZl0EZ6G3rKjgcZ34bExmVqXaCJkd/NkSVWt4MiUREpqhZvaatq0A2oqwUIK0s9RN5E4DlZNlniPz3g59tXQWHcvXVNraugkFZ6fKyljpTzuV/apW8mc7NYSyPAsykhvAdISIiIrum1iiQY+ChLuRZ5MWLF6NSpUpwd3dHo0aNcPDgQck1JiIiIqLixFgeLWwmLe55lJ2NREREZNdU2a5GH+bauHEjRowYgQ8//BBnz57Fiy++iJdffhk3b960QM2JiIiIqDjIL4+am0lLQh41qbMxICAA9+7dAwC89dZbePz4sUUrRURERJRHrXEy+gCA1NRUnUdWVpbRsubNm4cBAwbg7bffRq1atbBgwQKEhYVhyZIlVmmLv78/AgICTHqQLuZRIiIispX88qi5mbQk5FGTBnhQqVRITU1FUFAQVq9ejTlz5sDHx6fQOyUiIiIyVbbKFXBxM7A8d+yfsLAwneVTpkzB1KlT9bZXqVQ4ffo0xo8fr7O8Xbt2OHLkiLwK52PBggXa/9+/fx8zZsxATEwMmjVrBgA4evQofv75Z0yaNMkq9XEkzKNERERkK8byaO460zNpScmjJnU2NmvWDF26dEGjRo0ghEBsbCw8PDwMbrtixYpCV4bI1hR+nraugnlcJM56JWe8XgCAS2kveYURlVDOzrYZ/NoeqXPcoMjWD3fqnNwvrlu3bsHX11e7XKlUGizn3r17UKvVCA4O1lkeHByMpKQkiTU2rl+/ftr//+tf/8L06dMxdOhQ7bLY2FgsWrQIe/bswciRI61SJ0fBPEr24MSZoxbfR00Msfg+yHxO4UHSyvp7h+EOi8IoFZItrSyyHd+yEv8YkygrTd7fmwpJcxkpNHLKMZexPJq7zvRMWlLyqEm3Ua9duxYdOnTAkydPoFAokJKSgocPHxp8EBEREcmkUTsbfQCAr6+vzsNYZ2MehUI3OAsh9JZZw88//4z27dvrLY+JicGePXusXh97xzxKREREtpJfHi1MJi3uedSkvuXg4GB8/PHHAIBKlSphzZo1CAwMLPROiYiIiEylznaDwsBtK+ps864CCAoKgrOzs95Z4+TkZL2zy9YQGBiILVu24P3339dZvnXrVuYsA5hHiYiIyFaM5dHcdaZn0pKSR82+kDUhIaHQOyMiIiIyl0atgEatfzOGRm3e2V83Nzc0atQIu3fvxmuvvaZdvnv3bnTu3LnI9TTXtGnTMGDAAOzfv187Rs6xY8ewc+dOfP3111avjyNhHiUiIiJrMpZH89aZqqTkUZNuo/6/9u47rqmrcQP4E/YUQWUpggoqCG6rYitYB2itWN+3bsBqFV8VtW7faqVurau1ddS6d1tHrW2tOEAURIuiqNQq4qhC3eAGkvP7gx95DUlYJiSB5/v55FNy7sm55+RGeHpu7rmFxcbG4v3334enpye8vLzQo0cPxMXFlbkTRERERGrlmgA5Kh65pV/8Z9y4cfjuu++wbt06pKam4pNPPsHNmzcxfPhwLXS8aIMGDUJ8fDyqVq2K3bt3Y9euXbCzs8OJEycwaNCgcu+PoWEeJSIionKjLo+WIZNWhjxa6pS+ZcsWfPTRR+jVqxdGjx4NIQTi4+PRsWNHbNiwAf379y9zZ4h07pWBLbBsY6q5tl5KNdaUeKW5tpLaBGmsrRYnf9dYW5pmxJuBUCFGJvxMFJBIjSBRcSZZVVlx+vTpgwcPHmDmzJnIyMiAr68vfv31V7i7u2uiq6XWunVrbN26VSf7NmTMo6QrbjXu67oLpCPPNLhsw42/NLcuW9smzAsVgamb6hue6VruK819Vo00dHNTI1n5r2sIqM+jBdtKozLk0VJPNs6ZMwcLFy5UuCPNmDFjsGTJEsyaNYvhjoiIiDTKJAcwUZXhcsrW3ogRIzBixIg36pOmpKWlYf369bh27RqWLVsGR0dHHDhwAG5ubmjUqJGuu6e3mEeJiIioPKnNo0CZMmlFz6Ol/krAtWvX8P777yuV9+jRg+vnEBERkcYZS9U/DFlsbCz8/PyQmJiIXbt24enTpwCA8+fPY8aMGTrunX5jHiUiIqLyVFQeNeRMqq08WurJRjc3Nxw+fFip/PDhw3BzcytzR4iIiIhUMcsVMM9RfpjlGvalY1OmTMHs2bMRHR0NM7P/3d2wQ4cOSEhI0GHP9B/zKBEREZUndXnU0DOptvJoqS+jHj9+PEaPHo3k5GT4+/tDIpHg+PHj2LBhA7788ssyd4SIiIhIFROpgIlUOcQJFWWGJCUlBdu2bVMqr1GjBh48eKCDHhkO5lEiIiIqT+ryKGDYmVRbebTUk43/+c9/4OzsjMWLF+P7778HAHh7e2Pnzp06uU03kSaJp6903YXSsdDgDWJMyrj4mQriVZ7G2qosjEp/U12iSsP8lQymkCmVG71SLjMkVatWRUZGBurUqaNQfvbsWdSsWVNHvTIMzKMV261H32q0Pcmo7Rprq5qzBrOXGtc+6Kj1fVTtobmbnQCAw0ffa6yteyM6aKwtYy9bjbVlaTldY209f66xpopYRK70rrzfSWNtef18SGNtVQrVLTTW1OVumjuOpubGGmvrVZaG2tHRPV3V5VHAsDOptvJomf739oMPPsAHH3xQ5p0SERERlZSxmjPJMgM+iwwA/fv3x+TJk/HDDz9AIpFAJpPhxIkTmDBhAsLCwnTdPb3HPEpERETlRV0eBQw7k2orj2ruNAgRERGRFpjmyGD6Sqr8yDHcs8hA/h2Va9eujZo1a+Lp06fw8fFB+/bt4e/vj2nTpum6e0RERET0/9TmUQPPpNrKo7xwj4iIiPSakVQGI6mKy6hVlBkSU1NTbN26FbNmzcKZM2cgk8nQrFkzeHl56bprRERERPQadXm0YJuh0lYe1ek3G+fNm4dWrVrB1tYWjo6O6NmzJy5fvqxQRwiBqKgouLq6wtLSEoGBgbh48aJCnVevXiEyMhLVq1eHtbU1evTogb///luhzqNHjxAaGgo7OzvY2dkhNDQUjx8/1vYQiYiI6A0Z5+SpfRiymTNn4vnz56hbty7+/e9/o3fv3vDy8sKLFy8wc+ZMXXev0mAeJSIiouIUlUcNOZNqK4/qdLIxNjYWI0eOxMmTJxEdHY28vDx06dIFz549k9dZuHAhlixZgq+//hqnT5+Gs7MzOnfujCdPnsjrjB07Fnv27MGOHTtw/PhxPH36FN27d4dUKpXX6d+/P5KTk3HgwAEcOHAAycnJCA0NLdfxEhERURlIZeofBuzzzz/H06dPlcqfP3+Ozz//XAc9qpyYR4mIiKhYReVRA86k2sqjpb6MeubMmZgwYQKsrKwUyl+8eIEvvvgCn332WYnbOnDggMLz9evXw9HREUlJSWjfvj2EEFi2bBk+/fRT9OrVCwCwceNGODk5Ydu2bYiIiEBWVhbWrl2LzZs3o1On/LsubdmyBW5ubjh06BCCgoKQmpqKAwcO4OTJk2jdujUAYM2aNWjbti0uX76MBg0alPZtoIrqqY5ubVVGEmtzjbUl8Kz4SiUk0+DdqKVSicba0mfGxoa7qDBph5Hmbv5n8GTSHMjylCOLTJqjg95ojhACEony77hz587BwcFBBz0yHMyjFdsjjd6qF3DV4I2XTe9p/++1ibn2/6dV4lhV6/soKyN7Dd7xO09z76Us4S+NtWWruZtkI++xtPhKJWRswvChMxaa+9ybW2nuc6/JPHr/hplG2nmap5vvzKnLowXbDJW28mipj5I2z8JnZeXfC71gQOnp6cjMzESXLl3kdczNzREQEID4+HgAQFJSEnJzcxXquLq6wtfXV14nISEBdnZ28mAHAG3atIGdnZ28TmGvXr1Cdna2woOIiIjKnzQvF9K8HBUPwzpBVMDe3h4ODg6QSCSoX78+HBwc5A87Ozt07twZvXv31nU39RrzKBEREZUn9XnUMDOptvNoqb/ZqK1ZTyEExo0bh7fffhu+vr4AgMzMTACAk5OTQl0nJyfcuHFDXsfMzAz29vZKdQpen5mZCUdHR6V9Ojo6yusUNm/ePF7CREREpAdkQgqZUP7mhqoyQ7Bs2TIIITB48GB8/vnnsLOzk28zMzODh4cH2rZtq8Me6j/mUSIiIipP6vJowTZDo+08WuLJRnt7e0gkEvms5+sBTyqV4unTpxg+fHiZOzJq1CicP38ex48fV9pWOEyqC5hF1VFVv6h2pk6dinHjxsmfZ2dnw83Nrch9EhERkeZJ815BKlG+GEOa90oHvXlz4eHhAIA6deqgXbt2MDEp9bnfSot5lHmUiIhIF9Tl0YJthkbbebTErWlz1jMyMhL79u3DsWPHUKtWLXm5s7MzgPwzwS4uLvLyu3fvys8uOzs7IycnB48ePVI4m3z37l34+/vL6/zzzz9K+713757SWeoC5ubmMDfX3Hp4REREVDYV7ZuNBZ49e4bDhw8jKChIofz333+HTCZD165dddQz/cU8SkRERLpQ0b7ZWEBbebTEk42vz3r6+/vD1PTNFzAVQiAyMhJ79uxBTEwM6tSpo7C9Tp06cHZ2RnR0NJo1awYAyMnJQWxsLBYsWAAAaNGiBUxNTREdHS2/njwjIwMXLlzAwoULAQBt27ZFVlYWTp06hbfeegsAkJiYiKysLHkAJAIAh4n7dd2F0rG21FxbJpq7EYssR3M3iJFprikig2JswpsGFZDm5SBP5TcbDXcxbgCYMmUK5s+fr1QuhMCUKVM42agC82jl8EjDXxCpaa+ZmxIAgIm59v+H0sRc+7//JQ52xVfSEYmNBm8Qo0H3kq9prC1La83l7pfZmrtZhhFvWKgzEgvNfavMzFpzN4ixqKK5tq6c1czdZp7r6MbP6vJowTZDpa08WupPdEBAAGQyGf766y/cvXsXMpnikW7fvn2J2xo5ciS2bduGn376Cba2tvL1auzs7GBpaQmJRIKxY8di7ty58PLygpeXF+bOnQsrKyv0799fXnfIkCEYP348qlWrBgcHB0yYMAF+fn7yuwF6e3sjODgYQ4cOxerVqwEAw4YNQ/fu3XnnPyIiIj1XUb/ZeOXKFfj4+CiVN2zYEFevXtVBjwwH8ygRERGVp4r6zUZt5dFSTzaePHkS/fv3x40bNyCE4pkPiUQCqbTkb/LKlSsBAIGBgQrl69evx6BBgwAAkyZNwosXLzBixAg8evQIrVu3xsGDB2Frayuvv3TpUpiYmKB379548eIFOnbsiA0bNsDY+H8z51u3bsXo0aPldwns0aMHvv7669IMnYiIiHQgT/YKEqnyt0DyZIa3Ps7r7OzscO3aNXh4eCiUX716FdbW1rrplIFgHiUiIqLypC6PFmwzVNrKo6WebBw+fDhatmyJX375BS4uLsUujF2UwuFQFYlEgqioKERFRamtY2FhgeXLl2P58uVq6zg4OGDLli1l6SYRERHpkEwmhUym4puNKsoMSY8ePTB27Fjs2bMH9erVA5Af7MaPH48ePXrouHf6jXmUiIiIypO6PFqwzVBpK4+WerLxypUr+PHHH+Hp6VnmnRIRERGVlFSaAyMVk0lSqeGujwMAX3zxBYKDg9GwYUP5DUn+/vtvvPPOO1i0aJGOe6ffmEeJiIioPKnLowXbDJW28mipJxtbt26Nq1evMtwR6QH7977VXGPvaa4ph4maawuI0GRjesv7YLSuu0B6pu6ew7rugt6oqN9stLOzQ3x8PKKjo3Hu3DlYWlqicePGpVpvsLKq7Hn0/rj38MrszW8mUGPFUQ30RvMsNXyTCkl1zd1Qz9jkicbaIjU0eNNC8TRXY23duP+3xtpyc9RYU3rrZ5eg4iuVkH+Y5v7eixeaa+tRumZueAIA1Xro502bJJq7bw2MNdSWkY5uEFNRv9morTxaosN9/vx5+c+RkZEYP348MjMz4efnp3QXwMaNG79Rh4iIiIheJ5XmqLxM1pDPIheQSCTo0qUL2rdvD3Nz8ze6HLiiYx4lIiIiXVGXRwu2GTJt5NESTTY2bdoUEolEYU2bwYMHK3RMCFHqBbmJiIiIipN/9788leWGTCaTYc6cOVi1ahX++ecf/PXXX6hbty6mT58ODw8PDBkyRNdd1CvMo0RERKQr6vJowTZDpa08WqLJxvT09DI1TkRERPSm8s8kqy43ZLNnz8bGjRuxcOFCDB06VF7u5+eHpUuXcrKxEOZRIiIi0hV1ebRgm6HSVh4t0WSju7t7mRonIiIielMyoWbNRgM+iwwAmzZtwrfffouOHTti+PDh8vLGjRvjzz//1GHP9BPzKBEREemKujxasM1QaSuPlnqJzn379qksl0gksLCwgKenJ+rUqVPmDhERERG9Lk/N2WJ15W/q+vXrmDVrFo4cOYLMzEy4urpi4MCB+PTTT2FmZiavd/PmTYwcORJHjhyBpaUl+vfvj0WLFinUKcrt27dV3uBEJpMhN1dzNzWoiCp7Hn12TwIj0zdfT6mGBvqiDdZmRhptT1LVQmNtmZhnaawtdYw0d88J9fIM93+MS0M80tzfief3b2qsLVM3zd4ESVOMTDTXL6nqq03LRORqsF8v9fO9x0v9/DdppMEbNtnYaqYtiVQ361sXlTsNOZNqK4+WerKxZ8+eSuvlAIrr5Lz99tvYu3cv7O3ty9wxIiIiIgAQQgah4oyxENq5HeGff/4JmUyG1atXw9PTExcuXMDQoUPx7NkzLFq0CAAglUrx3nvvoUaNGjh+/DgePHiA8PBwCCGwfPnyEu2nUaNGiIuLU/rG3g8//IBmzZppfFwVCfMoERERlSd1ebRgmzaURybVVh4t9WRjdHQ0Pv30U8yZMwdvvfUWAODUqVOYNm0apk+fDjs7O0RERGDChAlYu3ZtmTtGREREBBScLVb+JkKeVDvf/gsODkZwcLD8ed26dXH58mWsXLlSHuwOHjyIS5cu4datW3B1dQUALF68GIMGDcKcOXNQpUqVYvczY8YMhIaG4vbt25DJZNi9ezcuX76MTZs2Yf/+/VoZW0XBPEpERETlSV0ezd9muJlUW3m01JONY8aMwbfffgt/f395WceOHWFhYYFhw4bh4sWLWLZsmcLdAYmIiIjKSibNgVRFuJP9f7DLzs5WKDc3N4e5ublG+5CVlQUHBwf584SEBPj6+spDHQAEBQXh1atXSEpKQocOHYpt8/3338fOnTsxd+5cSCQSfPbZZ2jevDl+/vlndO7cWaP9r2iYR4mIiKg8qcuj+dsMN5NqK4+WerIxLS1N5cxolSpVcO3aNQCAl5cX7t+/X+ZOERERERWQyaSQSJTXcCtYpNvNzU2hfMaMGYiKitLY/tPS0rB8+XIsXrxYXpaZmQknJyeFevb29jAzM0NmZmaxbebl5WHOnDkYPHgwYmNjNdbXyoJ5lIiIiMqTujxasA0wvEyqzTxa6snGFi1aYOLEidi0aRNq1MhfVvrevXuYNGkSWrVqBQC4cuUKatWqpdGOEhERUeWUJ8uFkCifSZbK8lefv3XrlsLEk7ozyFFRUfj888+L3Nfp06fRsmVL+fM7d+4gODgYH374IT7++GOFuhKJ8gLlBesFFsfExARffPEFwsPDi61Lyip7HnXfvL9El+obqkYuYzTbYKjm2rMP1VhTpIbDRP1cRiL8K133wLDU1nUHyoFT8VXoNZq6KVl2djZgZ6eh1kpOXR4FDDeTajOPlnqyce3atQgJCUGtWrXg5uYGiUSCmzdvom7duvjpp58AAE+fPsX06dM13lkiIiKqfIRMCqEqRP3/WeQqVaqUaOJl1KhR6Nu3b5F1PDw85D/fuXMHHTp0QNu2bfHtt98q1HN2dkZiYqJC2aNHj5Cbm6t0dlmdTp06ISYmBoMGDSpRffof5lEiIiIqT+ryaME2wDAzqbbyaKknGxs0aIDU1FT8/vvv+OuvvyCEQMOGDdG5c2cYGeV/pbRnz54a7SQRERFVXnmyHAgo3+Wv4CxySVWvXh3Vq1cvUd3bt2+jQ4cOaNGiBdavXy/POAXatm2LOXPmICMjAy4uLgDyF+g2NzdHixYtSrSPrl27YurUqbhw4QJatGgBa2trhe09evQoUTuVEfMoERERlSd1eRQw7EyqrTwqEUKo/h4oKcjOzoadnR2ysrIq9GUrRERE6pT338KC/Xl5vAVjI+Xzo1JZHq5cP6Xx/ty5cwcBAQGoXbs2Nm3aBGNjY/k2Z2fn/H1LpWjatCmcnJzwxRdf4OHDhxg0aBB69uyJ5cuXl2g/hcPi6yQSCaRS6ZsNhCoc5lEiIqrs9C2PAoadSbWVR0v0zcavvvoKw4YNg4WFBb76qujFKkaPHl2mjhARERGpIpXmQgjlM8kFi3Fr2sGDB3H16lVcvXpVac2/gnO0xsbG+OWXXzBixAi0a9cOlpaW6N+/PxYtWlTi/chkqs+Ok2rMo0RERKQr6vIoYNiZVFt5tETfbKxTpw7++OMPVKtWDXXq1FHfmEQivwNgRcMzyUREVNnp6kyyR60mMDIyVtouk0lx/e9zBvm3OS8vDxYWFkhOToavr6+uu2MQmEeZR4mIiPQtjwKGm0m1mUdL9M3G9PR0lT8TERERaZtUmgMhVE82GioTExO4u7vzUulSYB4lIiIiXVGXRwHDzaTazKPqL84uRk5ODi5fvoy8vNIthElERERUGjKZVO3DkE2bNg1Tp07Fw4cPdd0Vg8U8SkREROWhqDxqyJlUW3m01Hejfv78OSIjI7Fx40YAwF9//YW6deti9OjRcHV1xZQpUzTaQSIiIqrcpLI8lXf/M/Q1D7/66itcvXoVrq6ucHd3V7r735kzZ3TUM/3HPEpERETlSV0eBQw7k2orj5Z6snHq1Kk4d+4cYmJiEBwcLC/v1KkTZsyYwXBHREREGiVkUggoLzEtDDjYAUDPnj113QWDxTxKRERE5UldHs3fZriZVFt5tNSTjXv37sXOnTvRpk0bSCQSebmPjw/S0tI02jkiIiKiPFkujITyyi8yNXcENBQzZszQdRcMFvMoERERlSd1eRQw7EyqrTxa6snGe/fuwdHRUan82bNnCmGPiIiISBOEkKk8jywMONi9LikpCampqZBIJPDx8UGzZs103SW9xzxKRERE5UldHi3YZug0nUdLPdnYqlUr/PLLL4iMjAQAeaBbs2YN2rZt+0adISIiIiosLzcHEonymWRDD3Z3795F3759ERMTg6pVq0IIgaysLHTo0AE7duxAjRo1dN1FvcU8SkREROVJXR4FDDuTaiuPlnqycd68eQgODsalS5eQl5eHL7/8EhcvXkRCQgJiY2PL1AkiIiIidWQyGSQSFWs2CnXnlw1DZGQksrOzcfHiRXh7ewMALl26hPDwcIwePRrbt2/XcQ/1F/MoERERlSd1eRQw7EyqrTyqelq2CP7+/jhx4gSeP3+OevXq4eDBg3ByckJCQgJatGhRpk4QERERqZOT+0rtw5AdOHAAK1eulAc7IH/NwW+++Qa//fabDnum/5hHiYiIqDwVlUcNOZNqK4+W+puNAODn54eNGzeWeaeGqGCmOjs7W8c9ISIi0o2Cv4HlffY2J89wA1xRZDIZTE1NlcpNTU0hM+C7GpYX5lEiIqLKh3lUs7SVR0s82VjSUFOlSpUyd0afPXnyBADg5uam454QERHp1pMnT2BnZ6f1/ZiZmcHZ2RmZmZlq6zg7O8PMzEzrfdGGd999F2PGjMH27dvh6uoKALh9+zY++eQTdOzYUce900/Mo8yjREREgH7lUcBwM6m28qhElHA62MjIqMi7+wkhIJFIIJVKy9wZfSaTyXDnzh3Y2triyZMncHNzw61btypsmM3OzuYYK4jKME6OsWLgGPWfEAJPnjyBq6srjIxKvRJLmbx8+RI5OTlqt5uZmcHCwqJc+qJpt27dQkhICC5cuAA3NzdIJBLcvHkTfn5++Omnn1CrVi1dd1HvMI/+L49KJBKD/51SEhxjxcAxVgwcY8Vg6GPUxzwKGG4m1VYeLfE3G48ePSr/WQiBbt264bvvvkPNmjXLtGNDY2RkJH+TC0JulSpVDPIfZ2lwjBVHZRgnx1gxcIz6rTzOIL/OwsLCIINbSbi5ueHMmTOIjo7Gn3/+CSEEfHx80KlTJ113TW8xjxqpDP2G/DulpDjGioFjrBg4xorBkMfIPKo52sqjJZ5sDAgIUHhubGyMNm3aoG7dum/UASIiIqLKrHPnzujcubOuu2EQmEeJiIiINE/TebR8vnNKRERERACAI0eOwMfHR+X6g1lZWWjUqBHi4uJ00DMiIiIiqgy0nUc52VgG5ubmmDFjBszNzXXdFa3hGCuOyjBOjrFi4Bipsli2bBmGDh2q8tIlOzs7REREYMmSJTroGRmayvA7hWOsGDjGioFjrBgqwxipeNrOoyW+QUxhtra2OH/+POrUqVPmnRMRERFVNu7u7jhw4AC8vb1Vbv/zzz/RpUsX3Lx5s5x7ZniYR4mIiIhKT9t5tMRrNvbq1Uvh+cuXLzF8+HBYW1srlO/evbtMHSEiIiKqDP755x+Ympqq3W5iYoJ79+6VY48MB/MoERER0ZvTdh4t8WRj4bv9DBw4sMw7JSIiIqqsatasiZSUFHh6eqrcfv78ebi4uJRzrwwD8ygRERHRm9N2Hi3zZdREREREVHqRkZGIiYnB6dOnYWFhobDtxYsXeOutt9ChQwd89dVXOuohEREREVVk2s6jnGwkIiIiKkf//PMPmjdvDmNjY4waNQoNGjSARCJBamoqvvnmG0ilUpw5cwZOTk667ioRERERVUDazqOcbCQiIiIqZzdu3MB//vMf/P777yiIYhKJBEFBQVixYgU8PDx020EiIiIiqtC0mUeNNNRHg7JixQrUqVMHFhYWaNGiBeLi4uTbhBCIioqCq6srLC0tERgYiIsXLxbbZkpKCgICAmBpaYmaNWti5syZKDyPGxsbixYtWsDCwgJ169bFqlWrND62Apoe4+XLl9GhQwc4OTnJ+z9t2jTk5uYq1NOXMe7evRtBQUGoXr06JBIJkpOTS9SmIR3HsoxRH48joH6cubm5mDx5Mvz8/GBtbQ1XV1eEhYXhzp07xbZpKMeyrGPUx2NZ1Oc1KioKDRs2hLW1Nezt7dGpUyckJiYW26ahHEegbGPUx+NI5cPd3R2//vor7t+/j8TERJw8eRL379/Hr7/+yonGSqIy5FGAmZSZVDVDOo7Mo+oZ0nEEmEfV0cfjSOVDq3lUVDI7duwQpqamYs2aNeLSpUtizJgxwtraWty4cUMIIcT8+fOFra2t2LVrl0hJSRF9+vQRLi4uIjs7W22bWVlZwsnJSfTt21ekpKSIXbt2CVtbW7Fo0SJ5nWvXrgkrKysxZswYcenSJbFmzRphamoqfvzxR4MYY1pamli3bp1ITk4W169fFz/99JNwdHQUU6dO1csxbtq0SXz++edizZo1AoA4e/ZssW0a2nEsyxj17TgWN87Hjx+LTp06iZ07d4o///xTJCQkiNatW4sWLVoU2aYhHcuyjlHfjmVxn9etW7eK6OhokZaWJi5cuCCGDBkiqlSpIu7evau2TUM6jmUdo74dRyIqH5Uhj2prnPr2e5OZtGJkUuZR5lF1DOk4lnWM+nYcqWKodJONb731lhg+fLhCWcOGDcWUKVOETCYTzs7OYv78+fJtL1++FHZ2dmLVqlVq21yxYoWws7MTL1++lJfNmzdPuLq6CplMJoQQYtKkSaJhw4YKr4uIiBBt2rTRxLAUaGOMqnzyySfi7bfflj/XlzG+Lj09vcShx5CO4+tKM0ZVdHkchSj5OAucOnVKAJD/QVXFUI9lgZKMURVD+DdZICsrSwAQhw4dUtumoR/HkoxRFV3/myQi7asMeVQIZtLXMZMWzxCOYwHmUfUM6Tgyj6rHPEpvqlJdRp2Tk4OkpCR06dJFobxLly6Ij49Heno6MjMzFbabm5sjICAA8fHx8rJBgwYhMDBQ/jwhIQEBAQEwNzeXlwUFBeHOnTu4fv26vE7h/QYFBeGPP/5Q+nqyPo6xsKtXr+LAgQMICAiQl+nLGEvKkI9jSenzcQTKNs6srCxIJBJUrVpVXlbRjmVJxliYIf2bzMnJwbfffgs7Ozs0adJEXl6RjmNJx1iYrv9NEpH2VYY8CjCTMpMqqmjHkXlUNUM6jsyjgWr3wzxKmlCpJhvv378PqVSqdDcdJycnZGZmIjMzU/5c1fYCLi4uqF27tvx5ZmamytcUbCuqTl5eHu7fv/+GI/sfbY2xgL+/PywsLODl5YV33nkHM2fOlG/TlzGWlCEfx5LS5+MIlH6cL1++xJQpU9C/f39UqVJFXl6RjmVJx1hAH45lSce4f/9+2NjYwMLCAkuXLkV0dDSqV68u314RjmNpx1hAH44jEZWPypBHAWZSZlJFFek4Mo8a9nFkHtXv40gVh4muO6ALEolE4bkQQqGsuO3z5s0rUZuFy0tSR1O0MUYA2LlzJ548eYJz585h4sSJWLRoESZNmlRku6rKNaG4MRSnIhzH4hjCcVS3v8Jlubm56Nu3L2QyGVasWKGwraIcy9KOEdCvY1ncGDt06IDk5GTcv38fa9asQe/evZGYmAhHR0cAFeM4lmWMgH4dRyIqH5Uhj6rbHzOpIkM4lpUhkzKP5mMeNfzjyDxK+qBSTTZWr14dxsbGSmdv7t69CycnJzg7OwPIn7V3cXFR2q6Os7OzyjaB/531UFfHxMQE1apVK/ugCtHWGAu4ubkBAHx8fCCVSjFs2DCMHz8exsbGejPGsjKk4/im9OE4AiUfZ25uLnr37o309HQcOXJE4QyrKoZ4LEs7xgL6cCxLOkZra2t4enrC09MTbdq0gZeXF9auXYupU6eqbNcQj2Npx1hAH44jEZWPypBHAWZSZtKSMaTjyDyqniEdR+ZR9fThOFLFUakuozYzM0OLFi0QHR2tUB4dHQ1/f3/UqVMHzs7OCttzcnIQGxsLf39/te22bdsWx44dQ05Ojrzs4MGDcHV1ld8uvG3btkr7PXjwIFq2bAlTU1MNjC6ftsaoihACubm58jMa+jLGsjKk46hJujqOQMnGWRB6rly5gkOHDpXoj5mhHcuyjFEVQ/s3KYTAq1ev1G43tOOoSnFjVPcaXf2bJCLtqwx5FGAmZSYtPX0+jsyjJafPx1Fdf5lHVb+GeZTeyJvfY8awFNwqfu3ateLSpUti7NixwtraWly/fl0IIcT8+fOFnZ2d2L17t0hJSRH9+vUTLi4uIjs7W97GlClTRGhoqPz548ePhZOTk+jXr59ISUkRu3fvFlWqVBGLFi2S1ym4Vfwnn3wiLl26JNauXau1W8VrY4xbtmwRO3fuFJcuXRJpaWni+++/FzVr1hQDBgzQyzE+ePBAnD17Vvzyyy8CgNixY4c4e/asyMjIUDtGQzuOZRmjvh3H4saZm5srevToIWrVqiWSk5NFRkaG/PHq1Su14zSkY1nWMerbsSxqjE+fPhVTp04VCQkJ4vr16yIpKUkMGTJEmJubiwsXLqgdoyEdx7KOUd+OIxGVj8qQR7U1Tn37vclMWjEyKfMo86i6MRrScWQeJX1S6SYbhRDim2++Ee7u7sLMzEw0b95cxMbGyrfJZDIxY8YM4ezsLMzNzUX79u1FSkqKwuvDw8NFQECAQtn58+fFO++8I8zNzYWzs7OIiooSMplMoU5MTIxo1qyZMDMzEx4eHmLlypUGM8YdO3aI5s2bCxsbG2FtbS18fHzE3LlzxYsXL/RyjOvXrxcAlB4zZsxQO0YhDOs4lmWM+ngcixpnenq6yjECEEePHlU7TiEM51iWdYz6eCzVjfHFixfigw8+EK6ursLMzEy4uLiIHj16iFOnTim83pCPY1nHqI/HkYjKR2XIo0IwkzKTqh6jIR1H5lH1YzSk48g8qn6M+ngcyfBJhPj/78USERERERERERERvYFKtWYjERERERERERERaQ8nG4mIiIiIiIiIiEgjONlIREREREREREREGsHJRiIiIiIiIiIiItIITjYSERERERERERGRRnCykYiIiIiIiIiIiDSCk41ERERERERERESkEZxsJKpAAgMDMXbsWF13Q6XMzEx07twZ1tbWqFq1qso6gwYNQs+ePd94XxKJBHv37gUAXL9+HRKJBMnJyWV6vSplabMkPDw8sGzZMo22qU5MTAwkEgkeP35cLvsrT/PmzUOrVq1ga2sLR0dH9OzZE5cvX1aoI4RAVFQUXF1dYWlpicDAQFy8eFG+/eHDh4iMjESDBg1gZWWF2rVrY/To0cjKylJox8PDAxKJROExZcqUIvtX8N7b29vj5cuXCttOnTolb0cbXr16hcjISFSvXh3W1tbo0aMH/v77b4U6ZRkTERHR65hJ8zGTFo+ZlJmUmbRi4mQjURHu3r2LiIgI1K5dG+bm5nB2dkZQUBASEhLkdYoLAaVR0sCg7o/y7t27MWvWLI30RdOWLl2KjIwMJCcn46+//lJZ58svv8SGDRs0ul83NzdkZGTA19e3xK/JyMhA165dNdoPKj+xsbEYOXIkTp48iejoaOTl5aFLly549uyZvM7ChQuxZMkSfP311zh9+jScnZ3RuXNnPHnyBABw584d3LlzB4sWLUJKSgo2bNiAAwcOYMiQIUr7mzlzJjIyMuSPadOmlaiftra22LNnj0LZunXrULt27TcYfb6cnByV5WPHjsWePXuwY8cOHD9+HE+fPkX37t0hlUoV6pV1TEREpB3MpJrDTErlhZmUmbQyM9F1B4j02b/+9S/k5uZi48aNqFu3Lv755x8cPnwYDx8+1Pi+1P0iLg0HBwcN9EQ70tLS0KJFC3h5eamtY2dnp/H9Ghsbw9nZuVSvKW19XcnJyYGZmVml3b86Bw4cUHi+fv16ODo6IikpCe3bt4cQAsuWLcOnn36KXr16AQA2btwIJycnbNu2DREREfD19cWuXbvkbdSrVw9z5szBwIEDkZeXBxOT//35tLW1LdNnJjw8HOvWrUO/fv0AAC9evMCOHTswevRohf9Be/DgAUaNGoW4uDg8fPgQ9erVw3//+1/564D8b5D4+vrCzMwMmzZtQqNGjRAbG6uwv6ysLKxduxabN29Gp06dAABbtmyBm5sbDh06hKCgoDceExERaQczqeYwk2qerjOhrvevDjMpM2llxm82Eqnx+PFjHD9+HAsWLECHDh3g7u6Ot956C1OnTsV7770HIP+r3QDwwQcfQCKRyJ+npaUhJCQETk5OsLGxQatWrXDo0CGF9j08PDB79mwMGjQIdnZ2GDp0KOrUqQMAaNasGSQSCQIDA5X6df36dXTo0AEAYG9vD4lEgkGDBgFQvmSlYB9hYWGwsbGBu7s7fvrpJ9y7dw8hISGwsbGBn58f/vjjD4V9xMfHo3379rC0tISbmxtGjx6tcAZOlZUrV6JevXowMzNDgwYNsHnzZoV+7Nq1C5s2bVLob2GFL1kJDAzE6NGjMWnSJDg4OMDZ2RlRUVEKr7ly5Qrat28PCwsL+Pj4IDo6Wun9KjgzL5PJUKtWLaxatUqhzpkzZyCRSHDt2jUAyt8MOHXqFJo1awYLCwu0bNkSZ8+eVXj9hg0blC7D2bt3r8JlByX5TBSn4P2ZN28eXF1dUb9+fQD5f5xbtmwp/2Pcv39/3L17V+G1v/76K+rXrw9LS0t06NAB169fV9j+4MED9OvXD7Vq1YKVlRX8/Pywfft2hTqBgYEYNWoUxo0bh+rVq6Nz586l6r+uFFxmUvA/Punp6cjMzESXLl3kdczNzREQEID4+Pgi26lSpYpCqAOABQsWoFq1amjatCnmzJlT4v9JCw0NRVxcHG7evAkA2LVrFzw8PNC8eXOFei9fvkSLFi2wf/9+XLhwAcOGDUNoaCgSExMV6m3cuBEmJiY4ceIEVq9erbS/pKQk5ObmKozb1dUVvr6+SuMu65iIiEjzmEmZSQswk+ZjJmUmJf3HyUYiNWxsbGBjY4O9e/fi1atXKuucPn0aQP5ZqoyMDPnzp0+folu3bjh06BDOnj2LoKAgvP/++/Jf4AW++OIL+Pr6IikpCdOnT8epU6cAAIcOHUJGRgZ2796ttE83Nzf52a3Lly8jIyMDX375pdpxLF26FO3atcPZs2fx3nvvITQ0FGFhYRg4cCDOnDkDT09PhIWFQQgBAEhJSUFQUBB69eqF8+fPY+fOnTh+/DhGjRqldh979uzBmDFjMH78eFy4cAERERH46KOPcPToUfn7FBwcjN69exfb38I2btwIa2trJCYmYuHChZg5c6Y8vMlkMvTq1QvGxsY4efIkVq1ahcmTJ6tty8jICH379sXWrVsVyrdt24a2bduibt26Sq959uwZunfvjgYNGiApKQlRUVGYMGFCiftfoKSfieIcPnwYqampiI6Oxv79+wHkn82dNWsWzp07h7179yI9PV0hPN+6dQu9evVCt27dkJycjI8//lhpvRNNhQd9I4TAuHHj8Pbbb8svW8rMzAQAODk5KdR1cnKSbyvswYMHmDVrFiIiIhTKx4wZgx07duDo0aMYNWoUli1bhhEjRpSob46Ojujatav8Mq1169Zh8ODBSvVq1qyJCRMmoGnTpqhbty4iIyMRFBSEH374QaGep6cnFi5ciAYNGqBhw4ZK7WRmZsLMzAz29vZFjvtNxkRERJrHTMpMCjCTMpPmYyZlJjUYgojU+vHHH4W9vb2wsLAQ/v7+YurUqeLcuXMKdQCIPXv2FNuWj4+PWL58ufy5u7u76Nmzp0Kd9PR0AUCcPXu2yLaOHj0qAIhHjx4plAcEBIgxY8Yo7GPgwIHy5xkZGQKAmD59urwsISFBABAZGRlCCCFCQ0PFsGHDFNqNi4sTRkZG4sWLFyr74+/vL4YOHapQ9uGHH4pu3brJn4eEhIjw8PAixxUeHi5CQkIUxvP2228r1GnVqpWYPHmyEEKI33//XRgbG4tbt27Jt//2228Kx6Twe3rmzBkhkUjE9evXhRBCSKVSUbNmTfHNN9/I23j99atXrxYODg7i2bNn8u0rV65UaHP9+vXCzs5OoZ979uwRxf2KVfWZWLp0qdr64eHhwsnJSbx69arIdk+dOiUAiCdPngghhJg6darw9vYWMplMXmfy5MkqP0Ov69atmxg/frz8eUBAgGjatGmR+9Y3I0aMEO7u7gqfkRMnTggA4s6dOwp1P/74YxEUFKTURlZWlmjdurUIDg4WOTk5Re7vxx9/FADE/fv3hRD5x9ja2lpYW1uL4OBgIYTiv999+/aJOnXqiLS0NGFhYSHu37+v9NnJy8sTs2fPFn5+fsLBwUFYW1sLExMT8eGHH8rrBAQEiI8//lj+fM6cOfL9Wltbixs3boitW7cKMzMzpT536tRJRERElHhMRERU/phJ8zGTMpMKwUzKTMpMagj4zUaiIvzrX//CnTt3sG/fPgQFBSEmJgbNmzcvdsHoZ8+eYdKkSfDx8UHVqlVhY2ODP//8U+mMYcuWLbXY+3yNGzeW/1xw1szPz0+prOASh6SkJGzYsEF+Ft3GxgZBQUGQyWRIT09XuY/U1FS0a9dOoaxdu3ZITU3VaP8BwMXFRd7X1NRU1K5dG7Vq1ZJvb9u2bZHtNWvWDA0bNpRfjhEbG4u7d++id+/eKuunpqaiSZMmsLKyKvE+VCnpZ6I4fn5+SmvSnD17FiEhIXB3d4etra38UqeCtlNTU9GmTRuFS2gKj0EqlWLOnDlo3LgxqlWrBhsbGxw8eFAnn1lNiYyMxL59+3D06FGFz0jBui+FzxjfvXtX6czykydPEBwcDBsbG+zZswempqZF7rNNmzYAgKtXrwLIv1QoOTkZycnJ+O6775Tqd+vWDS9fvsSQIUPw/vvvo1q1akp1Fi9ejKVLl2LSpEk4cuQIkpOTERQUpHQZibW1tfzn4cOHy/ebnJwMV1dXODs7IycnB48ePSp23EWNiYiIyh8zKTMpMykzKTMpM6kh4WQjUTEsLCzQuXNnfPbZZ4iPj8egQYMwY8aMIl8zceJE7Nq1C3PmzEFcXBySk5Ph5+dX5C9ibXn9D1HBH3ZVZTKZTP7fiIgIhT8K586dw5UrV1CvXj21+3k9NAD5lwoULnvT/hfsp6Cv4v8vsymqH6oMGDAA27ZtA5B/uUpQUBCqV6+usq6qfRRmZGSkVC83N1fheUk/E8Up/Jl59uwZunTpAhsbG2zZsgWnT5+W302uoO2SjKEs4UFfCSEwatQo7N69G0eOHJGvO1WgTp06cHZ2VlhLKScnB7GxsfD395eXZWdno0uXLjAzM8O+fftgYWFR7L4L1k5ycXEBALi7u8PT0xOenp6oWbOmUn1jY2OEhoYiJiZG5eUqABAXF4eQkBAMHDgQTZo0Qd26dXHlypUi++Hg4CDfr6enJ0xMTNCiRQuYmpoqjDsjIwMXLlxQGHdxYyIiIt1gJmUmLQ4zqX5hJmUmrcx4N2qiUvLx8VFYqNnU1BRSqVShTlxcHAYNGoQPPvgAQP7aKIUXP1al4Oxg4fbKWq8smjdvjosXL8LT07PEr/H29sbx48cRFhYmL4uPj4e3t7fG+/c6Hx8f3Lx5E3fu3IGrqysAICEhodjX9e/fH9OmTUNSUhJ+/PFHrFy5ssh9bN68GS9evIClpSUA4OTJkwp1atSogSdPnuDZs2fy4JOcnKxQp6yfieL8+eefuH//PubPnw83NzcAUFpcvfBnVtUYXg8PQH7Av3LlitaPoTaMHDkS27Ztw08//QRbW1v52WI7OztYWlpCIpFg7NixmDt3Lry8vODl5YW5c+fCysoK/fv3B5B/9rhLly54/vw5tmzZguzsbGRnZwPIP97GxsZISEjAyZMn0aFDB9jZ2eH06dP45JNP0KNHD9SuXbvE/Z01axYmTpyo8gwykL/uza5duxAfHw97e3ssWbIEmZmZpT42dnZ2GDJkCMaPH49q1arBwcEBEyZMgJ+fn/xOgJoaExERaR8zqTJmUmZSfcJMqhozaeXAbzYSqfHgwQO8++672LJlC86fP4/09HT88MMPWLhwIUJCQuT1PDw8cPjwYWRmZsq/Cu7p6Yndu3fLz8D2799ffuazKI6OjrC0tMSBAwfwzz//yO9YVpi7uzskEgn279+Pe/fu4enTp5oZNIDJkycjISEBI0eORHJyMq5cuYJ9+/YhMjJS7WsmTpyIDRs2YNWqVbhy5QqWLFmC3bt3l2nR6tLo1KkTGjRogLCwMJw7dw5xcXH49NNPi31dnTp14O/vjyFDhiAvL0/heBbWv39/GBkZYciQIbh06RJ+/fVXLFq0SKFO69atYWVlhf/+97+4evUqtm3bpnRZU1k/E8WpXbs2zMzMsHz5cly7dg379u3DrFmzFOoMHz4caWlpGDduHC5fvqy2f9HR0YiPj0dqaioiIiLULkyt71auXImsrCwEBgbCxcVF/ti5c6e8zqRJkzB27FiMGDECLVu2xO3bt3Hw4EHY2toCyL90KzExESkpKfD09FRo59atWwDy7xa4c+dOBAYGwsfHB5999hmGDh2qdMfE4piZmaF69epqvwExffp0NG/eHEFBQQgMDISzs7PCHTJLY+nSpejZsyd69+6Ndu3awcrKCj///DOMjY01OiYiItIcZlJmUoCZ1BAxk6rHTFoJ6GapSCL99/LlSzFlyhTRvHlzYWdnJ6ysrESDBg3EtGnTxPPnz+X19u3bJzw9PYWJiYlwd3cXQuQvAN2hQwdhaWkp3NzcxNdff61yoWxVCy+vWbNGuLm5CSMjIxEQEKC2fzNnzhTOzs5CIpHIF7kuyT5QaPFwVQuAnzp1SnTu3FnY2NgIa2tr0bhxYzFnzpwi368VK1aIunXrClNTU1G/fn2xadMmhe1lXYz79fGoaufy5cvi7bffFmZmZqJ+/friwIEDRS7GXeCbb74RAERYWJhSPwq/RwkJCaJJkybCzMxMNG3aVOzatUupzT179ghPT09hYWEhunfvLr799luFBZXf5DOh7v0psG3bNuHh4SHMzc1F27Ztxb59+5T69/PPPwtPT09hbm4u3nnnHbFu3TqFxbgfPHggQkJChI2NjXB0dBTTpk0TYWFhxR4PIiIi0i5mUmbSAsyk+ZhJifSfRIgSLJxAREREREREREREVAxeRk1EREREREREREQawclGIiIiIiIiIiIi0ghONhIREREREREREZFGcLKRiIiIiIiIiIiINIKTjUSkVlRUFJo2barrbmhEZmYmOnfuDGtra1StWlXX3Sm1wMBAjB07tsg6EokEe/fuLVF7FenYEhERUcVVkTIL86iiinRsiUiRia47QERUHpYuXYqMjAwkJyfDzs5O193RioyMDNjb2+u6G0RERESkAvMoEVUWnGwkokohLS0NLVq0gJeXl9o6ubm5MDU1LcdeaZazs7Ouu0BEREREajCPElFlwcuoiTTkxx9/hJ+fHywtLVGtWjV06tQJz549k29ft24dGjVqBHNzc7i4uGDUqFHybUuWLIGfnx+sra3h5uaGESNG4OnTp/LtGzZsQNWqVfH777/D29sbNjY2CA4ORkZGhsq+yGQy1KpVC6tWrVIoP3PmDCQSCa5duwYAuHnzJkJCQmBjY4MqVaqgd+/e+Oeff9SOUdWlEz179sSgQYPkzz08PDB79myEhYXBxsYG7u7u+Omnn3Dv3j35vvz8/PDHH38otBMfH4/27dvD0tISbm5uGD16tML7t2LFCnh5ecHCwgJOTk7497//rbafhXl4eGDXrl3YtGkTJBKJvL8SiQSrVq1CSEgIrK2tMXv2bADAypUrUa9ePZiZmaFBgwbYvHmzQnsSiQSrV69G9+7dYWVlBW9vbyQkJODq1asIDAyEtbU12rZti7S0NLV9+te//oXIyEj587Fjx0IikeDixYsAgLy8PNja2uL333+X15HJZJg0aRIcHBzg7OyMqKgopX69ftnK33//jb59+8LBwQHW1tZo2bIlEhMTFV6zefNmeHh4wM7ODn379sWTJ09K/L4SERGRfmEezcc8yjxKRLrFyUYiDcjIyEC/fv0wePBgpKamIiYmBr169YIQAkB+WBg5ciSGDRuGlJQU7Nu3D56envLXGxkZ4auvvsKFCxewceNGHDlyBJMmTVLYx/Pnz7Fo0SJs3rwZx44dw82bNzFhwgSV/TEyMkLfvn2xdetWhfJt27ahbdu2qFu3LoQQ6NmzJx4+fIjY2FhER0cjLS0Nffr0eeP3Y+nSpWjXrh3Onj2L9957D6GhoQgLC8PAgQNx5swZeHp6IiwsTP7+pKSkICgoCL169cL58+exc+dOHD9+XB6A//jjD4wePRozZ87E5cuXceDAAbRv377E/Tl9+jSCg4PRu3dvZGRk4Msvv5RvmzFjBkJCQpCSkoLBgwdjz549GDNmDMaPH48LFy4gIiICH330EY4eParQ5qxZsxAWFobk5GQ0bNgQ/fv3R0REBKZOnSoPrq8H+MICAwMRExMjfx4bG4vq1asjNjZW3ueXL1+iXbt28jobN26EtbU1EhMTsXDhQsycORPR0dEq23/69CkCAgJw584d7Nu3D+fOncOkSZMgk8nkddLS0rB3717s378f+/fvR2xsLObPn1/i95WIiIj0B/OoIuZR5lEi0iFBRG8sKSlJABDXr19Xud3V1VV8+umnJW7v+++/F9WqVZM/X79+vQAgrl69Ki/75ptvhJOTk9o2zpw5IyQSibxPUqlU1KxZU3zzzTdCCCEOHjwojI2Nxc2bN+WvuXjxogAgTp06JYQQYsaMGaJJkyby7QEBAWLMmDEK+wkJCRHh4eHy5+7u7mLgwIHy5xkZGQKAmD59urwsISFBABAZGRlCCCFCQ0PFsGHDFNqNi4sTRkZG4sWLF2LXrl2iSpUqIjs7W+14i1O4n0IIAUCMHTtWoczf318MHTpUoezDDz8U3bp1U3jdtGnTlMazdu1aedn27duFhYWF2v6cP39eSCQSce/ePfHw4UNhamoqZs+eLT788EMhhBBz584VrVu3ltcPCAgQb7/9tkIbrVq1EpMnT1bo1549e4QQQqxevVrY2tqKBw8eqNz/jBkzhJWVlcJ7OnHiRIV9EhERkeFgHg2XP2cezcc8SkS6wm82EmlAkyZN0LFjR/j5+eHDDz/EmjVr8OjRIwDA3bt3cefOHXTs2FHt648ePYrOnTujZs2asLW1RVhYGB48eKBw2YaVlRXq1asnf+7i4oK7d++qbbNZs2Zo2LAhtm/fDiD/TOXdu3fRu3dvAEBqairc3Nzg5uYmf42Pjw+qVq2K1NTUsr0R/69x48byn52cnAAAfn5+SmUF/U9KSsKGDRtgY2MjfwQFBUEmkyE9PR2dO3eGu7s76tati9DQUGzduhXPnz9/oz4WaNmypcLz1NRUhbO3ANCuXTul96QkY3z58iWys7NV7tfX1xfVqlVDbGws4uLi0KRJE/To0UN+JjkmJgYBAQFq9wkU/RlITk5Gs2bN4ODgoHI7kH85j62tbYnaIyIiIv3GPKqIeZR5lIh0h5ONRBpgbGyM6Oho/Pbbb/Dx8cHy5cvRoEEDpKenw9LSssjX3rhxA926dYOvry927dqFpKQkfPPNNwDyF4guUHihaIlEIr/sQ50BAwZg27ZtAPIvWQkKCkL16tUBAEIISCQSpdeoKwfyL4cpvM/X+6iqrwVtqSoruIRCJpMhIiICycnJ8se5c+dw5coV1KtXD7a2tjhz5gy2b98OFxcXfPbZZ2jSpAkeP35c5PhLwtraWqms8PhVvSelHaOqfbRv3x4xMTGIjY1FYGAgfH19IZVKkZKSgvj4eAQGBqrdZ0Eb6tov7nNX2vaIiIhIvzGPKmIeZR4lIt3hZCORhkgkErRr1w6ff/45zp49CzMzM+zZswe2trbw8PDA4cOHVb7ujz/+QF5eHhYvXow2bdqgfv36uHPnjkb61L9/f6SkpCApKQk//vgjBgwYIN/m4+ODmzdv4tatW/KyS5cuISsrC97e3irbq1GjhsIi4FKpFBcuXHjjfjZv3hwXL16Ep6en0sPMzAwAYGJigk6dOmHhwoU4f/48rl+/jiNHjrzxvgvz9vbG8ePHFcri4+PVvidvomCdnJiYGAQGBkIikeCdd97BokWL8OLFC6Uz2qXRuHFjJCcn4+HDhxrsMREREekz5tGyYx5lHiUizTHRdQeIKoLExEQcPnwYXbp0gaOjIxITE3Hv3j15IIiKisLw4cPh6OiIrl274smTJzhx4gQiIyNRr1495OXlYfny5Xj//fdx4sQJpbv2lVWdOnXg7++PIUOGIC8vDyEhIfJtnTp1QuPGjTFgwAAsW7YMeXl5GDFiBAICApQu5Sjw7rvvYty4cfjll19Qr149LF26VCNncydPnow2bdpg5MiRGDp0KKytrZGamoro6GgsX74c+/fvx7Vr19C+fXvY29vj119/hUwmQ4MGDd5434VNnDgRvXv3RvPmzdGxY0f8/PPP2L17Nw4dOqTxfQUGBmLMmDEwMTHBO++8Iy8bP348mjdvjipVqpS57X79+mHu3Lno2bMn5s2bBxcXF5w9exaurq5o27atpoZAREREeoJ59M0wjzKPEpHm8JuNRBpQpUoVHDt2DN26dUP9+vUxbdo0LF68GF27dgUAhIeHY9myZVixYgUaNWqE7t2748qVKwCApk2bYsmSJViwYAF8fX2xdetWzJs3T2N9GzBgAM6dO4devXopXMogkUiwd+9e2Nvbo3379ujUqRPq1q2LnTt3qm1r8ODBCA8PR1hYGAICAlCnTh106NDhjfvYuHFjxMbG4sqVK3jnnXfQrFkzTJ8+HS4uLgCAqlWrYvfu3Xj33Xfh7e2NVatWYfv27WjUqNEb77uwnj174ssvv8QXX3yBRo0aYfXq1Vi/fr3SJSSa4Ovri+rVq6NJkybyIBcQEACpVKq0Pk5pmZmZ4eDBg3B0dES3bt3g5+eH+fPnw9jYWBNdJyIiIj3DPPpmmEeZR4lIcySiuEU2iIiIiIiIiIiIiEqA32wkIiIiIiIiIiIijeBkIxEREREREREREWkEJxuJiIiIiIiIiIhIIzjZSERERERERERERBrByUYiIiIiIiIiIiLSCE42EunAoEGD0LNnT113g3Row4YNqFq1aqleExgYCIlEAolEguTkZK30i4iIiCoH5lFiHiUibeFkIxEZNA8PDyxbtqxc9lWWQKZpQ4cORUZGBnx9fQEAMTExkEgkePz4sVLdpk2bIioqSl6nqMeGDRsAALt27UJgYCDs7OxgY2ODxo0bY+bMmXj48GE5jpKIiIjIcDCPMo8SkSJONhLRG8vJydF1F4oklUohk8l03Q2NsLKygrOzM0xMTEr8Gn9/f2RkZMgfvXv3RnBwsEJZnz598Omnn6JPnz5o1aoVfvvtN1y4cAGLFy/GuXPnsHnzZi2OioiIiOjNMI+WH+ZRIioOJxuJykAmk2HBggXw9PSEubk5ateujTlz5si3p6Sk4N1334WlpSWqVauGYcOG4enTp2rbU3U2tOAsYAGJRILVq1eje/fusLKygre3NxISEnD16lUEBgbC2toabdu2RVpamvw1UVFRaNq0KTZv3gwPDw/Y2dmhb9++ePLkidq+PHjwAP369UOtWrVgZWUFPz8/bN++XaFOYGAgRo0ahXHjxqF69ero3LkzAODSpUvo1q0bbGxs4OTkhNDQUNy/f7/I9zI+Ph7t27eHpaUl3NzcMHr0aDx79gwAsGnTJtjY2ODKlSvy+pGRkahfvz6ePXuGwMBA3LhxA5988on8jCjwvzO++/fvh4+PD8zNzXHjxg2cPn0anTt3RvXq1WFnZ4eAgACcOXNGoT+PHz/GsGHD4OTkBAsLC/j6+mL//v2IiYnBRx99hKysLPm+Co5PTk4OJk2ahJo1a8La2hqtW7dGTEyMQrsbNmxA7dq1YWVlhQ8++AAPHjwo8n3RJDMzMzg7O8sflpaWMDc3VyhLSUnB3LlzsXjxYnzxxRfw9/eHh4cHOnfujF27diE8PLzc+ktERETFYx5lHmUeJSJ9xclGojKYOnUqFixYgOnTp+PSpUvYtm0bnJycAADPnz9HcHAw7O3tcfr0afzwww84dOgQRo0a9cb7nTVrFsLCwpCcnIyGDRuif//+iIiIwNSpU/HHH38AgNJ+0tLSsHfvXuzfvx/79+9HbGws5s+fr3YfL1++RIsWLbB//35cuHABw4YNQ2hoKBITExXqbdy4ESYmJjhx4gRWr16NjIwMBAQEoGnTpvjjjz9w4MAB/PPPP+jdu7fafaWkpCAoKAi9evXC+fPnsXPnThw/flw+hrCwMHTr1g0DBgxAXl4eDhw4gNWrV2Pr1q2wtrbG7t27UatWLcycOVN+RrTA8+fPMW/ePHz33Xe4ePEiHB0d8eTJE4SHhyMuLg4nT56El5cXunXrJg+7MpkMXbt2RXx8PLZs2YJLly5h/vz5MDY2hr+/P5YtW4YqVarI9zVhwgQAwEcffYQTJ05gx44dOH/+PD788EMEBwfLQ2liYiIGDx6MESNGIDk5GR06dMDs2bNLetjLxdatW2FjY4MRI0ao3K7ry3WIiIhIEfMo8yjzKBHpLUFEpZKdnS3Mzc3FmjVrVG7/9ttvhb29vXj69Km87JdffhFGRkYiMzNTCCFEeHi4CAkJkW93d3cXS5cuVWinSZMmYsaMGfLnAMS0adPkzxMSEgQAsXbtWnnZ9u3bhYWFhfz5jBkzhJWVlcjOzpaXTZw4UbRu3bpUY+7WrZsYP368/HlAQIBo2rSpQp3p06eLLl26KJTdunVLABCXL19W2W5oaKgYNmyYQllcXJwwMjISL168EEII8fDhQ1GrVi3xn//8Rzg5OYnZs2cr1Ff13q1fv14AEMnJyUWOKy8vT9ja2oqff/5ZCCHE77//LoyMjNT2d/369cLOzk6h7OrVq0IikYjbt28rlHfs2FFMnTpVCCFEv379RHBwsML2Pn36KLVVnICAADFmzBiFsqNHjwoA4tGjR0r1C3+GChT+/AkhRNeuXUXjxo1L1R8iIiLSDeZR5tHXMY8Skb4p+SILRAQASE1NxatXr9CxY0e125s0aQJra2t5Wbt27SCTyXD58mX5GeeyaNy4sfzngnb8/PwUyl6+fIns7GxUqVIFQP4lMba2tvI6Li4uuHv3rtp9SKVSzJ8/Hzt37sTt27fx6tUrvHr1SmE8ANCyZUuF50lJSTh69ChsbGyU2kxLS0P9+vWVypOSknD16lVs3bpVXiaEgEwmQ3p6Ory9vWFvb4+1a9ciKCgI/v7+mDJlitq+v87MzEzh/QKAu3fv4rPPPsORI0fwzz//QCqV4vnz57h58yYAIDk5GbVq1VLZV3XOnDkDIYTSa169eoVq1aoByP9MfPDBBwrb27ZtiwMHDpR4P9omhJBf9kNERET6jXk0H/NoPuZRItI3nGwkKiVLS8sitxf1R1JduZGREYQQCmW5ublK9UxNTZXaUlX2+uLTr28vqFPU4tSLFy/G0qVLsWzZMvj5+cHa2hpjx45VWnS7cNiTyWR4//33sWDBAqU2XVxcVO5LJpMhIiICo0ePVtpWu3Zt+c/Hjh2DsbEx7ty5g2fPnsmDa1EsLS2V3u9Bgwbh3r17WLZsGdzd3WFubo62bdvKx1bcsVU3BmNjYyQlJcHY2FhhW0HQLXxsNangvcjKylK6tOTx48ews7MrUTv169fH8ePHkZubq/SZISIiIv3CPJqPefR/Y2AeJSJ9wjUbiUrJy8sLlpaWOHz4sMrtPj4+SE5Oli8qDQAnTpyAkZGR2jOUNWrUUFjfJTs7G+np6ZrteAnFxcUhJCQEAwcORJMmTVC3bl2FBbHVad68OS5evAgPDw94enoqPAoHwcKvKVzf09MTZmZmAPIX7F64cCF+/vlnVKlSBZGRkQptmJmZQSqVlnhso0ePRrdu3dCoUSOYm5srLBjeuHFj/P333/jrr79Uvl7Vvpo1awapVIq7d+8qjcHZ2RlA/mfi5MmTCq8r/LysvLy8YGRkhNOnTyuUZ2Rk4Pbt22jQoEGJ2unfvz+ePn2KFStWqNz++PHjN+0qERERaQjzqGrMo8yjRKQfONlIVEoWFhaYPHkyJk2ahE2bNiEtLQ0nT57E2rVrAQADBgyAhYUFwsPDceHCBRw9ehSRkZEIDQ1Ve8nKu+++i82bNyMuLg4XLlxAeHi40lnJ8uLp6Yno6GjEx8cjNTUVERERyMzMLPZ1I0eOxMOHD9GvXz+cOnUK165dw8GDBzF48GC14Wvy5MlISEjAyJEjkZycjCtXrmDfvn3yAPfkyROEhoYiMjISXbt2xbZt2/D999/jhx9+kLfh4eGBY8eO4fbt28XeadDT0xObN29GamoqEhMTMWDAAIWzxwEBAWjfvj3+9a9/ITo6Gunp6fjtt9/kl5d4eHjg6dOnOHz4MO7fv4/nz5+jfv36GDBgAMLCwrB7926kp6fj9OnTWLBgAX799VcAwOjRo3HgwAEsXLgQf/31F77++muNXbJia2uLiIgIjB8/Hnv37kV6ejpOnDiBfv36wdvbG126dClRO61bt8akSZMwfvx4TJo0CQkJCbhx4wYOHz6MDz/8EBs3btRIf4mIiOjNMY+qxjzKPEpE+oGTjURlMH36dIwfPx6fffYZvL290adPH/m6M1ZWVvj999/x8OFDtGrVCv/+97/RsWNHfP3112rbmzp1Ktq3b4/u3bujW7du6NmzJ+rVq1dew1Ewffp0NG/eHEFBQQgMDISzszN69uxZ7OtcXV1x4sQJSKVSBAUFwdfXF2PGjIGdnR2MjFT/qmncuDFiY2Nx5coVvPPOO2jWrBmmT58uv8xlzJgxsLa2xty5cwEAjRo1woIFCzB8+HDcvn0bADBz5kxcv34d9erVQ40aNYrs47p16/Do0SM0a9YMoaGhGD16NBwdHRXq7Nq1C61atUK/fv3g4+ODSZMmycOpv78/hg8fjj59+qBGjRpYuHAhAGD9+vUICwvD+PHj0aBBA/To0QOJiYlwc3MDALRp0wbfffcdli9fjqZNm+LgwYOYNm1ase9pSS1duhQff/wx/vvf/6JRo0YYMGAA6tSpg4MHD8LEpOSrZSxYsADbtm1DYmIigoKC0KhRI4wbNw6NGzdGeHi4xvpLREREb455VBnzKPMoEekHidDm4g1ERKQxgYGBaNq0KZYtW6brrhARERFRJcQ8SkQlwW82EhEZkBUrVsDGxgYpKSm67goRERERVULMo0RUHH6zkYjIQNy+fRsvXrwAkH93xIJFy4mIiIiIygPzKBGVBCcbiYiIiIiIiIiISCN4GTURERERERERERFpBCcbiUinoqKi0LRp03LZj0QigUQi4YLWRERERKSAmZSISHM42UhEWlMQpNQ9Bg0ahAkTJuDw4cNa78uECROQkZGBWrVqaX1fRERERKQ/mEmJiMqXia47QEQVV0ZGhvznnTt34rPPPsPly5flZZaWlrCxsYGNjY3W+1KwH2NjY63vi4iIiIj0BzMpEVH54jcbiUhrnJ2d5Q87OztIJBKlssKXrAwaNAg9e/bE3Llz4eTkhKpVq+Lzzz9HXl4eJk6cCAcHB9SqVQvr1q1T2Nft27fRp08f2Nvbo1q1aggJCcH169fLd8BEREREpHeYSYmIyhcnG4lI7xw5cgR37tzBsWPHsGTJEkRFRaF79+6wt7dHYmIihg8fjuHDh+PWrVsAgOfPn6NDhw6wsbHBsWPHcPz4cdjY2CA4OBg5OTk6Hg0RERERGSJmUiKisuFkIxHpHQcHB3z11Vdo0KABBg8ejAYNGuD58+f473//Cy8vL0ydOhVmZmY4ceIEAGDHjh0wMjLCd999Bz8/P3h7e2P9+vW4efMmYmJidDsYIiIiIjJIzKRERGXDNRuJSO80atQIRkb/Oxfi5OQEX19f+XNjY2NUq1YNd+/eBQAkJSXh6tWrsLW1VWjn5cuXSEtLK59OExEREVGFwkxKRFQ2nGwkIr1jamqq8Fwikagsk8lkAACZTIYWLVpg69atSm3VqFFDex0lIiIiogqLmZSIqGw42UhEBq958+bYuXMnHB0dUaVKFV13h4iIiIgqIWZSIqJ8XLORiAzegAEDUL16dYSEhCAuLg7p6en8FUl5AAABKElEQVSIjY3FmDFj8Pfff+u6e0RERERUCTC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pUtpJScuiug/Ms/1O9DW+g8yB3b3hnUhPEQ7Pmyibfr64XmynlcAup+joaFq1ahXNnDmTevXqRS+88AK9/PLLgd4tzQjleHTP4deoUFzBP/PlCssrQrT5xF/S2mpZqTLJdDXlguZiUl9LzvxWanvxUd1IjWTGMpGF/yetrZu7Jkpri/Th0pqKrFxLWlt7kpOktdVYL6/3mnHLDmltFR24gtTo+owe0tpa98D90trqYrkYlPGo1mPSaB/Fox4nG9evX0+PPfYYXb161eE57mIZrHfhrd1HOdGIZKN74szyEjv+OudhEgL3/BSSeCz6NHnnOEbifpn1MdLa0sVGSmuLYuS1FREv7xiZMcaguu+XLIvcYzTpMqW1FavSmz7mGOcBSKB/7lkKSfw+CsAQ34TYBIrQOX6PZCtGomTSvIkTJ1KDBg2ob9++Inn25ptvBnqXVC/U41FONMbFFzwGiI+PJVliC8mLoeIl/47N0mkvJvU5g9xzHB+lzvMiM5aJlPl7WWZ8KzPZGCfveyVWkVcwNl7iMRqj1RmryZQd6VWfMqeiC0n8+WkJzng0WGLSiZLjUY9vEXC1mocffpguXLgg7iLnXII1sAMAAIDAMegNZIhwsujlJen9yVlw3LlzZ9qzZw8dP36c2rVrF5D90hLEowAAAKCKeFSjManOx/Gox8lGHqry3HPPUalSpQr0xgAAAADuiI0pRIVi4hwWXq/Vic2duf3220WAV69ePb/vk9YgHgUAAAA1xKNajUkVH8ejHicbH3roIdq6dSvJYDKZaNKkSVS5cmUxTrxKlSo0bdo0u3l3+ARMnTpVTEzJ27Ru3ZqOHj1q105WVhY988wzVLx4cYqNjRXz9PAE2jnduHGD+vfvTwkJCWLhr2/evCnlOAAAAMB3XN5FjtDeXWTGcU7JkiWdPsfzD61du1ZMMA6uIR4FAAAAtcSjWoxJLT6ORz0ezD9v3jwxjHr79u0i0xkRYT/nwciRI91u6/XXX6f3339fVLypU6cO/fbbbzRo0CCRDBw1apTY5o033qBZs2bRsmXLRIZ1+vTp1L59e9Gt0zoh6OjRo8WJ+Oyzz6hYsWI0ZswY6tKlC+3bt4/Cw2/N79CnTx+RgOQ5ftjQoUNFwpFfB76REF1Mc6fWdNNx7ic1M6dcl9dYGXlNxUUlSGsr5ewlaW1RySLSmjKYSKqs6ymkNtk3L0ttz5wk8furgrymQoFy8YqcdtKNFAixMbEUGe44H1yW2fM5if79918aP348/fDDD5SRkSFii8WLF1OjRo1sNzl5UuxFixaJG5VNmzal9957T8Qpvnby5EmxT7Vq1aLbbpNXuCMYhXo8GhsZT4UiCz4XXXaqvN89ZWKd95Dwhj5M3nxjrFCkvLgkWBhN8uZRVjOZsUxkYWlNkZKeIa0tXYy8OfVkMll06owhMzH1myei9PJ+tlNgwkifx6PexKT/hkA86vFv8pUrV9KPP/4oehlyD8ec47z5a0+Cu927d1O3bt3ogQceEI8rVapEn376qQjyrCd4zpw59OKLL9rKb3MgyEO4eT+GDRtGSUlJ4qJ8/PHHtjHlK1asoAoVKtCmTZuoY8eOdOzYMRHU/fLLL+IisQ8++ICaN28ugsQaNWo47Bv3luTFKjlZ47N9AgAAaFSEwUAR4Y53jC1m+wrE+eFg7e6776Y2bdqI4I7v5p44cYIKF/7vL0h3kkoFxZUKX331Vdq/fz81a9aMJkyYQP369aPPP/9cPM9xybp160RcBM4hHgUAAAA1xKOexqQ3QiQe9XgYNQ975qHOnOQ7deqU6FZpXTgD6omWLVvS5s2b6a+//hKPf//9d9qxY4eYlJJxmxcvXqQOHTrYXhMZGUmtWrWiXbt2icd8tzg7O9tuGx5yXbduXds2nNTku9PWRCPjk8nrrNvkxmW/rUOueeHkJQAAAPhfdGwMxRSKdVh4vfWGYM4l583C3D3Y+Pf50qVL6a677hLBU9u2balq1apOb3JyLME3OdPT00VyS2a1vwULFoibp0uWLBHvdeDAAfEe3CtOr9eLfQDXEI8CAACAGuJRT2PS10MkHtV7k/3s1asXhYV5nKd0wN1GOWlZs2ZNMbyEq1lzZvXRRx8Vz3OikeUuRsOPT58+bdvGYDBQkSJFHLaxvp7/dzYWnddZt3F24rkQjhV/UJBwBAAA8L8IQwRF6O2HyTJz2K35DHL/fp4yZYqY7zm3NWvWiBEPPPx227ZtVK5cORo+fDgNGTLErZucPKJChi+//FLcqeabq3zDleOg77//nu6//35bfNK3b18p7xWsEI8CAACAGuJRT2PSNSESj3qcbBwwYACtWrWKXnjhBSooboeHPHPmlMeeHzx4UMx3wz0T+X1cleTmTK+zMt15beNs+7za4YvJCwAAAARWVKFoio5wMidV9q3/zp49S/Hx8bbVrn5/8wgMvoPLNxM5jtm7d6+Y/oW3f+yxx9y6ySnD+fPnqUGDBuJrHhrD71+tWjXb87zO1c1QuAXxKAAAAKgiHvUwJj0ZIvGox8lG7n3I48d53sb69es7TMjN48rdNXbsWDEuvHfv3uIxT/DNJ4+HMHMQWbp0abGeD7BMmf+qV1y+fNl24nkbvrvN495z9m7kbVq0aGHb5tIlxyITV65ccbiAIE8hfazmTqdyI4m0xJyizv1VMrPktXVK3jHq9AXvkW1lvHaepLqpvonapU7GLb6/blLQM3k2h6C/KJflnHsl4/8jKT/TR+pJnyveEOvDbu0PB3U5A7u8qu41btyYZsyYIR7fcccddPToURHwcXBXkJucnsZSOeMnHqZiLSDCePQIvyfkfQ5DOR6Ni4qnuKiCx1lZ/8qrel6kUMEL1liF6+T9vmbRBu3FpL6WkZ1GocB0TeKNG5nT6KbJKxCj5Pj9UVC6cOe9tgJdIEZqDJkamFhGqyJ0iEfyi0c9jUktIRKPepxsPHz4sDgZ7MiRI3bPeXrgPOY893BsPjg++axy5coiMNu4caPtPTmQ466mPM6dcbUePkG8zSOPPCLWXbhwQewbB6GMC8HwcG3OGPOYeLZnzx6xzhoAAgAAgDpFxkVRpMGx+p/F6FlylxNFtWvXtlvHlfZWr14tvnYnqSQLJ8l4TmjGcQ/PYW2Nq27eDIHEfAEhHgUAAAA1xKOexqRlQiQe9TjZuGXLFpLlwQcfFHM0cjltHkbNk1HynejHH3/clrzkYdWc8a1evbpY+OuYmBjq06eP2IZPzODBg2nMmDFUrFgxKlq0KD3//PPirrS1OjVfuE6dOokx8AsXLhTrhg4dSl26dHFaiRoAAADUQ2+IIH2kk56N5FkPDK78x1X8cuI5aipWrOj2TU5Zck4Xw3LPvyPzznUwQjwKAAAAaohHPY1J7w6ReNTjZKNMc+fOpZdeeklMhslZWp6rkQ9u8uTJtm3GjRtHGRkZYhsemsIVpTds2GBX7nv27Nmiyyf3bORtuZIPT3SZswvoJ598IsbBWyfZ7Nq1K82bN8/PRwwAAACeioqLpOhIxzvJisHsUTvPPvusGNHANy45ZuARD4sWLRKLuzc5ZbCO4AB1QDwKAAAA3sajnsakz4ZIPOpWspFLYHPyzp35kBhXrOEEoLMK0DlxwpBLevPiCp9ort7jrKqkVVRUlAgUeXGFezxyMRoAAADQlpjYaIqJcjIht96zIKlJkyb09ddf08SJE2natGnizjHHIDkr7blzkxMCA/EoAAAAqC4e9TAmbRIi8ahbycZvv/1WTF7tDp5Acu3atfTKK6/km2yE4JZ1Xd6EzNH++ihdV2fBFZdSUkmNTKk3pLVlTJQ3gbahsLwK8yaJn2+mXFVfgRjLlWuh/f0VTC5KKgSQGZhJ1Q2GcIo0OE6Ab7J4Pik+T6HCS0FuchbEmjVr3N6WR2HAfxCP/kfJNJJiKPgAJVOSe/G9O4qWulXRUgaL0UgyGcIDOphLldKN6os7Qqr4Y5rE8x9pkNaUTuL3SoZJYoEYiTGkkmmS1lYoiNZLLBAjr4aoquJRb2LSLiEQj+rdTSBy2WsAAAAAfyscbaBC0Y43CyJ02qso2b17d4dgMmelv5xz43CVQPgP4lEAAABQWzyqxZi0ux/iUb2vJuEuV66cN/sDAAAAYCcmIlwsuVlMnvdsDLSc8+Ns2rSJxo8fL+bhad68uQjsdu3aRZMmTRLrwB7iUQAAAFBbPKrFmNTih3jUrWRjq1atvH4DAAAAgIKIMmZRdLhjlT+TUdvjcXjy7/fff59atmxpW9exY0cxAfjQoUPp2LFjAd0/tUE8CgAAAGqLR7Uek472UTyKCUwAAABA1cKyjRQWnuV0vZadOHGCEhISHNbzulOnTgVknwAAAADA/XjU+pxWnfBRPIpkI/hMdMmBmju7yqUU0pLCrd+hYL/20Uu09znyirx59aUp0m6e1Pauz7WfG6QgFu17T1pbdYvJCw5qSWuJh2Osl9fWhXQp7SjGwEyqrktJJ12240Tzukw5xxUoXI2Q7yavWLGCypQpI9ZdvHiRxowZQ3fddVegdw9UzJR2k0xhBe9FoaTIi3uiFHlDyMzSe4jIa8+gnUKgeUozhsicsFeukxopqfI+k7pYoyoLxGQrEgvEXJP3s0pJCf4CMTp9mLS29GESC8RonKt4VOsxaRMfxaNINgIAAICqKdlmUsJMTtdr2ZIlS6hHjx5UsWJFuu2228S6M2fOiKJ833zzTaB3DwAAAADyiUetz2nVEh/Fo0g2AgAAgKopKVmkGB17TSlZ2p0fh1WrVo0OHTpEGzdupD///FNUAaxduza1a9fOrgogAAAAAKgzHtV6TFrNR/Eoko0AAACgakq2iRSds56N2h8KxUFchw4dxAIAAAAA2opHrc9pmc4H8ajHg/mvXbtGI0aMEJnO4sWLU9GiRe0WAAAAAJksKUaXi9a8++67lJmZ6fb2XB0wReK8esEC8SgAAACoJR7VWkz6rh/iUY97Nvbr109Uqxk8eDCVKlUKw3wAAADApxSjmRQlOOZsfPbZZ+nRRx+lqKgot7YfN26cuMscFxckVSkkQTwKAAAAaohHtRiTPuuHeNTjZOOOHTvE0qCBCkunAhRQ0WcwIT+Az5gs0pqqmpAtry2V9srPvHJOWlvmTDmVBM3GwFQkNKdkkinCcc4Yc7b7d2TVgufBadu2Len17oVgGRkZPt8nLQr1eNSccpPMVPDPv3I9iWTJun5RlRVxmWKWN7ztz+Q55Gv1y432+XvcWeFZqe1lXF4mra3okgOltaXclPcz9Pild6W1VSJTYmIiS509qsrGShxWmiov7lMy5cWjMn15ZK60ttpIjLnLS7zXqcuQ87NdpzerKh7VYkyq+CEe9fhq16xZE4EvAAAA+I3FaCKLxfGPFotJe/PjTJkyxaPtu3XrhmlqnEA8CgAAAGqIR7UYk07xQzzqcbJx/vz5NGHCBJo8eTLVrVuXIiIi7J6Pj4/3tEkAAAAAl4ypmWR0ErEYTdq6i+xNcAfOIR4FAAAANcSjWoxJp/ghHvU42Vi4cGFKSkqi++67z6EbJlewMZu1NVYdAAAA1M1kMpHJyRw5JolDI0FbEI8CAACAGuJR8Rxi0oInG/v27UsGg4FWrlyJAjEAAADgc1wtLzzccb7ITHMWzn6IQjwKAAAAaohHxXOISQuebDxy5AgdOHCAatSo4elLAQAglEmcDL1SgrziAUX1haW1lSqtJaLsy2eltSUr/jEHaA76dGMG6cIcJztPtyDZGKpCPR41pyWRWSdhyFZyqip/ZoUXkvdzmVmM8oqExJcsIq2tYGK8kCitreiS0poiuinvF1f5IhWltZUlMSaSWQRHpqa3VZPWVrppk+qK5skWZ5BYuEbi56tqiarS2jKlyznGcCVTVfGoeA4xqYMw8lDjxo3p7Fl5wQQAAABAXkzmbMp2svB6CE2IRwEAAEAN8ShiUuc87hryzDPP0KhRo2js2LFUr149hwIx9evX97RJAAAAAJfSjBmkhDnepU+3BKirpSRbt26l1q1bB3o3NAnxKAAAAKghHtV6TLrVR/Gox8nGXr16if8ff/xx2zouDIMCMQAAAOALJlM2mcJ0just2u7Z2KlTJypXrhwNGjSIBgwYQBUqVAj0LmkG4lEAAABQQzyq9Zi0k4/iUY+TjYmJ8ubjAAAAAMhPVmoqhekiHNcr2g3s2Pnz52nFihW0bNkymjp1KrVt25YGDx5M3bt3F8X4wDXEowAAAKCGeFTrMel5H8WjHicbK1aUNzEuAACEDiXTJK2t0gnyeoCl//U7qZHlyjVpbZmydHLayZbTjqcs2Uay6BwndLdoOLBjRYsWpZEjR4rl4MGDtGTJEhoxYgQ99dRTotoyB3oNGjQI9G6qUqjHo0pKCikShmzJLCxhuXhZWltUmqRSsuQVk4ouV05aW8FE6vWX+GNPuSnv2uvT5RWlyDJJLAaSKu93oWKWF6sZ/z5OaiQrJpKtSKS8z4SSIa9AjPmCvHodYTFxctoxyysUKSMe1XpMWtRH8ajXV+mPP/6gM2fOkNFoH+h07drV2yYBAAAAHOjDsknvJLjTKyYiefF0QDVs2JAmTJggAr7XXntNBHrz58+n5s2b0/vvv0916tQJ9C6qEuJRAAAACGQ8GkwxaUOJ8ajHycaTJ09Sjx496PDhw7a5Ghl/zczmIDjDAAAAoBrhesVpcBfOMYh2byQL2dnZ9O2334pgbuPGjaLK8rx58+jRRx+l69ev0/jx4+nhhx8WSTX4D+JRAAAAUEM8GgwxabYP4tEwT3eCK1FXrlyZLl26RDExMXT06FH6+eefxc5wFRsAAAAAmSIjFZeL1isqlylThp588km6/fbb6cCBA7R792564oknKDY2VkzQzXeV//zzz0DvquogHgUAAAC1xKNajkmf8VE86nGykd902rRpVKJECQoLCxNLy5YtaebMmWKMNwAAAIBM+jAifbiTxeMoxh7HLjwyY/To0bZ1PGKDJ8cuW7YsRUdHU+vWrcWNVV/gu8Nz584VE3PPmTOH6tat67AN78eWLVt88v5ahngUAAAAVBGPFjAmnRmk8ajHw6h5mHShQoXE18WLFxc7VKNGDTFR9/Hj6pwEFgAAVCBT3jQbEVkSJzD/6ySp0oWr0poyZYXJaSdbTjueMkQSGZy8dUHm1//1119p0aJFVL9+fbv1b7zxBs2aNUtU5OO7u9OnT6f27duLGCcuTs7E5lZTpkyhFi1akF5vH46ZTCbatWsX3XvvveK5Vq1aSX3fYBDq8aiSnkmKTkIxgavyCl4oUfKKWikx0dLaEu1lFbyYjlWhqMLS2gomyhmJBWIksqTIiz2yLpyQ1pYiMSYq0C9DHzL9clBiY0rQF4iJdRboeMmcKe98GY/LS3DFNm0jpR29JZLUFI8W5Nvw1yCORz3+RHOW89ChQ+Lrpk2bipOwc+dO0duxSpUqnjYHAAAAkKcwPc+T47jwepacnGy3ZOVTeTY1NVVU1/vggw+oSJEidneR+Y7uiy++SD179hQxz/Llyyk9PZ1Wrlwp/Sq1adNGzIOTW1JSkngOXEM8CgAAAGqIR72NSVODPB71ONk4adIkslhupW05u3r69Gm65557aN26dfTuu+96vSMAAAAAzhgMOjJEOlkMt3on8FwyCQkJtoWHo+RlxIgR9MADD1C7du3s1icmJtLFixepQ4cOtnWRkZHiTi7f2ZWNg0lrgb2crl27JubIAdcQjwIAAIAq4lEvY9IRQR6PejyMumPHjravuScjj+/mLChnYp3tIAAAAEBBhIffWhzW////Z8+epfj4eLuAzJXPPvuM9u/fL4at5MaBHStVqpTden7MN1dl4bvUjOOmgQMH2u0vDw/mESQ8nAVcQzwKAAAAaohHxXMexqSfhUA86vXEAP/88w/9+OOPlJGRQUWLFvV6B/7991/q168fFStWTFS3btiwIe3bt8+jiTG5aypX0OE5ezjz2rVrVzp37pzdNjdu3KD+/fvbMsz89c2bN73ebwAAAPDjHDkuFsZBXc7FVWDHASBXMV6xYgVFRUW5fL/cN09d3fH1ljUW4XZ53p2cd8BLly5NQ4cOFfsI+UM8CgAAAIGORz2JSc+GSDzqcc9G7kr5yCOPiEo0fKB///236OHIZbELFy5Mb7/9ttttcQLw7rvvFuPAf/jhBypZsiSdOHFCtOPJxJhctWft2rUiO8xJyzFjxlCXLl1E0jL8/1PPffr0EQnI9evXi8d84jjhyK8DAAAA9dLxXDhO7iR7Gm9xXHD58mVq1KiR3Z3bn3/+mebNm2crLMJ3lMuUKWPbhl+T++5yQSxdulT8X6lSJXr++ecxZNoLiEcBAABADfGopzHpvhCJRz1ONj777LMUERFBZ86coVq1atnW9+rVSzznSbLx9ddfF2ParQdpPVBXE2MynhiTTzBPjDls2DAxaeXixYvp448/to115+wrt7tp0yYxzObYsWMiyfjLL7+IojaMJ+Fs3ry5uJBcvRAAAHxLyZZXGS/znLxqs8rRS9LaovISq8NdTJPWVLakyoum7MBMl2IwKGTQO35+DB5Wp2zbti0dPnzYbt2gQYOoZs2aNH78eHHzlO/kbty4ke644w7xvNFopG3btomYRTau/gfeCfl4NDmVyGQo8MfHckNeleYwSpHWllIylaRKy5DWVEyE3ErZwUI5J/maSZKVJK8t/ZmzqqwgrWTIi68Us0laW5lb5VUoj6om7/suO0te1WeZYg0F/5luZc67Tp5HlMNnpLWlby0nmaXPJlXFo57GpG1DJB71ONm4YcMGMXy6fPnyduurV6/u8fjxNWvWiGTgww8/LE5cuXLlaPjw4TRkyBC3JsbkZCNnhbOzs+224SHXXLGHt+H2d+/eLbqCWhONrFmzZmIdb+Ms2chDs3NWDuJKQgAAAOB/+kiF9E6CO324Z39g8YgIjg9y4ru4PCrCup5HS8yYMUPENbzw1zzNC4+QkOHOO++kzZs3i7muOYDMazgMz+UDziEeBQAAADXEo57GpHEhEo96nGxMS0sTB5nb1atX85yQ3ZmTJ0/SggUL6LnnnqMXXniB9u7dSyNHjhTtPPbYY25NjMnbGAwGu1Lh1m2sr+f/eYh2brzOuk1uXDXo5Zdf9uh4AAAAQL7wcEUsDusVeb05rMaNGyfmo+abnzzdC9+o5MSWdeqWgurWrZstXuKvUVzPO4hHAQAAQA3xqC9i0nFBEI96nGy899576aOPPqJXXnlFPOadslgs9Oabb4q5Fz3Br2vcuLHI0jLOqHLxF05AcrKxIBNj5t7G2fZ5tTNx4kSRBM3Zs5GHZgMAAIB/6Q0K6SOc9GwMK3hgt3XrVrvHHBdwYTpefCHnUBVfvUcoQDwKAAAAaohHZcSkW4MwHvU42chJRa4I/dtvv4lx45xx5QTh9evXaefOnR61xZNd1q5d224dzwO5evVq8TWPU89vYkzehveDs705ezfyNtYy3bzNpUuOc3JduXLF5QSbnOX1tKcmAAAAyBemVyjcybCVMB/0bPQnnp+nX79+dN9996GHo4cQjwIAAIAa4lGtx6SDfBSPepxs5OTgoUOHRO9DrvTMw1i4eMuIESPsEoLu4ErU1ko7Vn/99RdVrFhRfF25cuV8J8bkCj5csIa34SrZ7MKFC3TkyBFRyZpxIRguJMPDtO+66y6xbs+ePWKdNSEJAAA+JnEydMvVq9LaMl/IlNaWvlKCtLYsN+XN7m0xyQkcLObAFIjRR1pIH+H4+dGHyftMBaqi8gMPPCDm6Onduzf179+fGjZsGOjd0oRQj0eV5AxSsgteyMGcaiZ55BWbCZdY0IUpSenS2jJlyN03Z7IuzPb5exSq+qzU9hSJxYYyr30irS2LzI/49SRVFs2T+X1svOxZDYa8pFzxONXgUlRNdRZ1uf5mF2ltlR/ztLS2LkqK+5hyUd7PT61zFY9qPSa95qN41KufABxwyZjPkCsJcnDFw6g5MOPga9GiRWJhnFXNb2JMLvIyePBgGjNmjDg5RYsWFWW769WrZ6tOzb0lO3XqJArPLFy4UKwbOnQodenSBZWoAQAAVC4s/NbisF67cZ2tUN7Nmzfp888/p5UrV9KcOXNEXMJ3lznOqVSpUqB3UdUQjwIAAECg41Gtx6RrfBSPFuh2A99FXrVqlZi4kqtBczLQE02aNKGvv/5azI84bdo0ceeYD6xv374eTYw5e/Zs0uv1ImHJ23Ip8WXLlok73VaffPKJKD5jrVrdtWtXmjdvXkEOHwAAAPw1R47ByZyNOu0OWbEqXLiwuAHKy7lz5+jTTz+lJUuW0OTJk8lkKnjPtVCAeBQAAAACFY8GQ0xa2AfxqNvJxjNnzojulFz2ulmzZrR48WJq3749/f333+L56Oho+uGHH8SE3Z7g3oW8uOLOxJhRUVE0d+5csbjCPR5XrFjh0b4BAABA4On0txaH9Rq+i5xbdna2mA+bh9WeOnXK5ZzSoQ7xKAAAAKgpHg2mmDRbYjzq9uQHPDSZ56fhuXF4GHPHjh1FT0aej4aLr3Tu3BlVFQEAAEC68Cidy0XrtmzZIqZ54WBuwIABYuTG2rVr6ezZs4HeNVVCPAoAAABqi0e1HpNu8UE86nbPxp9//lmM5eYJrTmxWLx4cdGt0prpnDRpkhi+DAAA4GuKxEnaLSaJwx708iYwVzJkzmovb5L2gAgPc35uzeqcMN5d5cuXF5Ny8w1cnlP6wQcfFKM1wDXEo/8vLZtIwo8IReJIfSXTosqCLoLEglum1BvS2nL5HmdP+fw9qKrc5mR+lixG3xfh8YaSaVJl0TyzvI83mf89L62trDSJv6P16kzkKDflFUYyXjpHaiQzHjUb5XxYzUZ5511KPKrxmLS8j+JRt//6uHLliq0qHw9J5t6NObtU8iTdPKciAAAAgEy66HDSRTrOyK0L0/b8ODwPzsMPP0xFihQJ9K5oBuJRAAAAUFM8qvWYdLKP4lG3k42Kooj5E61yfg0AAADgM9yjwVmvBrO2YxGehBs8g3gUAAAAVBWPajwmHeqjeFTvacaTezQynr/x1VdfpYSEBPE4PV3ycAcAAAAAFhlOuihnIYv27iL37NmTli1bRvHx8eLrvHz11Vd+2y8tQTwKAAAA6olHtReT9vRDPOp2spGrTB8/ftz2uEWLFnTy5EmHbQAAAAD8cidZpXM45YVv0lpHh3CAh5EinkE8CgAAAKrr2aixmDTBD/Go28nGrVu3Sn9zAAAAbyjXU1Q5qT2FO5/HJeD7pXWReiJnd5IVbd1FZkuXLrV9zXeUwTOIR/9fklFKQRZTlsw/LuR9P4anZpNMytVMaW2Z0+X9/nHFknjB5+9BreU2Z86Ud/0Vk7zrHybv1zJRqlGVv+ONEguxREn87GWmyywQI6+tcL3Ez2qKvM+q8Z8/pLVlkVhj0JQq73xZjJmqakdaPKrBmHSpH+JR7ZbMAQAAgNC6k+xs0bD77ruPbt686bA+OTlZPAcAAAAAGohHNRyT3uejeNSjORsBAAAA/C4q4taSm7ZuIjvtpcdzYOeWmZlJ27dvD8g+AQAAAIAH8ajGY9KtPopHkWwEAAAAVdNF6p1OyK2zaDOyO3TokO3rP/74gy5evGh7bDabaf369VSuXLkA7R0AAAAAuBuPajUmPeTjeBTJRgAAAFC38DDn82GGS5yUyI8aNmwoJuLmxdnwlOjoaJo7d25A9g0AAAAAPIhHNRqTNvRxPOpRstFkMtGrr75Kjz/+OFWoUMHrNwUAACiQq+nqPIH6cFVOtq95kYZbS27ai+uExMREUhSFqlSpQnv37qUSJUrYnjMYDFSyZEkKl1hsKNggHiVSbhpJMaitQIw8BskFYkw35FXj0KfeIF9T/nGcO0vtZBakUAJV/CE/mfI+RxaTvN/x2VkSi9P9kyStrexMvSoLxMhkTJJ3HcP+PE1qZEyX93vCYsyQ0052prriUY3GpIk+jkc9+gmg1+vpzTffpAEDBnj9hgAAAAAeJ3GdJXIlJnf9qWLFiuJ/i6XgyaJQhHgUAAAAVBOPWp/TmIo+jkc9vkXQrl07MYEkAAAAgF8Y/v9Ocu6F12vYzJkzacmSJQ7red3rr78ekH3SCsSjAAAAoIp4VOMx6UwfxaMe922+//77aeLEiXTkyBFq1KgRxcbG2j3ftWtXr3cGAAAAIDedPox0Tu4Y83otW7hwIa1cudJhfZ06dah37940fvz4gOyXFiAeBQAAADXEo9bntGqhj+JRj5ONTz31lPh/1qxZDs/xxJJctQYAAADA53PkSJzzKhC46l+ZMmUc1vOcORcuXAjIPmkF4lEAAABQzZyNGo5JL/ooHvU4/crjuV0tSDQCAACAdGHht6r/5V54vYZxsb2dO3c6rOd1ZcuWDcg+aQXiUQAAAFBFPKrxmLSCj+JRiSWiAAAA/FeJVY0VNEWwIYnMKrFms05V7XhKFxkpFof1cgvW+t0TTzxBo0ePpuzsbLrvvvvEus2bN9O4ceNozJgxgd49UDGugGo0FLwXhcUsb9iXKUtaU2S5Ie9nPMtKlvezKzI9nXwt7YTvf7gVk9yezN9ZilmdP9wVyVXSZclKl/d9nHJCXsXtjHSJPb308j5fYeHy9suYJu/cG46pswp9Vpq82NaSlaGqdmTFo+I5df54CGg86lWyMS0tjbZt20Znzpwho9E+GBg5cqTXOwMAAADgIDzMeSKX12sYB3HXr1+n4cOH2+KpqKgoMTcOz48NeUM8CgAAAAGPR63PadQ4H8WjHicbDxw4QJ07d6b09HQR5BUtWpSuXr1KMTExVLJkSSQbAQAAQKowQ5RYHNd7Xm3vq6++oj///JOio6OpRYsWospejRo1bNsoikIvv/wyLVq0iG7cuEFNmzal9957T0ySLRvPdc3v/9JLL9GxY8fEPlWvXp0iXdw1h/8gHgUAAAA1xKO3nnO/nZkhEo96nH599tln6cEHHxSZT96JX375hU6fPi0qU7/11lsF2hkAAACA3HRhEaQLd7KERXh0snhUxogRI0TssnHjRjKZTNShQwdx89TqjTfeEEXw5s2bR7/++iuVLl2a2rdvTykpKT6dmJvjqqpVq4rAjgNMyBviUQAAAFBFPOphTLotROJRj3s2Hjx4UJTGDg8PF0tWVhZVqVJFnIwBAwZQz549C7RDAAAAADnpDJGkc3InWefhnHXr16+3e7x06VIxKmPfvn107733iqBqzpw59OKLL9rimeXLl1OpUqVo5cqVNGzYMKkX5tq1a/TII4/Qli1bxF3lv//+W8RUPHdO4cKF6e2335b6fsEE8SgAAACoIR71NCZdHyLxqMfJxoiICLEDjA+W522sVasWJSQkiK8BrK7PfEDaySg68Xu/nNgjbTv4/D0qLX5QWlsnB6yV1lb9bRtIjX65o5O0tqp3kDfp9YXf5M7LUbS8vMnwr57ycGypCxVGlCKZzJnyemuFmbLVOan9ZXmTexslTvhuMamrHU/p9BFicVx/a4eSk5Pt1vPdWHeGfiQlJYn/eUoYlpiYKO7q8t3lnG21atWKdu3aJT244955HFdZYymrXr16ieeQbHQt1ONRk1FHJqXgP7uyJf78i5A4+l/JtEgvICqLcu4i+Vradd/X8Ly5dZTU9vSR8n7HWzL+692jpt/xlivyYrXsNJ0qj1FmsRmjxKJRlGlW5THKpL9kVmVRF5nnK/vU33LaSZP54Sp4PFrQmDQpSONRj3+T3XHHHfTbb7/R7bffTm3atKHJkyeLORs//vhjqlevnlc7AQAAAJD3HDnRTtbf+uO2QoUKduunTJlCU6dOzfOE8l3j5557jlq2bEl169YV6ziwsyavcuLHPGWMbBs2bKAff/yRypcvb7ee58nxxfsFE8SjAAAAoIZ4tCAxqRLE8ajHycYZM2bYxom/8sorYuj0U089RdWqVRPdPwEAAABk0oXpSReud7qenT17luLj423r3enV+PTTT9OhQ4dox44dju3+f4+5nIFg7nUy8Nw8XGAvN76JiyIxeUM8CgAAAGqIR63PeROTPh3E8ajHycbGjRvbvi5RogStW7fO6zcHAAAA8L4a9a2hlhzU5Qzs8vPMM8/QmjVr6Oeff7a7i8uTb1vvKJcpU8a2/vLlyw53l2XgeXk++ugjcfOWcQBpsVjozTffFKNHwDXEowAAAKCeatSex6TPBHk86vsJQQAAAAAKINxgoHCD453VcINnk0jyHWEO7L7++mvaunUrVa5c2e55fswBHlcG5GG6zGg0iqqBr7/+uvRryEFc69atxfQ0/D7jxo2jo0ePikqAO3fulP5+AAAAACA3HvU0JlVCJB51K9nIB+hud839+/d7vTMQZExyJ/cOFtlHDwd6FzRF5uTSlhR1Trwse/L6/+/FX2DK6Rskk8zJ0KVORG+SN/G1curWNCMyZKVJLBBj1qmqHU/pwsPE4my9J0aMGCGq+H377bcUFxdnmxOHi4pER0eLWGf06NFiiC7PU8MLf81DS/r06UOy1a5dWwydWbBgAYWHh4thLFx1kPcz551suAXx6H+MaeGUFVHwXxzZWTILJVhUWVCMRcTKa0s5cZV8LT3Z9wUslKRU9RbhSU+X1pbMz3hWkrw4MiNZ3gkzSyzeJjP2kHgZ5RaIkXiMaiX1cy8xfrf8c05OOxnyCkXKiEetz7lrRIjEo279Sdq9e3fb15mZmTR//nyxQ82bNxfrfvnlF5H5HD58uNc7AgAAAOCMWbGIxdl6T3AQxfjubU485/TAgQPF13w3NyMjQ8Q0N27coKZNm4qJszkY9AW+c/3yyy/7pO1gg3gUAAAA1BaPWp9z14IQiUfdSjZyBR2rJ554gkaOHGkbz51zG54MEwAAAEAmk8VMJovJ6XpP8LCV/PDdZK4amF81a2/xnWN31a9f3yf7oFWIRwEAAEBt8aj1OXcpIRKPejzY7osvvhBjuXPr16+fmKx7yZIlXu0IAAAAgDNGUxYZTeFO12tNw4YNRQCZX6DJ25jN8oaNBRvEowAAAKCGeNT6nJY09EM86vFgfh5D7qwsN6+LinJemccdM2fOtI1Nt+ID50xu2bJlxftyN1Merp1TVlaWmFyzePHiFBsbS127dqVz5+znAuBup/379xdj4Hnhr2/evOn1vgIAAID/mC0ml4vWJCYm0smTJ8X/eS28Dfg/HmWISQEAAMCTeFRrMWmiH+JRj3s2cjLwqaeeon379lGzZs1sczZyj8bJkyd7tRO//vorLVq0yKF75htvvEGzZs2iZcuW0e23307Tp0+n9u3b0/Hjx21j1Xl/1q5dS5999hkVK1aMxowZQ126dBH7x5NbMp5EkxOQ69evF4+HDh0qEo78OvAdBQVinJ+X/fKmGwhUsQZ/MmbJmyQ+K0laU5SdJffch0ep71qaE9OktmeSeM48HD3rt0m0M07Jm7A6K90grS2jpHNvDNDPnGxxJznM6Xqt6dGjB23evJmKFClC06ZNo+eff15M+A2Bj0e1FJOajDoyWXSqmvjfiz4Mfvl9waIKyWsr+2+5hVWcSUsuWMLcLclp6o1jsoyq/Cyl3QhXZewhOyaVJU1iIRYlU14iJzNdnQViZF7HcEnFIlmmzAIxh69JaUfJMqkqHrU+pyU9/BCPevydNmHCBProo4/owIEDYu5GXvhrDr74OU+lpqZS37596YMPPhAHmrNX45w5c+jFF18UlXDq1q1Ly5cvp/T0dFG5hyUlJdHixYvp7bffpnbt2okqhStWrKDDhw/Tpk2bxDbHjh0TAd2HH34oCtrwwu/13XffiQARAAAA1M2smF0uWsNxCVf5YzwRN8dB4DnZ8ShDTAoAAADexKNai0mP+SEe9Srn/cgjj4hFBi6n/cADD4hkId8ltuIum1wCvEOHDrZ1kZGR1KpVK9q1axcNGzZM3CnOzs6224aHXHNikrfp2LEj7d69Wwyd5uo9VnwHnNfxNjVq1HC6Xzw8mxer5ORkKccLAAAAnjGand9J5vVaw3PkDBo0iFq2bClurL711ltUqJDzblcF6aEXCmTGo2qNSRGPAgAAqDsetT6nJQ39EI9K7GDrOR5msn//fjFkJTcO6lipUqXs1vPj06dP27YxGAx2PSKt21hfz/+XLFnSoX1eZ93G1Xw9skt/AwAAgOfMFrNYnK3XGu55x1WVeYQFz1X9ww8/kF7vGI7xc0g2+o9aY1LEowAAAOqOR63PackyP8SjHicbuRLN7Nmz6fPPP6czZ86Q0Wg/p8b169fdaufs2bM0atQo2rBhQ54TefPB5cRZ19zrcsu9jbPt82tn4sSJ9Nxzz9n1bKxQoUKe7wsAAADyZZksFGEyO12vNdx7jRNbLCwsTMyX4ywBBf6JR9UekyIeBQAAUHc8an1OS2r4IR71eM5G7u3HE2TzsBWeM5ETcjynIu8gV452Fw83uXz5MjVq1EhkUHnZtm0bvfvuu+Jr693j3Hd6+TXW50qXLi2CS642ndc2ly5dcnj/K1euONyhzomHx8THx9stAAAA4H9mi+Jy0TKLxYJEo5dkxaNqj0kRjwIAAKg/HtVyTGrxUTzqcc/GTz75RBRY4TltONB79NFHqWrVqqJqH1cB5Am63dG2bVtRyCUnHjNes2ZNGj9+PFWpUkUEZRs3bhSFXxgHcRz8vf766+IxB4URERFiG+ucPRcuXKAjR46IqoGMC8JwELp371666667xLo9e/aIdS1atPD08METJu1+w/nSzf3yKuwFeCYEvzBKnP7CKLH6XEY6SaVT4aW8eVZe1UUmc3RBWLg69+v6uQhVVv+TJVBzX2eaFNI7+Z3C67Xu448/pvfff1/MC8hz+lWsWFH02OM4qFu3boHePdWSFY9qNSa9cTGcjP9f4bogzGZ1VlKNjJH7+0cfKa9y6Q2JP+ddSbrh+59tRfrf6tEiTX9SpRMTOqryMx4RqagyJg3TyztGs1li1WcXvcm8kZ2pzqrPat2vXAMHCiT1jJzPfWq2oqp41Pqcln3sg3jU458AfFe3Xr164mueQJIDJNalSxf6/vvv3W4nLi5OTJqdc4mNjaVixYqJr3k4yejRo2nGjBn09ddfi2Bt4MCBohx3nz59RBs8ofbgwYNpzJgxotsnVyHs16+f2D+e3JvVqlWLOnXqREOGDBHBJy/8Ne+vq+IwAAAAoB5m5db9q9wLr9eyBQsWiB55nTt3pps3b4qhwYzn/ZszZ06gd0/VZMWjDDEpAAAAeBuPaj0mXeCjeNTjZGP58uXFnVpWrVo1Mb8N4wm1eaiHTOPGjRMJx+HDh1Pjxo3p33//Fe/HQaEVZ1u7d+8u7iLffffdIhm5du1aCs9xt5fvfnNAyhUCeeG73py5BQAAAPXLMOlcLlo2d+5c0TvvxRdftItbOObJ3dMOAhePMsSkAAAAoS2veFTLMelcH8WjHnew7dGjh+hF2LRpUzGZNg9bWbx4sZic+9lnn6WC2Lp1q91j7t3I8+7kNfcOT+TNJ4cXV4oWLUorVqwo0L4BAABAYJgUHZksOqfrtYyHqliH5ebEybK0tLSA7JNW+DIeZYhJAQAAwJ141PqcViX6KB71ONn42muv2b5+6KGHRIXmnTt3irvKXbt29XpHAAAAAJzJNOkozMkdY16vZZUrV6aDBw+KeXFy+uGHH6h27doB2y8tQDwKAAAAaohHrc9pVWUfxaMFnjqU7yjzYh260qRJk4I2CeDgxo9P+eWspF4PC4r3CCbRsfJ+cN+4IG+25FjJBerNmeqb6OPaOblVaxJKyKwuIu98ZaWFqXLy+DCJpz88XM75cjUptq9lmHWkczLRPK/XsrFjx9KIESMoMzOTFEURhUM+/fRTmjlzJn344YeB3j1NCbV49OQ/OorWFfzzHxVlIVlMEv/QCtPrVfv7LDXZ9z8HL1+N8vl7hIq0ZHmfy8sX5V37kqV1qvxMxheR1hRFRkqM+0zyflYZjfLivliVfqua5dXEIoNBXlupN+T8LE6TeHwy4lHrc1o11kfxqMdXOzU1VYzjjo6Otq3jLOhLL71E69ats00mCQAAACCD2eL87wxer2Vc8dhkMon5ANPT00UBvHLlytE777xDvXv3DvTuqRriUQAAAFBDPGp9TqsG+SgedTutf+7cOVGAhStA88LVanhHHnvsMXH3mMdz79ixw+sdAQAAAHAmGCfj5qBu+fLl9OCDD9Lp06fp8uXLosLy2bNnafDgwYHePdVCPAoAAACBEIwFYkw+jEfd7tk4YcIEcReZs5urV68W/2/bto0aNGhAf/31lxjnDQAAACBbtkVHeicTcvN6rdLr9fTUU0/RsWPHxOPixYsHepc0AfEoAAAAqCketT6nRXofxqNuJxu3bNlCn3/+uejdyIVhypYtSw8//LAI+gAAAAB8JZOHpphdrNcwnmPwwIEDDhNyg2uIRwEAAEBN8ajtOY1q6qN41O1kI3elrFq1qvi6dOnSYs7Gbt26Sd0ZAFeUQ2f9cnKSbvh+0u8M76vHOygkuUiJGsksxHLpnLy2qtaXOzNxdpr67oZdkTgROitUVH3HyNKTw1RZ1MUQKe/8R8bIiYAsEido90S2hSjc4ny9lg0fPpzGjBkjhgY3atSIYmNj7Z6vX79+wPZNrRCP3tLn6mqKjw/eIODHSh2lttfx1I9S2wPtkFk85aHrP6ryM/7gBXV+vv9JkHeMSra869jm+HppbYWCX+7oJK2tups3SGknOTmZKCGB1BKPWp/TquE+ikc9+rOIC8NYhYWFUVSUSssvAQAAQNDINOlIcTIXTpZG58ex6tWrl/h/5MiRtnU6nU5UAuT/UXTPOcSjAAAAoJZ4VOsxaS8fxaNuJxv5jdq2bSvGdLOMjAwxiaQhVy30/fv3e7UjAAAAAK7uFocFYc/GxMTEQO+C5iAeBQAAADXFo9bntCrRR/Go28nGKVOm2D3GEGoAAADwh3QTkdnJzAVZcmcz8DvM1eg5xKMAAACgpnhU6zFpRR/NHe51shEAAADAH8wWImfTRfJ6CC2IRwEAAEBN8aj1ObAncSp7AHu6aoXlnZKbmX45vdXv8P0tiXPHI6S1VaqSOm+h3Nz+nCqLZFxNLyGtrfJpl0mmhNIm1U2+XJdCY7LqZgfUOVH4yR5tpbUVFi7n+8iU7d2cLQWVYQ4js8mxkI/RLK+4DwCoR/HSgd4DCBZlK5EqlSpPQS8y4XZpbSmZR6S1BYErfqh1ruJRhpjUEaJ0AAAAUDWTRedy8cb8+fOpcuXKotAdV93bvn279H0GAAAAgNCIR72JSecHeTyKZCMAAAComtEUTllOFl7vqVWrVtHo0aPpxRdfpAMHDtA999xD999/P505c8Yn+w4AAAAAwRuPehOTrgqBeBTJRgAAAFA1sznc5eKpWbNm0eDBg+mJJ56gWrVq0Zw5c6hChQq0YMECn+w7AAAAAAR3POppTDorBOJRt0bgFy1alP766y8qXrw4Pf744/TOO+9QXFyc7/cOAAAAQl52tp4o23G+2+zsbPF/cnKy3frIyEix5GY0Gmnfvn00YcIEu/UdOnSgXbt2+eU8FylShHQ694baXL9+3ef7oyWIRwEAAEBt8ainMakxROJRt5KNfDL4pHGycfny5fT6668j2QgAAAB+wXeLw5zcMbbeReY7wbkrFk+dOtVh+6tXr5LZbKZSpUrZrefHFy9eJH/gO9dW165do+nTp1PHjh2pefPmYt3u3bvpxx9/pJdeeskv+6MliEcBAABAbfGo9Tl3Y9KrIRKPupVs5Dfs3r27mLRSURQaOXIkRUdHO912yZIlXu8MBJfwJvJq2VoO/0n+UKR/UZ+/h/7z09LaKtRUnT2MlVPnpbWll1iNulHdJtLaunBuHclUtoF3hS5C1bnT8j4XahVXxkJqYzEG5rybTQbSZRucrL9Vxf3s2bMUHx9vW++sV2NOue/kcmzj7t3dghowYIDt6//97380bdo0evrpp23rOMaaN28ebdq0iZ599lm/7JNWIB4NHUXK3PreBiioklWzVHkSi1c0UrCreXtNaW1ZTIeltQWeMUj8W0zrXMWj3sakuiCPR92as3HFihXUuXNnSk1NFQeflJREN27ccLoAAAAAyGTKNrhcGAd1ORdXgR2P0AgPD3e4a3z58mWHu8v+wHeMO3Xq5LCe7yxzcAf2EI8CAACAGuNRT2LS4iESj7rVs5EP+LXXXhNfc2nujz/+mIoVK+b1mwIAAAC4SzGHi8XZek8YDAYxSmPjxo3Uo0cP23p+3K1bN79fEI6lvv76axo7dqzd+m+++QZxlhOIRwEAAEBt8aj1OXcZQiQedSvZmFNiYqLXbwYAAADgKUt2OFnC9U7Xe+q5556j/v37U+PGjcWw3EWLFtGZM2foySef9PuFefnll0Ulwq1bt9rmyPnll19o/fr19OGHH/p9f7QE8SgAAACoIR61PueJ50IgHvU42ci2bdtGb731Fh07dkwMq+ZS3ZwFveeee7zeEQAAAACnzGG3FmfrPdSrVy8xETbPTXPhwgWqW7curVu3jipWrOj3kz9w4EARQ7377rv01Vdfibl6ateuTTt37qSmTZv6fX+0BvEoAAAABDwetT7ngV4hEI/qvZkvZ9CgQdSzZ08xaSTvCJfnbtu2LS1btoz69OlDwezaxAfJGOlVjtZO8VmbKdhFFCsrra2s+HPkDxE1a/n8PWL3ySueoiuXIK2ti4+3ltaW4Y7C0tqKiTdLa0sXX1xaW+YzJJWCufA9UrgIBb3wMlHyGisvp5hUWEY20WLyu3BjGIWHOQZxitHzZCMbPny4WNSAg7hPPvkk0LuhOaEejwY7Q4z6CmQFmxvfD5XbYFqGtKYsv1+S1pahmOc94P0hUl4Ir1phEv5mtrWlRyHFQJFZrFPrXMWj3sakw4M8HvX4J8Crr75Kb7zxhl1FmlGjRtGsWbPolVdeQXAHAAAAUoWZby3O1mvdiRMnaOnSpXTy5EmaM2cOlSxZUgxbqVChAtWpUyfQu6daiEcBAABADfGo9TktO+GDeNTj9Cu/+YMPPuiwvmvXrpg/BwAAAKQzZBNFGh0XXq/1YcD16tWjPXv20OrVqyk1NVWsP3ToEE2ZMiXQu6dqiEcBAABADfGo1mPSbT6KRz1ONnJmc/NmxyHAvI6fAwAAAJAp3Ky4XLRswoQJNH36dFF9kCsTWrVp04Z2794d0H1TO8SjAAAAoJZ4VMsx6QQfxaMeD6MeM2aMmBvn4MGD1KJFC1EgZseOHWJ+nHfeecfrHQEAAABwJirLQhHkOIdbeJa253U7fPgwrVy50mF9iRIlxKTh4BriUQAAAFBDPKr1mPSwj+JRj5ONTz31FJUuXZrefvtt+vzzz8U6rlyzatUq6tatGwU743UzGQ2YoNbvIv/LsPuSoeRtPn8PY3GJRR/M8iaHMFSOJjWKLSHvGNOzpDVFCUXk/hzIuC61uaB3exMjBb1CEdKaMrS5R047KVwA4BvyN1d3jC0avovMChcuLCoQVq5c2W79gQMHqFy5cgHbLy0I9XgUoMCuJ8s9iWXkFeFLOyGvMGShOhLjbol08mqnqFZ2Sqa0tsLi1FnoJxRERGo3iSZbXj0YtRyTFvZRPOrVj7kePXqIBQAAAMDXDEYzGcjxxoPOqO3ZuLli8vjx4+mLL74QI0UsFgvt3LmTnn/+eXrssccCvXuqh3gUAAAAAh2Paj0m7eOjeNTz+twAAAAAfq/+pzhZtF9R+bbbbhN3jXky7tq1a9O9994rpqmZNGlSoHcPAAAAAPKNR7Udk77qo3g0BDpwAwAAgJbpjSbSk8lhvcXouE5LIiIi6JNPPqFXXnmF9u/fL+4k33HHHVS9evVA7xoAAAAAuBGPaj0mjfBRPBrQno0zZ86kJk2aUFxcHJUsWZK6d+9Ox48ft9tGURSaOnUqlS1blqKjo6l169Z09OhRu22ysrLomWeeoeLFi1NsbCx17dqVzp2zn+vjxo0b1L9/f0pISBALf33z5k2/HCcAAAB4T2e2uFy0bNq0aZSenk5VqlShhx56iB555BER2GVkZIjnwD8QjwIAAEBB4lEtx6TTfBSP6hTO5gVIp06dqHfv3iLhaDKZ6MUXXxSVcP744w+RNGSvv/666NbJ1a5vv/12UZL7559/FklJTlJaJwlfu3at2KZYsWKiQuH169dp3759FB5+azLZ+++/XyQgFy1aJB4PHTqUKlWqJF7njuTkZJGk3NumNRXSF7xDaK0NGynYZZq+k9fWr9vIH+KbtPX5e9xc+oG0tnQlC0trS7ksMfmeKa8fuXIxXVpbf37TXFpbETF7SKbi5bOltVXl681S2vmxUkeSqVxVeb9uyjST1hSlHJd3J7LSlz9Ja+vG5/2ltRXds5eUdpKT06lUsV6UlJRE8fHxUtp053fvoIeXkyEixuF5Y3Y6Lf1igN/2RzaOUXhCbr7hmhNX/uN1ZolFwCC44tGdLdpIiUezs+QVOwuXOF6qeEW5RcAsGvtWOnUo0ufvUXes3J+ZYTUrSmvrzITfpbVVvvet72EZkjcnSWsrrqq8Pj+XD8j7Ppb5MyHzppzidKzaQ7uktXVuk8Tik8nyrqNZYsc4meFDmaryduzGRTm/KFLNJmp7YLNq4lGtx6ThPopHw7zNeubmTdZz/fr1NHDgQKpTpw41aNCAli5dSmfOnBFBGeM86Jw5c0TQ17NnT6pbty4tX75cvL+1NDdfzMWLF4tqhO3atRPdPVesWCGCxE2bNoltjh07Jt7rww8/pObNm4vlgw8+oO+++86hJyUAAACoi2Ixu1y0jOMcnog7t99//52KFi0akH3SCsSjAAAAoJZ4VMsxqeKjeNTjZOPLL78sJo3MjROA/FxBcOKQWQ8oMTGRLl68SB06dLBtExkZSa1ataJdu27d3eDEZHZ2tt02POSaE5PWbXbv3i0y0U2bNrVt06xZM7HOuk1uPDSbM9g5FwAAAPA/symbzCajk0Vej2B/KlKkiIh1OLDjXnL8tXXh2KR9+/ZiCAu4hngUAAAA1BGPajMmLeLjeFSvlqwnt/vcc89Ry5YtRaKQcaKRlSpVym5bfnz69GnbNgaDQZyo3NtYX8//5+4SyniddRtn8/cUNHkKAAAABWcyZ1FYWJjT9VrEozY47nn88cdFrMEBnRXHNDyslkdhgGuIRwEAAEAN8aj1Oa2Z4+N41O1kIyfzOMlozXrmTDjyGG7u7fjkk096vSNPP/00HTp0iHbs2OHwXO7kpqsAM69tnG2fVzsTJ04UyU8r7tlYoUIFt44FAAAA5HE1PEWrQ1YGDBgg/q9cuTLdfffdpJcw916oQDyKeBQAACAQ8hourcWYdICP41G9GrKeXEl6zZo1YqLt8uXL29aXLl1a/M+9D8uUKWNbf/nyZVtvR97GaDSKatM5ezfyNi1atLBtc+nSJYf3vXLlikOvyZzDtXnJ7eq/4ZTx/5N8F0QtCn6LD5yR1lb/wvY9V30m3A8F2k3yKlXpa9WQ1pYp/aC0thQnUy14SxdV8O83K7Mir6iLUfLMChVLaO8XlKcKFZE3wXTWOZkV3/zwfe+FsBpVpLV1JeW8lHZSUjIoEMxmI5mc3Enm9VqWlpZGmzdvpo4d7Ysx/fjjj2SxWEQxEbCHePSWiEiFIvQFL7oVGSvvZ2nqdXm/r2UzZcn7Oa+P9H3FUYPv68NILTIoSPj7yMpillekRFc4Wlpb+kiJxRQlxrdhenkxZHSkvGJ+JxMvS2urmsTzFRmbrcoCMUaVhjThEn7XWFkk/SmgBOjPJlfxqPU5rUrzUTyq9ybryUm8iIgIKihOXnKi8euvv6atW7eKtnPix5wo3Lhxoyj8wjixuG3bNlEVkDVq1EjsC29jHU/OlXSOHDlCb7zxhnjMSVCeD3Lv3r101113iXV79uwR66wJSQAAAFAni8UsFmfrtWzChAn02muvOY2P+DkkGx0hHgUAAAA1xaPW57Rqgo/iUY/7SXJxFs5u/vXXX6L3IH+d07333ut2WyNGjBBVpb/99luKi4uzzZ/IvSajo6PFEOfRo0fTjBkzqHr16mLhr2NiYqhPnz62bQcPHkxjxoyhYsWKiXkjn3/+eapXr56oTs1q1apFnTp1oiFDhtDChQvFuqFDh1KXLl2oRg15PcMAAABAPpMli3ROerrwei37+++/qXbt2g7ra9asSf/8809A9kkrEI8CAACAGuJR63Na9beP4lGPk42//PKLSPRxgRbOdObEyUGev9FdCxYsEP+3bt3abv3SpUtp4MCB4utx48ZRRkYGDR8+XAyV5orSGzZsEMlJq9mzZ4vx5dyzkbdt27YtLVu2jMJzdOf/5JNPaOTIkbaq1V27dqV58+Z5evgAAADgZxbFLBZn67WMb5iePHlSTEWTEwd2sbGxAdsvLUA8CgAAAGqIR63PaVWCj+JRj5ONXASmcePG9P3334t5FPMr1JKX3MlKZ7j9qVOnisWVqKgomjt3rlhc4R6PK1as8HpfAQAAIDDM5mwK0zmbs1HevEuBwDc+eQQHTydTtWpVW2DHozX4OXAN8SgAAACoIR61PqdVXX0Uj+q96WL55ZdfUrVq1bx+UwAAAAB3WSwmsljCna7XsjfffFNM88LDVKwF8s6dO0f33HMPvfXWW4HePVVDPAoAAABqiEetz2nVmz6KRz1ONvIwZs5yhmqy8XSijqIL0JvTyv2ZLbXrwBL7gj8FMXj6dfIHebXv8pAq766H+fy/FOx01eRVIq/RUk5FXnZ8RxTJdP1UwYtuWWX3bSOlHUOk3HKY2RKrgUbFW1RZ9VKmmOp3Smtr8i45n/2stMAMETGZjU5HUvB6LeNhK7t27RJF7n7//XcxX3X9+vU9mv86VIV6PGqIsVBkhEVVlZVvXPD4zwqXZM+zn50l7+e83g+VoqNi5VV/dUVXNIHUyijxelF8IWlN6SVWatZFyft+iYg0Sf3ZIkvSJYlzD0fJ+8aLjpf3A+bqWXnXMSNN3ucrOlbmzzx5nwmzpI+qBzP3+SUetT6nVQk+ikfd+u44dOiQ7WuuHs3dKbmYCxdhyV2VmncKAAAAQBbFRfU/Xq91HLTyfNIc0EVGRhZoeppgh3gUAAAA1BaPWp/TMp0P4lG3ko0NGzYUb5ZzjsXHH3/cbsf4OU8LxAAAAADkxyzuJDtfr2UWi4VeffVVev/99+nSpUv0119/UZUqVeill14Sk3QPHjw40LuoKohHAQAAQG3xqPU5rbL4KB51K9mYmJjoVeMAAAAABRWs1ainT59Oy5cvpzfeeIOGDBliW88jR2bPno1kYy6IRwEAACBQgrUa9XQfxaNuJRsrVqzoVeMAAAAABWWyGInMLtZr2EcffUSLFi2itm3biurKOaek+fPPPwO6b2qEeBQAAADUFo/antOoj3wUj3o8o+maNWucruch1FFRUWKi7sqV5RUGURtdbFHS6eQVcghmHy64X1pbN/e9SP6Q+Zfvf0joKsoreGJJvCCtLV3ZYtLaonPJ8tqSOIG5vsRVaW2VqiS34liY88JmXomSdCnji8idoF7mVCYRJeRNyB1D8q7lyR5tpbVV5evN0tp6p52cdpKTk2khjSF/4/lxLDonPRt9ND/OqVOn6JVXXqGffvpJzFFdtmxZ6tevH7344otkMBhs2505c4ZGjBghtuPJtPv06SOq9uXcJi///vuv0wInPJwlO1teMbFgFOrxaEZKOIXrC/6Lo3hFed9DYfJ+LJPFpFPt71h/KFffD3+0miT//AyXd5KjJBYpoSx559IksXCNoZDMvyczpbUUKbFu0B3NJH7GCrn3e9UdkfGKKos5GY3yPl/RMaRKss6XyeT7IlqexKPW57Qak/7ro3jU47Cge/fuDvM35p63sWXLlvTNN99QkSLykioAAAAQmszmbNK5WO8LfBeXA6yFCxeK4OvIkSNiWElaWpoI3G69t5keeOABKlGiBO3YsYOuXbtGAwYMELHQ3Llz3XqfOnXq0Pbt2x167H3xxRd0xx13+OTYggXiUQAAAFBDPGp9TqsxaR0fxaMeJxu5HDZnUXkCybvuukus27t3L02aNElMIMlls4cNG0bPP/88LV682OsdAwAAAPhvjpwwl/PjcI/LnLiKHi/e6tSpk1iseJLs48eP04IFC2yB3YYNG+iPP/6gs2fPirvM7O2336aBAweKGCk+Pj7f95kyZQr1799f3FHmQPKrr74S78PDWb777jtc/DwgHgUAAAA1xKPW57Qak07xUTzq/EzlYdSoUTRr1iwxnjsuLk4s/DUf6NixY+nuu++mOXPmiCAQAAAAoKAsZqOo8pd74fWsQoUK4mandZk5c6b0k56UlERFixa1Pd69ezfVrVvXFtSxjh07UlZWFu3bt8+tNh988EFatWoVrVu3TowMmTx5Mh07dozWrl1L7du3l34MwQTxKAAAAKghHtV6TPqgj+JRj3s2njhxwmlmlNedPHlSfF29enW6elXe3GgAAAAQukxmIymkuByywndyc8YmBbmD7Cr24WEofJfYiufNKVWqlN12PH0Mz43Dz+XHZDKJu82PP/44bdu2Ter+hgLEowAAAKCGeFTLManJh/Gox8nGRo0aiR6M3KWSx4SzK1eu0Lhx46hJkybi8d9//03ly5enYGTOzCKzTuKkxeAW0ye/+OVMhXeuRVqiKxonra2IGrWltZV9+Ya0tpQzl6W1pSseJa2t4vXSSabsK/KKlOii5czSX6qq3AnqU65IrB7g4hd9oIWFq3O/tM6iWEj3/8NTcq9nHNS5M2x56tSp9PLLL+e5za+//kqNGze2PT5//rwYvvLwww/TE088Ybct3/3NzTp/dX70ej29+eabYk4d8Fyox6PpKWEUFu7xACUH+sIaq5zipXC9tn42h9/xX4+VUBQRqaiyQIwxveDfc1YxUer83guLk7df5VrLK3iiS5BX8SS8kLxjjJRYICZaYgEiqd9DEsnar4gAxduu4lHrc1qMSfU+jEc9/suP52Hs1q2bCN64iyjvPFe+4bHj3377rdgmNTVVzN8IAAAAUFA8PIXI8Uaf2exZkv7pp5+m3r1757lNpUqV7IK6Nm3aUPPmzWnRokV225UuXZr27Nljt+7GjRuial/uu8uutGvXjrZu3Srm1AHPIB4FAAAANcSjWo9J2/koHvU42VijRg0xfvvHH3+kv/76S2RLa9asKcZyh4WF2SoEAgAAAMjAk27rLDqXk3G7q3jx4mJxB0+SzUEd96BbunSpLcax4mCPh51cuHCBypQpY5ugm4fL8Gvccf/999PEiRNFZUF+TWxsrN3zXbt2dfvYQg3iUQAAAFBDPGp9Tqsx6f0+ike9GtPGvRlzV8UBAAAA8Nmd5P8fnmK33iJv+oGc+O5x69at6bbbbhMF8Hh4bs67x6xDhw5Uu3ZtUb2Ph59cv36dnn/+eRoyZIhbw2fYU089Jf7nwnvOYi2z2bPANdQgHgUAAIBAx6Naj0mf8lE86lay8d1336WhQ4dSVFSU+DovI0eO9GpHAAAAAJxRFIvTO8a83hf4bvA///wjltxz/vGIDhYeHk7ff/89DR8+nO6++26Kjo6mPn36iEDQXRYL5oD2BOJRAAAAUFs8atAqwykAAMc5SURBVH1OqzGpxUfxqE6x7mEeKleuTL/99hsVK1ZMfO2yMZ3OVpE62CQnJ4vS5Qsi2lG0ruBFDnpfkjcePrJwL2ltdf74C2ltrev/sLS2Tj10H/lDwv9u3R3wKZPEb+ai7vWecUfM3W2ltZW+4TtpbVFWtrymtuRfIdZdhlqFSCZzYpq8tjLlTJosu3DA+X3y2ivbQN7nQpF4IzIrSV5bEQvk/Z4Yuk5OcZ7s9HT6YfBQSkpKcrv3nozfvRXL1qWwMMfPj8ViptPnj/htf2Ti6n98E/fgwYNUt27dQO+OJiAe/e97QoufeQAAAC3+LswvHtVyTGryYTzq1l8fiYmJTr8GAAAA8DVxF1kp+Pw4asLV/ypWrIih0h5APAoAAABqi0dtz2mQ3ofxqP3Mkh4wGo10/PhxkQkFAAAA8BWzOdvlomWTJk0SE3Lz3DrgHcSjAAAAEOh4VMsx6SQfxaMej6tKT0+nZ555hpYvXy4ec0XqKlWqiLkay5YtSxMmTJC6gwAAABDaLDwPjtOejRbNz0HIc/Bw/MR3lXNX/9u/f3/A9k3tEI8CAACAGuJR23Ma9a6P4lGPk42c8fz9999p69atdtWo27VrR1OmTEGyEQAAAKTiu8WKEhZ0BVa6d+8e6F3QLMSjAAAAoIZ4VOsxaXcfxaMeJxu/+eYbWrVqFTVr1kwUhLHiUtsnTpyQvX8AAAAQ4sQ8OBYlqO4iM75JC95BPAoAAABqiEe1HpNO8VE86nGy8cqVK1SyZEmH9WlpaXbJx2BVvLiFYsIK/kHKvnaeZIksTKqsIC2TPlJOdd38KKlZPn+PiFZNpbVl2iNviF26QVpTRAnyKjUrf12Q1tbvP0VKa6tJZbmT6IYViZDWVvY5o5R2dBFyf6ZnZ0lsT+/1lMMOworIqdTMTJflzddy4uoNaW09UF3O5ysjNYN+IP+z8J1kneM1VzQc2OW0b98+OnbsmIij+ObtHXfcEehdUr1Qj0cBAABAHfFosMSk+yTHox7/hdWkSRP6/vvvxbyNzBrQffDBB9S8efMC7QwAAABAblwhT6dTgi6wu3z5MvXu3VtMTVO4cGFSFIWSkpKoTZs29Nlnn1GJEiUCvYuqhXgUAAAA1BCPaj0mveyjeNTjZOPMmTPFXI1//PGHqET9zjvv0NGjR2n37t20bds2r3YCAAAAwJVsU7bT3mocDGkZ37hNTk4WcVStWrXEOo6vBgwYIArvffrpp4HeRdVCPAoAAABqiEe1HpM+46N41ONxaC1atKCdO3eKKoBVq1alDRs2UKlSpUSysVGjRl7tBAAAAIArZrPJ5aJl69evpwULFtgCO8bDVt577z364YdADFjXDsSjAAAAoJZ4VMsx6XofxaNeTVRVr149Wr58OYUSa6Y63SLnQ5SckkmyWJKTKdilZPvnm9eUIW++NVciUjPVub/J6fLaSpMzZyBTJB5jmqTvX5acKW/OQCFL3hyQWUY5x2mUPIVpqkneHb+ULHnXUmdQ58+qNIm/JzJS5exXZlpmQO7eGk2+n083ELhyYUSE43yavE7LVQ39JZTjUe6BAAAAEIqsvwMRj6o7HtUpbl4hd4Oa+Ph4Ckbnzp2jChUqBHo3AAAAAu7s2bNUvnx5n79PZmYmVa5cmS5evOhym9KlS1NiYiJFRUWR1nTr1o1u3rwphqeULVtWrPv333+pb9++VKRIEfr6668DvYuqg3gU8SgAAIDa4lEtx6TdfBSPup1sDAsLy7O6HzfDz/OkmcGIM7rnz5+nuLg4SklJEYlH/nAHa3KVg3kcY3DAtQwOuI7BQevXkX/X8+9ADkQ4LvAHDvCMRte9pQ0Gg+aCOiv+HHCAd+TIEfG54DjqzJkzosfet99+65cAWmsQj/4Xj/LnRes/U9yBYwwOuI7BAdcxOGj9OqoxHtVyTHrWR/Go28Oot2zZYndxO3fuTB9++CGVK1eOQgF/iK0n2Zp05W9MLX5zegLHGDxwLYMDrmNw0PJ1TEhI8Ov7cdCmxcDNHRzQ7d+/nzZu3Eh//vmniK94jpx27doFetdUC/Hof/FosPxMcReOMTjgOgYHXMfgoOXriHhU/fGo28nGVq1a2T0ODw+nZs2aUZUqVQq0AwAAAAChrH379mKB/CEeBQAAAFB/POqfPqcAAAAAIPz000/ijrGz+QeTkpKoTp06tH37dpwtAAAAANBkPIpkoxciIyNpypQp4v9ghWMMHriWwQHXMTiEwnWE/M2ZM4eGDBnidOgSDwsaNmwYzZo1C6cS8hUKP1NwjMEB1zE44DoGh1C4jhD4eNTtAjG58cTUhw4dElV5AAAAAMA9FStWpPXr11OtWrWcPs/z5XTo0EFMzg2IRwEAAAC0Fo+6PWdjz549HarxPPnkkxQbG2u3/quvvvJqRwAAAABCwaVLlygiIsLl83q9nq5cueL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",
       "text/plain": [
        "<Figure size 1600x1000 with 12 Axes>"
       ]
@@ -2321,7 +2360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 82,
    "id": "2304f9a9",
    "metadata": {},
    "outputs": [
@@ -2349,7 +2388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 83,
    "id": "67a86978",
    "metadata": {},
    "outputs": [],
@@ -2403,7 +2442,6 @@
     "                                        insitu_list[0], \n",
     "                                        \"M1\", \n",
     "                                        discard=discard_var['sonde'],\n",
-    "                                        resample=\"mean\",\n",
     "                                        prefix=\"sonde_\")\n",
     "    elif \"wxt\" in stream:\n",
     "         if \"S13\" in stream:\n",
@@ -2437,7 +2475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 84,
    "id": "f856a8b2",
    "metadata": {},
    "outputs": [],
@@ -2448,7 +2486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 85,
    "id": "bdacebe3",
    "metadata": {},
    "outputs": [
@@ -2488,7 +2526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 86,
    "id": "a228f40f",
    "metadata": {},
    "outputs": [],
@@ -2533,7 +2571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 87,
    "id": "0459a58f",
    "metadata": {},
    "outputs": [
@@ -2603,7 +2641,7 @@
        " 'wxt_cumul_precip': {'_FillValue': -9999}}"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2614,7 +2652,7 @@
   },
   {
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-   "execution_count": 80,
+   "execution_count": 89,
    "id": "e542755e",
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    "outputs": [],
diff --git a/notebooks/data/radclss/bnf-csapr2-radclss.c2.20250305.000000.nc b/notebooks/data/radclss/bnf-csapr2-radclss.c2.20250305.000000.nc
index 3635811..0a4be29 100644
Binary files a/notebooks/data/radclss/bnf-csapr2-radclss.c2.20250305.000000.nc and b/notebooks/data/radclss/bnf-csapr2-radclss.c2.20250305.000000.nc differ
diff --git a/notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.1.nc b/notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.2.nc
similarity index 72%
rename from notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.1.nc
rename to notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.2.nc
index 328e7fe..5b9951e 100644
Binary files a/notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.1.nc and b/notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.2.nc differ
diff --git a/notebooks/wxt-test.ipynb b/notebooks/wxt-test.ipynb
new file mode 100644
index 0000000..831676f
--- /dev/null
+++ b/notebooks/wxt-test.ipynb
@@ -0,0 +1,1720 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b8a30e67",
+   "metadata": {},
+   "source": [
+    "<img src=\"images/arm_logo.png\" width=500 alt=\"ARM Logo\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17cf3615",
+   "metadata": {},
+   "source": [
+    "# Bankhead National Forest - RadCLss Verification\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "The Extracted Radar Columns and In-Situ Sensors (RadClss) Value-Added Product (VAP) is\n",
+    "a dataset containing in-situ ground observations matched to CSAPR-2 radar columns above ARM Mobile Facility (AMF-3) supplemental sites of interest. \n",
+    "\n",
+    "RadCLss is intended to provide a dataset for algorthim development and validation of precipitation retrievals.\n",
+    "\n",
+    "This notebook will allow for verification of the interpolation schemes within RadCLss through comparison with the input datastreams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd322c43",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "| Concepts | Importance | Notes |\n",
+    "| --- | --- | --- |\n",
+    "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
+    "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+    "| [GeoPandas](https://geopandas.org/en/stable/docs.html) | Necessary | Familiarity with Geospatial Plotting|\n",
+    "| [Py-ART / Radar Foundations](https://projectpythia.org/radar-cookbook/README.html) | Necessary | Basics of Weather Radar | \n",
+    "\n",
+    "- **Time to learn**: 30 minutes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e4eea78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<dask.config.set at 0x7fd84754b490>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
+    "\n",
+    "import os\n",
+    "import glob\n",
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import dask\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "from math import atan2 as atan2\n",
+    "from cartopy import crs as ccrs, feature as cfeature\n",
+    "from cartopy.io.img_tiles import OSM\n",
+    "from matplotlib.transforms import offset_copy\n",
+    "from dask.distributed import Client, LocalCluster\n",
+    "from metpy.plots import USCOUNTIES\n",
+    "from scipy.stats import linregress\n",
+    "\n",
+    "import act\n",
+    "import pyart\n",
+    "\n",
+    "dask.config.set({'logging.distributed': 'error'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "121de9c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_rad = xr.open_dataset(\"/home/obrienj/git-dev/bnf-radar-examples/notebooks/data/radclss/bnf-csapr2-radclss.c2.20250305.000000.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "df01f998",
+   "metadata": {},
+   "outputs": [
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+       ".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",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n",
+       "Dimensions:                                                (time: 142,\n",
+       "                                                            station: 6,\n",
+       "                                                            height: 32)\n",
+       "Coordinates:\n",
+       "  * time                                                   (time) datetime64[ns] 1kB ...\n",
+       "  * station                                                (station) &lt;U3 72B ...\n",
+       "  * height                                                 (height) int64 256B ...\n",
+       "Data variables: (12/67)\n",
+       "    base_time                                              datetime64[ns] 8B ...\n",
+       "    time_offset                                            (time) datetime64[ns] 1kB ...\n",
+       "    gate_time                                              (time, station) datetime64[ns] 7kB ...\n",
+       "    attenuation_corrected_differential_reflectivity        (time, height, station) float64 218kB ...\n",
+       "    attenuation_corrected_differential_reflectivity_lag_1  (time, height, station) float64 218kB ...\n",
+       "    attenuation_corrected_reflectivity_h                   (time, height, station) float64 218kB ...\n",
+       "    ...                                                     ...\n",
+       "    wxt_temp_mean                                          (time, station) float64 7kB ...\n",
+       "    wxt_precip_rate_mean                                   (time, station) float64 7kB ...\n",
+       "    wxt_cumul_precip                                       (time, station) float64 7kB ...\n",
+       "    lat                                                    (station) float64 48B ...\n",
+       "    lon                                                    (station) float64 48B ...\n",
+       "    alt                                                    (station) int64 48B ...\n",
+       "Attributes: (12/23)\n",
+       "    Conventions:           CF/Radial instrument_parameters ARM-1.3\n",
+       "    command_line:          python bnf_radclss.py --serial True --array True\n",
+       "    process_version:       \n",
+       "    dod_version:           csapr2radclss-c2-1.28\n",
+       "    input_datastreams:     bnfcsapr2cmacS3.c1\n",
+       "    site_id:               bnf\n",
+       "    ...                    ...\n",
+       "    vap_name:              \n",
+       "    known_issues:          False phidp jumps in insect regions. Still uses ol...\n",
+       "    field_names:           base_time, time_offset, time, height, station, gat...\n",
+       "    source:                Atmospheric Radiation Measurement (ARM) program C-...\n",
+       "    history:               created by user jrobrien on machine cumulus.ccs.or...\n",
+       "    location_descrption:   Southeast U.S. in Bankhead National Forest (BNF), ...</pre><div class='xr-wrap' style='display:none'><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-cf1860b9-73d9-4fb7-b7b0-cf59812f29b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf1860b9-73d9-4fb7-b7b0-cf59812f29b1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 142</li><li><span class='xr-has-index'>station</span>: 6</li><li><span class='xr-has-index'>height</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4d457037-f244-4ddd-b119-b896563db6a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d457037-f244-4ddd-b119-b896563db6a0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-03-05T00:03:11 ... 2025-03-...</div><input id='attrs-d914a06d-ae8c-4922-95d3-84c8e57b2723' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d914a06d-ae8c-4922-95d3-84c8e57b2723' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8827f693-1e77-4e40-84b8-3fc1daa241d4' class='xr-var-data-in' type='checkbox'><label for='data-8827f693-1e77-4e40-84b8-3fc1daa241d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-03-05T00:03:11.000000000&#x27;, &#x27;2025-03-05T00:13:13.000000000&#x27;,\n",
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+       "       &#x27;2025-03-05T01:50:03.000000000&#x27;, &#x27;2025-03-05T02:00:02.000000000&#x27;,\n",
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+       "       &#x27;2025-03-05T10:50:42.000000000&#x27;, &#x27;2025-03-05T11:00:45.000000000&#x27;,\n",
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+       "       &#x27;2025-03-05T23:20:04.000000000&#x27;, &#x27;2025-03-05T23:30:02.000000000&#x27;,\n",
+       "       &#x27;2025-03-05T23:40:02.000000000&#x27;, &#x27;2025-03-05T23:50:05.000000000&#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'>station</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;M1&#x27; &#x27;S13&#x27; &#x27;S20&#x27; &#x27;S30&#x27; &#x27;S4&#x27; &#x27;S40&#x27;</div><input id='attrs-7b8d76c3-6bd0-48ca-91c5-35d92d983dc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b8d76c3-6bd0-48ca-91c5-35d92d983dc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19218bd7-198b-4be8-abb9-5ffef3e72ff3' class='xr-var-data-in' type='checkbox'><label for='data-19218bd7-198b-4be8-abb9-5ffef3e72ff3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Bankhead National Forest AMF-3 In-Situ Ground Observation Station Identifers</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;M1&#x27;, &#x27;S13&#x27;, &#x27;S20&#x27;, &#x27;S30&#x27;, &#x27;S4&#x27;, &#x27;S40&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>500 750 1000 ... 7750 8000 8250</div><input id='attrs-a0669a9b-b841-469b-a403-a3a5e3d4706e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0669a9b-b841-469b-a403-a3a5e3d4706e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39667452-3a32-4fa8-a8c5-f82615d4cbc3' class='xr-var-data-in' type='checkbox'><label for='data-39667452-3a32-4fa8-a8c5-f82615d4cbc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Height of Radar Beam</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>description :</span></dt><dd>Height Above Sea Level [in meters] for the Center of Each Radar Gate Above the Target Location</dd></dl></div><div class='xr-var-data'><pre>array([ 500,  750, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, 3000, 3250,\n",
+       "       3500, 3750, 4000, 4250, 4500, 4750, 5000, 5250, 5500, 5750, 6000, 6250,\n",
+       "       6500, 6750, 7000, 7250, 7500, 7750, 8000, 8250])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8ebb3ba-21db-457a-b11a-92f0e82a9baf' class='xr-section-summary-in' type='checkbox'  ><label for='section-b8ebb3ba-21db-457a-b11a-92f0e82a9baf' class='xr-section-summary' >Data variables: <span>(67)</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>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1dfcf996-35b1-48bf-888c-bc9f8d489aaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dfcf996-35b1-48bf-888c-bc9f8d489aaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1adae00c-8594-4487-80ee-c5edb1a8e010' class='xr-var-data-in' type='checkbox'><label for='data-1adae00c-8594-4487-80ee-c5edb1a8e010' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43366723-ab58-4b33-bb3f-09b9b141f90b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43366723-ab58-4b33-bb3f-09b9b141f90b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d4f5208-7874-44bd-bc41-c7e5dabf6712' class='xr-var-data-in' type='checkbox'><label for='data-8d4f5208-7874-44bd-bc41-c7e5dabf6712' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Start time of individual radar scan volumes  from which column are extracted </dd></dl></div><div class='xr-var-data'><pre>[142 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gate_time</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e3fb148-4311-46fb-bd2f-0c30a80d8c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e3fb148-4311-46fb-bd2f-0c30a80d8c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28bf2b3d-c7d6-4122-bd71-2434d21ab0d6' class='xr-var-data-in' type='checkbox'><label for='data-28bf2b3d-c7d6-4122-bd71-2434d21ab0d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time in Seconds that Cooresponds to the Start of each Individual Radar Volume Scan before Concatenation</dd><dt><span>description :</span></dt><dd>Time in Seconds that Cooresponds to the Minimum Height Gate</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c062247-d550-4201-934c-3abeb4c18253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c062247-d550-4201-934c-3abeb4c18253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef32cdf-cc6e-4b90-b33e-bea516f5b3e9' class='xr-var-data-in' type='checkbox'><label for='data-1ef32cdf-cc6e-4b90-b33e-bea516f5b3e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected differential reflectivity</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_differential_reflectivity_lag_1</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19d5d251-8b89-4721-b147-4535ce1661ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19d5d251-8b89-4721-b147-4535ce1661ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62512773-be5f-4be5-b691-ca218f96a2e2' class='xr-var-data-in' type='checkbox'><label for='data-62512773-be5f-4be5-b691-ca218f96a2e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>radar_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Differential reflectivity estimated at lag 1 corrected for rainfall attenuation</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>attenuation_corrected_reflectivity_h</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f77871c0-a6db-4c76-9468-835eddc20dfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f77871c0-a6db-4c76-9468-835eddc20dfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa9ca70-76bb-4ed7-ad02-424ed3c82ca1' class='xr-var-data-in' type='checkbox'><label for='data-9aa9ca70-76bb-4ed7-ad02-424ed3c82ca1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd>Rainfall attenuation-corrected reflectivity, horizontal channel</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>copol_correlation_coeff</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e063c57-14dd-45ef-a249-37f6cbdc2c7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e063c57-14dd-45ef-a249-37f6cbdc2c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea605888-55d7-4f64-87c9-d73c78203f24' class='xr-var-data-in' type='checkbox'><label for='data-ea605888-55d7-4f64-87c9-d73c78203f24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>radar_correlation_coefficient_hv</dd><dt><span>long_name :</span></dt><dd>Copolar correlation coefficient (also known as rhohv)</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_velocity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-76e8fb04-9152-43a0-bf2d-bcf67d7d1d1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76e8fb04-9152-43a0-bf2d-bcf67d7d1d1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3545dff-2045-4e51-82d5-2d082d60b431' class='xr-var-data-in' type='checkbox'><label for='data-f3545dff-2045-4e51-82d5-2d082d60b431' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m/s</dd><dt><span>standard_name :</span></dt><dd>corrected_radial_velocity_of_scatterers_away_from_instrument</dd><dt><span>long_name :</span></dt><dd>Corrected mean doppler velocity</dd><dt><span>valid_max :</span></dt><dd>49.476</dd><dt><span>valid_min :</span></dt><dd>-49.476</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-781adbaa-7f2e-476d-b307-e7c1e37c142c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-781adbaa-7f2e-476d-b307-e7c1e37c142c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b47e465-9800-4440-852d-961e7a5a02c4' class='xr-var-data-in' type='checkbox'><label for='data-7b47e465-9800-4440-852d-961e7a5a02c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Corrected Differential Phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_differential_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-056a1c77-b964-4ef4-ac95-b2a1ce5abc26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-056a1c77-b964-4ef4-ac95-b2a1ce5abc26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ff1d8e4-ca6a-4713-b206-1ff7b57b4890' class='xr-var-data-in' type='checkbox'><label for='data-9ff1d8e4-ca6a-4713-b206-1ff7b57b4890' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>standard_name :</span></dt><dd>radar_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Filtered Corrected Differential Phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df40775e-cb65-41d4-a5dd-d3252e291a0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df40775e-cb65-41d4-a5dd-d3252e291a0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-304e68c2-3d83-4da8-a403-e7b2f85f5bbb' class='xr-var-data-in' type='checkbox'><label for='data-304e68c2-3d83-4da8-a403-e7b2f85f5bbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Corrected Specific differential phase (KDP)</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>filtered_corrected_specific_diff_phase</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6155478c-b4b2-44a9-a361-6e33b246023a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6155478c-b4b2-44a9-a361-6e33b246023a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09744f75-f9b6-4e9e-9f1a-39af37dc5404' class='xr-var-data-in' type='checkbox'><label for='data-09744f75-f9b6-4e9e-9f1a-39af37dc5404' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>standard_name :</span></dt><dd>radar_specific_differential_phase_hv</dd><dt><span>long_name :</span></dt><dd>Filtered Corrected Specific differential phase (KDP)</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_differential_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b6791aee-01c3-40e6-9d69-742a280a82d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6791aee-01c3-40e6-9d69-742a280a82d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd2c13f5-8d21-4a35-8270-cb433e95f9b9' class='xr-var-data-in' type='checkbox'><label for='data-dd2c13f5-8d21-4a35-8270-cb433e95f9b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>standard_name :</span></dt><dd>corrected_log_differential_reflectivity_hv</dd><dt><span>long_name :</span></dt><dd>Corrected differential reflectivity</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-225a1bcb-cbc5-44b5-b8ea-b7d6eedd7ed9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-225a1bcb-cbc5-44b5-b8ea-b7d6eedd7ed9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a817e4a-4f9f-4c68-9f4f-e37667e44710' class='xr-var-data-in' type='checkbox'><label for='data-6a817e4a-4f9f-4c68-9f4f-e37667e44710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd><dt><span>long_name :</span></dt><dd>Corrected reflectivity</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fb930670-93d8-45a9-b2bf-1b5e8d9fbb6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb930670-93d8-45a9-b2bf-1b5e8d9fbb6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd84d51-1ebe-4dbf-86c7-89429c5dc882' class='xr-var-data-in' type='checkbox'><label for='data-5bd84d51-1ebe-4dbf-86c7-89429c5dc882' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from specific attenuation</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Z</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb318cac-564d-4160-a29a-adaf9a8625fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb318cac-564d-4160-a29a-adaf9a8625fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-406e3638-4d37-4981-b915-3edf9e056ec7' class='xr-var-data-in' type='checkbox'><label for='data-406e3638-4d37-4981-b915-3edf9e056ec7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from reflectivity</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_Kdp</span></div><div class='xr-var-dims'>(time, height, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ea7da32-2a83-4a2f-b8ea-5325b2209bf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ea7da32-2a83-4a2f-b8ea-5325b2209bf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5037fccd-7b44-41be-8b14-cd86c6da17fa' class='xr-var-data-in' type='checkbox'><label for='data-5037fccd-7b44-41be-8b14-cd86c6da17fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>long_name :</span></dt><dd>Rainfall rate from specific differential phase</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[27264 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atmos_pressure</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65df15c4-4664-44e4-9ffc-b4ef3eec0eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65df15c4-4664-44e4-9ffc-b4ef3eec0eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddb0781c-62cb-449b-abab-db6a17afa753' class='xr-var-data-in' type='checkbox'><label for='data-ddb0781c-62cb-449b-abab-db6a17afa753' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Atmospheric pressure</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>80.0</dd><dt><span>valid_max :</span></dt><dd>110.0</dd><dt><span>valid_delta :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-838fdbed-3e18-42cd-b9f2-19a910bb4b60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-838fdbed-3e18-42cd-b9f2-19a910bb4b60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18340ff3-5b16-424c-94c2-92c751ab3505' class='xr-var-data-in' type='checkbox'><label for='data-18340ff3-5b16-424c-94c2-92c751ab3505' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-40.0</dd><dt><span>valid_max :</span></dt><dd>50.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5867893c-ddeb-4346-a4a0-4f01474b44c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5867893c-ddeb-4346-a4a0-4f01474b44c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ac0a8fe-5772-4570-94d9-0480de38e42a' class='xr-var-data-in' type='checkbox'><label for='data-5ac0a8fe-5772-4570-94d9-0480de38e42a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative humidity mean</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>-2.0</dd><dt><span>valid_max :</span></dt><dd>104.0</dd><dt><span>valid_delta :</span></dt><dd>30.0</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vapor_pressure_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac22578c-e598-4788-aff6-4627295363e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac22578c-e598-4788-aff6-4627295363e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f08462f-e87d-457a-a38c-6716e7a80a13' class='xr-var-data-in' type='checkbox'><label for='data-6f08462f-e87d-457a-a38c-6716e7a80a13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vapor pressure mean, calculated</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>valid_delta :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>water_vapor_partial_pressure_in_air</dd><dt><span>comment :</span></dt><dd>The calculation is done with respect to ice or water, depending on the measured temperature being below or above 0 degC, respectively</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_arith_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-068e9566-84d0-4ed5-b1ac-47dce838de89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-068e9566-84d0-4ed5-b1ac-47dce838de89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72e5fb56-8731-4f7b-b344-e8f9c68d4c8b' class='xr-var-data-in' type='checkbox'><label for='data-72e5fb56-8731-4f7b-b344-e8f9c68d4c8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed arithmetic mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>60.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_vec_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7f679794-639d-4dbb-9479-d0ea631a3fb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f679794-639d-4dbb-9479-d0ea631a3fb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-291406dd-caa9-42cf-851a-f925fff2590e' class='xr-var-data-in' type='checkbox'><label for='data-291406dd-caa9-42cf-851a-f925fff2590e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed vector mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>60.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdir_vec_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd82beba-a326-4b12-8199-85e436f7d459' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd82beba-a326-4b12-8199-85e436f7d459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-336a3fba-2415-476b-8df3-4e709b8dbf10' class='xr-var-data-in' type='checkbox'><label for='data-336a3fba-2415-476b-8df3-4e709b8dbf10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction vector mean</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tbrg_precip_total_corr</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d56d2544-f91d-410e-8498-24182a1206d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d56d2544-f91d-410e-8498-24182a1206d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-baeab46e-d38b-471d-8f91-0b408847dc12' class='xr-var-data-in' type='checkbox'><label for='data-baeab46e-d38b-471d-8f91-0b408847dc12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>TBRG precipitation total, corrected</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>source :</span></dt><dd>bnfmetM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_pres</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce8fe71a-3410-4f40-9e77-c81c60d72554' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce8fe71a-3410-4f40-9e77-c81c60d72554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-658bd714-9725-429a-b6c9-268add2955f9' class='xr-var-data-in' type='checkbox'><label for='data-658bd714-9725-429a-b6c9-268add2955f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1100.0</dd><dt><span>valid_delta :</span></dt><dd>10.0</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_tdry</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac9e7cc4-7481-40f1-ae48-954d9dd9a10f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac9e7cc4-7481-40f1-ae48-954d9dd9a10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a749aca2-6513-48d4-b68d-9a6e379d2775' class='xr-var-data-in' type='checkbox'><label for='data-a749aca2-6513-48d4-b68d-9a6e379d2775' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dry Bulb Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>50.0</dd><dt><span>valid_delta :</span></dt><dd>10.0</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_dp</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e7ede82-b964-4b0f-8efc-11ced1e38c75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e7ede82-b964-4b0f-8efc-11ced1e38c75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-271021d8-0b9d-4391-ae9c-cb10b4a1ed57' class='xr-var-data-in' type='checkbox'><label for='data-271021d8-0b9d-4391-ae9c-cb10b4a1ed57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dewpoint Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-110.0</dd><dt><span>valid_max :</span></dt><dd>50.0</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_wspd</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64ce9a63-13a0-4401-b7b6-8015a423cfad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64ce9a63-13a0-4401-b7b6-8015a423cfad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4766371b-9543-44fc-aee4-59471ba32178' class='xr-var-data-in' type='checkbox'><label for='data-4766371b-9543-44fc-aee4-59471ba32178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>standard_name :</span></dt><dd>wind_speed</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_deg</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ded5b0de-6e2f-40a9-981b-10882983d170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ded5b0de-6e2f-40a9-981b-10882983d170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41ea95c0-ece4-4b1c-86fe-a32b549fa211' class='xr-var-data-in' type='checkbox'><label for='data-41ea95c0-ece4-4b1c-86fe-a32b549fa211' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind Direction</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>resolution :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_rh</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b5fa2b9-d13d-4916-aa98-d576a23f13f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b5fa2b9-d13d-4916-aa98-d576a23f13f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd756489-78c2-4695-a6d9-bda83a2ad0d8' class='xr-var-data-in' type='checkbox'><label for='data-fd756489-78c2-4695-a6d9-bda83a2ad0d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>100.0</dd><dt><span>resolution :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_u_wind</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a65447e-a0da-48ab-8bba-692c3149b922' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a65447e-a0da-48ab-8bba-692c3149b922' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd947d21-2c9a-4ee7-8970-1a23cdf39f6a' class='xr-var-data-in' type='checkbox'><label for='data-fd947d21-2c9a-4ee7-8970-1a23cdf39f6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Eastward Wind Component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>-75.0</dd><dt><span>valid_max :</span></dt><dd>75.0</dd><dt><span>valid_delta :</span></dt><dd>5.0</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>calculation :</span></dt><dd>(-1.0 * sin(wind direction) * wind speed)</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_v_wind</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad960ac1-5954-47a9-8274-83ca4f707abb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad960ac1-5954-47a9-8274-83ca4f707abb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a28260b5-061b-45fd-99a6-157037074f43' class='xr-var-data-in' type='checkbox'><label for='data-a28260b5-061b-45fd-99a6-157037074f43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Northward Wind Component</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>-75.0</dd><dt><span>valid_max :</span></dt><dd>75.0</dd><dt><span>valid_delta :</span></dt><dd>5.0</dd><dt><span>resolution :</span></dt><dd>0.1</dd><dt><span>calculation :</span></dt><dd>(-1.0 * cos(wind direction) * wind speed)</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sonde_alt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93f8b17a-4157-4f88-a3da-7116cf67b08c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93f8b17a-4157-4f88-a3da-7116cf67b08c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9597132-0f07-43ce-ba20-e8137eacaaba' class='xr-var-data-in' type='checkbox'><label for='data-b9597132-0f07-43ce-ba20-e8137eacaaba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd><dt><span>source :</span></dt><dd>bnfsondewnpnM1.b1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3799f9a4-0263-4f2e-9627-4c7309c335f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3799f9a4-0263-4f2e-9627-4c7309c335f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfadf64f-273b-4184-9906-de7df01beac5' class='xr-var-data-in' type='checkbox'><label for='data-cfadf64f-273b-4184-9906-de7df01beac5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Heavy precipitation alarm</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>3000.0</dd><dt><span>threshold :</span></dt><dd>6 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>comment_1 :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd><dt><span>comment_2 :</span></dt><dd>The time bounds for the sum is [-60, 0]</dd><dt><span>source :</span></dt><dd>bnfwbpluvio2M1.a1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_rtnrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-349e6334-c13b-4aa3-8d75-c5414b9f2292' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-349e6334-c13b-4aa3-8d75-c5414b9f2292' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7976852-2f02-4136-9d01-2c330cb6900c' class='xr-var-data-in' type='checkbox'><label for='data-a7976852-2f02-4136-9d01-2c330cb6900c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Accumulated amounts of precipitation over the sampling interval exceeding a threshold of 0.05mm or the accumulated amount of fine precipitation observed over the last hour</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>500.0</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_rtnrt variable is calculated by first measuring the accumulated amount of rain in the last minute. If this measurement exceeds the threshold, it reports this real time value. If the real time measurement does not reach the threshold, it reports the non real time measurement using the same equation as the accum_nrt variable.</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd><dt><span>source :</span></dt><dd>bnfwbpluvio2M1.a1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_nrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b64c02f6-253e-4c4f-9317-327841fb0a66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b64c02f6-253e-4c4f-9317-327841fb0a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-063d2b37-cc85-4957-8b03-cbc28f71f0f5' class='xr-var-data-in' type='checkbox'><label for='data-063d2b37-cc85-4957-8b03-cbc28f71f0f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Accumulated precipitation over the sampling interval filtered and delayed by 5 minute</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>500.0</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_nrt variable is calculated by measuring the amount of rain accumulate in a sampling interval at most 1 hour long, with the end of the interval at the given time. The start of the sampling interval occurs within the past hour, but is unknown. The start of the interval is determined once the accumulated sum either exceeds 0.05 or the interval length reaches an hour</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd><dt><span>source :</span></dt><dd>bnfwbpluvio2M1.a1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bucket_nrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-72b74e68-1e7f-4046-acc5-f1295a18e60e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72b74e68-1e7f-4046-acc5-f1295a18e60e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26b6471a-0982-4fa4-a263-2b38abccc972' class='xr-var-data-in' type='checkbox'><label for='data-26b6471a-0982-4fa4-a263-2b38abccc972' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The currently measured, filtered bucket contents since last reset</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>20.0</dd><dt><span>valid_max :</span></dt><dd>1800.0</dd><dt><span>threshold :</span></dt><dd>0.01 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>comment :</span></dt><dd>Only increases that exceed the threshold are recorded. Any increase less than threshold is reported as no increase</dd><dt><span>source :</span></dt><dd>bnfwbpluvio2M1.a1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rtnrt</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18ca9500-7a14-4f6b-8fda-6d0de52c61a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18ca9500-7a14-4f6b-8fda-6d0de52c61a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90160dd4-1f11-41ed-a167-6d9935d27ba2' class='xr-var-data-in' type='checkbox'><label for='data-90160dd4-1f11-41ed-a167-6d9935d27ba2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Rain intensity based upon accum_rtnrt</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>30000.0</dd><dt><span>threshold :</span></dt><dd>0.3 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>equation :</span></dt><dd>Calculated by accum_rtnrt * 60</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd><dt><span>source :</span></dt><dd>bnfwbpluvio2M1.a1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_rain_rate</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-644acdb9-ae4d-4aad-8907-f35aa19f51a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-644acdb9-ae4d-4aad-8907-f35aa19f51a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5cfb24-1ee5-4166-b925-27a4a6ff48ac' class='xr-var-data-in' type='checkbox'><label for='data-aa5cfb24-1ee5-4166-b925-27a4a6ff48ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Instantaneous Rainfall Rate of Water Flux</dd><dt><span>units :</span></dt><dd>mm/hour</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_reflectivity_factor_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86677d76-c287-499e-967c-757e2b3ed8c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86677d76-c287-499e-967c-757e2b3ed8c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a66b590d-5c1a-44a5-921f-68d23a88b31c' class='xr-var-data-in' type='checkbox'><label for='data-a66b590d-5c1a-44a5-921f-68d23a88b31c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Horizontal Radar Reflectivity from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_differential_reflectivity_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9084da96-e6de-4815-a151-3aea6d634506' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9084da96-e6de-4815-a151-3aea6d634506' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-756e02ec-87af-44c2-b614-4406db14f932' class='xr-var-data-in' type='checkbox'><label for='data-756e02ec-87af-44c2-b614-4406db14f932' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Differential Radar Reflectivity (H, V) from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_differential_phase_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8e9e2810-3a60-40c9-960a-7284ff94dc17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e9e2810-3a60-40c9-960a-7284ff94dc17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c2d4a43-2951-4a73-885c-7d2bf798b6cf' class='xr-var-data-in' type='checkbox'><label for='data-5c2d4a43-2951-4a73-885c-7d2bf798b6cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Phase from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>degree/km</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc250ed7-c792-457d-96f3-39fa8f4e1b93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc250ed7-c792-457d-96f3-39fa8f4e1b93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ae778c4-cb32-45ed-afa9-b13e18bb0639' class='xr-var-data-in' type='checkbox'><label for='data-7ae778c4-cb32-45ed-afa9-b13e18bb0639' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_med_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2127b38e-58eb-4c9f-a648-eff7942915e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2127b38e-58eb-4c9f-a648-eff7942915e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3e35cf-a34e-4537-a3fa-a29f90ad3c41' class='xr-var-data-in' type='checkbox'><label for='data-0d3e35cf-a34e-4537-a3fa-a29f90ad3c41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Median Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_mass_weighted_mean_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eaa80f04-1b66-48af-bbae-f227682dffd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaa80f04-1b66-48af-bbae-f227682dffd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a416e21e-65f5-4022-b20d-f2cb922f05c1' class='xr-var-data-in' type='checkbox'><label for='data-a416e21e-65f5-4022-b20d-f2cb922f05c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_lwc</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f8dc0bd4-1de0-4eef-af94-1f3ae1496e53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8dc0bd4-1de0-4eef-af94-1f3ae1496e53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dbfb109-d843-41ed-b2bf-15609d1965b7' class='xr-var-data-in' type='checkbox'><label for='data-5dbfb109-d843-41ed-b2bf-15609d1965b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Liquid Water Content</dd><dt><span>units :</span></dt><dd>g/m^3</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_total_droplet_concentration</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64d75133-6fad-41ed-8cf4-21571e953b01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64d75133-6fad-41ed-8cf4-21571e953b01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c27d54c-8eb7-4f3e-8a5b-7218c27dc40b' class='xr-var-data-in' type='checkbox'><label for='data-4c27d54c-8eb7-4f3e-8a5b-7218c27dc40b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total Droplet Concentration</dd><dt><span>units :</span></dt><dd>1/m^3</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_mean_doppler_vel_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94731d66-1036-4aa1-b90a-2b622a9c6863' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94731d66-1036-4aa1-b90a-2b622a9c6863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23443d6f-ffb6-43b4-9bf1-51a99e687185' class='xr-var-data-in' type='checkbox'><label for='data-23443d6f-ffb6-43b4-9bf1-51a99e687185' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Doppler Velocity C-band when temperature is 20C</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ldquants_specific_differential_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-05e007b3-e34a-4238-ba09-a747cb5d6047' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05e007b3-e34a-4238-ba09-a747cb5d6047' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af366fa9-1e13-4eb1-9731-dc51dc15554d' class='xr-var-data-in' type='checkbox'><label for='data-af366fa9-1e13-4eb1-9731-dc51dc15554d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd><dt><span>source :</span></dt><dd>bnfldquantsM1.c1</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_rain_rate</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-639a1802-1485-4219-806c-ac65dd88b138' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-639a1802-1485-4219-806c-ac65dd88b138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af79a81b-112b-4f1e-ba03-5ad99d65924a' class='xr-var-data-in' type='checkbox'><label for='data-af79a81b-112b-4f1e-ba03-5ad99d65924a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Instantaneous Rainfall Rate of Water Flux</dd><dt><span>units :</span></dt><dd>mm/hour</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_reflectivity_factor_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45a53137-b3c2-40e1-9ec0-e0901838e861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45a53137-b3c2-40e1-9ec0-e0901838e861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea37dcd2-dbf0-494e-8347-990e8771842c' class='xr-var-data-in' type='checkbox'><label for='data-ea37dcd2-dbf0-494e-8347-990e8771842c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Horizontal Radar Reflectivity from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_differential_reflectivity_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b645569-407e-48c4-93b6-a5044f80dd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b645569-407e-48c4-93b6-a5044f80dd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce22ac8d-ef4f-44d7-af16-f457017be3b9' class='xr-var-data-in' type='checkbox'><label for='data-ce22ac8d-ef4f-44d7-af16-f457017be3b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Estimated Differential Radar Reflectivity (H, V) from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_differential_phase_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-51d25371-e383-48b7-a5a6-1093cd6b5ff3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51d25371-e383-48b7-a5a6-1093cd6b5ff3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b67a8cf-d3eb-41b0-bb7c-fc7fb75ad4fb' class='xr-var-data-in' type='checkbox'><label for='data-7b67a8cf-d3eb-41b0-bb7c-fc7fb75ad4fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Phase from Drop Size Distribution C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>deg/km</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-068c1345-ddb2-4962-9157-1f567e448201' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-068c1345-ddb2-4962-9157-1f567e448201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6000f418-bc3d-4d96-a37b-7ccd4d799ebb' class='xr-var-data-in' type='checkbox'><label for='data-6000f418-bc3d-4d96-a37b-7ccd4d799ebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_med_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-316a90e5-664a-4f5a-bc43-93f0edd20324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-316a90e5-664a-4f5a-bc43-93f0edd20324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-910616ad-c354-410d-ace9-3b7fb9fedf0d' class='xr-var-data-in' type='checkbox'><label for='data-910616ad-c354-410d-ace9-3b7fb9fedf0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Median Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_mass_weighted_mean_diameter</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4cbfb48-875e-48e8-a028-9f8c6a7e459c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4cbfb48-875e-48e8-a028-9f8c6a7e459c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58ac8ae5-3dc3-4fa7-b602-c3662695ab2c' class='xr-var-data-in' type='checkbox'><label for='data-58ac8ae5-3dc3-4fa7-b602-c3662695ab2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Drop Diameter</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_lwc</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b35f3485-7351-4578-97cd-777db830937b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b35f3485-7351-4578-97cd-777db830937b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84c2d6a7-9c42-4b8f-bad5-11a32f5127b2' class='xr-var-data-in' type='checkbox'><label for='data-84c2d6a7-9c42-4b8f-bad5-11a32f5127b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Liquid Water Content</dd><dt><span>units :</span></dt><dd>g/m^3</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_total_droplet_concentration</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8bba3d3c-d4cf-43e7-be3c-48be3b0d152b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bba3d3c-d4cf-43e7-be3c-48be3b0d152b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc799d0d-af9f-49ae-8a7c-35e569d46179' class='xr-var-data-in' type='checkbox'><label for='data-bc799d0d-af9f-49ae-8a7c-35e569d46179' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total Droplet Concentration</dd><dt><span>units :</span></dt><dd>1/m^3</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_mean_doppler_vel_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33c83428-c29a-4b8a-aec1-8a2aad0043ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33c83428-c29a-4b8a-aec1-8a2aad0043ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e078e0-d3a6-438d-87b5-158e220d40b6' class='xr-var-data-in' type='checkbox'><label for='data-b2e078e0-d3a6-438d-87b5-158e220d40b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean Doppler Velocity C-band when temperature is 20C</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vdisquants_specific_differential_attenuation_cband20c</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6eae16af-e92c-495c-b84e-ceaf7dce3a32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6eae16af-e92c-495c-b84e-ceaf7dce3a32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd8b51d8-cc9f-478d-95e4-f0dd8344b5d5' class='xr-var-data-in' type='checkbox'><label for='data-dd8b51d8-cc9f-478d-95e4-f0dd8344b5d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific Differential Attenuation C-Band when temperature is 20 degree C</dd><dt><span>units :</span></dt><dd>dB/km</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_temp_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ca03e04-75d3-4613-a629-6cba22de3b67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ca03e04-75d3-4613-a629-6cba22de3b67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf6246c-e787-494b-840a-8e9b17a9e513' class='xr-var-data-in' type='checkbox'><label for='data-6cf6246c-e787-494b-840a-8e9b17a9e513' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vaisala Weather Transmitter (WXT) Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>standard_name :</span></dt><dd>surface_temperature</dd><dt><span>valid_min :</span></dt><dd>-40</dd><dt><span>valid_max :</span></dt><dd>50</dd><dt><span>valid_delta :</span></dt><dd>20</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_precip_rate_mean</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-187da7fb-ac32-4c97-af5f-e653e9e8492a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-187da7fb-ac32-4c97-af5f-e653e9e8492a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89dd88ed-71b8-4966-a874-5bae5e8daf6d' class='xr-var-data-in' type='checkbox'><label for='data-89dd88ed-71b8-4966-a874-5bae5e8daf6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WXT mean precipitation rate</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>200</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wxt_cumul_precip</span></div><div class='xr-var-dims'>(time, station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1209570e-bc73-40c0-82a3-7004dac00e20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1209570e-bc73-40c0-82a3-7004dac00e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92101f82-80ea-4cbe-b35c-4630ddb6fb8c' class='xr-var-data-in' type='checkbox'><label for='data-92101f82-80ea-4cbe-b35c-4630ddb6fb8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>WXT cumulative precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd></dl></div><div class='xr-var-data'><pre>[852 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b5990d5-7284-459d-bc08-47391b12f646' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b5990d5-7284-459d-bc08-47391b12f646' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80d25034-7c32-46e6-a132-88896d36e42a' class='xr-var-data-in' type='checkbox'><label for='data-80d25034-7c32-46e6-a132-88896d36e42a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitude of BNF AMF-3 Ground Observation Site</dd><dt><span>units :</span></dt><dd>Degrees North</dd></dl></div><div class='xr-var-data'><pre>[6 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9615de4a-d235-41c5-80ce-d645b131a258' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9615de4a-d235-41c5-80ce-d645b131a258' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a74c3914-4510-4eb5-ba43-583f7aecaddb' class='xr-var-data-in' type='checkbox'><label for='data-a74c3914-4510-4eb5-ba43-583f7aecaddb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitude of BNF AMF-3 Ground Observation Site</dd><dt><span>units :</span></dt><dd>Degrees East</dd></dl></div><div class='xr-var-data'><pre>[6 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b7e5e3a-3338-45bc-8d14-89a375fecd23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b7e5e3a-3338-45bc-8d14-89a375fecd23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-411fe844-ef3e-4325-85d5-8001d5627910' class='xr-var-data-in' type='checkbox'><label for='data-411fe844-ef3e-4325-85d5-8001d5627910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level for each station</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[6 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1b63101-79c6-436f-a065-0404e4bab28f' class='xr-section-summary-in' type='checkbox'  ><label for='section-f1b63101-79c6-436f-a065-0404e4bab28f' class='xr-section-summary' >Attributes: <span>(23)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF/Radial instrument_parameters ARM-1.3</dd><dt><span>command_line :</span></dt><dd>python bnf_radclss.py --serial True --array True</dd><dt><span>process_version :</span></dt><dd></dd><dt><span>dod_version :</span></dt><dd>csapr2radclss-c2-1.28</dd><dt><span>input_datastreams :</span></dt><dd>bnfcsapr2cmacS3.c1</dd><dt><span>site_id :</span></dt><dd>bnf</dd><dt><span>platform_id :</span></dt><dd>csapr2radclss</dd><dt><span>facility_id :</span></dt><dd>S3</dd><dt><span>data_level :</span></dt><dd>c2</dd><dt><span>location_description :</span></dt><dd></dd><dt><span>datastream :</span></dt><dd>bnfcsapr2radclssS3.c2</dd><dt><span>institution :</span></dt><dd>U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility</dd><dt><span>references :</span></dt><dd>See CSAPR2 Instrument Handbook</dd><dt><span>doi :</span></dt><dd>10.5439/2586707</dd><dt><span>comment :</span></dt><dd>This is highly experimental and initial data. There are many known and unknown issues. Please do not use before contacting the Translator responsible scollis@anl.gov</dd><dt><span>attributions :</span></dt><dd>This data is collected by the ARM User Facility. Radar system is operated by the radar engineering team radar@arm.gov and the data is processed by the precipitation radar products team. LP code courtesy of Scott Giangrande, BNL.</dd><dt><span>version :</span></dt><dd>1.0</dd><dt><span>vap_name :</span></dt><dd></dd><dt><span>known_issues :</span></dt><dd>False phidp jumps in insect regions. Still uses old Giangrande code.</dd><dt><span>field_names :</span></dt><dd>base_time, time_offset, time, height, station, gate_time, attenuation_corrected_differential_reflectivity, attenuation_corrected_differential_reflectivity_lag_1, attenuation_corrected_reflectivity_h, corrected_velocity, corrected_differential_phase, filtered_corrected_differential_phase, corrected_specific_diff_phase, filtered_corrected_specific_diff_phase, corrected_differential_reflectivity, corrected_reflectivity, rain_rate_A, rain_rate_Z, rain_rate_Kdp, atmos_pressure, temp_mean, rh_mean, vapor_pressure_mean, wspd_arith_mean, wspd_vec_mean, wdir_vec_mean, tbrg_precip_total_corr, sonde_pres, sonde_tdry, sonde_dp, sonde_wspd, sonde_deg, sonde_rh, sonde_u_wind, sonde_v_wind, intensity_rt, accum_rtnrt, accum_nrt, bucket_nrt, intensity_rtnrt, ldquants_rain_rate, ldquants_reflectivity_factor_cband20c, ldquants_differential_reflectivity_cband20c, ldquants_specific_differential_phase_cband20c, ldquants_specific_attenuation_cband20c, ldquants_med_diameter, ldquants_mass_weighted_mean_diameter, ldquants_lwc, ldquants_total_droplet_concentration, ldquants_mean_doppler_vel_cband20c, ldquants_specific_differential_attenuation_cband20c, vdisquants_specific_differential_phase_cband20c, vdisquants_specific_attenuation_cband20c, vdisquants_med_diameter, vdisquants_mass_weighted_mean_diameter, vdisquants_lwc, vdisquants_total_droplet_concentration, vdisquants_specific_differential_attenuation_cband20c, wxt_temp_mean, wxt_precip_rate_mean, wxt_cumul_precip, lat, lon, alt</dd><dt><span>source :</span></dt><dd>Atmospheric Radiation Measurement (ARM) program C-band Scanning ARM Precipitation Radar 2 (CSAPR2) Corrected Moments in Antenna Coordinates (CMAC)</dd><dt><span>history :</span></dt><dd>created by user jrobrien on machine cumulus.ccs.ornl.gov at 2025-12-02 20:51:10.579190 using Py-ART and ACT ; Written to file by ACT-2.2.3 with write_netcdf() at 2025-12-03 03:03:58+00:00 UTC</dd><dt><span>location_descrption :</span></dt><dd>Southeast U.S. in Bankhead National Forest (BNF), Decatur, Alabama</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 3MB\n",
+       "Dimensions:                                                (time: 142,\n",
+       "                                                            station: 6,\n",
+       "                                                            height: 32)\n",
+       "Coordinates:\n",
+       "  * time                                                   (time) datetime64[ns] 1kB ...\n",
+       "  * station                                                (station) <U3 72B ...\n",
+       "  * height                                                 (height) int64 256B ...\n",
+       "Data variables: (12/67)\n",
+       "    base_time                                              datetime64[ns] 8B ...\n",
+       "    time_offset                                            (time) datetime64[ns] 1kB ...\n",
+       "    gate_time                                              (time, station) datetime64[ns] 7kB ...\n",
+       "    attenuation_corrected_differential_reflectivity        (time, height, station) float64 218kB ...\n",
+       "    attenuation_corrected_differential_reflectivity_lag_1  (time, height, station) float64 218kB ...\n",
+       "    attenuation_corrected_reflectivity_h                   (time, height, station) float64 218kB ...\n",
+       "    ...                                                     ...\n",
+       "    wxt_temp_mean                                          (time, station) float64 7kB ...\n",
+       "    wxt_precip_rate_mean                                   (time, station) float64 7kB ...\n",
+       "    wxt_cumul_precip                                       (time, station) float64 7kB ...\n",
+       "    lat                                                    (station) float64 48B ...\n",
+       "    lon                                                    (station) float64 48B ...\n",
+       "    alt                                                    (station) int64 48B ...\n",
+       "Attributes: (12/23)\n",
+       "    Conventions:           CF/Radial instrument_parameters ARM-1.3\n",
+       "    command_line:          python bnf_radclss.py --serial True --array True\n",
+       "    process_version:       \n",
+       "    dod_version:           csapr2radclss-c2-1.28\n",
+       "    input_datastreams:     bnfcsapr2cmacS3.c1\n",
+       "    site_id:               bnf\n",
+       "    ...                    ...\n",
+       "    vap_name:              \n",
+       "    known_issues:          False phidp jumps in insect regions. Still uses ol...\n",
+       "    field_names:           base_time, time_offset, time, height, station, gat...\n",
+       "    source:                Atmospheric Radiation Measurement (ARM) program C-...\n",
+       "    history:               created by user jrobrien on machine cumulus.ccs.or...\n",
+       "    location_descrption:   Southeast U.S. in Bankhead National Forest (BNF), ..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_rad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "926ce010",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_met = act.io.read_arm_netcdf(\"/nfs/gce/globalscratch/obrienj/bnf-insitu/bnfmetM1.b1/bnfmetM1.b1.20250305.000000.cdf\", cleanup_qc=True)\n",
+    "ds_pluv = act.io.read_arm_netcdf(\"/nfs/gce/globalscratch/obrienj/bnf-insitu/bnfwbpluvio2M1.a1/bnfwbpluvio2M1.a1.20250305.000000.nc\", cleanup_qc=True)\n",
+    "ds_sonde = act.io.read_arm_netcdf(\"/nfs/gce/globalscratch/obrienj/bnf-insitu/bnfsondewnpnM1.b1/bnfsondewnpnM1.b1.20250305.172900.cdf\", cleanup_qc=True)\n",
+    "ds_ld = act.io.read_arm_netcdf(\"/nfs/gce/globalscratch/obrienj/bnf-insitu/bnfldquantsM1.c1/bnfldquantsM1.c1.20250305.000000.nc\", cleanup_qc=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "05008f2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f7a7fd0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_met.temp_mean.plot(ax=axs, label=\"MET-M1\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").temp_mean.plot(ax=axs, label=\"RadCLss\")\n",
+    "\n",
+    "axs.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1e19f8b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd845843f50>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_met.atmos_pressure.plot(ax=axs, label=\"MET-M1\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").atmos_pressure.plot(ax=axs, label=\"RadCLss\")\n",
+    "\n",
+    "axs.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "bfdc9625",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f5cb710>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_met.rh_mean.plot(ax=axs, label=\"MET-M1\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").rh_mean.plot(ax=axs, label=\"RadCLss\")\n",
+    "\n",
+    "axs.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fd9e7330",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd84625f690>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_met.vapor_pressure_mean.plot(ax=axs, label=\"MET-M1\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").vapor_pressure_mean.plot(ax=axs, label=\"RadCLss\")\n",
+    "\n",
+    "axs.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e85c4bbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f38ffd0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_met.wdir_vec_mean.plot(ax=axs, label=\"MET-M1\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").wdir_vec_mean.plot(ax=axs, label=\"RadCLss\")\n",
+    "\n",
+    "axs.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "42b443ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f3ba750>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_pluv.bucket_nrt.plot(ax=axs, label=\"MET-M1\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").bucket_nrt.plot(ax=axs, label=\"RadCLss\")\n",
+    "\n",
+    "axs.legend(loc='upper right')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "20fecd98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f2905d0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_sonde.wspd.plot(ax=axs, label=\"Sonde\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").sonde_wspd.plot(ax=axs, label=\"RadCLss\")\n",
+    "axs.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4c622b7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f35fe50>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_ld.reflectivity_factor_cband20c.plot(ax=axs, label=\"LD\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").ldquants_reflectivity_factor_cband20c.plot(ax=axs, label=\"RadCLss\")\n",
+    "axs.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "943d0a49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd83f42b050>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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oBOkAAAAA4EB5eXmyd+9eadCggVSqVCneu4Ni1K5d2wTqet7S09OltLgUAwAAAAAO5Ha7zdeylk8jNuzzZJ+30iJIBwAAAAAHK0vpNBLvPBGkAwAAAADgEATpAAAAAAA4BEE6AAAAACCi+vXrJ7169Qr6WNOmTU1puN4qVqxo7l988cUyZcoUzgJBOgAAAAAg1kaMGGGmK1uyZImMHz9eDjjgADnttNPkkUceKfcngynYAAAAAAAxVbVqValXr575XucVP+WUU6R+/foybNgwufDCC+Xwww8vt2eEcncAAAAASACWZcnenLy43PS1o+3WW281r/N///d/Up6RSQcAAACABJCV65aWw76Ny2svGnGmVKoQ3fCxRo0aUqdOHVm1apWUZ2TSAQAAAACOYFlWuZ8Xnkw6AAAAACSAiumpJqMdr9eOti1btsh///0nzZo1k/KMIB0AAAAAEoBOWRbtkvN4eu655yQlJSXk1G3lRfKeYQAAAABA3OzYsUMWLlxYaNy52rVrl2zYsEFyc3Nl5cqV8s4778gbb7whI0eOlObNm0t5RpAOAAAAAIi4adOmSbt27fyW9e3b13zVqdb0VqFCBTMVW4cOHWTy5MnSpUuXcn8mCNIBAAAAABE1btw4c0PJ0d0dAAAAAACHIEgHAAAAAMAhCNIdbvWWvXLHR7/J3xt3xXtXAAAAAABRRpDucP3H/Swfzf9Xzn95drx3BQAAAAAQZQTpDrf8vz3m6659efHeFQAAAABAlBGkAwAAAADgEATpAAAAAAA4BEE6AAAAAAAOQZAOAAAAAIBDEKQjLPty3RwpAAAAAHHVr18/6dWrV1KfBYJ0FOuBz/+SI+7/Rv74dwdHCwAAAEBYwbTL5TK3tLQ0ady4sdxwww2ybdu2iB+9DRs2yM033ywHH3ywZGRkSKNGjaR79+4yefJk7zpNmzaVZ599NiHOHEE6ijVu9irz9dkflnK0AAAAAITlrLPOkvXr18uqVavkjTfekC+++EIGDhwY0aO3atUqad++vUyZMkWeeOIJ+eOPP+Sbb76RLl26yI033piQZ4ogHQAAAAAQcZrVrlevnjRs2FDOOOMMueSSS+S7774zj7ndbrnqqqukWbNmUrFiRTn88MPlueee83u+rjN48GA54IADpGbNmnLnnXeKZVl+62jQr9n6n3/+WS688EI57LDD5MgjjzTPmzt3blj7qdn9K664QmrXrm325dBDD5WxY8eax3JycuSmm26S+vXrS2ZmpsnIjxw5UqIpLapbR1Lx/+8AAAAAIKY0QM3dG5+Dnl5JxOUq9dNXrFhhMtzp6enmvsfjMcH7hx9+KLVq1ZLZs2fLtddea4Lhiy++2Kzz9NNPy5gxY+TNN9+Uli1bmvuffvqpnHrqqebxrVu3mm0+8sgjUrly5UKvqcF9OO6//35ZtGiRfP3112Zfli1bJllZWeax559/Xj7//HOzn1qyv2bNGnOLJoJ0AAAAAEgEGqA/2iA+r33POpEKhQPhonz55ZdSpUoVkxHft2+fWTZq1CjzVYP1Bx980LuuZtQ1UP/www+9QbqOIR86dKhccMEF5v4rr7wi3377rfc5GkxrZv2II44o01tbvXq1tGvXTo455hhzX7Plvo9pZv2kk04yGfsmTZpItFHujrCV/roZAAAAgPJGx4UvXLhQfvrpJ9PY7cwzzzRfbRp0a2CsZeYazL/++usmKFY7duww49lPOOEE7/ragM4OpJVd+q7Bc1loQ7v3339f2rZta0rq9WKBbwM8fQ9ajn/LLbd4y/WjiUw6AAAAACQCLTnXjHa8XruEtAS9efPm3rJxDdo1e/7QQw+ZjPmgQYNMCbsG4lWrVpUnn3zSBPTh0gy3BuiLFy8u07Rs3bp1k3/++Ue++uor+eGHH6Rr166m6dxTTz0lRx99tKxcudKUwutjmuU/7bTT5OOPP5ZoIZMOAAAAAIlAM8Zach6PWxmz1Wr48OEm8F23bp3MnDlTOnbsaBq/aam5BvPLly/3rlu9enUzPt23+VteXp7Mnz/fe79GjRomO//SSy/Jnj17Cr3e9u3bw943zeZr1vydd94xZfavvfaa97Fq1aqZpnea6f/ggw/kk08+MePho4VMOgAAAAAg6jp37mw6rz/66KMmCz5+/HgzxlzHo7/99tvyyy+/mO9tt956qzz22GNm3RYtWpjx7IGB9+jRo02wf9xxx8mIESPkqKOOMsH8999/Ly+//LLJstvWrl1rStd9aTM4Dcp1Gjfdt+zsbDOWXl9PPfPMM+ZigZbCp6SkyEcffWQ61ofblK40CNIBAAAAADGhU6P1799fli5dagJmzVBryfpll11msupff/21d90hQ4aYcema4dYAecCAAXLeeeeZ8eo2DeoXLFhgOrzb62tWXINuDdJ9aRZfb750qrUKFSqYBnU657pOwXbyySebMepKx8o//vjj8vfff0tqaqoce+yxMmnSJLM/0eKyAieai6EZM2aYcQdasqAHU9vpFzeWYMKECWaSej1IWgJx1llnmQOt8+aFY+fOneZ5emK1bMHpmt79lff7VY+dE9d96HpEHXmz37Fx2QcAAACgvNGO6DoeWgNRnaMbiXu+ShKHxnVMuo4baNOmjbz44othrT9r1izp06ePmfT+r7/+MqUGWhJx9dVXR31fwTzpAAAAABBtcS131y56eguXNg3QOeu09b3SKxTXXXedyawDAAAAAJDoEqq7uzYE+Pfff80YAK3S37hxo2l9f845ocvAdeC/lhb43lA6zJMOAAAAANGVcEG6jknX5gI6uN/uqvfCCy+EfM7IkSNN7b99a9SoUUz3OZnErXkBAAAAAJQTCRWkL1q0yJS6Dxs2zDSb++abb8zA/Ouvvz7kc7RLnw7Ot29r1qyJ6T4DAAAAAJCUU7BpVvzEE0+UO+64w9zXOfAqV65sWuQ//PDDZv66QBkZGeaGsqPcHQAAAIi9OE7IhTicp4QK0vfu3Stpaf67rHPVKT640cePBgAAACB20tPTzRzi//33n5n7W7+HM2k8qudJz5Get4QN0nfv3i3Lli3z3tfSdZ3QvkaNGtK4cWNTqr527VoZP368ebx79+5yzTXXmEnpzzzzTDO3+m233SbHHXecNGjQII7vBAAAAAAiSxOSDRs2NM2zV61axeF1OA3Q9XzZieSEDNLnzZsnXbp08d4fPHiw+dq3b18ZN26cCcJXr17tfbxfv36ya9cuM6/6kCFDTNO4U089VR5//PG47H95w3U7AAAAILaqVKkihx56qOTm5nLoHU4z6GUN0OMepHfu3LnIMnUN1APdfPPN5obYo9wdAAAAiD0N/CIR/CExJFR3dwAAAAAAkhlBOgAAAAAADkGQDgAAAACAQxCkAwAAAADgEATpAAAAAAA4BEG6g+3Ldcd7FwAAAAAAMUSQ7mAXvTIn3rsAAAAAAIghgnQH+2PtjnjvAgAAAAAghgjSAQAAAABwCIJ0AAAAAAAcgiAdAAAAAACHIEgHAAAAAMAhCNIBAAAAAHAIgnQAAAAAAByCIB0AAAAAAIcgSAcAAAAAwCEI0gEAAAAAcAiCdAAAAAAAHIIgHQAAAAAAhyBIBwAAAADAIQjSAQAAAABwCIJ0AAAAAAAcgiAdAAAAAACHIEgHAAAAAMAhCNIThMsV7z0AAAAAAEQbQXqCIEYHAAAAgORHkA4AAAAAgEMQpCcIF/XuAAAAAJD0CNITBOXuAAAAAJD8CNIdyrKseO8CAAAAACDGCNIdyhMQo1PtDgAAAADJjyDdodwBUbrLAQXvZPcBAAAAILoI0h3KE1juHv8YHQAAAAAQZQTpiRKkOwAd5gEAAAAgugjSE6bcPf4odwcAAACA6CJIdyiPx/8+jeMAAAAAIPkRpDuU23Je4zjK3QEAAAAgugjSE6Tc3QkodwcAAACA6CJId6jAgJhydwAAAABIfgTpCVPuDgAAAABIdgTpDuXEcncAAAAAQHQRpCdMd/f459KdsA8AAAAAkMwI0h3KieXuNI4DAAAAgOgiSHcoT0CQ7ogoHQAAAAAQVQTpDuVx4Jh0yt0BAAAAILoI0h2KcncAAAAAKH8I0hOkuztZbAAAAABIfgTpCdPdPV574rsPDtgJAAAAAEhiBOkJUu7uBHR3BwAAAIDoIkhPkO7u5LABAAAAIPkRpCdId3dKzQEAAAAg+RGkJ0jjOAAAAABA8iNIdygnTsEGAAAAAIgugnSHcmJ3dwAAAABAdBGkJ0jjOHLpAAAAAJD8CNIdyolTsAEAAAAAoosgPWG6u8dtVwAAAAAAMUKQ7lB0dwcAAACA8ocgPUHGpFP9DgAAAADJjyDdoQpPk84YdQAAAABIdgTpCVLuTiYdAAAAAJIfQXqilLvHbU8AAAAAALFCkJ4wmXTCdAAAAABIdgTpiRKkx21PAAAAAACxQpDuUKccVlueuqiN9z6JdAAAAABIfgTpDlW3WqZc2L6hjL7iaHOfcncAAAAASH4E6Q53eL2q5ivl7gAAAACQ/AjSHc5lf0OUDgAAAABJjyDd4Vyu/DCdGB0AAAAAkh9BeoJk0hmTDgAAAADJjyDd4QoS6Y7JpE/93ybp9dKPsmzT7njvCgAAAAAkHYJ0h3MV5NKdMgVb/3G/yMI12+WmdxfEe1cAAAAAIOkQpCdMJt0hUXqBHVm58d4FAAAAAEg6BOkJwimZdAAAAABA9BCkO5zTxqQDAAAAAKKHID1BpmAjSgcAAACA5BfXIH3GjBnSvXt3adCggQlGP/vss2Kfk52dLffee680adJEMjIy5JBDDpExY8ZI0k/BRpQOAAAAAEkvLZ4vvmfPHmnTpo30799fLrjggrCec/HFF8vGjRvlzTfflObNm8umTZskLy9PkpU3ke6AencH7AIAAAAAJLW4BundunUzt3B98803Mn36dFmxYoXUqFHDLGvatKmUiynYxJkZfgAAAABAOR2T/vnnn8sxxxwjTzzxhBx00EFy2GGHye233y5ZWVlFlsfv3LnT75aYmfT4h+kE5gAAAACQxJn0ktIM+qxZsyQzM1M+/fRT2bx5swwcOFC2bt0aclz6yJEj5cEHH5TEH5Mef07YBwAAAABIZgmVSfd4PKbB3IQJE+S4446Ts88+W0aNGiXjxo0LmU0fOnSo7Nixw3tbs2aNJBQHjUkHAAAAAERXQmXS69evb8rcq1ev7l3WokULUwr+77//yqGHHlroOdoBXm+JPibdCZyzJwAAAACQnBIqk37iiSfKunXrZPfu3d5lS5culZSUFGnYsKEkI3tMuhPGpZPMBwAAAIAkDtI12F64cKG5qZUrV5rvV69e7S1V79Onj3f9yy+/XGrWrGmmbFu0aJGZZ/2OO+6QAQMGSMWKFSUZ+Wav413ynucmTAcAAACApA3S582bJ+3atTM3NXjwYPP9sGHDzP3169d7A3ZVpUoV+f7772X79u2my/sVV1wh3bt3l+eff16SlY7Bt8U7RJ61bHPQ/QIAAAAAJMGY9M6dOxdZwq0N4QIdccQRJlAvL/wz6XqsCI4BAAAAIFkl1Jj08shvTHo8dwQAAAAAEHUE6QnU3T0eY9Lj3awOAAAAAMoTgnSn88ukxz5gJkYHAAAAgNghSE+oKdjEMciwAwAAAEDkEaQ7XLzbxDnougAAAAAAJD2C9ESags1BETNTsAEAAABA5BGkJ9IUbHEZk+6gKwMAAAAAkOQI0hNoTHo8EKIDAAAAQOwQpDtcvKdgAwAAAADEDkF6InV3j8Prc2EAAAAAAGKHID2BxGN8eDzGwQMAAABAeUWQ7nDxzqQDAAAAAGKHIN3h4j0mnXJ3AAAAAIgdgvRE6u5OKh0AAAAAkhpBusPFe550AAAAAEDsEKQ7nMsnlU65OwAAAAAkN4J0h6PaHQAAAADKD4L0ROruzhRsAAAAAJDUCNITqdw9Dq9Pd3cAAAAAiB2C9ATipIDZr+s8AAAAACAiCNITgB0Qx6O7u4OuCwAAAABA0iNITwCuOEbM8RgHDwAAAADlFUF6Ao1LJ1wGAAAAgORGkJ5AmfS4zJMe+5cEAAAAgHKLID0BxHVMOlE6AAAAAMQMQXoCcBXk0gmYAQAAACC5EaQnAm8mPQ5CvChTsAEAAABA5BGkJ9SYdOfUnjtoVwAAAAAgaRCkJ9KY9Lg0jiMaBwAAAIBYIUhPoDHp8UDGHAAAAABihyA9AcQzkw4AAAAAiB2C9EQakx6PKdhi/ooAAAAAUH4RpCcAV0EqPS5j0knfAwAAAEDMEKQnVCbdOZiCDQAAAAAijyA9EXjHpFPuDgAAAADJjCA9AcQzk061OwAAAADEDkF6AojnmHQAAAAAQOwQpCeA/eO/41HuzpUBAAAAAIgVgvREKncnXgYAAACApEaQnkjl7vF4cS4MAAAAAEDMEKQngHhm0kO9pMu7VwAAAACASCFIT6Ax6YwPBwAAAIDkRpCeEOLX3Z1x8AAAAAAQOwTpiZRJj0u5O4PSAQAAACBWCNITaUw6ATMAAAAAJDWC9AQQ10w6iXQAAAAAiBmC9ASQak/B5qDu7gAAAACAyCNIT6B50j0OSmvb2X0AAAAAQOQQpCeAlIKz5I5DkG456MIAAAAAACQ7gvSEKncnYAYAAACAZEaQngBSvOXusX/tUNcFuF4AAAAAAJFHkJ4A7PHf7nhE6QAAAACAmCFITwCpKc5rHAcAAAAAiDyC9AQqd2eedAAAAABIbgTpCTQFWzzK3a0QM6UzBRsAAAAARB5BegJILThLlLsDAAAAQHIjSE8AlLsDAAAAQPlAkJ4A4lvuDgAAAACIFYL0BJBaMAUb5e4AAAAAkNwI0hOo3D0e06RbTPsGAAAAADFDkJ5QQTrF5wAAAACQzAjSE0BKHLu7h3rFggp8AAAAAEAEEaQngPiWu8f+NQEAAACgvCJIT6QgPR5ROgAAAAAgZgjSE0BKSjzHpHNhAAAAAABihSA9ARTE6JS7AwAAAECSI0hPAJS7AwAAAED5kBbOSr///nuJN9yyZUtJSwtr8yhKbpYc6N4iNWWHo7q7AwAAAAAiL6woum3btuJyucQKM0hMSUmRpUuXysEHH1zW/cOv78jTa26XSenHyXbrpJgfj1CnXD8PAAAAAIDICjvV/dNPP0nt2rWLXU8D+VatWpV1v2BLyzRfMiRX3MyHBgAAAABJLawgvVOnTtK8eXM54IADwtroKaecIhUrVizrvsEvSM8Ju5IhkiwK3gEAAADAWUH61KlTS7TRSZMmlXZ/ECi9IEh35UpOnkfy3B5JS6XfHwAAAAAkI6K9BMmkZ0qOPPzVYun05DRxe2KXUafCHgAAAABip8Tt17Xk+uOPPzbZ9U2bNonH4/F7fOLEiZHcP6RleMekq7Xbs2Tb3hypVSV/ebQRpAMAAACAg4P0W2+9VV577TXp0qWL1K1bly7fMWwcFzhvOgAAAACgnAfp77zzjsmWn3322dHZIwTPpLv2B+mxnC+dxnEAAAAA4OAx6dWrV4/Y/OczZsyQ7t27S4MGDUxG/rPPPgv7uT/++KOkpaWZOdyTWlrFQpl0xqQDAAAAQHIqcZD+wAMPyIMPPihZWVllfvE9e/ZImzZt5MUXXyzR83bs2CF9+vSRrl27SnnJpGvjOFteDBvHAQAAAAAcXO5+0UUXyXvvvSd16tSRpk2bSnp6ut/jCxYsCHtb3bp1M7eSuu666+Tyyy+X1NTUEmXfE39MugbnLnG7CdIBAAAAIBmVOEjv16+fzJ8/X3r37h2XxnFjx46V5cuXm7HxDz/8sJSXTHqKy5J0cUuupEleQEf9aKK7OwAAAAA4OEj/6quv5Ntvv5WTTjpJYu3vv/+Wu+++W2bOnGnGo4cjOzvb3Gw7d+6URMykqwzJMUF6LMekAwAAAAAcPCa9UaNGUq1aNYk1t9ttStx1PPxhhx0W9vNGjhxpmt3ZN93/RMyk+zaPi+WYdLq7AwAAAICDg/Snn35a7rzzTlm1apXE0q5du2TevHly0003mSy63kaMGCG//fab+X7KlClBnzd06FDTaM6+rVmzRhKKyyW5rgp+zeOckElnpnYAAAAAcEC5u45F37t3rxxyyCFSqVKlQo3jtm7dKtGg2fs//vjDb9no0aNNcP7xxx9Ls2bNgj4vIyPD3BJZXkqGpLtz8udKt5iCDQAAAACSVYmD9GeffTZiL757925ZtmyZ9/7KlStl4cKFUqNGDWncuLHJgq9du1bGjx8vKSkp0qpVK7/na4f5zMzMQsuTjTulgog7XuXuAAAAAADHBul9+/aN2Itr+XqXLl289wcPHux9jXHjxsn69etl9erVUt7laZDuMybdCeXuAAAAAAAHBOm2TZs2mZsnYDqwo446KuxtdO7cWawi5vjSQL0oDzzwgLklOy13V/sz6bGcgo0LAgAAAADg2CBd50jXTPfixYsLBXA6Z7p2YUdkuQuC9ExXTuzHpMfslQAAAAAAJQ7S+/fvb6ZAe/PNN6Vu3bomMEcMxqQXzJMe6zHpAAAAAAAHB+na3G3ixInSvHnz6OwRQmbSvWPS3THMpId6Ka7NAAAAAED850nv2rWrmZscccik6xRsMc+kk7UHAAAAAMdm0t944w0zJv3PP/80U58FzpPeo0ePSO4fNEhPzYx5d/dxP66Ub/7aIAM7UzEBAAAAAI4N0mfPni2zZs2Sr7/+utBjNI6LTbl7LLq7P/DFIvO1SsaqqL8WAAAAAKCU5e633HKLXHnllWYOc51+zfdGZ/fo8KTml7tnFjSOi2V3993ZeTF7LQAAAAAo70ocpG/ZskUGDRpkOrsjNjx2Jj0OY9KZJh0AAAAAHBykn3/++TJ16tTo7A2CcqcGdHePUZDuEo9YTPcGAAAAAM4dk65zpA8dOtSMS2/dunWhxnFaDo/I8niD9NjOk35myjwZvfE5mZ3eUnrn3uv3GDOwAQAAAIBDurtXqVJFpk+fbm6BjeMI0qMZpNvzpEe/cZw9Bj5FLLGChORMzAYAAAAADgjSV65cGYXdQFhBuiu2mfTMgtfbJ/mN6wAAAAAADhuTDgdk0mMVpBeU1xOkAwAAAICDgvTBgwfLnj17wt6ojlnfunVrWfYLPqyAID0v1kG6RSYdAAAAABwTpD/33HOyd+/esDf60ksvyfbt28uyXwg2BVusM+mUuwMAAACA88akW5ZlurprY7hwlCTrjuJ50jLjMk865e4AAAAA4MAgfezYsSXecN26dUuzPwgmLcMvaHZ7YtfdXTEmHQAAAAAcFKT37ds3+nuCsBvHOWJMOnOwAQAAAEDE0d09oRrHFWTS3fEfk06MDgAAAACRR5CeAPLsxnExH5OeGzpItwjTAQAAACDSCNITQHZBubld7u6x4l/uTogOAAAAAJFHkJ4Asqw0v6A5Vpn0iq7sIjLpMdkFAAAAAChXShSk5+XlSVpamvz555/R2yOEDNK986THaEy6PQZ+n6RzVgAAAADAaUG6BuhNmjQRt9sdvT1CIXs9+UFymssjqeJ2xph0Ct4BAAAAIP7l7vfdd58MHTpUtm7dGvm9QZFBup1Nj/k86cHGpFPuDgAAAADxmSfd1/PPPy/Lli2TBg0amKx65cqV/R5fsGBBJPcPIrLHk+pXgp7rifUUbPnd5X0RpAMAAACAA4L0Xr16RWE3UJSLjmki2T+lSYYrz5Sg5+bFOJPOmHQAAAAAcGaQPnz48OjsCUJqXqeKWBkVRXJ2SYYrR3LcTih3p94dAAAAABwxBdv27dvljTfe8BubrmXua9eujfT+oYArPdM7Jj0nBpl0l3gk01VU4zgAAAAAQNwz6b///rucdtppUr16dVm1apVcc801UqNGDfn000/ln3/+kfHjx0d8J6FnKrZBuj3dW6ggHQAAAADggEz64MGDpV+/fvL3339LZmZ+4Ki6desmM2bMiPT+IViQHoNyd7vUPWQmnVQ6AAAAAMQ/SP/ll1/kuuuuK7T8oIMOkg0bNkRqvxAiSNeO67HIpNtBeo6lM7Pv7y5vY550AAAAAHBAkK7Z8507dxZavmTJEqldu3ak9guB0jJiWu6+f/q14KXuZNIBAAAAwAFBes+ePWXEiBGSm5s/Ztnlcsnq1avl7rvvlgsuuCAKu4jAcvfsmGTS889vdqggndMCAAAAAPEP0p966in577//pE6dOpKVlSWdOnWS5s2bS9WqVeWRRx6J/B4iIJMemynYKkp2yOnXFJl0AAAAAHBAd/dq1arJrFmzZMqUKWbaNY/HI0cffbTp+I4oSq9ovmS4nFHuTi4dAAAAABwQpOsUa5dccomceuqp5mbLycmR999/X/r06RPpfYRPJl0busVmCjY7SE/n+AMAAACAU8vd+/fvLzt27Ci0fNeuXeYxxGJMujtmY9JpHAcAAAAADg7SLcsyzeIC/fvvv1K9evVI7ReK6O7usUTyojwu3Z6CLcvKf91ANI4DAAAAgDiWu7dr184E53rr2rWrpKXtf6rb7ZaVK1fKWWedFYVdhF8m3ZWf4dbmcWmpJb7GEvaFGHtMesju7nSOAwAAAID4Bem9evUyXxcuXChnnnmmVKlSxftYhQoVpGnTpkzBFqNyd6Xj0iuF6ulWRhp/ZxYzJp1MOgAAAADEMUgfPny4+arBuDaOy8zMDxoR2yDdznBHs3mcBuAV7SCdKdgAAAAAIGZKXC/dt29f2bdvn7zxxhsydOhQ2bp1q1mu07GtXbs2GvsInzHpFQvK3bOjGaT7lLuHbhxHLh0AAAAA4j4F2++//27mRNcmcatWrZJrrrlGatSoIZ9++qn8888/Zoo2RC+TXjElzzsmPVq0Md3+KdiiVFMPAAAAACh7Jn3QoEHSr18/+fvvv/1K3rt16yYzZswo6eZQykx6dMvdrf3d3UNl0qP26gAAAABQfpU4kz5v3jx57bXXCi0/6KCDZMOGDZHaLwRKr2i+ZLoKMulRLXf3aRwXYkw6UToAAAAAOCCTrtnznTt3Flq+ZMkSqV27dqT2CyEy6Zl2d3d3lIP04qZg4wwBAAAAQPyD9J49e8qIESMkNzc/WNR501evXi133303U7AlTXd3a393dxrHAQAAAIBzg/SnnnpK/vvvP6lTp45kZWVJp06dpHnz5lK1alV55JFHorOX8GbSfedJj2W5e2a6/0eFTDoAAAAAOGBMerVq1WTWrFkyZcoUM+2ax+ORo48+2nR8R/Qz6RUKgudoTsHmCTIFW5uGB8hPK/On21PMwAYAAAAADgjSbaeeeqq5IbaZ9AqxGJNuMvb+3d1drqi9HAAAAACgLEH6zz//LNOmTZNNmzaZTLqvUaNGlWaTKE5afnf3Clbpx6Sv35El/cb8Ilee0ER6d2hSTLl7btFj0il4BwAAAID4B+mPPvqo3HfffXL44YdL3bp1TeM4m+/3iFImvQxB+mNf/0+WbNwl9332Z5FBugQZk+4S/3NLuTsAAAAAOCBIf+6552TMmDHSr1+/KOwOihuTnl4QpGfnuUt8sLJy3GGPSa/oys5/nRDl7jSOAwAAAAAHdHdPSUmRE088MQq7gnCC9DTJkxTxRHkKNp9MuqSHXgkAAAAAEN8gfdCgQfLSSy9Fdi8Qdrm73TwuulOwWfvHpFuhMulE6QAAAAAQ93L322+/Xc455xw55JBDpGXLlpKe7p9pnThxYiT3DwGZdHuu9NJ0dw83rNZmgBku/8ZxjEkHAAAAAAcG6TfffLNMnTpVunTpIjVr1qRZXKykpomkpIl48kwpelQz6XlZ3u+zJD+DT09AAAAAAHBgkD5+/Hj55JNPTDYdccim5+w2We7sKAbprtx93u9DT8EGAAAAAIj7mPQaNWqYUnfEb1x6acvdw50gzyoI0nOsVPEUfEQCp9fTcesAAAAAgDgH6Q888IAMHz5c9u7dG+FdQbjj0jNKWe4edlidt7dQFj2FKdgAAAAAwHnl7s8//7wsX75c6tatK02bNi3UOG7BggWR3D8EDdKj291dCjLp9hzpwbLwJNIBAAAAwAFBeq9evaKwGyhJkJ7pim7jOMnb5zf9WrBydwAAAACAA4J0LXVHYo5JD1tOfnf3rCIy6fa4dIJ3AAAAAIjjmHQkf7m7y12QSfcN0kmkAwAAAIDzMulut1ueeeYZ+fDDD2X16tWSk5Pj9/jWrVsjuX8IkUnfEtUx6VlBpl8rHKXruHSCdwAAAACIYyb9wQcflFGjRsnFF18sO3bskMGDB8v5558vKSkppvM7oii9ovli5kl3xyBIt0J3d1dMwgYAAAAAcQ7SJ0yYIK+//rrcfvvtkpaWJpdddpm88cYbMmzYMJk7d26Edw/BMumZpZyCLVyuvCDd3YMF6bR4BwAAAID4BukbNmyQ1q1bm++rVKlisunq3HPPla+++iqye4eQ86Rn57mjd3Ty7HL3/dPruYKVu3N+AAAAACC+QXrDhg1l/fr15vvmzZvLd999Z77/5ZdfJCMjP9OLGHR3L0UmPezEd9Ap2MqwPQAAAABAdIL08847TyZPnmy+v/XWW+X++++XQw89VPr06SMDBgwo6eZQmky6jkmPYrl7SkEmPUsyig7SyaUDAAAAQHy7uz/22GPe7y+88EKTWZ89e7bJqvfo0SOye4eQU7DtzMot8dEJuxO7nUn3G5POHGwAAAAA4LggPVCHDh3MDbEL0rVxnGbSs3LcUrFCatQax/mPSS+McncAAAAAiEOQ/vnnn0u3bt0kPT3dfF8UsunRH5NeMSU/i751b44cVCF/WrZwhBtUu+zGcX5j0smkAwAAAIAjgvRevXqZru516tQx34eigZzbHcWu4+VdQSa9amr+Md62J0cOOiD8IL1MU7AFWY9MOgAAAADEoXGcx+MxAbr9fagbAXpsMumV7SB9b05UXmZ/ubt/d/cHureUzofX9i6jcRwAAAAAxLm7eyTNmDFDunfvLg0aNDBZ+M8++6zI9SdOnCinn3661K5dW6pVqyYnnHCCfPvtt1JupOdnzSun5pmvW/dEN0jP8i13F5F+JzaTl69o711GJh0AAAAA4hika7Z8zJgxcu6550qrVq2kdevWZgz6+PHjxSpFxLZnzx5p06aNvPjii2EH9RqkT5o0SebPny9dunQxQf6vv/4q5SmTXqlgTPr2vSXv8B6OlCCZ9JSCMem+Q9OZJh0AAAAA4tTdXYNwDcg1QNbAWgN0XbZ48WLp16+fyXIXlwkPpM3o9BauZ5991u/+o48+Kv/3f/8nX3zxhbRr107Ky5j0iq7c6GbS3YWD9KCD0gEAAAAA8QnSx40bZzLZkydPNhlsX1OmTDEN5TSj3qdPH4kVzezv2rVLatSoIeUpk15B8oP0Pdn5Ze+R5sot6O7u1ziucJRemuoJAAAAAEAEyt3fe+89ueeeewoF6OrUU0+Vu+++WyZMmCCx9PTTT5uS+YsvvjjkOtnZ2bJz506/W6Jn0tOt/Ax6Vm50Oumn2Jl0vynY/L8qQnQAAAAAiFOQ/vvvv8tZZ50V8nEtW//tt98kVvSiwQMPPCAffPCBt/N8MCNHjpTq1at7b40aNZKElVYxNkF6EVOw+WbUSaQDAAAAQJyC9K1bt0rdunVDPq6Pbdu2TWJBA/OrrrpKPvzwQznttNOKXHfo0KGyY8cO723NmjWS6OXuaZ6CID3HHeUx6emFGselprhkwInNzK1CalwnBwAAAACA8jsmXedAT0sLvXpqaqrk5UVnjHRgBn3AgAHm6znnnFPs+hkZGeaWFArK3dM82TEpd8+y9h83u8xdg/Rh3VtG5XUBAAAAoLwrUXd37eIeKuDVsd8ltXv3blm2bJn3/sqVK2XhwoWmEVzjxo1NFnzt2rWmIZ3SwFwb0z333HPSoUMH2bBhg1lesWJFU8qe9Aoy6akFQfreaGTSPR5JcecUbhxHd3cAAAAAcE6Q3rdv32LXKWln93nz5vk1ohs8eLD3tbSb/Pr162X16tXex1999VWTrb/xxhvNzXffdP2kV5BJTzXl7pbsi0YmvWA8eqEp2JiDDQAAAACcE6SPHTs24i/euXPnIqfxCgy8p02bJuVaQSZdZUhuKTLpYfRjL5h+TZFJBwAAAIDYovNXIknP7+5uB+lRaRyXlx+k51ip4vH5eFDtDgAAAADRR5CeSFLSRFz5pyxDckpR7h5GqJ27L0ip+/7u7gAAAACA6CFITyQaKBeMS89wlabcPfxM+j7xbxBIjA4AAAAA0UeQnqDj0k25e667yDH9pRuTXpBJt/bPka7IowMAAABA9BGkJ5qCTHqm5Jqv+3I9Ucqk+5e7u0ilAwAAAIBzurv7WrJkibzwwguyePFiE7wdccQRcvPNN8vhhx8e+T2EP7vcXfLnMtdsesUKqZE7SrnBg3QAAAAAgAMz6R9//LG0atVK5s+fL23atJGjjjpKFixYYJZ99NFH0dlLFArSq6blma97c/K/RjtIp3EcAAAAADgwk37nnXfK0KFDZcSIEX7Lhw8fLnfddZdcdNFFkdw/hBiTXiPDI1rxvnVPjjQ8sFLEjlP2vj2mZdw+K7DcnVMBAAAAAI7LpG/YsEH69OlTaHnv3r3NY4hNJr1upfyoef2O/EZvkbJj1y7zNTtwTHpEXwUAAAAAEJEgvXPnzjJz5sxCy2fNmiUnn3xySTeHUmbS61TMv7t+e355eqR4cvK3l1WocVxEXwYAAAAAEIly9x49epiydh2T3qFDB7Ns7ty5Zjz6gw8+KJ9//rnfuoiw9PzovFZm/nRq63fui0qQXrjcnSgdAAAAABwXpA8cONB8HT16tLkFe8wO6txudyT2EUEy6TV1TLrJpIcfpIczpbo7e0/wKdg4CwAAAADgvCDd44nwvNwo1Zj06un5F0A27YpsJt1tZ9KZJx0AAAAAnD8mHc7IpFfQ1u4aVHvCSI8XCKdi3VvuLuklfi4AAAAAIA5B+vTp06V79+7SvHlzOfTQQ83Y82DN5BC9THqqJ9t8LUGMHhZPbogx6ZF9GQAAAABAJIL0d955R0477TSpVKmS3HLLLXLTTTdJxYoVpWvXrvLuu++WdHModZCeY756whloXiCsVe0g3cyWvh+ZdAAAAABw4Jj0Rx55RJ544gkZNGiQd9mtt94qo0aNkoceekguv/zySO8jggXp7n1RyaRL7r4QjePIpQMAAACA4zLpK1asMKXugbTkfeXKlZHaLxQzJt3OpFslyKSHJc8ud/cfk55CjA4AAAAAzgvSGzVqJJMnTy60XJfpY4hVJt0ekx7ZIN3O0Adm0ql3BwAAAAAHlrsPGTLEjEVfuHChdOzY0cyHPmvWLBk3bpw899xz0dlLFMqkp9iN40owI14448pTQgTpJNIBAAAAwIFB+g033CD16tWTp59+Wj788EOzrEWLFvLBBx9Iz549o7GP8JVe0XxJdUencVxaQYa+8DzpnAYAAAAAcFSQnpeXZxrHDRgwwGTPEb9y9xRPfsa7tNXuOpZdqyACpVvZIaZgI0oHAAAAAEeNSU9LS5Mnn3xS3G539PYI4ZW7l3FMeqinVbDyM/Rk0gEAAAAgARrH6Rzp06ZNi87eIPxMekG5+7a9OfLilL9lzda9JTp6oUJ7byY9oNyd7u4AAAAA4MAx6d26dZOhQ4fKn3/+Ke3bt5fKlSsXmooNscukb96dI099t1Q27cqWET1bBX3Kvly3XPLqHPnt3x3eZflTtxUuYa/gCVHuzqB0AAAAAHBm4zg1atSoQo9pIEcpfJSlVfTr7m5btG5nyKd88+cGvwA9ZCbd45Z0yQ0+BRsAAAAAwHlBuqckc34hepn0vPzGcbbGNSqVfUy6zzYZkw4AAAAACTAmHU4Zk+6fSXcX0UAuPbXwaQ7acC63iCCd7u4AAAAA4KxMumbRx40bJxMnTpRVq1aZ8vZmzZrJhRdeKFdeeSXjluMwJt3m9hQVpIc5fVpelvmSbaWJJ+D6DUPSAQAAAMBBmXRtNKZN4a6++mpZu3attG7dWo488kj5559/pF+/fnLeeedFd0/hl0l3mSB9f2Be1ExsFdIKn+ag6xdk0rODjEenuzsAAAAAOCiTrhn0GTNmyOTJk6VLly5+j02ZMkV69eol48ePlz59+kRjP2FLLwjSxZJ0cUtuwSksKpNeIUi5uxWsdVxBJj1Y0zjK3QEAAADAQZn09957T+65555CAbo69dRT5e6775YJEyZEev8QIpOuMiV/rvTixqSnpYabSS8I0q30Qg9R7g4AAAAADgrSf//9dznrrLOKnD/9t99+i9R+IZTU/VnujILp0vbPex5csMeCrm0H6Uy/BgAAAADODtK3bt0qdevWDfm4PrZt27ZI7RdC0ZR2QTY9wzeTXkS5e7BHgnZ3L5iCLWi5O6l0AAAAAHBOkO52uyUtLfQQ9tTUVMnLy4vUfiGMDu8Zrv2ZdHcRjeOCxeNFlbtnSf72fdE4DgAAAAAc1DhOS6a1i3tGRuEATmVn+08JhigymfQdfuXunqIy6UGjdAmZSc8ONia9tPsKAAAAAIh8kN63b99i16Gze4x4y919gvSixqQHXWaVaEw65e4AAAAA4KAgfezYsdHdE5Q4SM905Xgj8KLGpAcL4Ivs7h40SOcEAQAAAIBjxqTDgWPSw82kh1nt7p0n3Qo2TzoAAAAAINoI0hNRkO7uRSTSgwbwQYP63NDd3UmlAwAAAED0EaQnSSa9pFOwBU2825l0qSDHNa0hz13a1vsQ3d0BAAAAwEFj0uEg6RULTcFWdLm7FWbjuPxMepZUkNtOO1SqZO7/eLgoeAcAAACAqCOTnsCZ9Ey/cveSjUmXIjLp2VYF083dNzCncRwAAAAAODSTvnTpUpk2bZps2rRJPB6P32PDhg2L1L6hBFOwuf1Pg59glfBBQ3qfMekalPsG5jSOAwAAAAAHBumvv/663HDDDVKrVi2pV6+e3/zZ+j1Bepy6uxc1Jr0UU7ClBKTOyaQDAAAAgAOD9IcfflgeeeQRueuuu6KzRwg/kx7umPQgy4Ku752CLd00ivPLpBOlAwAAAIDzxqRv27ZNLrrooujsDUpf7l7ixnFhlLv7jknn3AAAAACA84J0DdC/++676OwNSj9PepHl7sGWhc6kZ0lGfuM4MukAAAAA4Oxy9+bNm8v9998vc+fOldatW0t6errf47fccksk9w/hBulWCRvHWUVn0nVMOo3jAAAAAMDhQfprr70mVapUkenTp5ubL82+EqTHsHGcz5h0d1GZ9ODF7UVMwVYwJp0p2AAAAADA2UH6ypUro7MnKNOY9KIax4WfSd/f3d3Mku5X7s4JAgAAAADHjUmHA6SXLEgPNv486PqFGsftFzglGwAAAADAAZl09e+//8rnn38uq1evlpyc/eOi1ahRoyK1bygmk57pMybd7ZGSNY4LtqJ3CrbC86QDAAAAABwYpE+ePFl69OghzZo1kyVLlkirVq1k1apVJlt79NFHR2cvUeyY9KLnSQ8yBVvg+h63iDvHP5NOd3cAAAAAcHa5+9ChQ2XIkCHy559/SmZmpnzyySeyZs0a6dSpE/Onx3Oe9CIax3mCZNkLrZ2XX+qusgq6u/sWvJNXBwAAAAAHBumLFy+Wvn37mu/T0tIkKyvLdHsfMWKEPP7449HYR4TKpIc7Jj3YMiv4ePT9U7AFZtI5DQAAAADguCC9cuXKkp2dbb5v0KCBLF++3PvY5s2bI7t3CC6tYuF50ovKpAcN4K2g49FzJE0sSTHT6dE4DgAAAAAcPia9Q4cO8uOPP0rLli3lnHPOMaXvf/zxh0ycONE8hthl0jN950kvIpMeLJVeOJO+f/o1lT8mnXJ3AAAAAHB0kK7d23fv3m2+f+CBB8z3H3zwgTRv3lyeeeaZaOwjQo5J98mkFxmjB5uCLXiQnl0QpKcEZNIpdwcAAAAABwbpBx98sPf7SpUqyejRoyO9TyjNmPQiy93DCNwLGsfp9GtKx6T7Y1A6AAAAADhynnQ4sLt7UY3jSlPuHhCUk0kHAAAAAIcE6TVq1JClS5dKrVq15MADD/Qbqxxo69atkdw/BJOeH6SnuTySKm5xS6oJunXu82DnJljjuEKL7Ey6z5h0X+TRAQAAAMAhQbqONa9atar5/tlnn432PiHMTLrKlBzZIxW9Ze2pQaLp4L3drSIz6SkpLr8S+vx50wEAAAAAcQ/S7XnRA79HnKTmj0m3S97tIN3tsSS18GByk2EvvEyCB+k+Y9It5kkHAAAAAOcF6Tt37gx7g9WqVSvL/iAcKSkiqRVE3Dn+zeNCjEsPa0x6XuEx6b7JcxLpAAAAAOCQIP2AAw4ochy6L7fbXdZ9Qrgl7xqku3K89eyhgvSgY9ILlbvb3d3TvZl0367wgY3kAAAAAABxCtKnTp3q/X7VqlVy9913S79+/eSEE04wy+bMmSNvvfWWjBw5Mgq7iJBBevZO/w7vnrJn0rO8jeM0k+4XpQMAAAAAnBCkd+rUyfv9iBEjZNSoUXLZZZd5l/Xo0UNat24tr732GmPWY9w8ThvH2UJNlR48ky5BM+lZBWPStXDCN3tO4zgAAAAAiL6Ukj5Bs+bHHHNMoeW67Oeff47UfqE4afnN4/zGpIeK0oMo1EwuYEy6BuV+Y9I5IwAAAADgvCC9UaNG8sorrxRa/uqrr5rHENtMeobLp9y9JI3jAhcETsFmMun70TgOAAAAABxS7h44Z/oFF1wg3377rXTo0MEsmzt3rixfvlw++eSTaOwjws2kl6RxXOCygnL3bLvcXUN0v0w6uXQAAAAAcFwm/eyzz5alS5eacehbt26VLVu2SM+ePc0yfQwxzqT7lbsHXzVY6F7sFGwp/oE5mXQAAAAAcGAmXWlZ+6OPPhr5vUH40u0gPafYcveSNI7zHZPuizw6AAAAADgwk65mzpwpvXv3lo4dO8ratWvNsrfffltmzZoV6f1Dcd3dXcU3jivRFGyWz5h033J3UukAAAAA4LwgXcedn3nmmVKxYkVZsGCBZGdnm+W7du0iux6XMem+U7CFCtLDH5PuLXcPGIVOjA4AAAAADgzSH374YdPd/fXXX5f09HTvcs2qa9CO+I1Jd5ckky7FjEk3mXSK3AEAAADA0UH6kiVL5JRTTim0vFq1arJ9+/YSbWvGjBnSvXt3adCggQkIP/vss2KfM336dGnfvr1kZmbKwQcfHHQ6uHKhRN3dgy0LzKQHmSc9grsLAAAAAIhCkF6/fn1ZtmxZoeU6Hl2D5pLYs2ePtGnTRl588cWw1l+5cqXpIH/yySfLr7/+Kvfcc4/ccsst5XPqt7SKheZJD5FIFytYf/fARQVBeraV7pNJj9jeAgAAAACi0d39uuuuk1tvvVXGjBljst/r1q2TOXPmyO233y7Dhg0r0ba6detmbuHSrHnjxo3l2WefNfdbtGgh8+bNk6eeesrM3V4eM+mZvt3dfaJ0HXP+7V8b5cgG1YIG74XL3Qt3dyeXDgAAAAAOD9LvvPNO2bFjh3Tp0kX27dtnSt8zMjJMkH7TTTdJNOnFgDPOOMNvmTaxe/PNNyU3N9dvjHx5H5P+w+JNcv078833t5zavNDTC1XGFzSOy5IMb3d333p3kuoAAAAA4NB50h955BG59957ZdGiReLxeKRly5ZSpUoVibYNGzZI3bp1/Zbp/by8PNm8ebMpxQ+k3eftDvRq586dklRj0n3K3X0D719Xb9u/PJwSeLtxXMEUbFolQbk7AAAAACRAkK4qVaokxxxzjMRaYMdxeyqxUJ3IR44cKQ8++KAkbybdp9zdJ0qvXjG9yIZyfos8bhF3/nb2Sbo3OCd7DgAAAAAODdIHDBgQ1no6Vj1a6tWrZ7LpvjZt2iRpaWlSs2bNoM8ZOnSoDB482C+T3qhRI0l46UWXu/sG6bv35RV6ul/gXtA0zh6TruPRFVOwAQAAAIBDg/Rx48ZJkyZNpF27dt7sdaydcMIJ8sUXX/gt++6770xGP9R4dB0vr7dkzaT7No7zPS9pqfsb9/+3e3+5v3fdIE3j7CA9jUw6AAAAADg7SL/++uvl/ffflxUrVpiseu/evaVGjRplevHdu3f7TeemU6wtXLjQbFe7uGsWfO3atTJ+/HjvPuh0bZoZv+aaa0wjOW0a995770m5E2RMum8mPc/t8X6/edf+QD5olF6QSbdSK4glKd6u7oxJBwAAAACHzpM+evRoWb9+vdx1110mm60l4xdffLF8++23pc6s6/RpmpnXm9LgW7+3p3LT11u9erV3/WbNmsmkSZNk2rRp0rZtW3nooYfk+eefL3/Tr4Xq7u5zHnJ9gvTgmXSrUCbdSs3f5v4x6ftHpVP6DgAAAAAOaxynZeOXXXaZuf3zzz+mBH7gwIFm+jPt9F7SDu+dO3cuMsDX7Qfq1KmTLFiwoESvk9SZdJ8g3W+YuXv/nc27ggTpwTLp6RXN1/1j0iO/2wAAAACACGTSA+VP0eUyQbZOw4YYS8sPqCuEaByX53NO9uW5iw7SCzLpnoJMupkjHQAAAADg7CBd5xvX8d+nn366HH744fLHH3+YMeJakh6LedJROJNe1ZUVotw9+PdFdXe3Ckro7dJ2MukAAAAA4NBydy1r18Zx2tCtf//+5vtQ054hBmo2lxwrVeq4tktT13pZZdX3GzrgOyY9GL+wvVCQXnhMOgAAAADAQUH6K6+8YgJ0bd42ffp0cwtm4sSJkdw/hJJZTX7xHCEnpv4lp6YslDHu+uIbl+cFyZ6HLnfPCih3L5xJJ6sOAAAAAA4K0vv06UOHb4eZ4mlrgvQuKb/KGHc3vzHpxWXS/XLpuftCZNIBAAAAAI4M0oN1Wkd8TfW0k/tlghyfslgqS5bfOPNg49CLzaSnBWbSCdMBAAAAICG6uyP+Vlj1ZZWnrlRwueXElD+9Qfr2vTky5seVhdavmpEWYkx68d3dCdcBAAAAIPoI0hOaS6Z62prvuqQs9Ja7D/7wt6BrX33ywXJ8sxqFu7sHjEn3dneP7s4DAAAAAAIQpCe4KZ525muX1IXe+eqn/G9T0HU19rYr2P3K3Qu6u7vtIN1nfQAAAABA7BCkJ7Avbz5J2p50jmS5MqWea5tU3f6/ItfXMnZ7WrVgU7AVNSadgB0AAAAAoo8gPYG1Oqi6DDn7KFmcebS5X3dj8GnxbBp078+k+4Tp6/PL43OqNg45Jh0AAAAAEH0E6Ungj0rHm6/1iw3Sg2TENYu+5ifz7a56JxSsR5QOAAAAAPFAkJ4E/qycH6TX3P6HyJ7NIdfTUne7lN2bSF/zs4g7R6RqfdlX/eD89YjRAQAAACAuCNKTwK70OvKXp4m4dKT5sh9Crudbxu7t7r5qZv7XpidLftu5/WPSAQAAAACxRZCeBFJSxDsVmyz9NuR6Gnunp+af8jx3QZC+siBIb3ayd5w6Y9IBAAAAID4I0pOAZr6nuPOnYpPlkyVV3CHXq1AQpGe7PSLZu0XWztufSS+I24OPSSe7DgAAAADRRpCeBFJTXLLQai770g8Q2bdDjnb9HXLdCmn5pzwnzyOyZq6IJ0+kemORA5t6x6kHi9FrVq4Qtf0HAAAAAOQjSE8CmiH3SIqsqZHfnf3U1F9DrucXpPuUumtkbo9T9x2T/kafY+TxC1pL01qVo/9GAAAAAKCcI0hPAnZQ/U/Nk83XLikLg66nq2UUBOnZeW6/pnHKDtJ9E+mntawrlxybP386AAAAACC6CNKTQMEwc1l1QAdxWy45ImWNNJDCU7Fp8G1n0mXfTpF1v+7PpKuCcne6uwMAAABAfBCkJwE7qM5KrSbzrcNClrynpOwvd6+3fYGI5RGpcbBI9YZm2f7GcbHbdwAAAADAfgTpSUCDbzvInlrQ5T1YybuulVGQdm+0Y75fqXv+8wuPSQcAAAAAxA5BehJILQiq3ZYlUwrmSz8x5U+pInv91tOp1exMetNdBUF6s1O8j3vHpBOjAwAAAEBcEKQngYJEung8liyxGskyTwPJdOXKB61+CtI4LlWqy26pv29Z/sKmJ3kfL6h2J5MOAAAAAHFCkJ5U5e6WpKemyBN5l5j7R6wYL/Vky/71CjLpHVIWS4qG5LUOF6laz/u45S13j/lbAAAAAAAQpCdfubuWtH/nOUayD+ogqZ5suT39o0Ld3TukLPLv6l7A47FXJEoHAAAAgHggk55MmXSPJTl5Gmm7ZG+nB8yy81NmypGuVfsz6akpcoIdpPs0jVN5BVF6Oql0AAAAAIgLgvQkYHdjzzYBesGyRsfI+kbnSorLkqFpE/JHnLtEqrq3mXnUgwfp+eXuaalk0gEAAAAgHgjSk0DBrGqSleP2LstIS5EVbQZLtpUmJ6X+JZ1TfjPBfN2tv5jHV6Y1E6lc0287ee6CID2FjwUAAAAAxAPRWBJl0vf5ZNK1rD23aiMZ5z7T3L8nbYKkePKk1uafzf3fUlsX2k6uO//5ZNIBAAAAID4I0pMoSM/KyTNf01JcZpx6aopLXsrrKdusKnJYylpp9u+ncuCm/GnZFqS0KrQdb7k7mXQAAAAAiAuC9CSgwbjKys0vd9cO7ma5yyU7pYo8n3eeuX/komek0s4V4rFcMt/VMmSQns6YdAAAAACIC4L0JGA3Y9+X6/EP0gseeMd9uqzy1JUKuTvM/T+tprIlr1Kh7eQVlLvbzwMAAAAAxBZBehJNwWY3jtPx6L7Bdq6kyWN5l3nXn+NpKTkFAXmwxnHpdic6AAAAAEBMEY0lAS1rV/sCyt3t4F194zlWNtc6znz/vbt9wXzq/nIL5knXMe0AAAAAgNhLi8NrIlrd3QOCdP9g2yW/dHxV2lffIfNeXycVggTpbnsKNjLpAAAAABAXZNKTqdzdDtILgmw7eLdZ6RVFarcw3+cVZM195Xq7u5NJBwAAAIB4IEhPAnYzdjtIz0hPzV8eEGzrXTug13jcsvKDcuX2WN7GccyTDgAAAADxQbl7ErADb7u7e0ZBJr1wRtzlHb9uB+oa4D/57f/knbmr5cTmNc1yGscBAAAAQHyQSU8CgWXtwRrHKV3Nd5lmz9WsZVtkR1auLFy93dxnCjYAAAAAiA+C9CQQGFSnF9S/+2bNlXZ0913XU1Dubpe57y0ol09nTDoAAAAAxAVBehIIjKlTU/znSbdla5DuKpxJt+dHt+dZp7s7AAAAAMQHQXoSlrvbM6gVDtLdUhC/G+6CTLo9P7oG8YrGcQAAAAAQHwTpSaBwF3dX8CA91z+T7gnIpNuYgg0AAAAA4oMgPRmD9IL7gcv35bn9ltnl7vZXW5pvuh0AAAAAEDNEY0nAFVjubmfSXYUz6b7resvdCxrHBTaeAwAAAADEFkF6EggMxu1sud293Vaveqb/4wWxeV5gJt0e1A4AAAAAiCmisSQQGFPbY9IrZ6R5l/Xr2FQubN8wf/2Cx0Nl0pknHQAAAADiY38Uh6Qpd7eHnWemp8r3g04xjzevU2X/4xrUu/c3jgsck065OwAAAADEB0F6Epe7q0PrVg25vl0OX7i7OwUWAAAAABAPRGNJ3N09FPtxO4Nuz5NuI5MOAAAAAPFBkJ4EAhLphTLrgXwby2mgHtBfTlLJpAMAAABAXBCkJ2EmvbjGb97GcR7t7O6fRVdpTMEGAAAAAHFBkJ4EAjPndnf3cMrdA8ejq3Qy6QAAAAAQFwTpSdzdPRTfxnHBgnQy6QAAAAAQHwTp5bHc3SeTHtg0TqUVF+UDAAAAAKKCIL1cdnfP/9rzpR9l/j/bCj2elsrHAgAAAADigWgsCQRmvovr7u47Zj1ouTuZdAAAAACIC4L0cphJLy6ITyeTDgAAAABxQZCeBAKD6uIS4cUG8WTSAQAAACAuCNKTsXGcq6yZdBrHAQAAAEA8EKQngbQSN44r+nEaxwEAAABAfBCkl8sp2PZ/HyypTuM4AAAAAIgPgvQkkJYa2XJ3gnQAAAAAiA+C9CQQmDkvJgb3W8EqPAMb5e4AAAAAECcE6UkgLSWlZN3Zg0XmPmgcBwAAAADxQZBeDseke4qO0ZmCDQAAAADihCA9Gbu7F1Pv7vHJpAdbNT0gMw8AAAAAiA2isXKeSQ+sfNenFjdFGwAAAAAgOgjSk0C675xqYXRvt3wi8zyPx+8x5kgHAAAAgPghSE8CgYnv4rq7+5a7uwMGqDP9GgAAAADED0F6EnC5XH7BdUnK3fPcBOkAAAAA4BQE6UkitURB+v7APDeg3D2wdB4AAAAAEDtEZEnCN5NeXHd3q6hMeipN4wAAAAAgXgjSy2Em3bdxXK47oHEc068BAAAAQNwQpCcJ367sxc2g5jsmvVDjODLpAAAAAFB+g/TRo0dLs2bNJDMzU9q3by8zZ84scv0JEyZImzZtpFKlSlK/fn3p37+/bNmyRcq71BKUu/uOSc+juzsAAAAAOEZcg/QPPvhAbrvtNrn33nvl119/lZNPPlm6desmq1evDrr+rFmzpE+fPnLVVVfJX3/9JR999JH88ssvcvXVV0t5V5Lu7r5j0gPL3WkcBwAAAADlNEgfNWqUCbg1yG7RooU8++yz0qhRI3n55ZeDrj937lxp2rSp3HLLLSb7ftJJJ8l1110n8+bNk/LOL5Negu7ugY3jigvwAQAAAABJGKTn5OTI/Pnz5YwzzvBbrvdnz54d9DkdO3aUf//9VyZNmmSan23cuFE+/vhjOeecc6S8882Ap5al3J0p2AAAAACg/AXpmzdvFrfbLXXr1vVbrvc3bNgQMkjXMemXXHKJVKhQQerVqycHHHCAvPDCCyFfJzs7W3bu3Ol3S0YlG5O+//u8wHJ3MukAAAAAUH4bx7kCAkrNkAcusy1atMiUug8bNsxk4b/55htZuXKlXH/99SG3P3LkSKlevbr3puX0ST9PejFn1Soyk065OwAAAACUuyC9Vq1akpqaWihrvmnTpkLZdd+A+8QTT5Q77rhDjjrqKDnzzDNNd/gxY8bI+vXrgz5n6NChsmPHDu9tzZo1kvTzpJcgk8486QAAAADgHHEL0rVcXadc+/777/2W630taw9m7969khKQJtZAPzA77CsjI0OqVavmdyvv3d19x6QzTzoAAAAAOEdcy90HDx4sb7zxhsmEL168WAYNGmSmX7PL1zULrlOu2bp37y4TJ0403d9XrFghP/74oyl/P+6446RBgwZSnpWou7tPKj03oLt7WnG18gAAAACAqEmTONIGcFu2bJERI0aYcvVWrVqZzu1NmjQxj+sy3znT+/XrJ7t27ZIXX3xRhgwZYprGnXrqqfL4449LeecbXBdX7u5bdJDn8YTMyAMAAAAAylGQrgYOHGhuwYwbN67QsptvvtncUPru7r6588B50mkcBwAAAADxQ21zkvANrourWPcdkx7YOM53vnUAAAAAQGwRkSVjd/eyNI6j3B0AAAAA4oYgvRyOSa/gky3PLQjS7WWUuwMAAABA/BCkJ4m0EnR3H9PvWKlfPVNe6X205BWUu2emFwTpdHcHAAAAgLghSE8Sqb5j0ovJpB/TtIbMGdpVzmpV39s4rlKF/B6CZNIBAAAAON2+XLfs2pdbqMdWMiBIT8JMenHl7r7sKdgaHJBpvtaumhGFvQMAAACAyLn89bnS+oHvZMr/NiXdYY37FGyIwhRsJbj0klcwJn3ASc3k2pQUOeWwWpwSAAAAAI6WUpCYtHyaYicLgvQkUbmgXD2c7u6+cgvK3atmpkunw2pHY9cAAAAAICpBuif5YnTK3ZPFMU0PLF25e8EYjnSmXgMAAACQIFyu4FNKJwPGpCeJEw6uWapMul3unuYzLRsAAAAAOFlqQczjodwdTlWnWqbccebhsjMrV2pWyShx4zi6ugMAAABIvDHpknQYk55EbuzSvMTPsadg8+0ODwAAAACJUO7uScIonRrncs5uHJdWkpbwAAAAAOCATLqbMelINna5e3oqmXQAAAAAiTUm3Uq+RDqZ9PLObWfSaRwHAAAAIEGkUO6OZJVrN45jTDoAAACABOFinnQkK2/jOMrdAQAAACSIFHue9CSsd6dbWDlmWdb+edJpHAcAAAAg4cakW5JsCNLLsQ0795mvWilSOSM13rsDAAAAACUrd6e7O5LJtCX/ma9tGx0glSqkxXt3AAAAAKBEU7AlYYxOJr08m7Zkk/na+bA68d4VAAAAAAhbMnd3J31ajvXu0ETqVM2UM46sG+9dAQAAAICwpXoz6QTpSCInH1rb3AAAAAAgkbgodwcAAAAAwBlSkrjcne7uAAAAAIDEbBznIUgHAAAAACCuUgpS6UkYo5NJBwAAAAAklhTK3QEAAAAAcIYUGscBAAAAAOCwTLon+erdaRwHAAAAAEjQMemWJBuCdAAAAABAQkmh3B0AAAAAAGeVu1tk0gEAAAAAcEYm3c2YdAAAAAAA4stFuTsAAAAAAM6QWtBdjcZxAAAAAAA4pNzdYkw6AAAAAADOKHd3E6QDAAAAAOCM7u6e5JsmnXnSAQAAAACJJZVydwAAAAAAnCGlIJXu8UjSKeiJBwAAAABAYnAVlLszJh0AAAAAAId0d/fQOA4AAAAAAKeMSZekQ7k7AAAAACAhy909SRilE6QDAAAAABK03F2SDkE6AAAAACAx50n3JF+UTpAOAAAAAEgoqfYUbJS7AwAAAAAQXy66uwMAAAAA4AwpjEkHAAAAAMAZUhiTDgAAAACAM6QwJh0AAAAAAGdIodwdAAAAAACHlbtbTMEGAAAAAIBDMulW0p0J5kkHAAAAACTmmHSPJB2CdAAAAABAQkmh3B0AAAAAAGeVu1vJV+1OJh0AAAAAkJiZdHcSRumUuwMAAAAAEkoKjeMAAAAAAHCGFOZJBwAAAADAGVIKasItyt0BAAAAAIgvV0Em3e1hTDoAAAAAAHGVSrk7AAAAAABOm4LNkmRDd3cAAAAAQEJOweYhSAcAAAAAIL5cjEkHAAAAAMAZUgtS6UmYSKfcHQAAAACQWFIodwcAAAAAwFnl7h4y6QAAAAAAOCOT7k7CKJ3u7gAAAACABB2TbkmyIUgHAAAAACTkPOme5IvRCdIBAAAAAInFReM4AAAAAACclkm3JNlQ7g4AAAAASMgx6Z7ki9EJ0gEAAAAAiSWFcvfoGT16tDRr1kwyMzOlffv2MnPmzCLXz87OlnvvvVeaNGkiGRkZcsghh8iYMWOiuIcAAAAAAEfOk+5JvlR6Wjxf/IMPPpDbbrvNBOonnniivPrqq9KtWzdZtGiRNG7cOOhzLr74Ytm4caO8+eab0rx5c9m0aZPk5eXFfN8BAAAAAPFxYKUKcsupzSUjPTXpToHLiuPEcscff7wcffTR8vLLL3uXtWjRQnr16iUjR44stP4333wjl156qaxYsUJq1KhRqtfcuXOnVK9eXXbs2CHVqlUr0/4DAAAAABDJODRujeNycnJk/vz5csYZZ/gt1/uzZ88O+pzPP/9cjjnmGHniiSfkoIMOksMOO0xuv/12ycrKitFeAwAAAACQhOXumzdvFrfbLXXr1vVbrvc3bNgQ9DmaQZ81a5YZv/7pp5+abQwcOFC2bt0acly6jmHXm+8VDAAAAAAAnCjFKQP+bVp9H7jM5vF4zGMTJkyQ4447Ts4++2wZNWqUjBs3LmQ2XcvmtazAvjVq1Cgq7wMAAAAAgIQN0mvVqiWpqamFsubaCC4wu26rX7++KXPXYNt3DLsG9v/++2/Q5wwdOtTU/du3NWvWRPidAAAAAACQ4EF6hQoVzJRr33//vd9yvd+xY8egz9EO8OvWrZPdu3d7ly1dulRSUlKkYcOGQZ+j07TpwHzfGwAAAAAAThTXcvfBgwfLG2+8YcaTL168WAYNGiSrV6+W66+/3psF79Onj3f9yy+/XGrWrCn9+/c307TNmDFD7rjjDhkwYIBUrFgxju8EAAAAAMq3hWu2y6OTFss7c/+J964ktLjOk37JJZfIli1bZMSIEbJ+/Xpp1aqVTJo0SZo0aWIe12UatNuqVKliMu0333yz6fKuAbvOm/7www/H8V0AAAAAAJZt2i2vzVghJzWvJb075Md0SLB50uOBedIBAAAAIPJm/b1Zer/5kzSvU0V+GNyJQ5xo86QDAAAAAJJHveoZ5uvGnfvivSsJjSAdAAAAAFBmdatlmq+79uXJ3pw8jmgpEaQDAAAAAMqsSkaaVKqQar7fsINsemkRpAMAAAAAyszlckm9gmz6BkreS40gHQAAAAAQ0ZL3TTuzOaKlRJAOAAAAAIiIetXJpJcVQToAAAAAICLqVMvv8M6Y9NIjSAcAAAAARIQ9Jp1p2EqPIB0AAAAAEBEE6WVHkA4AAAAAiIi6BWPSN9I4rtQI0gEAAAAAEe3uruXuHo/FUS2FtNI8CQAAAACAQHWqZshDvVpJ3aoZ4rEsSREXB6mECNIBAAAAABGRnpoiV3ZowtEsA8rdAQAAAABwCIJ0AAAAAAAcgiAdAAAAAACHIEgHAAAAAMAhCNIBAAAAAHAIgnQAAAAAAByCIB0AAAAAAIcgSAcAAAAAwCEI0gEAAAAAcAiCdAAAAAAAHIIgHQAAAAAAhyBIBwAAAADAIQjSAQAAAABwCIJ0AAAAAAAcgiAdAAAAAACHIEgHAAAAAMAhCNIBAAAAAHAIgnQAAAAAAByCIB0AAAAAAIcgSAcAAAAAwCEI0gEAAAAAcAiCdAAAAAAAHIIgHQAAAAAAhyBIBwAAAADAIdKknLEsy3zduXNnvHcFAAAAAFAO7CyIP+14tCjlLkjftWuX+dqoUaN47woAAAAAoJzFo9WrVy9yHZcVTiifRDwej6xbt06qVq0qLpdLnH61RS8mrFmzRqpVqxbv3UEJcO4SF+cusXH+EhfnLnFx7hIb5y9xce4Si4bdGqA3aNBAUlKKHnVe7jLpekAaNmwoiUQDdIL0xMS5S1ycu8TG+UtcnLvExblLbJy/xMW5SxzFZdBtNI4DAAAAAMAhCNIBAAAAAHAIgnQHy8jIkOHDh5uvSCycu8TFuUtsnL/ExblLXJy7xMb5S1ycu+RV7hrHAQAAAADgVGTSAQAAAABwCIJ0AAAAAAAcgiAdAAAAAACHIEgHAAAAAMAhCNIjoF+/fuJyueT6668v9NjAgQPNY7pOpP3xxx/SqVMnqVixohx00EEyYsQI8e0DOG3aNPPagbf//e9/Ed+XZDN69Ghp1qyZZGZmSvv27WXmzJnexx544AE54ogjpHLlynLggQfKaaedJj/99FOZz5eaPn26eT193YMPPlheeeWVqLy/8nru1OLFi6VHjx5SvXp1qVq1qnTo0EFWr15d5DY5d/E/dxs3bjQ/Rxs0aCCVKlWSs846S/7+++9it8m5i74ZM2ZI9+7dzbnR3zGfffaZ97Hc3Fy56667pHXr1uZnpq7Tp08fWbduXbHb5dzF//z5/o3je9Ofm8Xh/MX/3O3evVtuuukmadiwofnbo0WLFvLyyy8Xu13OXfSNHDlSjj32WPN3SJ06daRXr16yZMkSv3UmTpwoZ555ptSqVcuc34ULF4a1bc5fktDu7iibvn37Wo0aNbKqV69u7d2717s8KyvLOuCAA6zGjRubdUorJyen0LIdO3ZYdevWtS699FLrjz/+sD755BOratWq1lNPPeVdZ+rUqRoBWkuWLLHWr1/vveXl5ZV6X8qD999/30pPT7def/11a9GiRdatt95qVa5c2frnn3/M4xMmTLC+//57a/ny5daff/5pXXXVVVa1atWsTZs2hdxmOOdrxYoVVqVKlczr6evq6+t+fPzxxzF53+Xh3C1btsyqUaOGdccdd1gLFiww5/DLL7+0Nm7cGHKbnLv4nzuPx2N16NDBOvnkk62ff/7Z+t///mdde+215mfr7t27OXdxNmnSJOvee+81P9f0d86nn37qfWz79u3WaaedZn3wwQfmvM2ZM8c6/vjjrfbt2xe5Tf7fOeP8Kf375ayzzvL7O2LLli1FbpPz54xzd/XVV1uHHHKI+Xtw5cqV1quvvmqlpqZan332Wchtcu5i48wzz7TGjh1r/o5cuHChdc455xT6nTZ+/HjrwQcfNL8X9fz++uuvxW6X85c8CNIjQH+B9ezZ02rdurX1zjvveJdrMKfL9DE7SP/666+tE0880QT0Gizof0oNHGz6Q1T/I+ofNJ06dbIyMjKsMWPGFHrN0aNHm23s27fPu2zkyJFWgwYNzB+0vkH6tm3bIvE2y43jjjvOuv766/2WHXHEEdbdd98d8geiHucffvgh5DbDOV933nmneR1f1113nQlOEJlzd8kll1i9e/cu0eHk3MX/3OmFRv0/pn/M2PRio/4M1T9eQuHcxV6wQCGQXmjR9eyLZ8Fw7uIjVJCuf8eUBOfPGefuyCOPtEaMGOG37Oijj7buu+++kNvh3MWHJnr0HE6fPr3QY3ZsEE6QzvlLHpS7R1D//v1l7Nix3vtjxoyRAQMG+K2zZ88eGTx4sPzyyy8yefJkSUlJkfPOO088Ho/feloeeMstt5jSXC11CTRnzhxTOp2RkeFdputpCeGqVav81m3Xrp3Ur19funbtKlOnTo3gO04+OTk5Mn/+fDnjjDP8luv92bNnB13/tddeM6XTbdq08SsP7Ny5c4nOl64T+Lq6zrx580zJKMp27vT/2FdffSWHHXaYOa5aXnb88ccHLe3k3Dnr3GVnZ5v7WgZvS01NlQoVKsisWbO8yzh3iWHHjh2mdPOAAw7wLuPcOZsOn9Ofmfrz85prrpFNmzb5Pc75c6aTTjpJPv/8c1m7dq0ZXqd/Ay5dutTv70rOnXN+LqoaNWqU6Hmcv+RFkB5BV155pfmDUYOuf/75R3788Ufp3bu33zoXXHCBnH/++XLooYdK27Zt5c033zRjRxYtWuS33m233WbW0/GZOtYo0IYNG6Ru3bp+y+z7+pjSwFwDyE8++cSMazn88MNNoK5jmBDc5s2bxe12Bz229nFVX375pVSpUsUEDc8884x8//33ZsyQTY9948aNS3S+Qq2Tl5dn9gtlO3f6R6WOz3vsscfMeObvvvvOXCDT/2faC4Bz59xzpz0gmjRpIkOHDpVt27aZoF7Poz62fv167/r8v3O+ffv2yd133y2XX365VKtWzbucc+dc3bp1kwkTJsiUKVPk6aefNkmGU0891XvxTHH+nOn555+Xli1bmjHpelFTf/dp7w8N3m2cu/jTCyiawNPz0qpVqxI9l/OXvNLivQPJRIO0c845R9566y3zH06/9w3c1PLly+X++++XuXPnmj9M7Qy6Nq7y/Y95zDHHeL8/8sgjTdCvTj75ZPn666/N95qJ8GU3IbOXa1CuN9sJJ5wga9askaeeekpOOeWUKByB5BHs2Pou69Kli2ngoefw9ddfl4svvtg0j9NMg90QJJxtBi4PZx2U7tzZ/9d69uwpgwYNMt/rhTLN1GqDPq104Nw589ylp6ebi41XXXWVyTJoFl0bNmrw4Iv/d86mFUGXXnqp+b+ogYIvzp1zXXLJJd7v9e8U/ftEL5ppZZJe5FScP+cG6fr3pmbT9ZxpkkYbGmtgpz9DFecu/rS53++//+5XGRYuzl/yIkiPMC1v1/9s6qWXXir0uHbhbNSokQnsNEOuf6zoLz3NDPnSLri2SZMmecudtTunqlevnl9mV9nlZ4HZKF/akfWdd94p03tMZnpRRQOAYMfW97jq+WnevLm56THVygititBMXzDhnK9Q66SlpUnNmjUj9h7L67nTx/VYalbBl3a7LeoXI+fOGf/vtNu7XhjTkkD9eVm7dm0zXMH3gmYgzp1z6O8wvZi5cuVKk5H1zaIHw7lzLg3wNOAranYFzl/8ZWVlyT333COffvqpSRqpo446yvwc1WSNHaQH4tzF1s0332wuougFFK14KCvOX/Kg3D3CtJRI/4DUW+BY8i1btpgx5vfdd58pO9fgQEs3i6O/DO2AUKfusrPi+h/aN7jX8l0N/Js2bRpyW7/++qv5BYvgtBxMgwEtX/el9zt27BjysGnGz7f0L1A450vXCXxdXUeDEM0komznTh/X6U4CpzjR8Xn6f4xzlxj/77T/gwboGiBovwatjAiF/3fOCtD1nP3www9hXXTk3DmX/i2jVXlF/S3B+XPG/zu9ae8jX3pBNLAPki/OXWzo342a1NPhqHrhUoe3RgLnL4nEu3NdMgjsfKrdvvVms7u7u91uq2bNmqa79N9//21NnjzZOvbYY/06cobbwVGntdEpvS677DIzpdfEiRPNNGC+U3o988wzZrtLly41XZG1S7JuW6fqQPFTQb355ptmKqjbbrvNTAW1atUqMzXG0KFDzTRCen/+/PlmCjbtwu/beVqP9ZVXXlmi82VPwTZo0CDzuvr6TMEWuXOn9Ljr46+99pr5P/jCCy+Y6WhmzpzJuXP4ufvwww/NjBU6bZ5OH9SkSRPr/PPP99sG/+/iY9euXeZ3lt70d8yoUaPM99q9PTc31+rRo4fVsGFDM82Q7zRe2dnZnDuHnz99bMiQIdbs2bPN3yf6f/CEE06wDjroIGvnzp3ebfB/z3nnTuksQdrhXc+b/o2hU35lZmaaDuCcu/i64YYbzKw/06ZN8/u56DuVs051qOfzq6++MudXf0/qfV3Pxv+95EWQHgHFTU/iOwWbzq/dokULE9QdddRR5j9naYJ09fvvv5t5g3Vb9erVsx544AHvdF7q8ccfN/Nj6g/kAw880DrppJPMf3QU76WXXjJBQIUKFcx0JfaUGFlZWdZ5551npk7Tx+rXr2/+ANUphQI/E/rLsSTnS+nnoV27dmbbTZs2tV5++WVOV4TOnU2DwObNm5v/F23atCk0Xyznzpnn7rnnnjOBngbyOpesTiHkG+Qpzl182NN9Bt70fNi/04Ld9HmcO2efPw0YzjjjDKt27dre/3u6fPXq1X7b4P+e886d0mCuX79+5m8W/Z13+OGHW08//bTf3x6cu/gI9XNRL6TY9Ptg6wwfPpzzVw649J94Z/MBAAAAAABj0gEAAAAAcAwaxwEAAAAA4BAE6QAAAAAAOARBOgAAAAAADkGQDgAAAACAQxCkAwAAAADgEATpAAAAAAA4BEE6AAAAAMAYOXKkHHvssVK1alWpU6eO9OrVS5YsWeJ3dCzLkgceeEAaNGggFStWlM6dO8tff/3lfXzr1q1y8803y+GHHy6VKlWSxo0byy233CI7duzw207Tpk3F5XL53e6+++4iz8S0adPMegceeKDs27fP77Gff/7Zu51oyM7ONu+rVq1aUrlyZenRo4f8+++/ZX5PgQjSAQCIMP1j5bbbbnPkcd2wYYOcfvrp5o+LAw44IOg6/fr1M3+UlZX+YfLZZ5+Z71etWmXuL1y4sFTPD6Y02wyH/oH17LPPSizYf2xu3749Jq8HAMWZPn263HjjjTJ37lz5/vvvJS8vT8444wzZs2ePd50nnnhCRo0aJS+++KL88ssvUq9ePfO7ZdeuXebxdevWmdtTTz0lf/zxh4wbN06++eYbueqqqwq93ogRI2T9+vXe23333RfWSdKLCJ9++qnfsjFjxpgLAmWVk5MTdLn+btfXfP/992XWrFmye/duOffcc8XtdkfkPXlZAAA43MaNG61rr73WatSokVWhQgWrbt261hlnnGHNnj3bu47+Svv0008j8norV6402/v111+LXG/q1KlmvW3btvkt37Jli7Vz507Lie68807ryCOPtJYuXWqOazDbt28v9J5Kw/ec5OXlWevXr7dyc3PDfr6uv2/fvjKfp5Jq0qSJ9cwzz1ixEOozBABOsWnTJvNzavr06ea+x+Ox6tWrZz322GPedfRndfXq1a1XXnkl5HY+/PBD8zvc9/dAaX7eTi34uXnfffdZp512mnf53r17zT7cf//95nHb5s2brUsvvdQ66KCDrIoVK1qtWrWy3n33Xb9tdurUybrxxhutQYMGWTVr1rROOeWUoL8b09PTrffff9+7bO3atVZKSor1zTfflOk9BSKTDgBwvAsuuEB+++03eeutt2Tp0qXy+eefm2y1ltNFWqir5yVRo0YNc4XfiZYvXy7t27eXQw891JQxBlO9evWQWfbSSk1NNZmWtLS0sJ+j62dkZIjTReIzk8ivDyC52SXq+rtNrVy50lRlaXbdpj+rO3XqJLNnzy5yO9WqVSv0e+Dxxx+XmjVrStu2beWRRx4J+2falVdeKTNnzpTVq1eb+5988omphDr66KP91tOSeP299+WXX8qff/4p1157rXnuTz/95Lee/o2h+/bjjz/Kq6++Wuj15s+fL7m5uX7vW8v9W7VqVeh9l/Y92QjSAQCOpmXAWlKmv/C6dOkiTZo0keOOO06GDh0q55xzjllHfymr8847z5QO2/c1IO3Zs6fUrVtXqlSpYsbY/fDDD37b13UffvhhU+Ktwek111wjzZo1M4+1a9fObE8vCAQrtdb9UTouTtfTbQQrd7dfo0+fPmY/9D383//9n/z3339m/3RZ69atZd68eX6vob/0TznlFDPer1GjRmY8n2+5YTAvv/yyHHLIIVKhQgUzFvDtt9/22w/9I2b8+PF++1tcubu+H33tO++80/yRpsGzjkX09ffff5t9zczMlJYtW5oSyVCl6R6PRxo2bCivvPKK3zoLFiww66xYsSJoubuONdRzoq9xzDHHyK+//ur3fC2nDLy4oM/3HZsYzmeiOPbx0XGb+gfaYYcdZpa/8847Zr/0Ao0eo8svv1w2bdrk99xJkyaZ9fWc6udHj4uvLVu2yGWXXWaOj47j1M/Fe++957eOno+bbrpJBg8ebMZFaokpAESDFkXpz5qTTjrJBKNKA3SlP0d96X37sUD6s+2hhx6S6667zm/5rbfeakrHp06dan6u6VCjgQMHhrVveqG5W7du5me/Xeo+YMCAQusddNBBcvvtt5uA+eCDDzZjys8880z56KOP/NZr3ry5KePX351HHHFEoe3oe9Pfrfo7v6j3XZb3ZCNIBwA4mgZSetNgSxu2BKPj4dTYsWPN2C/7vo4VO/vss00QpgGd/lLu3r2796q77cknnzR/fOhV8vvvv98Eg0qfp9ubOHFiodfUoFkDXqUNdXS95557LuT7eOaZZ+TEE080+6EXF/QqvgbtvXv3NsGp/nGg9/OrxMWM4dP9Pf/88+X333+XDz74wFys0F/4oeg4Of3jYMiQISZboH8M9e/f3/yhYB+ns846Sy6++OJi9zeQZhh0HLtmHvSPGB1vZwfiGnTrfmq2XMcwavB91113hdxWSkqKXHrppTJhwgS/5e+++66ccMIJ5o+oQHpxQsf96R9Pep70IoH+0VVS4X4mijN58mRZvHixOQaanVGaKdE/QrXqQz+vmm3yvRCyZs0ac5z09fVixdVXX12omVCkMj4AEAn6O0d/BwVeLFSBzdn091ewhm07d+40v/f0Au7w4cP9Hhs0aJDJwB911FHmZ6L+/njzzTdNUK+OPPJI798BGpAH0qBcg3S9uDtnzhy54oorCq2j48U1m62vodlt3dZ3331X6Oe+XmS1Pfroo97X1VtRvyMC33dx7yksZSqWBwAgBj7++GPrwAMPtDIzM62OHTtaQ4cOtX777Te/dcIdk96yZUvrhRde8Bs71qtXr4iOSdexbbfeeqvfa/Tu3dtvrLU+T8fN2ebMmWOW6WPqyiuvNOPwfc2cOdOMfcvKygq6P3psrrnmGr9lF110kXX22Wd77/fs2dPq27dvke9LH9f1fN/PSSed5LfOsccea911113m+2+//dZKTU211qxZ433866+/9jsngcd0wYIFlsvlslatWmXuu91uM17wpZde8m7D9/mvvvqqVaNGDWvPnj3ex19++WW/bY4dO9aMR/Slzy/uz51gn4mixhPq8dG+CNnZ2UVu9+effzavvWvXLnNfP7ctWrQw4zltegyLG5Ou52/IkCF+56Nt27ZFvjYAlNVNN91kNWzY0FqxYoXf8uXLl5ufW/pz3FePHj2sPn36+C3T/iwnnHCC1bVr15C/u3z9+++/Zttz58419/V3xN9//21u+ljg717td1K/fn2rc+fO5vddsJ/7jz/+uBln/vbbb1sLFy402zrnnHMK/Z7z/b2tvWXs19WbjqOfPHmy2e7WrVv99vmoo46yhg0bFvZ7CgeZdABAQoxJ1y6xOhZdM5/aEVvHnNklbqFo9lVLtPXqvZZB69Xw//3vf0VePY8WvaJus0sEtZQ5cJldHq3ZYn1/vlfy9b1r1loztMFoZlez9b70vi6P5P6r+vXre/dVt6/ddLVE26YZ8aJo2bqWE9rZGe0mrNvTLH8w+hpt2rQxJeDhvkZZPhPF0XOnZY++NDOvpfQ6nEFL3u1hEva29T106NDBL+MS+B5Kk/EBgEjSa6SaQdcqsilTpniHgNn0vg7p8R3WpJVE+nO8Y8eOfhl0Hb+tPyv197cOVSqOPYypfv365qv+PNVKM71p2XogreDSaiP9uyBYqbvScev6s1kr1/T3iFZr6RCtoujQLvt19aaVS1rllJ6e7ve+tSpNq55833dx7ykc4XdvAQAgjvSXu4691duwYcNMCZmWzYUaV63uuOMO+fbbb80UMPpLVscBX3jhhYUauGgZd7TpL3abHaQFW6ZBuP1Vy9V1LHigoqaXCbf8sCz7b7+Ova92iX5R+xGMliVqibuWfOtXvQihY6yDCfYawcroA9fTJj+l+UwUJ/Azo8G//jGqNx2bXrt2bRNY63uytx3Oe3j66afN0Agdw6gXAvR1tL9BPD6zAMonnX5NfyZr7xS94GiPt9a+LfozU3++688lLQnXJqR60+/1Iqr24lA6FZv+PNy7d6/5magBu96U/nzU4FrL03WIlPbn0G3rkCwtFe/Ro0eJplHTYUb6s10vbAajP+t1eJr2edHx5Dp1nL6nFi1alOi46D7qFHI6pExfSwN5HXalP6tPO+00s06k3hNBOgAgIWkm1LepmAaRgfOU6tVzDeK1oZw9HjmwUVcwdoY0cHulXa80tFLgr7/+Mn9chEv/4NBx6zq23aZ/lJT0D5HSnAsNSLXaQRup2X+oFEf/mNO5Y7Vq4OOPPzZN74p6DW2Cl5WVZf5IVPqHkC/9w0//MNSA2Q5iA+dQL+1nojiajd+8ebM89thjpl+BCmwEGPiZDfYefDM+Si+EaMYn2ucQAGz2z+LApqna98W+MK4VSfrzWBuibdu2TY4//nhT9WPPbKI/1+1eGoG/x7QaTBuZakd47bfy4IMPmp4zmjXX5q133nlniU6G/i4OdYFXaa8ZfU29aKoXErTXhzb/tLvWl4ReRNWsulZ96fvv2rWrqXrTiw4qUu+JIB0A4GjaaOWiiy4yZWxaAqx/AGjwo83LNJix6S98beal5d36S1KvlusfBlqup43B9Mq//qK2s7/FdYzVQPCbb74xJdyaxdcr4oH0l69uV5t8aTMwfY6WJ0eCNl7T0mjNaOgveA067UZlL7zwQtDnaCZB/3DQAF//cPjiiy/M+y9p9/KS0gyCNnTTiwOaCdZsyb333lvs87RkUksENTORl5fndz6DBfS6TV1XA3sNrDUb7kv/SNQ/wO655x7TvVcbAAYOiSjtZ6I4miHRPxT13Fx//fWm/FGzO750uR4f7ZSsVRL2kIZoZHwAoLTCqfrRn5/awDNwpg+bBvjFbUd/VwVeqAxH52K2rQG47+Oa8Q68QBpIy+XDoX8P6M/5UL+HS/ueAjEmHQDgaBr0avClV691ii/twq6BlQauL774onc9DX40gNUspo53VvocDXQ0ENSgTK+iB86fGoxeJX/++edN12zNDIcKHnV8nF4t13JtHVNeVOf1ktILEjq+T7OoJ598snlP+r6LGtOmf5hox3btVq8dcXX/NfMRbAq5SNIyc+0sr1kDnR5PhyLouOpwaMm7dkPXrud2hjzU50AvOixatMgcCw3YdVo+X/qHmJZV6jRn9tRlgX9AlvYzURzN4mvArVP6aMZcM+qBFxE0kNcAXN+HjovUjr9aIupLz7Huj+6Xnjcd9+k7HR4AIPm5tHtcvHcCAAAAAACQSQcAAAAAwDEodwcAAAAAwCEI0gEAAAAAcAiCdAAAAAAAHIIgHQAAB9Pu5G3btpVkoFOJnX766WY6uQMOOEASjXZbv+2224qdlqi4qX6S8dwCACKHedIBAEBM6PRn69evl4ULFwaddz4Z6PvTKd4AACgtgnQAABATy5cvl/bt28uhhx4acp3c3FxJT09P2DOi85oDAFAWlLsDAJLGxx9/LK1bt5aKFStKzZo15bTTTpM9e/Z4Hx8zZowceeSRkpGRIfXr15ebbrrJ+9ioUaPMc7UUu1GjRjJw4EDZvXu39/Fx48aZEu1vv/1WWrRoIVWqVJGzzjrLZE6D8Xg80rBhQ3nllVf8li9YsMCURK9YscLcX716tfTs2dNsr1q1anLxxRfLxo0bS1Ry3atXL+nXr5/3ftOmTeXhhx+WPn36mO02adJE/u///k/+++8/72vpe503b57fdmbPni2nnHKKOX56DG655Ra/4zd69GgTYGdmZkrdunXlwgsvlHDpPn3yyScyfvx48/7t/dXv9Rjpfumx1/1WL7/8shxyyCFSoUIFOfzww+Xtt9/2254+79VXX5Vzzz1XKlWqZM7JnDlzZNmyZeYY6bZOOOEEc2EglAsuuEBuvvlm7309rrrdv/76y9zPy8uTqlWrmnPue17vvPNOqVGjhgnItWS9qHL3f//9Vy699FKzvu7TMcccIz/99JPfc/S96fHR6gJdd9euXWEfVwBA8iFIBwAkBQ2WL7vsMhkwYIAsXrxYpk2bJueff75YluUN+m688Ua59tpr5Y8//pDPP/9cmjdv7n1+SkqKPP/88/Lnn3/KW2+9JVOmTDHBmK+9e/fKU089ZYKqGTNmmAD79ttvD7o/uj0NuCZMmOC3/N133zXB48EHH2z2TQPsrVu3yvTp0+X77783QeUll1wSkdLyE088UX799Vc555xz5MorrzRBe+/evc2FAn3vet8+PnpMzjzzTHPMfv/9d/nggw9k1qxZ3gsZGtBr0D5ixAhZsmSJfPPNNyagD9cvv/xiLmroRQg9V88995z3seHDh5sgXfdBz9+nn34qt956qwwZMsScj+uuu0769+8vU6dO9dvmQw89ZN6Dls8fccQRcvnll5t1hw4d6r0A4XshJpAG8/o5sek5qFWrlvlq7/O+ffvMcbTpZ0ODbQ20n3jiCXM89LwFoxd5OnXqJOvWrTOft99++818pjTQt+n51qD+yy+/NDd97cceeyzs4woASEIWAABJYP78+RptWqtWrQr6eIMGDax777037O19+OGHVs2aNb33x44da7a/bNky77KXXnrJqlu3bshtLFiwwHK5XN59crvd1kEHHWSep7777jsrNTXVWr16tfc5f/31l3mdn3/+2dwfPny41aZNG+/jnTp1sm699Va/1+nZs6fVt29f7/0mTZpYvXv39t5fv3692eb999/vXTZnzhyzTB9TV155pXXttdf6bXfmzJlWSkqKlZWVZX3yySdWtWrVrJ07d1qlFbifSvfhtttu81vWsWNH65prrvFbdtFFF1lnn3223/Puu+++Qu/nzTff9C577733rMzMzJD78/vvv5vz899//1lbt2610tPTrYcffti8lnr00Uet448/3u/Yn3TSSX7bOPbYY6277rrLb78+/fRT8/2rr75qVa1a1dqyZUvQ19dzW6lSJb9jescdd/i9JgCg/CGTDgBICm3atJGuXbuaMu6LLrpIXn/9ddm2bZt5bNOmTSabqY+Holla7Tx+0EEHmRJnzdBu2bLFr9xby6q1BNumJfO67VDatWtnMrzvvfeeua9ZUl1fs8lKM/5aVq43W8uWLU1ZvT5WFkcddZT3ey1NV3psApfZ+z9//nxT0q+l8PZNM+ua9V25cqU5Nlo2rxUAmpXXCgGtLIgELQH3pe/dN3ut9H7gMQnnPWomfOfOnUFft1WrVmZYhJ6XmTNnms9Qjx49vJl0zbJrJjzUaxb3GdAMv34GtNQ9FC1z189bONsDAJQPBOkAgKSQmppqyo6//vprE+i+8MILZiyzBpg6xroo//zzj5x99tkmaNNx0xqwvvTSS95GZrbAhmY6/tguFw/liiuuMCXuSr9q4Ksl1Uqfq9sIFGq5XUYf+Jq++xhsX+1tBVtml17rVy0V18DSvml59t9//20uTGggqWXyesFBA8lhw4aZoHb79u1SVlo+Hijw/Qc7JiV9j8FeQ0v2NRjXwFzL3/Uz4Ha7Tem9jtHXZaFe095GqO0X97kr6fYAAOUDQToAIGlogKMZ1wcffNCMxdamYzq+WQNMzVhOnjw56PN0/LI2CXv66aelQ4cOcthhh5nMeyToOGkN+DTw18Z2GrTb9GKCjmtfs2aNd9miRYtkx44dphFaMLVr1/ZrVqcBpY7bLqujjz7aNEzTseqBNz2OKi0tzTTj07HYOm591apVZux+pOl71/HwvjRgDnVMysIel643/V4/QyeffLLpPZCVlVUoo18SmnXXix3acwAAgHAxBRsAICloIy8Nws844wypU6eOua/dzO3ATrtwX3/99eaxbt26mQ7aP/74o+nurZliDdI1+969e3ezPLAre2k1a9ZMOnbsKFdddZV5DW2QZtOAVwM5DdyfffZZ87h2ldcS68AScNupp54qgwcPlq+++srstzaIi0Q2+6677jIXKLS53jXXXGOy21pertUJely0qZl2pNfMs84DPmnSJJPx1WqFSLvjjjvMkAC9cKBDFL744guZOHGi/PDDDxF/LQ3MtUmdXoDQ4Nxepk3r9PW1435paSPDRx991DQHHDlypKlA0ItHDRo0MM0DAQAIhkw6ACApaDClHde1bF0z4ffdd5/JjGtArvr27WsCYZ1GTKdh06m7tJRbtW3b1kzB9vjjj5tyZx1vrUFVpGgQrqXj2jndtwTanq5Lg14NfjVo1zHf2lk9FO1+ru9Fx8xrMK8XAbp06VLmfdSLBVryrcdEg1UdS33//febwFLpOHkNlPUigV740IsYWvquxzLSNKjV7u9PPvmk2b5OtTZ27NhCpeeRoOdbhx9o6b4dkOtx1QqFwPHoJaUVCN999525MKSfSx0vr53bdWgGAAChuLR7XMhHAQAAAABAzJBJBwAAAADAIQjSAQAAAABwCIJ0AAAAAAAcgiAdAAAAAACHIEgHACBC+vXrZzqTo/waN26c6YQPAEBpEaQDAADHadq0qZkyLxYIrAEATkKQDgAAgsrJyXH0kdG5zD0eT7x3AwCAiCJIBwCggAZ8jz/+uDRv3lwyMjKkcePG8sgjj3iPzx9//CGnnnqqVKxYUWrWrCnXXnut7N69u0TZ4LZt28oDDzzgve9yueTVV1+Vc889VypVqiQtWrSQOXPmyLJly6Rz585SuXJlOeGEE2T58uXe5+jzdTtvv/22eY3q1avLpZdeKrt27Qq5L1u2bJHLLrtMGjZsaF6ndevW8t577/mto6930003yeDBg6VWrVpy+umnm+WLFi2Ss88+W6pUqSJ169aVK6+8UjZv3lzk52b27NlyyimnmGPVqFEjueWWW2TPnj3msfHjx5tt/f333971b775ZjnssMPMOrof//zzjwwaNMgcH735Zry//PJLadmypTlHut4vv/xi9lX3WY9Fp06dZMGCBX77s337dnO+dP8zMzOlVatWZjvTpk2T/v37y44dO7yvZZ8fvUhx5513ykEHHWTOw/HHH2/W96X7pJ8TPabnnXeeOc4AAJQFQToAAAWGDh1qgvT777/fBKbvvvuuCerU3r175ayzzpIDDzzQBIUfffSR/PDDDyaoLauHHnpI+vTpIwsXLpQjjjhCLr/8crnuuuvM/sybN8+sE/g6GrR/9tlnJtDU2/Tp0+Wxxx4L+Rr79u2T9u3bm3X//PNPE7BqsP3TTz/5rffWW29JWlqa/Pjjj+biwfr1603QqxcFdF+++eYb2bhxo1x88cUhX0svZpx55ply/vnny++//y4ffPCBzJo1y/se9L1q0H/FFVdIXl6e2aa+1oQJE0wwPHHiRHMxYcSIEeb19WbT8zBy5Eh544035K+//pI6deqYixN9+/aVmTNnyty5c+XQQw8127cvWujFl27dupkLB++88445t3qsUlNTpWPHjuZCSrVq1byvdfvtt5vnafCux+H999837+Oiiy4ynwH74oIeuwEDBsjAgQPNuevSpYs8/PDDpfgEAADgwwIAANbOnTutjIwM6/XXXw96NF577TXrwAMPtHbv3u1d9tVXX1kpKSnWhg0bzP2+fftaPXv29D7epEkT65lnnvHbTps2bazhw4d77+uv4vvuu897f86cOWbZm2++6V323nvvWZmZmd77+vxKlSqZfbbdcccd1vHHH1+iM3n22WdbQ4YM8d7v1KmT1bZtW7917r//fuuMM87wW7ZmzRqzj0uWLAm63SuvvNK69tpr/ZbNnDnTHKusrCxzf+vWrVbDhg2tG264wapbt6718MMP+60f7NiNHTvWvO7ChQuLfF95eXlW1apVrS+++MLc//bbb81rh9pf3W716tX9li1btsxyuVzW2rVr/ZZ37drVGjp0qPn+sssus8466yy/xy+55JJC2wIAoCTSfAN2AADKq8WLF0t2drZ07do15ONt2rQxmV7biSeeaLK0S5Ys8WbcS+Ooo47yfm9vR8vRfZdpJnznzp0m46u0zL1q1arederXry+bNm0qcvy2Zo81q7127VrzXvXm+37UMccc43d//vz5MnXqVFOeHkiz+VqiHkifo+X6mhm36fUIPVYrV640Jf1akfDmm2+ajLtms++++24JR4UKFfyOl9L3PWzYMJkyZYrJ8ut71Yz76tWrzeOa5dbMfLB9DUXL5XWfA5+jx0yHOtifCS1x96VDE7QyAACA0iJIBwBAxIydLooGbPbY6EChlqekpJjn+crNzS20Xnp6eqFtBVvm2yTN93F7naKaqD399NPyzDPPmNJuvQCgwfltt91WqDlcYNCu2+zevbsZBhBILwwEo8/Rcn0dhx5Ix2/bZsyYYUrO161bZ8ai2xcgijtPgcdbp77777//zHtr0qSJGauuwbL93oo7t6Heg+6bXnDQr77sCxaB5xYAgEggSAcAQMSMY9ZgbvLkyXL11VcXOibaqEzHa2swaQeyOl5ZA/FQGdratWv7jafWTLhmkuNBx2v37NlTevfu7Q1CdWy1ZrWLcvTRR8snn3xiMvc6Vj0c+hwdL64N+ELR8eFPPPGEfPHFFyaLro3j9Pj6Zsw1Ix7uexs9erQZh67WrFnj19hOM+///vuvLF26NOi5CvZa7dq1M8s0S3/yyScHfV39TOgYeF+B9wEAKCkaxwEAIGI6ft91112mm7d2H9dSbg24tCRbaZMzXUcblGnjNS0B18BSm6+FKnXXTvDagV2DSH2OPjcwKxsrGjB///33JjjWMm3NdG/YsKHY5914442ydetW0xn+559/lhUrVsh3331nGqaFCqL1OGqHen2ulprrxYDPP//cHC+lDd30uOl9beimDfo+/PBD04zPphcFNNOupfnFdZLX96bHWd+XNnPTc+WbPdfGd9pp/oILLjDHQC+UfP31196ydH0t7dKvF2j0tbRUXoN53Y42udNGdvocbRioFQWTJk0yz9NKAd2GXmzQCwAvvvgipe4AgDIjSAcAoIB2dR8yZIgZ36wZ5ksuucQ7zlun2Pr2229NwHrsscfKhRdeaMava2AWinZn1+BQp1fTLG+vXr3kkEMOidt70wy3jgHXKc7q1atn9qc4DRo0MBUDGpDrc3XqsltvvdVMdaZVBMFo5lq7zWtwrllozUrr69vl8fp8rUZ49NFHzf0jjzzSBL/XX3+9CcqVdnZftWqVOV5akVCUMWPGyLZt28zraPCvwbN2ffel1QB63vRig2bA9WKMfZFBx8Tra+v51tfSoFuNHTvWBOn6mTj88MOlR48e5iKATimnOnToYLrMv/DCC6b7vV68uO+++8I4GwAAhObS7nFFPA4AAAAAAGKETDoAAAAAAA5BkA4AAAAAgEMQpAMAAAAA4BAE6QAAAAAAOARBOgAAAAAADkGQDgAAAACAQxCkAwAAAADgEATpAAAAAAA4BEE6AAAAAAAOQZAOAAAAAIBDEKQDAAAAAOAQBOkAAAAAAIgz/D+zSIP6uoT4FAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 1, figsize=[12, 8])\n",
+    "\n",
+    "ds_ld.med_diameter.plot(ax=axs, label=\"LD\")\n",
+    "ds_rad.sel(station=\"M1\").sel(height=1600, method=\"nearest\").ldquants_med_diameter.plot(ax=axs, label=\"RadCLss\")\n",
+    "axs.legend(loc=\"upper right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58ab00a3",
+   "metadata": {},
+   "source": [
+    "## Interactive Visualization - Panel + hvplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fd7ee21",
+   "metadata": {},
+   "source": [
+    "### CSAPR-2 / CMAC Fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "076bbc98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['attenuation_corrected_differential_reflectivity',\n",
+       " 'attenuation_corrected_differential_reflectivity_lag_1',\n",
+       " 'attenuation_corrected_reflectivity_h',\n",
+       " 'copol_correlation_coeff',\n",
+       " 'corrected_velocity',\n",
+       " 'corrected_differential_phase',\n",
+       " 'filtered_corrected_differential_phase',\n",
+       " 'corrected_specific_diff_phase',\n",
+       " 'filtered_corrected_specific_diff_phase',\n",
+       " 'corrected_differential_reflectivity',\n",
+       " 'corrected_reflectivity',\n",
+       " 'rain_rate_A',\n",
+       " 'rain_rate_Z',\n",
+       " 'rain_rate_Kdp']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[var for var, dim in ds_rad.items() if \"height\" in dim.dims]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "398a46d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"holoviews.plotting.bokeh.raster.HoverModel\",\"properties\":[{\"name\":\"xy\",\"kind\":\"Any\",\"default\":null},{\"name\":\"data\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"2070da8a-1a93-4b6f-8585-d1d131b13c2b\",\"roots\":{\"f26f0170-bd69-4d7e-b924-df09604a85e1\":\"d06476f6-f027-40ac-a4b2-62e18cf0c6c0\"},\"root_ids\":[\"f26f0170-bd69-4d7e-b924-df09604a85e1\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  async function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t  for (const child of root_el.children) {\n",
+       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
+       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
+       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(version);\n",
+       "    } else if (root.Bokeh.version === version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "f26f0170-bd69-4d7e-b924-df09604a85e1"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = false;\n  const version = '3.8.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = true;\n  const Bokeh = root.Bokeh;\n  const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n  const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n      const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n      const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version).Panel == null;\n      if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.8.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false;\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true;\n      root._bokeh_onload_callbacks = [];\n      const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version).Panel));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n        if (Bokeh != undefined && !reloading) {\n          const NewBokeh = root.Bokeh;\n          if (Bokeh.versions === undefined) {\n            Bokeh.versions = new Map();\n          }\n          if (NewBokeh.version !== Bokeh.version) {\n            Bokeh[NewBokeh.version] = NewBokeh;\n            Bokeh.versions.set(NewBokeh.version, NewBokeh);\n          }\n          root.Bokeh = Bokeh;\n        }\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        let retries = 0;\n        const open = () => {\n          if (comm.active) {\n            comm.open();\n          } else if (retries > 3) {\n            console.warn('Comm target never activated')\n          } else {\n            retries += 1\n            setTimeout(open, 500)\n          }\n        }\n        if (comm.active) {\n          comm.open();\n        } else {\n          setTimeout(open, 500)\n        }\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        })\n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8df3343a7ce4473a71566e91fad541a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'a87ec0a2-dbd7-4a48-a9de-9676feb0c7e1': {'version…"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import hvplot.xarray  # registers .hvplot\n",
+    "import panel as pn\n",
+    "\n",
+    "pn.extension()\n",
+    "\n",
+    "# --- Widgets ---\n",
+    "\n",
+    "# separate out the CSAPR-2 / CMAC variables by checking for the height dimension\n",
+    "cmac_var = [var for var, dim in ds_rad.items() if \"height\" in dim.dims]\n",
+    "\n",
+    "# --- Widgets ---\n",
+    "\n",
+    "# variable selection\n",
+    "var_select = pn.widgets.Select(\n",
+    "    name='Variable',\n",
+    "    options=cmac_var,\n",
+    "    value='attenuation_corrected_reflectivity_h'\n",
+    ")\n",
+    "# station selection\n",
+    "station_select = pn.widgets.Select(\n",
+    "    name='Station',\n",
+    "    options=list(ds_rad['station'].values),\n",
+    "    value=str(ds_rad['station'].values[0])\n",
+    ")\n",
+    "\n",
+    "# --- Plotting function ---\n",
+    "\n",
+    "def make_plot(var_name, station, kind):\n",
+    "    da = ds_rad[var_name]\n",
+    "\n",
+    "    # Ensure we are working with a 2D slice for 3D variables (time, height, station)\n",
+    "    if 'station' in da.dims:\n",
+    "        da = da.sel(station=station)\n",
+    "\n",
+    "    if {'time', 'height'} <= set(da.dims):\n",
+    "        return da.hvplot.quadmesh(\n",
+    "            x='time',\n",
+    "            y='height',\n",
+    "            colorbar=True,\n",
+    "            cmap=\"ChaseSpectral\",  \n",
+    "            width=800,\n",
+    "            height=400,\n",
+    "            title=f\"{var_name} (station={station})\"\n",
+    "        )\n",
+    "    else:\n",
+    "        # Fallback: time series at this station (height-aggregated or first height)\n",
+    "        if 'height' in da.dims:\n",
+    "            da = da.isel(height=1500)  # or da.mean('height')\n",
+    "\n",
+    "        return da.hvplot(\n",
+    "            x='time',\n",
+    "            kind='line',\n",
+    "            width=800,\n",
+    "            height=400,\n",
+    "            title=f\"{var_name} (station={station})\"\n",
+    "        )\n",
+    "\n",
+    "# Bind widgets to function\n",
+    "interactive_plot = pn.bind(\n",
+    "    make_plot,\n",
+    "    var_name=var_select,\n",
+    "    station=station_select,\n",
+    "    kind=\"quadmesh\",\n",
+    ")\n",
+    "\n",
+    "dashboard = pn.Column(\n",
+    "    pn.Row(var_select, station_select),\n",
+    "    interactive_plot,\n",
+    ")\n",
+    "\n",
+    "dashboard\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dfe5890",
+   "metadata": {},
+   "source": [
+    "## MET (RadCLss version)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "43593d99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const version = '3.8.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n  const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n  const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n      const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n      const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version).Panel == null;\n      if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.8.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.1.min.js\", \"https://cdn.holoviz.org/panel/1.8.4/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false;\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true;\n      root._bokeh_onload_callbacks = [];\n      const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version).Panel));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n        if (Bokeh != undefined && !reloading) {\n          const NewBokeh = root.Bokeh;\n          if (Bokeh.versions === undefined) {\n            Bokeh.versions = new Map();\n          }\n          if (NewBokeh.version !== Bokeh.version) {\n            Bokeh[NewBokeh.version] = NewBokeh;\n            Bokeh.versions.set(NewBokeh.version, NewBokeh);\n          }\n          root.Bokeh = Bokeh;\n        }\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && 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google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        })\n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: 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output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='fd0a05db-5b51-4d5e-891d-a4181bc884b9'>\n",
+       "  <div id=\"f4140082-a4c4-42cf-984f-431e33229d15\" data-root-id=\"fd0a05db-5b51-4d5e-891d-a4181bc884b9\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"6c58d7c1-74c9-4864-8713-723b4ef3e74c\":{\"version\":\"3.8.1\",\"title\":\"Bokeh Application\",\"config\":{\"type\":\"object\",\"name\":\"DocumentConfig\",\"id\":\"e0f022e3-11f7-4b2e-9037-be6d24ae5be4\",\"attributes\":{\"notifications\":{\"type\":\"object\",\"name\":\"Notifications\",\"id\":\"163304b1-1632-4e20-8956-12790db0c13d\"}}},\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"fd0a05db-5b51-4d5e-891d-a4181bc884b9\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"4a14749c-e8a2-4837-b79f-b543a9eeaa22\",\"attributes\":{\"plot_id\":\"fd0a05db-5b51-4d5e-891d-a4181bc884b9\",\"comm_id\":\"41753c077c6c47b1967d28e15a40a1c3\",\"client_comm_id\":\"04c01714ec0644c890dd950c72bbd96c\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"start\",\"kind\":\"Any\",\"default\":0},{\"name\":\"end\",\"kind\":\"Any\",\"default\":100},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"use_shadow_dom\",\"kind\":\"Any\",\"default\":true},{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas 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+       "  var render_items = [{\"docid\":\"6c58d7c1-74c9-4864-8713-723b4ef3e74c\",\"roots\":{\"fd0a05db-5b51-4d5e-891d-a4181bc884b9\":\"f4140082-a4c4-42cf-984f-431e33229d15\"},\"root_ids\":[\"fd0a05db-5b51-4d5e-891d-a4181bc884b9\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  async function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t  for (const child of root_el.children) {\n",
+       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
+       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
+       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
+       "\t  }\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(version);\n",
+       "    } else if (root.Bokeh.version === version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "fd0a05db-5b51-4d5e-891d-a4181bc884b9"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"esms-options\">{\"shimMode\": true}</script><style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "  > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = false;\n  const version = '3.8.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = true;\n  const Bokeh = root.Bokeh;\n  const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n  const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; 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i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false;\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true;\n      root._bokeh_onload_callbacks = [];\n      const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version).Panel));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n        if (Bokeh != undefined && !reloading) {\n          const NewBokeh = root.Bokeh;\n          if (Bokeh.versions === undefined) {\n            Bokeh.versions = new Map();\n          }\n          if (NewBokeh.version !== Bokeh.version) {\n            Bokeh[NewBokeh.version] = NewBokeh;\n            Bokeh.versions.set(NewBokeh.version, NewBokeh);\n          }\n          root.Bokeh = Bokeh;\n        }\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = 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handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97368c24f7074678b33b26cbd43bdd88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'147af14a-4201-458a-bfe5-b1eaf207ba59': {'version…"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import hvplot.xarray  # noqa: F401, needed to register .hvplot\n",
+    "import panel as pn\n",
+    "import numpy as np\n",
+    "\n",
+    "pn.extension()\n",
+    "\n",
+    "met_var = []\n",
+    "for var in ds_rad:\n",
+    "    if hasattr(ds_rad[var], \"source\"):\n",
+    "        if ds_rad[var].attrs['source'] == \"bnfmetM1.b1\":\n",
+    "            met_var.append(var)\n",
+    "\n",
+    "# ---- 2. Widgets ----\n",
+    "\n",
+    "var_select = pn.widgets.Select(\n",
+    "    name='Variable',\n",
+    "    options=met_var,\n",
+    "    value=met_var[0],\n",
+    ")\n",
+    "\n",
+    "kind_select = pn.widgets.Select(\n",
+    "    name='Plot Kind',\n",
+    "    options=['line', 'scatter'],\n",
+    "    value='line',\n",
+    ")\n",
+    "\n",
+    "height_slider = pn.widgets.DiscreteSlider(\n",
+    "    name='Height',\n",
+    "    options=list(ds_rad['height'].values),\n",
+    "    value=float(np.array(ds_rad['height'].values)[0]),\n",
+    "    visible=False,  # start hidden; shown only for 3D vars\n",
+    ")\n",
+    "\n",
+    "# ---- 3. Plotting function bound to widget values ----\n",
+    "\n",
+    "def make_plot(var_name, kind, height_level):\n",
+    "    da = ds_rad[var_name]\n",
+    "\n",
+    "    # If the selected variable is 3D (time, height, station), slice at the chosen height\n",
+    "    if 'height' in da.dims:\n",
+    "        # Show the slider if this variable has height\n",
+    "        height_slider.visible = True\n",
+    "\n",
+    "        # Slice at the nearest height level\n",
+    "        da2 = da.sel(height=height_level, method='nearest')\n",
+    "\n",
+    "        title = f\"{var_name} at height={float(da2['height'].values):.1f}\"\n",
+    "    else:\n",
+    "        # Hide the slider for 2D fields\n",
+    "        height_slider.visible = False\n",
+    "        da2 = da\n",
+    "        title = var_name\n",
+    "\n",
+    "    # We assume dims include 'time' and 'station'\n",
+    "    return da2.hvplot(\n",
+    "        x='time',\n",
+    "        kind=kind,\n",
+    "        by='station',   # each station gets its own line/color\n",
+    "        frame_height=450,\n",
+    "        width=750,\n",
+    "        title=title,\n",
+    "        legend=\"bottom\",\n",
+    "        legend_opts={\"label_text_font_size\": \"8pt\"}\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Bind function to widgets; Panel will re-run when any widget value changes\n",
+    "interactive_plot = pn.bind(\n",
+    "    make_plot,\n",
+    "    var_name=var_select,\n",
+    "    kind=kind_select,\n",
+    "    height_level=height_slider,\n",
+    ")\n",
+    "\n",
+    "# ---- 4. Layout ----\n",
+    "\n",
+    "dashboard = pn.Column(\n",
+    "    pn.Row(var_select, kind_select, height_slider),\n",
+    "    interactive_plot,\n",
+    ")\n",
+    "\n",
+    "dashboard  # in notebook, or `panel serve` if running as app\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b7bd03b",
+   "metadata": {},
+   "source": [
+    "## MET - RadCLss Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d842fa9",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "amf3-radar-examples-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/bnf_radclss.py b/scripts/bnf_radclss.py
index fc9f570..6af2211 100644
--- a/scripts/bnf_radclss.py
+++ b/scripts/bnf_radclss.py
@@ -190,7 +190,6 @@ def match_datasets_act(column,
                        ground, 
                        site, 
                        discard, 
-                       resample='sum', 
                        DataSet=False,
                        prefix=None):
     """
@@ -215,11 +214,6 @@ def match_datasets_act(column,
         List containing the desired input ground instrumentation variables to be 
         removed from the xarray DataSet. 
     
-    resample : str
-        Mathematical operational for resampling ground instrumentation to the radar time.
-        Default is to sum the data across the resampling period. Checks for 'mean' or 
-        to 'skip' altogether. 
-    
     DataSet : boolean
         Boolean flag to determine if ground input is an Xarray Dataset.
         Set to True if ground input is Xarray DataSet. 
@@ -244,7 +238,8 @@ def match_datasets_act(column,
         grd_ds = grd_ds.compute()
         # check if a list containing new variable names exists. 
         if prefix:
-            grd_ds = grd_ds.rename_vars({v: f"{prefix}{v}" for v in grd_ds.data_vars})
+            skip_vars = {"wxt_precip_rate_mean", "wxt_cumul_precip"}
+            grd_ds = grd_ds.rename_vars({v: f"{prefix}{v}" for v in grd_ds.data_vars if v not in skip_vars})
         
     # Remove Base_Time before Resampling Data since you can't force 1 datapoint to 5 min sum
     if 'base_time' in grd_ds.data_vars:
@@ -253,21 +248,62 @@ def match_datasets_act(column,
     # Check to see if height is a dimension within the ground instrumentation. 
     # If so, first interpolate heights to match radar, before interpolating time.
     if 'height' in grd_ds.dims:
-        grd_ds = grd_ds.interp(height=np.arange(3150, 10050, 50), method='linear')
-        
-    # Resample the ground data to 5 min and interpolate to the CSU X-Band time. 
-    # Keep data variable attributes to help distingish between instruments/locations
-    if resample.split('=')[-1] == 'mean':
-        matched = grd_ds.resample(time='5Min', 
-                                  closed='right').mean(keep_attrs=True).interp(time=column.time, 
-                                                                               method='linear')
-    elif resample.split('=')[-1] == 'skip':
-        matched = grd_ds.interp(time=column.time, method='linear')
-    else:
-        matched = grd_ds.resample(time='5Min', 
-                                  closed='right').sum(keep_attrs=True).interp(time=column.time, 
-                                                                              method='linear')
+        grd_ds = grd_ds.interp(height=np.arange(500, 8500, 250), method='linear')
+
+    # Create blank DataSet to store individual interpolation results
+    matched = xr.Dataset()
     
+    for var_name, da in grd_ds.data_vars.items():
+        if var_name == "tbrg_precip_total_corr":
+            interpol_da = da.resample(time='5Min', 
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "intensity_rt":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "accum_rtnrt":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "accum_nrt":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "intensity_rtnrt":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "wxt_cumul_precip":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "ldquants_rain_rate":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "ldquants_lwc":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "vdisquants_rain_rate":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        elif var_name == "vdisquants_lwc":
+            interpol_da = da.resample(time="5min",
+                                      closed='right').sum(keep_attrs=True).interp(time=column.time, 
+                                                                                  method='linear')
+        else:
+            interpol_da = da.resample(time="5min",
+                                      closed='right').mean(keep_attrs=True).interp(time=column.time, 
+                                                                                   method='linear')
+        # Check for ancillary variables
+        if hasattr(interpol_da, "ancillary_variables"):
+            del interpol_da.attrs['ancillary_variables']
+        # assign to the matched dataset
+        matched[var_name] = interpol_da  
+
     # Add SAIL site location as a dimension for the Pluvio data
     matched = matched.assign_coords(coords=dict(station=site))
     matched = matched.expand_dims('station')
@@ -284,7 +320,7 @@ def match_datasets_act(column,
     # global attributes will be lost on merging into the matched dataset.
     # Need to keep as many references and descriptors as possible
     for var in matched.data_vars:
-        matched[var].attrs.update(source=matched.datastream)
+        matched[var].attrs.update(source=grd_ds.datastream)
         
     # Merge the two DataSets
     column = xr.merge([column, matched])
@@ -551,31 +587,31 @@ def radclss(volumes, serial=True, outdir=None, dod_file=None):
                                 'alt'
                     ],
                     'wxt' : ['base_time',
-                         'time_offset',
-                         'time_bounds',
-                         'qc_temp_mean',
-                         'temp_std',
-                         'rh_mean',
-                         'qc_rh_mean',
-                         'rh_std',
-                         'atmos_pressure',
-                         'qc_atmos_pressure',
-                         'wspd_arith_mean',
-                         'qc_wspd_arith_mean',
-                         'wspd_vec_mean',
-                         'qc_wspd_vec_mean',
-                         'wdir_vec_mean',
-                         'qc_wdir_vec_mean',
-                         'wdir_vec_std',
-                         'qc_wxt_precip_rate_mean',
-                         'qc_wxt_cumul_precip',
-                         'logger_volt',
-                         'qc_logger_volt',
-                         'logger_temp',
-                         'qc_logger_temp',
-                         'lat',
-                         'lon',
-                         'alt'
+                             'time_offset',
+                             'time_bounds',
+                             'qc_temp_mean',
+                             'temp_std',
+                             'rh_mean',
+                             'qc_rh_mean',
+                             'rh_std',
+                             'atmos_pressure',
+                             'qc_atmos_pressure',
+                             'wspd_arith_mean',
+                             'qc_wspd_arith_mean',
+                             'wspd_vec_mean',
+                             'qc_wspd_vec_mean',
+                             'wdir_vec_mean',
+                             'qc_wdir_vec_mean',
+                             'wdir_vec_std',
+                             'qc_wxt_precip_rate_mean',
+                             'qc_wxt_cumul_precip',
+                             'logger_volt',
+                             'qc_logger_volt',
+                             'logger_temp',
+                             'qc_logger_temp',
+                             'lat',
+                             'lon',
+                             'alt'
                     ]
     }
 
@@ -625,28 +661,28 @@ def radclss(volumes, serial=True, outdir=None, dod_file=None):
         if volumes['met_m1']:
             # Surface Meteorological Station
             ds = match_datasets_act(ds, 
-                                    volumes['met_m1'][0], 
+                                    volumes['met_m1'], 
                                     "M1",
                                     discard=discard_var['met'])
         
         if volumes['met_s20']:
             # Surface Meteorological Station
             ds = match_datasets_act(ds, 
-                                    volumes['met_s20'][0], 
+                                    volumes['met_s20'], 
                                     "S20",
                                     discard=discard_var['met'])
 
         if volumes['met_s30']:
             # Surface Meteorological Station
             ds = match_datasets_act(ds, 
-                                    volumes['met_s30'][0], 
+                                    volumes['met_s30'], 
                                     "S30",
                                     discard=discard_var['met'])
 
         if volumes['met_s40']:
             # Surface Meteorological Station
             ds = match_datasets_act(ds, 
-                                    volumes['met_s40'][0], 
+                                    volumes['met_s40'], 
                                     "S40",
                                     discard=discard_var['met'])
             
@@ -666,8 +702,7 @@ def radclss(volumes, serial=True, outdir=None, dod_file=None):
                                     grd_ds, 
                                     "M1",
                                     discard=discard_var['sonde'],
-                                    DataSet=True,
-                                    resample="mean")
+                                    DataSet=True)
             # clean up
             del grd_ds
         
@@ -675,44 +710,41 @@ def radclss(volumes, serial=True, outdir=None, dod_file=None):
         if volumes['pluvio']:
             # Weighing Bucket Rain Gauge
             ds = match_datasets_act(ds, 
-                                    volumes['pluvio'][0], 
+                                    volumes['pluvio'], 
                                     "M1", 
                                     discard=discard_var['pluvio'])
 
         if volumes['ld_m1']:
             # Laser Disdrometer - Main Site
             ds = match_datasets_act(ds, 
-                                    volumes['ld_m1'][0], 
+                                    volumes['ld_m1'], 
                                     "M1", 
                                     discard=discard_var['ldquants'],
-                                    resample="mean",
                                     prefix="ldquants_")
 
         if volumes['ld_s30']:
             # Laser Disdrometer - Supplemental Site
             ds = match_datasets_act(ds, 
-                                    volumes['ld_s30'][0], 
+                                    volumes['ld_s30'], 
                                     "S30", 
                                     discard=discard_var['ldquants'],
-                                    resample="mean",
                                     prefix="ldquants_")
             
         if volumes['vd_m1']:
             # Laser Disdrometer - Supplemental Site
             ds = match_datasets_act(ds, 
-                                    volumes['vd_m1'][0], 
+                                    volumes['vd_m1'], 
                                     "M1", 
                                     discard=discard_var['vdisquants'],
-                                    resample="mean",
                                     prefix="vdisquants_")
         
         if volumes['wxt_s13']:
             # Laser Disdrometer - Supplemental Site
             ds = match_datasets_act(ds, 
-                                    volumes['wxt_s13'][0], 
+                                    volumes['wxt_s13'], 
                                     "S13", 
                                     discard=discard_var['wxt'],
-                                    resample="mean")
+                                    prefix="wxt_")
 
         # ---------------------------------------------------------------
         # Cumulus cannot access the DOD API, Requires locally stored file
@@ -987,7 +1019,7 @@ def ith_val_subdict(input_dict, i):
     )
     
     parser.add_argument("--dod_file",
-                        default="/ccsopen/home/jrobrien/git-repos/bnf-radar-examples/notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.1.nc",
+                        default="/ccsopen/home/jrobrien/git-repos/bnf-radar-examples/notebooks/data/radclss/bnf-csapr2-radclss.dod.v1.2.nc",
                         dest="dod_file",
                         type=str,
                         help="[str] RadCLss DOD file"