diff --git a/conda-env/dev.yml b/conda-env/dev.yml
index b925538a1..72fe63119 100644
--- a/conda-env/dev.yml
+++ b/conda-env/dev.yml
@@ -14,7 +14,7 @@ dependencies:
     - matplotlib >=3.7.1
     - eofs >=2.0.0
     - seaborn >=0.12.2
-    #- enso_metrics >=1.1.5
+    - enso_metrics >=2.0.0
     - xcdat >=0.10.0
     - xmltodict >=0.13.0
     - setuptools >=67.7.2
diff --git a/doc/jupyter/Demo/Demo_6_ENSO.ipynb b/doc/jupyter/Demo/Demo_6_ENSO.ipynb
index c0df30eea..f39961a20 100644
--- a/doc/jupyter/Demo/Demo_6_ENSO.ipynb
+++ b/doc/jupyter/Demo/Demo_6_ENSO.ipynb
@@ -46,10 +46,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "9c822ad9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n!conda install -c conda-forge enso_metrics\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "!conda install -c conda-forge enso_metrics\n",
@@ -74,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "ea19cd07",
    "metadata": {},
    "outputs": [],
@@ -96,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "b800cfe7",
    "metadata": {},
    "outputs": [],
@@ -117,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "2f8e10ad",
    "metadata": {},
    "outputs": [
@@ -149,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "38fc987f",
    "metadata": {},
    "outputs": [
@@ -170,8 +181,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "d56dfa78",
+   "execution_count": 6,
+   "id": "43b86f8b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "1c1abe78",
    "metadata": {},
    "outputs": [
@@ -238,7 +249,7 @@
       "\n",
       "options:\n",
       "  -h, --help            show this help message and exit\n",
-      "  --parameters PARAMETERS, -p PARAMETERS\n",
+      "  --parameters, -p PARAMETERS\n",
       "  --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n",
       "                        Path to other user-defined parameter file. (default:\n",
       "                        None)\n",
@@ -246,19 +257,19 @@
       "                        cmip5)\n",
       "  --exp EXP             An experiment such as AMIP, historical or pi-contorl\n",
       "                        (default: historical)\n",
-      "  --modpath MODPATH, --mp MODPATH\n",
+      "  --modpath, --mp MODPATH\n",
       "                        Explicit path to model data (default: None)\n",
       "  --modpath_lf MODPATH_LF\n",
       "                        Directory path to model land fraction field (default:\n",
       "                        None)\n",
       "  --modnames MODNAMES [MODNAMES ...]\n",
       "                        List of models (default: None)\n",
-      "  -r REALIZATION, --realization REALIZATION\n",
+      "  -r, --realization REALIZATION\n",
       "                        Consider all accessible realizations as idividual -\n",
       "                        r1i1p1: default, consider only 'r1i1p1' member Or,\n",
       "                        specify realization, e.g, r3i1p1' - *: consider all\n",
       "                        available realizations (default: r1i1p1)\n",
-      "  --reference_data_path REFERENCE_DATA_PATH, --rdp REFERENCE_DATA_PATH\n",
+      "  --reference_data_path, --rdp REFERENCE_DATA_PATH\n",
       "                        The path/filename of reference (obs) data. (default:\n",
       "                        None)\n",
       "  --reference_data_lf_path REFERENCE_DATA_LF_PATH\n",
@@ -271,19 +282,18 @@
       "                        File name for output JSON (default: None)\n",
       "  --netcdf_name NETCDF_NAME\n",
       "                        File name for output NetCDF (default: None)\n",
-      "  --results_dir RESULTS_DIR, --rd RESULTS_DIR\n",
+      "  --results_dir, --rd RESULTS_DIR\n",
       "                        The name of the folder where all runs will be stored.\n",
       "                        (default: None)\n",
       "  --case_id CASE_ID     version as date, e.g., v20191116 (yyyy-mm-dd)\n",
-      "                        (default: v20250829)\n",
+      "                        (default: v20260528)\n",
       "  --obs_catalogue OBS_CATALOGUE\n",
       "                        obs_catalogue JSON file for CMORized observation,\n",
       "                        default is None (default: None)\n",
       "  --obs_cmor_path OBS_CMOR_PATH\n",
       "                        Directory path for CMORized observation dataset,\n",
       "                        default is None (default: None)\n",
-      "  -d [DEBUG], --debug [DEBUG]\n",
-      "                        Option for debug: True / False (defualt) (default:\n",
+      "  -d, --debug [DEBUG]   Option for debug: True / False (defualt) (default:\n",
       "                        False)\n",
       "  --obs_cmor [OBS_CMOR]\n",
       "                        Use CMORized reference database?: True / False\n",
@@ -316,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "49a4ac2c",
    "metadata": {},
    "outputs": [
@@ -342,7 +352,7 @@
       "# OBSERVATIONS\n",
       "obs_cmor = True\n",
       "obs_cmor_path = \"demo_data/obs4MIPs_PCMDI_monthly\"\n",
-      "obs_catalogue = \"demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20210805_demo.json\"\n",
+      "obs_catalogue = \"demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20260522_demo.json\"\n",
       "\n",
       "# METRICS COLLECTION\n",
       "metricsCollection = 'ENSO_perf'  # ENSO_perf, ENSO_tel, ENSO_proc\n",
@@ -373,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "5940ef4e",
    "metadata": {},
    "outputs": [
@@ -391,18 +401,19 @@
       "debug: False\n",
       "obs_cmor: True\n",
       "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n",
-      "egg_pth: /Users/lee1043/miniforge3/envs/pmp_devel_20250715/share/pmp\n",
+      "egg_pth: /global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/share/pmp\n",
       "output directory for graphics:demo_output/basicTestEnso/ENSO_perf\n",
       "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_perf\n",
       "output directory for metrics_results:demo_output/basicTestEnso/ENSO_perf\n",
       "list_variables: ['pr', 'sst', 'taux']\n",
       "list_obs: ['AVISO-1-0', 'ERA-INT', 'GPCP-2-3', 'HadISST-1-1']\n",
       "PMPdriver: dict_obs readin end\n",
-      "Process start:Fri Aug 29 15:21:52 2025\n",
+      "Process start:Thu May 28 10:51:56 2026\n",
       "models: ['ACCESS1-0']\n",
       " ----- model:  ACCESS1-0  ---------------------\n",
       "PMPdriver: var loop start for model  ACCESS1-0\n",
       "realization: r1i1p1\n",
+      "run_in_modpath: 4\n",
       " --- run:  r1i1p1  ---\n",
       " --- var:  pr  ---\n",
       "var_in_file: pr\n",
@@ -505,7 +516,7 @@
       "            \"sst\": {\n",
       "                \"areaname\": \"areacella\",\n",
       "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/MOHC/HadISST-1-1/mon/ts/gn/v20210727/ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc\",\n",
+      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/MOHC/HadISST-1-1/mon/ts/gn/v20260416/ts_mon_HadISST-1-1_PCMDI_gn_187001-202501.nc\",\n",
       "                \"path + filename_area\": null,\n",
       "                \"path + filename_landmask\": null,\n",
       "                \"varname\": \"ts\"\n",
@@ -518,405 +529,205 @@
       "\n",
       "\u001b[94m     ComputeCollection: metric = BiasPrLatRmse\u001b[0m\n",
       "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasPrLatRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1213: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageZonal(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1214: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageZonal(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasPrLatRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1213: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageZonal(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1214: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageZonal(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
       "\u001b[94m     ComputeCollection: metric = BiasPrLonRmse\u001b[0m\n",
       "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasPrLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageMeridional(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1495: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageMeridional(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasPrLonRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageMeridional(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:1495: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageMeridional(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
       "\u001b[94m     ComputeCollection: metric = BiasSstLonRmse\u001b[0m\n",
       "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoAmpl\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n"
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/cdms2/dataset.py:2179: Warning: Files are written with compression and no shuffling\n",
-      "You can query different values of compression using the functions:\n",
-      "cdms2.getNetcdfShuffleFlag() returning 1 if shuffling is enabled, 0 otherwise\n",
-      "cdms2.getNetcdfDeflateFlag() returning 1 if deflate is used, 0 otherwise\n",
-      "cdms2.getNetcdfDeflateLevelFlag() returning the level of compression for the deflate method\n",
-      "\n",
-      "If you want to turn that off or set different values of compression use the functions:\n",
-      "value = 0\n",
-      "cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1\n",
-      "cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1\n",
-      "cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included\n",
-      "\n",
-      "To produce NetCDF3 Classic files use:\n",
-      "cdms2.useNetCDF3()\n",
-      "To Force NetCDF4 output with classic format and no compressing use:\n",
-      "cdms2.setNetcdf4Flag(1)\n",
-      "NetCDF4 file with no shuffling or deflate and noclassic will be open for parallel i/o\n",
-      "  warnings.warn(\"Files are written with compression and no shuffling\\n\" +\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3065: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_mod, keyerror_mod = AverageMeridional(sst_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3066: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_obs, keyerror_obs = AverageMeridional(sst_obs)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoDuration\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n",
-      "it is now: MV2.copmress(array,condition), if your code seems to not react or act wrong to a call to compress, please check this\n",
-      "  warnings.warn(\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3065: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_mod, keyerror_mod = AverageMeridional(sst_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3066: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_obs, keyerror_obs = AverageMeridional(sst_obs)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSeasonality\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstDiversity_2\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstSkew\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstTsRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstTsRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = SeasonalPrLatRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalPrLatRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = SeasonalPrLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalPrLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = SeasonalSstLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = SeasonalTauxLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n",
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n"
+      "\u001b[94m     ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO::2025-08-29 15:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "2025-08-29 15:23:13,255 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "2025-08-29 15:23:13,255 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "INFO::2025-08-29 15:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n",
-      "2025-08-29 15:23:18,085 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n",
-      "2025-08-29 15:23:18,085 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "figure plotting start\n",
-      "metrics: ['BiasPrLatRmse', 'BiasPrLonRmse', 'BiasSstLonRmse', 'BiasTauxLonRmse', 'EnsoAmpl', 'EnsoDuration', 'EnsoSeasonality', 'EnsoSstDiversity_2', 'EnsoSstLonRmse', 'EnsoSstSkew', 'EnsoSstTsRmse', 'SeasonalPrLatRmse', 'SeasonalPrLonRmse', 'SeasonalSstLonRmse', 'SeasonalTauxLonRmse']\n",
-      "filename_js: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "met: BiasPrLatRmse\n",
-      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasPrLatRmse.nc\n",
-      "failed for  ACCESS1-0 r1i1p1\n",
-      "'BiasPrLatRmse'\n",
-      "PMPdriver: model loop end\n",
-      "Process end: Fri Aug 29 15:23:18 2025\n"
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
-    }
-   ],
-   "source": [
-    "%%bash\n",
-    "enso_driver.py -p basic_enso_param.py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46ff9996",
-   "metadata": {},
-   "source": [
-    "This run saved metrics to two files:  \n",
-    "- `basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json`  \n",
-    "- `basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json`\n",
-    "\n",
-    "diveDown metrics are not available in all cases. \n",
-    "\n",
-    "Example dive down (i.e., diagnostics) figures:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "af28cd74",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1affcb610>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
     },
     {
-     "data": {
-      "image/png": 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pQZIkevbsSUhISJPLW61W0tPTyc3NxWAwkJSUpLbSu91u0tLSOHr0KFarFbPZTEJCAlFRUef15t3bxbfu1FJ1P6upqSE1NZVu3bo16K6cmZlJaWkpvXv3Pq+tqHWnHjqduvBmPD948CBZWVlUVVURFBREfHw8ycnJaLVaEeS2YTqdDo1GI7qkC4IgCIJwUiKwFVqM3W7n7bffZseOHQwYMACLxUJqairDhw/nmWeeQa/Xk5+fz9/+9jeKiorYsGEDnTt3BqC8vJzZs2dz4MABvv/+e6644gr279/Pv/71L4xGI0FBQdTU1JCfn89HH31EZGQk//vf/5g7dy7jxo1Ty2A0GklMTDytwHbnzp089dRTSJJETk4OH330EcOGDWvy2H744Qc+//xzYmJiKCkpQZZlZs+eTWxsLC6Xi6+++opjx45hNBopKirCbrfz3HPP0aVLlxYLxDZs2MCcOXN46623GgS2CxYsYN26dXz55ZetpnuwoigcPXqU119/ncWLF3PgwAHsdjtGo5GUlBSuvvpqbr/9dkJDQ0VwKwiCIAiCcAETgW0LkSRJtCT9bsSIETz22GNIksQ333zD7Nmzueaaa+jTpw9wfDztvHnzeOqppwDIzs5Gq9USFBSkbue9995Dr9fz0ksv4evri91up7y8vF6raseOHZk5c6b6WpIk9Hr9aZWzZ8+efPjhh1itViZPnnzSZQsKCnjnnXe4/fbbmThxItXV1dx55528+eabvPjii2i1Wu699160Wi16vZ6ioiL+7//+j3nz5vHPf/6zwfa83XMNBgM2mw23242Pjw96vZ6amhocDgd6vR4fHx80Gg1Op5Pq6mqMRiNWqxW3243JZEKj0WC1WikvL8fHxwcfH596raKLFy+md+/ehISE4HK56m27bku3t+XXZrOhKAqyLGM2m9FqtbjdbiorK9XyeZevqKjAZDJhMBhOq75PxTuf8aOPPsr333+Pw+FQP7NarWzZsoW9e/eSl5fHM888Q0hIyHm73txuN4sXL+aDDz4gMjKSmTNn1js3Tyy3t+7cbjdarRaj0Yher8flclFdXY3T6USSJHQ6HWazGVmWURQFu92OxWLB5XLVW89qtVJTU4MkSWg0GoxGI0ajscnjdblc1NbW4nA4kCRJ/V5OXL5ueTQaTZPLCYIgCIIgtDQR2LYQnU5H3759W03LV0vS6XTqDXNKSgqKolBVVaV+rtFomDx5MvPnz+euu+4iODiY3377jWHDhrF06VJ1ucrKSgC1xc5sNjcILmRZxmg01nvPe5OekZFBRkYGI0eObLQLrJ+fH35+fhQVFZ3yxr6qqory8nJ69uxJQEAAAQEB9O/fn0WLFlFSUkJoaCjh4eEoioLb7VYzfgcEBDS6vaysLC677DLGjx9Pbm4ueXl5DB48mLFjx/LNN9+QkZGBr68vzz33HCkpKezatYs777yTMWPGcPDgQXJzc+nbty9XX30133//PQcPHsTHx4enn36afv36IUkSVVVVbNiwgVdffRVJkli0aBGvvvoqTqeTxMREqqqq1OC2qqqK1157je3bt2Oz2TAajVx77bVcddVV1NbWMmnSJO655x6uueYaNBoNubm5TJgwgZdffpkRI0actO5Ol91u57///S/fffddo12rwdNl/f3332fIkCFMnjz5nLp5u93uJudNLikp4YsvvuCvf/0ro0ePVrvSN2bnzp188MEH5OXlodFoCA4Opnfv3tx1111kZGRw0003ERYWhk6nw+12c++993LFFVdQWFjIe++9x44dO1AUhaCgIPr06cNNN93El19+yRtvvEHHjh0BSExM5OmnnyYsLKzRMuzZs4c5c+aQl5eHoih06dKFxx9/vMH5l5+fzxNPPKFej927d+fee+8lMjLybKtREARBEAThvBCBbQtxu90UFhYSGBj4p5/yxxvcFRUV8fPPPxMbG0uXLl3qLdO7d29WrlzJmjVrGDp0KNu2beO+++7jt99+U5e5/vrrefDBB7ntttvo1KkTvXv3ZtSoUcTFxanL7N+/n3vvvVd9HRUVxW233UZ0dDSLFi3izTffZOfOnY0GQGfSSlV3LLD3GIuLi6mpqaGoqEjt+rxgwQIWLlxIfn4+YWFhXHPNNU1uMz8/H5PJxMyZMzlw4ACPP/44hYWFTJs2jYCAAJ544gm++eYbOnXqBHi6a1utVp5//nkKCwu55ZZbKCkpUbvnvvbaa3z99dd0794dk8nEunXr8PX1JSUlhdLSUp577jkuu+wybrzxRrKysnjmmWcIDw8HPA8jxo4dy4033ohWq2Xfvn28+uqrJCUl0bNnT0aNGsWSJUsYM2YMvr6+fPfdd0RGRjbrfNMVFRV8//33TQa1Xg6Hg7lz53L11VefcWC7cOFCysvLKSsro7y8nPvvv5+dO3eyaNEitFotY8aMoU+fPnz22Wds3rxZbSm/5ZZbGt1ecXGx+jBh+vTphIWFUVxcTHZ2NpIkoSgKRqORTz/9FK1WyyeffMLrr7/OkCFDeP3110lPT+ef//wn8fHxlJeXk5aWBnjOr6uvvppHH32UnJwc7r//fpYsWYIkSXTv3p2UlBQkSWLhwoU4nU569+7NLbfcQmxsLJWVldx3332sX7+eMWPG1CtvcHAwTz/9NKGhoRQWFvLkk0+ybdu2et35BUEQBEEQWgMR2LYQp9NJdnY2BoPhTx/YfvLJJyxevBibzUZNTQ1z5sxp0NKk1+sZN24cy5cvJzMzk+DgYEJCQup1jx05ciTffvstq1atYv369bzyyiu8/fbbzJ07l5SUFABCQ0MZO3asuo63FRbgr3/9K+PGjTvtrsknExoaSq9evXj77bcxGo0cOHCA1atX43a765W5U6dOuN1utm/fzrJly9i/fz8xMTGNbjM8PJxx48aRlJRESEgIERERdOnShf79+6MoChdddBHbt2+ntrYW8Iwfvv7660lOTiYpKYmYmBjatWtHfHw8UVFRXHLJJfzwww/Y7XYMBgMLFy5k5MiRmEwm1qxZQ21tLTfffDMdOnQgISGBVatWsXfvXsDT8p2ens5///tfioqKcDgcauKufv36MWrUKKZPn05eXh5+fn4sXLiQm266qVnnzy0tLWXbtm2ntey6deuora09427Q27ZtY968edx3331cfPHFbN++nbfffptrr70Wu93OK6+8wpNPPsnAgQNZuHAh48aNY8iQIbz//vtUVVXx0EMP1Xsgsnr1avLz83nooYfw9fWlpqYGf39/UlJS1KBbkiR8fHwwm8106dKF77//nn379rF48WLefvtttYt+cHAw8fHx6vmk0+nw8fEhISGBkJAQysrKcDgcHD58mM6dO+N0Ovn444+5/fbbiYmJUc+zgIAAtev+iUwmE3FxcVRWVlJeXg4gsrgLgiAIgtAqicC2BbndbpHpE7jqqqu45557yM/P59VXX2XWrFn06NGj3nQ0kiQxfPhwfvzxRxYuXMiTTz7ZoBu3TqejW7dudOvWjXvuuYf09HRuu+02PvjgA958800AwsLCuPLKKxstR2BgYLPdtAcFBfGvf/2LDz74gGeeeYZ27dpx2WWXsWHDBrXVEzyBbadOnZgwYQKSJPHmm28yevToRrfpHRPrHXup1WoJCwur19Jnt9vVTNtms1ld3juO0nt8kiTVG6+bnZ1Namoqf/vb3wBPt25ZlvH39wc8gayfn59a54sXL+Y///kPTzzxBCkpKTidTu6++27sdjuSJNGjRw969uzJ3Llz6d27NxaLhSuuuKJZ6tbLZrOddlZxh8PRaOB2Onr06MEdd9wBwP33309gYCCKoiBJEjabjS1btnD55ZcTHBxMSkoKiYmJZGdnq631deXk5BAWFoavry/gaRH+7LPP2LFjB+vWrVPL+v333+N2u5k/fz6jRo3C6XRSW1tLcnJyve3VzRacmprKt99+S3p6OuXl5QwfPpyKigpeeeUVampq2L9/P1arlSFDhqjrW61W/vvf/6LVaptMhJaZmclrr73GwYMH6dmzJ0lJSWdVj4IgCIIgCOeTCGyFFhcUFESHDh3o0qULwcHBTJ48mQULFnDzzTfXWy4qKor+/fsjyzKDBg0iNze33ufV1dWYzWa1hSwiIoKIiAi1BdPrj0h8I0kSiYmJzJw5Ux2f+H//938MHjy4XjIrb1m83bFbavqn1NRUAgIC1Fa8xMREamtrOXToEKGhoVRXV5ORkaGWLzs7m+joaCZNmoQsyxw8eJCcnBx1eyaTiauuuoqHH36Y1NRURo0addJxp2cjICCAwMBAtSXxZEJDQ9Vg8kzFxcWpUzR5s1h7p6gaMmQI3bt3r7e8JEmMHDmy0W35+/tTVVWlBsYTJkxQA2dvgOp2u8nJycHPz4+//vWvjBw5Uk2WVlpa2uT8wxUVFeTm5hIZGckLL7xAp06dqK6uRlEUdu/eza5du+jTp486xtztdvPrr7+ybNkyHn74YfUhxokSExN58cUXOXbsGLNnz+a3335jypQpIj+AIAiCIAitighsW4hWqyUuLg4fH5+WLkqrkpKSwpQpU/jf//7XIMmQn58fjz32GA6HA1mW1UytXs888wwA/fr1w2AwsHr1alJTU3n99dfVZUpKSli0aFG97fbt25fQ0FC++OIL5s6dyy+//NJol9XCwkLWrFlDQUEBlZWVLF26lMLCQi6//HL8/f155ZVXOHjwIP/5z38wm80sW7aM4uJiDAYDy5cvJzc3l+eeew6NRkN5eTmPP/44gwYNwt/fn127dvHrr7/y5JNPNmd1NiDLMu3bt68X5NntdjZv3kz//v3VbvHdunVj4MCBPPfcc0yZMoXs7GxWrVpFt27dAOjatSuffPIJb7zxBqGhocybN69BUq5hw4YRHBzMoUOHePTRR9HpdM16LIGBgYwbN44vv/zylMted9116rjnM+VdT5IkOnfujEaj4c4771SzEZvNZoqKiuqt4w0ofX196z1IGTp0KG+//Ta//PILI0eOVFve6/bcMBgM3HPPPQQEBKDRaJAkCaPRSHJyMnPnzuWRRx7BZDLhdDopLy9XE6QNHDiQ++67T81+DZ7gf/jw4Xz88cfIsswNN9yATqfD4XCwaNEiPvroI5555hl69uypBtsWiwWr1UpQUBBWq1XtDRAREYG/vz/FxcWip4kgCIIgCK2OCGxbiEajISQkpNlv9tsSWZYZPHgwfn5+9cYX/u1vf6O2tpbKykrCw8O58sorCQ8PR5Ik/Pz8UBQFp9NJZGQk48ePJzo6GoCpU6eyfPlyNmzYgMViITo6mk8++YS+ffuq3WOzsrL4+uuv65UjMjKS0NBQ2rVrR79+/ZpsiSovL2ft2rWUlZUxevRo8vLyqKys5KKLLsLf35+4uDi0Wi1arRaNRoPBYGDXrl3U1NTQtWtX/vnPfxIVFQV4xr/27NmTnTt3Ul1dTbt27XjzzTcZMGBAoy3Kfn5+TJw4UW3t1el0XHbZZWrXVG/QBZ7AKCQkhCuvvLJeltvRo0eTnJxMUFAQWq2WxMRELrvsMqxWK7t372batGnq+ajT6Xj55ZeZO3cuGzZsoEePHsyYMYPi4mI0Gg3Dhg3j3//+NwsXLiQvL48HHniA3bt3k5iYqO7P2zU8MTGRHj16NHtLeUBAADfeeCOrV6+u11p8os6dOzN16tRmGTs9depUZsyYwcyZMwkLC6OoqIjbbrutQWvwRx99RGVlJU8++WS9405KSuLhhx/mgw8+YNWqVURGRlJYWEh4eDhms5mamhrAU3d1E12ZTCaeffZZ/vnPf/Loo4+SmJiIzWbDbrdz3333AZ7fFO+55yVJEuPHj2f27Nn079+fzp07I0kSmzZt4qGHHmLYsGFs2LCBjRs3MnjwYPr378/SpUv57rvvmDt3Lhs3bmTFihX4+/tTVlZGfn4+N9544zlllxYEQRAEQTgfJOVP8uhdURR+/PFH9u3bxxNPPNHiN2YOh4MDBw4QGxvb5BQvQuMURaGgoIDq6moSEhJa/LtsSxwOB2lpaURGRhIUFIQkSdTW1rJ+/Xr69OnTZDfXM6EoCiUlJezcuZNHH32UGTNmNDmuWVEUVqxYwaJFi5g5cyZarRaHw0Fubi5RUVEnTfakKAq1tbV88803PPzww5SWljZYJiEhgZdffpkxY8ac1fyre/bswe1207NnT8Azr2tmZiZ79+7FZrMRFhZGv3790Gq1bN26le7duxMcHMyePXtwOBz07t27wT6dTiepqalkZGRgsVgIDAykS5cuapKmTZs2MWLEiAYPvRRF4ciRI+zdu5fKykp1vZiYGHVbdZNQebndbn766SfCwsIYMGAAWq2WzMxMNm/erI47liSJXr160b17d7KyssjMzGT48OHk5+ezZcsWtZt/9+7diYuL+0OvOafTSV5eHuHh4X/6RHuCIAiCIDRNBLYtxGazsW3bNjp16lRvzKVwat4b/PLycrp163bWXUz/jOx2Ozt37iQuLk5tBW9uDoeDOXPm8MUXX/CXv/yFBx98sMnW0nMJbL3r2+12MjIymDNnDkuWLKGqqorg4GAmTJjAbbfdRrt27dDpdH/I2Gqh+YnAVhAEQRCE0yEiAkEQmpVOp+Pvf/87f//738/7vrzZnbt06cLs2bNxOp1YrVZ1nKl3jKogCIIgCIJwYROBbQvxJoQRmUXPjlarPauupX923vOupXssNDfvlEZ6vb5ZxtIKgiAIgiAIbYsIbFuITqejR48eIrA9S5GRkURERIj6O0NarZauXbuKehMEQRAEQRAuKOLutoW43W6KiorU5C3CmampqaG8vFxMO3KG3G43JSUlWK3Wli5Ks1IUzz+nCywOcLrB/ft7giAIgiAIwoVPtNi2EKfTSWZmJnq9vsH8n8KplZWVUV5eTmBgoGh9PAMul4vs7Gzi4uIuiEQ83oC2wgY/p8GGXKi2Q5ARLomDMYlg1IEEiF7rgiAIgiAIFy4R2AqC0CYpCjjcsDkPnlwBR6vA7vJ8JgEL0+Gz3fDMJdAjAmREcNucXC4XR44cUefgFQRBEARBaEkisBUEoc3alQ//XAlZ5fXfVwCbC3bmw7Or4OXLIDHo7PbhdDpZt24dR44c4brrrlOTUymKwtGjR1m/fj3Hjh3D19eXHj160Lt3b2RZpqqqipUrV5KdnY1Op6NTp07069cPSZL49NNPsVgs6j66du3K6NGjqa6uZt26dWRmZqLT6UhISGDUqFFoNBr27t3Lxo0bsVgshISEMHToUOLj48nNzWXNmjUUFhYyatQounbt2mhSNbfbze7du9mwYQNGo5ERI0YQGxt7ygRs5eXlbNq0ifT0dOLi4hgzZgyyLONwOPj1118ZM2YMHTp0YNeuXezcuROLxcKECROIioo66XYVRSE7O5sNGzZQUlLCiBEj6Nq161l8Q4IgCIIgCGKMbYvRarUkJiaKlo6zFBwcTExMjOiGfIZkWSY+Ph4/P7+WLso5q3V4uh8fLjv5crsKYEmGZ9ztmVIUhYqKCubNm8fLL79Mbm6u+n5mZib/93//x4oVKzAajVitVt5//32OHj1KZWUlTz75JJ9//jngud5/+OEHNm7cSGVlJZ999hkAUVFRREVFERQUhM1mY86cOfzwww+YzWbcbjerV6/G5XKxc+dOnnnmGSoqKvD19SUnJ4e9e/cCkJaWxq5du/jiiy/Ytm1bk8exa9cunn32WaqqqtizZw///Oc/Gx1r7XK5cLlcOJ1ObDYbxcXFbNq0iU2bNvH555/jcnmaxQ0GA7fffjtxcXEAbN68mR07dvD555+TnZ2tbs/tduN0OtVtulwuFEXB7XaTlpbGzp07T1p2QRAEQRCE0yFabFuIRqPB398fnU7X0kVpk4xGI3q9Xkz3c4a8551W2/Yv/Uob/JrmaZ09GQX4JhVu6w1nc7Wlp6fjcrm48cYb+fbbb3nkkUdQFIXXXnuN8PBwZsyYgb+/P4qiYLFY0Gq1fPrpp+zZs4fvv/8ef39/ACwWCxqNhoqKCoKDgxk7diydO3cGPNMVFRYWsnPnTm6++WZGjhwJeFqLdTodW7duJSwsjHvuuQeDwYDbfTxKv/jiixk6dCjFxcVNHoPL5WLRokX06NGDadOmYbPZmDJlCuvWrWPUqFH1lp0+fTo2mw2LxUJgYCCvvvoqjz32GIsXL+bLL79Ul6upqeHWW2/l4YcfZuDAgdx8883k5+fz+OOP19vewoULmTVrFgMGDGD79u1069aNxx57jOjoaIYPH86ll17KHXfccRbfjCAIgiAIwnGiuauFOBwODh48SFVVVUsXpU0qKioiKyur3g2+cGpOp5P09HQqKipauijnrNwGhbWnt2xeFdScZQLy+fPnM3jwYCZPnszKlSspLS3F7XazYcMGLrroIoKCgpBlGa1Wi5+fH4qisH37dkaOHElgYCCyLCPLMr6+vvj4+ACQn5/Pp59+yssvv8zLL7/M+vXrMZlMJCYm8vnnn/Phhx+yevVqtUW1a9eu5Obm8vLLL/Pdd9+RkZGhPpzQarWnfMhjsVjIysqiZ8+eGAwGAgICSE5OZt++fQ2WrampIS8vj0cffZR//vOfaDQaDAZDg7mPvYG89xrU6/XodLoG5XA6neTn53PxxRfz0Ucf4XA4+Oabb3A6nadVdkEQBEEQhNMhAtsWoigKtbW1arc+4cw4HA6sVquY7ucMec87p9PZ0kU5Z2fy43W2WZGLi4vZsGEDF198MWFhYURFRbFmzRoAamtrGx1K4HA4sFgsBAQENLldvV5PREQEMTExxMTE4O/vj9ls5t5772XSpEnk5OTw+uuvc88991BdXU3//v3597//jb+/PytWrOCee+7hu+++a/L8VxSFL7/8kokTJzJx4kSysrKw2+3q+GDwdCVurCuyVqvl4osvpnv37oSGhjZL0BkbG8vll19OfHw8Y8eOZf/+/dTWnuZTCUEQBEEQhNPQ9vsjCoLwpxRsgo5BkH6KMbYAXcPArD/1cnUpisKPP/5IYWEhU6dORZIkqqurMRgMjBgxgri4ODIzM3G5XGprpqIoGI1GoqOj2bt3Ly6XS21Zrdu7IDg4mDFjxtTrigwQERHBhAkTGDt2LJWVlUyePJlly5YxceJEevXqRY8ePXC5XPz000988MEHjB49uskA+oorrmDgwIEAhIWFERwcTEFBgTq+taCggO7duzdYT5IkAgMDz6yyToP3GEXrrCAIgiAI54MIbFuIGGN7bgwGA76+vuIm+QxpNBr8/PwuiPPOzwBjk+CtLeA+ScO9LMG1XUF7hqdKRUUFS5cuVceHAuTl5TFr1iwyMzO5/vrr+fzzz0lISKB///5YrVY2bNjA8OHDmTBhAg899BBff/01l156qTpONjY2lrCwMNxuNxaLRW21lGUZl8vFpk2biIyMJDIykqysLKxWK0FBQezdu5fS0lKSkpIAyM7Oxs/PD61Wi81mo6ysDIvFQnl5OcXFxQQEBBASEkJISAjgCar79OnDggULGDp0KPn5+WRmZjJkyJBGj73udeV0OikvL6esrAyr1Up+fj6hoaEN1qmqqqKoqAiLxUJJSQnFxcUEBwcDkJOTw9KlS0lJSWHRokV07twZHx8frFYr5eXlWCwWKioqKCgoIDQ0tEG3Z0EQBEEQhFMRgW0L0Wq1dOzY8YIIMFpCaGgowcHBIivyGZJlmQ4dOlwQyaMMMoxPgvU5sO1Y40mkNBKMSIDh8Z7/PxP79+/HbrczcOBAwsLCAPDx8SEqKordu3fzl7/8BbfbzWeffcarr75KYGAgffr0Ydy4cQwYMIAXXniBOXPm8Omnn2I2m+natSvJyclIkkR2djb33HMPRqMRgP79+/Poo4+ye/du3n77bSorKzGbzUycOJF+/fqxa9cuNduwJElERkby+OOP4+Pjw+bNm5k+fTo5OTls3bqVNWvW8PTTT9OjR4/j9aDRMGbMGA4fPsx9992HVqvl9ttvJz4+vsFx6/X6eoFlTk4OM2bMYMuWLZSWlnLdddfx+OOPM2LECAwGg3oNfvHFF8ydO5ecnBwyMjLo3bs3b775JgB+fn4sX76cV155hU6dOjFp0iR0Oh1r167lySef5MiRI+zZs4dffvmFzz//vNHAWRAEQRAE4WQk5U8ySNHbrXDfvn088cQTLd4i4HQ6KSgoIDg4GJPJ1KJlaWsURaGyshKbzUZoaKgIbs+Ay+WisLAQf39/fHx8WrzFW1EUVqxYwaJFi5g5cyZarRaHw0Fubi5RUVEYDIaTrOsJZrPK4YW1sLsAimrBpYBWA5FmGBoLDwyAqN9nNzqTw3W73WpXYm89KYqCy+VCkiQ0Go362u12I0kSsiyj0WiQJAlFUXA6nepnGo1G/d1xOBz1xsdqNBq0Wq26T0VR6m3Pux/vOpIkqeVyu904HA51W97PTrwuvF2QveP6ves3luzJu29vPTidznrl9W7f6XSqZfRO5VO3HDqdjgULFvDJJ5/w7bffqt22ZVlGkiR1CqAT1zmxxTgvL4/w8HDxWykIgiAIQpPafrNNG+VyucjOzsZoNIqbtbNQXl5OeXm5aLU9Qy6Xi5ycHOLi4tQMvW2VJHmSQsUHwmuXw+ajsL8Iqh0QaIDu4dAvCnSas0scpdFoGpxb3qCx7uumzj9vkNaYukmc6vIGfY1tq6n9eLMWn4o3WD3VQ70TW/M1Gk2T5a17fFqt9qQ9ARr7/HTKIwiCIAiCcDpEYCsIQpumkcBHB8Pi4NI4z3hbze9BL5xdUCs0n0svvZRu3bq1dDEEQRAEQbjAicC2BXm7LApn7mQtWMLJXYj15r2MJM58LK1wfgUGBp6XLMuCIAiCIAh1icC2hWi1WhISEtp8d9CWEhQUhNlsviCDtPNJlmXi4uLw9fVt6aI0yfudOp3O0+piK1zYvGOYxbUuCIIgCMLJiMC2hWg0GkJCQsTN2lkym82tIvlRW6PRaAgKCmrV551Go8FsNlNcXIzb7b4gMjgLZ8ftdlNRUYEsy+I8EARBEAThpMSdQgux2+3s3LmTpKQkda5H4fTl5uZSWVlJ586dxQ3vGXA4HOzZs4f27durU9i0NpIkERoaSklJCWVlZfxJErcLTdDr9URGRrbqhzGCIAiCILQ8ERG0oLrTdwhnxjv9iXDm2sJ5p9FoCA0Nxe12t/qyCueXd2ogQRAEQRCEkxGBrSC0Um4FjlbBuhwwyjCyA5h1f54sv3XnUhUEQRAEQRCEkxGBbQvRaDQEBAQ0Oc+lcHJGoxFfX98LtiVHUWBXPjy9AvYUgVYDU1PgkSFgbnxK0dOi0Wjw9/cX550gCIIgCIJwQRGBbQvRarUkJSWJ8aFnKTQ0lODg4Aty3J2iwN5CeG6NJ6gFcLphfS5kVUC3cxgaK8syiYmJF2S9CYIgCIIgCH9e4u62hbhcLo4dO4bVam3porRJlZWVFBcXX3DjL90K7CqAf62C7cfqf5ZZDumlnmXOevtuN/n5+dTW1p5TOQVBEARBEAShNRGBbQtxuVzk5eVhsVhauihtUmVlJYWFhbjd7pYuSrPxttQ+tQK2HoMT41e7Czbmev57trwPVERgKwiCIAiCIFxIRGArCK2AokBqETy3GvYUNr3cxlywOf+4cgmCIAiCIAhCWyAC2xYkprE4e5IkXTDjRBXFE8w+swq2HD3+vkaC9v4QZDz+XlYFHCo5t/2J804QBEEQBEG40FwYkUEbpNVqSUhIwGw2t3RR2qTg4GCio6MviOB2f7En+/GWo/W7H1/UHt64AnpGHH/PrcBvmWe/L1mWiYuLw8/P7+w3IgiCIAiCIAitTNuPCtooSZLw9/cXWZHPksFgwGw2t+mWR0WBA8We7sc7C+p/NjgGnroEekVC1zDPdD9eG3LBepbdkTUaDb6+vmK6H0EQBEEQBOGCIgLbFuJ0Otm9ezcVFRUtXZQ2KT8/n7S0NFyuc8ik1MJKLfDmZs+4WS8JuCQWZgyD5GBPd+Sh7cFU5/nHsSrYV3R2+3Q6naSmplJaWnouRRcEQRAEQRCEVkUEti1EURScTucFN13NH8XtdrfpoFZRIKMM1h6Bunmdh7b/PagNAW9jdK9ICPE5vkyFDbYe9WzjzPcrzjtBEARBEAThwiMCW0FoAQqesbXltuPvdQ2DJy6C+MD6y5p1MCDq+Guby5NBucr+R5RUEARBEARBEFo/McCzhXjHOooxtmdHr9fj4+PTZsfYOt2e1tq6Lo2DpODjLbV1XZ4I81KPJ5dKLYa8SvALbXz5pkiShNlsFmNsBUEQWkhTPWbO5O/ZyXrdSJJ0yl45dfd1rj14TqfcZ1Ke012vrf79v5A09v009r2czvfYHOfh6Zxn51KWcznXz/R8PZtynuz4mmv/rf26E1FVC9HpdPTo0aOli9EmSZJEu3btWroY56TWAduOHX/tp4cuoaCXG1++Sxi084OjVZ7X2eVwuAw6hXrG5Z4unU5Ht27dzrbYgiAIwjlQFIWjR4/y6quvsmrVKiwWCyNHjuSVV15Blpv4A3DC+qWlpSxevJgFCxZw+PBhXC4X7dq1Y8iQIUydOpWYmBj27dvHddddp64nSRKBgYEMHjyYO++8k44dO6LRaFAUhezsbObPn8+SJUs4duwYPj4+9O3bl1tvvZWePXvy4Ycf8vrrr6vbkmWZyMhIxo4dy4033khoaOgpy+1yufj2229ZsmQJ6enpVFdXExcXx/XXX8+VV16Jj49Po+spisLLL7/M6tWrOXbsGJIkkZSUxF//+ldGjhyJ0WhsdD3hj1FZWcn999/Ptm3b0Ov1/OMf/2DSpEn1gh+Xy8WOHTv47rvv2LBhAxUVFYSGhjJs2DDuuOMOwsPDATh69CgLFixg0aJF5ObmYjAY6NmzJ7fccgv9+/fnm2++4fnnn8ftdtcrg8Fg4KeffiI6Opqamhq+//575s2bR25uLmazmfbt2zNy5Eiuv/56fHx8OHLkCJ9++inLli2jurqawMBAkpOTue666xg2bBiSJLFp0yY+//xztm3bRlVVFV27duXNN98kMjLypPXhdrvZuHEjH330Ebt27SIoKIirrrqKG264gcDAwDOqW0VRWLx4MfPmzWPXrl1YrVaGDBnCW2+9hcFgAGDfvn1MmzaNoqIifvnlF2JjY9m/fz8fffQRmzZtory8nLCwMD7//HNiYmLOaP9Hjhzh/fffZ+PGjRQUFBAUFMRbb71Fr169zmg7fzQR2LYQp9NJdnY2ERER+Pr6tnRx2hTvH3aLxUK7du1O62agtUktgnLr8dcRZugQ2HjrqyR5At++7Y4HtjYXbD0GoxObDoYb43Q6yc3NJTg4GD8/v1b/5E0QBOFC4na7+fLLL3nttdfw8fGhS5cuZ3QPUFxczL333sv8+fMBiI2NJSAggB07drB06VKio6O56aabsFgspKamotFo6N69OwaDgT179rB+/XrWrVvHN998Q3R0NHv27OG+++5j7dq1+Pn5ER8fT1lZGR9//DF6vZ7u3btTXFxMamoqvr6+dO7cGZvNxurVq1m+fDmHDx/mhRdeOOXUhXa7nX//+9/k5OQQHx9PcXExO3fuZOnSpbz++uvcdtttjf49crlcfPnll5SXlxMSEsKRI0fYunUrS5Ys4euvv2bkyJHi71gLys7O5ocffqC6uhqA+fPnM27cOPVBhcvl4ocffuChhx4iNzeX0NBQ2rdvT0ZGBmvWrKFv375cfvnlpKenc//997NkyRLMZjPx8fHY7Xa+/PJLHA4HPXv2pKysjH379mE0Gunatav6UEOv1yPLMi6Xi88++4yHH35YfQBSXV3N4sWLyc3NZdy4cciyzPTp05k/fz6hoaHExsaSl5fHpk2bCAwMZNiwYQAsXryYuXPnotFoqKqqwmAw4HA4TloXiqKwdetWpk6dSk5ODklJSWRlZbFq1Spyc3N5+umnMZlMp123iqLwzTffMG/ePBRFoba2lujo6HotqVqtloCAABwOhzr95aZNm/jggw+QJInq6moiIyOx28987NrevXt59913URSFiooKQkJCsFgsZ7ydP5oIbFuIy+WioKCAwMBAEdieherqasrLy0/59Kw1UhRYle2Zk9Yrxh8SgppexzvOdmG6pxszeLbxf4POLLB1u90UFhZiMpnEXLaCIAhnwWq18ttvv1FSUsKAAQOorKwkPT2diy++mPbt21NWVsaOHTvIz8/HZDKRkpJCx44dcTgcrFy5kiVLluByuejevTvXXXcd/fv3x+l0Mn/+fGpqarj44ouJj49vsF+Hw8Hs2bP5/vvvCQ4OZvbs2UyYMAGdTofVamXFihXExsbWW8doNPLaa6/Rv39/fv31V+644w42btzI6tWrmThxIk888QRr166lc+fOvP/++/Tu3RvwtASVlpbWCxqTkpL46quviIyM5N///jcvvvgi//vf/5g6dSq5ublUVVUxcuRIoqKikCSJ3bt3s2PHDsLDwxk+fDi33XYbo0ePJiEhgby8PG644Qa2b9/OF198wc0339zo0CxZlvniiy+IiYlBp9ORmprK1VdfTW5uLtu3b2fkyJHN++UKZ8R7zgYHB2O1Wlm9ejW5ubkkJSUhSRJpaWk89NBD5OTkcNVVVzF79mzCw8NxOBz89ttvxMfH43Q6+de//sWSJUuIj4/nnXfe4aKLLgIgPT2d7OxsNWgDiImJ4dNPPyUhIQHw9EYwGo1UVlby/fffY7VaeeWVV7j99tuRJImysjL279+Pr68v+/btY82aNfj7+7N48WKSkpJwuVxkZGRgtR5vbZg0aRJ/+ctfWL16NQ888MBp1YXFYuGtt97i8OHDTJkyhXfffZe1a9cydepU3nvvPSZNmkSfPn0arHfkyBFWrlyJv78/ffr0YdeuXbhcLsaNG8d9993H9OnT+eSTT3jttdcarBscHMzEiROpra1V7+kuvfRSli1bxsGDB7n77rsbrKMoCj/99BPl5eX06dMHh8PBgQMHCA4OZtCgQfj7+yNJEr169eKXX37B5XJxySWXnFYdtAYisBWEP5jFCTvyj4+XlSVIiQBffdPryBpPt+N2vpBT6XkvsxzSS6F32+6VLQiC0KZUVlYyY8YMtmzZwogRI8jIyKC4uJgvv/wSq9XKgw8+yObNm7HZPNkB4+PjmTFjBsOHD+fDDz9k9erVAGzdupU9e/Ywffp0OnfuzGOPPUZOTg6fffZZo4FtXl4eixYtwuVyMXnyZG644QY1GDSbzVxzzTUN1pEkCYPBgK+vL127diU0NJSqqiqOHTvGgQMHWLt2LXq9nvvvv5+hQ4eqAcTAgQMbjLHTaDSYTCZ8fX0ZMmQIAKWlpVRXV/PJJ5/w888/M2vWLB5++GHsdjuzZ8/m888/Z9q0aVx++eVMmzZNLW/Hjh1JTExk+/btOByOJsfzaTQaunXrRllZGWVlZRQXF2O32/H19SUpKeksvj2huZSWlvLbb78BcNNNN7Fu3Tq2bt3KunXr1O9m8eLF5OTkEB0dzaOPPkp8fLz6sMR7vqalpbFs2TK0Wi133nkno0aNUs+TXr160bNnz3r7dbvdVFZWUl5eDnhabI1GI263W73mVq9eTa9evYiPjyc4OJjRo0cjSRI2mw23243VamXBggWMHz+eiIgIunTpgsFgUMvWrVs3FEVh27Ztp10fxcXF6vKXX345AQEB9OjRg4SEBHbs2MGWLVsaDWy3bdvG7bffTnBwMF26dGHfvn1ERkZy+eWX07dvX5xOJ3p94zeIR44c4cknn+To0aOMHj2agIAAOnToQIcOHdSu+415+umn2bt3LxdddBGFhYVkZWVhMBi45ppreP311/H19SU6Opro6Gj27dt32nXQGoisyC1IdJ85N221/jLLIL/6+GudXD/rcVM6BEJ7/+OvvS2/Z5pvoa3WmyAIQmuzb98+7rrrLt5++21iY2N59tlnWbRoEYMGDeKtt97i7rvvJjMzk0ceeYTCwkKmTZumtjIOGzaMOXPmMHnyZDQaDbIso9Vqm/yNLi4uJjfXM/H5JZdcgizL5Ofn89JLL/HEE0/wj3/8g88++6zeGERFUXC5XNTW1rJ27VoKCgqQJIn4+Hj279+P1WpVW4rqtoqB52/FiUmmnE4nZWVl/PrrrwBEREQQFRXFuHHj0Ol0/Pjjj9jtdrKysti8eTMmk4lrr70WjUajBiuKonDgwAF2794NwGWXXXbSRJpOp5PnnnuOYcOGcc0111BRUcH//d//MXr06DP9uoRmoigKe/bsYe/evZjNZm655RYGDx4MwLfffqtOx7hr1y4URSEmJkZtxfXynl+HDh2ipqYGo9HIgAEDGpwLJ56HOTk5TJ06lZEjRzJy5Ej+9re/UVNTg6+vLxdffDFarZYffviBESNGMHToUG666SZ+/fVXnE4nXbp0oWPHjlitVp555hkGDRrEyJEjeeihhzh48OBpJ7DyXlfef263m9raWgoKCgDPdQHg4+OjtqRmZmaedJtFRUVotVpefPFFnnjiifOeXNbtdpOdnc1dd93FY489htPp5L///S8//vhjm54SUrTYthCdTkeXLl2aTJggnFx4eDjBwcFtbnytosDeIiipM0zBT396ra6hPtAzAjblgUvxtPiuyoa7+4LpNJMca7VakpOTRcINQRCEZnDDDTfw6KOPIssy6enprF27FoDCwkK+//57qqo8iREOHz7M3r17GT9+PB06dAAgISGBiRMnYjAYcLvdrFixApfLRXBwcKP7UhRFDVq9N71lZWV8++237N27l9raWsaNG8f111+vrmOxWJg0aRI6nY6KigqsVitjx45l6NChLF26FPAEDicGtY3Zs2eP2lJbUlKCyWTi1ltvJTY2lnHjxvHvf/+bgwcPsn37drKyssjIyOCiiy6ic+fO9Y7h0KFD3HvvvWRkZDBx4kTuvPPOkz5wlSSJiIgIEhMTMZlMHDhwgHnz5jF69GiGDh16ynILzc/tdrNkyRJKS0sZPXo0sbGxjB8/nvfff5/du3ezd+9eevXqpZ6vJwanJ27Lu8zpnIc6nY7Y2Fj1/jk6OhqNRoNOp+ORRx7BaDTy66+/kpmZSX5+PvPnz2fz5s3MmzePIUOGMGfOHF599VU2b97MkSNHOHDgAIcOHWLfvn0sWLAAf3//U5QA1q1bx+zZs9UA8IorrmDYsGEnDQhPTHp1osjISJ555hm1G/b5ptFomDRpEtOmTcPhcLB69WpWrFjB2rVrmTx5cpOtxK2dCGxbkEajEa1nZ+l0fwBbG5sLDhZDTZ1x/H3becbQnookweD2MHe3J6syeFp+D5ZArzMYaizOO0EQhHMnSRLdunVTg0yHw0F1dTWKopCWlkZ2djbg6Sbs6+tLTU1Nk9vSaDSnzPYfEBBAeHg45eXl7Nq1i4kTJxIXF8fbb7/N7Nmz+d///tfodhMSEggKCiIgIIABAwYwadIkIiIiSEhIQKfTUV1dTVpaGv369TvpNEAmk4lOnTrh4+NDREQEI0eOZPz48ZhMJiIiIhg9ejSfffYZS5YsITU1FfDc8HsDdUVR2L9/P3fffTebNm1izJgxvP7666fMlaHVannsscd4+OGHOXToEHfccQcbNmzg888/Z8iQIeLvWQuwWCz8/PPPgKcFdfr06VitVtxuN/n5+axatYoePXrQsWNHwPOgJy8vj8DAwAZT1cTGxmIwGKiqqmL//v1ceuml9e7vTjwPY2JimDNnDomJifXeVxSFgIAAnnrqKe6++24yMjJYvnw5L730EseOHWPr1q0MHjyY7t278/7773PkyBHS09P59NNPmTdvHgcOHCAtLY2+ffue8viLi4vZvHmz2jLduXNnjEYjwcHBlJaWUlZWBoDNZqO2thaAqKiTd80LCAio11X7fJMkibCwMLWByJvdvKamRj2utkgEti3E6XSSlpZGhw4dCAo6SdYgoVHFxcVUVVWRnJzcplptyyxwoOT4+FqAoe1Pf8qePpEQaDge2JZZYVeBpyX3dH4LXS4Xhw8fJioq6rSmaBAEQRCaVrdVw9fXl3bt2lFeXs6//vUv7rrrLjQaDTabjd27d9O9e/cmt2Oz2fjqq6+oqqriiiuuIDk5ucEy7du356KLLiItLY0vvviC0aNHM2TIEHr27NlkUGwwGHjppZcYNGiQ+kDY23rWs2dPUlJS2LhxI++88w79+vVTx0ZmZ2dTUlKiJpMCz7jYTz75hOjoaCRJqve3V6vVMnHiRObNm8d///tfNYiZOHGiOq3Qjh07uPvuu9m+fTtTp07lhRdeUINaRVGQJIn58+dz+PBh+vbtyyWXXEJ+fj4Oh4OYmBhkWSY4OFid6qSkpKRNd5lsqxRFYf369Rw+fBiAAwcOcODAgXrL/PTTT9x+++2MHTuWV199laysLN577z2ef/55/P39cbvdbN26laioKLp06ULfvn1ZsmQJc+bMUc9p8EwBlJOTQ79+/RotR11Wq5UlS5aQnJxMcnIy4eHhRERE8NVXX1FZWYlOpyMvL49t27YxZMgQdSxqVVUVP/74I5IkodPp6m37xH14X48fP54xY8ao78uyTG1tLd27dyc9PZ01a9YwefJkMjMzyc7OxmQy0b9//5PWq3coQlPHd7JjP53PTnzf7Xaze/duampqsFgsHDhwQJ1OU6/XN7odRVHU91vrAyUR2LYQRVHUQezCmXO5XNjt9jb1R01RoLAGDhQff89XD33OIPmTrx4ujoP//T6W3+KEnflwdWfwM5xOGcR5JwiCcD5ERkYyZcoUZs6cyXPPPcfixYvx9fUlKyuL0tJSfvnlF7Ub8oksFgszZ84kJyeHyMjIRgNbk8nEo48+yp49e9ixYweTJ0+mT58++Pj4sH79egD1xtzLG4Ce+D54WpJnzJjB/fffz4YNG7jyyitJSUnB4XCQmprKpEmT6iXukSQJrVbb6LYkSaJ///507tyZrVu3Ap6xs97MtTabjQcffJBt27ZhMpkoKSnhiSeeADzJtR5//HGMRiMff/wxP/30Ew899BCXXHIJmzdv5qmnniIqKoqgoCAOHTrE3r178fX1Zdy4cW2y51Zb53A4+PXXX6mpqWHQoEG8++679aaaeeSRR9i0aRMZGRl0796df/zjHzz//PPMmTOHjRs30rFjR4qKikhNTWXu3LnExMTwzDPPkJ+fz86dO7n66qvp2bOnOhZ7+PDh9eZOPXbsGI8++qg6dlWr1TJ9+nSioqJ455132L9/Px07diQyMpK0tDTS0tJo3749AwcOpKCggAceeACTyURycjJ6vZ5t27Zht9vp2bOn2sL8/fff88UXX5CVlQV4hhLccccd+Pn58eGHHxIUFNSgUcXPz4/bbruNdevW8dVXX5Gbm8vhw4cpLS3l+uuvP+mDraaCyHfffZfFixezf/9+AHbu3Mm1115LbGwsL730UqPbWrVqFW+99RY5OTnYbDZKS0u5++67iYiI4LHHHlPLoSgKv/32GxMnTqS6uprU1FQiIiKYMGECsiyza9cunn/+eYqLPTetlZWVPP744wQHB/OPf/yDAQMGnOpUaREisBWEP4iCp9tw3fG1nUMgxHR6ra1ew+sEtuAJlAtqTi+wFQRBEM6NJEmYTCZ8fHzqtbDo9XoeeOABfHx8+Oabb9i4cSMajYb4+HiuvfZawsLC1OV8fHzqtfY2tc0TJScn89VXX/HRRx+xcOFC1q9fjyRJREVFccUVVzB58mS0Wi2yLOPj44OPj0+TvZo0Gg2jRo3is88+46OPPmLlypUsX74ck8lEz549GTZsmDp20cfHB5PJdNJWGm8m1/3796PT6bjmmmvUINjtdlNWVqbO47l8+XJ1vV69evHwww8DnhZms9ms1k379u3p0KEDu3btUtcfOnQoU6dO5dprr221rUYXMm83XJPJxFVXXUWPHj3UwDYyMpJ3332X9PR0li5dSs+ePbn//vuJi4vjq6++YsuWLaSnpxMSEsKoUaPU7sSDBw/m888/Z86cOSxbtoyVK1diMBjo1q0bo0ePRqvVotVqMZvNOJ1OFi1apJZHr9dzww030LFjR2644Qa+/fZbDhw4wM6dO9Hr9Vx66aXcf//99OjRg7KyMqZOncqqVavYsWMHVVVVBAYGcvPNN/PQQw+p52dmZia//fYbbrcbHx8fHA4Ha9euxWAwqJmXTyRJktpC/d5777Fu3ToCAwO55557eOSRRwgICGh0Pe9xnXh9ebvuL1u2DPAkoqqpqWH58uUkJyfjdDrVTOVms1ld99ixYyxbtgy73a72bli/fj1BQUHcdddd6vZlWWbSpEkUFRWxc+dOunbtyoMPPqgmASstLWX58uVYLBZ1PPO2bduQJKnedlobSWlLTV7nQFEUfvzxR/bt28cTTzzR4t1XHQ4HaWlpxMTEnNZAdeE4RVEoLCykpqaGuLi4Fv8uT5fTDY8she/r9Ni5qQc8cRH4nGbyJ0WB7AqY8h0c+z2zskGG98bB8PhTB8hOp5OMjAzCw8PrjXVpKYqisGLFChYtWsTMmTPPexZAQRCEc+V2u6mursblcuHj46PePMLxBE8WiwWn0wl4blwNBoP6+2axWLDZbBgMBvVmVlEUKisrcbvd9QK7pjgcDqxWqzoWTpZl9Ho9er0eSZJwOp1UVVUhSRK+vr4n/W1VFKXe9rzBrNFoVKdIsVgsyLKMr6/vSVtJrVYrFovn6a2vr2+9rp3e4zuRLMv4+fkhSRLV1dU4HA4MBgM+Pj7q1CwOhwO32612FzWZTKK1toW4XC6qq6vVoM97zoHne66qqsLlcqnfIaBOxWO323G73er5WneKHe95aLPZ1PPaex5qNBrsdrs6XvVE3nPc7XZjt9vV8wWOTwfk7RLvdDqx2+04nU7cbjcajQaj0VjvOOqexycKCAg46bnnvf4dDoe6bZ1O1+T9lt1up6ampsH1pSgKtbW12O32ButoNBr8/f1xuVzU1NTgdrvx9/dHluV643rrkiQJs9mMVqulV69epKam8sorr3DHHXdgs9nUh2He4QrenAGNOZ3fqJbS7HeRdrud4uJiKioqsNvtyLKM2WwmJCRE/eESPH/oOnXqJH6Yz1JYWBihoaFtqv5sTtiQe/y1jw66h4HpDK5CSYIgI/SPggWHft+uC9YegUviQHuKy0uWZTp27CiuQ0EQhLPkvalsjLfrr6+vb5Pre1tST1yvqRadxuh0uka7BHtptdrTzt8hSZIaFDfGaDSedib9ppY93eM7sd40Go2YPaKVkWW5ye9SkqRGrw1vy6K3RbSpdU92HhoMhnoPkZoq28n2430wcrJrB87snD+RRqPBbDaf9vJNHbM3ED3ZtrRabYPv4lT11FhSuMbqS6fTtckcQM0W2FZVVbFs2TLWr1/P/v37ycvLo7a2Fp1OR3BwMAkJCfTr14/LLruMxMTENtPKdr64XC6ys7OJiIg46R9AoXFlZWVYLBbatWvXZs6lAyVQXOchWqgPdAw5s27I4Bln2yMCfk33tAIDrDkCDhdoTxHnu1wucnNzCQ4OVsenCIIgCIIgCH8O9957L0VFRQwaNKili9LszjmwtdlsLFu2jNdee43Dhw/j7+9P3759ufjiiwkICMBisZCfn8/u3bt55ZVXmDNnDuPGjeOBBx4gPDy8OY6hTXK5XBQUFBAYGCgC27NQXV1NeXn5KacJaC0UxdNa667zoCzCDB3P4mGYRoIuoRDuA0d/7yWSVQEZZdD9FJeU2+2msLAQk8kkAltBEARBEIQ/kdY+RvZcnXNgu2bNGmbMmMGoUaN46aWX6NKlizqWo26/eUVR1KyA8+fP58EHH+Srr746190LQptgc8GmXHD9HthqJM8UPf5nkfBJkjxJp2IDjge2Thcsyzx1YCsIgiAIgiAIF6JzDmy7devGhx9+WC+gPZE3wA0LC+OWW27hL3/5i5q6WhD+DI5UHE/2BCBLMCD6zLshewX7QNcw2HzU0wrsBjbngdUJRpF/SRAEQRAEQfiTOefMO+3atSMlJQWtVovT6cThcJxybtGgoCCGDBlyrrtu03Q6HT169DijZBHCcZGRkSQlJbWJ8bWK4pnmp6jO+FqTzpMA6mxpJLg4FnR1ruC8KsgsP/l6Wq2Wbt26ERwcfPY7FwRBEARBEIRWplnbdjZv3szy5csZMWIE3bp1w9/f/6yyr7pcLtLT08nLy6OiooKOHTvSvXt3dVs2m42DBw+SlZWF0Wike/futGvXrs1lenW5XKd8CCA0zjulQlvgcMP+IqiwHn+vT+TZdUOuq2878NOD7feM9CUWOFDk6aZ8sktBnHeCIAiCIAjChaZZA1uHw8F///tf5s6dy6BBgxgxYgRXXnmlOin56aqpqeFf//oXBQUFpKWl8de//pUXXngB8NyUL1myhHfffZeEhARKS0uRZZlXX331jPfTkpxOJ4cOHaJjx45tMp12SysuLqayspLk5ORWP/dppQ0OFEPdUHJQjKfV9Vz4GaBbGKw64nldbYe0Uk+mZF0TDdkul4uMjAxiYmIIDQ09twIIgiAIgiAIQivRrJOADho0iMWLF/P0009z9OhRnn32WUaOHMnTTz/N3r171QmdT8VsNjNr1iw+/vhjLrnkknqflZeX89Zbb3HllVfywgsvMGvWLMrKyvjss8+a81DOO+9E1G2l1bG1cblcOByOli7GKSkKlFpgX9Hx90xaT2vrufYvkIChscdfu3/v8lxubXIVcd4JgiAIgiAIF6RmbeoyGAxER0czdepUpkyZwvbt2/nf//7H4sWLef/99xk+fDjjx4+nf//+JCUlodE0HldrNBrat2+P3W5vMEFyQUEBhYWFDB48GD8/P/z8/Bg+fDiLFi1i+vTp9bbp7a7qdrtRFAWXy4XL5cJutyPLMpIkodVqcbvduFwudT1ZltFoNDidTrXL5qmWrRtkeZd1uVz1Aghvy6J3LHLdbTmdznrL6nQ6FEXB6XTWqxdZlhts92TLnskxnK/jrdv19XTq5lTH613W7Xar9djYspIkqWNwm7NuvHV+WstKEgeLteTXHA9jOwa5iTB7gl6n8/TqRpKkenXuPYbeEW70sga7y7P9A8VQXOsmQFf/eLVaLQ6HQ60vbxlP57w5k+M903NMlmURZAuCIAiCIAjnrNn7cHrHuer1egYOHEhkZCROp5O0tDS+//57VqxYQceOHZk6dSq33HILJpOpyW00Ji8vD19fXwICAtTlYmJiKC0txWKxYDab1WXdbjeffvopK1euBCAnJ4dOnTqxc+dONBoNZrOZpKQkSktLOXLkiLpeYmIifn5+7N+/H5vNBkBAQAAJCQkUFhZy9OhRwHOzHhcXR2BgIDt37lRv2AMCAkhKSuLIkSMUFRWpy3bs2BFZljl48CAOh4OqqioKCgoIDw/n8OHDlJWVqcffvXt3rFYrmZmZauARHh5OTEwMmZmZ6rKyLJOSkkJNTQ0ZGRlqkBAdHU14eDgZGRlUVlYCYDKZ6NixI7W1tWRkZKjH2759eyIiIkhNTcVq9TT3mc1mOnbsSFlZmVo33gcOoaGh7NmzRy2XyWSie/fuHD16lGPHjqnHkJiYiNFoJC0tTa1HX19funbtSk5ODoWFhWoZunTpAkBGRgZ2ux2A4OBg4uPjycnJqVePnTt3RqvVUlxczK5du5AkiYiICKKjo+vVjcFgIDExEZfLxcGDB9V9RUZGEhsby4EDB6itrVWXTU5OpqamhszMTPW7jI6OJjIykv3792OxeAazarVaevfuTUFBAbm5ueqy8fHx+Pv7k56erm7XaPJhfWFX4Hjf4CB3IbLdiNWqJy0tTa3zwMBAEhISyM/PV+tRlmUSExMxmUzs3r1b3VdwcDAJCQk4y48Rrg0k1+WZk/ZYNRwqsFB5eB/ezs8hISF07NiR9PR0ysvLqaiowO12ExQUhNVq5fDhw2oQGhERQUxMDOnp6ep5I0kSffv2paSkhOzsbPUci4mJISQkhMOHD1NVVeU5XqORLl26UF5eTmZmpnrM8fHxBAYGcvDgQbUe/fz86NChA9XVddJFC4IgCIIgCMJZaPbA1uVyUVFRwcGDB/nqq69YunQpABMmTODOO+/EbDbz2Wef8dJLLxESEsLkyZPPePsajaZey6wsy40mE9JoNIwdO5YhQ4agKArLli0jLy+P+Ph4ZFlGq9Wi1WrVoNXL19cXrVZLTEyMuk29Xo9WqyUoKAiDwZP1R5Ik/Pz8kGWZ+Ph4dX29Xo8kSYSGhuLr66suazKZ0Gg0xMXFqS3I3hbpiIgIAgMD623DGzh7y2A0GpFlud6y3hYys9lMfHy8Gvj4+PggyzKRkZGEhIQAnoBMr9ej0WjqHa/ZbEaj0RATE6MGON668ff3V5eVJEldtm65vC2KwcHB9VrYzWYzsizTvn17dbs6nQ6A0NDQeg8hvOu1b99e3a7BYECW5Qb1aDAYCAkJISUlRT0PTCZTg7qRZRmDwYCiKPWO18fHB/AErd7gXJZl9Xur+116jzc6Olo9Bo1GgyRJBAYGqq3l4AnUdDpdve26JS3rt3vKqABGWWFAvC+hATq0slyvzr3fed16lCQJHx8ftFotCQkJ6r4MBgMajYb4yEB6RenI/T2GdCmwo8jA37rGq2N4vedru3btCAkJweVyIcsyOp1OPce82/Weo3XPG+8x+/n51TvHzGYzOp2Odu3aqeN1ZVlGlmX8/Pzq1bmfn596TXnrRqfTodVqG/TKEARBEARBEIQz1ayB7dGjR1mwYAHz589nz549dOrUidtuu42rr76aDh06qC2sKSkp7N69+6zmso2IiKC2trZeK09hYSG+vr5qwOIlSRKRkZFERkYCcPDgQSoqKggPD1cDIm/AWbfl2FvOujf23vfNZnO9gMyrscRV3q7SJwoNDcXpdJKVlaV+7u/v32A5WZYbLcOJy3q7dJ6YDMgbfDW27Inl9QamJ/Lx8WlQr9CwboAm6+bE7UqShK+vrxqsnmy73ocHJ9ZjRUUFdrud6Ojoet9lY/UIDb8fSZIaTdoly3KjgVZjdXPieeNVd7t7i1C7IUtAiEmif7wZg77xOm/sHPOej419v5EhvvSMhkXZnqRRAJuPyTx5SRjaE3r6BwQE4HK5OHLkCCEhIWqX7saSSDV23hiNxgZ109Q51tSyjdW5Xq9v8J4gCIIgCIIgnIlmDWw3btzIzJkzueyyy/j73/9Or169iImJaTCWVqPRMGXKFMLDw5vcVmPTkSiKQrt27ZBlmcOHD9OtWzesVitbt26lX79+jc5p6g0KTtxe3e7OTXV9buz95ppSyO12U1RURHBwML6+vme03XMt75msf76cy/EqikJNTQ3l5eVERUWd1nd5rmU4G4oCW48eDzgBgk2QFCyp0/Gc6/ejkSQSgyHIeHye3KxyiYIaiPFvuL7b7aa4uBiz2XzG03E1x3XS2HcpCIIgCIIgCOeqWQPboUOH8ttvvxEdHY2Pj89Jb25vuummJpNHASxevJisrCwOHTrE0aNH+eijjxgwYACdOnXi+uuv5/XXX6eoqIiMjAwOHjzI3Llzm/NQBOGcOd2wKe94YCsB3cM9wW1zSg6GcPPxwNbmgs15DQNbQRAEQRAEQbhQNWtgGxERQUREBOAZC2uz2eplRgVPt0ODwaCO+2vKwYMHSU1NpWvXrgBs2bKF9u3b061bN26//Xaio6NZt24dAQEBvPPOOyQmJjbnoZx3kiSh0+lOGtwLTfOOEW3NcirhSMXx1xoJLmoPzd1QHOUHcQHHpxRyuWFjHkzs3HCuXHHeCYIgCIIgCBeiZg1sFUWhtraWr776ilWrVlFVVdUgodNVV13FbbfddsptPfDAA01+5uvry6RJk5g0adI5l7mlaLVakpOTGx2/KpxaaGgogYGBjXY/bw0UBXIroaT2+Hs6DfSJav59yRro0w4WZ3iSRylAeqlnPtsTW4e9WZZFwiZBENqK9957jx9++IE777yTa665Ru0Nlp2dzVNPPYXL5eL9999vNKfF+eR2u3nwwQfJyspCq9ViMBjo2bMnt9xyCxEREaSnp/Pkk0+qUwz6+voyatQorrrqqkZzTAhCcysvL2fq1KmEhYXxn//8Rx0C6J01ZN68eUydOpUbbrjhDy/b2rVrefHFF9FqtWg0GkJCQpg0aRKXXnopGo2Gt99+myVLlqiJUxMSErjpppvo0qWLeDgvNKlZz4yKigoeeeQRZs2axcGDB1m+fDkul4vc3FzWrl1LSUmJSBRTh3cuXeHMSZLU6n/YssqhxHL8dccQCG3mbsheQ2KolyyqoAbSShtfVpx3giC0JQcPHmT16tV8+umnai8wRVFYtWoVS5cuZdWqVfXm+f4jbdy4kfDwcK6//nouuugi5s2bx//93/9RWVlJZWUlO3fuZPDgwVx33XV06NCBf/zjH3z44Ydi/m7hD+FwOFi2bBmLFy9mw4YNal4Lm83Gp59+yurVq+tN//hHKigoYNeuXUycOJFrrrkGo9HI1KlTWbFiBW63m9TUVGw2G9dffz1jx44lNTWV66+/nry8vBYpr9A2NGuL7c6dO1m2bBmzZs0iPDycW2+9lU8++QSj0cjXX3/Njz/+yJAhQ5pzl22Ww+Fg9+7ddOrUqdEMw8LJ5efnU15eTrdu3dTsvq2J1QmHy8BR596lV4SndfV8SAyGCDMc8Uw9S0ktpJfAgKj6XZ+dTif79u0jLi7upMnbBEEQWpOuXbtSVVXF3r176dWrF1arlV9//ZWRI0eqc9WDJ+AtLS3l8OHDWK1WIiMjSUhIQKvVoigK5eXlZGdnU1VVpc517v0bbLVa2blzJ8nJyRw7dozS0lIiIyPp0KHDSXsH9e7dm2uuuQbwDMl67LHHOHz4MODJnj9ixAj69euHw+EgPz+fpUuXcuuttzbIKC8I54NGo+GSSy5h4cKFXHHFFRgMBjZt2gRA586d6y3rcDjIzMykoKAAg8FAUlISgYGBSJKEw+HgyJEjFBUV4XA4CA8Pp0OHDuqwsMzMTKxWK0FBQRw+fBhZlunUqRMBAQFNPkw3m82MHz+eoKAgrrjiCg4cOMB3333H8OHDAUhISFCvrYEDBzJkyBB27txJ+/btz1d1CW1cs0YExcXFmEwmLrroIg4dOgR4bqQDAgK4+eab+f7771m0aBH33HNPc+5WEFqdGgekl9V/LyW84ZjX5qLTQN92xwNbixMOlXoCbFPrHoosCIJwSgkJCQQFBbF48WJ69OhBfn4+hw4d4q677lIDW0VROHz4MI899hhHjhzBaDTicDh44IEHuPbaa3E4HGr3Rr1eT2lpKZ07d2bWrFnExMSQn5/P9ddfz4QJEzhw4ADl5eW43W5mz57NJZdccsqeLt45wO12O3a7vd5DV29+g+DgYDIzM0WLrfCHuuyyy3jzzTcpLy8nODiYxYsX07t373qttQ6Hg48//piPP/4Yg8GAxWIhOTmZV155hYiICDIyMpg2bRpOpxOn00lNTQ3333+/mgz2k08+Yc2aNURGRlJYWMjRo0cZNmwYr7766mkNfzIYDISFhVFRUdHgM0mSCAgIwGQy4XQ6m7VuhAtLs7YfybKMyWRCkiR8fHwwmUxkZ2cDoNPp0Gq1HD16tDl3KQitjqJAlR0OlRx/z08PiUGezMjngyTB0Nj6299X5BlnKwiC0NbJssxVV13FkiVLKC8v5+eff6ZHjx7Exsaqy9TW1vLcc88RHBzML7/8wpIlS3jwwQeZOXMm+fn5aLVabrzxRn755Rd+/vlnFi1aRGVlJd98840aaLrdbmpra/nyyy9ZtmwZPXr04OOPPz6trs4lJSV8//33hIeHN+gR43a7OXToEBs2bODSSy8lICCgeStIEE4iMTGR+Ph4fvvtN0pKSlixYgVXXXVVvYc127Zt46233uLZZ59l8eLFzJ8/H4vFwgcffIDT6aRdu3Z89NFH/PLLLyxevJhHHnmEt956i+LiYnUbaWlp3Hbbbfz888+89957rF+/ni1btpyyfE6nk127drF+/Xr69u3b4POamhoWLlxIYGAgvXr1apY6ES5MzdpiGxoaitVqpaqqiujoaGJiYnj33XcBOHr0KPv372fChAnNucs2S5ZlIiIiTpkdWmicr69vqx4rerQSCmuOv44LgBCf5s+I7CUBnUM889mW/h7MHizxjPGN9D2+X41GQ1hYGCbTeRrsKwiCcJ707duXqqoqtm7dysqVK5k0aVK937KysjKWLVvG888/rz5EDw4ORpZl9uzZQ1RUFAaDgR9++IH9+/djsVgoLi5mz549aiuQLMtMmTKF0NBQFEVh2LBhvPfeew1meKjrrbfeYsGCBdjtdqqqqnjkkUdo3749paWlFBcXM23aNHx9fcnNzaVr167cfvvtrTbxoXBh8vf3Z+TIkSxZsgQfHx98fHzo1KlTvWUWLVpEZGQkQUFBaq/Lrl27smXLFmpqajCZTOzbt48vv/ySwsJCSkpKKC8vJz09XX2Q069fPwYOHIjJZCIlJYXg4GCOHDnSZLny8/OZNGkSsixTWVnJgAED6iWy8nafrq6uJj8/n1mzZoluyMJJNWtgm5iYyLBhw8jLy+Oiiy7i5ptvZvr06WzZsoWqqiqSk5MZO3Zsc+6yzZJlmfj4+FafAKm1CgoKIjAwsNXW3+5CT3Zir9gACDyPiYglyZMBuWMwbP69U0SlDQ4UQ7ew48vJskxsbGyrfSAgCILQlODgYC699FJefvllSktLGTlyJKmpqerndrudoqIiXn/99XoPjf39/ZFlmbKyMu6++25qamq49NJLCQ8PJz09ndraWjWpjiRJ9VpbzWYzdrv9pOUaOXIkV155JSaTiaSkJNq1a6f+xvr6+nLDDTfQoUMHNm7cyCeffMLatWsbtJYJwvkkSRJjx45l7ty5ZGVlccUVVzToNVBQUMDu3buZPn16vfd79eqFoij8/PPPPPHEE4wYMYIOHTrg4+ODVqvFYjmeJTMwMFC99vR6PbIsn7S3Q0BAAPfccw/+/v6EhITQtWtXDAaDuk5KSgoPPvggJSUlfPDBB3z88ccMHz5c5KYRmtSsgW27du2YNWsWGo0GjUbDtddeS69evdi0aRN+fn6MGDFCdL/5ndPpJC0tjZiYGPz9/Vu6OG1OUVERNTU1xMXFtcon31vq9LjXSJAQCAHnuXE+2ARdQo8HtgBrjsDVXY53UXa5XGRkZBAeHi4SlwiC0KZotVrGjBnDl19+yaRJkwgKCqr3uV6vJyIiglmzZtG/f/96n5lMJvbv38/mzZtZs2YNHTp0QJIk9u/fT1nZCQkRzlCXLl247LLLGn3QajQaGTRoEP369WPEiBFUVFTw1ltvMWTIECIjI89pv4JwJqKjo0lOTmbNmjVcccUVDe6d2rVrx+DBg3nvvffq9YTQarXo9XoWLFjAmDFjmDVrFnq9nq1bt/LDDz+cU5m8ydVOvJa9YmJiuOyyywBP6/GVV17JTz/9xC233HJO+xUuXM3a3CVJEnq9Xk2YIMsynTt35uabb+bqq69WM6sJnvE25eXlLTZFQVvn7fLufcremlidsL/o+GuzDjoEnb+MyF5GLSSHgKnO46ptx8BWJ8+C2+2moqLilC0QgiAIrY0kSQwePJj//e9/PPzwww0y4oeEhDB+/HjmzJlDXl4edrud6upqtmzZgsPhUKcbPHz4MKWlpaxYsaJeRuXzXXaTycTdd99NRkYGK1asaJV/v4QLl06n49lnn+Wrr76ie/fuDT4fP348WVlZLFq0CKvVis1mU4cRenPnHDt2jPz8fI4ePcrnn39eb3zt+SRJEl26dGHy5Ml89tlnlJSUnHol4U/pnFts8/Pz2bp162kv36FDB7p27XquuxWEVutwGVTWiRv99J7A9nyTJE9gG2QCS5XnveJaSC+FlIjzv39BEITzQZIk9aG4yWTi4osvbvA5eFpHn3zySZ577jmmTJmCVqtFkiTi4uLo27cvcXFx3HPPPUybNo2AgACioqK46KKLqK6ubrCtxrbfVNlOVfa6kpOTueaaa/jggw+YMGECZrP55AcvCOfIew5KkkRiYiKJiYmAZy7butdWjx49eOaZZ3j99dd55ZVX0Ol06HQ67rnnHvr06cOdd97J9OnTGTduHIGBgfTp04e4uLh6+znxfG/svdP5rLHP9Xo9t99+O9dccw1Lly7luuuuE41lQgOSco6PDJcsWcK9996rnlwOh4PCwkL0er06d1VVVRU1NTWEhoZy77338vjjjzdL4c+Eoij8+OOP7Nu3jyeeeKLFu6/a7Xb27NlDhw4dmuyCITROURTy8vKoqqoiKSmp1c1j+9UeeGYV2H/PNdI1FL6+BgLO4xhbr3Ir3Pwj7CzwvNZq4LEhcNfvSQYdDgf79u0jOjqa0NDQFv+joCgKK1asYNGiRcycObPVfZeCILS8wsJCnE4nUVFRDT6rra3l2LFjxMfHI8syiqJgsVgoKirCbrej0+kIDAxU70esViv5+fk4nU6Cg4NRFAWbzUZkZCQul4ucnByio6MxGAwoikJ1dTWlpaW0b9++QVdjRVE4cuQI/v7+jfZI8+4rMjJSne5EURQqKiooLS0lNjZW/OYJ55XT6SQzM5P27ds3mHLH7XZz7NgxjEajOmbV5XJRWlqqTrnj4+NDSEiI2tuhuLiYiooKdDodISEhlJaWEhISgtlspri4GIfDQWRkJJIk4Xa7ycvLw9/fv9FhiFVVVRQXFxMbG9vgnlxRFAoKPDcyERER6rXldrvJzs4mICCAoKCgFr+HEVqfc/5F7d27Nx9++CGSJGGz2fjf//7HwYMHmTRpEh07dkSWZbKzs/npp5/UweuCZ8xCUlKSyE57lkJDQwkMDGzxBxQnciuQWgTOOlMUxgeC/x+U/DrAAJ1DYVeBJ3mVy+0Jcu0u0Mue4QGJiYkiG7cgCG3GiVPn1OXj46O2QAFql8m6LUl1GY1G4uPjG/1Mo9HQoUOHetvy8/PDz8+v0eW9rcFNaWxfkiQRGBgochwIfwjvvWZjNBoN0dHR9d6TZZmwsDDCwsIaXefEz3x9fdX/Dw0NbbD9k2UwPtW11dgYdI1GQ0JCQpPbFIRzDmzDwsIYNmwY4JkDKz09ndmzZ9O/f3816HC73YwbN47rr7+ejIwMevToca67vSCICdrPnqIorXJ8UqUNsis8AS54kjb1ijx/0/ycSJJgYDR8k+qZT1cBciogv9qTmRk8511rrDtBEARBEARBOFvNms4mKysLh8NBYmJivZY071Oh8PBwtm/f3py7bLMcDgf79++nsrKypYvSJhUWFpKZmXnSuQVbQm6lJ4j0kiTo17D33Hk1KAYMdRqyj1V7xtmCp1vSoUOHKC8v/2MLJQiCIAiCIAjnUbMO7jAYDBw7doz9+/c3GL+XnZ3NwYMHGThwYHPusk0TrWbnpjXW37EqT8ImL3/DH5M4qq4QEyQFe+bSBSizQkYZXPp7j7nWWG+CIAgnUhQFq9UqZg8Q2hStVovJZGrR8Z+KouB2u7FYLKJ3oNBmSJKE0WhUE/+djWYNbHv06EHnzp3529/+xpQpU+jfvz86nY7du3fz7bffAnD55Zc35y4FodVwueFIJZTbjr/XJbT+9Dt/BFkDA2OOB7ZOt2f6oVoHGESeBUEQ2giXy8XkyZPZsmULcXFxjU5RcqacTiepqamkpKSoN06KolBYWIjBYGg1Y18rKiooKiqiY8eO520fiqJQW1uLwWA46yRWbrebPXv2kJKS0ug8vufT0aNHmxynWVNTw/bt2zEajURFRREYGMiRI0cIDAzE398fgF27dlFWVoZWq1V7fxmNRnr06IHJZGLHjh1IksSAAQPQ6XRNlqOiooKtW7ditVpJSEggMDCQb775psnxo3+UVatWMXXqVFwuF0OHDm2WczsrKws/Pz812RSgJl2rmyStJSmKwt69e+ncufNJv7dz5XK5sFgs9cYZn6n8/HxcLleDsc7nm91u59ixY40mxQPIycnh4MGDREVFERkZiVarJT09neTkZCRJora2lt27d2O325EkSb1+IiIi6NKlC9XV1ezevZv27dvTuXPnk5YlIyODAwcOoNVqSU5OZtKkSfz9738/62Nr1lvu9u3b88YbbzBjxgy+/vprPvjgA9xuN35+fnTo0IHZs2fTs2fP5txlmyXLMhERESKJz1ny9fVFluVWlRHP4oQDxcfH1wL0jgD5Dy6iLMGAKPhk5/EkVrsKPeN/I3w0hIWFtYo/PoIgCCfjdrtxu90kJSXRv39//vOf/5zzb77VauW9997j/vvvrzdkKi0tDaDJRDt/tLS0NLZv387kyZPP6985b2B7tokYnU4nb7zxBtOmTfvDMzzn5+fj6+vbaGBRU1PD119/zU8//YTL5WLEiBEUFBTQu3dv9TvevXs36enp7Nixg4SEBKKjo9Hr9QwcOBAfHx/uvvtuzGYzb7zxxkmnZcrNzeW2225j165dtGvXDlmWcTqdTS7/R6murqZPnz6sXbuWhx9+mH79+p3zNhcsWEBCQgIpKSnqey6Xi+3btxMXF3fSRG9/FJfLxTvvvMOtt956TkHnqXhbxM9lyq5169bhcDjUXEV/FLvdTkFBAdHR0Y0GtocPH+a9995j165d+Pr6MmnSJFauXMkdd9yBVqtVA9utW7dSUVFB586dMZlMxMTEkJKSQlpaGvfffz/XXHMN999//0nLMn/+fKZPn47NZiM5OZni4mIURWm5FltvFweNRoMkSSQlJfH++++zd+9e8vPzcbvdhIWF0aVLF5Gauw6tVlsvk6Nw+iRJqve0sLWwOiGt9PhrjQRdwjwtqH8kSYIYf4j28ySyAsgu94z9jfLTioyCgiC0Gf7+/gwZMoTt27fjdrvP+aGcRqNhwoQJ6PV69YZOURQSExPRarXqvUxLa9++PWazGZ1Od17L09g0LGdClmX+8pe/YDAY/vAW25Nl3A0MDOSuu+7itttuY9WqVbz//vtUVlZSU1ODwWAgPj6enj17Ul1dzeDBg7niiivU1j1JknA4HBiNRnr37s2GDRswm81cdNFFjc69Gh8fz48//khaWhoRERE899xz5/W4z0T//v1RFIWMjAwGDx58TttSFIWBAwfi6+tbr6uoVqulR48eahfSlqbVarnyyivx9fU9ry22wDk1TimKQrdu3XC73efU9fZs6HS6ehngT5ScnMzs2bOprKxk7ty5vP7664SGhrJkyRKGDx+On58fffv2JTs7mzFjxpCcnFxvfZfLRXBwMB07dmT+/PkMGDCg3vVat7fMVVddxYABAyguLqa2tpbly5ef07Gd8xm4Zs0a3n33XcaNG8eIESMIDAzEYDAwYMCAc930Bc2bPCouLu6c/7D82SiKQn5+PtXV1XTo0KFVTPmjKFBth7SS4++F+niCy/P1U6UoCi6XC6vVikajqTemJ9QHOgYfD2wdbth6DHqEOTh06BDt2rUTD5oEQWj1iouL+fnnn7n11lubpYeT1WrlnXfeYfbs2fUC202bNhEdHX3Sm70/UkZGBitXrmTatGmt+nfa6XTy1ltv8corr6hznbYWGo0GvV7PqFGj6NevHzNnziQnJ4fZs2cTEBCgdq+sqKggKyuLW2+9Ve0+vGPHDnbt2kVVVRU//PADVquVwMBAxo0bx7333kt4eLj6vUiSREBAAP369WsVLbV1rV+/noqKimbLb7Nw4UK6du3KkCFD1PdsNhu//fYbI0aMULt5tySXy8X777/P008/3WqGFjRl06ZN2Gw2rr766pYuSj2SJCHLMkFBQdx3332MGzeOO+64g127drFo0SIiIiIoKSnh2LFj/PTTT9x7770MGTJEnUt82bJlZGRk8M4775CamkpkZCQ6nY6HHnqICRMmqA9AvPuJjY0lNjaWHTt2nHPZzzmwDQ8Px9fXl3feeYfnnnuOTp06MXr0aPr06UNsbCzR0dGtIvBobdxuN9XV1a3uR7CtsNvt1NbWtqpESAeKobpOjpO4AAg1n7+pfhwOB1988QVz584lPDycBx98kEGDBiHLMsEmSA6GFVnHu0avzoZbuivU1NSIZCyCILQJFRUVpKenExwcTG1tLXq9/pxaNxRFwWKx1HvP7XarN1StJbB1uVzYbLZTL9gKnFifrY038AwPD2fSpElERUWxZ88eamtr6du3L2VlZbz11lu8++673Hnnnfj5+dG5c2eefvpptcVcq9VSVlbGrFmzsFgsvPDCC62idfJU1qxZQ8+ePdXz/lxb1u12e73ZKLzbXb9+PQMHDmwVgS14zsnWdH/YFIfD0ervx7RaLREREXTu3JnXXnuNgwcPcujQIYKCgkhJSWHNmjV88MEHWK1WLrnkErXFPC4uTm1A0ev1/Prrr9x11134+/szatSo81fec91Aly5deO+998jOzmbPnj3s3r2bn3/+mXfffZeYmBh69+7N0KFDGThwIBEREa36yaMgnIudBfVfR/tB8HkayqooCgsWLOCRRx6htNTT/zktLY1XX32VSy+9FK1GQ6dQ8NV7xtYCHCiBktZ9/yEIQitms9nYtm0bR44cITQ0lGHDhqHValEUhSNHjrBp0yZ0Oh0XXXQRYWFhABQVFbFy5Urat29Pv379kGWZFStWUF1dzZgxY9DpdFgsFlauXMnQoUMb3BgrikJZWRlTp06lZ8+ejB8/noceeuisuyQbDAb++te/qg/cFUVh27ZtREdH06NHj3OroGYUGxvLZZdd1urvmWRZ5sYbb2z1DRiSJDF69GjatWtHREREvW6R8fHxvPLKKzz//PP84x//oH379gwaNIiEhASMRqPaQON2u8nJycHHx+eMvxdFUcjJyWHPnj2Ul5dzySWXqGWwWq1s2rSJnJwcOnXqRO/evdFqtTidTrZu3Upubi6XXHIJYWFhFBQUsHLlSvr27UtSUhKKorB7927Ak8D1xHLV1tayceNGhg4dSkpKCk899RQjRow463q89NJLCQ4OVl9bLBZWrVrFmDFjiIiIOOvtNieNRsOUKVPw8fFp6aKcUq9evdpEA5fRaOSGG27AYDDQq1cvevXqpX52zTXXkJiYyIwZM1i4cCGdO3emd+/e9OzZE39/f3XoYKdOnTh27Nh5/16a5XGTd7xohw4dGDt2LOXl5eTn57N27VoWLFjA/PnzMRgM9O7dm/Hjx6sXaGv/wT6fvE8A/8x1cC40Gk2r+0O6/ejx/9dqICHQE1g2N++N2MyZM9WgFjyJMB588EHeffddBg0aRPcwDUHG44FtjR22HZOIFuedIAhnoaSkhLfffhuLxYLD4WDw4MFotVqOHj3Ko48+SkpKCtXV1SxcuJCXXnoJPz8/3nnnHXx9fdmwYQN6vZ7evXvzzjvvsG7dOn788UcGDBhAVVUVL730EklJSU22+NhsNjZt2kRkZCTZ2dkkJSWxLH0vH29eUW+5AC34a0GngcbapdxON1X7s9hbtRetrEGjKCz9cRlhUSGU+xQiaTy/jYE6PQaNBp2kabLXjVnWYdYe/5HXajTNNvSksLicnLxC9PbMZhvPoijgVM5t6heX4qbEbgU8rWFul8KWTfuocWd6Eks0odLpxOJyoihgb6QlrcLhotblxqHUT8B4pvSapqurKC2PoLAAYiOD0ElSve91QKfB3Dv9HjIzMjmWk8+PP/5IVlYW1dXVvPDCC3Tr1g1Jkti/fz8333wzGo2GqupS9uxaTIDO00XeaAjAPziu0X27XC4WLlzI+vXr2b59O4GBgbRv3x6Xy8XcuXNZu3YtgwcP5sUXX2Tq1KmMHz+eAwcO8MUXXxAXF0dqaipPP/00Bw8eZPr06YwbN47XX38do9HIzz//DNDkgxlFUSguLmbt2rXs27ePXr16eTI3r/6K3KpyrC4FnUYiVH+KeyoFjmYcJSE8hPAIzzyGFeVVfD73O67763h+W3NEXdRPq8es1SOfxb2GjykUjUaLJGnQaM48THG7FdL3bSfKX8ZobN0JWjMzsnA5XZhcFQ0+c7tdKIqrkbXAYinF5bYDnmu67iVlcTmocno+szcy1VOty0WV09NK7FLA2cj16HArFNs9AbcCWO0uivZl8nPhDqpcEhV1YvF2foHcO/RyHn7uGWqLy9i2cRPvvfcelZWVREVF8cwzzxAcHMyxY8dwOBz06dMHgDfW/MLRwnTMskSEQUeIyUyYfO7ZoZu1H4W3uTk8PJywsDBSUlK4++67OXLkCCtXrmTFihW8+OKLbNq0ieeff77Vjcf4I3kH25/vge0XqsjISMLDw1tNcFv6+1yxXr466BTS/N2QvdNSzJw5s8FYBO+T29tuu425c+fSp29/YgM06jhbmwu25msZOagrel3r70IlCELr0q5dO7744gvWrl3Liy++CHh+d3788UfatWvHE088gdPpZOrUqaxdu5bLL7+cY8eO8eijj/LLL79QVFQEeJ7+9+vXj/fff59u3bqddJ9arRYfHx+cTid2u5358+eTl5fH/PnzKaquZHvu4UbX89eCQQOhetD9/jssSRJOq50di1dxNNiNj06LWdYQ3DeWIIOeI0XZ6nJH1O3okCUJo6zBVz7+uylJEkUn7NPv9xt5g0bGt07Aq3DmsWlWxhE2b99PUnvfJh9EKkC5w4r7NLtcOtxualyeG9qz7aZZ6XRgrxN5Op1Oflq0BKWdBmedYtoVhQqHSy2nl0uB8t/fL3MoWE+47y5zeHJCVFhOHeD6GkB3wi1AsB60dcohAWF6z0OOgxu2Ep4UQ4a1He0MWgK0npUlSeJIoee7bx8Wx+3X/43rrruO1NRUvvrqKz7++GMee+wxgoODOXDgAE6n03MuOR1kFhwmzOCDv1aPIcIP/7DGb8xlWeauu+7illtu4eabb1bfr62t5euvv+bll1+md+/exMfH8/HHHzN27FhKS0tp3749EyZM4I033lDXiY2NJSsriy1btnDxxReftI78/Pyorq5GURQcDgcPPPAA+fn5zJgxg6PFOZTXlFNid6IAWYBRo8FPq1HrpS5FUdiycRslidH0k49nD590w3AUqZaiOl3CigCdpMGs1eEj6zCdcO2cTHV5/u91ZkCn87buNb6ORiMja3Q4nFb1PZfLxa8LfyIp1hd/v7PPiqzTmtDpzj7jMYDLbcdmq2ziU4Wd2zdjtzsID3Q3OESHw4LTebxOHYqbSoetztoeNU4HFQ47Na6GLb/5VhsVzsaDY/Bcg3nWpj8HcCpQXGXj6C9rMLsV0DS87/5i23Z0Mnx14908Puxx7rvvPjZv3syLL77IggULmDp1KhkZGRQUFKixX1pxAWsyDpPgA6FGPQ+MuglH2bm3Xp+3u1vviSvLMgkJCcTHx3PDDTdw+PBhXC5XmxibcD4pikJVVRX+/v6tJjhrS2w2Gw6H47xnjDxdh4o9gaOXjx4Sg5te/lx88MEH/PLLL02X5dAhpk+fzquvvsqAqAGsPSKh4LlJOFSqkFtcQ/tQH3HeCYJwRuomy/FyOBwcPHiQ3r17I8syGo2GDh06kJGRgSzLXHvttTz77LN07NiR/v37q+tdddVVzJ8/nzVr1tC3b98m9xkfH098fDxff/01Pj4+1NbWcvjwYQ4ePAgBTT8Yrvz9/qjs9+FrZhmCdAouF7h9fHAqYHUrWN2eH+5Ki+eG0U8rY66Tyt6tKGgkCcmB2voUqjegaeTvjktRkCUJCSi2e25IHXYn5eVVhIUFgQQSEiF6E5VOG3Z30zeUxS4rdoNEdm1lo/f0bgUsLmejrS2no9RuO+m6NS53owGzSwHF21qrQIHFRoVex8ayWjTa439TlDotQQ4FCmyeud7tzvrbcgNOF1RYj68oORRcJ2lYVmQJZIlqW8OHx1UmT48pWQOG328zSx2e7ZZpDPhKMn5uhXybkzKHi1C9Fq0kYfr9O7fYa6msrSDAHEhKSgr//Oc/ef7555k5cyYzZswgMjJSHYet0xmIjuyIhIRZbyQwIg6b3UF1dXWDMjd27QAUFBSg0+mIjIxEkiTi4uIoLi7GarXSvXt3VqxYwQsvvMBNN92krhMcHMzUqVOZM2cOvXv3brqigOnTpzN79mxsNhuyLGO321m6dCnPzniWGpebPKuDmjqVLeFCth+/DkwaDdo6LfEGs5FqSSGztka9DvQaTaMtsw7FTbnDRoXDpl4vPrIOX+2J162ESdY2uKZcLhsFhYUYDDr8fE/WfVWi7uMTl8uFv58eu70Cq9V+0vo5GSsSGunM75Ocihvb79e2oribbHUFcGDBqXGSU5FHtctT1lqnS73G6m9X4ajVisVVf3u1Lih1uKj8PYB1usDu8tTIUauCo5FrSXIqKG6F0loa/fxEih3sGhOuSpA09VdQtBJoPL97T/zyPZ/fcCeJoRGMGDGCuLg47r77bvz9/TEajfVmg0kIao89wYlZhhizjo7RyWzM2nDqwpxCs0aXiqJgtVrJzc1l9+7dVFRUqNMBeaWkpDRbdra2zOl0cvjwYZKSksRctmehtLSUiooK/Pz8/vApBk6kKLC3qH5gG2z0JI9qTi6Xi0WLFvH6669jt3t+ACVJom/fvsiyzNatW3G5XGqGz2nTpnHPs++hkXrhUjx/MHIqFFbvyWFCvygxl60gCOfM7XZjtVrrjZsymUzYbDYURWHEiBEMGTIEvV6vZswEzzQ+N954I3PnzqVLly4n3b53Gp7a2lrAMyYxIyMDS1QQ9tIKNAY9Gq2Ms8YTTGr9zLitNtwOJw5ZRjabqC2toUhR0Bj10LUjaceq0MkSWl8fXLU2FKeTcF8dNSY9zmrPfvS+Ppjcblw2OxqtjMFsRG+xUySB3mRA0miw11hAkjD5m5GtDhSXC51ei9HHSFV5NU6HE61BR2lxCT5OBZ2soSbQl9ySCqwOBwajHq1OS21VLUgSPn4+2K12ah02/OLDSauowFZtwe12Y/AxoqBgr7WhSBLK/7N33nFWVOf/f59pt5ftuyxlqUsH6aCAWDEKNixYYv2KMRE1sYstlmgsMWo0JorYYkcFFRQURQVF6U1632XZfndvvzPn98fdvezC0hRL8rvv18uEnXtm5ty5c2bOc57n+TxOG5H6MJZpott0qhEEAyFAYvc4ScQSJKIxFEXB7nURDgSRloVqtyFUhUQwnEyLcjsxG66XommoDhu1tfVYlkR1OYgnTEprowhVQTgdWKEwmBbCZoCqYnVpz+rSOuJ2F/WBKDKeQKgq2O3IUBAkCJudhCWJBqMgQLG5ELUhME3QdISuI8NhkBJFtSPMBMTjoCgImx0ZiYC0wDAQmoJMJK8BDidWIoqlWQhNozJmQwaDaAJsHgdYJjIaw20X6J3aUZpIULujFrvdINelUxGqQVcEmX43GVJSXV7DM4F/UJBXgJJQGdZtBKNGjeLVV1/lqaeeIjMzk/fffz9liHZsMyxlMNZFJMsWzGH+/IOfnEciEXRdTzl7GhfrY7EYmZmZTJo0iXg8js1mSxnFiqJw8skn8+677zJr1qz91vw0TROPx0MoFEqJPgUCAdauXcvaHRVUBWvQ3U7MaBwzFkfRVDSnndqKAFJKfB4HmhDEQhF8ukab4jaYsQTlu6qpMXRsDoNYXRhDUXB5nCTiJmY0TpbdhtvrIhhIntfusKFqKqV1lQhFwet1EQ5FiMXiGIZOttdDXSCIZUk8HieWaREMRQiFIuTnZ7JlezWmaeGwGxg2ndraIAB+v5tIJEYkEkNVFXx+NzXVdXTu3Z7t9QHUcD2hYARFEbi8TiLBCPG4idHQ92AgKULq8jqJxxJEIzE0TcHpdlLf0B+704aqKgTrwgjA43cTDkWIxxJouorL7SBQU4+U4HI7iJkJtlTVEEfi8LqIBsOYieQYVQ2daF0YBOguB/UuBTMq+GTTFsKGTrw+TDAeR9htIMAKR6lKQMzhIh4MEYrFEbpOwDSI1yevQVhzJPN0A1GEFEjdTjgUBWkhNB0Fga02CAhU3YZlxiGaQAgVQzdQY5HkuNNtIC2sRByEQLU5MKPJMSkUFSOrHdGdyRBAxeZAJuJIMwE2FelzYcZDLKyt5bfP/4OeBfmM7dSLngVtGTFiBE899RSnnHIKtbW1zJ49my5dunB6u07YuiTLHcXjcSrKynn66acPGMVzIA6rYVtTU8Pjjz/Ok08+id/vx+l0NjM6pJT89re/TRu2DTQaIWkOHSnlXosmvxQJK6mIHG9i2PbKgwOlqxwKUkpWrFjBHXfcQWXl7ppCrVu35t5776V3795cfvnlzJw5E8uysCyLBQsWELz5Cuxjn6M+sxdCCMqCChtqFKwfk8SUJk2aNA1omkZmZia7du1Kvc+qqqooLCxM1fx0OBx77SeEYPTo0bz77rtMmzZtn+/C2tpa2rdvnyovYRgG9fX1vPbaawwdNpQjazQ+De1Az/ASWLoGJLgH9aJyzQ5iZZWoXjeePsXUzF6AVBRcXdsTWbQC0+tFMQwyBnWHjZuIVwcoy8/G3iafwOLVIKGgXxci5dVUbd6Jw++hVZ8OVC9agxqNkte1LbrdYMeyDSiqQscRfdixZD3BygDZrbLo0LOIb2ctREroMbgbwboQW1ZtQbfpDDiuH8u+Wkmwpp7Cjq3IzM9k+byVaLpKjyHdKd28k60rN4MQdD9pMNu+W0MsHKVVrw5YlkXZqi3UawatB3dn28I1RGpDZLTNxdc6h83zVyKlpGhoDwKllVRt3onhctDhqJ6s/3wZiUgcZ8fWqE4HdcvXIjQNT79u7Fq9nXh5NXpWBs7ObQgsWo1MmLh7dCJRHyK8aTuqy4lS3Jn46nUQDiFatULRnSQ+/wwlvxC1b2+srduRlVUItwe1qCOJVcvAslDaFiVdvNu3gKKgdOuFuWUjsr4OkZGFmpuHufb75IS/dQeUqmrM8jIsrxvZsROJTeshGkEpaI0wDMytm0BV0bp0R27dDPUB8Gcg2rfB/H4FMSmJd+0CoRBy63bqdR0cdthVjnDYyemcT1VOJoFFq1E0hV7DurFq+y5qtpfjy/qewYO6snTeSqZ6ZnDp2Rdy6aWX8vbbb7Nhwwa+++47HnvsMex2O/F4HFVVicfjFBcXo6rqIalZN6p9N6r4BoNBhBCphSJVVVuMrnK5XFxyySU8+eST9OrVK1WmaE++++47hg0bxowZM7DZbNTX17Nlyxaee/Y5CjSdlWtLCXXMoXZrGdHScvQML+5u7an9bhVWLE5Bz/YgJTu/WUlOtgeHz0XN9nIMl4Oc1tm0K27Dwk+XANBjSDdqKwOUrttBnt9Fn6N6smL+KoKBEG2LW+Pxe1i14Hs0Q2PI8N6sX72F0h0V5Bdk0a1nB+Z9sZRoJMbAIT0IBsOsWrERl9PBqOMHMmf2twTrw3Ts3JpWrXP5Ys4iAI4+bgCbN5aweWMJHp+LEaP6MXvmN6xZsp6Rpx2J3WFnzeJ1YOi0H9SVjSs2Ub2rhtzWORR2asWKeaswEyY9hnSjelcN29fvwO13031wV5Z+sZxoKEpR93Y4PU5WL/gehGDgcf1Yu2wj67dV4c/20aV/F76bvRDLtCju34VYJMbGFZvRbDrZ/Xqwfcl6QtX1ZLTNxdsqi23frkEoAn/fYuoXryW2Yxd6uza4iouoW7IGvTaE0r4IhMDauBk0Da1Hd+SylRAMomRkoeTmE1+xFBIJnK07IkL1xKrKEKqOP689yq4tmIkYDm8Wmm6nvnI7QtFwFrQnHKomFq5Dc3kxslsRLNlEQhM4CtphxWPEKkpRDBtG605Et2/AikXQPBlEy7aiOj0IwNmumHhNOeFgNZbXg9qpK+baFSQUkwU1tSxQV/Ha1hf53chjOPmkk1i2bBnTp09PCQGGw8lFNUVRUouXffr0YdWqVb8uw3bRokU8//zzXH311Rx77LG4XK69VpEalRLTpPlfoTIMJXXN84j65B2+/FopJbW1tTz66KMp9UNITgwnTpzIqFGj0HWdhx9+GNM0mTVrVsroX7lsIb749ThOeQS9oCcxS7A17CEm02HIadKkOTQsy6K2tpZAIEA8Hqeqqorc3FyGDRvGq6++yumnn04oFGLDhg1ceOGFB0wTcbvdXHbZZdx66637LHmRl5fHH/7wB9auXctnn31GZmYmRx11FJs3b+b6P12PbrczaearvL9qETkF2dRFoDIiSHRyobSNIx12AkKgjBoJikLETJCwrUcbfiQoCgFFoHVLlkOJC0EdIAcOBaDMJnD6s/B07AiKYJcQ6EMHYuiSmobvZhybBwi2KED/3tikpE4ISmyCDmOOAiAqBFqepEPHNiBglxDkjegLJEOT6wDj+CNBwmoBdPWiuXzUr9/KdocXMXwgNikpRyQXUHML0SWUItAGHYEbiCMotSAy5EgA1goBrTKRBZ2ICVhWJ6DfoKR4U0Pf5dBcBBARgkR3P0JKEkIQABgyGAEEhYBMUNoUJVNaEOhd+yIaF0fjFqYvC/2IwaCqKB26QnuZegHq/YeAlNhiFlrchK4Nc0BLQJviJgnIAoqTojKKBFHgg4J2SEWQ0FTCvfslw6MavZY5u1+yWreeye+jCRCgDR2G1MXul3D7IgDMbxeh9O6JUpBPQBPkuCH7pOT1KlcFvt4ZdOxXjAB2agrZo/ph2Nz8s3Qp554wkmkXX8wF55/P/PnzicViOJ1O4vE4breburo6tm7dSv/+/SksbDnPNhwOU1tbm/Tu1tVRW1tLRkYGOTk5fP3117jdbj7//HN69ep1UPorRx11FG+88QYffPAB5557bottxo0bh9/vZ968edTU1NCzZ09cLhcOh4M77riDC8p3MvIffyPePgNZVExMCIKA7N6bzAyDel0DKXGfko9laOz4Zhn2nt2wdSokIAQ7bYIOY5LXMCIEtpws2nctQlcUtghwDeuNWwiiiiAG5J40BBCsEyD7dCGvT2ckgtUC/McMACTbhIAsH3ltkmPru3gC94i+uIF6IVgH5P0mOUa/FwLZtYi8ru0AwcK4ie/ofth21VKTl4Nit+M/IZv6BHwXk9CzJ0hJmRCUARw5GCzJcgm08kNBEfXAgrCA/gNIJCRLghIRBtlzCACzdoLM6YLlNam2JN+utaBNXwSwtlyC5UBp3Qc1ZlKzKoZLa4MrBwhJQuslWf4uCMtCW1aPWQkOIwtXPBOW1eCMeFE0D2J7uOGezgUkLNqGsNygu6EeqK8Ae0FyTFRHAR1crZM/em0Yvy0HbICZ/C/T1w6hqRCX6HoGIrM1qCpIcBR0xjR04l4XUgjsuYU05j94OvUCJDIeJ1ZVhr9r/waROIHu8qLaVYINKqlGn4FYTiU5DoF4Xis+wsba75bx178/ztJvvmbixIlUVFSkonAMw0iVoFyxYgWnn376Ae/7A3FYDdvq6moyMjK4/PLLadWq1eE89P8cqqpSWFjY4kp2mgPj9XoxDOMXD0MGKK2DnU1SahQB/fIP3/FN02Ty5Mm8+uqrKVn4xty1K664IpWIX1xczOOPP85VV13Fp59+mvSASEnt6k+JxCaSec6/0LI7sV3mJV/6adKkSXMIhMNhbrjhBtavX8+WLVu46qqruOGGGzjuuONYsmQJ1113HZZlMWbMmGblIPbEbrenQi8HDx5M9+7dWbFiRYvPc8uy+Ne//sUXX3yBoigcd9xxOJ1OVq1aRXV1NUVFRfgdDQJLQiTncRKs7TuQldWo/fsmPzN0kBIpVZTCVigxGowzSZOqnLtPLCBgCmqDYayqKtTWhSmZZZfRoKgbjyNjMYSricCMSDbaUddwEJIiR2osQqK2HiMvi7rVm4h7fIispkIMzZ/JCRwkvDnEA7s/Ny0Ix/dsu+ezfH9/p1S0mv/daC82XYjYc1Giyd+WS0UkJEhQTIlS0CqZ1Cr2PP7ufaN2haj90KecUhdIY/fxhGhZRkgKGtolc/2atRENOZi52eheJz5P8jdSVRCKQEqIWrArlvwPwKbIpGJsXT014Xq+2fQe4zr15S9/e4wlX8/n6aefZt26dXTp0oU//elPPPDAA/Tr148zzjiDqVOntvhdPvroI1544QWWL1/Orl27WLBgAffeey9//OMfeeKJJ3j33XdxOBzcdNNN+5zbqKqK3W5HCIHH42HChAnMmDFjn2KsgUCA++67j6qqKjRN47zzzuPjjz9m48aNxONxct0+ZMDCTOXZSmQiQWLREnZ16oySmSzVYjl0CIHlyESVLoJ1ySu8o9mSftN/J49nVlQhTBMlL2evdrI+mAy53UtzZ+9jyopKhMuJtCxkaRlK60KEzdasrRIxk4actAjZc6neaBETuxfMFEtiDzckeTeJEBFxiTAlWiSKSDSPBBSWxFEfTiauNt0uJe5QhD2TwWUisVfbFHu2lRJbPOmpV8JlLS4E/rCZWgvRL6YFmpp85ukqKEpKxTxht2HabcjUPdfCM0RTMbLykKrSMMIaPxLN+q2Gks/Uxuu7traMrRt3cF15gLOOHMTLL7/M9OnTee2116iurmb8+PHY7XY++OADxo4dS7du3di8efMP+taNHFbDtqCgALfbnVJg+zWI+vxaUVWVgoKC/+9FtH4oXq8Xt3vfapE/F1ImJzA7g7u3tfJA/g8X4muGZVl88skn3H///c08GgMGDGDSpEnNwo8URaFLly4888wzXHbZZcydO7fBuDWJrv+Mmhl3kn3e82wPO6mKW7Q9PF1MkybN/yc4nU4effTRZmkgTqcTXde55ZZbUuFlDodjv++2xx9/PPW50+nkH//4B4lEosX6hpWVlaxbt46LLrqIzMxMFi1axOLFi8nLyyMzM5OacJCNlbuLiBtqclqmFBZCfvO6miImUessLCMDJST3G1UjATVqIhMqqp6BqDFTBlujzpFVXo6MxVALG/QKFLDsTdKvNAGaIBgFogIrqCI2B7AqIyDcCNe+I3skNoTiIVxqNptn/thYG8utNDMUfxCaSH43KTENDdk2BzNDO6QwJVUBx0EUhZB7GNw+e1Ic6lCRQDzhRXXb0PeQl4jEYdfeek+795Wg1IV5oXQeQ7t3YsKECYwePZq1a9dSWFhI165dqa+vZ9asWSlhqZYYPXo0Rx99dOpvVVVxOBwMGjSIf//738TjcQzDSBmuLTFkyBBefPHFlOE7aNAg1qxZ0ywHtynz588nGo3yyCOPMHXqVL766is+//xz7rzzTmw2G18uX4OvJES9IbCaiETJtt0Qwg61DfOOhgWWRNyOCChYkYMMt47pSMWGFd6jvZTEN25EKyhEOA+sPCyDYCZqEIqKtbUCPZ6JJxxqbqCm/ilRwnZcK8qT1ySRQJpWcsGjob2MxJL53U36Q8JEmtZeBmjSUGuhTwfs9YFRFf2wHGcfBwdA6BrC3pAP73EjNQWpqkS8DsyGBamYoZLQleRCUot10iwsLZ9onhNhJsWnoGF87olMLiA0LiIICUtWrEONxPn3rVczdOhQzjvvPCoqKhg0aBCWZbFmzRry8vLw+/0/+msfVquqd+/enHDCCTzyyCP8/ve/p7i4uMVVpF/aGPk1kEgkWLduHW3atNln3b40+6aiooL6+nqKiop+UXXfhAWbayHUJIque/beJQh+CFJK1q1bx4MPPtisXm1WVhY33ngjxcXFLY6lDh068Mgjj3Dttdfy5ZdfprZHVn1IZMNc6HIc87Ym6JufLjWVJk2ag0cIsc/3la7rBxU+2TR/sPHv/UUu1QZq6TOwN7ffdAeWZXHbbbfRuXNn7r77btAET376Gt9sWZtq7zQg0wW1ik7C2qM/AkiYJNauxhiS3bIhJiWK1ThpE0hVg8ZSJXvMQJXMpBcqNaE2QamJImNRFJcbqSS9iJZLRRo2ZDiKVbYLpXtXxAGijWRtLdb2bdC/3yEZjKJBVdgy9j6+IkDT9zEZPdBxBWQ49yxVK5CmRdXcleR2yUE7BItTEdC06pwCFNh31x7WFfEDvVX7QArWf7sFb4dW+HIclEQafYqgGWDzQawF2Y5gDMIxEG4FJNz84VReWfINloTbjj+JHh06IYTgiCOOYMqUKSlhx5aw2+0tijbuOSb2h6ZpzRaNFEXB59u3UuX0L+Yw/uoruPC3F1JcXMztt9/OOeecw0UXXcSn3y7jpseeRauowb/njpaVtCb3oHbnJgynF4c366D6u1+ED8qCIEKpTYlYBFXTEYqadKk3IVRZgmZ3YXjbQHkIJWE2u0dkMJQ0TqWktmwtzpwOKKoOppkyaHc3lsh4Yg8ns3V4rNWWaMkbKyAaDSKlRNezOCT/rNKkYLMQCF1FqkryGLqGsBlYmoqpa5iOBq+4gLDXRrxhgmppgoRbRVV313QWast9lbEo5ndrUDvnIVCQZnL+K+ISJZq8bqLJtbNFTJQ9ruWSFWu59Lq78RoGndu24obrr8DlcmJZFgUFBaxevXq/C0MHy2E1bB0OByeeeCJXXnklxxxzDHa7fa9wiiuuuIJJkyYdztP+V9KYq5SffxhjVv8/IhKJpCIDfkmiJqzao5hh78MkHBUIBHj44Yd3e15JTh4nTpzImDFj9ukRaXzJPvnkk5x33nmsWrUKABkJUDf3cYzCvszdmsnvBv3QMJc0adKk+XnIzPHRdkAWy0sWoWsGF14zntraAN8t+ZZPVs9ni1FLB5dCyJRUxiRRKxn667JBRT2EmtgZUhPJNIx9CA+KhlBFeyQ54U1oCgldIWaomJrYa8LXonHadDJuQWOcsxACfF6E7xAWshWZLMK7D8NWAF5HS89x0eKip6aAXUZx23V8NmWPPX4YlglxxaK7V9DGvXfJlkOh6Z4Zupo6ll/XWywnc7BUx2KYUhI0VPw2lTynTocmdmTMsqiOW1TE9jefaDx/hHBoAzVRuPjVF3j1wssY0q49LpeLeDz+qxG1bKTdqCPY6Qny6qIZeD0eTrr9T9TtCvHI3x/ns5WlRANhDEBGIphNQ2jj8aThB2DJpCcTEIFaRDABdfuvfSpUZc9VkJZaNbu1JRIlEUMoetJ2sNua3ftuzQMJoLZu9z6KgEZvtdYYNSCTitm6lqy5mkg0OUfjqQWK05FMUUh9KJH7qfkqpUSaCZS9yhXtH6kITI+rxUFmVVpIy8LMOXhbIKEpxD2O5s8fkQwptuxKshwWkDBUTE1BEeC1g1RBtwma917gMkDbY85qiGRNaEjWsd0VgHLdItcPQhUkzOSij5RQF1WxohLioEQthCWIOLRmod8CsDSVFSVlKPEEC75dSjgc4b57b0BVk7m2+1sUOhQOq2G7bt06rrvuOnRdZ/z48fh8vr0M2wEDBhzOU6ZJ84sSTcDqJoatXYUumaD+SIvRsixeeeUVXnjhhZREvxCCk08+mWuuueaAnhFFUejTp0+y5M9VVzW8bCXRjV8SXvkB67IuYGd9Mmw6TZo0aX5KTNNk1apV7Ny5k/z8fLp27YqmaVRXV7N8+XJyc3Pp0qVLi9E3qhAU2Gxs3LSQXdEopmmycNYSZL3glJNO5YYLJgKwvmwLT8x+lZJwmNJosnZ3tjtZN7W8viG6UBGYfgO1b5+WPSgNE7HGT/SEiZ6wMKImtRkHV5ZPKArC1ZCLooDl2j0HampsSsuCUBicyQmqXW8+75W5fpxZNpwZe59DV6Ax4llrcNI06QEAeUaynSYE9ibzsB3Ld2LF4nQYWJzalqWrGA1tBOBQ1YN2EktL0v+Mo8jKcqOryXqm2Ubza6UIgV394dNNTfw4722B3YmUUHDMIJxuB6pDJ2FZmFJSGYsm1x/2WCSPW5KYZWEoCnoTA82Ukp3ROEGHRUFBJ3rkFwDJKLLS0tJDUkQ+GKSUVFVVsXLlSizLokePHuTk5GBZFitXrqSqqoq+ffvu02vr0wVuFWav+pptYVixfBcZm8voqiu89PcnyMrOBmDrpu386U/3ULazPBl5G0806cTuvjgMP4pQkiWd9nOTyIR54NUSRQFD213jF4GmNbl3YrGGhSIBTkfzPG8zjpQWqmHDsjXMh4zdkRUeoxvYPVhCYCqkwqxNm47ZaMyK3YtGB6M7YpkJQptX4OzdpyG/9+CxbCqWIbCrkAqmkGAPZyWtQ6fzoFeXVAS6Bn5H49cQOFUlWc23SYK5Xwe3mhw76sEHUzQcExo0oAiZFtmaQfD4vri9aur4VXEImUlBu7qEoDoElimIxMAyBMKSROwajnAcPWahaiqmMNDCUTJb5XHib0ahqgqJRILa2tqUhsyP5bAatmvWrGHnzp28/PLLDBo06BcNEf1vQFXVdFj2D0QI8au4v6rCsC2w++88N+S6DylybC+klMybN4+HH364WV5tz549uemmmw4pdP3MM8/kueee49tvv00eO1xDcMEU6vv+hsU7cyj4kX1NkyZNmv0hpWTmzJlMmTKFHj16sGzZMi6++GJOOukkJk+eTGVlJRUVFdx66620b99+r/1dDi8jBp+BommELZPyXeXUr4hx2cWX43K5mPz0vznrrLPo27orecJFVAtjU6EiCkETFB1yPRCMQn0UTGlhVpchcjNRwiRD6BrOZSmCek/LIjx7fa995cbaREoVVGgCRdvd0O/YPcGUkTj1S1fhG9Qb1WHDYTSf1zoDEcI7q2hV5EfdIyzXUMDR5PWXqWsYe3jHVJIqS7oQOJrMansPLMah60Sjyfqhhk1L5iQ3iF4pgFPV9vleUBBkGo7U56ZpMu2bdQzqXoy6p9unyT7Og/ByCbH/d7quOTBse7//wuEKLGv/HkQpJVvLtpBjOMjJ9JCQSQ9kUUN6pyUllbFwqn3csohYFjZFSRn9kDRsW9lNWhV0YdTAsdgNG9999x0PPPAA48aNI7vBUDxcBINBbr31Vmw2G6qq8tprr3HPPfewa9cu/vnPf5KZmcl3333HxIkTW1zwHt1rOL8ZPQZTQl0CvvZ9g1pcyf9deimrV6/ms09nc9VVV+G06fQoLqJ0c8l+56XxRBhNNVCUgzAf9ucA11RE03u28Zy6nvSiiqRgUVLhC2IuRxNxI4gGKonX1uAoLCbob57KIKQksW4XWvs2SJeBqSlYDd6GxuHotjeJrBMg9WRor0JzR3OeIXbndEuJ1n8gmk0jFo3jcjuSJWsE5Bj6QdmljqaGLbBjYw2YFm27ZLOv5RufrmPbT+qCANz7GHsHgyUlEknUtKhN7K1OHzItItE4S7ftomOHguTvJsFEEjItquMm9QnAAxETNtWDzFCpDUtiIUm9R0WvS2BETBz1Jgpwz81XccyIwZimyVNPPcWCBQv485///IO/Q1MOq2Grqip5eXl06NDhV2F0/JoxDIP+/fv/KlR9/xtp3bp1UknuF75+S8og3iTyqLUX8g6sg7BPpJSsX7+eu+66q5kyXHZ2NrfeeisDBw48pMWQzMxMrr/+ei6//HLq6pLhO9GNX1D17Rt8M/D3jO74473LadKkSbMvamtr+de//sUf//hHjjrqKD777DOefPJJhg0bRllZGZdeeinTp0+npKSkRcM2EomxY2s13bp1I9fp5LP359Iqr5DOnTtz9dVX8+qrr1JUVMRZZ52FPz+Lbdsr8QqBR5XUm1AVgxqSk1ivHXZVWYR2lmH16o60K4hIQ46YmVTIjdv2nrs0zs+lIVJJoJZNaVHJya6D0TB7tutJgSRFQKbefBFR6oJyM0xnRwxnpg27quBs8j6rCMTZXlVNe6eKv4nhqgiBV2s+iW7plZChG6nwXVUIPA2esMa2d9/9HAMGdOXk3wxDVRT8uv2gJuaG4UZVdhv/8USCrRt2kenKQT/EEM1mCIHDnnmA99veIdZSSgzdzYGSIy3LYvuOueTlFOB3tlx2MqfJMUwzRsKMYDP2NqQtEvQefDqqZlBbW8uNN95IVlYWEyZMOKg880Phs88+o66ujr/+9a/Y7Xb+7//+j88//xxd1+nbty/HHnssTz/9NOFwuMVzB3ZU4o0atG7dmqqqKqYtXsqll15KOBzmwgsvRFEULr/8cnw+H5mZTfJmFZH0qDZBIImHowgEun4QFT32DidANAqQCIGwGbtvSIcdy2YgGwzZhM0g7Laldk8YChHP7u9nVZuwphSliwPdsft+dKoCr2axa30l7Tp3RXMm97EpAp+mkmNr/C7NMRQFl6bhVhWcTRaC9mwnBFRvreA/Uz7klpt+S06Ov6HdD5tIzV+7E7u0cUpR733e+4drihaN1iLl3qHytfEo8Ybt+8ruC4cjrNpZw5GZWYSsBDFpUdvgeGncJy4ttkeidHIlN2yPmNTEJVvrIOLXCVWo6NE4Qgh6FCdrcn/55Zc888wzXHnllfTp04fvv//+R3/Pw2rY9ujRg9zcXD7//HPOOOOMtOLvfrAsi8rKSnw+X4tiAmn2TygUIhaL4ff7f1Gv96LS5n+39kLWj6jgFA6Heeyxx/j8889TebWNL54zzzzzkBeMFEVh5MiRnHjiibz99tsNKskWtZ8/wfcnjqF8YLvDpuCcJk2aNHtSV1dHRUVFKtS4V69ehEIhysvLGTp0KPfeey+tWrWie/fuLe6/Y8eOpNHq96NpGrt27eLPf/4z4XCYDz74gFAoxPbt26moqCBQU5t6boZNSUlYYkmQUoCUqAKyPbDD42oo/SORdoHUFZSwBdHdszqpJiODpJRJL6yugCqQjcaPSM7bVSFx28BuJNuqAlRFoghBrgFeLXkcu5LMIdSFQFcVXLrGfMuiUFXIdNpQpMRQFGyqikTicDtw52Qy0O9rsNl2V5rQhZIK77UaJ6pCYFNUfCkDtiHXsKGUj03VsBpEsVRV464/XYamqWQ43Giqgd2ehUQiEMkSOJaFhFSeqyUlAtA0W2oiK4RAT5gU5Bdit2djM4zk9Wo4qSJEKue0se9N32tWQ0k60VAyxLKs1IJ16jgIFEU09N1CKCpCUbAaPEv7Os6e55RSkJtTgNebi92etVdbRVFImCbl5ZVkZWbgcCiYZgJV0ffuu6YgEZSVlXH11VcTDoe54IILsNvthz3HdunSpXTr1g2v14uUkv79+7NkyRImTpzIX/7yFz7//HPGjx+P293yi/yZZ55h6tSpuFwuYrEYhmHw+9//ntmzZ1NRUYHP56OmpoZ4PE4oFN79Pa2kargwmhvLmsOJotlR7PuY6CRMaChOLAyt2XKDsNt3hywIkTRkFUGiwRub0BTiNo2oXQVVkLApWHaBpiooiiTDCS5doCsQ1SG6w6J7loXLI1KLOJmGileB7/L9DM5xYnPYkFLiUjVy7DZUdrdtHN8Abs0gw7Cl1keSIdHJ8S6EQCBSY03xFzL0nj4U5Odh6DpCaXIf7TFehKKA0JIpCg0LM43HEUKhoo0kFkvg9bZBKEqL4w5AVZTk+Gjo95737u7xsndbacaxzASG7t39TGsy1r37GOtCUYjFgkSjtUSEg6L8QvIdGZhCIq148ns0XK+gFWdnbQCHVMnI9BCXFrsiEYKmyWZnnK0hky2qIFytJEWtLIs5c+Zw/fXX079/f4477riW76cfwGG1PMPhMB6Ph9tvv50ZM2bQuXPnvSTI+/fvz8iRIw/naf8rSSQSbNq0iS5duqQN2x9AVVUVtbW1eL3eX8xra1qwbHeVCWwqdPD/cOEoKSVTp07lxRdfbJZrcNxxx3H11Vf/4JXg3NxcLrjgAubMmUNlZSUAiarNrJr1PFtOnESea99hZ2nSpEnzY2jUCGhclHM6nUgpicVinH766Zx44onYbLZ9LoSbpklFRQUej4eamhoMw2D79u2sX78+JTby8ssvM3PmTJZvWYMz20uPXj3YWRlg69ZttGpVyBWXXcKTjz9BLBrl5FNOYddRw/nwk0+wVA2lT0/M9RsQZTUoGdkoufkkNn6P1EHp3hVZWYUs2QleN0rnTlgrV0M0it6hLW6fjfj364moCo5hfalfvo54VQBbXiZtehZR+tFiSqSkTb/OVIWi7Pp+Kz6njREnDWLJ3GVEQxG2r95CuLqO9Us34DB0Tjx2ABvWb2fDuu1kZHqp2VXDl58vIRyMMGBwd7AkC7/7Hp/byfljjmbmJwsor6qle5d2dO3clv98+BWWtDjtpKPYsHkHK1Zvxu9zc+4Zo3n5zRkEgyFGDBtGTnYe02d+it1m46zTTmL+glms37SFDkVtGD5kAG9Om0k0GuU3xx9NZVU18xYsJjsrgzEnHsO0mZ9QWVXDwCN6UViQT0VlFQ8/+SwXnn0aXy1YxLoNm2lTmM/JJ4zi2ZfeIJEwOWHUUViWxezP56HrGpecN44Zsz9n245SuhV3pn+fHrz+zgcIIRgz+hg2bdnOomUrKcjP55STT2bqu+9SWVnJqFHH4PNl8P70dzFsNi668EJmzJzJ5i1b6FpczPCjjmLylCmYpslpJx1HeWUVXy1YhMvpYNyY0Uz94GPenvYxR/TpTqeidrw5bQaGrnPGKSfw3ZLl/Oet6fTt1Y3TTz6BOV98TTgS5cQTjsc0TWZ98gk+j5szx4zmrY8fZtmy5axatYpJkyYxbdo03n77bcaNG3dYx08wGCQ7Ozs1j/Z4PGzatIns7Gz++te/Eo/HcTgc+1zgj0QiVFRUYBgGlZWVdOnShYULF1JZWYlpmtTV1XH99dezZs0adu2sJhJJ0L9/P5YsWUIwGOTksWPxeNxMnz4dw2Zj4h//wOuvv86WrVvRvBnYswuo37QKEHiyWxOPBIgEKlA0A1+bzgQqtmHGoji82Whxk7rS7QhNw9W6E8HaHcTra9HcfmTbtoQ2rYN4HNG9C9KKYW3ZimK3kTG8N+HvlhMKhcnq3IqCNllsWLwc6sLo5VXUfx+gtrQSl9dJ8TH9+eqjBURqgljbKjENnW+/WYnL6eDk4wezYOEaysuq6NS+kN49OvLhrK+JxxOMPnYQq3ZWs3T5WvJysjljzBjenDaLurp6jhzUj6zMDKZ//CmKEFw8/ky+Xvg1GzfPpG3rVpx07Aiee+UtTNNkzMknUx8M8umcObjdbi44/zw+/Hg2pWW7OKLvEXTv1pU33nwLVdM468wzKauJs2zZMkqrwpxw/PG88eZbBOrqOPH441EUhY9nzcJut3P5ZZfxxptvUrpzJz179GDI4ME8O/l5AM49+2y2bNnC/G++wevxcNkll/DSKy9TWVXFsKFDycn08tZbb+BwODjrzDP5ZsECvl+zho4dOjDiyGG8+fbbBENhfnP8SGoDdXz1zSL8Pi/jxo5m6vsfUVlVwxG9ujN00BD+OWUmUkrOHnsMi5avZOXqdWRn+Rg7ehhvTJ5Byc5KTjvpSOIaLF6+HptNp//RfSlbtAq2lRPVMqjw5nH7XXewee0aBg4cSPfu3XnggQfIz8+nd+/eP3rMCHkYZWU//PBDbrzxRhKJBIqioCjKXoPtoosu4vrrrz9cpzxopJS8++67rFy5kltuueUXD5WORqMsXLiQ4uJisrIOg2z6/0dIKdm6dSs1NTX06NHjF4sM2FQNZ78FuxqU6v12eGI0jGh36MeyLIsFCxZw7rnnsmXLltT2nj178q9//YshQ4b8KM90PB7nkksu4ZVXXklts+V344l/PselY4agHlC98KdBSsmcOXOYOXMm999/fzrKI02a/zF27NjB2WefzRtvvEFhYSElJSVceuml/OMf/6Bjx4773TcWizF8+HAWLVoEQPv27bnzzjux2+14PB7OP/98CgsLeeyxx7jtttuYN28eAwYMoFu3bnz22WcUFhayevVqJk2aRFZWFh06dKC4uJjbbruNDiefxNrKCirMBLOWLUWtioNQQFMxnRZWdTXdO3akb+cuiEQCVIWPN22ksqoSv13i9eoIRcGMRAGB5rJjRmJI08RlqLT325ChMIlY0rNo9zjJjsXRkGTlZxIJRrFMk6pdNaz/dg1nnTkKTVNxuuzEYnHWr93O+rVbGXvGSCKhKFm6neyGuuXhcARFUfB4nNTXhzFNE5thYLPpBOqSJVo8bheWVJHSjsPux+9zU11bh2VJXA47qqoSqK9HEQo+r5tgOJL06Ok6LqeT2ro6pCXxuF0kTJNQOIymqng8bgJ19ZimicNuRwi47b6/cdPEK8jJyiQUDhONxdA1HY/HRXVNLQgFf2YuUkqCwRAIQYbfR319kHg8jjuzAKfLRW1tAITA63YTi8cIR6JomobX46E2EMA0TVxOJ6qqUldXhxACv99PfX09sXgcm2Hgcrmorq5GAl6PGzNhEgyHEcDUd6bSpUsxPXr0wGG3YRgGtYHkcXweD5u3bOGGm2+mtraW4UceybhxZ9KqoBVut7uh70GQJg4RxtuqC2edfTaDBw/moosuSlVpcLvdPPjggzz55JNkZLSg/HWI/PWvf6W+vp67774bKSWPPPIIkUiE2267bb+L+lJKpk+fznnnnUcoFEJVVSZMmMCAAQMoLCxk3rx5PPDAA0yYMIGhQ4cyceJEqqqqOPvss6murmb79u1UVlZiGAYPP/ww4XCYfv36MWfOHAy7k2XbKrFsNuatXs/W6gogmaetR02UujpkXQ2jR5+APcOLEo1RVVbFNwuWYmIl81J1A1NI4oaGZbcjdJ2IYhIzFEynHcsASzORkQieTCc+u0SGI2R47OT4bNgaRLrKFq8jx+OgS5+OqKpCts+NVR/lvVdnc/4Fo3G57ITDUeyaTiu/j3A4iiEVbDYdu82gvj6ElOB2O0gkTExTx2734PW4qQ3UIaXA581CU1UC9UEE4Pd7CQbDxOIxdF3H43JSXRtAyuQ9Z1kW9cEgiqLi93mpq6snnojjsNux2WzUhWLoDi8+n5cPZ84iUFfHmN+ciNvtoba2FsuycLvdCAF1dfUoiiAjIzMlsGS32XA4nVRXVwPg8/mIx2OEQmEURSEzI4PqmhpM08TpdKJpGoFAAEUReL0+wuEQ0WgMXdcwZJia2lqshrFuWSbBUARVVfB5PNTW1WGaJpYlefLZl/j9ZRegqip+n4dIJEokGk0+F9xO3p3xCU9P/g/t2xbQq3tbRh8zEFVViBqCnYEAO4JBtkQTrI0pXNVxGH+9617uv/9+2rZtSzgcRtM0duzYwfLly5k0adIPdlod1lnkiBEjmDZt2n7b7K/eVpo0/02sqoBwExE3pw7FP2CNQkrJ9u3buffee9m2bVtqu8fj4frrrz/kvNqW0HWdP/3pT3ww4yNqqpIvoWjZGqa9/RpnH9sHn/vgauilSZMmzaHg9Xpp37498+fPZ/To0XzxxRdkZ2fTunXrg9rfsiwsy2LQoEHU1NTw17/+lXA4TJ8+fTjmmGPw+Xxs2rSJSCTCmWeeybJlyygpKSEUCtGuXTs2bdrEo48+SqdOnXC5XHTt2pWysjJu7zeAVq1aETZNLnr2eT5evJL40oVYZSUoI4ditxmcqNm5ZMiRFBQU4PP5mLp8Kee/PIWIBrvFb/VkjVcJwu5AFRBTYEsMNN1FviepYKwKQdRpJyEgjkB321GFwOF24M3wEHLp2BQVp6ZiaCpurxNd19B1DZtfR1M1gsLEb9ixOwwUBHX1YVxOO0aTcNHsLH/q34qiYxhu7DY3iqKRk5XZ7No6HLujxQyjuWjWnm097t3iEdmZuw22WDyO3WaQk5WZFKPaI3Q1NzsLhIKq6yi6nYzcNqnPXP5mTcnJ2n1cm6Hh9XoRDaJWOXuIMjWt+5qZ2byveXl5zfvu9WJZFqZp4ff7yW/yucOx+zhdunTh8cceo7q6msLCQrIyM5sttjaG+8bqy/H7fZimyezZszn99NNTpRsPl7JrI0OGDOGxxx5j27ZtOJ1OFixYwGWXXXbQc4JgMEjr1q1xuVx89913zJ49G4BTTjmFvn370q9fP2bPnk2bNm0oKipix44dBAIBamtrOfLII5k2bRqTJk2ibdu2vP3228TjccaNG8fdEy/F7/czd+kqzrvv79QSIbroG3TNwNOxF1akhlFHdOKoYUPJy8sjEovzhz/cyYKFy5PhyKpEA7SIiWXFkLqF6rBhjynEhCQiNCx0LLuDIAoJE1SHE8WAYFTg0TVcGmQN6IouJdU2g0xDo9Y0sQyVuKpQSYK4sMj0J3+3skQE1RCgKkSlSWl1FS63A5+RFOZSdbUhiLqecCSE3SYQQsGyqohZyepDAOFwiMZqRBAmFA5gM0BVNAxdACr2lMiZic/rABpDtyUZHh0IYwbDiEQddjWGnXqsUBSv0ZhUnFQl9TcMUTNYjlujwWoLI8Ph1GdEqzEAww5gkQiW49EBHbDqIbb7OESqsEkTQ2+sQ0bq+iRRcNgbx7BFhi857uuDIUwzQVaGB61BqMrtsuF27VaHPunYI+nVrQuaqmGz1eNqyG+2pMRnd+CLBHGHg7RNWPidDmpqapg9ezZXXnllShR1586dB3Vf74/Dati63e7Uylaa/aOqKu3atTvootxpmuP3+3E4HL9YGLKUyTI/TQ3btj7I+QHCUbFYjGeeeYZPPvmkWW7QhRdeyFlnnXXYvJjdunXjggsu4MnHH0tukBafvPcy6665iAH9+x2Wc6RJkyZNU9xuN9deey0PPvgg06dPp66ujhtuuGEvQ2pfGIZBq1atOPbYY7nqqquor6/no48+4sUXX6Rfv34sXLiQOXPmkJGRwfjx41m6dCmffPIJAN9//z3t2rXjlFNO4bTTTqOqqorPPvsMVVW56KKLGDRoEMOHD+f8fkfgEBrv7tpJwusAhx2b38/6Vas48sgjGTx4MHfeeSfDe/TgtuNH8/e5c6jfo6xLuDb5/w49WXXEbUvm4NYHwaeBTZFkGxJNQH0ohhDgVBUcisCX5SVsmoRNM6VKmnDqZHXIpy6RwKNphBJJ0ZVAIoZNUXEpOgsXrcbncdGvd2dUde8IOcuKE4lUEY3W4HEXomnJyXWzskNN5ms/ZAFVVRSOGT4UdX/1RKSFGQthxkLEg1UHfWzN7kGoze8T3elDNCjyJiKBVK7tgZH0Ki4i060RratA1W2otuSEvjHvUAhBUbt2FLXbf9iVZnOjqCojR47k0UcfpaamJmXYHm4GDx5Mr169uOGGG4Dke/zII4886P1dLhd+v58nn3yS9u3bU19fz6233sqGDRto164dDz30EMFgkKOPPjrluY1EIgghaN++PcOHD+d3v/sdxcXFrFy5krlz5/LKK6/w5ptvcswxxzBg8BBuOPMU/vb5Z5R2LCJh16nP9IDI5vY776C+poYLLriAP/7xj1x4/mk47TY++/xrZHR3zVJF00DXUIXA8rgwDB13FUS8TuKGCtUQduvE7YLSWlCcCq4GO1FVbLRyCSpDFhtCMdwauBSJs0sbdsRNdgXDeKIx7A1RaU5Vw6GqRKMxvpz1HYOG9qCoYG8xMbdmYCiNkZ3BvT5XhMCvNy/5Ewci0cBebRsEylskOxNMU6O6ZhN2uw9F/PRRawkzTDweOqR9YrE4A49oR6Bu837n3a1beVAVnUg0nhKqUoTAqWq0dnhQhcBE0rlDB3Jzc1m4cCHhcPiwpmQe9isYCoXYtGkTZWVl1NXV7WXkdurUiZ49ex7u0/7XoaoqWVlZh11B7/8XXC7XfvNKfmoCUVhXBYkmOhFDCg+iHvkeWJbF9OnTeeSRR1L174QQHHPMMdx+++04HD9CiWoPbDYbp55zEW+8/ym7Ni4DIByo4p577uW1V185rOdKkyZNGkg+z/r168fTTz9NKBTC6XSSmXkg9dvdmKaJlJIpU6bQvXt3xo8fTyAQ4KWXXmLhwoVIKenVqxdlZWWsXbuWe++9lwkTJhAOh1m9ejVXXnklJ598Mg6Hg9atW3POOefQrl072rdvz4IFC3j55Zeprq7m/Isuht8cQ0BLzkLLa+p49ra7+PD995kwYQK33norN998M7efMJoCj5c/vPE6wmo+v5FCEEYQjkNdJPk+8DshHAe7lqz7KACXKsk0oC5h4lKhJmE2ii3j01QcqkLEsrBsGmXRCBUxgUvVcGsa8Wgcj8NGRCTILy4gHIqyI1JPtt2Zmjw7VG0P49Wirn4HQijYbX5UzY6uORq8URZbtpXQvt3BedBbIjPD/4P33R+JSN1e26xEFNFgcEgzjhk7uAm6lBKXIVASIeLBSuJCoDt8GJ7c5HHjYSK1O5vJwhqebDS7d697VTWc7NxZxurVq7nmmmtaVPM+XBiGwS233EJlZSVSSrKysg7pXZ1IJKiurua+++7jiSeeoHv37gwZMoRXXnkFp9OJ3++nuLiYDRs2cO6553L++efzwgsvoKoqGzdu5K677kqlfPXr1w+3282pp55KOBxm7ty53H/Pn2ndujW/HzWKj7u1we60UxeKsHSHm/efeIh7J93OP/7xD1RVZeLEiRx11O3cfNMDzJg2O9VHSRQUBaGpiGAYVYBw2HE1hNWjqjj9bmTD71Cf4SDasJAS96rUBXb/Pi6HwG2HhLBTWpHAbkCR20SlQUQsnkAxdBxCktunAzuxqK9rfp8la8Imz51l2NBaqnkNlChBYuEoIm7hz3CzL/PVqxvY9yiPlBSUU7HbbQ0ODUk0WnvQv+vPjRACv/fASqOxWB2KoqOpuw1VKa2kwriq0trhQQJLFi3C5/Nx5ZVXHvZI3sNq2NbW1vLss8/yzDPPUFVVhcvl2suy/7//+7+0YUvyYbN+/Xratm17SHVJ0ySpqKggGAxSVFT0i+RLlwVhxx7v3AEFh3YMKSWLFy/mrrvualbUvWPHjtx2223k5uYeVsNdCEHX4mL6H3MaM7augUTynHM/n8PHH3/M2LFj03WV06RJc9hRFOUH1/dUVZX6+npqa2uZPn06Z511Fv369ePdd99Nedry8vIIBAIpZdBrr70Wl8vFypUrWb9+PZMmTWLkyJGMHj0aIQQffPAB99xzD6effjqjR49m4cKF/OupfyS9t+efT05ODl+uXM2z//43C775hkgkQnl5ORUVFbz22mscc8wojsor4uu1G5r1VYpkGR+pCSybwAIq6gAhcBpJD67Pkaz1WB1PqhV7taT2KkCeDcJmAiGgdkcFNWu20ycngwxdJWJZVMVjbFy2kaK2+TicNio2l9G2MIewlWBHuC6laupSk+HRAkGmYUcRAkVKhLAIhZOpKLruRAgNw8jA5bLTqGR7qJiWxVvTZtC3Z7ef9l0sVFTDgd2Xv9uwlRaJSH0LjSWxuoo9HCuSz7/+jsH9+5Lfuj2K7kCzOVPvvFiwGmk29/7GQzVo9pbnZ4sWLSIcDnPJJZccdPTBD0EIgd1up7Cw8Aft3759e9auXUtlZSXz58+nc+fOXHfddZx77rnYG3I+/X4/paWlZGRk4PF4yM7OJiMjg2+//ZZXX32Vjz/+mHHjxtGrVy++/vpriouLGTx4MN27d6esrIwZM2bw5mOPcf311zN48GAiUvLom68z9bXXWbRoEYqisG3bNj777DP69OnDhAnn8+nMuUSbeG0xLaS521Mgo7t/C2HTsQV2/85GrSdZFgiI2zVibmfq1o05VErtKrGvVmEM8qE6oMylYBkCGY2R2LwFrWN7/KEaDCuKszAXNRxJHduvQyu7mlpo2hDcPTdTRXLM5hhacqxZEjOewEBQHWxQW24yhFyqhktTiUR3h/YpJNXCBeBUdb5ZtZZYPMGIjCPINOxoipJq92uaj8VicT6Y9TU9u7XfswoUsLsGtd3ux9A9DarSSSzLJB4PkjAjRKPJ5/QH73/ACSecwIABAw779zyshu3ixYt54oknOOecczjzzDP3UkQGyMlpuX7Y/29YlkUgECAeP9gwmjRNiUajKbGGnxspYWd9c8PWZ4NOh5BfK6WksrKSxx9/vFndLqfTyTXXXMOwYcN+kjDrbLdKj1Hn8sm014jtWgskF6ReeOEFBg8eTF5e3q/qYZomTZpfF3s+c/cV1tr0s5a27+vZvefzp3Xr1px00km89dZbGIbB6tWr6d27N61atWp27IyMDKSUvPnmm0yfPh1d12ndujWTJ09m4cKF/PnPf6akpITLLrssZYAJIXA6nRx55JEEg0H++c9/8tJLL2FZVqoEitoQcvrCCy9QVVXFqFGjuP/++5ly2W95fNacVDmOldtL+GLNOkRMImMSGhyJlkMBRRKSyVlxXcMc2mNPGroBwGGArkKgYf7rVEFJQHVCUhM3qTMtBKArgozitgSSf+DtXEBACOpCQZyqilPVUMTuayKEoD4RQxGCDN2Orig41WSUWDwewrIsNm/dRE6Wn/r6GJruxNBdKIre4m+xLw7JoBUKuuPQFvOFqqM7/exZx1bs41hSSjS7Z69tDk8OzozWGJ69F401mwtF1YiHA6i6HUUz0J37Fn/Ky8ujsrKSbdu20aFDh4O+Vj92/OxrTO15rEYuuOACvv32W0pKStiwYQORSASn00lRUVHqOEIIWrVqRTQa5ZlnnmHjxo1s2LCBu+66i7Fjx/Lyyy9z2223cffddzcThRVCkJeXxwUXXMCGDRuYNGkS8Xic6upqIpEIpmni8Xi45ZZbuOaaa7j33nt57LHHmDx5Mvf99WaWLVkNJOfDU9/8sKHcUMMCS1MjNxRNenUbv2conOqDDbA5bIiGxQUJxF12qupi+LfUoOo2pICQx4Zp6OhGa9huUivcxD0+EmUa0FgyCxwehVWN+aekNuO2Q44BugKbQsnP4+EoHpnA7XdDIEiGrqI3uz9BFwKPtnt8uDUNe8PCTBUxQpaJKS3qEzGCZjxlF/t0G5pQUmP21zAv03UbdrsfbY/xrigadnvjWNnbIFcUFZvNiyE9OB3ZSKBr11LmzZvHaaedhs3WPKT7x3JYDdvy8nL8fj/XXnst+fn5v4ofIk2aw40lYUM1VIV3b+uVC+5DjCpftGgR06ZNS5XDUBSF8ePH/yRF3htRBHQpcJFz/J/Y8ervwUogpeTjjz9mxowZ/Pa3v/3FFcPTpEnz6yUej/PUU0+xYMECTNPk4Ycfpk2bNpimybx583jxxReJx+OMGDGC8847D7vdzvr163nyySdT4YhFRUWUlJQwZcoU1q9fj5SSjh07cvnll1NQ0Dz0xWazMWHCFbTpZGBZkpycnNTk2W63s2vXLnJzc3G73SxatAjDMLjnnnt45JFHME0TXdcZPXo05eXl/OUvf2HMmDFcfvnlqKqKlDJlwI4cORK/38+VV17J+eefT/v27Vm5ciWjRo1i9OjR5Ofnk5mZybhx4/jzn//MG2+8wb333ktxcTEZGRlUh8Kc8MCjrCkta+b3VMPJGpFEwHIqSCO5YBnY7SRCaxK2rCoS05RIbwayp5P1oaRH16OBT5eEFAtNCGoSVqpMW5auETItVBHHUBRqGhbMPZqOU1URQMIKoQiBriQNYLdmoAmFnCwfqiqIxeuJxeuJKDpCKKiqgcOeFGVSVSMl4rQnmqpyxW/PSQnK7AuhaGh2N7rTj1CNn3R+mDz2nseXnHbqqXi8e4cWA+hOP1LKZP8ULeUV3hfFxcUcffTRPPLII9x+++173bctIaVk2bJl/Pvf/+b777/n3HPP5fLLLweSJQz//e9/s2LFCrKysrjyyispLi4mkUjw0ksv8dVXXzFq1CjOOeccAGbPns0HH3xAIBDA6XTym9/8hjFjxuz13VwuF2dc/3senTUdZ4/u6LpOMBgkFEoubDRGvgWDQWbPns15551HfX09V1xxBcFgkLy8PCZOnMi3337L9OnTueKKK1JRhvF4nMrKShwOBzfffDN///vfef/993nooYdYsWIFAGeeeSYDBw7E6XRy6aWXMnXqVM4991xGjhzJpEmT8Pl8eDweuhR34I5bH2rhd2vhOsabG57EE8jGlSQkWkAjU8tEL6mgcZD4bAapxNwGEg4b8SbbLFVQn2GnqbvJNFQsp0KgHkoaN2rgtQt01cCQGmqNRFVAU5IrUw4Vshqc+AoCt5ZAbfhaPi2BsyGMWgDOohyEhK3hUCrs2aYo1MSjqf1tqkqW4cBQ1FRUxsGgKFrKkwqgqjYcKQP00HC7LK685CK8noKDGruKZoBQsXlyQAgS4cDutAEhOO744/l6wXc899xzXHbZZYfVuD2shm1ubm6qCHSa/dO4Upw2In4Yuq5jt9t/kcWTmAkrdjXf1iP30OrXJhIJpk2bRk1NTWpbv379uPnmm3/SkjdCCLr6o2T1OZmKr18humEukFROfOqppzjllFPSURVp0qTZL0VFRRQWFvK3v/2NaDSKlJJt27Zx33338cc//pGcnBzuuOMOWrduzXHHHcdbb73FSSedRCKR4K233uKaa67hscceQ1EUbr31VgC2bNnS4oJeIpEgEo1SsqOUUCjMrMQstm7dyuzZs+nWrRvFxcW0bduWeDzOPffcw4gRI/jrQ3/F5/fi92fgz/ATj8WZPXs269ev56abbmLEiBH89re/ZcGCBbz11ltUVVXRp08fevfuTdu2benRowe/+93vUFU1+f0UhUg8jmoY3DJpEm6vl+efe47bbrsNt9vN8ccfz/jx45t5a5oiAExQ6i2kTSa9uI1NhUhpNeyqAywLR2UZQlOwh+rQvG6EIgjHYFcM3GpSiAokPg1cGmw3YymPbqN3KFNXCZsmgqQis1fXydQNTCmJmglq4lEcqoahqPhUG5qUCJKCUwCmGSUWS4YlGbobm92Prjn3euealsVnXy3gwrNP3WeUke7KRLN7UfXD65k5VBYtXkzXrl3xuFvOFRRCILSD66Pf7+f+++/n/vvv5/XXX+d3v/vdQU3OHQ4HRx11FOFwmLKyMiBp8L7yyiusW7eOSZMm8eWXX3LXXXfxzDPPEI/HWbBgAddddx1PPfUUo0ePpqqqivvuu48777yTLl267FdJtqamBkc0yq5Nm9iV0YqXX36ZL7/8kp07d1JYWMjIkSP59ttvKS8v55ZbbuHtt99mxKijeXbKFIYNG4ZpWaxatYoVK1awbt06wuEwl112GYqiMH/+fKZOnUpBQQEDBw4kNzeX1q1bM2jQIK644gpM0ySeSICiEI3FKGrfgWeffY77/3I/U6ZMoaKiAlVV+d3vfofdoae8x4eMTP0PCTNOPFxLLBHG48xBaVygiMSgIZxZaBoI0DUVXdOTY9FhB0XBUZv8DRN2A6mpRG0ashoSmkLMljyWZVeoqm1IOzAUQKJrAruWtKN9DqhsMIOEkHhUiaFAjg2iTfPypSS2bRc+RdDObackkvSW5Nps+LSkl9ZCEjYTbA/XkaHbybLtL79aYDO8qTBgTXM2CwneP/u/7lJVmffdXDp3O+KA0YRCkMxNb7I4pOrNxaEG9cjn3//+NxdffDFLly49LNU/GjmsM+h+/fpx5JFH8uijj3LjjTeSkZGxd7iHpqUFk0gaZsXFxT9pbsb/Mjk5OWRlZf0iqsgxE5Y3MWx1BbpmHZphW1lZyZtvvrn7GLrO2WefTbt27X5SY13TNPp3bUPvjRrbh04gVrIMGa4BkqkEr776KldffXU62iJNmjQtous6p556KpWVlTz55JOp7StWrCAzM5Ojjz4aXdc56aSTmDFjBscddxwFBQV89dVXWJZFnz59SCQSlJWVccQRR5CTk4PP56NTp04tnm/jxo307dOXSDSMpmpY1ivE43FisRjRaJTBgwfTrl07vv76a4YMGcJtt93Glu3L2Fb5DV9uCxIzfkO7zA5UVFQwcOBA/H4/c+bMwe/38+ijjyKl5N5778UwDF5++WXOP/983nzzTWw2G3379uXyP11PRZv2EI2h5BUgVI1ij4eRI0fy8ccf061bN+69914efvhhlB596Nh3ABt2lbf4XYQEIhI1aiJtAsu59/tLmhb1gSgyFqM+ECDUDrI9ybBlgHoz+V8iGKZcSlRDQ7cZFDmTE8rqBm9WbdxEEdDKpuPSVGricQLxOIoQZBs2XJpG2EwQNhPUxaMIIcg0HLhUPZXn10jSmxtE0xx43AUoDUI4UkpKSsuY/91izhxzIl7P3gaj5vBhuLN/8XeKlJLNmzcflGf1YEjE4xg2G1dddRWXXnopixcvZvDgwQfcr3PnznTq1In169entsXjcT777DN+97vf0aVLF7KysnjhhRcoKyujoKCAeDzORx99hBACXdcJBAJYlkVeXh6FhYW0a9dun0bhww8/DH9TiMViPPveXMyGsSOl5OKLL6Znz55s3bqVkpIS/vznP9OhT08ue+d5FmypgG+/YuLwURTXBfH5fAwfPpx169bx3nvvUVFRwQcffECnTp244YYbmDlzJps2bWL48OHcfffd3HTTTXz11Vc8/N4H2LIL8FaFcNi9SCTjTjyFrVu28PXXX+N0OjnnnHNIxGFw/1GsW7PtR6WYWVYCiSQcDeB2ZAENE7Mmoc1WIkrCjKJqNhSbkZxHRmOgCFS3G6GpqNFkfWrV7cC025CYOINxYjaVEDoIgYxKECBtgrhTIW42iFtFk+O1oEETqc4ETIhY0KlJ5QwJbFq3g0K3nXbFu8tg1cTiOFUNY4/fU7Kv6yIwDDdORxZKQ8TFAREKms3V8E8Vw53F/ozb+mCQNZu2ozl8h8X5Eo/FyM/P5+qrr+af//wn3bp1w+PxHHjHg+CwGrZOp5Px48dz8cUX8+KLL1JYWLjXCtaFF17ItddeezhP+19JY46tz+dLe21/AJFIsph8S4snPzUVIVjfpGJBgTtZ6udgkVLyn//8h4qKitS2zp07c9xxx/2k3lpI3nfB+jqOLPQwp9uJRLqNJrT4jWQ5BtPk73//OyeccAJdu3b9SfuRJk2a/0729bwtLS2loKAglYPXqlUr5s+fTyKRYNy4cXz55ZcoisJRRx2F3W5n/Pjx/OUvf2H27NkUFRVxxhlnMHz48L0We30+H5dddhldunThqaeeYtmyZeTk5DBhwgQGDBhAUVER2dnZnHvuuQwfPpzVaxZSVbeByopKQrW1vLnrFcYdfy633HILiUSC1q1bc8UVV/DOO++QmZnJhAkTOPbYYwEoKSnhnXfeoXPnztxyyy3JOqqGTk7HIpavWI6e4UDG4vTOL+aee+7hggsuYNWqVUyePJmSkhI6OJ38/uLfcusHHxNSVAg0lP5wu8A0IRzBsiuQ5UOG65FhE2G3ga4j6+sBgXC7EVmZsHMXliUJh+IEQ0HsioXaUG/crA+BEAi3k2BJBardYL3LTlGGDVlbhwScGW4SkTirIzE8hkb7gkzqKgNYpkWt24HLZqDUR3HqGplZPurrQmyJlOF1OmiV6SdaE0JKSWaGj3giQSAQRNc1CvJihMI24okEPq+HyuoaynZV8PKb73HBWadSHwwRDIWx2wxys7PYsnMLujNATk4OUkoqKioQikKb1q3ZVV5OJBzG7Xbj8/koKSmBhrzNUChEoLYWw2YjNyeHsrIy4vE4mQ21ZcvLyxGKQmGrVlRUVBAOh3G5XGRkZLB9xw5oEBWLxWJUV1ejKApZ2dlUVFSwdu1avD4fLqeT0tJSFEUhPz+fQF0d9XV12Ox2cnNyKC0tJWGaZGdlpXQxNF2nID+frYsXY28432mnnca7775LZmbmXjV0Wxo7expugUCASCSSSuEzDAO3201NTQ2dOnXi+uuvZ+XKlYwdOxaPx0Pnzp3p2bMn11xzDTk5OQwZMoSzzz67xdrQ/fv356STTgLg/vvvJxwOM2zYMC644AKOOOIIbDYbRx99ND169KAmUMv9bz/LrnAYUVNCJGby1w/f5fZjR/HnP/8Zl8vFqlWrePzxxzFNkz59+qQiM84880zuu+8+tm/fTmVlJWeccQbRaJQuQ4fx3fb1lEkNv9OBEqinR+9evPTSS6xatYp58+bxwgsvJPNxMyVHHd2Pzz75BikllhVHINA0G4lEFIlEERpCUTDNpEtUUw0sy8SSZqqtlBaKULGs5CKPacUBgaYamFZjGRqBlYgTtyKomo6q2ZDSxIqGEB43ms1JIhbGCgWQmRlIw8CMhlARGNJPRCQgHgdNA6cTK5xUURYuF6a0cMowsbiC7neTqAsh4wny/Hbiik64NpjM2/W70cNR1m/YgS/LS8fu7TADYTShEM2AKBCoDaKpKj3aFFJXWc+meC1Opx2vx8nOsipU1UZR285EIoKdu3aiqQqFBfmUlO0iHk/g93mw2eyU7SpHVRXatC+mLhghGArj9jnIzMhgZ1kZZlmA3JwcEokEVVVV6LpOQUEBpTt3Eo/F0A2D7Oxs1q1fj6ooFBYWEggEqKurw7DZaFVQwNZt27BMM+V0Ki8vR1FVWhUUUFlZmRqjfr+frdu24fH7GTx4MC+88AIzZsxgyJAhh6UO9GGdRW/cuJEbb7wR0zQZP348fr9/L49aesKcJJFIsGHDBoqLiw9r/ab/X6iqqqKmpgafz/eze22XlIHZ5L1U4IF8dyqV44BUVVXx7rvvpmrWKorCoEGD6Nat209upJumyaZNm+jmLcLuycE15HLCqz9ChqsB2L59Oy+88AJ33HFHuvxPmjRpDhpVVVPPNEg+axqNXJfLxYknnpj6TAjB6NGjGThwIIsXL2bBggVMnDiRhx56iJNOOqnZczAajTJr1ixWrlzJ1q1bGTt2LEOHDmX8+PG8+uqrzJs3j6FDh+L3+3nppZcoKSnh5ptvJhrYxLJ1n1IX3sj04PNs3VhLMByix+AjOO6UY/j0w8/Ytm0bJ5xwAl999RWbN2+muLiYa6+9ltdee41WrVqxatUqWrVqxXB/BjuLcrGcJua2jXy97XvuStTi9Xjw1NUyfvx4LCl5fvJkbvjdFYQH9Yeitphz5yHrg2gnHIOMRZElJcl6nRk9sTZtRoZCiPw8hN+H9f1aUFWU4s7IXeVYpTsRdht6NEx88wZi0Riu4nZgWgQ3bEO1GXj7diW6YxeJ+hDRgmy+j+Qglq/BJiTtBnUlsLOK6q27MFx2OLInO79dgxmN0a64DQ63g++/W4tu6Jw4qj8bv99KaWkFeflZRHq2Z/4XyzCk4Ngjj6Cmtp6lK9aT4fMw8kiFz75aSjii0q93T/JysuncoYjNW7dTWVXDkhWr2Lh5G63yczlu5JFM++Bj0Owce8wxmKbJZ59/jq7rXHD++Xz2+efs2LGD4uJi+vbpw7vTpiGE4DejR7N5yxaWLltGbk4Oxx93HB99/DHVNTUMGzoUt9vNJ59+imEYnDVuHJ/Nncv27dvp1LEjgwcP5p2G9+vJv/kNFRUVfLNgAQ6Hg5NOPJEPZsxg0eLF9OnThw7t2/POu+9iGAZjx4xhxcqVfP/99xTk53Psscfy4cyZ1NfXc/TIkViWxedz5+LzehkzZgyzZs8mUF9Pv3796NevH3//+9/5+9//zsSJEw957DTe73uOH1VVURSFrl27pubOQgi8Xi9PPfUUq1evZunSpbz55pt89dVXvPTSSzidzmbHDgQCTJ06lWg0SnZ2NuPHj+e4446ja9di3n57KkIIzjrrLD755BOWLl2KkJJ///GPvD31bd5bMIvqDC+PvjqFo5w5rC3bwCXnXcDo34xm6ltT6dKlC927d2fy5MkA3HTTTbz33ntUVlYCSWFKdzTC0cVFfJsIU4kFJaVceNfdnDHuTAJbN9MxK4vrr7+e9Rs28Oabb+JwZ5CV2xkzFKSiegM2m5ssbxHBSDWWlcBmuNA1B/WhpGPA7cwhFg8Si4dQFA2PM4dQpAaJJG5GEJZCOFKDECpuZxbhaC0JM4Zhd2HTPIRDNUgpcXtyiCeiRImiWiHcma2oryohpisYNgU1YSdcuiVZVzenJ2bJdmRdLcLtQenQHmvtOpAWSsf26FYcuXM79YaO94iuaBu2oNXXE2+TS6ggk/LF6/EaGu0HFRPM8ZEIhAhur8TbvjULF20gEo4wbEAPDFVl9ZJ12Aydbjl5fP3tSiqr6uhe3J1e3f18Pi+pyn7yCXmUlJaxePkqXC4nZ5/6G+Z8uYD6mKBf7x7kF7Ri5heLcdjtjMluz6LFS1m/YQPt2rZl2NChfPDhh4RCIU44/ngCgQBff/MNPp+P0089lY9nzaKqqopePXsysH9/3ps2DSkl5559NsuWL2fV6tXkZGdzxumnM/3994lGIqkFytmffILdbufMM87giy+/ZOvWrXTq1IkB/fox7YMPMGw2Tj/9dI488kgeeughamtrD0vVHCEPo6zse++9x7XXXsurr75Kz5490fW9lbwURfnJvVItIaXk3XffZeXKldxyyy2/uJc0Go2ycOFCiouLyco6BDndNEgp2bp1KzU1Nan6aj/fueHmT+C1lbu3ndkNHjj24EKRpZRMmzaNK664gl27kvHMTqeTt99+m9GjR/9Evd5NLBZjyZIleHPbcd1Xuawsi1P1+gSCC6ak2nTs2JGXX36ZwYMH/+SGtpSSOXPmMHPmTO6///5f5NmQJk2aQ6eiooIzzzyT5557jo4dOzJnzhyefPJJ/vOf/2AYBg899BDBYJC77757r+eIlDI1cRdCEI/HueKKK+jSpQs33XRTarEyFotx2WWXcccdd7BgwQJ8Ph8jR47E6XSybd18EvEI7YqPIhiKcPTRR1NeXk40GqVDhw5cffXV5Bfk8c47b7F48RKmTZvG+i0LmbfiSxbP2UKHtp148MEHee211zj11FMB+HLBVKqqdzKs/5lYlmDLli24XC4K27ZhxPljKNm4jdyj+uLs2KYhak/gdngJr9lKuLSc7Q4nLqLE27XDblOo3l5NfPM2yMvDaJVHNJHSX22ZJhV35K5yPOXbyTjqCFSl+Wd7VeZp8nc7B/g10bxtsqv4NJVWNn2vzxyaSmu7E5CEw1EUIbDZbahC4DNsZBmOZkrLUkoMw4PHU0ginuC+vz3Nn666FLeruVHVKOJk8+Wn1Iv3PE7Ttgf72aEcp7GtlJLnX3iBAf360bt378NyzqzcXPSGCIPnnnuOGTNmcNttt/Hoo4/y5JNPkpHRslCPZVncf//9CCG47bbbME2T3/72t5x11lmceuqplJaWcu655/LCCy+0WCPXNE2EECiKgpSSefPmceONN/LKK680UzuePn06O3bsYPjw4cyePZsTTjiBjh07EouH+OTL/6B4cjll6Jn85z//YdKkSSkxqXHjxnH66adTUVfNgw8/zOjRJ3HnLbcy5bPXeP+117nslMv526N/IxKJ8M4779CmTRvC0RAfLHgPr93HoM7DCAQCbN26leLiYt5cNo8bJ1xNQrfhGDOaWEKBkIVbN8jWbcRXLiHs9FBZW4tHOPFYBiQShKvLSFRV4PHmoah6cgLW4NGTCTOp5NkCUkqqaraQ4WudCpsXurb7vleUZJ4tyfBeIQQ4HUnvhKIgFYGl68RddqSiJPPgdQVLEQTdeqqeLgpIQ0mKwglQLAuPFcSb7UZVkmMwyxC0soFCUiAu367jUhQ0IdBVhfVLNuC0YNDg7rhtNlShNAhFJRcFk0JudmyGG113NXRfb35/Kgqq7kiJn2mGI/lIULQfPF72/CwYDPLXhx/m9ttuQ9O0Hz1GDZuNjOxkikJVVRV/+MMfGDx4MO3bt2fp0qVMmjTpBzutDussUghBQUEBHTp0wL2P5Pw0af6biZqwpIlOg02FHjnJPNuDob6+ng8//DBl1AL06NGDY4455jD3dP84dBjSGlaW63iPuZHI2k8wa7YBsGHDBp5++mn69OmT9tqmSZNmL5YsWcLGjRuprq5m/vz5WJZF9+7d0TSNKVOmkJ+fz5w5c7jvvvtaXByLRqO88MILuN1u2rdvz7Zt21i3bh3jxo3bW5woHiZRt56zzjwNw747QU1aCWoqtlDQri9C6NhsNqqrq7HZbHTo0IFZs2bx7bffsmHDBjp16oShO+jRZQir1iygribA/B3z6dChA6+88gqnnHIKuq4TiQYJRepwOG143Jnk5+djWibzVn7B4MGdqemShyzMQbUnH/hSSgKJANVOSY1d4G2fi8Opo4hkVE9+UQZmtg0zGMbwQTgu9jUXBySJmjqEoaM67cTrJJa0UJWGiSESpEQoSeEpp6ajKirheAQJeLRkTVyXCkJpcg2b/NOjqy1+FrMsQpaJu2HCqmoqipKsrlufiOPVbRhNhGCEEJhmhEQiBMIgGo1ht9n2ORGN1e1CUTU0m2uPcj3Nf+uD/eyHtLUsi1gshiVls37+2HMmjQ/B6aefzvTp0/n666/3myNaXV3N4sWL2bBhA0II5syZw5AhQzjttNN46aWXcLlcfPnll/Tt23efYc3r1q3jzTffZOjQoei6zttvv03r1q3Jzc3dq21d5UZyfCO45pqJqdzLWDxEPBElQ0n2PTMzk7q6OkzTJDs7G0VReOKJJ1iyZAnl5eWcevxJ2DWDbhkZfKmolOwoYdGiRbRp04avv/6atm3bsqumjNVbltO30wD8fj+ZmZkUFRVhSYsMp0K/E/phOR3UZKvUxwQBp0J91CQYqyPhs4MArWNnIqZFIiHBkrgyvViZGcRsTnSpooUjEIu3WHFZShMzFkmWakIgIyWITF9SnTfZILmTUNB0DYfbSdy0iDTU0rV0FbMhdVIqAtPQkSKplBx26MQNpVlYnnQILEMBreE+ADLcAhc6iiIQisCnQSt7UrwNIM+m4deTZpcqBIV2B5WaRjyWwO9wkGd3oTbJjzUMD3abH11vvmCUPKGCIkQyf13RUPcYW3s1/7FjSwgikQiKoqTGz48do43/ZWZmcsUVV3DPPfekVL9/DIfVsO3atSter5cFCxZw8skn/+JiAb9mNE2jffv2uFyuAzdOsxcZGRm4XK6fPQx5UzVUNynTYNOge/bBhSE3eprnzJnTbPtFF130swmqqapKu3btcLk99MoFuyaIZLXHNegiArMfACu5Gjp16lQuueQSjj766J+lX2nSpPnv4ZtvvmH16tWMGDGChQsX4na7GTt2LA899BCvvfYapaWl3HHHHfTt27fF/XVdp1+/fsydO5fly5fjcDi4//77GTZs2F5tzUSMytJ1aJpK594nJI3JQIDFqyvIy+uCbveycskSqqqqePzxxxkyZEjqeRqJRJgwYULSWFNVLJlA01S6dG3Fd9+s46677uK5555jy5YtKfEqKS2+XPAuwwefgdvlZ3v5Nj5Z8hGFbXNo1SYbC6iMJbAkhC0rqXraNgPaZlAeS9agdalJVdTquCDkdgICIxpBcziapbE0RUqwPElRGsUAM9tDXNstKGMGw4i6enLb5tImM5/iKgUXKrtyY2wJJUsM7W/O5VAELrXl96UFxCwT0HA6m6dGJaSVqtXbvL8m9fUlOB35nHLC0aj7OHaysUUiHEA1HAcnbPMTIIRg2NChh008CiBQXY0/KwvdMMjKyuLaa6/lgQce2K8oaGVlJTNnzkwJ5cycOZPevXszduxY7HY7c+bMoXXr1lx77bX7XFjOz8+nQ4cOfPnll0QiEXr06MFtt93WYvv6mjI2r56Lx5+H05MNQGnJLiI12XTs0Y94PM7cuXM54ogjuOOOO8jKykLXdSzLYtGiRVx77bV4PB5UVcWhqgzo1QkzEWHEiBEcd9xxzJ07l7POOguHqtLe5WLz1uWszG5Nz87JiK8lGxaybuM39OrfmUDCImpZlMcg6oLqmMBCI5zdlkiqxo5KMAyELCI+HVo5IVCHy7Qh5H4cZlJiRoLYNAf2QAiH6IT0+DEVFZBE62tQ7U7IyOCsY4ZgC5RRj8Z7SzYSjifnPTGbitWw8COFIOxsMJGaGrQaWDYFaRPNtusqOG0Kqrr7N7Cru41aAMce81UJtGmbj2VZLcpCaaq9ZaMWcGQUouh2aKFu7E+BzTAYc8oph23OHYtGqautxePzIYTgyCOP5IgjjmDmzJkcccQRP+rYh9WwVVWVLl26cP311zNr1ix69eq1V/5ot27d6N+//+E87X8liqKQk5Pzi6j6/i/gdrt/uDT8j2B1BQSbFDlzatD9IKvjSCn59NNP2bBhQ2pbp06dUsIlPweKoqQS+7tkQZEfvq+w4ex7NuFVHxLfvghIepYffPBB+vfvf9iU6tKkSfO/wYQJE1rc3q5dO2666aYD7q+qKgMHDmTgwIEHbGtzeOk+6AycLg9SSlauXMlVV13Fli1byMzM5Nxzz+Wbb77hmGOOYfjw4c3SGbZs2UJpaSk+nw/TNHG7fZw48iI2rHwGKdfSq1cvHA4Hl19+OQ888AAD+56UEpwxjOTcJT+zgP876fct9i0hJYn9eOfiFixZsZJXX3yJvn16cea55+4uP3IASkpL2bB+fbJubyJBj27dUCwLh13H63Az890PmDx5MgVtWnHm2aczaNCg/R5PEwJtP+9LXRHNvEVNMRQFsQ/FVClh5dY6WncbeMAUL0W37fM4PzVSSuxOJ96MDLJa8Gz+UJSG7yyEoGfPnkSj0WYRWXvSqVMn/vrXv7b42ZgxYxgzZswBz+n3+zn//PMPqn+tOw2kx+AzsTl9JBIJnnvuOR577DEsy6KoqIizzjqLl19+mb///e8UFham9hNCsHjxYkzTpK6ujkQiQa9uw6mvgi/mfk2nTp3o3Lkzb7zxBtdddx033PAnTj4mWZNXU3enIbbNKeL/TroKgLiUmE3GS3iPcrSNxMzkfRWNRXl/+nTmfPQxN98yiTZt27S8QxMUS0LC4sMPP2TU0UezcNEiunfvTqbfh1BUhKKQ43Hw0IMPsnr1arr7srj25j+SmZmJqQrkgW5PpeU2yTq2zbdpCuhN2toUpdndrymC9d+vIWEm6NmzJ4aiNhsfiqKlQqn36oa279rSPwXRaBTNMMjKzf1J7BZd1xk0aBBvv/32r8uw/f7775kzZw6apvHpp5/y6aef7tXmoosuShu2JOXdV6xYQfv27feZh5Fm35SWllJXV0fnzp1/tnxp00oatqEmhm33HPAeZJmwUCjE5MmTm4lGnXzyybRp0+ZnM9ATiQSrV6+mVatWdMjIonsOrK0U6AU9cQ26mJqdKyGRLAw+f/58Xn/9dS655JJfPCc9TZo0/3+iqBoubw6GYVBeXs6ECRMoLS3lL3/5CyUlJdx///0YhsFFF120V45+eXk5NpuNIUOGJI+lqLicmXw258tUfuLEiRP57LPPuPfee3nhhRfIympu9Nh0G4XZe6vNHgyWZfH09Ef44v2Z1GzZwaXjzqNdu7YH3E9KSf2WEqrXb2HcqBMAyM5uXjLnggsuYPz48WzcuJHMzEyysrJ+0veIZVmUlJTg9XrxeDypHOl4PM6Mjz/h9HHn/KrLF1qWxTcLFmB3OFrMWz0c5OTkcOmll/KXv/zlJzn+D8Hm8OLyJhWpP/74I2688UZOPvlkrrzySh566CFuvfVWjAaPc1OklOzYsYP27dvToUMHpJS4HD6+/34zpTt3YRgGnTt35k9/+hMvvvgiU6e+26JwVrbvIFf+W6CkpIRpz06mpqaGr2d8wNiGvOT9IaUkHo/zj5VLufK355Hvc1FQUNDMySal5JFHHiEajbJ69Wp69+79k85xGvu0bds2OnToAOwWB/uy9Gui0Sgjjir4VUe5hiMRPpwxgzFjx/4kEYZCCI4//ng+/vjjH32sw2rYDhkyhOeff36/bfYnhf7/E1JKIpFIMxW8NAdPIpEgGo3+qHpnh0ptFLbUNNcr6H8IUU3z589n9erVqb9zcnIYNWrUXiqGPyWN951pmjg0GNgKZqyHSELg6n8ewW9fIL5tIZBUNHzttdc4/vjjadeu3c/WxzRp0qRpimmavPvuuzz77LN8/fXXvPjiiwwaNIhIJELfvn3JyMhoMQzzqKOOwrIsnn/+ea655hrGjBlDIBAgHA5TWFiIpmkUFRUxbtw45s6dS0lJCZmZmYdtgimEYNy4cUydOpUFCxZQW1u73/aNeaAzZ87k4YcfZuTIkWRkZLQoqieEQNM0unTpclj62khVVRX19fV7Lbju3LmTO++8k2uuuYZdu3YRj8fp378/fr+f7Ozsw9qHnwqfz7dXCcrDzTnnnMPnn3/+k57jUNm1axcvvvgiTz31FF27duWaa64hKyuLW2+9FdM0cTqde0VmKYrC9ddfz7/+9S/+8Y9/UFFRQUFBAWvWrGHjxo2ceOKJ2O12Bg4cSEVFBUuXLiUWix3WxY28vDxOO+007rvvPlatWkU8Ht/v8ePxOJWVlUyZMoXPP/+cjRs30r9//33mftrt9h/tHWyKaZps27YNv9+P3+9v9tmbb77Jjh07OPbYY6murkZVVY4++mhcLtd/hWimoih7La4dbhrLr82cOfNHHeewXs2srKy0wm+a/1l2BWFLYPffgqRheDDEYjGmTJlCLBZLbevWrRvDhw//RVfpRrQFrwGRBCjOTLzH3ULVS+chE8l+zp07l2nTpnHVVVelvbZp0qT5RZg9ezbXXXcd1dXVXHfddfTq1QshBI4DeN80TWPUqFH07t2bGTNm8Oc//5lYLEZ+fj79+/dPhdTZbDaKi4uZNm3aYSk30YgQgqFDh/Loo49y//33EwqF9tlWSsmqVat49tlnefXVVwmHw/Tt25f33nuP008/PSW08lOzc+dOFi9eTMeOHamrq+OYY45BVVUyMjK44YYbyM7O5ptvvuGcc87BZrNRUVHBpZdeiqZpeymf/poQQvCb3/xmL4PjcJ9D07RflaESjUZ54IEHeOGFF8jIyGDixIlkZGQghGhRbKoRIQQ+n4/rrruODRs28J///IfHHnuMrl27kkgk6NevX+o3PuKII3jzzTdZunTpQaUXHCyqqnLttddSX1/Pli1bqKur26+N8frrr/POO+8wc+ZMfD4f06dPJzc392eLirMsi6+++ooOHTpQXl5Ojx49Uh7ao446CpfLxeLFi7Hb7QwePJjq6mqKi4tTzo09VYV/Tdjtdi655JKfPH3ycHiDf3QPKysr2bRpE6a5j2D5Fqivr2f58uU/9tT/1SiKgsfj+VU9AP+bsNlsuFz7V4E7nEgJpfWwrcmCe54b2voObv/GWo2NDy5N0zj77LN/9jB0IQRutzv18GjthX4Fuz9zdB6Fs9epKVGEeDzOo48+yvbt23/WfqZJkyYNJJ9Bs2fPZsCAAdx6661ceumlhxTlIoQgKyuLCy64gMcee4xBgwYRCoU47rjjUu8PTdMYMGAAc+fOJRaLHXIkUGM5mZbQNI3Ro0fTrVs3li9fTjweb7EdJBVzn3nmGcLhMCNHjkTXdb799ltKSkpSbUzTZPv27SQay540nPtwRS9169aN8ePHs23bNv71r38RjSZTUxwOB127diUrK4vLL78cj8fDpk2buPLKK3n66acxTZOqqipef/11Fi1a9LNGUx0sc+bMYevWrb90N35Wampq+Oijj7j++uu57bbbGDjwwLnQTWmMCrj99tu5++67qa2tZdiwYc0WlPLy8sjLy+Obb7455CjEA92/WVlZnHXWWQBs3bp1v/fVl19+yTvvvENeXh49evQgOzubuXPnpu5hKSU1NTVUVFQc9PkPBU3TGD9+PF26dGHevHnMmDEDy7IQQtCuXTuys7M57rjjOOqoo9A0jccee4zbb7+dxYsXA7BixQqef/55qqurf3RfDjfRaJQ333zzvyLK9EdbVUuWLOGBBx7glFNOYezYsakaWi1RX1/PnDlzmDZtGtFolJdeeunHnv6/Fk3TkiUIfsU5Kb9msrKyyMjI+NnEtywJG6qaC0d1y06WzTmQbZ1IJPj888/Ztm1baltWVhZjx4792VfmNE2jQ4cOzRZURneEmQ16VsLhJ3vw+cjNXxKqLgWSAizPPPMM9957b1rsLE2aND8rO3bsYNu2bVx//fUUFxcDHNJCelNatWrFH/7wBx544AG8Xm+z4xQUFFBbW8uTTz7Jueeee0hpU3PmzCEzM5M+ffq0+LndbufOO+/kd7/7Ha1ateLEE09ssV2fPn248cYb+fbbbzn77LNZsGABRx99NHa7PdXXaDTKmjVr8Hg8uFwupJR8+eWXtGnTZr/zr0NlzJgxHH300ei6njKi96SwsJC77rqLRx55hHg8jsPhoKioCL/fTyKR+FV5nqSUlJSU0KlTp31+n8NBIpH4VRn1b7/9Nv369ePEE09MiW7+0O8/fPhwNmzYQI8ePfYag41e2w4dOjBixIi9hGP3RTAYZNq0aYwZM2afZUL79+/P2LFjuf7663n//ff36dW77rrrWLVqFaeccgqLFy+mbdu2ZGVloapq6jvX1tZSUlKS8tzX1tby1Vdfcfzxxx+23FGfz8ett96aMpj3db0vuugiZsyYQXl5OaZpkpGRQbdu3YjFYj/pPfpDiMVibNy48Sfv1w99tjflRxu2gwYN4vzzz+epp57imWeeoV27dgwaNIhOnTrh8/kIh8OUlpaybNkyFi5cSCwW46ijjuLOO+/80Z3/b8ayLKqrq/eZG5Rm/4TDYaLR6E8umNFI3ILFO5tv65EDzgM8B6WU7Ny5kxkzZqRWDQHOOuss8vPzf4Ke7h/LsqipqcHj8aS8HgNbQb4bdtaDEAqi0/Hk9j2RLZ+/iLQspJS8/vrrnHLKKQwdOvRXNVlJkybN/y6KouByudi+fTuvvvrqYVlYM02TtWvX7lVjV0pJNBrlvvvuY/bs2bRte2CRp0bWrl2LYRgHNCzLysr4xz/+wbRp0/bZZtOmTZSVlfHOO+9QVlbGunXrmDp16l6GwhtvvJH697Zt28jIyNinYfBTYlkWq1at4pprrvnVL3x+//33LFy48CdNmWss6/drSN1xuVxs2bIFn8/Ho48+eliOuXnzZpYsWbJXHmQoFGLTpk38/ve/58gjjzzoUpaxWIyFCxcye/bs/RrD5eXlbN26lauvvnqf1zaRSFBRUcEXX3zBjh07mDJlCvF4vJnacyNTpkwBkgtFpaWlTJ8+/Re5f7dv345lWXz55Zc/+7kPhUQiwZIlS5g4ceJPOgcMBAIMHTr0Rx3jRxu2Ho+Hiy66iDFjxjBt2jTmzp3LJ598wmuvvUY4HEbXdXw+H61ateL000/ntNNOo2fPnr+KQf9Lkkgk2Lx5MzabLW3Y/gCqq6upqan52by2MROWNDFsnRp0yQL9IE69dOlS5s+fn/o7IyODs88++xd5iJqmydatW2nXrh1OpxMhIMMBR7WBtxp0rSzVQb/T/0jdyo+o3JX02m7evJnJkyfTvXv3nzRHKU2aNGkaUVWV1157rdmiYJo0v3Y0TftVlMkbMWIEq1at+q8IH02TBpKpIz82zfCwJHg25rBccsklnHPOOZSUlFBRUUEkEkHTNLxeLwUFBT+5olaaND8VJXXJHNtGcl1Q6DlwGLJlWfznP/9pNjE78sgj6dy5869mLDh0GFAA09dC1ASEYKvRnTPP/S3/evxBIPk93nvvPc466yxOOOGEX03f06RJ87+LEAKPx/OrMBLSpPlvQgiBrutkZmb+0l1Jk+Zn5bArFzmdTjp16kSnTp0O96H/51AUJW0g/ECEED+rx/PbkuZlfgo80MZ74P02btzIBx98kPrb5XJx0kkn7VeN8Kdmz+smgF550M4Ha6uS2yojCsUnXEbfuR+xZMkSACoqKrjvvvsYOnQoXu9BfPk0adKkSZMmTZo0aX4mft0JEf/DaJpG27Zt02HIPxC/309+fv7PYtxKCd/ugEa7VpA0ArMPIMxpWRYvv/wywWAwta2oqIjjjjvuF8tFUlWV1q1bN8vFEgI6ZCRDqxuXWSSC1YkiLr3s/5p5S+bPn88rr7ySDm1KkyZNmjRp0qRJ86siXWvmF0JRFPLy8n71Ygu/Vtxu989W7qc+Buuqdv9tqNA9G9QD/HQlJSV8+umnKRU5IQQjRoxI1TX7JVAUhZycnL2um0ODwYXw8cZkPjHAuhqdS489nQED3mLOnDlAMjd88uTJjBo1iuLi4nTEQZo0aX4RamtrefbZZ1m5ciVFRUXceOONzcRnKioquOWWWxg2bBiXXHIJkBRvev7559m8eTM9evTgoosuwuv1Eg6HmTx5Mhs2bOC8886jf//+bNq0iRtuuAG32516T59yyimcccYZP/i5Z5oms2bNYtasWZSXl3PTTTfRo0cPLMti6dKlvPHGG9TU1NClSxfOO+88cnNzSSQSfPTRR8yYMQO32825555L3759kVKyYMECXnvtNbp168Yll1zyg6ss7Ny5k8mTJ7N69WoGDRrE73//+2Zzk02bNnHnnXcybtw4xo4di5SSzZs38+KLL1JaWsrgwYM599xzcTgc1NTU8K9//YuqqiouvvhiOnTowN13383ZZ59Nnz59qK6u5q677mLkyJGcccYZSCl5/PHH6dKlC6NHj97ntZVSsmjRIqZOncqGDRu47LLLOP7445FSUl1dzSuvvMKKFSvo1KkTF198MdnZ2Ugp+fbbb3njjTeIx+OMGTOGUaNGoaoq69ev5/nnn8flcnHVVVcRiUR48MEHmTRpEtnZ2axcuZInn3ySK6+8kj59+hAIBLjzzjuZOHHifmsn/zdgWRZffPEFH3zwATt27ODaa69tVn/WNE3effddPvroI2699VaKiopIJBJ89tlnvPfeexiGwbhx4xg8eDBCCJYuXcpLL71EmzZtmDBhApqm8cQTT/Ddd99hs9kAyM3NZeLEiS0KOR0sFRUVPPfcc6xevZru3bvzxz/+EU3TCAaDvPrqqyxevBjDMBg7dizDhw9H0zS2b9/OlClT2L59O0cccQQXXHABLpeL+vp6/v3vf1NSUsKFF16Yqo19qJimyYwZM/j000+prq7m5ptvTqm3w+450/Lly7nzzjvJzs4mHo/z/vvvM2vWLLxeL+eddx69evVCSsm8efN444036Nu3LxdeeCHz58/nq6++4sYbb0RVVT788ENmzpzJbbfdRl5eHmvWrOHZZ5/lnnvu2a/4VjQa5fnnn2fJkiW4XC4mTZpERkYGlmWxYMECXn31VaSUjB07NjVG6urqmDJlCitXrqRdu3Zceuml5OXlkUgkeO2111iwYAGnnnoqI0aMYPLkyeTk5KTG9N/+9jcikQg33ngjmqYxc+ZMVq9ezcSJE39yjaW0VfULEY/HWbhw4a+yXtV/Azt27GD16tWHRRr8QKyrgqrI7r8NFfoeQNBYSsk333zD999/n9pmt9tThex/KRKJBEuXLm1Wxw2SXttj24OnybyoPARbzHxuvPGmZl7bpUuXMmXKFMLh8M/V7TRp0qRpRiKRQFVVioqKWLhwYbN3QTweZ/LkySxbtiz1DE4kEjz44IMEAgEmTJjA6tWr+ec//5lS+ywrK2PMmDG8/fbbBINBAoEA5eXlXHvttdxxxx3ccccdHHPMMT+qz1JKQqEQ3bp1Y+PGjVRVJVdMa2pquPbaa+natSu/+93vWLVqFU888QQA8+bN46mnnuL888+nd+/e3HrrrVRWVhIIBHj77bc566yz2LlzJwsXLvzB/YpGozgcDrKysli+fHmzcjXRaJR///vfLFmyhM2bNwPJ63vnnXfidDq54oor+OSTT3j99debqbsOHTqUqVOnoigKpaWlfPPNN0gp2bVrF59++ikffPABpmkSi8X48MMPD1hqRUpJOBymsLCQurq6ZrXVn376adasWcNVV11FbW0tDz74IPF4nG3btnHrrbcyYsQITjvtNB544IH/1959x0dRpw8c/8z2Te+NkJBAEkoAKdLBAkgRPUFEf4oKnqiInt55p6J3KndivWY5C4rtBLGDBRRROQRUpPdOICQEQnrbbJn5/bHskIVESjo879crr2RnZ3e+MztJ5pnv9/s8bNq0SQ/cevfuTUhICN988w0Wi4Uff/yR3NxcNE1jzZo1/Pjjj6xcuRJN08jPz2f58uXnRIlGTdOoqKigQ4cO5OTkkJ+f7/f8nj17+Pjjj1mxYgVlZWVomsamTZt4+umnGTduHIMHD+bPf/4zBw8epLKykg8//JDRo0dTVVXFihUrUFWVLVu2MGDAAP1356677iI6Orpe7Xa5XFgsFuLj41m/fr0+cmzu3LksWrSIyZMnM3z4cB599FG2b9+O2+3m8ccfR1VVbr/9dlatWsVbb72Fx+Nh1apVlJeXM3ToUD766KOzTlCnqiqVlZV07tyZHTt2UFxcrD+naRqrV6/mm2++YcWKFTgcDjRN4/vvv+eNN97g5ptvJiMjg4cffpiSkhKKior49NNPueGGG8jKymLDhg1YLBYWLVqEw+HA4/Hw1VdfsWzZMr2m78aNG9m3b98pf388Hg+qqtK5c2dWrVpFdXU1mqaxZ88eHnvsMS677DLGjBnDM888w5YtW/B4PLz66qts3ryZ2267jYqKCp5++mmcTie7d+9mw4YNTJw4kUWLFlFQUICqqixcuBC3243T6eTzzz/nyy+/1Nv9zTff4HQ6m6QzTwLbZuKrbdWS6p21JqqqNklQC7CzAMpq/M0LtULaKaoFVFVVsXz5cr8A8sILL6Rr166N1MrT82vnXXww9KwRsJc7Yc0hhX5DhjJ27Fh9ucvlYs6cOaxfv17OXyFEs4iIiOCee+7h4osv9luuaRorVqwgOzub3/zmN/ry7OxstmzZwh133EGPHj2YOnUqS5YsoaioCLvdTklJiV6pwNejYLPZSEpKol27dnp91vqMUjEajYwdO5aJEycSHh6uL1ePlVXr1q0b6enpdOnShaqqKlRV5auvvmLkyJH079+fcePGERkZycqVKzEajZhMJvbu3UtZWdlpl1epTVJSEvfeey99+vTxW66qKl988QUGg4FLLrlEX75jxw4KCgq4+eab6dmzJ5MmTeKLL77A4XBgt9s5evQo2dnZxzLvKwwcOJBVq1bhdDpZt24dY8aMoaysjMOHD7Nz505UVaVjx46/emx973Pbbbf5lVWqrq7mhx9+4MYbb6Rbt25MnDhRD1pWrFhBhw4dGDFiBBdddBGDBw9m8eLFgPezzc7O5siRI3pN4K5du7Jq1Sr9BvDEiRPZtGkT1dXV/PLLL2RkZJwTVQEMBgOjRo1i0qRJJ5UdrKysZPbs2VxzzTWEhobqy7/99lsGDhzIkCFDGDVqFOnp6fzvf//DYDBgsVjIysqiqKhIn+akKApRUVH6705iYmK9bwrExcVx7733MnDgQL/lFRUVJCcn07lzZ3r16oXdbsfpdLJnzx6ysrKYMmUKPXr0YMqUKSxcuJDy8nLsdjtFRUXs378fu91+1gGXyWTimmuu4YYbbvA7XgBlZWX897//ZeLEiXrg6evh/c1vfkOfPn249tprCQgIYNWqVRiNRoxGI3v27KGiooKAgADatWtHQEAAW7dupbCwkLy8PMaNG6cH9j/99BP9+/c/ZfvtdjtTp05lzJgxfuv+9NNPtGvXjlGjRjFs2DD69u3LV199RUFBAd9//z1Tp06lZ8+e3HXXXWzcuJEDBw5gtVpxOBzs3btXT1LWr18/Dhw4oP9Ot2nThk6dOrFt2zaKiorYunUrAwcObJJRfk0W2Gqahsvlkl4e0aq4PLC7ECpdx5d1ifEO3a2LpmkUFBTwv//9z2/5uHHjmrW39lQUYNQJOd82H4H8KiN33nmn38XEwYMHee6555rs5oIQQtSkKEqtF0mlpaXMmTOHG264wS/Yy8/Px+PxkJCQAEBsbCzl5eVUVlaSmZnJsGHDcDgcTJo0SR/St3PnTu6//36mTZvGtGnT+OWXX+p1M6+uNoeGhjJx4kRmzJjBHXfcwdKlS5k4caI+5Nc3fcVutxMbG8uBAwcICgrilltuobS0lOHDh5OZmVmvdtUmLy+P+fPnc+ONN/oFJTk5OdhsNqKiogCIj4/n6NGjuFwuBgwYwIUXXojNZuO6667DYDDQo0cPtm3bhsPhYN26dQwYMIDY2Fj27dvHxo0biY6OJj4+/pRtrK2dBQUFaJpGTEwMiqIQEhKCoiiUl5ezb98+2rZtq9+sSEpK0mvMTpgwgaCgIDIzM7n44ouxWq306tWLn376Sa9t2qdPH4qLi6moqOCnn37iggsu0Ou/t2Z1HUtVVfnyyy8xGo1cdtllJw1Hb9euHQaDAavVSps2bdi/fz82m41JkybhcrkYNGgQvXv3BrwB3BtvvKH/7jz77LOUl5eftM0zbXdtxo4dS35+PlOmTGHKlClcfPHFdOzYkcOHD2M0GomNjQW8gXFxcTHV1dX06tWLwYMHoygKN9544yl7PH+tTbW1y+128+6775KSkuJ3w8jj8bB//379dzogIIDo6GgOHDhAaGgot9xyC4WFhYwePZqOHTsSHR1NUlISq1evJjc3l6CgIPr168fmzZtxuVxs2LCBnj17nlU7VVUlOzub5ORkPSFrcnIyOTk5eg9ycnIygJ5du6ioiHbt2nH11VeTn5/PpEmTiIiIID09HYfDQXZ2NuvWraNr16706tWL1atXc+TIEQoKCujYseNZHeMz1WBX2b7i5gBWq1U/gJqmUVJSwvbt2/nPf/5Dhw4dePTRRxtqs62WoigEBASc9/V8z5bZbMZmszX63Z8iB+wpPp44CqB/4qlft2HDBjZs2KA/bteuHYMHD272OdWKomC32+sMsHslQEKwt7wReHurdxQoDOt+AbfccgszZ87Uf88///xzFixYwNixY5t9v4QQwuVyMWvWLDp37kz37t1ZuXIlHo9Hz3MAtV8cm0wmxowZg6ZpfheAiYmJ3HrrrXpPTH3mB/6a0tJSvvnmG8aMGUNmZiZz587l888//9V5f4qi0KFDB9q3b1/nxXV9OBwOXnzxRYYPH05ycrJ+HOu6menbvt1uZ/z48Wiapv9fiI+Px2Kx6MMmf/e735Gdnc369evZvXs3ffr0adJrIUVRiI2NZfLkyX7HrkePHsyfP5+NGzdiNBrp1KkTAQEBbNq0iT179tRrfnVrkJOTw8cff8wjjzyCpmmoqorL5dJv5tS274qikJSUxJ0LmvQAAFg7SURBVG233aYfS4/Hg8FgYMSIEYwaNQrw9pD/2hzQs6WqKitWrMBgMHDrrbdSWFjIW2+9xRVXXFHr+r59sFgsXHXVVX7naUPaunUry5Yt4+9//7s+IsPlctW5vu/YZWRkkJ6e7nde9u3blx9//JHAwEA6dOhAt27deP3119m0aROqqtKuXbt6nZe/9jemtseKonDRRRcxZMgQvZ1Wq5WOHTuydu1adu7cyW9+8xtCQkJ49dVXCQkJITU1tV6jSs5EgwS2BQUFfPnll2zatAmAzMxMrrjiCkJCQliyZAnz5s1j4cKFxMfHM2bMmIbYZKtnNpvPerK68N55a4rkWwWVsL/4+GOTAbrHnvp18+bN88sc3LdvX/2uWHMymUx07ty5jn9QEGGHfm3gk2NTg90afJcFI9pbuf766/niiy9YtWoV4B1u/cILL3DhhReSlJTUhHshhBAnq6qqYtOmTRw9epQff/yRXbt2UVVVRefOnRkyZAgmk4mcnBySkpI4fPgwQUFBeg9cbcFhQEAAGRkZjV4LNCsri5KSEsaOHUtERATV1dXMnDkTVVVJSUlhz549+v4dPnxYH37dGAGtT1FREdu2bWPLli188cUXbNy4EbvdTmpqKu3bt8fhcHD06FFiYmLIzc0lKipK7/U6sV2RkZFkZGTwzjvvEBQURFxcHJmZmcyePZt9+/Zx7bXXnnU7IyMjURSFI0eO0K5dO0pLSwEIDg6mffv2fPfdd1RXV2MymThw4IDeAwUnl77zJfJ699136dSpE5GRkWRmZvLBBx9QWlpKt27dzrqdrcH27dvZv38/jzzyCOAdsfDEE0/wj3/8g/bt27N3715UVcXtdnPw4EEGDRoE1H4eKopCYmIinTt3btQ2O51OlixZog83V1WVTz/9lI0bN9K7d29UVSUvL4+EhAQOHTpEWFiYntCqMX9/Nm3axP79+/n973+vD4t+4oknmDFjBu3atWPPnj1ccsklVFRUkJ+fr19D1damfv368cYbb+DxeBg3bhwxMTFER0fz5ptvkpCQoI9COVMGg4GkpCSWL1+Opmlomsb+/ftJTEwkLCyM8PBwsrKyCA8P10dG+KZQnNhOo9FI//79WbBgAWazmQ4dOhAYGEhJSQkLFiygb9+++nFvbPUObMvLy/nXv/7Fyy+/jMPhwGQyYbFY2LlzJ+Hh4bz00ku43W5uv/12JkyYQHp6er2253Q6+e9//8uHH36o/1FSFIWnn366XkNxmprH49H/GZwLQ1uaWmlpKQ6HQx9+1Bg0DY5UwMHS48sSgyH2FDedDh8+rGcRBu/FUf/+/f3mVDUXVVX1P+613T0LMkPPeFi4CxzHbswv2w8VLkhJSeGOO+5g7dq1eg/I6tWrmT9/PlOnTj3roTxCCHGm3G4327dvZ9euXZSUlLBhwwYyMjJ49tln9VElb7zxBocPH2bs2LEEBweTmZnJyy+/zPjx45k9ezaXXXbZr/5drqqqYt++fXqSx5CQEKKiour1PycnJ4eDBw9SWlrKzp07iYuLIyoqipKSEhYtWkTXrl2ZP38+SUlJmM1mRowYwVNPPUWvXr30eYz9+/c/6+3Xprq6mu3bt5OVlUVBQQHr1q0jLS2Nl19+GafTCcDMmTNJSEjg0ksvxWazER0dzVtvvcXQoUN5++23ueKKK+rskTObzfTs2ZNHHnmEe++9Vx/ymJWVRWVlJYmJpzEMCigsLGTfvn0cPXqU/fv3s3nzZjIyMrjooot45513sFqtfPjhh/Tt25fAwEAGDBjA66+/zqJFiwgJCWH58uX8/e9/r/P9g4KCSE5OZsGCBbz55psYDAZ69erFc889R//+/f2SKLZ2hw8fZv/+/RQVFbFnzx62b99Ov379+OCDD/Texeuuu46pU6cSHx/PpZdeyv3338/SpUspKSlh9+7dPPzww3W+v6ZpHD58WL8pYzQaiYuLq1evrcvlYtu2bezdu5eioiLWr19PWloaqampLFq0iLS0NAoLC8nKyqJt27akpqaSkpLCa6+9xpgxY3j99de5/PLL/codNoSDBw/qv9M7duwgKiqKMWPGMHjwYMB73k6ePJmpU6cSHR3NqFGjeP7558nMzNSH6NfMSn0i3zzozZs3M336dAwGA5mZmTzyyCM88MADpzV3WdM0duzYwe7duykvL9c7Ivv168fcuXP1BG6rVq3iH//4B5GRkVx66aW88sor3HbbbXzyySd07979VzsxOnXqxJNPPknPnj0JDQ3FbDbrHZy33357k3Xs1Duw3bJlCx999BE333wzDz74IIGBgXpq6t27d3PllVfyt7/9jeTkZIxGY713TFVVdu7cSVhYGHfddZd+B6DmXbjWwOPxkJOTQ2BgoAS2Z6GkpISSkhKioqIarddW1WDjEaiuMfKqSwwEWby9m7XRNI3PP/9cz3YJ3mFYQ4cObRHDdT0eD4cOHcJsNtca2CqKN+NzYqh3bjFAQZU3uL0yw8B1113Hhx9+yKJFiwBv0oaXX36ZYcOG0alTp2bvkRZCnB+qq6uZPXs2+fn5REVF8eqrr3LnnXfSp08f/e9Qr169KCws1IPX+++/n7feeovZs2fTpUsXbrrppjqnZYSGhpKQkMCLL76o/+0eMmQIN910U73avWLFChYtWkR0dDTff/89BQUF3H333Tz//PMsWLCAH3/8kdTUVP7v//4PgAEDBnDXXXfx/vvvExgYyBNPPEFk5CmyF56hkpISXnnlFUpKSjAajbz44os89NBDfh0R/fr1IyoqipCQEDRNY8aMGbzzzjvMnj2b4cOHM2HChDr/x/lK3V100UV6Zmnf/0WTyXTaPeI7duxg9uzZ+kX67Nmz+dvf/sYdd9zB3LlzeeWVV0hLS+Pmm2/GbDaTmJjIk08+yQcffIDT6WT69Omn7AC56qqrqKiooFevXgB0796dIUOGcMUVV7SI/+ENZc2aNXz00UeEhITw008/6eWx2rZtC3hvHA0dOpQOHTpgsVjo2rUr06dPZ/78+VgsFmbOnFnn0HyDwaAn4vJNyQoNDeWuu+6qV7nDiooKZs2apSepeumll/jDH/7A3Xffzbx583jnnXcwm838+c9/pl+/fphMJv785z/rv/P9+vXjhhtuaPBh70uXLmXJkiXEx8ezePFiCgoKmDZtmj6FITQ0lKFDh+o3qy6++GIqKyuZM2cOISEhzJw586TEUzUFBQUxfvx48vLy9FwnvqHAw4YNO63rLk3TmDdvHnv37qVdu3b897//ZcKECYwaNYrHHntMH2X4wAMP0KlTJwwGA7fddpv+O56cnMy9995bZxCtKAppaWmMHj2a7t276+U4R4wYgdFoJDMzs8muDxWtnmlNv/zyS/74xz+yaNEifail0+nkqaeeYtasWaxcubJBhyk6HA4effRRHA4HTz31FHa7/bRep2ka8+fPZ8uWLUyfPr3Z57ZWV1ezZs0aMjIyGvyf1LlO0zQOHDhAcXExXbp0abSETNUeuPNLWLLP+1gBft8P7uwN5jpOn/Lycm655RY++ugjfV7KuHHjmDdvXovo0XQ6naxfv57k5OQ6e7srXfD7r+HrPcfnFl+RDs+NAIOisWTJEm666Sby8vL019x9993861//OuPfK1/q+6+++oonnniiRSfXEkIIIYQQLVe9ryKdTiehoaF+iXzMZjMJCQm0a9fuV+9CnC2DwcCSJUu46qqriImJ4eqrr2b06NEn3Unw1Yzz1WsqLy/H4XBQWlqK0WjEYDAQEBCAy+Xyq2Fls9n0os++4MRoNOopxH1Dc2quWzPbm9FoJCAgAIfD4TdZ3G63oygKlZWVOJ1O3G63vt3Kykq/BBeBgYGoqqrXvQLvcfWl2fatqygKgYGBeDwev4zTFosFi8VCVVWVnuzBYDBgt9vxeDw4HMcLs1qtViwWCxUVFfq8UN+6NdtY17ExGAwEBgZSXV3td2x8KdQrKyv9jmNtx8bXa11VVaWvazKZsNlsVFdX17qu0+mkrKwMo9FY57Gx2+16/buax8Zqtfrtry+ZV81jU+pUWHsoCG9IC4FmlViLg6oKD6bgIJxO50nHZtu2bX51AA0GA1dfffVJn4/v2PhKOtTc3xPPMbvdjtFo9DvHfOvWtr/gPZ9qrhsQEEBFRYV+3CsrK1FVFVVV/dplNpuxWa30iXXx7T4LLtW779vyYfdRJ/E2B507d2bUqFG8/fbbetvfe+89rrzySn0ImNvt9jvHfOeNb7u+c8Fms/md90IIIYQQQpyNBukecblc5OTk+F3kFxUV4fF4OHjwoF/B4pCQkHrNNTQajQwbNozevXsTHx/Pjz/+yPTp0ykpKeHmm2/2W1dVVV555RUWLlyIpmkcPXqUPn36sGHDBgwGA0FBQXTp0oX8/Hz27t2rv85Xq8w39h0gPDyc9PR0Dh06RHZ2NuANGNq3b094eDgbNmzQg5nw8HA6d+5Mdna23qvly7BnMpnYvHkzbrcbVVUpKCggISGBffv2UVBQAHgDlJ49e1JVVcWuXbv0oC4+Pl6fl+Krj2oymejZsydlZWXs2LFDDxqSkpKIj49nz549+vEPDAykY8eOVFRU6EXrwTt3MiEhgW3btulBTnBwMB07dqSgoEA/NgaDgZSUFGJjY/VU4+ANNHv06EFubq5eNF1RFDp27Ijdbvc7jiEhIXTv3p2DBw9y6NAhvQ3du3fXhxf5zqPo6Gjat2/vdxxNJhOZmZkEBgbqiUIAEhISSE5O9juONpuNjIwMVFXV1wNvZsvU1FR27typB4s2m40uXbpQVlbGrl270DSNtUWhFDuOJ4sIMzlwH9nOFkc1ffv25ciRI+zfv18/5qmpqfz000/6nBLwlpQYMWKEPp/Fp3Pnznog7DvmERERpKWl+R1Ho9FIx44dCQwMZOPGjfq2oqOj6dChAwcOHODIkSOAN2BPT0/HbDb7nY/R0dF06tRJPxd882yjo6NxOBzs2LFDv/nhO47tDVnYje1xqd4/EXkV8NWmQnrZ9qB6PAwZMoSlS5eyb5+3O7ugoIAZM2bw6KOPMnjwYAoLC9m1a5e+v2lpaYSHh7Njxw4qKioA7/Cc9PR0/bEQQgghhBBnq95DkT/99FMmTZpEly5d/HpM8/LyOHz48ElDRa+99lqmTp1an036cbvdzJgxgxUrVvDNN9/4DYXUNE0PIH1zHzdv3sz999+vz/e1WCwnlQMwm80YDAacTqdfz5vZbD4p3b1v3ZpB/a+t6xuq7euNtdvtek9wzSy6FovlpPTgvoLsLpfLb12r1aqnZf+1dX2FlE98X5PJhNFo9Ntf37q+DHgnrltzf33Hsa79PTFdfF3r+j7PUx1zi8WC0+nE4XDopaXOZH99655qf59YYeSdzSa0Yz22Fyep/HOYk0Cz95ifeN6Ul5dz22238emnn+rLpkyZwgsvvIDRaDzpHDvx2JzNOXbiuVDzHKu5ru+Y+XqOLRYLAQEBdR6b6moXd35t4tus43OKxqW7eWigm2CL96bR888/z0MPPaS3PzIykn//+9/83//9n/675/Nr59h3333H4sWLZSiyEKLB+LJ8ulwuv/8rQgghGp6vXJpvRGJz5Vyp91VkSkoKN9xww0nL60rx7SuU3FB8mdZKS0tRVdUvsPVdOIP3gJtMJr3+ac31TCZTrRfUtaWmNpvNtc6VrC3T26+t63Q69cLrdru9zgnZtc1ZrG1do9F42uvW9b617a/BYKj12JzJ/p7JcaytXSeuq2kaBQUFlJWVkZaW5veahtrfcidsOnp8jqnJAD3iDUQE2fTEUTXPG03TyMrKYunSpfp7BQQE6EPkFUVplHOsrv2ta12Xy8XBgwdJSEggMDAQg8FQx7GxcHm6t9SP7xj8dMjEEYeJqGBvkqlbbrmF9957j40bNwLeXtt3332XESNGEBUVdVr721g15IQQ5x/f9KN9+/axd+8+9u7dw8GDuZSWFKNp6qnfoAGpLkcTb1PD7apCo+kCeA1weNwcv2eg4Zu6c/Jj388nfj/xHall+XEqGo46auk2FremUa027Y0Rp0qTb9PlAXfT/pqguIEmvulkdqk04a8JAIZfqWHbKDTN+4E21ebQKDxaRKDdzsSbJ5DYJpH09HS6ZHYmJCSkSYPcege23bt354UXXjjt9eu7c06nk927dxMSEoLdbic3N5dFixYxYMCAVtXbo2kaDoejzmLn4tf55v421l34bfmQd3xKK2YDDE6qOxsywIIFC/SyEOAt9t7SSlCdyXnXJwE6RsE276h3csvgqz3eZUYFoqKi+NOf/sQdd9yhDydevHgxCxYs4Le//W1j7oYQ4jzn8XgoKyujtLSUI0eOsGXLVrZt28ahQ4eIiowmsW1bUlPTGNB/CMGBNhSa9ordUZyLx1l56hUbUElpFh5P0+UsUDWNA5UlTRojVKsq2VVNe1zL3R5yq5s2MDnqhKPVTRt9FVZAefWp12tIxlIPirtp9zO0uBqDpwm3qWnYCktp0mja7UErK2u67QEmUwSVZQ5eem4uLreDsPBAomLCCAgIpF+/Cxk12tvpERoaSnR07Z0fDdKO+r6Bbxjozp07Wbt2LQ6Hg7Zt29KvX79ay4nUV0VFBf/+978pLi7GaDRSUlJCTEwM99xzj5QaEQ1C02BrPhQdz31EVAB0iq77NU6nk08++UR/bDQaufDCC0lMTGyV56WiQHQgXNwOdhaA73/Agh1wSw8It3l7ty+99FIuueQSvvjiC8AbOD///POMHj36rIuGCyGEj29IsaZpVFdXs3v3bjZs2Mj27dspLioGFIJDQkjrkMboUVfQvn177PbjJfQURcHlrER1O+vcRmOoUsvwOJuu+oKmaahqIKradAGYR1MJNDibNLA1eTzYDU17k8Lt9mBp4koaJqOGsYkHMymeX7953yjbrHajNOWh1TQUMxiMTXjWqioGk/lXxiE0PA03GJu2EoemGTCYFExGGzZLMJ5qyDtQjdtTypYN7/Pyf95CVd10SGvHgIEXEhUVw4iRw+nUqSN2ewBG4/Hhy/W5bq53YKuqKp9++imPP/44hw8fRlVVbDYbl112GY899hjx8fENemEfEhLCww8/TGlpKW63G6vVSmxs7GnXQWspfNmMW1Mvc0tisVj0LNMNrcIFW49CVY0b333bgO1XPqo1a9b4JUsKCwvj4osvrnWocXM6k/POavT2Un+2A3KO3fjLKYNv98LVnbz/AOPi4rjxxhv58ccf9aRdO3bs4M033+TBBx9s9rJaQojWS1VVli9fzs8//8yBAwcpKyslMjKKjIxOjLhsNHFx8Xqeiuac0yWEEMKr5t9hRVGwGOyYTTYCCEfTVI4ecvLpB0txeRy88focTGYFq9XK+AneyhrDhg2tc5rd6ah3VLVz506effZZAgIC+Otf/0pYWBjLly9n3rx5ZGZmcueddzZo8GY0GklOTm6w92suZrOZbt26nXpFUau4uDji4uIa5b3zK2FL/vHHCnBJu7pn/bjdbhYsWEBZjWEfSUlJDBkypMVdaJlMJn3++6napijQKx66x3qHIWt45998vA2Gp0LosV7b3/zmN3z00Ud8+OGHgLf3+r333uOyyy6jd+/eLe4YCCFaB03TWLNmDQsXLsLlctHnwr707t2HjhkdCQsLx2azYbFYZJ6+EEK0YMevA4/dgFQUFMVIdVXVsfKQFbwx+21KS0u56KIhzRvYHjhwgPz8fObOnUufPn1QFIVRo0ZRWVnJypUr+e1vfyu9krXwlUKKjo5ulCHb57qioiIcDgdxcXENGjhpGuSVwZ7C48uiAyAjsu7XHDp0iFWrVvllAR4xYkS9ylo1FlVVycnJITw8nKCgoFMeO5sJfpMB3+wF17HRX7sKYfUhuLSdN/i1Wq3cd999LF68mJKSEsDba/vhhx+SmZmp19YVQogzYTAY+N3vfsddd91FSUkJ27ZtY8OGjbzzzkoc1U4sZgsxMbGkpaWRlpZOmzaJftcbclNNiBagtjxh4px2Yv4bj8eJ012Fy+3A7XESERHKJZf0Izo6hiEXDaZXr55ERERgMpnqfaOy3hFnZWUlkZGRJCUl6f9EAgIC6NWrFx988IEkR6qDx+MhNzeX4OBgCWzPQllZGcXFxcTExDTo3XpVgzWHoLLGMOTMGIiw1z73RNM0Nm7cyNatW/VlJpOJa6+9tsHa1JA8Hg95eXlYrVaCgoJO6zWDk6B9OGz3jjTmaCX8bz/0awOBx26qde/enZtuuokXX3xRL/XzxhtvcMMNN9C9e/dG2hshxLnMl8PDaDQSFRXF4MGDGTRoEC6Xi+LiEkpKijlw4ABbtmxl2bKlVFZVkpCQSEq7FNq1SyE6Oprw8AisZrmqFqLZyK/feUNVPaiqG4/qxuVxYLEaiI4NIzAwjPapvbj6mitJTm5HYGAgbdok6CU7G1K9A1tfGZ0TG+arCSq140Rr4tFg6f7jjw0KXBAHIXVMla2urmbp0qUcOXJEXzZo0CDat2/fyC1tOnYz/F8mzFjmDfw14Lt9cH2mtyfb12s7adIkvvvuO7Zs2QJ4y//MnDmTd955p9byQ0IIcaZ8tdBjYqKJiYkmLS2NoUOH6mXgdu/eze7du/nxp+UcPpxPZWUFwYEB2APsTXp97aoqRfM0bSZdh6MITWu6zgQVKHFWN2nyKLfqodjpbNLh59WqRpm7aTtpKjxQ3sT9QpoTzE28TbXShQEDSpP9dmq4qjy4mzA2UTRQHY6mLTGkqlDdtAnzPG43qseFyWhi3IQxdOnckbZtk+nVuwexsbFN9jvbIGOE9+3bx0MPPURAwPFMhNu2bWPXrl386U9/8hsrPXToUMaOHdsQmxWiwRVUHi9vAxBmhfRIbx3b2pSVlfH111/rjxVFYfTo0efU8FsFGNAWkkNhX7F32cFS+OGA99goePe7c+fOjB07lt27d1Nd7a0ZsGTJEr777jtGjx7dXM0XQpwHFEUhKiqKqKgo+vbti8fjwel0UlFRwfPPP8+QIUMICQlp7mY2snO/IyEraz+7du1i6LChTRgMNb1z/5P0jiCbN28eEyZMwGxu2gy+Ta5ZPtCm3ejatWtp3749/fr1JyDAjtlsbpbpIPUObH1ZXxctWlTr859//rnf4+joaAls8fZod+rUSYYhn6WYmBjCw8MbPOvuzzlQVeMme1zQ8V7J2qxfv95vGHJSUhL9+/dvsdmATSYT6enpZxR4Kwq0DYGLkyGr2PunUgPmbfb22vqGI9tsNqZMmcKHH37Ijh07ACguLua1116jV69exMTEyJw3IUSjUxQFk8mEyWTCarVSVFREly5dpATZOUEhKyuLvn36StKwVs7pdPL6a6/R58ILZVRXK6dpGrt378bhcBAaGtKs13r1DmxHjRpFbm5uQ7TlvFPbEG5xegwGQ4MHjx7VO3dUPXaTSwFSwiEptPb1NU3jv//9r9888l69etGpU6cW/bmezXlnM8HQFFi0G/IqvMsOlMCSfd7kUj5t27blj3/8I3fccQcejwdN0/j2229ZuHAhN910U4sN+IUQ5y5FUVr032RxZuSzPHfIZ3luaQmfZ71vd/n+YZzJl/CWiNm1axcVFRXN3ZRW6ejRo2RnZ6OqDVeo/WilN+Ovb/CG0QB9EuoehnzgwAGWL1+uP7bZbPTv379FZkP28Xg87N27l9LS0jN6nXJsrnH6sezQGt4syQt3Qbmz5noKV155Jf369dOXlZWV8eqrr1JcXFz/HRBCiDOgKArXXHPNeTAM+fzQrl07hg0bJteS5wCj0cjEiROlcso5ok+fPnTt2rW5m1H/wFacHU3TcDgckjX6LLndbqqrqxssOZmmeefWHqlxn8FsgIFta19fVVUWLFhAXl6eviwqKoqRI0e26OFR9TnvgiwwrpM30FfwBrcbj8D6PO/x84mKimLatGmEhYXpy1avXs2cOXMa9EaEEEKcisFgYMiQITLt5xwRGxtLz549JbA9BxiNRoYOHSqB7TlAURQyMjJITU1t9t/NlnsFLkQTUjXYkg8FVceXpYR5v2pz+PBhFi1aRFXV8RcMHjyYLl26NGo7m5OiwLAUbxIpn0Nl3uHb1TXKIxkMBoYNG8Zll12mB/kej4eXXnpJpi0IIZqMpmknfYnWTz7P1k9+N1u32j672j7T5vhcJbBtJgaDgfDw8HM/E1wjsdvthIQ03AT1MidsPwruGh2KA9tCbZ2vvtq1a9eu1ZcZDAYmTZrU7HeqTsVgMBAWFuaXqfxMBJjhqhpzan2lf/KrTu61nTx5MsHBwQDExcVx44036snmhBCisRUUFPC3v/2Nm266iT/96U/s2rVLLqBbOVVV+fbbb7nooov4+eefm7s54iyoqsqqVau46667uPnmm7nvvvv0hJOi5dM0jW3btnH33XczfPhw/vOf/+hB7Ny5c5k2bRqTJ0/m4YcfZu/evU3+N1cC22ZiMplo3749QUFBzd2UVikyMpLExMQGGfaraVBYBZuOl6LFqMCgpNrrijscDj7//HO/2rX9+/end+/e9W5LYzMajaSkpJz1fDODApemQFKNl+8p8vba1qQoCkOHDuW6665jypQpLF68mD/+8Y9ER0fXo/VCCHF63G43r7zyCqWlpTz22GPEx8fz9NNPy3SIVu7IkSO8/fbblJaWUlZW1tzNEWehoKCAxx57jEGDBjFjxgxCQ0N59tlncbvdp36xaBHsdjtDhgyhY8eO+pQ8TdMICwvj1ltv5dFHHyUsLIy//e1vTZ5LSALbZuLxeMjJyfEbyipOX0lJCfn5+Q12J2hfMewvOf449Vg25No6YA8fPswnn3yiPzabza0mOYnH4+HQoUNn/YdGUaBdmDfo9/3x8JX+qTrhf5LJZOIf//gHL7/8Ml27dpXeWiFEk6msrGTp0qVMnDiR9u3bM2HCBHJzc9m/f/+pXyxaJLfbzdtvv82gQYNITk5u7uaIs+Qrx5WQkEBsbCzR0dFYrdYWP+JNHNeuXTvGjx9PamqqvsxgMDB69Gh69OhBSkoKl1xyCYcOHWryOEcC22biCzAcDkdzN6VVKisrIz8/v0HuvmvATwePl/kB6BgFEbWUetU0jYULF3Lo0CF9Wfv27RkwYECrKGWjqip5eXn1+kMTaIbBSRBao+zc7kLvMax5n0FRFAIDA1vFcRFCnFvcbjcOh0OfDhEREYHJZPIbaSNaD03TWL58OQcOHOCaa66R/yutWHh4OLfeeiszZ87kpptu4rPPPpNygK1IXRVuai4vLS1lzpw5XHTRRX6JRJuCBLbivOdW/YfSmg3QNQZCaulgLCsr480339QfGwwGBg0aRGZm5nlzt1FRvIFtWsTxZQ43vLvRO6RbCCFaAkVR9FE9vpugcvHcOpWVlTFr1iyuueYaDAaDXhlBhq+2PgUFBcyZM4dbbrmFmTNnMmzYMN555x0pf3kO0DQNp9PJP//5T8rKypg2bVqTZ72WwLYZGQyG8yYYamiKojRYWZ19Rf7DkCPt0DnKO5/0RN9++y179uzRHwcHBzNu3Djs9lq6d1sg33Gr73kXbIUJnY8fIw1YlQuL9/on4BJCiOZgsVgICwvTR9fk5OTgcrlo27aOGm6iRSsrK+PIkSP861//YvLkyaxZs4Z//etfrFy5srmbJs5Qfn4+OTk5jBgxgoyMDK644gp27dpFQUFBczdN1FNFRQVPPfUUeXl5zJw5s0GTvJ4uKR7VTMxmMxkZGVJb7yxFR0cTFhZW77vvmgY/5fgHY5EBkBF58roVFRV8/fXXlJQcj4Lbt2/P4MGD69WGpmQ0GunQoUODBOJDU6HbJlh/2Pu4zAnztsDQdhAdWPv8ZCGEaAp2u52rrrqK119/nerqar788kt69epFTExMczdNnIW4uDjee+89NE3D5XJx2223MXnyZPr06dPcTRNnKDIykoiICF5//XV69+7NF198QXJyMlFRUc3dNHGaSkpKWL16NTt37qSsrIzvvvuOCy+8kFdeeYVFixYxffp0srOzOXr0KGlpaU2aY0UC22ZktVplWNRZMplMDXIXyKXCyuzjga0CZMZ4A7OafOnNly5dqg9pUxSFW2+9tVXdnFAUpcHOuzAbTO0Nf/oGSp3eZRvyvMHtXX1qzygthBBNwWg0cuONNxIeHs53331H165dGTdunIySaqWMRqOeVd/j8XDVVVeRmZmJzWY7xStFSxMbG8uLL77I/Pnz+fbbb+nYsSNjx45tNSPfBBQXF7NkyRJsNhtWq5Wvv/6a9PR0bDYbffv2ZdmyZQDExMQwZcoUCWzPB263m02bNpGWlkZERMSpXyD8HDp0iNLSUjp27Fiv8fsHSvyHIRuOzR898drH4/GwbNkydu3apS9LTk5m1KhRZ73t5uB2u9m6dStt27atd+kdgwJ923h7bhfs8Cbf0oA5m+Gy9t4EXEII0VwsFgvjxo1j3Lhxzd0U0YCMRiNTpkxp7maIs6QoCsnJydxzzz3N3RRxlpKTk3nyySdPWv673/2uGVrjT+bYNhNN0/B4PFIs/iz5jl/93gP2FMLhGvkKrCa4MOHkdauqqnj//ff9sjBffvnlxMTEtKoegIY+78Js3rm2kTVutOZXwJxNUOVqkE0IIYQQQghxShLYivOWW4XN+f6ZfHvG1V7mZ8WKFaxZs0Z/HB8fz+jRo8/7oTPKsV7b32QcX+bR4Os9sCLbv4SSEEIIIYQQjUUC22ZiMBiIiorCYrE0d1NapYCAAMLCwurVW1pcDWsP+S8blATGE97S5XLx5ptv+vUQX3DBBfTu3btV9daC97yLjIxs0PkORgNM6g7JoceXHa6AuZuhrLrBNiOEEEIIIUSdJLBtJiaTiZSUlFaVeKgliYiIoE2bNmdd8kfTILsE1uYdXxZkhgti/cv8aJrG5s2bWbVqlb7MarUyYsSIVpnBz2g0kpSURHBwcIO+b3ww3NjNWwPY54cD3vrAMtpeCCFES6FpGqqq+n1pmqZP0TnV86d63+aYYlZXGxuyTc25f0KcLglsm4nb7Wb//v1UVlY2d1NapaKiIg4dOuQ35/VMLdgBlTXmgfaMh5Rw/3XcbjefffYZubm5+rKYmBiuvfbaBquj25Q8Hg/Z2dmUl5c36PsaFRjVAfonHs+G7PTAC6vgiNRcF0II0UJUVlbSq1cv+vXrx5AhQ7jkkkt48MEH9ZrHCxcu5IILLmDgwIEMGTKEkSNH8tJLL53yei0vL49Jkyaxc+fOptgNnaqq3HvvvXz00UcnPVdVVcXtt9/OnDlz6r2d6upq7r77br755hsJbkWL1fquzM8Rqqpy5MgRqqtlrObZqKiooLCw8Kz/uBY74Nt9xx9bDN65olEB/hmRc3Jy+P7773E6nfqyq666itjY2LNterNSVZWjR4/icDga9H0VBdoEwzWdIajG6PqsEnh/izfIFUIIIZqbpmlkZWUxefJknnnmGe68806+/vprnnzySSorK/Ubvw888ABPPfUUI0aM4JlnnmH+/Pl1XnN4PB7mzp1LUFAQKSkpTbk75ObmsnnzZlJSUk6aHqVpGnl5eZSWltZ7OxaLhQEDBjBr1qwGv4YQoqFIuR9x3tE07zDZ/Bo9iVGB0C8RTIaa62ls2LCBX375RV8WGBjI9ddf34StbT0UBYalwGdt4JtjNw2cHvh8J1zcDrrGnFxGSQghhGgOnTt3ZsCAAWiaxuHDh3nttdcoLCwEvHk8evXqRWJiIhdeeCHffvsty5YtY/z48bXmqCgsLOSDDz7gySefxGKxoGkav/zyC+Xl5cTFxbFmzRoCAgIYOnQoNpuNpUuXkpubS2ZmJr169cJoNFJcXMw333xD79692bRpE4WFhfTu3ZvOnTuzY8cOVq1aRUhICJdccgmhoaEoioKmaaxevRqr1UrXrl0Bb6C7dOlSVFWle/fufu10OBxs3ryZHTt24HA4aNeuHQMHDsRms1FYWMjChQsZNWoUkZGRADidThYvXkxiYiIXXHABl156KbNmzWLFihUMGzaskT8hIc6cBLbNRFEUzGZzqxzO2hIYjUbMZvNZvbbKBQt3g6NGL2KnKOh+QidsUVERs2bN8ht+NGLECNLT089quy1BY593djPc2w9+OeTtFQfYWQjvbYa0Id7nhRBCiJbEYrHUWgpPURSMRiMmkwmr1VpnwsgdO3ZQXl5Ox44d9WVffvklX3/9NXFxcYSEhLBlyxaWLFlCfHw827Zto7Kykn/+85/MmjWLAQMGcOTIER555BEyMjIIDAykuLiYF198kalTp/Ltt99iMpnYtGkTy5cv55lnnsFoNOJ2u1m+fDn9+vXDYrGwb98+7rrrLiorK2nXrh3z5s0jOztbb9OuXbt49tlnsVgsqKrKnj17GDVqFA888ACKovDcc89hNBq57rrrUBSF3Nxc7r33XmbPng1AXFwcSUlJLFu2jIsvvhiTScII0bJIVNVMTCYT7du3l+RRZykiIoLExMSzCtB2FMKOguOPFWBMGpiNx5e5XC5ee+01vv76a31ZYGAgI0aM0O+UtkZGo5GUlBRCQkIa5f0VBTpEwPhO/km4Pt8JGw83yiaFEEKIM+ZyuaiqqmLz5s18+OGHdO3albCwMMA7Yqu6upqysjK+/PJLDh48yJgxY+q8ob5mzZpaE4Lm5OTw+OOP8/bbb/PMM8/w8ccfU1FRwVtvvcV7773HBRdcwAcffKAH1EVFRfTu3Zu3336bOXPmEBMTw0svvcSDDz7I22+/zYwZM/jqq684cuQI4J1Du3LlSoYOHYqqqsybN4+CggI++OAD3njjDX7/+9+ze/duvT2pqanMmjWLN998k7feeou///3vvPvuu+Tk5BASEsK4ceOYN2+eHuQvWrSI5ORkMjMzAW+g361bN9avXy9T6USLJLdampHVam2wnjPfTUZVA+3YY9/PquZ9XOmCo5WQV+EdhnukAvLK4UglFFR6gxK7CQLMEGjx/hxohgCL93vnaG/WYJvJG7Q0Z2xnMplQFOWMA0yP6i3xk11yfFlMIFyUfPyxqqosXLiQF1980a/ET/fu3Rk6dChGY40IuBWyWCyNOlLAaoSrOnrr2G476l1W5oSXVkO3WOm1FUII0fzuvvtugoODMRqNJCcn8+ijjxIUFAR4ezavvvpqFEUhOzubm266iYsuuqjOa46cnBzCw8NPCnz79u1L+/btURSFzp07ExYWxiWXXKKXekxPT2f79u16Ho+IiAj9OiM0NJTU1FQiIiLo2LEjiqLowXNeXh7x8fHs2LEDs9lMamoqHo+H5cuXM3LkSKKiolAUhd69e+tBqc8333zD119/TX5+Pg6Hg9zcXPLz80lNTWXUqFHMnz+ftWvXkp6ezueff86VV15JePjxzJqxsbEcPnwYt9vdYJ+FEA1FAttm4nK52LhxI+np6fpchjOhaVDt8Q73LKyCoiooqYaCKm+QWljlDWILqrxfRyu9wYV2LNjF953j30904p9vowE6RsIV6TCwrTeDcKC5eQLcQ4cOUVJSQufOnU97KIymeY/Rt/vAVSOZ8uVpEGL1raOxceNGHn/8cQ4ePKivExsby4wZM0hNTW3I3WhybrebLVu2kJSURExMTKNsQ1GgS5S31/aZld7z1GL0HvPsUkg/89NdCCGEaFD3338/vXv3xmq1kpCQQGBgoB64JiUl8cwzzxAVFcWiRYt48803ufzyyxk6dGitwa1vvuuJgoOD9RvJZrMZq9WK3W7X38M3JNj3WpvNhs1m83s+ICBAD5iNRiMGgwG3242maSxZsoRu3boRHR2NqqqUl5cTGnq8qLzRaNSDdVVV+fTTT3niiSe45ZZbGDNmDA6Hgw0bNlBdXY2iKHTp0oW0tDQWLlxIaWkpBw8e5IorrvC7Ga5pWqsdtSbOfRLYNqMzzejr8sCBUsgqhr1F3q/cMm+wkFsGVQ188+zE1rlV2Jzv/UoJgz5tvAHukGQItzXstk+rfWeREXl/MayrUbs2xOJtvy9pVGVlJU899RRr1qzR17FYLNxzzz1ceuml58Qf86ZI028weHttP98JRQ4Y29H7lRx66tcKIYQQjS01NVVPuHQim81Gp06dSExMJCMjg82bN/Of//yHPn361DqVJzExkU2bNuF0OgkICNCXN8Q1Q12j08rKyvjll1+4/vrrMZlMuN1ukpKS2LZtm/5/vrKykqysLMCbuXnp0qWMHDmSP/zhDyiKwvbt2/3K/5nNZq6//nqmT5/O7t276d27N8nJyX7bzcvLIy4uTubXihZJzsoWTNWg2u0dzrlghzcgK6yCChdUOL09YfWl4B1WXHM+pG8oc83vJ9pX7A2wv9oNbUNgdJq3J7dNsLdnt6VauNu/dm3XGEiP8PYyut1uXnzxRb+U/gaDgWuuuYZbb731nAhqm1KkHf52ibe3NiXM+10OoRBCiNZCURQCAwOZNm0a48eP55dffmHo0KEnrde7d2/+85//UF5ers/TbWxZWVnk5OQwaNAgFEXBZDJx1VVX8fDDD/PJJ5+QlpbGxx9/rGd6NhgMJCYm8v3337N69Wo0TePVV1/1m3IF0KtXL4KDg/n66695++23/XprVVVlw4YN9OzZs9bs0EI0Nwlsm4nBYCAyMlKfZ1GTpkF+JazP8wa0i/eAU63lTU6D2eCdE2s3e+fM2s3eObQBx+bSRtq9tVsjA7yZxKrc3q9K17GfXd6fixywp8g7L1dvJ96hvSXHenHf2QgzLoKhKf6JmBpDQEDAGQ+HKayCL3cdf2w2eEv8xAV5g9oFCxbw3HPP+SVE6NevH9OnT9fnq7R2BoOB8PDwJvmHpCjeGwe+n4UQQojmpigKoaGhdfY4WiwWgoOD/f7n9+jRg5EjRzJv3jyGDBly0lzatLQ0IiIi2Lx5M4mJiYC31/fE3tvg4GC/7dZcx2Aw6HN+fex2O6p6/ALQN7TYaDSybt06kpOTiYiI0N9/5MiRHDhwgMcff5ygoCAGDBjA4MGD9dwaN998M/v27eO2224jOjqayy67jE6dOvm1KTIykv79++tzdGseh9zcXA4ePMiUKVOkx1a0SIrWFOMSWwBN05g/fz5btmxh+vTpLTIBkKZ5A8jvs+DLnbD6kDdwPBWj4u01bRMM4XYIs0Go1Ts8ONjqnQcbZPF+BVsh+NjPgRbva08VdPjatS4Plh/wti+ruPae3LYh8PBgGJ7qXxO2JViwAx5YcnzIdmwgvHw59IzT2LlzJzfddBOrVq3S1w8LC2POnDmMGjXqnAhqWyJN0/j+++/56quveOKJJ+QfpRBCiEalaRqVlZXYbLZarwXdbjdOpxObzab3VmqahtPpRFVVvzmwPh6PhxdffFEfsmyxWPT1fWWCNE2jqqoKq9Wqb7fmOpqm4XA4/J6vrq5G0zRsNu98L1VVcTgcWCwWZs6cSUZGBhMmTPDrVXW73foNeovFgtvtxmQyYTab0TQNl8uFy+VCURS9nTW3WVRUxPXXX8+ll17KH//4R31fVVXlv//9LwsXLuTtt9/W2yRESyJXkc3E5XKxe/duEhMTsQcGU1btHSb7/hbv3Nky58mvMRm8GWfNBmgT4s1QnBnj/Yq0g9Xkfc5kOPbd6D/E+GwpCkTY4dJ2MCARftsDftgPH27zls2pdHmHTYN3vu/jy7zbv6Rd4wxL1jSN/Px8KioqSEpKOq2bFJUu+HYvOGrMQ06P9PYoVlVV8cgjj7B69Wr9ObPZzAMPPMDw4cPPqaDW7Xazd+9eoqOjCQsLO6f2TQghhDgV3/DiuphMppNusiqK8qsjnYxGI9dffz0PPfQQ+/btIyMj46QReYqi+PXgAn7r1Pb8ids0GAz6iLVp06YREBBwUpWDE9tfs3fZF8zW3K5vXY/Hw/z58/nggw8oKipiwoQJftcITqeTdevWcccdd8gwZNFiSWDbTFRVpaioCHdALGv2wHubvUFibb2gdhNkRHqTHPVJ8AZkUQH+QWtTxCeK4h3K3MYE12XCuE7e4dJzNsPXe44HjQfLvD2jzwz3ltFpjJ7bqqoqSktLTysRkqbB9qOw8Yj/8R3fCVSXgxdeeIFPP/1UH+7jS55w6623nnM9iKqqUlxc7Jc1UQghhBD1ExUVxaxZs5pkW4qiEBUV1eDvGxoaysUXX8zgwYNp27at33NWq5V//etf+vaFaInOrav2VqTKrbCmNJZl+wPYWHg8KNQ4XmbHqECPOG8AOSARkkJbRmIm398zq8mbGbl9hHe+7ifbvAmtNOBoFTz+g3e9i5Obt90eDTYchoOlx5fFBUK/NirffvstL730Ei7X8YxSXbp04fe//z2RkZHyx1sIIYQQp9TarxeMRiPDhg1j2LBhtT7f2vdPnB8ksG1CvtqzW/PhhVUmfj7Yjgq3f8RnwDsnNjMGbu4OFyZ4hwG3hIC2NoriHQb9p/6ABh9tO14jdl8RPPY/mHkJDGrrLQHTUAwGw2nPky53nly7dkR7jaMHdzNjxgyys7P15SEhITz99NN07dr1nPwj7suceC7umxBCCCGEOH9JYNtEXB7YdMRb1/P9LVDhUvD2zR4PMKICoH8iXJ4GQ9sdzyzc0mMQ3xzcvwzx9o7O3wHOYz23B0rg/iXw7DAY0LbhAvTo6GgiIiJOK7jNKoafc44/DrVCr8gyHp/xKL/88ou+PCAggOnTpzN06NCT5qycK4xGI+np6bVm4xZCCCGEEKK1ksC2Efmmf+aWeYPZL3Z56796Ey35olUNs0Ghf+LxHtoQa8sPZk+kKN7hyPf1B48Kn+083kN6qNw7LPkvQ7zBbUMktFJVFY/Hc8qSP5rmTcrl1Mu0aaSHu/lh/mwWLJivr2cwGBgzZgyTJk06Z4NaH7fbfVKpAiGEEEIIIVozCWwbgaZ5g7ojFfDRVm8P5sFS/6GwACY8tI+AOy80clGyt0xPawtoa1IUbwmd6YO9vbWf7QT3sX3eUQB/+R7+MRx6xNd/P48ePUppaSkdO3b81UC0yAFf7T7+2IRKyeaveP/tf+NwOPTlXbt25S9/+QuxsbHn9DBdj8fDnj17aNu2LdHR0c3dHCGEEEIIIRqEBLYNzKPCtqOwNMs733Rf8cnrGBToEO6hi2k/d18cQfv4sCZuZeNRFIgOgMcu8ga1i3Z7A3oN2FsMjy2DF0d5E2HVh6ZpeDyeU6zj/RyOVtZ4TcFudn7yNw5mH9DXi4yM5IknniAzM7N+jWoFfMftPClfLYQQQgghzhMS2DYAX4xwoNTbQ7t4D+wpOrmHFiDE4i2Vc3mqh5J9eYRZzs2yKyFWeGCgd9j1ot3eubfgnWf8wiqYPgjCG7GHWtPgcIV3GHKlyxvQ4a6maPFMyrb516udNm1anVkAhRBCCCGEEC2fBLZnSdW8AVO5E3LKYNEubwCXV3F8+K2P4Vjm4EFtYfIFkBEFJgxkVcWcs0WuFQXaBMMjQ6DCBd9neZermneIcnwwTO0FNtPZBbeBgYEYDIY6hw07Pd7awEuzvL3FmsdJ2Q8vULb+Y7230mg0Mn78eKZOnXrezDk1GAxERUVhs9mauylCCCGEEEI0mPM+sPWV4AEwG7xBaF2BlqZBfiXsLfL2yK7Lgw153iG2Jwaz4E0PlRDsDWjHdfImhvJlBdY0IykpKed0oiJFgdgg+MtgbwKtHQXe5Q43zF4HySHe43I2IiIijm2j9g9rZbZ3Gy4VNNWDY/tiyn/4D5qzUl+nW7duTJ8+nbi4uLNrRCtkNBpJTk4+p+cRCyGEEEKI8895H9g6Pd4AaHUumAzeEjs2I1hN3t5EixHsJu/yw+Xe4CynzDvM1fkrUzyDLTAmHcZ1hE5REGTxD5jdbje7d++mTZs2hISENP6ONqOUcPh9P3hsqbdHG7w93c+vgtQIuCD2zHtt8/PzqaysJCkpya/kj6Z5E3X9+2cocx6bU1qSS8k3j+Mp2q+vFxAQwKOPPnpezKutyePxsHfvXqKjowkLC2vu5gghhBBCCNEgzvvA1q3C+jz4Luv4Ml+MpSjHKs0eW6BqvlI9tTMZvLVoL0mGG7tB+wiwGmsP2lRVpaioiJiYmAbak5ZLAYaleG8M/P1Hb8AJkFUCT/wATwyFDuFnFtw6HA5KS0v9kiBpGpRWw4u/eOfyapqKWllM0ecP4Np/vF6tzWbjoYceYuTIkeddz6WqqhQXFxMaem7O7RZCCCGEEOen8z6w1YBq98nL4HhSKH4lmDUq3uHGGZHQMQouaQc94+AcHmF8xhTF2+N9bRfYXwJvrj9+SFcfghdXwYyLIbSe9Xs9Gny+E77cBS6nA8fOb6lcM4eqDZ/g26LRaGTs2LHccsst5+z8ZiGEEEIIIc43531gC+BUj/fS+tQVyyp45+EmhcLAttA7AVLDvMFtuN3baytqZzfDHb1gVyH8cKzajqp5MxenhsPdfU7+HH5Nzd5WTYO9RRov/6JSsH87Zcuew7F1IZ6SXGp+mqmpqTz44IPn1bzaE51vvdRCCCGEEOLcd94HtlYj/LYHjGzvTWpU5fZ+1392gcMDVS7vur0SvMmg2oV55+GaDP7DlU+XxWKhV69e5002Xp+YQHhwIJQ4jg0X5vg858RguCLDm8TrVMezTZs2JCQk6PNrj5Y5+cvH+9n8wWzKf3kHtewwaP4ZvSIjI/n3v/9N165dz9vgzmw20717d795yUIIIYQQQrR2531gazbC8NSm366qqpSVlREcHHxeDYlVFOgcDff2gwe/hSPHkkmVVMO/foaCKri6E0QG/Pr7VFdX43Q6CQkJYeeuPfz7nc9Y+O5/Kc/eeNK6JpOJ/v37M3XqVIYNG3beBrXgTaZVVlZGYGDgeXXeCSGEEEKIc9t5H9g2F1922g4dOjR6gOFLsKRp2kk/a5pGVVUV+fn5FBQUUFxcTFFREUVFRfrPhYWFlJaW4na7/d6v5s9Go5G2bdvSs2dPLrjgAlJTU7HZbCiKopc08gWUBgWGJMG03vDXZd65sQDZx7IZr8iGqb2hV/yxHnHFfx88Hg9bt25l8eLF7Nixg/UbNrJz1y6qq46X8vFtLzExkXvuuYexY8eSlJSEyXR+n/Iej4f9+/eTmJhIdHR0czdHCCGEEEKIBnF+X+U3I03TcLvdfkFiQ763y+WitLSUkpISysrKyMnJ4eDBg+Tl5ZGTk0NeXh55eXnk5uZSXFyMqqqoquoX8Nb88r3vrzEYDBiNRoxGIyEhIVxwwQVccMEF9OjRg7S0NEJDQ/Uvs9nMhC4KB0rg3U3HawlXuGDpfm+N4N9kaNzYxU2kqYLSkiKys7NZsWIF33zzDT///DNOpxOPx3NSuxSDgXbJyYwfP5477rhDAtoaGvO8E0IIIYQQornI1f45QNM0KioqyM7OZt++fWRlZXHgwAH27NnDvn372L9/P0ePHm30dviCY5fLhcPhYPHixSxevBiA0NBQUlJSaN++PR06dKBdu3akpqZyaUwq7oxEFuyxUlTlQa0oxF2czaHiHP7zXS5fGbJJNWZzYO8uNm7cSGVl5a+2wRYaw+VX/IZ7bruRgQMH6r3FQgghhBBCiHPXeR/Y1uyRrDn3srHnYRqNRuLj47HZbKdc19c+X+Doq0W6ceNGNm3axIYNG9i1axfFxcX6V1VVVYO0U1GU0zoumqahqmqtzwGUlJSwfv161q9fj6Io2O12wsPDCQ8PxxIciSewLYU5R3CWHUVzlKI6ytCqy1jtqmT1qRppMILRQkDGUK6afB/P3NiDhKiQ83oubV2MRiNxcXEEBJxiErMQQgghhBCtyHkf2LrdbpYuXcrevXuJiIggPDwcu92OxWLx+zKbzbUuO5vgSdM0DAYDsbGxAJSXl+N2u3G5XHpSJIfDgdPppLq6GofDQVFRETt27GDt2rVs3ryZrKwsXC6X3xDiM2E0GrHZbNjtdmw2GzabDavVqu+X1WolKCiI8PBwIiIiCA0NxWKxAMeD25r7XlFRwY4dOzhw4IA+P7ekpASPx1Pr/ldWVlJZWUlOTo7+Xqe9D4qCISAKY3AsxugO2DuOILzTJXRNb8fjoy0khCn1qod7LjMYDMTFxUlWZCGEEEIIcU457wNbp9PJW2+9xdy5c/VlNpuNwMBAgoKC9O81fw4MDPRbHhQUpGc3VlUVj8ejf6/5c81A1eFwUFJSos95rK6upqKigrKyMsrKyigtLdV/LisrqzVAPB1Go5Ho6Gjat29PmzZt9EA1LCyMsLAwPWgNCwsjNDRU37fAwEBMJtMZBe6qqpKfn09WVhZZWVns3r2bffv2sXfvXnbu3EleXl6d+3E6Qa0hJAFLmwswt7kAa9teBCZ2Ja19O3q2MdElWmFQkrcMkwS1dfN4PGRlZRETE0NoaGhzN0cIIYQQQogGcd4HtqqqUlJS4rfMF3gWFBSc8vVms1n/MhqNdSZf8vWs1gx2G4qiKHrSJovFQkpKCt26daNnz55kZmYSGxurB62+ntkzDVpPh68XOjY2lj59+qCqKhUVFZSWllJcXExeXh4bN25k9erVrF+/nuzsbFwul348fPthMBiw2+3EJyZTFdGF0uBOGKI7Yo1NwxoSQ7v4CHpFltMlpJSBmQZighUCzd5sy+LX+YaxS1ArhBBCCCHOJRLYHrvQP1sulwuXy9VwDToNAQEBREREEBkZSXh4OB06dKB79+506dKFzMxMwsPD9bmxtQ0bbgq+IDUkJISQkBDatGlDly5duPTSS/VA/8iRI6xfv55NmzZx8OBBYmJiSE9Pp1OnTt5yQXY75U6Fz3YqbMlXSIuAQUkKKeEahw6WUVpSQnJoIpLwWAghhBBCiPPbeR8SmM1mhg8fTtu2bfUhwOXl5foQ4PLycioqKpq8XQEBAYSEhOjlcUJCQoiOjqZDhw4kJSWRlJREcnIybdq0ITAwsMUnSjoxwDYajbRp04Y2bdpw+eWX1/m6cBPcfIH/Mk0Ds8mI2WxurOaesxRFwWKxSLZoIYQQQghxTjnvA1u73c59992H0+nUe199iZx83x0OB8XFxRQVFVFQUEBRURGFhYUnfTkcDoxGIyaTSf+q+dhqtRIQEOA3j9U3VzcgIIDQ0FCio6OJiIggKChIH+J8YkKns01adS6JjY0lKipKkiCdIZPJREZGhtwUEEIIIYQQ55TzPrBVFIWgoKA6n68tqdGJy84kI7EvIPXN7f21QPV8D15/je/Gg8VikeN0BjRNw+FwYDAY5KaAEEIIIYQ4Z8h4xFOoOVfV92UwGPy+fImbTufL9xqPx8OOHTsoLy/HYDDUuh1Rt/z8fPbt29egSbjOB263W695LIQQQgghxLlCAlshhBBCCCGEEK2aBLZCCCGEEEIIIVq1836ObXMxGo20bdsWu93e3E1plUJDQ7FarZLd9wz5slEHBgY2d1OEEEIIIYRoMK0ysK2oqODDDz/kgw8+wOVyMXz4cO644w5CQkKau2mnzWg0EhcXJwl8zlJwcDBBQUEyF/kMGQwGYmJi5IaAEEIIIYQ4p7S6q1uPx8OHH37I7NmzufPOO3nwwQf5+uuvefHFF88oO3Fzc7lcbN++nbKysuZuSqt05MgRsrKyJHnUGfIljyopKWnupgghhBBCCNFgWl2PbXl5OV9++SVXX301I0eOxGQycfToUf75z38yZcoUoqOjm7uJp0XTNMrKynC73c3dlFbJ6XRSUVHRqm5mtASaplFeXo7L5WrupgghhBBCCNFgWl1g63A4yM7OJiMjQx/G26dPH8rLy9mzZ49fYKtpmh74+H5WVRW3260vNxgMfuvB8RI/J/YGNsS6vrZ4PB5UVdW/n7jur23rTNZtjH04m/09m3Xr2l/f9n3H71Tr1vW+renYnPi+Z7u/J553zX1salsmhBBCCCHEmWp1ga3H48HlcmG1WvWL+JCQEDweD+Xl5X7raprGsmXL2Lp1KwDr16/nwIEDPP/883o9WqvVisfjwel06q/zJSVyOBz6xbnRaMRiseB2u/XeLkVRMJvNGI1Gqqqq9NcbjUasVitOp1PvkVUUBYvFgqIoVFdX43a7ycvLIzo6mrCwMKqrq/UgDcBms6FpGk6nU2+DyWTCbDbjdDr9AjqbzYaqqn7rms1mTCaT37oGgwGLxYKmaVRXV+vbMpvNmM1mHA6HHmT4jk1d+1vz2BgMBmw2Gy6Xy68n0PcZOZ1Ov/e12Wx+x8a3vwDV1dUn7a/L5fI7jlarleLiYsrKyoiKikJRlDqPjdVqPaP9PfFc8B3H6upqfV1FUbDb7fqx8bXXYrFgMBhq3d/TOTZ1nWO+963tfDzx2PjOMYfDoW/LZDJhsViorq7G5XJx+PBhQkNDiYiIOK1zDCAgIOCk/a3rHKvtOFosFoxGo99x9K27fft2/bwUQgghhBDibLS6wNZgMGA0GvVeV99FvO8i+URGoxGz2QxAjx496N69OxaL5aT1astOXNt6tS2ra7lvuyfyBYzvv/8+EyZM0AOt2vgCvtNpQ33Xra0NDbW/9Vm3tm3l5+ezZ88errjiCr9ESPXdXzj9c8FsNte6bmPsb13L61q3rjZ4PB4WLFjAsGHD9HVO97ypa3/rasPprpuZmUmnTp0koZUQQgghhDhrrS6wtVqtREZGkpubqwe227dvx2q1kpSU5LeuoigMGjSIgQMHAseHXLaETLoul4vFixdz7bXXkpyc3NzNaXW++uorVq5cyS233CKZpc+A2+3m559/Zty4cXTt2rW5m+OnJfxeCiGEEEKI1qnVBbaBgYEMHz6c+fPn0717d2w2G2+++SaDBg2qNbCt+b0l8c059A2JFqfPd0PDd+zk+J2+mued70sIIYQQQojWrtUFthaLhWuvvZZDhw7xwAMPoKoqSUlJ/P73v29VF+kGg4EBAwYQGBjY3E1pleLi4ujSpUur+sxbAkVR6N27N6Ghoc3dFCGEEEIIIRqMorXCjC2+ZEAVFRWAd45gQEBAqwpyfPvgSwwkTp8vu7XH48FsNreqz725+ZJF+Y6bHDshhBBCCHEuaJWBrRBCCCGEEEII4dPqhiK3JCfWyD1x3uKpnvc9d+TIET755BOSkpIYPXr0Sc8DtdYwld6281NDnXcFBQXMnz+f8PBwxo0bd9Lzqqqe9NqapXqEEEIIIYRoKeTqtB48Hg+ffPIJ1157LV27duXqq6/m8OHDfs/Pnz+fsWPHMmTIEH7729+yZs0avyDVV2s3KSmJ3bt3s2XLFr9tlJWV8dJLLzFu3DguuugiRo4cyS233EJeXl6T7adoWVRV5auvvuL666+nR48ejB49mv379+vPa5rG4sWLGT9+PEOGDOGmm25i5cqVfnVpAX755RdCQ0PJzc1l/fr1fs8tXbqUpKQkpk2bRmlpqb78wQcf5He/+12j7p8QQgghhBBnSgLbetA0DZvNxqBBg7jiiisoLy+n5sju7du389RTT3H55Zfz6quvEhMTw4wZMygpKfF7n6CgIHbu3InT6SQoKMjvublz5/LOO+9w22238cYbb/DXv/6Vfv36YTJJZ/v5zGKx0LdvX8aPH095ebnfzZKsrCweeeQRLr74YmbNmkV6ejp/+9vfyMvL8zs/AwMDycrKoqKiguDgYL/3d7vdVFRU8L///Y+ffvpJf11lZSWVlZVNs5NCCCGEEEKcJomO6sFkMjF69GgAPvnkE9auXev3/GeffUbbtm254YYbCAwM5KabbuK3v/0tW7du1WvrGgwG+vfvT0xMDHa7/aSSRT///DPdu3dn5MiRGAwG0tLS6NevX9PsYDPzJTpaunQpH3/8MYWFhfTp04dJkyYRHR1NcXExb775JitXrqRDhw4MGjSIpUuX8uCDDxIVFdXczW80BoOBSy+9lEsvvZSlS5eyePFiv+cXLVpEaGgokydPJjg4GJvNxvfff8+GDRtISEgAvMPZe/ToQXBwMCaTidTU1JO2ExcXR+/evXnjjTe46KKLsNlsTbJ/QgghhBBCnCnpsa2HU81zXb9+PRkZGQQEBADQpk0bYmJi2LBhg996YWFh9OrVi86dO580d7Fv377873//4/HHH+d///sfu3btory8vOF3poWaN28ejz76KD179mTy5MmsW7eOP/7xjxQWFvL444/z2WefMXHiRDp06MDMmTP5+eefqa6ubu5mN6rTOe/S09P13v+YmBjatm170nkXHBxMjx496Nq1a61zZm02G9dffz1ZWVl8/fXXSJ45IYQQQgjRUkmPbSOqqKggMDBQD0IsFgsWi+WMAtOJEydSUVHB8uXL+fTTT1EUhSFDhvDAAw8QHx9/TieQcrvdzJo1i4kTJ3LrrbdiMBiIiori1ltvZdmyZfzwww888cQTDB06FEVROHTo0Em9l+ej8vJyIiIi9HPDbDZjtVopKys7o/dRFIXOnTtz9dVX8/LLL9O/f//GaK4QQgghhBD1JoFtIwoICKCyslLPTOtyuXC5XAQGBp72ewQFBXHPPfdw4403cvToUdasWcPTTz9NdHQ0Dz/8cCO2vvmVl5eza9cuXn/9dRYsWACAw+Hg8OHDbN26FVVVSUhI0AO4Cy64gG+++aY5m9wiBAYG+p13breb6urqk+Zvnw5FUZg8eTKffPIJn3/+ufTaCiGEEEKIFkkC20bUvXt3Nm7cSFVVFQEBAeTm5pKfn0+3bt1O+z1cLhcmk4mYmBiio6NJSUlh6dKlrFmzphFb3jLY7XZiY2O5/vrrueyyy/QAVlEUSktL+fTTTyktLdUDuIMHD0rgBXTr1o2FCxdSUVFBUFAQ+fn55Obmcs0115zV+0VGRjJ16lTeffddLBYLsbGxDdxiIYQQQggh6kfm2NZTQUEB27Zt4+DBg5SXl7Nz506ysrLweDxceeWV7N+/nw8++IDt27czd+5cwsLC6NKly2m//wcffMAnn3zCli1b2Lt3L19++SUbNmxgwIABjbhXLYPVamX8+PGsXr2a4uJigoKC0DSNnTt3kpaWRkZGBq+99hobN25k2bJlfPzxx83d5CZTXFzM9u3b2b9/P5WVlezatYu9e/fi8Xi4/PLLKSoq4t1332XHjh28//77GAwGevTocVbbMhgMDB8+HKvVyurVq+XmgRBCCCGEaHGkx7aeFi1axAsvvEBhYSHFxcXcfffdDBgwgH/84x906tSJe++9lzlz5vD666+TnJzMn//8Z0JDQ0/7/Y1GIx999BFHjhzB4XAQGBjIVVddxc0339yIe9VyTJ06lXfeeYdnnnmG0tJSwsLC6NSpE1deeSUPPPAATz75JLfffjtJSUkMHDiQ7777rrmb3CSWLVvGk08+SVFREfn5+dx33310796dV155hZSUFB566CFmz57Nu+++S0JCAg8++CBxcXGnPSfbYDBgs9n09WNjY5kwYQLr1q3DbDY35q4JIYQQQghxxhRNul/qxePx4PF4/HqxDAaDXmdW0zT9eYPBgNFoPKOET6qq+r2/oigYDAYMBsM5nTjKR9M0VFVFVVX9GPiOI3iPv6qqKIrCsmXLmDFjBu+99x5t2rRpzmY3ujM57xRFwWQyndV5V/N1qqridrv9tiOEEEIIIURLIFen9WQ0GvUgqza+QPRs+YLY85WiKL96jGsGWOdDoO/THOedwWDAYrGc9XsKIYQQQgjRWKTHVpwzSktLOXToECkpKRKACSGEEEIIcR6RwFYIIYQQQgghRKt2/o5xFUIIIYQQQghxTpDAVgghhBBCCCFEqyaBrRBCCCGEEEKIVk0CW9EglixZwsMPP4zT6az3ezmdTj766COef/55qqurG6B1tTt69CiPPfYYP/30EzLVXAghhBBCiNZLAlvRINatW8dbb72Fy+U67ddUVFRw991389BDD1FZWakv93g8rF27lu+//x6PxwPAp59+yqhRo9iyZUuDtbm0tJR3332X7du3S2ArhBBCCCFEKyZ1bEWz0TSN3NxcwsLCUFVVX261Wrn//vtxu93YbDbAG4Tu27evUXtwhRBCCCGEEK2TBLaiUXg8HpYtW8Yvv/xCTk4OZrOZHj16cOWVVxIUFISiKDz33HOsXbsWi8XC3XffjclkYsyYMQwfPpyvvvqK3Nxcpk2bxs8//8y7775LXl4ejz/+OJGRkbRt25Zp06axceNGli1bxq233kqbNm307T/77LOEhYUxZcoUfdn27dv58MMPyc/Pp1OnTgwYMOCkdmuaxu7du/nss8/IysoiNDSU4cOH079/f6mNK4QQQgghRAslQ5FFo6iqquIPf/gDixYt4ujRo2zcuJGHH36YP/zhD7UOV645FNjtdvPjjz/y5Zdf4na7a13P933r1q188sknFBUV+a33+eef89133+nr/vzzz0yePJm5c+eSn5/PRx99xH333efXA6xpGj/++CNXXXUV7777LocPH+aHH35g8uTJzJkzx69XWQghhBBCCNFySI+taBQ2m4158+aRlpaGoih4PB7ef/99pk+fzsqVK7n44ou55557WLt2LWFhYTz33HMEBgYC3qC4psGDBzNx4kSys7P5y1/+Qo8ePc6oLRUVFcyaNYuioiLmz59PRkYGVVVVTJkyhW+//VZf7/Dhwzz00EN06tSJ5557joSEBCoqKpg5cyYzZ85kxIgRJCQk1P/gCCGEEEIIIRqU9NiKRmE0GklOTmb16tXMnTuXN998k+zsbFRVZd26dbW+RlEUFEWpdXlt69W2bm3Ky8tZunQp48aNIyUlBUVRsNvt3H777X7Di9esWcPevXu5/fbb9QA2MDCQK664AkVR+Omnn05394UQQgghhBBNSHpsRaMoKCjg9ttvZ926dVitVgwGAx6Ph+LiYgoLC5u0LS6Xi4MHD5KWlqYHsoqiEB8fT0REhL5ebm4uRUVFJwW8LpeLsrIyjh492qTtFkIIIYQQQpweCWxFo5g9ezarV6/m8ccfZ9SoUYSHh5Obm8tll13WoKV1DAYDmqb5zX/VNA2Hw6E/VhQFk8mE0+lE0zS9p1fTNL85vAaDAZPJxG233UZsbOxJ2+ndu3eDtVsIIYQQQgjRcCSwFY1i8+bNJCYmctVVVxEcHIymaWzZsoXS0lK/9YxG42klZTIajQAnBcWhoaFUVVVRUlKiP5eTk0N+fj7t27cHwGKxkJqayoYNG6iursZut6NpGtu3b/dLOpWUlERYWBgZGRlcddVVgDcolhq3QgghhBBCtGwS2IpGkZqaysqVK/nuu+/o3bs3eXl5zJ492y+QNBqNxMXFsXHjRg4cOEBERIReCuhE8fHxVFVVsXXrVhISEjCbzYSHh3PhhRdSXFzMZ599RmJiItXV1bz22mt+w4ZDQ0O5+uqreeWVVxg5ciS9evWiuLiYv//9737b6t+/PwMHDuTxxx/HZrORmZmJoihUVFSwatUqRo4cSXR0dOMeOCGEEEIIIcQZk8BWNIrJkyezevVq7r33XqKiorBarQwZMsRviK/VauXyyy9n2bJlXHfddQQFBTF58mQmTJhw0vtdeOGFdO/enb/+9a+8+OKLZGRk8Mwzz9CuXTt+97vf8dZbb7Fw4UKCg4MZNGgQqampftuZNGkSubm53HXXXcTExGA2m+nTpw/Z2dn6ekFBQTz55JM88sgj3HfffRiNRiwWC06nk+TkZC677LLGPWhCCCGEEEKIs6JoMs5SNIBdu3axe/duhg8fjslkQtM0cnJy2LZtG9XV1cTGxtK+fXvWrVtHfHw8nTt3BsDhcLBjxw6ys7NxuVxkZGSQlpbGtm3bKC0tpV+/fvr7ZWdns337diorKwkJCaFfv37Y7XbKysrYtGkTBQUFhIeHk5mZyaZNm7Db7Vx44YWAdwhzfn4+mzdvpqKigujoaNLT01m9ejUZGRkkJSXpw46Li4vZvn07BQUFKIpCSEgISUlJtG3bFoNBEokLIYQQQgjR0khgK4QQQgghhBCiVZPuJyGEEEIIIYQQrZoEtkIIIYQQQgghWjUJbIUQQgghhBBCtGoS2AohhBBCCCGEaNUksBVCCCGEEEII0apJYCuEEEIIIYQQolWTwFYIIYQQQgghRKsmga0QQgghhBBCiFZNAlshhBBCCCGEEK2aBLZCCCGEEEIIIVo1CWyFEEIIIYQQQrRqEtgKIYQQQgghhGjVJLAVQgghhBBCCNGqSWArhBBCCCGEEKJVk8BWCCGEEEIIIUSrJoGtEEIIIYQQQohWTQJbIYQQQgghhBCtmgS2QgghhBBCCCFatf8HwBuVIpV2roYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# figure size in inches optional\n",
-    "rcParams['figure.figsize'] = 12, 10\n",
-    "\n",
-    "# path to images\n",
-    "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse_diagnostic_divedown01.png\")\n",
-    "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse_diagnostic_divedown02.png\")\n",
-    "\n",
-    "# display images\n",
-    "fig, ax = plt.subplots(1,2); ax[0].axis('off'); ax[1].axis('off')\n",
-    "ax[0].imshow(mpimg.imread(plot1))\n",
-    "ax[1].imshow(mpimg.imread(plot2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b5d97b22",
-   "metadata": {},
-   "source": [
-    "The results section of cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json is shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "511a1809",
-   "metadata": {},
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:4158: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_mod, keyerror_mod = AverageMeridional(taux_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:4159: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_obs, keyerror_obs = AverageMeridional(taux_obs)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{}\n"
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "metrics_file=demo_output_directory+\"/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\"\n",
-    "with open(metrics_file) as f:\n",
-    "    results = json.load(f)[\"RESULTS\"][\"model\"][\"ACCESS1-0\"][\"r1i1p1\"][\"value\"]\n",
-    "print(json.dumps(results, indent = 2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d29412",
-   "metadata": {},
-   "source": [
-    "### ENSO Metrics Collections\n",
-    "\n",
-    "There are 3 metrics collections available:  \n",
-    "ENSO_perf  \n",
-    "ENSO_tel  \n",
-    "ENSO_proc  \n",
-    "\n",
-    "They can be selected using the `--metricsCollection` flag. The first example used the \"ENSO_perf\" collection.\n",
-    "\n",
-    "The next example runs the teleconnection collection. To save individual metrics in netCDF format, it uses the `--nc_out` flag."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6629c5d8",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mip: cmip5\n",
-      "exp: historical\n",
-      "models: ['ACCESS1-0']\n",
-      "realization:  r1i1p1\n",
-      "mc_name: ENSO_tel\n",
-      "outdir: demo_output/basicTestEnso/ENSO_tel\n",
-      "netcdf_path: demo_output/basicTestEnso/ENSO_tel\n",
-      "debug: False\n",
-      "obs_cmor: True\n",
-      "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n",
-      "egg_pth: /Users/lee1043/miniforge3/envs/pmp_devel_20250715/share/pmp\n",
-      "output directory for graphics:demo_output/basicTestEnso/ENSO_tel\n",
-      "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_tel\n",
-      "output directory for metrics_results:demo_output/basicTestEnso/ENSO_tel\n",
-      "list_variables: ['pr', 'sst']\n",
-      "list_obs: ['AVISO-1-0', 'ERA-INT', 'GPCP-2-3', 'HadISST-1-1']\n",
-      "PMPdriver: dict_obs readin end\n",
-      "Process start:Fri Aug 29 15:23:25 2025\n",
-      "models: ['ACCESS1-0']\n",
-      " ----- model:  ACCESS1-0  ---------------------\n",
-      "PMPdriver: var loop start for model  ACCESS1-0\n",
-      "realization: r1i1p1\n",
-      " --- run:  r1i1p1  ---\n",
-      " --- var:  pr  ---\n",
-      "var_in_file: pr\n",
-      "var, areacell_in_file, realm: pr areacella atmos\n",
-      "path:  demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
-      "file_list:  ['demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc']\n",
-      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
-      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
-      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
-      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
-      "PMPdriver: var loop end\n",
-      " --- var:  sst  ---\n",
-      "var_in_file: ts\n",
-      "var, areacell_in_file, realm: sst areacella atmos\n",
-      "path:  demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
-      "file_list:  ['demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc']\n",
-      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
-      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
-      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
-      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
-      "PMPdriver: var loop end\n",
-      "dictDatasets:\n",
-      "{\n",
-      "    \"model\": {\n",
-      "        \"ACCESS1-0_r1i1p1\": {\n",
-      "            \"pr\": {\n",
-      "                \"areaname\": \"areacella\",\n",
-      "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n",
-      "                \"path + filename_area\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
-      "                \"path + filename_landmask\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
-      "                \"varname\": \"pr\"\n",
-      "            },\n",
-      "            \"sst\": {\n",
-      "                \"areaname\": \"areacella\",\n",
-      "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n",
-      "                \"path + filename_area\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
-      "                \"path + filename_landmask\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
-      "                \"varname\": \"ts\"\n",
-      "            }\n",
-      "        }\n",
-      "    },\n",
-      "    \"observations\": {\n",
-      "        \"ERA-Interim\": {\n",
-      "            \"pr\": {\n",
-      "                \"areaname\": \"areacella\",\n",
-      "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/pr/gn/v20210727/pr_mon_ERA-INT_PCMDI_gn_197901-201903.nc\",\n",
-      "                \"path + filename_area\": null,\n",
-      "                \"path + filename_landmask\": null,\n",
-      "                \"varname\": \"pr\"\n",
-      "            },\n",
-      "            \"sst\": {\n",
-      "                \"areaname\": \"areacella\",\n",
-      "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/ts/gn/v20210727/ts_mon_ERA-INT_PCMDI_gn_197901-201903.nc\",\n",
-      "                \"path + filename_area\": null,\n",
-      "                \"path + filename_landmask\": null,\n",
-      "                \"varname\": \"ts\"\n",
-      "            }\n",
-      "        },\n",
-      "        \"GPCPv2.3\": {\n",
-      "            \"pr\": {\n",
-      "                \"areaname\": \"areacella\",\n",
-      "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/NOAA-NCEI/GPCP-2-3/mon/pr/gn/v20210727/pr_mon_GPCP-2-3_PCMDI_gn_197901-201907.nc\",\n",
-      "                \"path + filename_area\": null,\n",
-      "                \"path + filename_landmask\": null,\n",
-      "                \"varname\": \"pr\"\n",
-      "            }\n",
-      "        },\n",
-      "        \"HadISST\": {\n",
-      "            \"sst\": {\n",
-      "                \"areaname\": \"areacella\",\n",
-      "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/MOHC/HadISST-1-1/mon/ts/gn/v20210727/ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc\",\n",
-      "                \"path + filename_area\": null,\n",
-      "                \"path + filename_landmask\": null,\n",
-      "                \"varname\": \"ts\"\n",
-      "            }\n",
-      "        }\n",
-      "    }\n",
-      "}\n",
-      "\n",
-      "### Compute the metric collection ###\n",
-      "\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
       "\u001b[94m     ComputeCollection: metric = EnsoAmpl\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n"
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/cdms2/dataset.py:2179: Warning: Files are written with compression and no shuffling\n",
-      "You can query different values of compression using the functions:\n",
-      "cdms2.getNetcdfShuffleFlag() returning 1 if shuffling is enabled, 0 otherwise\n",
-      "cdms2.getNetcdfDeflateFlag() returning 1 if deflate is used, 0 otherwise\n",
-      "cdms2.getNetcdfDeflateLevelFlag() returning the level of compression for the deflate method\n",
-      "\n",
-      "If you want to turn that off or set different values of compression use the functions:\n",
-      "value = 0\n",
-      "cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1\n",
-      "cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1\n",
-      "cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included\n",
-      "\n",
-      "To produce NetCDF3 Classic files use:\n",
-      "cdms2.useNetCDF3()\n",
-      "To Force NetCDF4 output with classic format and no compressing use:\n",
-      "cdms2.setNetcdf4Flag(1)\n",
-      "NetCDF4 file with no shuffling or deflate and noclassic will be open for parallel i/o\n",
-      "  warnings.warn(\"Files are written with compression and no shuffling\\n\" +\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
      ]
     },
     {
@@ -924,173 +735,2275 @@
      "output_type": "stream",
      "text": [
       "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoPrMapDjf\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim_ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoPrMapJja\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and ERA-Interim_ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSeasonality\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstMapDjf\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoSstMapJja\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstMapJja = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n"
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO::2025-08-29 15:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "2025-08-29 15:23:53,700 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "2025-08-29 15:23:53,700 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "INFO::2025-08-29 15:23::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n",
-      "2025-08-29 15:23:59,279 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n",
-      "2025-08-29 15:23:59,279 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "figure plotting start\n",
-      "metrics: ['EnsoAmpl', 'EnsoPrMapDjf', 'EnsoPrMapJja', 'EnsoSeasonality', 'EnsoSstLonRmse', 'EnsoSstMapDjf', 'EnsoSstMapJja']\n",
-      "filename_js: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "met: EnsoAmpl\n",
-      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n",
-      "failed for  ACCESS1-0 r1i1p1\n",
-      "'EnsoAmpl'\n",
-      "PMPdriver: model loop end\n",
-      "Process end: Fri Aug 29 15:23:59 2025\n"
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
-    }
-   ],
-   "source": [
-    "%%bash -s \"$demo_output_directory\"\n",
-    "enso_driver.py -p basic_enso_param.py \\\n",
-    "--metricsCollection ENSO_tel \\\n",
-    "--results_dir $1/basicTestEnso/ENSO_tel \\\n",
-    "--nc_out True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22b4008b",
-   "metadata": {},
-   "source": [
-    "All of the results (netCDF and JSON) are located in the output directory, which uses the metrics collection name."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f963d430",
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n",
-      "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n"
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
-    }
-   ],
-   "source": [
-    "!ls {demo_output_directory + \"/basicTestEnso/ENSO_tel/*.nc\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "46bfec14",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1b050e5c0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1000 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# figure size in inches optional\n",
-    "rcParams['figure.figsize'] = 16, 10\n",
-    "\n",
-    "# path to images\n",
-    "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown01.png\")\n",
-    "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown02.png\")\n",
-    "plot3 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown03.png\")\n",
-    "\n",
-    "# display images\n",
-    "fig, ax = plt.subplots(1,3); ax[0].axis('off'); ax[1].axis('off'); ax[2].axis('off')\n",
-    "ax[0].imshow(mpimg.imread(plot1))\n",
-    "ax[1].imshow(mpimg.imread(plot2))\n",
-    "ax[2].imshow(mpimg.imread(plot3))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c48822c8",
-   "metadata": {},
-   "source": [
-    "Finally, this example runs the remaining metrics collection ENSO_proc:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "71ad2751",
-   "metadata": {},
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoDuration\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mip: cmip5\n",
-      "exp: historical\n",
-      "models: ['ACCESS1-0']\n",
-      "realization:  r1i1p1\n",
-      "mc_name: ENSO_proc\n",
-      "outdir: demo_output/basicTestEnso/ENSO_proc\n",
-      "netcdf_path: demo_output/basicTestEnso/ENSO_proc\n",
-      "debug: False\n",
-      "obs_cmor: True\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoSeasonality\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoSstDiversity_2\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11783: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon, keyerror = AverageMeridional(sstmap)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11783: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon, keyerror = AverageMeridional(sstmap)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11783: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon, keyerror = AverageMeridional(sstmap)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoSstLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14907: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14908: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14907: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14908: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoSstSkew\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:12116: UserWarning: areacell is None for variable 'skewness'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:12116: UserWarning: areacell is None for variable 'skewness'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:12116: UserWarning: areacell is None for variable 'skewness'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoSstTsRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstTsRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:17288: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssthov_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:17289: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssthov_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstTsRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:17288: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssthov_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:17289: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssthov_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = SeasonalPrLatRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalPrLatRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24819: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLat_mod, keyerror_mod = AverageZonal(prStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24820: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLat_obs, keyerror_obs = AverageZonal(prStd_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24901: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageZonal(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24902: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageZonal(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalPrLatRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24819: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLat_mod, keyerror_mod = AverageZonal(prStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24820: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLat_obs, keyerror_obs = AverageZonal(prStd_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24901: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageZonal(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:24902: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageZonal(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = SeasonalPrLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalPrLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25151: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLon_mod, keyerror_mod = AverageMeridional(prStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25152: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLon_obs, keyerror_obs = AverageMeridional(prStd_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25232: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageMeridional(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25233: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageMeridional(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalPrLonRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25151: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLon_mod, keyerror_mod = AverageMeridional(prStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25152: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  prStdLon_obs, keyerror_obs = AverageMeridional(prStd_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25232: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_mod, keyerror_mod = AverageMeridional(pr_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:25233: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  pr_obs, keyerror_obs = AverageMeridional(pr_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = SeasonalSstLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26493: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstStdLon_mod, keyerror_mod = AverageMeridional(sstStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstStdLon_obs, keyerror_obs = AverageMeridional(sstStd_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26574: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_mod, keyerror_mod = AverageMeridional(sst_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26575: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_obs, keyerror_obs = AverageMeridional(sst_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26493: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstStdLon_mod, keyerror_mod = AverageMeridional(sstStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstStdLon_obs, keyerror_obs = AverageMeridional(sstStd_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26574: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_mod, keyerror_mod = AverageMeridional(sst_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:26575: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_obs, keyerror_obs = AverageMeridional(sst_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = SeasonalTauxLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, SeasonalTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:27166: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tauxStdLon_mod, keyerror_mod = AverageMeridional(tauxStd_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:27167: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tauxStdLon_obs, keyerror_obs = AverageMeridional(tauxStd_obs)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:27254: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_mod, keyerror_mod = AverageMeridional(taux_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:27255: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_obs, keyerror_obs = AverageMeridional(taux_obs)\n",
+      "INFO::2026-05-28 10:57::pcmdi_metrics:: Results saved to a json file: /pscratch/sd/l/lee1043/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
+      "INFO::2026-05-28 10:57::pcmdi_metrics:: Results saved to a json file: /pscratch/sd/l/lee1043/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "figure plotting start\n",
+      "metrics: ['BiasPrLatRmse', 'BiasPrLonRmse', 'BiasSstLonRmse', 'BiasTauxLonRmse', 'EnsoAmpl', 'EnsoDuration', 'EnsoSeasonality', 'EnsoSstDiversity_2', 'EnsoSstLonRmse', 'EnsoSstSkew', 'EnsoSstTsRmse', 'SeasonalPrLatRmse', 'SeasonalPrLonRmse', 'SeasonalSstLonRmse', 'SeasonalTauxLonRmse']\n",
+      "filename_js: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
+      "met: BiasPrLatRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasPrLatRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse\n",
+      "                    curve 10:57\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:57\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: BiasPrLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasPrLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLonRmse\n",
+      "                    curve 10:57\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:57\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: BiasSstLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasSstLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasSstLonRmse\n",
+      "                    curve 10:57\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: BiasTauxLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasTauxLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasTauxLonRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoAmpl\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoAmpl\n",
+      "                    dot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoDuration\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoDuration.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoDuration\n",
+      "                    dot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    boxplot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSeasonality\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSeasonality\n",
+      "                    dot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstDiversity_2\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstDiversity_2.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSstDiversity_2\n",
+      "                    dot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    boxplot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSstLonRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstSkew\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstSkew.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSstSkew\n",
+      "                    dot 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstTsRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstTsRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSstTsRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: SeasonalPrLatRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalPrLatRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalPrLatRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: SeasonalPrLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalPrLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalPrLonRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: SeasonalSstLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalSstLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalSstLonRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: SeasonalTauxLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalTauxLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalTauxLonRmse\n",
+      "                    curve 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    map 10:58\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 10:58\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "PMPdriver: model loop end\n",
+      "Process end: Thu May 28 10:58:18 2026\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "enso_driver.py -p basic_enso_param.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46ff9996",
+   "metadata": {},
+   "source": [
+    "This run saved metrics to two files:  \n",
+    "- `basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json`  \n",
+    "- `basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json`\n",
+    "\n",
+    "diveDown metrics are not available in all cases. \n",
+    "\n",
+    "Example dive down (i.e., diagnostics) figures:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3265f064",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f076880d590>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# figure size in inches optional\n",
+    "rcParams['figure.figsize'] = 12, 10\n",
+    "\n",
+    "# path to images\n",
+    "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse_diagnostic_divedown01.png\")\n",
+    "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse_diagnostic_divedown02.png\")\n",
+    "\n",
+    "# display images\n",
+    "fig, ax = plt.subplots(1,2); ax[0].axis('off'); ax[1].axis('off')\n",
+    "ax[0].imshow(mpimg.imread(plot1))\n",
+    "ax[1].imshow(mpimg.imread(plot2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5d97b22",
+   "metadata": {},
+   "source": [
+    "The results section of cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "511a1809",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"BiasPrLatRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 1.0865432651564593,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": 1.8799937669351745,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"BiasPrLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.568085855536746,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": 1.6074270393816383,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"BiasSstLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.625782965480205,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 0.482453339105798,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"BiasTauxLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 5.992234121008959,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoAmpl\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": 0.6630260736952586,\n",
+      "        \"value_error\": 0.0530845705527287\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.9001341048707652,\n",
+      "        \"value_error\": 0.1423236985494241\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 0.7688706055408969,\n",
+      "        \"value_error\": 0.06298833428079066\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 26.341411784363938,\n",
+      "        \"value_error\": 17.543852271121864\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 13.766234667168376,\n",
+      "        \"value_error\": 13.968772137924121\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoDuration\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": 12.0,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 11.0,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 13.0,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 9.090909090909092,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 7.6923076923076925,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoSeasonality\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": 1.1798342563448676,\n",
+      "        \"value_error\": 0.18922890859200112\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 1.4547701017161387,\n",
+      "        \"value_error\": 0.4629690002655354\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 1.223684198498758,\n",
+      "        \"value_error\": 0.20083433711187593\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 18.898920526819975,\n",
+      "        \"value_error\": 38.81725124591041\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 3.5834361682275975,\n",
+      "        \"value_error\": 31.28801335338797\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoSstDiversity_2\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": 10.0,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 32.0,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 49.0,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 68.75,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 79.59183673469387,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoSstLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.16378772100156302,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 0.14599676988358967,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoSstSkew\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": -0.3339196537261565,\n",
+      "        \"value_error\": -0.02673496883520171\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.4050153562604934,\n",
+      "        \"value_error\": 0.06403855065638478\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 0.4032072801499268,\n",
+      "        \"value_error\": 0.03303202744844833\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 182.44617113021008,\n",
+      "        \"value_error\": -19.636860842263534\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 182.81587911855,\n",
+      "        \"value_error\": -13.415118084475575\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"EnsoSstTsRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.09946192140712347,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 0.07289513613537553,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"SeasonalPrLatRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 1.1595800016277198,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": 1.5277759179048984,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"SeasonalPrLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 1.183968647553551,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"GPCPv2.3\": {\n",
+      "        \"value\": 1.4562160603155088,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"SeasonalSstLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 0.2846354718626762,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"HadISST\": {\n",
+      "        \"value\": 0.3043109894071552,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  },\n",
+      "  \"SeasonalTauxLonRmse\": {\n",
+      "    \"diagnostic\": {\n",
+      "      \"ACCESS1-0_r1i1p1\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      },\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": null,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    },\n",
+      "    \"metric\": {\n",
+      "      \"ERA-Interim\": {\n",
+      "        \"value\": 4.25475826495948,\n",
+      "        \"value_error\": null\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "metrics_file=demo_output_directory+\"/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\"\n",
+    "with open(metrics_file) as f:\n",
+    "    results = json.load(f)[\"RESULTS\"][\"model\"][\"ACCESS1-0\"][\"r1i1p1\"][\"value\"]\n",
+    "print(json.dumps(results, indent = 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52d29412",
+   "metadata": {},
+   "source": [
+    "### ENSO Metrics Collections\n",
+    "\n",
+    "There are 3 metrics collections available:  \n",
+    "ENSO_perf  \n",
+    "ENSO_tel  \n",
+    "ENSO_proc  \n",
+    "\n",
+    "They can be selected using the `--metricsCollection` flag. The first example used the \"ENSO_perf\" collection.\n",
+    "\n",
+    "The next example runs the teleconnection collection. To save individual metrics in netCDF format, it uses the `--nc_out` flag."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6629c5d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mip: cmip5\n",
+      "exp: historical\n",
+      "models: ['ACCESS1-0']\n",
+      "realization:  r1i1p1\n",
+      "mc_name: ENSO_tel\n",
+      "outdir: demo_output/basicTestEnso/ENSO_tel\n",
+      "netcdf_path: demo_output/basicTestEnso/ENSO_tel\n",
+      "debug: False\n",
+      "obs_cmor: True\n",
+      "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n",
+      "egg_pth: /global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/share/pmp\n",
+      "output directory for graphics:demo_output/basicTestEnso/ENSO_tel\n",
+      "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_tel\n",
+      "output directory for metrics_results:demo_output/basicTestEnso/ENSO_tel\n",
+      "list_variables: ['pr', 'sst']\n",
+      "list_obs: ['AVISO-1-0', 'ERA-INT', 'GPCP-2-3', 'HadISST-1-1']\n",
+      "PMPdriver: dict_obs readin end\n",
+      "Process start:Thu May 28 10:58:27 2026\n",
+      "models: ['ACCESS1-0']\n",
+      " ----- model:  ACCESS1-0  ---------------------\n",
+      "PMPdriver: var loop start for model  ACCESS1-0\n",
+      "realization: r1i1p1\n",
+      "run_in_modpath: 4\n",
+      " --- run:  r1i1p1  ---\n",
+      " --- var:  pr  ---\n",
+      "var_in_file: pr\n",
+      "var, areacell_in_file, realm: pr areacella atmos\n",
+      "path:  demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc']\n",
+      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
+      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
+      "PMPdriver: var loop end\n",
+      " --- var:  sst  ---\n",
+      "var_in_file: ts\n",
+      "var, areacell_in_file, realm: sst areacella atmos\n",
+      "path:  demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc']\n",
+      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
+      "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
+      "PMPdriver: var loop end\n",
+      "dictDatasets:\n",
+      "{\n",
+      "    \"model\": {\n",
+      "        \"ACCESS1-0_r1i1p1\": {\n",
+      "            \"pr\": {\n",
+      "                \"areaname\": \"areacella\",\n",
+      "                \"landmaskname\": \"sftlf\",\n",
+      "                \"path + filename\": \"demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n",
+      "                \"path + filename_area\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
+      "                \"path + filename_landmask\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
+      "                \"varname\": \"pr\"\n",
+      "            },\n",
+      "            \"sst\": {\n",
+      "                \"areaname\": \"areacella\",\n",
+      "                \"landmaskname\": \"sftlf\",\n",
+      "                \"path + filename\": \"demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n",
+      "                \"path + filename_area\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
+      "                \"path + filename_landmask\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
+      "                \"varname\": \"ts\"\n",
+      "            }\n",
+      "        }\n",
+      "    },\n",
+      "    \"observations\": {\n",
+      "        \"ERA-Interim\": {\n",
+      "            \"pr\": {\n",
+      "                \"areaname\": \"areacella\",\n",
+      "                \"landmaskname\": \"sftlf\",\n",
+      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/pr/gn/v20210727/pr_mon_ERA-INT_PCMDI_gn_197901-201903.nc\",\n",
+      "                \"path + filename_area\": null,\n",
+      "                \"path + filename_landmask\": null,\n",
+      "                \"varname\": \"pr\"\n",
+      "            },\n",
+      "            \"sst\": {\n",
+      "                \"areaname\": \"areacella\",\n",
+      "                \"landmaskname\": \"sftlf\",\n",
+      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/ts/gn/v20210727/ts_mon_ERA-INT_PCMDI_gn_197901-201903.nc\",\n",
+      "                \"path + filename_area\": null,\n",
+      "                \"path + filename_landmask\": null,\n",
+      "                \"varname\": \"ts\"\n",
+      "            }\n",
+      "        },\n",
+      "        \"GPCPv2.3\": {\n",
+      "            \"pr\": {\n",
+      "                \"areaname\": \"areacella\",\n",
+      "                \"landmaskname\": \"sftlf\",\n",
+      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/NOAA-NCEI/GPCP-2-3/mon/pr/gn/v20210727/pr_mon_GPCP-2-3_PCMDI_gn_197901-201907.nc\",\n",
+      "                \"path + filename_area\": null,\n",
+      "                \"path + filename_landmask\": null,\n",
+      "                \"varname\": \"pr\"\n",
+      "            }\n",
+      "        },\n",
+      "        \"HadISST\": {\n",
+      "            \"sst\": {\n",
+      "                \"areaname\": \"areacella\",\n",
+      "                \"landmaskname\": \"sftlf\",\n",
+      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/MOHC/HadISST-1-1/mon/ts/gn/v20260416/ts_mon_HadISST-1-1_PCMDI_gn_187001-202501.nc\",\n",
+      "                \"path + filename_area\": null,\n",
+      "                \"path + filename_landmask\": null,\n",
+      "                \"varname\": \"ts\"\n",
+      "            }\n",
+      "        }\n",
+      "    }\n",
+      "}\n",
+      "\n",
+      "### Compute the metric collection ###\n",
+      "\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoAmpl\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoPrMapDjf\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim_GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and HadISST_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and HadISST_GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoPrMapJja\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and ERA-Interim_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and ERA-Interim_GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and HadISST_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and HadISST_GPCPv2.3\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoSeasonality\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoSstLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14907: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14908: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14907: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14908: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoSstMapDjf\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoSstMapJja\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstMapJja = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO::2026-05-28 11:14::pcmdi_metrics:: Results saved to a json file: /pscratch/sd/l/lee1043/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
+      "INFO::2026-05-28 11:14::pcmdi_metrics:: Results saved to a json file: /pscratch/sd/l/lee1043/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "figure plotting start\n",
+      "metrics: ['EnsoAmpl', 'EnsoPrMapDjf', 'EnsoPrMapJja', 'EnsoSeasonality', 'EnsoSstLonRmse', 'EnsoSstMapDjf', 'EnsoSstMapJja']\n",
+      "filename_js: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
+      "met: EnsoAmpl\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl\n",
+      "                    dot 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoPrMapDjf\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapDjf.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoPrMapDjf\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoPrMapJja\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapJja.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoPrMapJja\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:14\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSeasonality\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoSeasonality\n",
+      "                    dot 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoSstLonRmse\n",
+      "                    curve 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstMapDjf\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapDjf.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoSstMapDjf\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstMapJja\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapJja.nc\n",
+      "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoSstMapJja\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:15\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "PMPdriver: model loop end\n",
+      "Process end: Thu May 28 11:15:47 2026\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash -s \"$demo_output_directory\"\n",
+    "enso_driver.py -p basic_enso_param.py \\\n",
+    "--metricsCollection ENSO_tel \\\n",
+    "--results_dir $1/basicTestEnso/ENSO_tel \\\n",
+    "--nc_out True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b4008b",
+   "metadata": {},
+   "source": [
+    "All of the results (netCDF and JSON) are located in the output directory, which uses the metrics collection name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f963d430",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "demo_output/basicTestEnso_xcdat/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n",
+      "demo_output/basicTestEnso_xcdat/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapJja.nc\n",
+      "demo_output/basicTestEnso_xcdat/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n",
+      "demo_output/basicTestEnso_xcdat/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n",
+      "demo_output/basicTestEnso_xcdat/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapJja.nc\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls {demo_output_directory + \"/basicTestEnso/ENSO_tel/*.nc\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46bfec14",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f07676c5bd0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# figure size in inches optional\n",
+    "rcParams['figure.figsize'] = 16, 10\n",
+    "\n",
+    "# path to images\n",
+    "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown01.png\")\n",
+    "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown02.png\")\n",
+    "plot3 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown03.png\")\n",
+    "\n",
+    "# display images\n",
+    "fig, ax = plt.subplots(1,3); ax[0].axis('off'); ax[1].axis('off'); ax[2].axis('off')\n",
+    "ax[0].imshow(mpimg.imread(plot1))\n",
+    "ax[1].imshow(mpimg.imread(plot2))\n",
+    "ax[2].imshow(mpimg.imread(plot3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c48822c8",
+   "metadata": {},
+   "source": [
+    "Finally, this example runs the remaining metrics collection ENSO_proc:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "71ad2751",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mip: cmip5\n",
+      "exp: historical\n",
+      "models: ['ACCESS1-0']\n",
+      "realization:  r1i1p1\n",
+      "mc_name: ENSO_proc\n",
+      "outdir: demo_output/basicTestEnso/ENSO_proc\n",
+      "netcdf_path: demo_output/basicTestEnso/ENSO_proc\n",
+      "debug: False\n",
+      "obs_cmor: True\n",
       "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n",
-      "egg_pth: /Users/lee1043/miniforge3/envs/pmp_devel_20250715/share/pmp\n",
+      "egg_pth: /global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/share/pmp\n",
       "output directory for graphics:demo_output/basicTestEnso/ENSO_proc\n",
       "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_proc\n",
       "output directory for metrics_results:demo_output/basicTestEnso/ENSO_proc\n",
@@ -1100,17 +3013,21 @@
       "\u001b[95mObservation dataset GPCP-2-3 is not given for variable thf\u001b[0m\n",
       "\u001b[95mObservation dataset HadISST-1-1 is not given for variable thf\u001b[0m\n",
       "PMPdriver: dict_obs readin end\n",
-      "Process start:Fri Aug 29 15:24:07 2025\n",
+      "Process start:Thu May 28 11:16:01 2026\n",
       "models: ['ACCESS1-0']\n",
       " ----- model:  ACCESS1-0  ---------------------\n",
       "PMPdriver: var loop start for model  ACCESS1-0\n",
       "realization: r1i1p1\n",
+      "run_in_modpath: 4\n",
       " --- run:  r1i1p1  ---\n",
       " --- var:  ssh  ---\n",
       "var_in_file: zos\n",
       "var, areacell_in_file, realm: ssh areacello ocean\n",
       "path:  demo_data/CMIP5_demo_data/zos_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
       "file_list:  []\n",
+      "path:  demo_data/CMIP5_demo_data/zos_Omon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
+      "file_list:  ['demo_data/CMIP5_demo_data/zos_Omon_ACCESS1-0_historical_r1i1p1_185001-200512.nc']\n",
+      "PMPdriver: resolved ocean variable ssh using alternate path demo_data/CMIP5_demo_data/zos_Omon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n",
       "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
       "file_list:  ['demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc']\n",
       "path:  demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n",
@@ -1183,7 +3100,7 @@
       "            \"ssh\": {\n",
       "                \"areaname\": \"areacello\",\n",
       "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": null,\n",
+      "                \"path + filename\": \"demo_data/CMIP5_demo_data/zos_Omon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n",
       "                \"path + filename_area\": \"demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\",\n",
       "                \"path + filename_landmask\": null,\n",
       "                \"varname\": \"zos\"\n",
@@ -1332,7 +3249,7 @@
       "            \"sst\": {\n",
       "                \"areaname\": \"areacella\",\n",
       "                \"landmaskname\": \"sftlf\",\n",
-      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/MOHC/HadISST-1-1/mon/ts/gn/v20210727/ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc\",\n",
+      "                \"path + filename\": \"demo_data/obs4MIPs_PCMDI_monthly/MOHC/HadISST-1-1/mon/ts/gn/v20260416/ts_mon_HadISST-1-1_PCMDI_gn_187001-202501.nc\",\n",
       "                \"path + filename_area\": null,\n",
       "                \"path + filename_landmask\": null,\n",
       "                \"varname\": \"ts\"\n",
@@ -1345,154 +3262,1445 @@
       "\n",
       "\u001b[94m     ComputeCollection: metric = BiasSstLonRmse\u001b[0m\n",
       "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3065: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_mod, keyerror_mod = AverageMeridional(sst_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3066: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_obs, keyerror_obs = AverageMeridional(sst_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3065: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_mod, keyerror_mod = AverageMeridional(sst_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:3066: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_obs, keyerror_obs = AverageMeridional(sst_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:4158: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_mod, keyerror_mod = AverageMeridional(taux_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:4159: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_obs, keyerror_obs = AverageMeridional(taux_obs)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeCollection: metric = EnsoAmpl\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6950: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsodSstOce_2\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +1.99 to +243.32\u001b[0m\n",
+      "\u001b[93m     range new = -243.32 to -1.99\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -3.28 to +31.07\u001b[0m\n",
+      "\u001b[93m     range new = -31.07 to +3.28\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +1.99 to +290.82\u001b[0m\n",
+      "\u001b[93m     range new = -290.82 to -1.99\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -3.28 to +62.31\u001b[0m\n",
+      "\u001b[93m     range new = -62.31 to +3.28\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:7820: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:7821: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  thf_map, keyerror2 = AverageMeridional(thf_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +219.02\u001b[0m\n",
+      "\u001b[93m     range new = -219.02 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -4.19 to +33.07\u001b[0m\n",
+      "\u001b[93m     range new = -33.07 to +4.19\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +243.35\u001b[0m\n",
+      "\u001b[93m     range new = -243.35 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -9.37 to +72.85\u001b[0m\n",
+      "\u001b[93m     range new = -72.85 to +9.37\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:7820: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:7821: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  thf_map, keyerror2 = AverageMeridional(thf_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = HadISST_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +219.02\u001b[0m\n",
+      "\u001b[93m     range new = -219.02 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -4.19 to +33.07\u001b[0m\n",
+      "\u001b[93m     range new = -33.07 to +4.19\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +243.35\u001b[0m\n",
+      "\u001b[93m     range new = -243.35 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -9.37 to +72.85\u001b[0m\n",
+      "\u001b[93m     range new = -72.85 to +9.37\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:7820: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:7821: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  thf_map, keyerror2 = AverageMeridional(thf_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoFbSshSst\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7286: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tab, keyerror = dict_average[average](tab, areacell, region=region, **kwargs)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8495: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssh_map, keyerror2 = AverageMeridional(ssh_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_AVISO\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7286: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tab, keyerror = dict_average[average](tab, areacell, region=region, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8495: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssh_map, keyerror2 = AverageMeridional(ssh_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = HadISST_AVISO\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7286: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tab, keyerror = dict_average[average](tab, areacell, region=region, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8494: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8495: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssh_map, keyerror2 = AverageMeridional(ssh_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoFbSstTaux\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8129: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_map, keyerror = AverageMeridional(taux_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8129: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_map, keyerror = AverageMeridional(taux_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = HadISST_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8129: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux_map, keyerror = AverageMeridional(taux_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeCollection: metric = EnsoFbSstThf\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +1.99 to +243.32\u001b[0m\n",
+      "\u001b[93m     range new = -243.32 to -1.99\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -3.28 to +31.07\u001b[0m\n",
+      "\u001b[93m     range new = -31.07 to +3.28\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +1.99 to +290.82\u001b[0m\n",
+      "\u001b[93m     range new = -290.82 to -1.99\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -3.28 to +62.31\u001b[0m\n",
+      "\u001b[93m     range new = -62.31 to +3.28\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6629: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6630: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  thf_map, keyerror2 = AverageMeridional(thf_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +219.02\u001b[0m\n",
+      "\u001b[93m     range new = -219.02 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -4.19 to +33.07\u001b[0m\n",
+      "\u001b[93m     range new = -33.07 to +4.19\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n",
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +243.35\u001b[0m\n",
+      "\u001b[93m     range new = -243.35 to -3.32\u001b[0m\n",
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoAmpl\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n"
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -9.37 to +72.85\u001b[0m\n",
+      "\u001b[93m     range new = -72.85 to +9.37\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6629: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6630: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  thf_map, keyerror2 = AverageMeridional(thf_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = HadISST_ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +219.02\u001b[0m\n",
+      "\u001b[93m     range new = -219.02 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
+      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
+      "\u001b[93m     range old = -4.19 to +33.07\u001b[0m\n",
+      "\u001b[93m     range new = -33.07 to +4.19\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/cdms2/dataset.py:2179: Warning: Files are written with compression and no shuffling\n",
-      "You can query different values of compression using the functions:\n",
-      "cdms2.getNetcdfShuffleFlag() returning 1 if shuffling is enabled, 0 otherwise\n",
-      "cdms2.getNetcdfDeflateFlag() returning 1 if deflate is used, 0 otherwise\n",
-      "cdms2.getNetcdfDeflateLevelFlag() returning the level of compression for the deflate method\n",
-      "\n",
-      "If you want to turn that off or set different values of compression use the functions:\n",
-      "value = 0\n",
-      "cdms2.setNetcdfShuffleFlag(value) ## where value is either 0 or 1\n",
-      "cdms2.setNetcdfDeflateFlag(value) ## where value is either 0 or 1\n",
-      "cdms2.setNetcdfDeflateLevelFlag(value) ## where value is a integer between 0 and 9 included\n",
-      "\n",
-      "To produce NetCDF3 Classic files use:\n",
-      "cdms2.useNetCDF3()\n",
-      "To Force NetCDF4 output with classic format and no compressing use:\n",
-      "cdms2.setNetcdf4Flag(1)\n",
-      "NetCDF4 file with no shuffling or deflate and noclassic will be open for parallel i/o\n",
-      "  warnings.warn(\"Files are written with compression and no shuffling\\n\" +\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsodSstOce_2\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
       "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
       "\u001b[93m                         hfls sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = +1.99 to +243.32\u001b[0m\n",
-      "\u001b[93m     range new = -243.32 to -1.99\u001b[0m\n",
+      "\u001b[93m     range old = +3.32 to +243.35\u001b[0m\n",
+      "\u001b[93m     range new = -243.35 to -3.32\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
       "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
       "\u001b[93m                         hfss sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = -3.28 to +31.07\u001b[0m\n",
-      "\u001b[93m     range new = -31.07 to +3.28\u001b[0m\n"
+      "\u001b[93m     range old = -9.37 to +72.85\u001b[0m\n",
+      "\u001b[93m     range new = -72.85 to +9.37\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n",
-      "it is now: MV2.copmress(array,condition), if your code seems to not react or act wrong to a call to compress, please check this\n",
-      "  warnings.warn(\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6629: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst_map, keyerror1 = AverageMeridional(sst_map)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:6630: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  thf_map, keyerror2 = AverageMeridional(thf_map)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
-      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = +1.99 to +290.82\u001b[0m\n",
-      "\u001b[93m     range new = -290.82 to -1.99\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
-      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = -3.28 to +62.31\u001b[0m\n",
-      "\u001b[93m     range new = -62.31 to +3.28\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoFbSshSst\u001b[0m\n",
-      "\u001b[94m     ComputeCollection: ENSO_proc, metric EnsoFbSshSst not computed\u001b[0m\n",
-      "\u001b[94m          reason(s):\u001b[0m\n",
-      "\u001b[94m           no modeled ssh given\u001b[0m\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoFbSstTaux\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
-      "\u001b[94m     ComputeCollection: metric = EnsoFbSstThf\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
-      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = +1.99 to +243.32\u001b[0m\n",
-      "\u001b[93m     range new = -243.32 to -1.99\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
-      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = -3.28 to +31.07\u001b[0m\n",
-      "\u001b[93m     range new = -31.07 to +3.28\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
-      "\u001b[93m                         hfls sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = +1.99 to +290.82\u001b[0m\n",
-      "\u001b[93m     range new = -290.82 to -1.99\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n",
-      "\u001b[93m                         hfss sign reversed\u001b[0m\n",
-      "\u001b[93m     range old = -3.28 to +62.31\u001b[0m\n",
-      "\u001b[93m     range new = -62.31 to +3.28\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n",
-      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
       "\u001b[94m     ComputeCollection: metric = EnsoFbTauxSsh\u001b[0m\n",
-      "\u001b[94m     ComputeCollection: ENSO_proc, metric EnsoFbTauxSsh not computed\u001b[0m\n",
-      "\u001b[94m          reason(s):\u001b[0m\n",
-      "\u001b[94m           no modeled ssh given\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7286: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tab, keyerror = dict_average[average](tab, areacell, region=region, **kwargs)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8835: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssh_map, keyerror = AverageMeridional(ssh_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: twoVarmetric = ERA-Interim_AVISO\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5534: UserWarning: areacell is None for variable 'tauu'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  taux, keyerror = AverageHorizontal(taux, region=\"nino4\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7286: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  tab, keyerror = dict_average[average](tab, areacell, region=region, **kwargs)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:7389: UserWarning: areacell is None for variable 'zos'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  variable, areacell, keyerror3 = Read_mask_area(\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:8835: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  ssh_map, keyerror = AverageMeridional(ssh_map)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[94m     ComputeCollection: metric = EnsoSeasonality\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11532: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror1 = AverageMeridional(sst2)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11533: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_NDJ, keyerror2 = AverageMeridional(sst3_NDJ)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:11534: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst3_MAM, keyerror3 = AverageMeridional(sst3_MAM)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[94m     ComputeCollection: metric = EnsoSstLonRmse\u001b[0m\n",
       "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14907: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14908: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14907: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_mod, keyerror_mod = AverageMeridional(sstmap_mod)\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:14908: UserWarning: areacell is None for variable 'unknown'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sstlon_obs, keyerror_obs = AverageMeridional(sstmap_obs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n",
       "\u001b[94m     ComputeCollection: metric = EnsoSstSkew\u001b[0m\n",
-      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n",
-      "\u001b[93m               EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n",
+      "\u001b[94m     ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:12116: UserWarning: areacell is None for variable 'skewness'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[94m     ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:12116: UserWarning: areacell is None for variable 'skewness'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[94m     ComputeMetric: oneVarmetric = HadISST\u001b[0m\n",
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n",
-      "/Users/lee1043/miniforge3/envs/pmp_devel_20250715/lib/python3.10/site-packages/share/cdutil/navy_land.nc\n"
+      "\u001b[93m                         NOTE: Estimated landmask applied\u001b[0m\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO::2025-08-29 15:26::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "2025-08-29 15:26:11,380 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "2025-08-29 15:26:11,380 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
-      "INFO::2025-08-29 15:26::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n",
-      "2025-08-29 15:26:16,871 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n",
-      "2025-08-29 15:26:16,871 [INFO]: base.py(write:347) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n"
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py:5753: UserWarning: Generated land mask is entirely ocean for this region. This may be expected if the region is all ocean.\n",
+      "  sft = cdutil.generateLandSeaMask(d(*(slice(0, 1),) * n),debug=False) * 100.0\n",
+      "/global/homes/l/lee1043/.conda/envs/pmp_devel_20250908/lib/python3.13/site-packages/EnsoMetrics/EnsoMetricsLib.py:12116: UserWarning: areacell is None for variable 'skewness'; synthesising cosine-latitude weights. Provide areacella/sftlf for accurate spatial averages.\n",
+      "  sst2, keyerror = AverageMeridional(sst2)\n",
+      "INFO::2026-05-28 11:29::pcmdi_metrics:: Results saved to a json file: /pscratch/sd/l/lee1043/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
+      "INFO::2026-05-28 11:29::pcmdi_metrics:: Results saved to a json file: /pscratch/sd/l/lee1043/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n"
      ]
     },
     {
@@ -1504,10 +4712,128 @@
       "filename_js: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n",
       "met: BiasSstLonRmse\n",
       "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_BiasSstLonRmse.nc\n",
-      "failed for  ACCESS1-0 r1i1p1\n",
-      "'BiasSstLonRmse'\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_BiasSstLonRmse\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: BiasTauxLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_BiasTauxLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_BiasTauxLonRmse\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoAmpl\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoAmpl\n",
+      "                    dot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsodSstOce_2\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsodSstOce_2.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsodSstOce_2\n",
+      "                    dot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoFbSshSst\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSshSst.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoFbSshSst\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoFbSstTaux\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSstTaux.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoFbSstTaux\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoFbSstThf\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSstThf.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoFbSstThf\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoFbTauxSsh\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbTauxSsh.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoFbTauxSsh\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    scatterplot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSeasonality\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSeasonality\n",
+      "                    dot 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    hovmoeller 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:29\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:29\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstLonRmse\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSstLonRmse\n",
+      "                    curve 11:30\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:30\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:30\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:30\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
+      "met: EnsoSstSkew\n",
+      "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstSkew.nc\n",
+      "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSstSkew\n",
+      "                    dot 11:30\n",
+      "                              took 0 minute(s)\n",
+      "                    curve 11:30\n",
+      "                              took 0 minute(s)\n",
+      "                    map 11:30\n",
+      "                              took 0 minute(s)\n",
+      "figure plotting done\n",
       "PMPdriver: model loop end\n",
-      "Process end: Fri Aug 29 15:26:16 2025\n"
+      "Process end: Thu May 28 11:30:05 2026\n"
      ]
     }
    ],
@@ -1520,23 +4846,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "e91aaa1e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1b8e107c0>"
+       "<matplotlib.image.AxesImage at 0x7f0bd16039d0>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x1000 with 3 Axes>"
       ]
@@ -1560,11 +4886,19 @@
     "ax[1].imshow(mpimg.imread(plot2))\n",
     "ax[2].imshow(mpimg.imread(plot3))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06a2150a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pmp_devel_20250715",
+   "display_name": "pmp_devel_20250908",
    "language": "python",
    "name": "python3"
   },
@@ -1578,7 +4912,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,
diff --git a/doc/jupyter/Demo/basic_enso_param.py.in b/doc/jupyter/Demo/basic_enso_param.py.in
index 94904fa24..8b6e5e423 100644
--- a/doc/jupyter/Demo/basic_enso_param.py.in
+++ b/doc/jupyter/Demo/basic_enso_param.py.in
@@ -16,7 +16,7 @@ modpath_lf = '$INPUT_DIR$/CMIP5_demo_data/sftlf_fx_%(model)_amip_r0i0p0.nc'
 # OBSERVATIONS
 obs_cmor = True
 obs_cmor_path = "$INPUT_DIR$/obs4MIPs_PCMDI_monthly"
-obs_catalogue = "demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20210805_demo.json"
+obs_catalogue = "demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20260522_demo.json"
 
 # METRICS COLLECTION
 metricsCollection = 'ENSO_perf'  # ENSO_perf, ENSO_tel, ENSO_proc
diff --git a/doc/jupyter/Demo/demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20210805_demo.json b/doc/jupyter/Demo/demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20260522_demo.json
similarity index 97%
rename from doc/jupyter/Demo/demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20210805_demo.json
rename to doc/jupyter/Demo/demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20260522_demo.json
index 11edf0da3..a8bb45722 100644
--- a/doc/jupyter/Demo/demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20210805_demo.json
+++ b/doc/jupyter/Demo/demo_data/obs4MIPs_PCMDI-CEM2021_monthly_bySource_catalogue_v20260522_demo.json
@@ -228,12 +228,12 @@
     "HadISST-1-1": {
         "ts": {
             "CMIP_CMOR_TABLE": "Amon",
-            "MD5sum": "99c8691e0f615dc4d79b4fb5e926cc76",
+            "MD5sum": "96224d2e9a6ad837819e7372fb1d443c",
             "RefTrackingDate": "Tue Jul 27 11:32:36 2021",
-            "filename": "ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc",
-            "period": "187001-201907",
+            "filename": "ts_mon_HadISST-1-1_PCMDI_gn_187001-202501.nc",
+            "period": "187001-202501",
             "shape": "(1795, 180, 360)",
-            "template": "MOHC/HadISST-1-1/mon/ts/gn/v20210727/ts_mon_HadISST-1-1_PCMDI_gn_187001-201907.nc",
+            "template": "MOHC/HadISST-1-1/mon/ts/gn/v20260416/ts_mon_HadISST-1-1_PCMDI_gn_187001-202501.nc",
             "version": "latest"
         }
     }
diff --git a/pcmdi_metrics/enso/enso_driver.py b/pcmdi_metrics/enso/enso_driver.py
index def4221ee..e927e8ba7 100755
--- a/pcmdi_metrics/enso/enso_driver.py
+++ b/pcmdi_metrics/enso/enso_driver.py
@@ -290,15 +290,18 @@ def _get_model_file(path, var, realm2):
                         print("file_name:", file_name)
                 else:
                     file_name = param.reference_data_path[obs].replace("VAR", var0)
+
                 file_areacell = None  # temporary for now
                 try:
                     file_landmask = param.reference_data_lf_path[obs]
                 except Exception:
                     file_landmask = None
+
                 try:
                     areacell_in_file = dict_var["areacell"]["var_name"]
                 except Exception:
                     areacell_in_file = None
+
                 try:
                     landmask_in_file = dict_var["landmask"]["var_name"]
                 except Exception: