diff --git a/climanet/predict.py b/climanet/predict.py
index f959870..09ecb26 100644
--- a/climanet/predict.py
+++ b/climanet/predict.py
@@ -124,6 +124,10 @@ def predict_monthly_var(
     model.to(device)
     model.eval()
 
+    # Length of the dataset should >=1
+    if len(dataset) < 1:
+        raise ValueError("The dataset is empty. Please provide a non-empty dataset.")
+
     use_cuda = device == "cuda"
     dataloader = DataLoader(
         dataset,
diff --git a/climanet/utils.py b/climanet/utils.py
index 98bcb14..3278665 100644
--- a/climanet/utils.py
+++ b/climanet/utils.py
@@ -789,7 +789,9 @@ def data_preparation(
 
     # rechunk data
     input_da = input_da.chunk({"M": 1, "T": -1, "lat": 100, "lon": 100})
-    input_da_nan_mask = input_da_nan_mask.chunk({"M": 1, "T": -1, "lat": 100, "lon": 100})
+    input_da_nan_mask = input_da_nan_mask.chunk(
+        {"M": 1, "T": -1, "lat": 100, "lon": 100}
+    )
     monthly_da = monthly_da.chunk({"M": 1, "lat": 100, "lon": 100})
     padded_days_mask = padded_days_mask.chunk({"M": 1})
     time_features = time_features.chunk({"M": 1})
@@ -891,3 +893,29 @@ def read_st_data(data_path=".", var_name="tos"):
 
     # if one of the datasets is None, we need to compute them
     return input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features
+
+
+def coarsen_land_mask(
+    input_lsm: xr.DataArray | xr.Dataset, coarse_factor: int = 2, threshold: float = 0.5
+):
+    """Coarsen spatial resolution of land mask data by coarse_factor.
+
+    It also applies a threshold to values outside of [0,1].
+    see https://confluence.ecmwf.int/spaces/FUG/pages/673550380/Section+2A.1.3.1+Land-Sea+mask
+
+    Args:
+        input_lsm (xarray.DataArray): Land-sea mask from ERA5-Land data.
+        coarse_factor (int, optional): Factor by which to coarsen the resolution. Defaults to 2.
+        threshold (float, optional): Threshold for determining mask value. Defaults to 0.5.
+
+    Returns:
+        xarray.DataArray | xarray.Dataset : Coarse land-sea mask.
+    """
+    coarse_lsm = input_lsm.coarsen(
+        lat=coarse_factor, lon=coarse_factor, boundary="trim"
+    ).mean()
+
+    # Apply threshold
+    coarse_lsm = coarse_lsm >= threshold
+
+    return coarse_lsm
diff --git a/notebooks/data_vis.ipynb b/notebooks/sst/data_vis_sst.ipynb
similarity index 100%
rename from notebooks/data_vis.ipynb
rename to notebooks/sst/data_vis_sst.ipynb
diff --git a/notebooks/example_daily.ipynb b/notebooks/sst/training_daily_sst.ipynb
similarity index 99%
rename from notebooks/example_daily.ipynb
rename to notebooks/sst/training_daily_sst.ipynb
index bc0e2cb..e07c011 100644
--- a/notebooks/example_daily.ipynb
+++ b/notebooks/sst/training_daily_sst.ipynb
@@ -182,14 +182,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "ca9b6c44-1f4a-4ff2-b15e-7b330bbc501e",
    "metadata": {
     "jp-MarkdownHeadingCollapsed": true
    },
    "outputs": [],
    "source": [
-    "# read data\n",
+    "# read data \n",
     "data_dir = f\"{run_dir}/data_train\"\n",
     "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
    ]
@@ -259,12 +259,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "d02dace1-c194-49bd-a283-94f7a04e666b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# read data\n",
+    "# read data \n",
     "data_dir = f\"{run_dir}/data_validation\"\n",
     "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
    ]
@@ -475,19 +475,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "4c30511a-49fc-4a4d-8bfc-61ec3857fde0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# read data\n",
+    "# read data \n",
     "data_dir = f\"{run_dir}/data_test\"\n",
     "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "ac99fdf5-dfd8-44a0-957b-dea177ac92a1",
    "metadata": {},
    "outputs": [
@@ -503,7 +503,7 @@
     }
    ],
    "source": [
-    "spatial_crop_size = monthly_da.shape[1:]  # the whole dataset\n",
+    "spatial_patch_size = monthly_da.shape[1:]  # the whole dataset \n",
     "\n",
     "dataset_test = STDataset(\n",
     "    input_da=input_da,\n",
@@ -513,7 +513,7 @@
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
     "    model_patch_size=patch_size,\n",
-    "    crop_size=(1, *spatial_crop_size),\n",
+    "    crop_size=(1, *spatial_crop_size), \n",
     "    stride=None,  # no stride in inference\n",
     "    sh_embed_dim=96,\n",
     "    sh_order_L = 10,\n",
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "7c2deb40-bee8-4973-80f0-9d9485eabf0c",
    "metadata": {},
    "outputs": [
@@ -591,7 +591,7 @@
     "# inference on test data, verbose is True\n",
     "predictions = predict_monthly_var(\n",
     "    model=f\"{run_dir}/best_model.pth\",\n",
-    "    dataset=dataset_test,\n",
+    "    dataset=dataset_test,  \n",
     "    dataloader_config=dataloader_config,\n",
     "    prediction_config=prediction_config,\n",
     "    run_dir=run_dir,\n",
@@ -647,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "7ba5d204-aefc-4103-8475-c802c603ad3f",
    "metadata": {},
    "outputs": [
@@ -668,7 +668,7 @@
     "\n",
     "ocean = ~lsm_subset[\"lsm\"].values\n",
     "masked_err_baseline = err_baseline.where(ocean)\n",
-    "masked_err_predictions = err_predictions.where(ocean)\n",
+    "masked_err_predictions = err_predictions.where(ocean) \n",
     "\n",
     "plot_results(masked_err_baseline, masked_err_predictions, label=\"error K\", title=(\"err_Baseline\", \"err_Prediction\"), error=True)"
    ]
diff --git a/notebooks/example_hourly.ipynb b/notebooks/sst/training_hourly_sst.ipynb
similarity index 99%
rename from notebooks/example_hourly.ipynb
rename to notebooks/sst/training_hourly_sst.ipynb
index b1bd7a7..001b1d3 100644
--- a/notebooks/example_hourly.ipynb
+++ b/notebooks/sst/training_hourly_sst.ipynb
@@ -670,9 +670,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "climanet",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "climanet"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -684,7 +684,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.6"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/example_tuning.ipynb b/notebooks/sst/tuning_sst.ipynb
similarity index 100%
rename from notebooks/example_tuning.ipynb
rename to notebooks/sst/tuning_sst.ipynb
diff --git a/notebooks/data_vis_watervapor.ipynb b/notebooks/watervapor/data_vis_watervapor.ipynb
similarity index 100%
rename from notebooks/data_vis_watervapor.ipynb
rename to notebooks/watervapor/data_vis_watervapor.ipynb
diff --git a/notebooks/watervapor/training_daily_watervapor.ipynb b/notebooks/watervapor/training_daily_watervapor.ipynb
new file mode 100644
index 0000000..78ae6c0
--- /dev/null
+++ b/notebooks/watervapor/training_daily_watervapor.ipynb
@@ -0,0 +1,764 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8fe27a3-710c-47b0-95c7-00d764eec025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-09-10 16:14:01,684\tINFO util.py:155 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
+      "2026-09-10 16:14:02,808\tINFO util.py:155 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import xarray as xr\n",
+    "import torch\n",
+    "import torch.nn.functional\n",
+    "from climanet.st_encoder_decoder import SpatioTemporalModel\n",
+    "from climanet.utils import set_seed, configure_compute_resources, plot_results, plot_histograms, plot_loss, data_preparation, read_st_data, coarsen_land_mask\n",
+    "from climanet.train import train_monthly_model, TrainConfig\n",
+    "from climanet.predict import predict_monthly_var, PredictionConfig\n",
+    "from climanet.dataset import STDataset, DataLoaderConfig\n",
+    "\n",
+    "from tbparse import SummaryReader\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0f5c8e2-693b-4f6e-8e88-e3c002001b24",
+   "metadata": {},
+   "source": [
+    "## Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abf5a034-02ee-497f-85d0-33b4e99f43a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# without diurnal cycle\n",
+    "data_folder = Path(\"../eso4clima/watervapor\")\n",
+    "run_dir = Path(\"./runs_daily\").resolve()\n",
+    "\n",
+    "var_name = \"prw\"\n",
+    "\n",
+    "# 1 month train, 1 month validation and test\n",
+    "daily_data = xr.open_mfdataset(data_folder / f\"202001_day_ERA5_masked_{var_name}.nc\")\n",
+    "daily_data_validation = xr.open_mfdataset(data_folder / f\"202101_day_ERA5_masked_{var_name}.nc\")\n",
+    "daily_data_test = xr.open_mfdataset(data_folder / f\"202201_day_ERA5_masked_{var_name}.nc\")\n",
+    "\n",
+    "monthly_data = xr.open_mfdataset(data_folder / f\"202001_mon_ERA5_full_{var_name}.nc\")\n",
+    "monthly_data_validation = xr.open_mfdataset(data_folder / f\"202101_mon_ERA5_full_{var_name}.nc\")\n",
+    "monthly_data_test = xr.open_mfdataset(data_folder / f\"202201_mon_ERA5_full_{var_name}.nc\")\n",
+    "\n",
+    "# Make land-sea mask for watervapor from sst mask\n",
+    "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloded from era5 and regridded using the function `regrid_to_boundary_centered_grid`\n",
+    "lsm_mask_sst = xr.open_dataset(file_name)\n",
+    "lsm_mask = coarsen_land_mask(lsm_mask_sst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9d62c368",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort in lat dimension to make it ascending\n",
+    "daily_data = daily_data.sortby(\"lat\")\n",
+    "daily_data_validation = daily_data_validation.sortby(\"lat\")\n",
+    "daily_data_test = daily_data_test.sortby(\"lat\")\n",
+    "\n",
+    "monthly_data = monthly_data.sortby(\"lat\")\n",
+    "monthly_data_validation = monthly_data_validation.sortby(\"lat\")\n",
+    "monthly_data_test = monthly_data_test.sortby(\"lat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4be672b-a2c1-4a6f-89a1-2c22cc6cde02",
+   "metadata": {},
+   "source": [
+    "## Subset data (for fast example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f2fda038-7201-42cd-b81b-83825f6338ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(31, 40, 40) (1, 40, 40)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# coordinates of subset\n",
+    "lon_subset = slice(-10, 10)  # one lon -179.9 is nan, check data\n",
+    "lat_subset = slice(-10, 10)\n",
+    "\n",
+    "daily_subset = daily_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_subset = monthly_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "lsm_subset = lsm_mask.sel(lon=lon_subset, lat=lat_subset)  # True=Land\n",
+    "\n",
+    "daily_validation_subset = daily_data_validation.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_validation_subset = monthly_data_validation.sel(lon=lon_subset, lat=lat_subset)\n",
+    "\n",
+    "daily_test_subset = daily_data_test.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_test_subset = monthly_data_test.sel(lon=lon_subset, lat=lat_subset)\n",
+    "\n",
+    "print(daily_subset[var_name].shape, monthly_subset[var_name].shape)  # (time, lat, lon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d5a6d46-1423-4313-b7d2-1eb939e2c876",
+   "metadata": {},
+   "source": [
+    "## Training workflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b69667c-18f9-4f2b-b7ff-0f737a6e1543",
+   "metadata": {},
+   "source": [
+    "### Build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5839cfb3-a79c-4b0b-b40b-9734233e0b57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the model (small)\n",
+    "set_seed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1ceb7756-f274-4823-bded-87c0f8f16a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "patch_size = (1, 4, 4)\n",
+    "model = SpatioTemporalModel(patch_size=patch_size, embed_dim=64, dropout=0.1, hidden=64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5840197d-3a4f-4942-ac42-f56e0fdaed0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Device and resources\n",
+    "device = \"cpu\"\n",
+    "compute_threads = 4\n",
+    "dataloader_num_workers = 0\n",
+    "model = configure_compute_resources(model, device=device, compute_threads=compute_threads, dataloader_num_workers=dataloader_num_workers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "069ae003-6ba6-4f06-b156-1e0e1536acc9",
+   "metadata": {},
+   "source": [
+    "### Build the datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9e6325a-de1f-4352-9183-caf57ee6185a",
+   "metadata": {},
+   "source": [
+    "#### Prepare the train data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "de9eb760-a412-4c63-b3f6-0d764e7ae0ef",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# this should be done once\n",
+    "data_dir = Path(f\"{run_dir}/data_train\")\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(\n",
+    "    daily_subset[var_name], monthly_subset[var_name], calculate_residuals=True, is_hourly=False, save_to_zarr=True, run_dir=data_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ca9b6c44-1f4a-4ff2-b15e-7b330bbc501e",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# read data \n",
+    "data_dir = f\"{run_dir}/data_train\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "98604568-a2c7-47c3-a5f8-9ead2bfb717a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset:\n",
+      "Crop grid (m x i x j): 1 x 1 x 1 = 1 data crops\n",
+      "Overlap: 32 pixels (height), 32 pixels (width)\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create dataset config\n",
+    "num_subsets = (10, 10)\n",
+    "spatial_crop_size = (patch_size[1]*num_subsets[0], patch_size[2]*num_subsets[1])\n",
+    "stride = (spatial_crop_size[0] // 5, spatial_crop_size[1] // 5)  # for data agumentation\n",
+    "\n",
+    "dataset_train = STDataset(\n",
+    "    input_da=input_da,\n",
+    "    input_da_nan_mask=input_da_nan_mask,\n",
+    "    monthly_da=monthly_da,\n",
+    "    padded_days_mask=padded_days_mask,\n",
+    "    time_features=time_features,\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    model_patch_size=patch_size,\n",
+    "    crop_size=(1, *spatial_crop_size),   # based on the patch_size in model\n",
+    "    stride=stride,\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    verbose=True,\n",
+    "    load_lazy=False,   # set it to true when data is large\n",
+    ")\n",
+    "print(len(dataset_train))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4243ef72-8295-4689-bef9-1ecf0ec6deb1",
+   "metadata": {},
+   "source": [
+    "#### Prepare validation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ab17c513-095a-420c-8193-c2d81c280082",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this should be done once\n",
+    "data_dir = f\"{run_dir}/data_validation\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(\n",
+    "    daily_validation_subset[var_name], monthly_validation_subset[var_name], calculate_residuals=True, is_hourly=False, save_to_zarr=True, run_dir=data_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d02dace1-c194-49bd-a283-94f7a04e666b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read data \n",
+    "data_dir = f\"{run_dir}/data_validation\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b85070a2-69f7-496c-8696-8f22802e4b8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset:\n",
+      "Crop grid (m x i x j): 1 x 1 x 1 = 1 data crops\n",
+      "Overlap: 32 pixels (height), 32 pixels (width)\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_subsets = (10, 10)\n",
+    "spatial_crop_size = (patch_size[1]*num_subsets[0], patch_size[2]*num_subsets[1])\n",
+    "stride = (spatial_crop_size[0] // 5, spatial_crop_size[1] // 5)  # for data agumentation\n",
+    "\n",
+    "dataset_validation = STDataset(\n",
+    "    input_da=input_da,\n",
+    "    input_da_nan_mask=input_da_nan_mask,\n",
+    "    monthly_da=monthly_da,\n",
+    "    padded_days_mask=padded_days_mask,\n",
+    "    time_features=time_features,\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    model_patch_size=patch_size,\n",
+    "    crop_size=(1, *spatial_crop_size),   # based on the patch_size in model\n",
+    "    stride=stride,\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    verbose=True,\n",
+    "    load_lazy=False,  # set it to true when data is large\n",
+    ")\n",
+    "print(len(dataset_validation))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5452ea81-86c2-49c4-81cd-43b6395c754d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DataLoaderConfig(batch_size=10, shuffle=True, num_workers=0, pin_memory=False, persistent_workers=False, device='cpu', multiprocessing_context=None)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create dataloader config\n",
+    "dataloader_config = DataLoaderConfig(\n",
+    "    batch_size=10,\n",
+    "    shuffle=True,\n",
+    "    num_workers= dataloader_num_workers,\n",
+    "    pin_memory=False,\n",
+    "    persistent_workers=False,  # set it to true when num_workers >0 and lazy data\n",
+    "    device=device,\n",
+    "    multiprocessing_context=None, # set it to \"spawn\" when num_workers >0 and lazy data\n",
+    ")\n",
+    "dataloader_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "acccd5d8-b4fe-489f-9f3e-0b90b6114334",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TrainConfig(calculate_residuals=True, num_epoch=100, patience=10, accumulation_steps=2, optimizer_lr=0.001, optimizer_weight_decay=0.01, scheduler_lr_factor=0.1, scheduler_min_lr=1e-05, device='cpu', verbose=True, verbose_epoch_interval=20, tune_checkpoint=False, store_model=True, store_logs=True)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create training config\n",
+    "training_config = TrainConfig(\n",
+    "    calculate_residuals=True,\n",
+    "    num_epoch=100,\n",
+    "    patience=10,\n",
+    "    accumulation_steps=2,\n",
+    "    optimizer_lr=1e-3,\n",
+    "    device=device,\n",
+    "    verbose=True,\n",
+    "    verbose_epoch_interval=20,\n",
+    "    tune_checkpoint=False,\n",
+    "    store_model=True,\n",
+    ")\n",
+    "training_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "945ec311-53b3-4cac-a1e3-d16f4508e7a7",
+   "metadata": {},
+   "source": [
+    "### Start training loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cd442780",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 4, 4)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_train.model_patch_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b9742e67-165f-4b5a-bab0-27b8b0d7070d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: gap between train and val loss: 0.044098\n",
+      "Epoch 0: best_loss = 0.298102\n",
+      "Epoch 20: gap between train and val loss: -0.008917\n",
+      "Epoch 20: best_loss = 0.092625\n",
+      "Epoch 40: gap between train and val loss: -0.013420\n",
+      "Epoch 40: best_loss = 0.079654\n",
+      "Early stopping triggered at epoch 47. Best loss: 0.079654\n",
+      "Training complete. Best loss: 0.079654\n",
+      "Model saved to /home/oku/Developments/ESO4Clima/ClimaNet/notebooks/watervapor/runs_daily/best_model.pth\n",
+      "CPU times: user 29.5 s, sys: 2.16 s, total: 31.6 s\n",
+      "Wall time: 16.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# verbose is True\n",
+    "trained_model = train_monthly_model(\n",
+    "    model=model,\n",
+    "    dataset_train=dataset_train,\n",
+    "    dataloader_config=dataloader_config,\n",
+    "    training_config=training_config,\n",
+    "    dataset_validation=dataset_validation,  # here validation dataset is used to improve model generalization\n",
+    "    run_dir=run_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e27a96aa-5560-47b6-91eb-dc4530dca090",
+   "metadata": {},
+   "source": [
+    "## Inspect results and compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a2b81805",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_loss(run_dir, list_loss_var=[\"Loss/train\", \"Loss/validation\"], unit=\"kg/m2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4440c610-ac0f-4f91-a21c-5a9ebd284cee",
+   "metadata": {},
+   "source": [
+    "#### Prepare the test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8e92bb78-8822-4b6b-8423-8e9dd3c04c74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this hsould be done once\n",
+    "data_dir = f\"{run_dir}/data_test\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(\n",
+    "    daily_test_subset[var_name], monthly_test_subset[var_name], calculate_residuals=True, is_hourly=False, save_to_zarr=True, run_dir=data_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4c30511a-49fc-4a4d-8bfc-61ec3857fde0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read data \n",
+    "data_dir = f\"{run_dir}/data_test\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac99fdf5-dfd8-44a0-957b-dea177ac92a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset:\n",
+      "Crop grid (m x i x j): 1 x 1 x 1 = 1 data crops\n",
+      "Overlap: 0 pixels (height), 0 pixels (width)\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "spatial_crop_size = monthly_da.shape[1:]  # the whole dataset \n",
+    "\n",
+    "dataset_test = STDataset(\n",
+    "    input_da=input_da,\n",
+    "    input_da_nan_mask=input_da_nan_mask,\n",
+    "    monthly_da=monthly_da,\n",
+    "    padded_days_mask=padded_days_mask,\n",
+    "    time_features=time_features,\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    model_patch_size=patch_size,\n",
+    "    crop_size=(1, *spatial_crop_size), \n",
+    "    stride=None,  # no stride in inference\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    verbose=True,\n",
+    "    load_lazy=False,\n",
+    ")\n",
+    "print(len(dataset_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c3dadb35-a6a8-413c-869b-1fa90adfec7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DataLoaderConfig(batch_size=10, shuffle=False, num_workers=0, pin_memory=False, persistent_workers=False, device='cpu', multiprocessing_context=None)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create dataloader config\n",
+    "dataloader_config = DataLoaderConfig(\n",
+    "    batch_size=10,\n",
+    "    shuffle=False,  # no shuffling in inference\n",
+    "    num_workers=0,\n",
+    "    pin_memory=False,\n",
+    "    persistent_workers=False,\n",
+    "    device=device,\n",
+    "    multiprocessing_context=None, # keep this when num_workers >0 and lazy data\n",
+    ")\n",
+    "dataloader_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "dc9bffb1-5db9-46d0-bd0a-987772321451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create prediction config\n",
+    "prediction_config = PredictionConfig(\n",
+    "    calculate_residuals=True,\n",
+    "    return_numpy=True,\n",
+    "    save_predictions=True,\n",
+    "    return_loss=False,\n",
+    "    device=device,\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7c2deb40-bee8-4973-80f0-9d9485eabf0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processed batch 1/1, with loss: 0.0850\n",
+      "Average loss over all batches: 0.0850\n"
+     ]
+    }
+   ],
+   "source": [
+    "# inference on test data, verbose is True\n",
+    "predictions = predict_monthly_var(\n",
+    "    model=f\"{run_dir}/best_model.pth\",\n",
+    "    dataset=dataset_test,  \n",
+    "    dataloader_config=dataloader_config,\n",
+    "    prediction_config=prediction_config,\n",
+    "    run_dir=run_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "370bf4d4-0f97-418e-ada3-9e958248d386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions_res = xr.open_dataset(f\"{run_dir}/202201_{var_name}_prediction_residual.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "76a795e8-b77c-4fb3-8c55-43d9996e2c5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to monthly average\n",
+    "input_data_averaged = daily_test_subset.resample({\"time\": \"MS\"}).mean(skipna=True)\n",
+    "input_data_averaged[\"time\"] = monthly_test_subset[\"time\"]\n",
+    "predictions = input_data_averaged[var_name] + predictions_res[var_name]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "14789287-c63f-49ad-bb45-e5fe4a7f039f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0lxMQva1iY2ONlxfFxMTgxIkTKFasGDZt2mQcUg28Gsn0+l1UVCoV1q1bhzZt2qQbOt+vXz8sWrQI+/fvR4sWLbBjxw706tULQUFBxjr+/v7o0KGD6G09NRoNNmzYgI8++ijdjPmenp4mo7ekSk1NxbZt29CxY0cULlzYuNzDwwODBw/Gp59+ijNnzqBWrVoAAEEQ0K9fP5MYjRs3xsaNGxEVFYV33nnH5hwEQUCfPn2MfysUCtSpUwerVq3Cxo0bTerWrVvXZGZ4R+UvNU61atWwZcsWSTHTcjTn4MGDmDhxIt599118/vnnJmUajcbuNshaXKl1L1++jKlTp5rU/eabb/DOO+9kKDdLrG3PFsnJyXj+/DnGjh2L2bNnAwB69+6NwMBATJ8+He3atUOzZs1szo/IWbATT7JYuHAhhg8fjo8//hh169aFv78/lEolLly4gPj4eJO6SqUSRYoUMVl29+5dADB7EH/nnXfw9OlT499pw6VXr16N3377zfhjAfBqplK1Wo2nT5+avS7dXuauY86TJ4/F5U+ePDH+nfbczA1/LlmyJI4cOWLyo4O5fRAUFGRzJ15qnGrVqhmHxtlr2rRpWLBgAXr06IGvv/7apCxtGF5ap/R1SUlJAGDxtoTW4tpS99y5cyZfHENDQzOcmzWWtmeLtO2WKlXK2IEHXv3g0LVrV0yYMAFnzpwx+RGJ6G3m6elpvLzI3d0dCxYsMHt7uWLFipn8HR0djaSkJFy9ehWdO3c2tieCIBg7Kffv38fDhw+h0WjSrQ8AxYsXF80vOjoaycnJJpczZVRaTpbaFwC4c+eOsYOpUCjStb9pP0g8fvzYrk68QqFId4zLkycPgPRtZ548efD8+XMYDAYolUqH5S81Tv78+Y0/DNnr9OnTaNu2LUJDQ7F79+50P7x6e3vb1QaJxZVa98mTJ+luuTdu3Di88847dudmjbXt2SJt2927dzdZ3rNnT0yfPh27d+9mJ55yNHbiKcO0Wi2++OILdOnSBevXrzcpGzFiRLr6np6e6X7ZTbu++c1begHpO1hpnd0mTZpY7By9PpmeHMxdf61QKCwuf/22NtaeW1JSElQqlcn+kBIzIzm/Gceea+JfN2fOHEyePBldunTB6tWrTc54Af/78eLNeQ1eX2buC7BYXFvqDhw4EI0bNzb+nTYHgb25ibG0PVu88847UKlUxi/Dr0tbFhsba3NcopzK3d1dUofrzY5HWptStmxZs+v36NED7777rvGYaq5TY26ZufyA/3WA5CDWvry+XeDVj+hvtr9px0t7b8dmLeab7dDr23p9fpWszl9qHHuviU9z/vx5NGvWDIGBgTh48KDZkwslSpQw/tj/urQ2yNwPHFLiSq377rvvphttkPZDlT25ibG2PVuULFnSOGHl69g+0tuCnXjKsPj4eCQkJKQb6nz9+nU8fPhQUicobRbbs2fPoly5csblycnJ+PfffxEcHGxclvaLvK+vr+gXNhcXF4fdJzZN5cqVAbyaIOb1M94GgwERERGoWLGi1Q6qJXI9tzNnzqT7VdyStC9eaRYuXIjPPvsMnTt3xrp168wOt8+XLx/KlSuHw4cPpys7dOgQatSokW4CHilxbalbuXJl4+uQ0dyksLQ9W3h6eqJu3bq4du0a9Hq9yfNKG41izxcfIjJVqFAhhISEID4+3mqbIggCgoKCEBERka7szJkzotspWLAgQkJCjJO5WpLWuZRyfC9YsCDy5s2LU6dOpStLW5bRY1Fmyqz8bdmH1jx69Ehy+/jme+fKlSto0qQJcufOjUOHDplcLvC6Ro0aYerUqbh165bJMf3gwYNQKBQmPwjbEldq3bx581p839uamxTWtmeLZs2aYceOHbh+/ToKFChgXM72kd4WSvEqRNblyZMHISEh+OOPP6DVagG8moF3zJgxkoe0161bF++++y5mzJiBx48fG5dPnTo13Vn1mjVromXLlpg4caLJtefAq+GKq1evNv4dEhJi9iwr8Oq2eB06dMDJkycl5WivOnXqoGrVqpg9ezbu3LljXP7tt9/ixo0bGDVqlF1xrT03WwwdOhRbtmyR9Hi9I7lq1SoMHz4cnTt3xvr16612tD///HNcuHDB5PrItWvXIjIyEuPHjzepa0tcW+rKkVtW++KLL/D06VPMnTvXuOzff//FypUr0bBhQ5QtW9aB2RHlHFOnTsVff/2FhQsXGofTA6+u2V65ciViYmKgUCgwdOhQ7N69G3v27DHWOXDgQLq5XyyZNGkSTp06hdmzZ5ts58CBA8bjedrdMKQc3xUKBYYPH47Dhw9j06ZNxuXnz5/H0qVL0bZtW7tujZk2v4DYdf4ZlVn527IPrUm7Jl7K4/U5aP777z80btwYPj4+OHz4sNVLqgYNGgRfX1+MHz/e+KPDvXv3sGTJEnTp0sWk421LXFvqypFbVuvZsydCQkIwdepU40iYlJQUfPnll/D29jZ7a1ainIRn4kkWq1evRnh4OEqWLIlSpUrh+vXr+Oabb/Ds2TNJ6yuVSvz2229o06YNypQpg+rVq+Phw4cYOHAgypYtiwcPHpjU37x5M4YPH45atWqhfPnyyJ8/P+7fv4+UlBSMGzfOWO/TTz/Fhx9+iGrVqqFIkSIm91L/77//sHXrVvTo0UO+HWGGQqHAtm3b0LlzZ7z77ruoUaMGnjx5gtu3b2P27Nno2rWrXXGtPTdb2HNN/J07dzBgwAAolUqo1WqEh4eblNeoUQNjx441/t29e3fcvXsXvXr1wtKlS2EwGHDmzBl8++23aNu2rV1xbc3BEqm5Aa/OtqVNoJM2t0D//v3h7u6OwoUL47vvvhPdni0xGjVqhBUrVmDEiBH4+eefkSdPHpw8eRJhYWHYsGGD6LaISJrevXtDp9NhwoQJmDt3LsqUKYPY2FjcvXsXH3zwgfE4PWHCBNy5cwcffPABqlevDoVCgcDAQIwYMcLi/eJf169fPyQlJWHSpElYunQpSpcujTt37iA0NNT4me7QoQO++uorNGzYEFWrVoWLi4vZe5yn+fzzz/H8+XP06NEDc+bMgZeXF06dOoVmzZphzZo1du2P5ORkbN26Fbly5bJrfVtkRv627kNL7L0mvmvXrnjy5Alq1qxp9tZ9r1+ali9fPuzcuRMff/wxypQpg+LFi+Off/5B3bp1sXTpUrvj2lLXEltyA4Dw8HDo9Xrjd79Vq1YZR7otWLAABQsWtLo9W2J4eXlh9+7d6NSpE4oXL453330XV65cgVKpxK5duxxya1iirKQQXv8pmEgCQRCwdetWBAcHm8zGGh8fj7Nnz0Kn0xmHIR86dAhKpRL16tUD8KrTcvfu3XSdozR6vR6nT59GTEwMKleujIIFC6JKlSrw9fU1O+Q5JiYG58+fh1qtRtGiRVGqVKl0Z2OfPn2Kc+fOISkpCT4+PmjatCmAV7/iHjp0CDdu3DC55u5Ne/bsQUBAgPFetWn27t2L3Llzp5uR9q+//oKPj4/Ze6deu3YNt27dQq5cuVCtWrV0Q7UtbSsiIgKPHz9ON4u8pedmaxxbxcfHm8y+/qZChQqZff7Pnz9HREQEFAoFqlevjoCAALvj2puDJWK5Aa8mkTp+/LjZ9f38/NCkSRPR7dgTIzExEadPn0ZSUhJKlSplnPCJiF45fPgwEhMTrR7b4uLisG/fPtSqVcvkEq3XaTQanD9/Hk+ePEH+/PlRpkwZs5N33bx5E5GRkQgODkblypVx7949nDlzBs2aNTPW//fff3HlyhW0a9cuXWcpOTkZERERSEhIQOnSpdMN/U1NTcXp06fx7NkzGAwGNG3aFL6+vlZjPnv2DOfOnYNOp0PZsmXTncG+fPkybty4gXbt2pksf/z4MY4ePYoGDRoYryfeuXMn2rRpg7///ht169a1uE8txbxy5Qr+/fffdB3gq1evIjIyEu3bt093GZmc+Vvbh7bGscdff/2VblLf15l7/lqtFidPnkRsbKzxhEhG4tqTgyVScgOA3377zeIlDK9/NqyxNYYgCDh79iwePnyIoKAgVKtWTda7ExFlV+zEU7YWGxuL4OBg9O/fH/PmzZM1dnBwMKZOnYq+ffvKGpeIiMiZjRgxAtevXze5bICIiLIPDqenbOPcuXPw9vY2nmXUarUYNWoUUlNTTe5BKwetVovvv//e4ogAIiKit1W7du3S3YKNiIiyD6c8E6/X65GQkABvb+90twh5nVqttnrfTLnWIXlERkaiY8eOcHV1Rb58+XDp0iWkpKTghx9+sPu6cSIiZyIIAuLi4pArVy6zt4lMk5KSAk9PT5ti27MOERERZT9ONTv9w4cPMWXKFISGhsLf3x9Hjx41W2/y5MnInTs3vL29UaJECezdu1c0tj3rkLzKlCmDS5cuYf369Rg6dCg2b96Mhw8fsgNPRDney5cvMXfuXJQqVQr+/v74+eefzdZbvHgx8uXLBx8fHxQqVAjr1q0TjW3POkRERJR9OVUn/scff4QgCPj1118t1lm4cCHmz5+PXbt2ITk5Gb1790bbtm1x8+ZNWdehzKFUKlGuXDm0bt0aderU4VkjInor/P7774iOjsauXbss3ipxx44dGDFiBJYuXYqUlBTMnDkTffr0wZEjRyzGtWcdIiIiyt6ccjj9gwcPEBISgkOHDqF+/fomZcWKFUPbtm1N7qtcpEgRdOjQAXPmzDEbz551iIiIMoOLiwtWrlyJXr16mSxv1KgRcufOja1btxqX1atXD0FBQRZ/3LZnHSIiIsrenOpMvJjnz5/j9u3bqFOnjsnyunXr4tSpU7KtQ0RElNVOnTqVrq2qV6+e1bbKnnWIiIgoe8tRs9M/ffoUABAYGGiyPG/evBa/sNizDvDq/qOpqanGvw0GA2JiYpAnTx7J990kIiLLBEFAQkICChYsmO6e2G+b5ORkJCUlmW2r0toxOdYB2L4REWU2tm+UUTmqE5/GYDCY/K3T6US/eNi6zsyZMzF16lT7kyQiIkmioqIQHBzs6DSyBbZvREQ5B9s3sleO6sQXLFgQAPDkyROT5U+fPjWWybEOAIwfPx6jRo0y/h0XF4fChQsjKioKvr6+duVPRET/Ex8fj5CQEPj4+Dg6FYfLlSsXcufObVNbZc86ANs3IqLMxvaNMipHdeJz586N8uXL48CBA+jYsSOAV2cgDh48iAEDBhjrpaSkQKfTwcfHR/I6b3J3d4e7u3u65b6+vvySQ0QkIw7hfiUsLAwHDhzAZ599Zly2b98+hIWFGf9OTU2FWq2Gn5+f5HXexPaNiChrsH0jeznVRRgajQaxsbGIj48HACQmJiI2NhZqtdpYZ8KECVizZg02bNiA27dvY8iQIUhNTcWgQYOMdYYNG4batWvbtA4REVFm0el0iI2NRWxsLIBX17PHxsYiOTnZWOfzzz/HgQMHMG/ePNy5cwfTpk3DpUuXMHr0aGOdb7/9FoUKFbJpHSIiInIuTtWJ//XXX1GkSBG899578PPzQ7du3VCkSBHMnz/fWOfjjz/GkiVLMHv2bNSuXRs3btzAwYMHUaBAAWOdXLlymZxNkLIOERFRZjl58iSKFCmCIkWKwNvbGxMmTECRIkUwZswYY52wsDD89ttv2LhxI2rVqoW9e/di9+7dqFixorGOh4cHcufObdM6RERE5Fyc8j7x2VF8fDz8/PwQFxfH4YZERDLgcTV74OtARCQvHlcpo5zqTDwRERERERHR24ydeCIiIiIiIiInwU48ERERERERkZNgJ56IiIiIiIjISbATT0REREREROQk2IknIiIiIiIichLsxBMRERERERE5CXbiiYiIiIiIiJwEO/FEREREREREToKdeCIiIiIiIiInwU48ERERERERkZNgJ56IiIiIiIjISbATT0REREREROQk2IknIiIiIiIichIujk6AiIiIiIiInJ9arYZGo5EtnpubGzw8PGSLl1OwE09EREREREQZolarkcfTG8nQyxYzf/78uHPnDjvyb2AnnoiIiIiIiDJEo9EgGXp0RSG4yXDVtgYGbHgcDY1Gw078G9iJJyIiIiIiIll4Qgk3RcY78SpBhmRyKHbiiYiIiIiISBYqhQIqhSLjcaAA2JE3i7PTExERERERETkJnoknIiIiIiIiWSgVgCrjJ+JfnW3mmXiz2IknIiIiIiIiWcg6nJ7M4nB6IiIiIiIiIifBM/FEREREREQkC5VMw+lVGQ+RY7ETT0RERERERLLgcPrMx+H0RERERERERE6CZ+KJiIiIiIhIFhxOn/nYiSciIiIiIiJZcDh95uNweiIiIiIiIiInwTPxREREREREJAsF5DlTzPPwlrETT0RERERERLLgcPrMx+H0RERERERERE6CZ+KJiIiIiIhIFpydPvOxE09ERERERESyeNWJl2M4PVnC4fREREREREREToJn4omIiIiIiEgWHE6f+diJJyIiIiIiIllwdvrMx+H0RERERERERE6CZ+KJiIiIiIhIFkqZhtPzbLNl7MQTERERERGRLBwxnP758+fYtm1buuUffvghgoKCTJZFRkbi3LlzCAgIQIMGDeDh4SEa3551MhM78UREREREROS07t69i/79+6N79+5wc3MzLm/YsKFJJ37y5MmYO3cuGjVqhBs3bkCr1eLgwYMICQmxGNuedTIbO/FEREREREQkC0fOTr9gwQLkzp3bbNnp06cxbdo07Nu3D40bN4ZGo0GdOnUwYsQIbN26VbZ1skKO68R37NgRKSkp6ZY3b94cQ4cONbvO4sWLsXv3bpNloaGhWLRoUabkSEREZKshQ4bg3r176ZZXrlwZX331ldl1fv31V6xdu9ZkmaenJ7Zs2ZIZKRIRETm0E797924olUqUKVMGFStWNCnbuHEjSpUqhcaNGwMA3NzcMHDgQHzyySdITEyEt7d3unj2rJMVclwnvm/fvtDpdMa/b926hREjRqBz584W17l06RJiY2Mxbtw44zJfX99MzZOIiMgW4eHhiI+PN/4dHx+Pbt26oVatWhbXuXnzJq5fv4558+YZl7m45Limn4iIcrDX2z4AcHd3h7u7e7p67u7uWLt2Lby8vHDw4EHUrl0bW7ZsgY+PDwDgypUrKFeunMk65cqVg1arxX///YcqVaqki2nPOlkhx7XkzZs3N/l7/Pjx8Pf3R4cOHayulz9/frRu3TozUyMiIrJb3bp1Tf5etmwZlEolevfubXU9Pz8/tm9ERJRl5J7Y7s1rzydPnowpU6aYLCtQoACuXr2KYsWKAQAePnyIqlWrYsKECVi4cCGAVz8GhIaGmqwXEBBgLDPHnnWyQo7rxL9Or9fjxx9/RLdu3URnELx48SI6deoEPz8/1KlTB926dYNSyRsbEBFR9rRq1Sq0bNkShQoVslrvwYMH+Pjjj+Hu7o7q1aujf//+JpP+EBERyUkFmYbTC6/+jYqKMhklbe4s/JttYcGCBdG7d2/88ssvxmWenp5ISEgwqZf2t6enp9kc7FknK+ToXuoff/yBR48eoX///lbrubq6omHDhmjbti1KliyJsWPHonXr1hAEweI6qampiI+PN3kQERFlhcuXL+PMmTOi7ZtCoUCDBg3QsmVLVKtWDfPnz0etWrWgVqstrsP2jYiIshNfX1+Th7lOvDmenp6IiYkx/l28eHHcvXvXpM6dO3cAwHgG/032rJMVcvSZ+FWrVqFmzZqoUKGC1XrTp083+XWnefPmqFSpEn7//Xe0a9fO7DozZ87E1KlTZc2XiIhIipUrV6JQoUJo2bKl1XqDBg0yad86dOiAkiVLYvHixRg1apTZddi+ERFRRihlGk6vtCHG3bt3UaRIEePfOp0OW7duRe3atY3LPvjgA6xevRo3b95E8eLFAQA///wzateujcDAQADAs2fPsH37duP95aWs4wg5thP/+PFj7N69G0uXLhWt++YkdhUqVEBoaCjOnj1rsRM/fvx4ky9A8fHxDr1XIBERvR1SU1OxYcMGDBo0CCqV9bl732zf8ufPj2rVquHs2bMW12H7RkREGSHb7PQ2xFi7di3++ecfNGzYEC4uLti8eTOeP3+On3/+2VinTZs2aNGiBZo1a4b+/fvj8uXL2LNnDw4ePGisc+vWLfTv3x/ly5dHUFCQpHUcIcd24n/88Ud4enpanZXeEkEQEBsbC1dXV4t1LM2KSERElJm2bduGmJgY9O3b1671X758icKFC1ssZ/tGRETOZsqUKTh58iT27NmDly9fYvDgwejSpQty5cplrKNQKLB9+3asX78eERERKFasGC5evIgSJUoY6+TNmxd9+/ZF3rx5Ja/jCArB2oXfTqxUqVKoX78+li1blq5s4cKF+Pfff7Fo0SJotVqsW7cOffr0geL/h2xMnz4dkyZNQkREBCpXrixpe/Hx8fDz80NcXBxvT0dEJAMeV81r2rQpFAoF/vzzz3Rlmzdvxq+//mq8D/zKlSvRs2dP44/S69evR/fu3bF9+3a0adNG0vb4OhARySunHlfTntePgaWQS2nPXd5NJRv06Pn8eo7bT3LIkWfi//nnH/z333/YsGGD2fKLFy/i5MmTAACVSoWIiAhMnjwZJUqUQFRUFBISEvDTTz9J7sATERFlhXv37uHAgQPYvHmz2fIbN25gz549xr8fPnyIokWL4p133kFMTAzu37+P7777TnIHnoiIyFaOGE7/tsmRZ+Jv3LiBW7dupbtnfJpLly4hNjbW5J67L168wJUrV+Dv74+SJUuK3pLuTTn1FzUiIkfhcTW96OhonD9/Hs2aNTN7ydfNmzdx8+ZNk/YvISEBFy9ehKenJ0qVKgVvb2+btsnXgYhIXjn1uJr2vNYHyXcmvtsznok3J0d24h0hp34YiYgchcfV7IGvAxGRvHLqcTXteW3MW1q2TvzHT//NcftJDjlyOD0RERERERFlPaVCYdPt4azFIfOUjk6AiIiIiIiIiKThmXgiIiIiIiKShUKlgEKZ8bPoCp6Jt4ideCIiIiIiIpKFUqWAUoZOPIfTW8bh9EREREREREROgmfiiYiIiIiISB4qJRRKGc4VK3gTNUvYiSciIiIiIiJZKJQKKFQyXBMPDqe3hMPpiYiIiIiIiJwEz8QTERERERGRLJQqBZQynIlX8ky8RezEExERERERkSwUSnmuiVcIvCbeEg6nJyIiIiIiInISPBNPREREREREsuBw+szHTjwRERERERHJQqHi7PSZjcPpiYiIiIiIiJwEz8QTERERERGRLF6diZdhYjsYZMgmZ2InnoiIiIiIiGTBa+IzH4fTExERERERETkJnoknIiIiIiIiWSgUCiiUMkxsZ+CZeEvYiSciIiIiIiJZKFVKKGW4Jl4pcNC4JdwzRERERERERE6CZ+KJiIiIiIhIFrLdJ17gcHpL2IknIiIiIiIiWbATn/k4nJ6IiIiIiIjISfBMPBEREREREcmCE9tlPnbiiYiIiIiISB4yDacHh9NbxE48Edml1tf7rZa7uKpEYyhEfmCVEkOMUsJ9SqXcy9TTzXouOwfUlpwTERG93Rot+Ee0jouL+FnI1FSd1fLDI+tJzomInAc78URERERERCQLpUIh6SSKlDhkHjvxREREREREJAuFSgmFDNfEKwy8Jt4S7hkiIiIiIiIiJ8Ez8URERERERCQLpUoBpQwT2ykNHE5vCTvxREREREREJAuFTLPTK9iJt4jD6YmIiIiIiIicBM/EExERERERkSw4sV3mYyeeiIiIiIiIZKFUQaZr4mVIJofizxtEREREREREToJn4omIiIiIiEgWCqUCCqUME9vJECOnYideZuoD6+Dm5Wm2zKNp3yzOht5GfTedF62jNwgZ3k7l0kFWy33cxQ8v3h7W66gkHLw1uoyPtZKyHU83VYa3Q0REzm/6gf9E64i1GW2qFhKNkajWZbjO5D//FY0xtVlp0TpEtlAqlVDKcE28Us9B45ZwzxARERERERE5CZ6JJyIiIiIiIlnIdp94O2O8fPkSPXr0AADs3LnTuHzJkiXYunVruvpKpRJ//fWX2Vj//fcfBg8enG75woULUaZMGbvykwM78URERERERCQL2W4xZ2eMPn364MaNG3jw4IHJ8kaNGqFEiRImywYOHIgiRYpYjBUfH48DBw5g27Zt8PLyMi4vVEj8kpjMxE48EREREREROb0ffvgBL1++xODBgzFhwgSTspIlS6JkyZLGv69fv47bt29j+vTponHr1auH3Llzy52u3XJcJz4iIgI3b940WZY7d240b97c6noGgwGnTp3CkydPUL58eRQvXjwz0yQiIrJJZGQkLl68aLLM1dUV7du3F133/PnzuHv3LooXL44KFSpkVopERERQKJVQKGU4E29jjIsXL2L69Ok4ffo0tm/fLlp/1apVyJMnDz766CPRuv369YNOp0OZMmUwZMgQBAcH25Sb3HJcJ37lypX4448/8P777xuXhYSEWO3Ex8XFoXnz5rh//z7Kli2L48ePY9iwYZg1a1ZWpExERCRq+/bt+Oabb9C0aVPjMg8PD6udeI1Gg44dO+LYsWOoWrUqTp06hdatW2PdunVQyvAFi4iI6E1KlUyz0/9/jPj4eJPl7u7ucHd3N1mWlJSEzp07Y/78+QgJCRGNrdPpsG7dOvTo0SNdrDfVrVsXLVu2hJeXFzZs2IAyZcrg2LFjePfdd218RvLJcZ14AKhZsyY2bdokuf4XX3yBmJgYXLt2DX5+fjh69Cjq1KmDJk2aoFGjRpmYKRERkXTvvPOOTe3bggULcPz4cVy4cAHBwcH477//ULlyZaxZswZ9+/K2p0RElP292SmfPHkypkyZYrJs6NChqF69OsLDwyXF3LVrF548eYJ+/fpZrVeuXDkcPnwYCsWrSfbCw8NRt25djB07Fnv37pX+JGSWI3+Gf/nyJXbs2IG///4bsbGxVusKgoANGzagb9++8PPzAwCEhYWhRo0aWL9+fRZkS0REJE1KSgr++OMP7N+/H0+fPhWtv379enTs2NE47K9kyZJo3bo12zciIso8/z+xXUYf+P8z8VFRUYiLizM+xo8fb7K51NRUrF27Fv/99x8aN26Mxo0b44cffoBarUbjxo2xa9eudCmuWrUK77//PsqWLWv1qXh6eho78GlatGiBc+fOZXAnZUyOPBN/8eJFLF26FNHR0bh79y6+//579OrVy2zdBw8eIDY2FuXLlzdZXqFCBVy4cMHiNlJTU5Gammr8+81hHkRERHKLiorCggULEBcXhwsXLmDy5MnpvsykMRgMuHr1KgYMGGCyvEKFCpg/f77FbbB9IyKijFAoZZqd/v8v+/L19YWvr6/Feq6urti3b5/Jsu3bt+PevXsYN25culvBPXr0CHv27MHq1avtyuv58+fw8PCwa1255LhOfOfOnTFv3jx4enoCAObPn4/+/fujatWqZifziYuLAwAEBASYLM+TJ4/Vs/gzZ87E1KlT0y03JMbCYFCbXSd56xzR/HO1HyNax1nceZ4gWkcQKdcbMp6Hm4R7TIpVUSnFYxTI7SVaR8zft56L1nmerLFa3qhUUIbzcHcRP/C6u6islntIiCG2W1UK8f2uF8TeReKkbMdV5E1y6WGcaIx3C/pJzonoTfXr18cnn3xinB33999/R7t27VCpUiW0aNEiXf3k5GTodDrZ2jciZ/DndesjVKS0TWJtYINieURjKCW0K2K0evH2TWvI+Bclse8e9YoFZngbRJlJqVSicePGJsv+/fdfqFSqdMsBYO3atfD29kbHjh3TlUVGRmLYsGHG+8Bv3LgR9erVQ8GCBQG8Olm8cuVKh1+SluOG09evX9/YgQeAESNGwM/PD3v27DFbP20ig8TERJPliYmJVn9hGT9+vMmwjqioKBmyJyIiMq9WrVomt7f56KOPUKVKFezcudNsfbZvRETkCGmz08vxyAyrV69Gt27dTPqMaeLi4nDgwAHjiV5/f380bNgQFSpUQOXKlVGzZk107doVM2bMyJTcpMpxZ+LN8fHxwbNnz8yWFS5cGC4uLrh//77J8nv37uGdd96xGNPcrIhERERZydfX12L75urqipCQELZvRESUpV5d0259xKa0OHq7123btm26y6WBV5eMLVmyxOLM8mXLlsW+ffuM18o3b94c165dw/Xr15GUlIQSJUoY51FzpBx1Jt5gMKSb6Ofs2bO4d+8eqlevblx25swZ42yC7u7uaNSoEbZs2WIsf/78OQ4ePIhWrVplTeJEREQiHj16ZPJ3VFQUzpw5Y9K+Xbt2DVu3bjX+3bJlS/z+++/Q6XQAALVajR07drB9IyKiHC04OBj169dPt9zd3R2NGzdG3rx5za7n6+uLxo0bm1yDr1QqUaZMGVSrVi1bdOCBHHYmXq/Xo169emjRogXKlSuH+/fvY+HChWjVqpXJfXSXLVuGkydPGu8dP2vWLISFhaF79+6oXbs2Vq5ciTJlylicDI+IiCirdezYEeXLl0eVKlUQExODRYsWoWzZshg8eLCxzm+//YZZs2YZ27yJEyeiWrVq+PDDD9G6dWts2bIFSqUSo0ePdtTTICKiHM44u7wMcci8HLVnXF1dERERgeLFi+PkyZNISUnBjz/+iJ07d0L12pCOGjVqmEwCVKlSJZw7dw4FChTAqVOn0KVLFxw5coTDCYmIKNs4dOgQateujXPnzuHJkyeYPXs2Tp48CW9vb2OdcuXKoUOHDsa/g4ODce7cOVStWhUnT55E/fr1ERERgcBATlRFRESZQ6lUyvYg8xSCIMPUzoT4+Hj4+fnh0bqv4JvL/lsOcHZ6U5ydPj2x2elTdRnfaZydPj2x2em93cQHNnF2etukHVfj4uKs3lqGMhdfB3ImWTE7vUFCu+NMs9OLtYGcnV5+OfW4mva8rozsAh93twzHS0jVoPy8n3PcfpJDjhpOT0RERERERI7D4fSZj514IiIiIiIikgU78ZmPe4aIiIiIiIjISfBMPBEREREREclCoVBCIcOkdAoFzzdbwk68zASNGoKFvar0FJ/4TP3XKqvlSp/cojHcare3Wq6JeSgaw+Dpb7U8VcI8Kv4e1ic+k0IhZcIxkdnRZJhbBlLmTotJSLZanqwV32mF/cTviFDI13odCXPwiU5iI2EuQNHXRkoeYuTIQwo5cpUSIi4pxWq5n5dnxhOR4IXIe9VbUIvGcPcNkCsdchLJv8+Hi4WJWxXu4hO6erb5VO6UHCY20fpnCJDnGClWxUVKDINOvJIIdy+fDMd4HJckWkfs67qU4331gt6idUTzENmMQYYpobPTrNJyPB+i13E4febjniEiIiIiIiJyEjwTT0RERERERLLgmfjMx048ERERERERyUKpUkIpQwdcjhg5FfcMERERERERkZPgmXgiIiIiIiKShUKpkGd2ejlmHs6h2IknIiIiIiIiWfCa+MzHPUNERERERETkJHgmnoiIiIiIiGTBM/GZj514IiIiIiIikoVCoZTnmngFO/GWcM8QEREREREROQmeic9KLm6iVZSeXlbLVX55RGPooy5bLVe4+4jnoY6zWu4pGgGAXidaRaHXipRrxLdjENmOlF/xlNY/CoLKVTSEi8jr6y2yjVd5iFcRJbY/AMBgsFqsECmXQpCw3xWCyHYMevEYUp6vCFlylWE72mTx95ng6mG9goT94ZeaZL2ClM+db4B4HcpZDHqLn0mFm8j7EoDmn01Wy1V58ovn4OVvtdjg7i0eQ8LxXOyz6iXpOGv9+KXUJkuIIXLckeFMl5TjnzYxV4a3k0fCMVS0zZfSjspBJFdBSh4ynEGU8h1IUi4ZpEsSf+1cCpTI9Dy0z+6LVxL5XgkArvmLyZANWaNQqaBUqWSJQ+ZJ/uSr1WqbAnt4iDfoREREjpaamgpBECTXd3d3h0LB294QERGZw2viM5/kTrynp6Rzr0a2fCEiIiJylFKlSuHevXuS69+5cwdFihTJvISIiIiIrLBpDM4///wjqV6dOnXsSoaIiMgRtmzZgvz5xYdzd+zYMQuyISIicl48E5/5JHfiQ0NDERYWJrkuERGRMwgODkatWrUQHBwsWrdYsWJwceF0MkRERJYolDLNTi9DjJxK8jeRu3fvSg5qS10iIiJHOnr0aKbUJSIiIsoMPJ1AREREREREsuBw+sxndyf+2rVrOHnyJGJiYtKVjRkzJkNJEREROcrdu3dx5MgRPHv2LN0krQMGDICvr6+DMiMiIsr+FEqFPJ14Je8EY4ldnfilS5diyJAhCA4Ohr9/+nu2shNPRETOaNeuXWjfvj0CAgKQL1++dOVdunRhJ56IiIgcyq5O/MyZM7F27Vp0795d7nyIiIgc5ttvv8WXX36JiRMnOjoVIiIip8SJ7TKfXZ342NhYtG/fXu5ccgSFUgWFUmW+zMVVPIBIHUGrEQ2hTE2yWq7SpornIUYnHsOQlCBeR209V0GnFc9F5AOucHETDSH22kh57ZQq8697GkGvF40Bg0G8jgwEnfj7KKOUFj4HJnkYrO8TKa+/WAy5CGLPR0oeIjEsHTtM61h/v0vZZ7qEWNE6YlyCy4nWSdr4tdVyr4+zX0c5NjYW7dq1c3Qa2ZJBp4VBZ6F9k3KsFjuOSvgMKVITrZarRMoBiH4OAUAQaZsktW8pIjGkHIdF2i+FSLsjiYT2TdL3F5H2S9CoRUMIYhWk5CH2XpTSKZDhWC22nezUvokRJHw30dw8a72ChP2ucPOwnoeUfSbyuQMA5C8mXocyxFp/yNY4ZJ5dP29Uq1YNERERcudCRETkUGzfiIiIKLuTfCZ+165dxv83btwYnTt3xmeffYbixYtDoTCddKB169byZUhERJSJDh48iOTkZABArVq1MGLECNy/fx8VKlSA6o0znY0aNYKnp6cj0iQiInIOSpWkUU+S4pBZkjvxHTp0SLds/PjxZuuq1eJDpoiIiLKDQYMG4d69eybLvv7a/KUA169fR2hoaFakRURE5JyUSmmXrkiJ4+SioqJw6tQphISEoGbNmiZlL168wJ49e9CtWzeb40ruxLNjTkREOdH169cdnQIRERHlMLt370aHDh2QkpICAKhfvz42b96MvHnzAgAePXqEWbNm2dWJt+vnjcDAQLvKiIiIsrMqVaogKirK5jIiIiJ6RaFSyfZwZkOGDMHYsWORkJCAEydOICYmBnXr1sXDhw8zHNuuTvyLFy/MLtdqtYiPj89QQkRERI4SExMDvYU7STx58iTdNfJERET0hrRr4uV4OKnHjx8jKSkJkydPhre3N2rVqoXjx48jNDQUDRo0yHBH3qZbzP36669m/w8ABoMBJ0+eRPHixTOUEBERUVbbs2cPkpKSkJycjD179iAoKMhYJggCbty4gYSEBOTLl8+BWRIREZGzUCqVJhPAe3l5YceOHWjXrh3q16+PH374we7YNnXi+/XrZ/b/AODq6ooiRYpg4cKFdidDRETkCJ9//jnu37+P+Ph4fP7551C+NpmOSqVCwYIFsXz5cp6JJyIiEqNUyjQ7vfNObJc/f374+Pjg1q1bKFasmHG5u7s7fvvtN7Rv3x6dO3dGwYIF7YpvUyc+NjYWAFC8eHHcvHnTrg0SERFlN5cuXQLwatKZDRs2oFChQg7OiIiIyDkplEooZOiAyxHDkcaOHYvvv/8eCxYsMFme1pHv2LEjbt26ZVdsmzrxaTp37mzXxoiIiLKzDz74AF5eXo5Og4iIiJxc//79LZa5ublh27Zt0Gq1dsW2qxM/Z84cTJ48Ga6urnZt9G0l6MRfJCHJ+sSA+lTxW/0ZEmOlpmQ5j5Qk69tIThDPQ+S5AIBgMEjOyRJ5fukTGfKTVRNrGMxPqPU6QaSOQauTK5tMJ+gz/vqLvYekbEOhyppfesVykSMPXUqqaB1NQrL1GEnixxn9gSOidcSeb9GPRUNkuRUrVqBNmzbInTu3o1PJdpQurlC6WGj3JRy7DHHmJ8VNI0ho38Q3IuEYqpHQjibEWi3Xp1j/DAHZ51gsRxup8nDLcAwp+0OsjhxthhzHWaWr+NdnOV7/7PJ8peSh11j/jivl+56Lh3uGtgEAerVGtE7ehj1E61AGKWSalE6Rsy9hUygUcHOz7/hqVye+atWqOHLkCBo1amTXRomIiLKjtPatRIkSjk6FiIjIOck1s3wGYiQmJkKv18PPz8+4TK/XIyEh/YlIHx8fSXPeqNVquLu7m0xWJ1VkZCQuXryIxMREk+UBAQFo166dzfHs6sS3atUKnTt3xvDhw1G2bNl0vyC0bt3anrBEREQO1aJFCwwZMgQ3b95ElSpV4OnpaVLeqFGjdMuIiIgo+zh9+jTCwsLg5uZm0mk+f/48qlevDl9fX5OO+P79+1GtWjWL8Y4dO4ZPPvkEkZGR8PDwQP/+/TFnzhzJk92OHz8es2fPRmBgIHLlymVSVqpUqazrxE+bNg0A8PXXX5stV6tlGBJHRESUxb766iukpqZi3rx5ZsuvX7+O0NDQLM6KiIjIeThyYru4uDh07doVH374Ifbs2WO2zr179yRfNvf48WO0bNkSn3zyCc6cOYPIyEg0bdoUvr6+mDp1quj6sbGxmDt3Lo4cOYKwsDBbnopVdnXi2UknIqKc6Pr1645OgYiIyLk5cDj9gAED0LFjRxQsWNBiJ95gMECj0Ui6Hn3NmjVwcXHB9OnT4eLigsqVK+PTTz/F999/j0mTJomejY+JiUGhQoVk7cADgHPP209ERERERERvvRUrVuDWrVuiZ8iDg4Ph7e2NEiVKYOXKlVbrnjp1CrVr14aLy//OfderVw8vXryQdMv1IkWKQKlUyn57drvOxAOATqfDtm3bEBkZCUEQULZsWbRt29bkCTrC33//jTlz5uD48eNwcXFBWFgYZs2aZXWSok8++QTLli0zWVauXDlcuXIls9MlIqJsRhAE7NmzBxcvXoRarUaJEiXQrl27dNexZbULFy5g9uzZOHz4MFJTU1G9enXMmDEDVatWtbjOrFmzMH78eJNlXl5e6SbWISIiko1SKdOZ+Ffnm+PjTe945e7uDnd307sZXLt2DRMmTMDRo0ct3kHNw8MD8+fPR8+ePeHp6YlNmzahX79+UKlU6N27t9l1nj59ipIlS5osy5s3r7GsVKlSIk9BiVWrVqFevXro1q0bChQoYFIeGBiIbt26WY1hjl097lu3bqFly5a4f/8+ihUrBoVCgZs3b6Jw4cLYvXs3ihUrZk/YDNPr9Zg8eTI+++wz/Pjjj0hOTsbQoUPRuHFjXL16Fd7e3hbXbd++PX799dcszJaIiLKbFy9eoFWrVjh79iyKFy8Od3d33Lp1C59//jl+//131KhRw2G5TZo0CV27dsW8efPg4uKCiRMnolGjRrh8+TJCQkIsrle1alVERERkYaZERPQ2U6hUUEic9E0sDoB0bdzkyZMxZcoU498GgwHh4eEYO3Ys8uXLh9jYWKSkpAB4dU16rly54ObmhvLly6N8+fLG9Xr27Im///4bCxcutNiJT4v/Op3u1S0kpc5S//333+Px48fYsmVLuhMCJUqUyLpO/PDhw1GmTBkcO3YMgYGBAIDnz5+jX79+GD58OHbt2mVP2AxTqVQ4fPiw8e+AgAAsWLAAoaGhOHnyJBo3buyQvIiIyDlMnjwZSqUSt2/fNn5pSExMxJgxY9CnTx+HjtDasWOHyd8LFy7EmjVr8Oeff6Jfv34OyoqIiChzRUVFwdfX1/j3m2fhtVotoqKiMH36dEyfPh0AoNFokJKSgiJFimDevHkWO+mlS5fG77//bnHbhQoVwpMnT0yWPX36FABQsGBB0dwfPXqEP/74AxEREahcubJofansuib+0KFDWLx4sbEDD7waCrB48WIcOnRItuTk8OzZMwAwuUegOXv37kWuXLlQoEABdOrUCXfv3s2C7IiIKDs5dOgQ5syZY/Krv7e3N3744QfcuXPH2KZkBy9fvoROpxNt365cuQIfHx8EBgaiRYsWuHTpUhZlSEREbyWlUr4HAF9fX5PHm514d3d3xMbGmjy++eYbeHl5ITY21upZ9oiICBQuXNj4t16vR2xsLPR6PQAgLCwMx48fN5nYfd++fShYsCCKFi0qaXcUKlRI1g48YGcnXqVSQavVpluu0Wgcfk3863Q6HUaNGoXKlStbvWawePHi2LhxIx4/foyDBw8iLi4OderUQVxcnMV1UlNTER8fb/IgIiLnZql90+l00Ov12aqNGzlyJAoUKIAWLVpYrJM3b16sWrUK9+/fR0REBAIDAxEWFoZ79+5ZXIftGxERZUja7PRyPGQ0ZswYLF68GNevXzdOgPfrr7/i888/N9Y5c+YM/P39cebMGQBAr1694OPjg379+uHmzZv4/fffsWDBAowfP17ScPq0a+AjIyNlfS52fRtp3rw5+vTpg5UrVxp/gbh9+zb69u2LZs2ayZqgvQRBQL9+/XD9+nUcPXoUSiv3GRwzZozx/76+vti8eTMKFCiAzZs3Y8CAAWbXmTlzptmZD/UaDfQq8y+oCuJfhPQpSaJ1MsqgEb9FoC7Jeh1NvHieOrVGck6ZSdAbROsoVDLcy1KGGFJyFasjGMRjiDFodBmOkVX7Q6/NmlytHUOkEstV+P9ffa0xiMTQinx2AUCTlL6TahIjUfyzKxYDAHRq67lK+/06azVv3hyffvop1q9fjwoVKgB4dY/YTz/9FO+++y78/f0dnOErkyZNwvbt27F//36r87306dPH+H9/f3+sXr0axYoVw7JlyzBjxgyz61hq33QpauggmF1HZRB/7yLJehuokPBlTRDZjtjnA5DWfmkSkq2Wi7WRchE7Br55naY5KlfrX/WkHGdVHuK3YRIjpW2So+2Rsk8ySkp7IEceenWqaB057sWtdDM/CZhNMUTy0El4LmLvESntmy5FvG2KbtvUarmU9q3mvuw18pjSc3d3T3cv+PHjx2PmzJlYtGgRkpKSULp0aezbtw+NGjUy1nFxcYGfn5/xR3s/Pz8cOnQIo0ePRlhYGAICAjB9+nQMHTpUUh7Pnz/Hu+++i7CwMLMT2wUFBaFv3742Pz+FIAjmW2QrHj9+jPbt2+P48eMICgoC8GrY+nvvvYetW7cif/78NiciJ0EQMGDAAOzYsQOHDh1C2bJlbY5RokQJtG3bFt9++63Z8tTUVKSm/u+AFB8fj5CQEDxYOg6+nu5m11G5m19uQuZfnMxhJz49duJNsROfHjvxtsUAxDvxdU8dt1oeHx8PPz8/xMXFmVwLl5kSExPRpUsX7Ny5E/7+/nB3d8eTJ09QtmxZbNmyBWXKlMmSPKz5+uuvMXPmTOzevRv16tWzef0mTZogd+7c2LJli9lyS+1b1KKxlts3GTp57MSnx0687diJt93b1onXirRfWdGJd0T7lhXSntfzAxvg65XxO7rEJyUjsFFXp95Pt27dQnh4uMXy4sWLY9OmTTbHtetMfP78+XHs2DEcPXoUV69ehUKhQNmyZWW/ib09BEHAJ598gh07duDgwYN2deBjY2Px4MEDq5MVmLu1AREROTdvb2/s2LEDZ8+eNd5irmTJkmjQoAFUMsy0m1HTp0/HzJkz8ccff9jVgddqtbh+/To++ugji3XYvhERUYYo/nc9e4bjOLlixYplyh1iMrRnwsLCMHDgQAwYMCBbdOABYMiQIdi2bRsOHjyIcuXKma3Tr18/4+0FUlNT8dFHH+H06dNISkpCZGQkwsPD4ePjg65du2Zl6kRElE1UrVoVffr0weDBg9G4ceNs0YGfPXu28Qx8/fr1zdb5+uuvTYbXh4eH4/Dhw4iPj8fdu3fRp08fPH/+HAMHDsyirImIiN4+0dHRmDNnDv79999MiW/3DD3Xrl3DyZMnERMTk67s9WvMs9Lz58+xZMkSADC5ByAArFixwuwteNzd3dGvXz+MGTMG58+fh7+/P+rUqYNTp04hb968WZI3ERFlH3fv3sWRI0fw7NkzvHnF2YABAxw2pO+rr75CUlJSug78F198ga+//trsOiNGjMC0adNw4sQJeHp6onr16jh58qRdo9SIiIikUChVki6RkhLHWbm4uODw4cOYNGkSChYsiNatW6N169aoW7cu3NwyfomSXZ34pUuXYsiQIQgODjY7yY+jOvGBgYHpvnCZs3LlSpO/W7VqhVatWmVWWkRE5CR27dqF9u3bIyAgAPny5UtX3qVLF4d14hMTE0XrTJw4ERMnTjT+Xbt2bezZsycz0yIiIjKlVMozz5ccQ/IdJF++fNi1axdSUlJw4MAB7Nq1C3369EFsbCyaNm2K1q1bo2XLlnafNLarEz9z5kysXbsW3bt3t2ujRERE2dG3336LL7/80qQjTERERGQPT09P41l4ALhw4QJ27dqFpUuXon///qhatSpat26NDz/80HhXHCns6sTHxsaiffv29qxKRESUbcXGxqJdu3aOToOIiMh5KWWa2M6Jz8RbUqlSJVSqVAkTJ07Es2fP8Mcff2DXrl3Ys2cPjh07JjmOXXumWrVqmTLLHhERkSOxfSMiIsoYhUol2yMnCwoKQq9evfDrr7/i6NGjNq1r15n4xo0bo3Pnzvjss89QvHhxKBQKk/K04QJERETOpGHDhhg2bBju37+PChUqpJuVvlGjRvD09HRQdkRERORMHj9+jDlz5pgtUygU8PT0RNmyZdGmTRvkypVLcly7OvFTp04FAIwfP95suVqttidsjmDQ6mBwMf+rkaA3yBJfjE6darVcE58sGkOTYL2OXmQbAGDQiOcqGkPCPhMMGd+vYhQShvMoVRkf8iPH85XyPhP01ieAlJKH2PNVqBRWy6UQyxOQlqsYKa+d0s36r8FS9rsmSWu1XJci/pnRJGmslhs0UvKwHkMsTwDQJorXcUbTpk1DcnKyxdner1+/jtDQ0CzOKnuw1r4pJHyG9Grr7zspnyG91vr7Tpsk/v0jNVZ8gkBdUor1PCS0b2LttZTjm+g2JOwzleixSzwPlYeraB2x46heyveXFOuvr5Rjtdg+EQwSnq+r9X0m1h4A4u9nKftdqxbfZypX6/tE7PUHxD+/Bo1eNIYYKc9Fm2j9GFFt9wHRGP/Ufl+0Tmq89e1oRdpIkkCpkmliO+c/E6/RaPDPP//g9OnTKFSoEEqWLImUlBRcunQJWq0WtWvXxg8//ICJEyfizJkzZieNN8euXodarbb6ICIickbXr1+32r69rR14IiIiydI68XI8nFzhwoXh5eWFuXPn4v79+zh48CBOnDiBqKgo1KlTB127dsW9e/cQGhpq8QSCOZk6W0D+/PkzMzwREZFDVK9eHVFRUY5Og4iIiLKxuLg4REZGYtSoUVC+NrI3ICAAX375JX7//Xf4+Phg7NixNs3JY9dweqmePHmSmeGJiIgc4tmzZ9DrMz7ElIiIKKdRKJWSLkWVEsfZxcTEICEhAWq1Gh4eHiZlT58+RUxMDADA3d3dpjl3nH/PEBERERERUfagkGkovcL5h9MXLVoURYsWxYcffojTp08jISEBT58+xebNm/Hpp5/igw8+AABs27bN+H8pMvVMPBEREREREdHbavv27Rg4cCBq1qxpXObp6YmhQ4di3Lhx0Ol0CAsLQ7t27STHZCeeiIiIiIiI5KFQAAoZBnwrMn6no+zgnXfewb59+xAVFYVbt24hV65cKFWqFPz8/Ix1OnToYFNMDqcnIiIiIiIieSiU8j2c3N27d3HhwgUAQEhICOrXr48aNWrAz88Per3ephnpX+f8e4aIiIiIiIgom9FoNGjevDlu3rxpslyn06FLly74888/7YqbqZ34NWvWZGZ4IiIih5gzZw4CAwMdnQYREVG2IyiUsj2cXcmSJdG/f380adIEDx8+BPCqA9+1a1ecO3cOmzZtsiuu3Xvmxo0bmDp1Knr27Glctn37dqSmphr/7tWrl73hiYiIHOLx48eYOXMm+vTpg+fPnwMA9u/fb7wNDPDq2jVvb29HpUhERJR9cTi9ia+++gotWrRA06ZN8fTpU3Tp0gXnz5/H4cOHUahQIbti2jWx3d9//42WLVvi/fffx759+/Djjz8CAI4fP47bt29j5MiRdiWTE1i7L6JgMIiub9DorJZrk1JEY2gSkq2XxydlOIYuRSsaQzAIonUMeut1BL34PhNEYjgTsf0BSNsnotsReW1UruK39FC5WT+wOtProlBJmDhF5GOjS7H+2QWA1HiNSHmq1XIpdaS8h1I01u9vniLhPaaR8PnuG/OvaJ3s5r///kPNmjVRvnx5nD9/HpMmTUJgYCBu3LiBnTt34vvvv3d0itmSXm39vQ0Aeo31dsOgFf8MaeJF2jeRtgsA9Grxz5k2SW21XKcWz1Uv8jmTcowUiyGFQmX9WK2UcPxTJEl5fa0fN6S0XWK56rXi+0Nsv7p4iH/1VblZ345CnfGOhUGG1xYQ369SvouJvc/EXltAvG2S1r5Zf5/9EfKuaAyNhPdIish7REobSGSrH374AR9//DGKFy+O/Pnz4/DhwyhYsKDd8ezqxI8bNw4LFy5Enz59oHht1sCePXuibdu2b3UnnoiInNfkyZMxbNgwTJs2DUWLFjUu79GjB/Lnz485c+bA1dXVgRkSERFlcwqFPDPLO+ns9M+fP8fKlSvTLS9Xrhz++OMPNG/eHOvWrQMABAUFoW/fvjZvw65O/OXLlxEeHg4AJp340NBQ3L17156QREREDnf58mVMnDgRgGn75uXlBQ8PDzx79ixDv5wTERHleErlq4cccZxQXFwcfv31V7NlpUuXxvHjx41/Fy9ePOs68V5eXnj69CmKFi1q8iXn3LlzyJ8/vz0hiYiIHC6tfStXrpxJ+3b//n3Ex8cjICDAgdkRERFRdlesWDFERERk6jbs+nmjffv2GDNmDBISEozLTp06hX79+qFTp06yJUdERJSV2rdvjy+//BJPnz41Lvv3338RHh6ODz74AB4eHg7MjoiIKPvj7PSZz649M3v2bMTFxSFPnjwwGAwICAhArVq1EBISgmnTpsmdIxERUZYYNWoUQkNDUaBAAdy7dw9VqlRBmTJloFarsWjRIkenR0RElP1xdvpMZ9dweh8fH+zfvx8nTpxAREQEDAYDqlSpgjp16sidHxERUZZxcXHBhg0bMHbsWBw/fhxqtRply5ZF48aNoVKJ37WBiIiIKLPZ1YlPU7t2bdSuXVuuXIiIiLKFihUromLFio5Og4iIyPnIdRadZ+ItsnvP3LhxA1OnTkXPnj2Ny7Zv347UVPH7QBIREWVXjx8/xsyZM9GnTx88f/4cALB//37ExMQ4ODMiIiInwOH0Rvfv38fw4cNlj2vXnvn7779RqVIlHDt2zHiPOwA4fvw4Fi9eLFtyREREWem///5DmTJlsHv3bvzyyy9ITEwE8L8fromIiIikMhgM2Llzp+xx7erEjxs3DgsXLsRff/1lsrxnz55YsmSJLIkRERFltcmTJ2PYsGH4559/EBQUZFzeo0cPrF69Glqt1oHZERERZX+CQiHT7PQK8Y1lc6GhoXB1dcWFCxdkjWvXNfGXL19GeHg4AJjcRzc0NBR3796VJTFnpXRVQelmfrfq1RrR9fUiXxB1EmKI1dEmqUVjaJOsx9Akin+RFfQG0Tp6rXidjDJIyEOMUpXx4Txy5CGFlFxVrtbrKFXiB01BL0jOKTMZRPKQ632o1+itluvUOtEYyc9TrJaniHzuACBF5PnqBfHXRWOwXkdsG6+2I1rFKV2+fBkTJ04EYNq+eXl5wcPDA8+ePUPBggUdlZ5DuXp7wjWX+Vvs6ZLF2xXBYP1zJqV9E2tH9WrxS/qktIGaJOttnEHkeACIHzO0Eo4ZYqQchxUix3MpbYZYDED8+WZVrlnRvikkzHEp1vZI26cSvjeI5KpLEX+fib0XU+PFP5vql9Y/V81vnReNIeanwDKidcTaN0C8jZMSg0TwmnijFy9eoFKlSqhTpw46d+6MIkWKmEyUGxQUhL59+9oc165OvJeXF54+fYqiRYuafMk5d+4c8ufPb09IIiIih0tr38qVK2fSvt2/fx/x8fEICAhwYHZERETkTOLi4nDr1i2UKlUK58+fx/nzpj9oFS9ePOs68e3bt8eYMWOwdu1a47JTp06hX79+6NSpkz0hiYiIHK59+/b48ssv8dtvvxmX/fvvv+jduzc++OADeHiYPxNNRERE/0+hePWQI46TK1asGCIiImSPa9cYhdmzZyMuLg558uSBwWBAQEAAatWqhZCQEEybNk3uHImIiLLEqFGjEBoaigIFCuDevXuoUqUKypQpA7VajUWLFjk6PSIiouyPs9NnOrvOxPv4+GD//v04ceIEIiIiYDAYUKVKFdSpU0fu/IiIiLKMi4sLNmzYgLFjx+L48eNQq9UoW7YsGjdubHINGxEREZGYx48fY86cORbLCxQogNGjR9sc165OfIUKFXD58mXUrl0btWvXticEERFRttOsWTOsWbMGFStWRMWKFR2dDhERkdNJm11ejjjOLjU1FVeuXDFZptPpcPXqVbx48QJ9+vSxK65dnfioqCjExcXBz8/Pro0SERFlR0+ePHmrZ6AnIiLKMIUSUHJ2euDV3dv27t2bbnlqairCw8Px/vvv2xXXrj3Trl07LF++3K4NEhERZVft2rXDihUrIEi4VR8RERGRPdzd3TFq1Chs3rzZrvXtOhOfmJiIsWPHYvPmzShbtizc3NxMyleuXGlXMkRERI4UFxeHJUuW4ODBg6hcuTI8PT1NymfPno08efI4KDsiIiInwPvESxIbG4vY2Fi71rWrE69UKhEeHg4A0Gg00Gg0dm2ciIgoO9FoNOjYsSMAQK/XIzEx0aTcYDA4Ii0iIiLnwU680cuXL7F169Z0yx8+fIglS5bgk08+sSuuXZ34TZs22bWxt4FCqYTCwjUglpbbWkeMQaO1Wi5I+BJq0FsfSqpyE89TL+G3HYXB+nYEkTyciVIlz4FIobJ+z0ylUvyemio367NsK0XKs4qgl9BhEnmPaNU60RC6FAl1ROJokqx/7gAgJcn6h+Kjx1dFY4hZkru0aB2NyOdOrBwA9Dl0uPnChQsdnUKOJenzLELsRxQp29Br9KJ1VK7Wj9dStqMQOeZLaRMMMuwzObahtO/qSxNi7Y6kGCKvi5TtuHja9dXXZgYZ9hkg4f2stf5+To1LlRDD+nZSnieLxlBLaGszqvvzSNE63/uWEq2TIvKeH5d0Q3JORGKePXuGr7/+2mSZQqFAUFAQBg0ahHHjxtkVN2uOZERERERERJTzOfhMvCAIWLduHXQ6Hfr27WtSlpKSguPHj+PRo0coUaIEatasaTXWs2fPzJ5Jb9euHfLmzSuaS8mSJXH37l2b8pfCrk5827ZtLZa5u7vjnXfeQbdu3VCuXDl78yIiIspyAwYMwNOnT82Wubq6IiQkBB07duTtVYmIiCwQFAqZbjEnPrrUnHnz5mHixIlQKpUmnfiNGzdiwoQJeOedd1CgQAF89tlnKFmyJP744w94e3ubjXXv3j0MGjQIvXv3NpkHrlmzZnblJhe79q6vry+2b9+OO3fuwN/fHwEBAbh9+za2b98OQRDw999/o3Llyjhy5Ijc+RIREWWaoKAg7Ny5E1evXoWvry+CgoLw5MkTbN++HcnJybh06RLCwsLw888/OzpVIiIiesPZs2cxf/58fPbZZ+nK8uTJg9OnT+PAgQNYv349rl69iv/++w8zZ84Ujfvdd99h6dKlxkfRokVtyuv69esYO3Ys2rVrh+7du+O7775DUlKSTTFeZ1cnXqfTYfr06bh48SLWrFmD1atX49KlS/jqq6/g6uqK48ePY9KkSRg/frzdiREREWU1QRDQv39/XL9+HevWrcOKFStw4sQJrFu3DrGxsdi/fz9+/PFHs18OiIiICP8bTi/HwwYJCQno3Lkzli1bhqCgoHTlTZs2NVkeEBCA6tWrIzJSfL6FnTt3Yv369Th79qxNOQHATz/9hPLly+Ovv/6Cl5cXkpOTMXv2bJQpUwZRUVE2xwPs7MTv378fQ4YMSbd86NCh2L9/P4BXQxKvXLliV1JERESOsH//fgwaNAjKNyYZ7d69OyIjI5GcnIyuXbsiLi4OL1++dFCWRERE2ZhCId8DQHx8vMkjNdX8hI2ffPIJmjdvjhYtWkhKMzY2FseOHUPlypWt1vPw8MDGjRuxfft2NG7cGI0bN0Z8fLykbeh0OowYMQJLlizBhQsX8NNPP2Hr1q24d+8eKlasmG7SO6nsuiZeo9EgMjIStWrVMll+7do1407VarUIDAy0K6mMunXrFlavXo0nT56gQoUKGDBgQLp7/cqxDhER5Sxp7VvFihVNlj948ABxcXFITU2Fm5sb3NzcLF4/l5mePHmC5cuX4+7duyhevDgGDhyIgIAA2dchIiLKLkJCQkz+njx5MqZMmWKybM2aNbhw4YLkM+V6vR49evSAn58fPv30U4v1ChYsiMjISBQpUgQA8PjxY1StWhXjx4/HokWLRLdz9+5duLu7o1+/fibLPTw8MGbMGIwePVpSvm+y60x8t27d0KFDByxZsgRnzpzB6dOnsWTJEnTo0AHdu3cH8GrYQJcuXexKKiMuXbqEypUr4/bt26hQoQJWr16NevXqWb2XvT3rEBFRztOtWzcMGjQI3377LU6cOIFz587hxx9/RKNGjdCyZUv4+/vj119/RatWreDq6pqluUVHR6Ny5co4duwYKlWqhL1796JatWqIiYmRdR0iIqIMkXk4fVRUFOLi4oyPNy/Z1mq1GDZsGOrVq4e1a9di6dKlOHr0KHQ6HZYuXYpr166Z1DcYDOjbty/OnDmDvXv3ws/Pz+JTKViwoLEDDwD58+dHr169sHfvXkm7wsfHB7GxsUhMTExXdv/+ffj4+EiK8ya7zsTPmzcPgYGB+OKLL4zDCf39/fHpp5/iiy++AAA0adIk3ZmMrDBu3DjUrl0bGzduBAB8/PHHCA0NxY8//oj+/fvLtg4REeU8Y8aMgbu7O2bNmoWxY8cCAHLlyoV+/foZh7yVL18eH3zwQZbnNn36dPj7+2PXrl1wcXFBv379UKJECcydOxfTp0+XbR0iIqKMEBRKmWanfxXD19cXvr6+FuspFAp069YNBoMBFy5cAPDqR2y9Xo8LFy6Y3DEtbe6bP//8E4cOHULJkiVtzsvDw0PyJXX58uVD9erV0apVK3z99dcoV64cUlJSsH//fowaNcruttiuvevq6oopU6YgJiYGjx49wuPHjxETE4MpU6YYz0xUrVoVLi5Zext6jUaD/fv3o1OnTsZlefPmRcOGDbFr1y7Z1iEiopxr2LBhiI6OxvPnz/HgwQMkJibi+++/N/5aXr58eXh5eWV5Xrt27UK7du2Mbaunpyc+/PBDq22VPesQERE5ExcXF5OZ45cuXYrw8HC4u7tj6dKlqFOnDoD/deB3796NgwcPonTp0uliPX36FEuXLjXebvbWrVsm5VqtFr/++ivee+89yflt3LgRvr6+qFu3LvLkyYPg4GAMHjwYI0aMwMCBA+17znat9Zr8+fNnNIRs7t+/D61Wi9DQUJPloaGh+Oeff2RbBwBSU1NNJlWQOrkBERE5hzx58jg6BSOtVouoqCizbdW6detkWwdg+0ZERBlkx8zyFuPI6Msvv8SqVaswfPhw/P333/j7778BvDpb/tFHHwEAbt++jUGDBqFSpUrImzcvNmzYgAMHDqBhw4ZwcXHB5s2bERcXh82bN0vebsGCBbFz505ER0fj5s2b8PLyQqlSpeweSg/Y2Yk3GAxYvHgxVq1ahdu3byMuLg4A8Pnnn2PYsGEIDg62O6GMSElJAYB0kw35+PgYy+RYBwBmzpyJqVOnpluuUKqgUKpsyvt1gsFg97pplG7Wr9N08XATjaFQWv/QGLQ60Rh6jV68jtb68zVIiOEsDHpBtI5SpRCtoxCpo1CJH/CkbEc0D2XGYwgG6/tEL+HlF/QZ/8yIvQ8BQKe2/p7XJmpFY2hEnu/aPGUyHEMviL/PpNQRo1Jk/PXPrjZv3ozvvvsON27cwIULF1C4cGHMmTMHjRo1Ep3BNrOo1WoAtrVV9qwD2Ne+ibUZgLRjkxiVq/WvLQYJ7ZurhGOGWFuschNv58XaQDmOw1LaFZWb9f0uSIghVy5ixI7nLp7iX1vFXhuVq/3f0WyhUFp/LlLaHSnEXj+xdhYQ/66lTRVvjMXaJjl871tKtI6U9i0nt1/ZhaBQQJBhP2ckRvny5dNNJhccHIyBAwdCrVYbh90DwDvvvGP8f758+TBw4EDky5cPADBp0iS0bNkSe/bsQUJCAkaNGoXOnTvDw8PD5pwKFSqEvHnzwsXFBYoM7h+7OvHz58/HggULMHbsWJNbzZUuXRpfffUVli1blqGk7JU2KcGb1yjExMRYnLDAnnUAYPz48Rg1apTx7/j4+HQzJxIRkXP57bffMGTIEIwfPx6zZ8+G4f87c1WrVsW4cePw559/OiQvLy8vuLi42NRW2bMOwPaNiIicX/369VG/fn2TZZ988onoekWLFsXSpUtNllWrVg3VqlWzOxe1Wo25c+dixYoViIqKgpubGypVqoRJkyZJvh3em+z6WXzp0qX45ZdfMHjwYJPlTZo0we+//25XInIICQlB7ty5092f/vLly6hQoYJs6wCAu7u7cZIFsckWiIjIOSxduhSLFy/G6NGjTa57r1u3Lo4ePYqEhASH5KVUKlG2bFmb2ip71gHYvhERUcYIgnyPnKBr165YsmQJhgwZgp07d+Knn35C5cqV8eGHH2Lr1q12xbSrE3/v3j3jF4DXhwLkypXLodfOKRQKdO7cGatWrTJ+0Tp+/DhOnz5tcru7VatWGW9NIHUdIiLK+Sy1byqVCi4uLkhKSnJUaujSpQu2bNmChw8fAgBu3ryJXbt2mbRV27dvNxk+KGUdIiIiORkEQbaHs3v8+DF27NiBo0eP4rPPPkPLli3RoUMHLF68GDNmzJB0r3lz7OrEFylSBGfPngVg+iVn69atZmf5y0ozZsyAj48Pypcvj5YtW6JZs2YYOXIkmjZtaqxz4sQJ7Ny506Z1iIgo57PUvu3btw8uLi7Imzevo1LDiBEjUL16dVSqVAmtWrVCzZo10apVK/Tt29dY5/Lly9i0aZNN6xAREVHmEAQBBQoUMLnXfJratWsbL9uzlV3XxI8aNQo9evTAjBkzAAB///039u7di/nz5zvsevg0/v7+OHnyJI4dO4YnT57gu+++S/fDQr9+/dC2bVub1iEiopxvxIgR6NmzJ9RqNVJTUxEREYFNmzZh1qxZGDVqFJQSJnDLLO7u7vjjjz9w+vRp3Lt3D1999RWqVKliUqdt27YoUaKETesQERHJSfj/hxxxnF2BAgXg5uaGI0eOoG7duiZlP//8M8LCwuyKa1cnfuDAgdBoNBgxYgQMBgPq16+PwMBAfPvtt+jRo4ddichJpVKl20mvq1Wrls3rEBFRzteiRQssXLgQ48ePR3R0NDp27AgfHx+MGDECEydOdHR6AIAaNWqgRo0aZsvKly+P8uXL27QOERGRnAzCq4cccZzd8+fPUaVKFTRu3BgffvghypYti5SUFBw6dAgXLlzAF198gVmzZgEAgoKCJI+Us/s+8cOGDcPQoUPx4MEDGAwGhISEOPQMBRERkRzCw8MRHh6OR48eQa1WIzg4GK6u1m/dSURERPSmuLg43L59G++++y7u3LmDO3fuGMsqVqyIXbt2Gf8uXrx45nfigVfXC/K2M0RElBMVKFDA0SkQERE5HUEQIMgwKZ0cMRytWLFiiIiIkD2u5E7869eQi9m2bZsdqRAREWW9AQMG4OnTp5LqLl++3KGT2xEREWV3HE6f+SR34l+fUS8mJgY//fQTypcvb7zx/ZkzZ3D16lV0795d9iSJiIgyS3BwMHLlygUASE1NxfLly1GkSBG899578PDwwKVLl3D69Gl8+OGHcHHJ0AA2IiIiegvp9XrcvXsX0dHRJjPSe3l5oXr16jbHk/xtZP78+cb/d+nSBVOmTMHkyZNN6kydOhX//fefzUmQdAqV+LwDLh5uVsulzF2g1+qslhu0WtEYgl78lglidQwSYohuw85bN9i8HZFc9Rp9luThTAx66z+xCiLlAGAQ+ZlWSgwp71W9RuS9KmHIl1gqehliaCT8bC1HjC+Sb4jWcRaTJk0y/n/ChAno1asXli9fDpVKZVy+du1arFixAgEBAY5IMVsQDHoIBvPHMSnHWZXIvAIGV+vtzqs61r+2KN3E5y5we+11tZeUNtCgsf583H09xWOIHJvkaN8UEr4TGES+EwCATm29jpQ2UKGy/vqqXDM+95KU7xVKke9aUmLotSKvnQzfkQDx/SrWzgKAJsn6+1lK2yTWbiz3F7/rk1gMKXlI8Vki+ypZgSfR/+fUqVPo2rUrbt26la6sXLlyuHLlis0x7Toa7t+/H8OHD0+3fPjw4di/f789IYmIiBxu//79GDZsmEkHHgB69eqFyMhIJCcnOygzIiIi55A2nF6OR07QvXt39OzZE8ePH0e5cuXw4sUL/PLLL8ifPz9++OEHu2La1YnXaDS4du1auuVXr16FRqOxKxEiIiJHs9S+RUVFIT4+nm0cERERSfb48WPExMRg4sSJ8PX1hU6nQ0BAADp27IiZM2diwYIFdsW16+K+nj17okOHDpg4cSKqV68OQRAQERGBr7/+Gr169bIrESIiIkfr2bMnBg0ahAcPHqBOnTpwc3PDlStXMGPGDHzwwQfInTu3o1MkIiLK1jg7/f+8ePEC+fPnh0KhQEBAAJ49e2YsK1OmDBYuXGhXXLs68XPnzkVQUBC+/PJLxMTEAADy5MmDkSNH4vPPP7crESIiIkcbOXIk3N3dMWPGDGN75uXlhYEDB2LatGkOzo6IiCj7M/z/Q444zu71HyLy5csHDw8PrF69Gm3atMGyZctMJo+3hV2deBcXF0ycOBETJ07EkydPjEkRERE5u8GDB2Pw4MF48eIFUlNTUaBAASgUCkenRURERE4md+7caNGiBYBXk4vPnz8f3bt3R9++fZEvXz78+eefdsWVfE38hQsXzC7Ply9fug68pbpERETZzbVr18xe654nTx4ULFjQpANvqS4RERG9IgjyPZzVixcvcPLkSQQHB+Pbb781Lu/YsSOePXuGy5cv486dO6hYsaJd8SV34itXriw5qC11iYiIHKlly5Z4+PCh7HWJiIjeRpyd/tWEdrVr10b58uUxb948PH/+3Fjm4+OD8uXLw9NT/Fajltg0nH7MmDF2b4iIiCi7mjFjBnx9fUXrvXz5MguyISIiImdWrlw5nDp1CqtWrcKUKVMwbtw4fPjhh+jXrx8aN24MpdKum8QZSe7EN2vWTPKN6Js1a2Z3QkRERFmpXr16uH//vqS6tWvXztAv50RERDkdZ6d/pUaNGqhRowbmzZuHLVu2YNWqVWjWrBlCQ0PRu3dv9O7dG4ULF7YrtuRO/N69e+3aAP2PQiX+i4uLh7vVckEvPk+jUmQ7Bg/xGC4i25GSh0GrE60jFsdgEN+OWAxBrxeNIboNCXkYNNafr0KV8f0hhaAXP+AZxPaZDOOXDFLy0Fh/bfQi5YD485Ujxqs61veZXoaGRkIasmyHTP3444+OTsHpKSScURBrAyW1kZ5uGSoHAF1Kxuc0yC7tmxRibaCk9k3CayMHKe2GGL024/tMjhhi7ZuU5yolD7H2SyyPVzHE2jfREFkiu+RB4jg7valcuXKhZ8+e6NmzJ27cuIHVq1dj2bJlmDZtGnr06IE1a9bYHDNrjspEREREREREb7ESJUpgwIAB6N27N1xcXBAREWFXHHbiiYiIiIiISBYCZJqd3tFPREYpKSnYsGEDGjVqhGLFimH9+vUYN24cdu3aZVc8u+4TT0RERERERPQmgyDAIMPlf3LEcLSIiAisXr0aGzduRHJyMtq0aYPdu3ejadOmGZrcjp14IiIiIiIiIpk8fPgQLVq0wKVLl1C2bFlMnDgRPXv2RGBgoCzx2YknIiIiIiIiWQiQZyi8M5+HT0pKQrVq1bBkyRK89957sse36xy+wWDADz/8gMqVK8PPz8+4/PPPP8eDBw9kS46IiCirbd68GTVr1kRAQIDx1nNz5szB+fPnHZwZERFR9mcQ5Hs4qxIlSmDVqlWZ0oEH7OzEz58/H3PmzEH//v0RHx9vXF66dGl89dVXsiVHRESUlX777TcMGTIEnTp1gouLi/E2YFWrVsW4ceMcnB0RERGRnZ34pUuX4pdffsHgwYNNljdp0gS///67LIkRERFltaVLl2Lx4sUYPXo0vLy8jMvr1q2Lo0ePIiEhwYHZEREROQE5ZqaXa0x+DmXXNfH37t1DhQoVAAAKhcK4PFeuXCZn5omIiJyJpfZNpVLBxcUFSUlJ8PHxcVR6RERE2Z4BAgwy9MDliJFT2XUmvkiRIjh79iwA0y85W7duRenSpeXJjIiIKItZat/27dsHFxcX5M2b11GpERERkZO5c+cOevToIXtcu87Ejxo1Cj169MCMGTMAAH///Tf27t2L+fPnY9myZbIm6HSUqlcPc0Wu4rtbobL+u4qUGAatzmq5oDeIxtBrtRmOIZYHABhE4hg0EmLI8nzFtyNG7LVT6O2/F+TrDBq99XJJz9d6HSn7TIygF//11CBSxyBhRhO9yP4QJMQQy0NKHY2E7YjVkZCGaB0pMcTy0Eq4L+sE92KidWak3hJPJpsZMWIEevbsCbVajdTUVERERGDTpk2YNWsWRo0alaF7uuZkKg830TpKN+vtl5T2TZuUYj2Gynz7a+t2xOjUmgzHkKMdlSUPg3geYu2bFAqlQrySyAFMrO0CxPerlLZJjJQ2QywPOdo3ANBrrdcRKweypn2TEkO8fct4DACY4lncennKTfEgZJVxOLwMcZydSqXCkSNHZI9rV0s2cOBAaDQajBgxAgaDAfXr10dgYCC+/fbbTPmlgYiIKCu0aNECCxcuxPjx4xEdHY2OHTvCx8cHI0aMwMSJEx2dHhERUbYn18zyzjw7fZrChQvDz88P//zzD+rUqSNbXLt/jh42bBiGDh2KBw8ewGAwICQkhGcoiIjI6YWHhyM8PByPHj2CWq1GcHAwXF1dHZ0WEREROZnHjx+jcOHCaNSoEVq1aoUiRYpA9dqosQIFCmD06NE2x83QmDKFQoGQkJCMhCAiIsqWChQo4OgUiIiInA6H0/9PamoqtFotGjZsiJSUFERGRpqUJyYm2hVXcie+bdu2koNu27bNjlSIiIiy3oABA/D06VNJdZcvX87J7YiIiKzg7PT/Exoair1798oeV/L49yJFihgfvr6+2L59O27dugV/f3/4+/vj5s2b2L59O3x9fWVPkoiIKLMEBwcb27cCBQpg586duHz5Mnx8fBAUFIRHjx5h+/btAAAXl4xPikZERESUEZK/jcyfP9/4/y5dumDKlCmYPHmySZ2pU6fiv//+ky05IiKizDZp0iTj/ydMmIBevXph+fLlJtesrV27FitWrEBAQIAjUiQiInIaHE5vSqfT4bvvvsO2bdvg5eWFffv2QaPR4IsvvsDMmTPtOkFg10x0+/fvx/Dhw9MtHz58OPbv329PSCIiIofbv38/hg0bZtKBB4BevXohMjISycnJDsqMiIjIORgEQbZHTjBixAisWbMG77//Ph49egQAcHNzQ0pKClavXm1XTLs68RqNBteuXUu3/OrVq9BoMn7PUiIiIkew1L5FRUUhPj6ebRwRERFJlpqaijVr1uDQoUPo1auXSVmLFi3wyy+/2BXXrov7evbsiQ4dOmDixImoXr06BEFAREQEvv7663TJEREROYuePXti0KBBePDgAerUqQM3NzdcuXIFM2bMwAcffIDcuXM7OkUiIqJsTW949ZAjjrOLiopC/vz5kT9/fsTExJiU+fn54eXLl3bFtasTP3fuXAQFBeHLL780JpMnTx6MHDkSn3/+uV2JEBEROdrIkSPh7u6OGTNmGNszLy8vDBw4ENOmTXNwdkRERNmfXEPhc8Jw+nz58uHJkyd4+fIlFAqFSdmuXbtQokQJu+La1Yl3cXHBxIkTMXHiRDx58sSYIAEKdw8o3D0sloky6K0WK920GY5h0InHcBH56cug1YmnoRGvo9daz0WvEh+6qldZvypErxaPoRL5KOj01vdpVjKIvDaCQfyAJ4jE0Gsy/tOn2DYAwCCSq14jvt/F6kiJIRjEcxX0md+QqBTy1MkovYRGU0qdT1VFrZYv0N+RnFNWGjx4MAYPHowXL14gNTUVBQoUSNfwvo2Unl5Qelpox5Qq88tfI2itH4uVLuJtk9It43cHkBJD7PildM14HrpktWgdhUj7JuU4q4f1tlhQS4ghoT0XoxR5LgCg12a87RFrv/RaCW1CFhzvpbRNmiTxz4TYe8Ag4bnI8XxVIsdIaW1XxvOQ0jZRzvfixQt069YNALBnzx6Tsri4OMyZMwcREREICAhAv3790KBBA6vx7FknjY+PDzp16oSOHTuiR48e0Ol0OH36NH755RcsWLDA7vnkMtwKsfNOREQ5UZ48eRydAhERkdMxCIIsP6jYeya+T58+uHv3LqKiokyWa7VaNGzYEK6urhg9ejSuXLmCJk2aYNu2bWjdurXZWPas86ZFixZhxIgR6NevH7RaLWrWrImgoCCsXr0adevWtes52tWJb9u2rdXybdu22ROWiIjIoQYMGICnT59aLF++fDny5s2bhRkRERE5F4Mgz1B4CYNL0/n++++RkJCAwYMHY/z48SZlmzZtwuXLlxEdHY2goCB07NgRDx8+xLhx4yx2yO1Z53WpqamIjo7GsmXLMHfuXNy8eRNubm4oVaoUdDodbt68ieLFi9v8PO2anT44ONjkUbBgQSQlJWH79u3w9va2JyQREZHDFShQIF0bZzAYsGvXLgiCYNe9XImIiCjznT9/HrNmzcK6devMXgb3119/oXbt2ggKCjIua9u2La5evYqHDx+ajWnPOq+7ceOG8QS4t7c3KlWqhLJly0KlUpmU2cqubyM//PCDxeXnz5+3KxEiIiJHmzp1qtnlu3btwrx58xAQEJDFGRERETkXuWenj4+PN1nu7u4Od3d3k2WJiYno3LkzFixYgODgYLPx7t27l66sUKFCxrKCBQvKso5UCQkJdp8Al/WUQo8ePTBjxgw5Q9olJSUFV65cgYuLC0qXLg1PT0+r9a9du4b79++bLPPx8cH777+fmWkSEZGTaN26NXr37o2UlBTRNiUzabVaXLt2DampqShTpgx8fHys1r9z5w6uX79usszFxQWNGzfOzDSJiOgtJvfs9CEhISbLJ0+ejClTppgsGzx4MN577z107NjRYjyNRpOuDc+VK5exTK51AODRo0eYPn06YmJi8OjRIwwdOtSkXKvV4p9//pE8Qd6bZO3E37592+qTyWyCIGDChAlYvXo1QkNDkZycjCdPnmDx4sVWX9AFCxZg27ZtqFSpknFZkSJF2IknIiIAwNOnTxEfHw+dLuOzdNtr3rx5mDt3LgIDA+Hi4oLr169jxowZGDZsmMV1Nm/ejJkzZ6J27drGZZ6enuzEExGR04iKioKvr6/x7zfPwqempuKnn35CtWrVUL9+fQBAdHQ0UlJSUL9+fYwaNQpt2rSBv78/Xrx4YbJu2t/+/v5mt23POsCrTvqDBw+QkJBg/P/rPDw8EB4ejhEjRlh+4lbY1YkfN25cumUvX77E77//jg4dOtiViBwEQYCfnx9u3bplHJrwzTffoHv37njvvfeMQx/MCQsLw6+//ppVqRIRUTY0e/ZsvHz50mRZYmIidu3ahQYNGoie+c5MgiDgwoULCAwMBAD88ssv6Ny5M2rVqoXq1atbXK9EiRLYu3dvVqVJRERvOb1Ms9OnxfD19TXpxL/J1dUVhw4dMln2+++/Y/ny5ZgyZQpKliwJAKhcuTI2bNhgUu/MmTPIlSsXSpUqZTa2PesAQOHChbFt2zY8fPgQy5Yts3i5nr3smtguIiIi3SMmJgYTJ07EggULZE3QFkqlEuPGjTO5tqBnz55ITU3FxYsXra6blJSEI0eO4OLFi1Crxe/dSkREOc/ly5fTtW/R0dHo378/tmzZ4tDcRo0aZezAA0CnTp3g4eGBM2fOWF1Po9Hg2LFjiIiIQFJSUmanSUREbzkD0maoz+BD4vaUSiXq169v8ihRogRUKhXq169vvG69e/fuiI6Oxrp16wAAsbGxWLx4MT7++GPj2f1r166hfv36uHbtmuR1rClYsKDsHXjAzjPxU6ZMQVhYmNmyo0ePWixzhOPHjwOA8RcYS44cOYK4uDg8fPgQycnJWLJkCdq3b2+xfmpqKlJTU41/vznhAhEROZ8RI0agQoUKZhvmiIgIi2WOcP78eaSkpFg9EwAA169fx6hRoxAXF4fo6GjMnj0bgwcPtlif7RsREeVEZcqUwYoVKzB48GB88803ePDgASpVqoS5c+ca68THx+Pvv/82tn1S1pEiKSkJV65cQXR0NAyG//084efnhyZNmtj8XBSCYPtYB4VCAUurWSuzR9p9+aypW7eucYKB1z169AjVq1dH48aNsXbtWovr7969G++//z78/PwgCAKmTZuGmTNn4uLFixa/HE2ZMsXsrypPty2Cr5f5SY8EgwzTNOrE5xwQ3Y6UGHq9SAytaAyDlDoa69eX6tXiuerUqVbLDVrxa1h1Kda3IymGSK4Grfj+EMsDAPRq63EMEqYD1alF9rtGPIZea/09IuhlGEalEXkfAtAkiewPCTHEngsAaBKtb0cjIUaKyD5JkfDaiccQ3+9i21FLuDGrRoY6C/R3rJbHx8fDz88PcXFxVofRyalIkSI4fPgwihQpYlOZPW7evImbN29arVO9enXkyZMn3fLExES8//77CAgIwMGDB83eSgcADh8+jFKlSqFAgQIAgLVr16JPnz44cOCAxcl0LLVvT7bMhW8uC5P6KVVWn4cUgkbCKDiDyHFHwnFWUi4i7aSUNkEQ+ZxJa1est2/aJPF9JtaOirVdAGDQiO9XsVzE2i5AvP0SJBx3dCnW96tWpP0DxNuerGrfxNrqV3Gs7zMpMTTJ1l8bKcOinaV9AwCtyPOR0r7N1twWrWONI9q3rJD2vLZG3ISXd8YvP0tKTED7asXt2k/R0dG4ffs26tSpYzbPa9euISAgIN2J3vj4eJw7dw5VqlQx2aa1dcQcOHAAXbt2RUxMDPR6PVQqFbRaLZRKJWrWrGk86WwLWSe2i42Nlf16wb/++gv79u2zWqdixYrpOvHPnz9H06ZNUaJECSxZssTq+i1btjT+X6FQ4Msvv8T333+PXbt2WezEjx8/HqNGjTL+HR8fn27mRCIiyhkMBgPi4+Nl/bJ18uRJrF+/3mqdWbNmpevEp6Sk4MMPP4Rer8eWLVssduABGCf4SdOrVy/Mnz8fv/32m8VOPNs3IiLKCEGm2ekzcmK4UKFCFudD8/X1Ra1atSyWvdl2iq0jpnfv3pg0aRLCwsLQpUsXXLx4EUeOHEHPnj0xadIku2La1Inv1q2b2f8Dr77gXLlyxe4nZ8no0aMxevRom9Z58eIFGjdujICAAOzatcvm2wEplUrkzp0bjx49sljH3P0JiYjIOY0cORLPnj3D8+fPMXLkSHh5eRnLBEHArVu3EBgYKOt94rt165auLRWjVqvRpk0bPH78GIcOHTK5Rl6qgIAAtm9ERERZ4OHDh0hNTcXgwYNx7do1aLVaqFQqNGjQAN988w0WL16M5s2b2xzXpontXFxc4OLiYvL/tIeXlxfCw8NFzypktpiYGDRu3Bh+fn7YvXu3yRexNFevXsWxY8cA/O/sypvl9+7dM7nlHBER5VwqlcrYvqX9P+3h4eGBJk2a4M8//3Rojmkd+IcPH+LgwYPImzdvujq3b9/G/v37jX+/OdP+kydPcPbsWbZvRESUafSCfA9nFxMTg6CgIABAYGAgnjx5YrwmvkiRIqKXjVti05n4tOvKAwMDMWfOHLs2mJk0Gg2aNGmChw8fYuLEifjnn3+MZeXLl0dwcDCAV/faPXnyJK5cuQK9Xo8aNWqgS5cuKFeuHO7fv485c+YgLCwM4eHhjnoqRESUhdLatJIlS2LQoEFW7/3qKB07dsTx48exePFinD9/3ri8WLFiKFGiBADg559/xqxZs5CYmAjg1eVi9erVQ5UqVRATE4N58+ahUKFCGDJkiEOeAxER5XwGmYbTyxEjOwkKCkJAQADmzp2LDz74AN9++63N19enseua+OzYgQdenaUICgpCUFAQVqxYYVI2cuRIYye+fPnyxjMurq6uOH78OJYuXYpNmzbB398fs2bNQteuXaFU2nUHPiIiclITJkxwdAoWubi4ICwsDD///LPJ8i5duhg78cWKFTOZ5fbAgQNYsWIFfvvtN3h6emL48OHo168f3NzcsjR3IiKit1GePHnQpUsXAK/mXluyZAk+/vhjjB07FsWKFcPu3bvtiit5dvrOnTsDADZt2mT8vyWbNm2yKxlnljYbI2en/x/OTv9mDM5ObyvOTm9PDM5Ob6thw4bh2bNnWLhwIaZNm4Znz55ZrLtw4ULjsLi3RdrrwNnpX0uDs9PbnAtnp7dtGwBnp7e1XOp2ODt95kl7XutPXEcuGWanT05MQLfapZxyPyUmJuLFixcIDQ1NV2YwGPD06VPky5fP6uS01kg+E+/t7W32/0RERM7My8sLKSkpUCqVxv9bwhFaRERE1nE4PXD37l1UqFABRYsWRcOGDdGwYUM0aNAABQoUgFKpRP78+TMUX3InfuXKlcb/9+rVC2FhYWbrHT16NEMJERERZaVZs2YZ/9+hQwdUqFDB7OzsERER/BGbiIiIRJUqVQp79uzB4cOHcejQIaxduxZ6vR6lS5dGgwYN0LBhQ9SvX9+uu8wANs5On6ZOnTp2lREREWVnHTp0sHj7NWtlRERE9Apnp38171rz5s0xa9YsnDp1CjExMdi1axdatWqFU6dOITw8HHnz5kWbNm3sim/XxHaWxMbGwscn49c/EBERZSdptyN1tmvyiIiIshqH06fn6+uLVq1aISwsDPXr18e2bduwbt063L5t3/wKNnXiu3XrZvb/wKsvOFeuXEGtWrXsSiSnUPoGQOmdy3yhDBPbiU22I2U7glbCxHYik9JJmYBIJSFXpZv17ag8xGOo1NZnWZYyOZ7S1fpHQcrEPwqV9YEtOnXWXEurkDBZkriMT9qkl/DzqZRJ+MSoXDO+Xw0SclW5WZ+4y1VCDI3IpFxykDIBkQxTbMpiiLKI1XKNkHWZjhw5Es+ePcPz588xcuRIeHl5GcsEQcCtW7cQGBiIgICALMspu1EF5IfKy3z7Jkvb5OFltfxVDJEJxyRMqCppcleROEoJn2WxSfYMEtpRsbZJ5eoqGkOTkGS1XCFhngeDSB5SKEXaSEC8rZUyoZzY8VwlIYbY5HjZiVJl36RYr1O5iLw2OvFjsUqR+Z0tudo3sYnrpExsN9KlqNXyeTrrE7fS2yMxMRFHjx7FoUOHcOjQIZw/fx4lSpRAvXr1sG7dOtSrV8+uuDYdldNuy/bm/4FXQwbCw8PRv39/uxIhIiJyFJVKZWzXXv9/2t9NmjRBnz59HJUeERGR0zAYBBgk/BgiJY6zio6ORocOHXDu3DmUKlUK9erVw9ixY1G3bl3kzZs3w/Ft6sSvXbsWABAYGJht7xVPRERkq7Q2rWTJkhg0aBD8/f0dnBEREZFzMsh0PbsT9+ERFxeHkydPokKFCmjXrh3q16+PWrVqwcPDQ5b4do2PYgeeiIhyogkTJjg6BSIiInJyZcuWxd27d43D6FevXo2nT5+iVq1aqF+/foY79XZf5JScnIyTJ0/i/v370OlMrx3q16+fvWGJiIgcSqfT4cSJE7h79y5SU1NNyjp37szbzBEREVnBie1eCQ0NRa9evdCrVy8AwK1bt4yd+hUrVuDFixfo2LEjfvrpJ5tj29WJv3jxIlq3bo3ExETExsYiX758ePLkCQCgcOHC7MQTEZFTio6ORpMmTRAVFYWUlBQEBATg+fPnEAQBBQsWRPPmzdmJJyIiskIvCJImIpQSJycpWrQoYmNjERsbi7i4OPz11184f/68XbHs6sSPGjUK3bt3x4wZM6BQKPD48WPcu3cP3bp1Q9OmTe1KhIiIyNEmTZqEqlWr4tKlSyhevDgOHz4Mb29vDBgwAP7+/ggODnZ0ikREROQEBEHApUuXjGffjxw5gtjYWAQGBqJevXqYN28emjRpYldsuzrxZ8+exZYtWwAACoUCGo0GoaGhWLVqFRo3bowvv/zSrmSIiIgc6ezZs1i9ejVcXFygVCqh0WgQGBiIn376Cfny5cOiRYtkm5SGiIgoJ+Ls9MDt27dRvXp1xMTEIHfu3Khbty6mTJmCBg0aoEKFClAoMnZ7SLs68XFxccZ75QYFBSE6OhpFixZFgQIF8PTp0wwlRERE5CixsbHp2reSJUvCy8sL7u7uiI2NRf78+R2cJRERUfalhzyz0+szHsJhPDw8MH78eDRo0ACVK1eGUqmUNb7dE9ulCQsLw5dffomhQ4fixx9/RLly5eTIi4iIyKHCwsIwY8YMeHh44K+//oJCoUC+fPkcnRYRERFlcwULFsSYMWMyLb5dnfgvvvjC+P/Zs2ejY8eOqF27NkJDQ7Fp0ybZkiMiIspKw4YNQ+7cuQEA48aNQ6dOnRAWFoY8efJgzZo1GR7+RkRElNNxdvrMZ1cn/uuvvzb+v3jx4jh//jzUajWvEwSg8s8LlY+FmYsFg3gAg/WBI4JBQgydxnoMrVY0hCAWQ6MWj5EqoY5IHEEnnqvCJclqudJNPA+lm/WPgtJV/KMieFl//+vV1vcpAGiTxHNVeViPo1enWi0HAINGZ7VcqRKPoXJTWc9DIz4ISq+x/n7Wa8VjKFRinSrrzxUABL2UIU7W3wOCXvyz6Sby+fVUSclDZJ8JGY8hhTyzzoqUZ3gLths9erTx/0FBQTh06BDbt/+nyh0IlbeX2TJZ2ia9hFdcbDsibSgAGNTW2wxAQjsp8lwA8fZLIaEdVYq0owpVsmgMMSoPN9E6Ym0GIN5OSmkDlckpGc5DrE1QirYZ4qQc7/Va63XE2y5pxLajktCOim5Dl/E2QyXhB1BP618roDGIx5AyhFsjmot4ELHtfKIoYj0HGdrh7Iyz02c+2Qbnp33BqVatmlwhiYiIHC6tfWvTpg0ePXrk4GyIiIjobZfha+LfdPbsWblDEhEROdylS5eQmio+SoWIiOhtZjAI0L/ls9NnNtk78URERERERPR20svUiZcjRk4l71z3RERERERERJRpeCaeiIiIiIiIZMEz8ZnPpk58+fLlMysPIiIih2nWrBmio6Ot1nn48GEWZUNEROS89AZ5OuASbgTx1rKpE9+8eXNZ6hAREWUn9erVQ0xMjNU6zZs3h5+fXxZlRERERGSeTZ34OXPmZFYeREREDjNhwgRHp0BERJQjcDh95uM18URERERERCQLduIzHzvxMtN7B0Lv7WO+UBC/sEMhVsegE48hUkdhEM9Dqddar6ATv1eyoFGL1jGkJFmPoU4Wj+HmYbVcISEPlZfGarmrVmR/ADCIbEevtr4NAHDJZf25AIBBa/311UnYjlgu7hrx95leJA+DhH2mS7Geh9hzBQCdWmR/pIjH0IrEAABtovXn4+Ipfjh1FcnFI0n8tfNO1Vst91SJf74TdQqRcgnHCNEaAJDBi9kEBcD2O9sweAfB4ONtfwC9+PtbjHgbKf6ec5HSfmmt1xFSU8RjiLQJhqSEDMdQuIu3GWLtm0FCOytIuDDVTaRd0anF97sm3nouUvIQa9+k5OGWZH2/CxLeZwaN9WO1Xmu9HJDWfulFtqNVu2V4O6pY8e9Rbhrr+8RbJE8A0IjsE5XCetsFSGu/VCJhxMqlbMdNKRKE7RtlEDvxREREREREJAuDTGfiDTwTbxE78URERERERCQLvSDTcHqBnXhLpI2IJCIiIiIiIiKH45l4IiIiIiIikgUntst87MQTERERERGRLNiJz3wcTk9ERERERETkJHgmnoiIiIiIiGShMwhQyXAWXccz8RaxE09ERERERESy4HD6zMfh9EREREREREROgmfiiYiIiIiISBYGmc7EG+yMER8fD19f33TLExISkJSUZHad/Pnzm12u0+nw/PnzdMvz5MkDV1dXu/KTAzvxMjN45YHBO/2bBgAgGMQDCCJvVgkxFAad9Qpi5QAUepE6eq2EGBrROkof63UUulTRGEJqski5WjyGxnodQSf+XMS246I2f9B4nZtIHgAgaK3veym5GrTWX1+9WjyGXmM9D0kxZMjDoLEeQ6cWfw9pk8T3u15t/flq1eKfK12K9TqaJPHnmxpvvY6nhBh+idafS5yE5xKnFT8Weeqs10nSWy9PFRSA+MtHWUTvHQi9j4X2zaDPcHyFpDZSpI6E45+k7Yi0cQqd+DFDqbX+5lUFiH/OxNo3Q1K8eAydSJshQxsJAC4iddxSxNtAzyDrr59Y2yWljjYpRTyGaLuS8XZWSgwpdCLPR0r7phM55us13qIxNEnW32di7R8AqF9azzWXhLZJK9K+AcDzVOtxYjTixzMvlfXBzHqRr/NqwQCIvzROSy8I0Iv1aSTGkSouLg7ffPMNVq5cCbVaDZ1Oh/DwcCxYsADe3q/ew99++y2WL19usl5sbCzc3NwQH2/+eHrhwgVUr14defPmhUKhMC7fvXs3qlSpYsezkgeH0xMREREREZHTOnXqFPLly4fIyEjExcXh0qVLOHz4MEaNGmWsM23aNDx+/Nj4iI6ORmBgILp06SIa//r16ybrOrIDD/BMPBEREREREcnEERPbNW3aFE2bNjX+XaxYMTRu3BhXrlyxuM6ff/6J6Oho9O/fXzS+RqNBYmKi8ay+o/FMPBEREREREckirRMvx8NWz549Q1RUFHbs2IFt27ZZ7aCvWrUKlStXRtWqVUXjhoaGIjAwECEhIfjhhx9szktuPBNPRERERERE2dKb16u7u7vD3d3dbN1mzZrh9u3biIuLQ79+/dCtWzez9Z49e4adO3fi+++/t7ptT09PLF26FN27d4e7uzt+/fVXdO/eHW5ubhgwYIB9T0gGPBNPREREREREspD7THxISAj8/PyMj5kzZ1rc9rlz5xAbG4vIyEgcPXoUvXr1Mltv3bp1cHV1Fb0evly5chg4cCBy5coFlUqF8PBw9OzZE0uWLLF7/8ghx3XiR40ahdy5c5s83nvvPdH1Vq9ejYoVKyJ//vxo0qQJzp8/nwXZEhERSTN//vx07VtwcLDoejt37kTNmjWRL18+vP/++zhw4EAWZEtERG8rvWCA3iDD4//vJhIVFYW4uDjjY/z48aI5lC5dGmPGjMHmzZuhNXNnp1WrVqFTp07w8/Oz+fkVL14cd+7csXk9OeW4TnxycjLq1auHu3fvGh9//vmn1XU2btyIQYMG4bPPPsOxY8dQrFgxNGzYEI8ePcqirImIiKxTq9UoWrSoSft29epVq+scPXoU7dq1Q+fOnXHy5Ek0a9YMLVu2xMWLF7MoayIioozx9fU1eZgbSi+YuR1dTEwM3N3doVKpTJYfP34ckZGRZq+X12q1ePz4sbHjby7uqVOnULRoUXufjixy5DXxrq6uyJ07t+T6M2fORO/evY3XTCxatAjbtm3DkiVLMG3atEzKkoiIyDYqlcqm9u2bb75Bo0aNMHLkSADApEmTsHPnTsydOxfr1q3LpCyJiOhtZpBpdnqDDTGGDx+OYsWKoX79+siVKxeOHj2K6dOnY9CgQVAqTc9br1q1CmXLljU7Wvvs2bOoXbs2Tpw4gVq1amHkyJEoXLgwGjVqBBcXF6xfvx7btm3D5s2bM/z8MiLHnYkHgAMHDqBgwYIoU6YMBgwYgCdPnlisGxsbi8uXL+P/2rvv+Ciq/X/8r+3JEpIQCEkEEjpIBKSXSBNBL4ggoIh8pCuoWJCPV6ygFy94bXjBjwWRCyiEDkZpFkoCwqUESJQSegSSEEIK6dk9vz/4Zn8s2Z0zSWYTFl7Px4PHg51T5uzZybz37Jw507dvX8c2g8GA+++/H3FxcVXRXCIiIlWOHTuGBg0aoGnTphg5ciROnTqlmD82NtYpvgFAv379GN+IiMhjqmN1+vfeew+XL1/G+PHjMWDAACxfvhyff/45PvzwQ6d8BQUF2LlzJ55//nmX9ZjNZoSEhMBsNgMA3n33XWRkZGD8+PEYNmwYjh49itjYWDz22GMV7yAN3HZX4uvVq4d///vf6NWrFy5cuIBXX30VUVFROHz4MGrUqFEm/8WLFwEAISEhTtvr1q2LgwcPut1PYWEhCgsLHa9vXjWRiIhIS4GBgfjggw8wYMAAZGVlYebMmejcuTMSExMRFhZWJn9eXh4yMzNdxrfS2OcK4xsREXmbwMBAzJo1C7NmzVLM5+Pjg6SkJLfp7du3R0pKiuN1QECAqnqr2i0/iH/uueewbNkyxTzx8fGO+xLefvttx/bw8HCsW7cO9evXR3R0NCZMmOC2jpunWRiNRpf3QJSaPXs23n333TLbM0v0sJW4nuCgVzHxwaDXKabrlJOv12FSzmRQU4ekHXpb2QUiyrDbpFl0tiJJHSXyOooLldPV1CFph75E/n5ldQhJOwFA5OfK85RI9lNUIK0DdrtyHYX58nZI9qOmHcLFQiNO6ZL3CgC2AuU89mL5519SIP9s7EXK9ZRI2gEAJbnKfVKUI//8i7KVP5vCbPl7yb+q3A6rijpqSeoAgOw85c83q1j5OMwXNkDeFK81Z84czJkzRzHPhg0b0KtXLwDA5MmTndKio6PRuHFj/N///R/+8Y9/uK1Ds/hmM8Jmc/21waQ3ua2vlCz06FXMDdRJgqDJIg9weqEiNknihk5NDJTEBJ1Nfm6CZD+6QHkdelmc1SC+AfJzvj1X/mOQNG5IYhcAiGLltloKVMRZWXyTxC5AHr9ksQtQF79sRcptURcDK9/Womzlfi2WxD8AKMpRziOLXQBQfE3+2Viu5CmmB2XKA8+1Ekn8ssniGwAVX9e8VYkd0GkwnV7SzXe0W34Q/9FHH+Gf//ynYh5/f3+3acHBwYiIiMDx48ddptetWxcAkJ6e7rQ9LS3NkebK66+/jldeecXxOjs7Gw0aNFBsJxERUamXX365zMD8Zn5+fm7TfHx8cM8997iNb1arFTVq1GB8IyKiKmWzC+g1GMRrcV/97eqWH8RbrVZYrdYKl8/NzcVff/3l9gtLnTp10LhxY8TGxmLIkCGO7Tt37sTw4cPd1muxWFyujEhERKSGj48PfHx8KlzebrcjKSkJAwcOdJunS5cuiI2Nxcsvv+zYtmPHDnTp0sVtGcY3IiKiW9tttbBdYWEhxo0bhxMnTkAIgUuXLmH06NEwm80YOXKkI9+LL77otBrhSy+9hG+++Qa7du1CUVERZs+ejZSUFEyaNKk63gYREVEZzzzzDA4dOgSbzYbMzEy88MILuHDhgtOtYh999JHTs+NfeuklxMTEYN26dSgpKcGSJUuwa9cuvPDCC9XxFoiI6A5QHQvb3Wlu+Svx5WGxWPDAAw9g+PDhSEpKgslkQo8ePbBr1y7Uq1fPkS8vL89poZ4XXngB6enpGDBgAHJzc9GwYUOsX78ezZo1q463QUREVMbQoUPx7LPP4siRIwCADh06YNu2bWjXrp0jT0FBATIzMx2vH3nkEcydOxeTJk3CY489hpCQEHz77beO++yJiIi0xun0nqcTSqvbeLGioiLHowFulp+fj5KSEtSsWdNpuxACBQUF8PX1Lff+srOzERAQgBPnL6Kmm3v01Ux7kC5sp0EdXNjOBdkCRFzYrtz74cJ2LvJ4ycJ2auooqKKF7V7OOomsrCzFtU/uNMXFxTAajS4XeSssLERBQQECAgLKpOXn53ssvpkkMQNQsbCditgkXdhORSW31cJ2GizceqctbCe4sF0Zd9rCdnmShe3yq2RhOxteyLz94ltpvHjgk60w+ZZ9Klh5Fefn4pdX+t92/aSF2+pK/I3cDeABuP0So9PpKvQFh4iIqKqYTO5Xgle6n53xjYiIqoJdo6nwdl6Jd+u2HcQTERERERFR1bLZhSaPmON0evduq4XtiIiIiIiIiG5nvBJPREREREREmhBCQGhwFf02XbpNExzEayw1txi5eteLasgWiwMAg2TRHoOKuROyhX2MmrRD3hCT3iDfj9H92gXX96Oirb6SRfg0WMRIk0X6VCxipGaxJNl+9CoWIJItZKRqESPJ4kCiUMXCdpJ+FSoWXDJJ8sgWOQJULsIna4uKRfjskoULC65kSesozLymmF6Uo7xgDwD4SRYgKsiU13EtVb44lCVdeRE+f8kCenl2GyDvEqoil/NKkG9wfR6TxQxAHr9UVAGzbOFWFTFDzeJ3Bp3y1yOzwf26BKV0RuVFnVQtBijJolMTm6pgkT4A0EnOgbpalY9vqhaZLdFg0VXJuVpNbJItbGdUsTiemrgibYuKY0RWh5pFd0vylM/3WsQ3q4rFX9UsoGdNV14fRE18882ULBCbpfzZ5dlv78nQdrvQ5H523hPv3u19BBERERERERHdRnglnoiIiIiIiDQhhNBkKjyn07vHQTwRERERERFpQtg1uiee0+nd4nR6IiIiIiIiIi/BK/FERERERESkCS5s53kcxBMREREREZEmhP36Py3qIdc4nZ6IiIiIiIjIS/BKPBEREREREWmCq9N7HgfxREREREREpAneE+95nE5PRERERERE5CV4JV5jJ67kwVpocJlm0uuk5S1G12XLU4ePUfm3GYskXU0dBhXtMOjkeWTVqKhC2idGVW1V/lMw6eV/Kgajj3I7zPJ2mA0q+sxWrJxBlg5AZytSTi8ukNdhLazUPgBAZ5esWGK3qaijRDmDiv4QJSryFCu/H1Ek7zNRkKeYbqiVLa3DN+uKYnpRVo60jsJM5TyWq9ekdZhrZknz5NfKVW5HtnKfWkpKgGTpbqiKKMU3Nawm5bIqTn/S+CWLXWraAchjnJr4ZpA0RY/K16FFfDMbTPI6TFZpHpNP5WOxXhJ7pOd7AJDVUaIcuwBAX6h8DlRxqErjl07Fil2iKL/S+1ETR0Wxcgy0FyifywHAkK+cx1Q7U1qHb2aGYrosdgFAoYr4ZaqhHL8s/hZpHXnpyvG8KFe5Ty0lJUCqdDdei8+J9zwO4omIiIiIiEgbGg3iwUG8W5xOT0REREREROQleCWeiIiIiIiINGEXAjoNVpa3c3V6tziIJyIiIiIiIk0IodE98RzEu8Xp9EREREREREReglfiiYiIiIiISBNcnd7zOIgnIiIiIiIiTdjtgE6DAbjsScR3Mk6nJyIiIiIiIvISvBJPREREREREmhBCaLIoHRe2c4+DeCIiIiIiItKEsF//p0U95BoH8Ro7cyUPPgWu71Iw6HXS8maj8h0OsnQA8DEo5zFJ0gHAJGmrUYM6AHmfqKlD9n6sJnlbLQaDYrrZqKIdsj5T817UHCOS92vU+8jrMPsqp/sGSuvQ24oV03UlhdI6YCuS1KGcDgCw25TrsJfI61ARJWT16EqU+wMAdMX5iun2/FxpHfaA2orp+pxMaR3mrCuK6T61s6V1+NT2l+axZl5TTC/MzFFMNxcVA9ulu6EqcjYjDz6FFb8LT4v4ZtIr5/FRUYdeJz/PWiT1aBGb1JDtx2pSjl0AUNOinEfWpwBglO8GBkm/mg1qYqBJUodZWofZWkM5Xchjgt1aSzFdTVzRFRcoZ5DEUADQ+dSU5pHFL1VttSnHUaMkVgOAKFaO+SJXHlf0foGK6aY68hhpVREDCzOyFNPzryinA4BvXeX4VpKrHO99i4qB3dLdELnFQTwRERERERFpwm4XGi1sx+n07nAQT0RERERERJrgI+Y8j6vTExEREREREXkJXoknIiIiIiIiTfBKvOdxEE9ERERERESasAsBnQaPh7PzEXNucTo9ERERERERkZfglXgiIiIiIiLSBKfTex4H8URERERERKQJITQaxHM6vVscxBMREREREZFXs9ls2LlzJ5KSkhAaGooHHngAVqvVkZ6WloZly5aVKffEE08gNDRUse79+/fjwIEDCAoKwt/+9jf4+flp3v7y4CBeY2fTc2HO01W4vEGvXNZskC9jIKtDlg4AFqPyftTUYZbUoSaPmjp8TQbFdKskXas6rCbltpr08vfio+L9yj4bo5rPxqCcx0eSfr0OyWdnsCqmA4DRVEMxXc1xprPbJOkl0jqg4pdeaT3FBfI6SgqV02sUSeswFOUrpwdkS+uwB9RWriNXXoc5OFOaxzczQzG94EqWYrq+QLm/qGqdSMmB2er6b0XV+V5yzlDz9y4jOz+q3Y8WsUm2H1+zPK7I6lAXm5TzBFjkXwXV9KssxqmJb7K2qvgKBIskfqn5HuVr9FFMN5nlx5DRx18xXW8rltYBSXwDAAi7YrJOkn59P8rxTVcij03y+BYorcNUK0QxXeQpxwwAsOVkSvMYaivHOEvdK9I6SrKV6yjMzFFuw20e34RdwF7F0+mPHDmCUaNGISwsDI0aNUJ8fDzGjx+PTZs2oVOnTgCA8+fPY+rUqZg8eTIsFoujbGGh8ucxbdo0LFy4EAMHDsSxY8cwbdo0bN++HY0bN67YG9MAB/FERERERESkCSGEJlPhy1OH2WzGpk2bUL9+fce2fv364Z133sGmTZuc8s6ePRuBgYGq6o2Li8Mnn3yC2NhY3HfffSgpKUGfPn3w0ksvISYmRnX7tMZBPBEREREREXmtli1bltlWq1Yt5OSUnRWxatUq6HQ63H333ejevTt0Oveza1auXInIyEjcd999AACj0Yinn34a48ePR05ODmrWrKndmygHDuKJiIiIiIhIE1qvTp990+0LFovFaTr8jRYuXIiMjAzEx8cjKSkJ33//vVO6n58fNm3ahBo1auD1119Hy5YtERMT4/bK/J9//lnmB4KWLVvCZrPhxIkT6NChQwXfXeXwOfFERERERESkCfv/uydei38A0KBBAwQEBDj+zZ492+2+k5OTcerUKRw9ehRms9lpSn69evVw7NgxrF27FkuXLsXRo0dx7tw5vPbaa27ry8nJKTPAr1WrliOtuvBKPBEREREREd2SkpOT4e///y8W6e4qPADMnDnT8f+RI0fiiSeeQEJCAgAgLCzMKW+dOnUwevRolyvWl7JarWUG66UzA25c+b6q3XaD+GnTprlcYbB79+548sknXZZZsWIFYmNjnbbdddddeOONNzzSRiIiovL65z//iYsXL5bZ3rJlS0yZMsVlmZ9//hkbNmxw2maxWPDxxx97pI1ERETCboNQ83QFFfUAgL+/v9MgXq2BAwciOjoahYWFbgf+ZrMZWVnun3zQvHlzHDhwwGnbqVOnoNPp0LRp03K3SSu33XT6Fi1aoGXLlo5//v7++Pzzz8vcS3Gjbdu2YdeuXU7lGjVqVIWtJiIiUtaoUSOnONWwYUP83//9H/766y+3ZQ4cOICYmBinci1atKjCVhMR0Z2mdBCvxT+1jh07Vmbbtm3bEB4e7hjA35ynsLAQK1ascCxaBwApKSmYO3cuUlJSAACDBw/GwYMH8eeffzryLF26FD169EBQUFC5+kVLt92V+Geeecbp9fvvvw+r1er2KnypJk2auL2SQUREVN1Gjhzp9Lp0sZ4JEyYolgsODmZ8IyKi29rq1auxadMm9OzZE1arFXFxcdizZw+WL1/uyLNu3Tr88MMP6N27N4xGI1avXo3i4mKn2Wlnz57F1KlT0bVrV4SGhmLAgAEYOnQoHnzwQYwZMwaJiYnYsWMHduzYUR1v0+G2G8TfSAiBRYsWYcSIEdIpGCdPnsTf//53BAQEoEePHujZs2cVtZKIiKj8Fi5ciN69e6NZs2aK+dLS0vD666/DYrGgU6dOGDhwYBW1kIiI7kTCbtdoOr1ddd633noLjzzyCLZs2YLs7GyMGDEC0dHRjkXoAOD111/Hww8/jM2bNyM3NxczZszA0KFDYTabHXnCwsLw0ksvOd0/v2rVKqxZswb79+9H9+7dMW/ePDRo0KDS768ybutB/Pbt23Hq1CksXbpUMZ9Op0NISAgCAwNx4cIFPPTQQxgzZgy++OILt2UKCwud7r1Xmq5PRESkpdOnT2P79u1lHp3jSv369WG1WpGVlYWnnnoK3bp1Q0xMDPR613fUMb4REVFlCJsNwqbBIL6cdbRp0wZt2rRRzNO6dWu0bt3abXpERATmzp3rtE2n02H48OEYPnx4udrjSbf8IP67777Dnj17FPPMnDkTderUKbN94cKFiIyMRLdu3RTLv/HGG06/pjz++OPo3bs3hg4din79+rksM3v2bLz77rtltp9NuwajT8Wfi6jX6ypcVi2din0YJHlk6WrzWM0GxXRfs/wQ9ZXUUdMiryPAalJM9/OR11HDpNwOi1E5/Xoe+TIVJkm/+qiowyppqywdAHxMyvsx6OSfv+y9GFSs2iHbj1Ev/+zU7MekNyumG31rSOswG5Tbqi8puyjnzXSSPDprrrQOfc3aiumGonxpHfZrmdI8hloZiunGwDTlfeQVSPfhzTZu3IiNGzcq5nnhhRdc3sP+7bffolatWhg6dKhi+f/5n//B9OnTHa+feeYZtG3bFgsXLsTTTz/tsoy7+Jacngujj+LuFBlUnJukdWgQI7WoQxZ3AMAo2Y+fj3LcAeRtDfSV1yGLX0F+yuc2QGVMkJxI1Xy/0SI2+Uk+GzUx0ix5L1rEJln8AwCjXv75yqqRHYcAIAuTZouatkriW7H8fK6zFSmnW+WP8zLUvCbfT7FyjDPkZMr3k3VFMd2UrZx+u8c38rxbfhAfFhaGli1bKuYxmcqe5DIzM7F27VrF5wiWunk6RK9evdCgQQPExcW5HcS//vrreOWVVxyvs7Ozq31aBREReY86depI41uNGmV/HLLZbPjPf/6D0aNHKz5mB7h+Ff5GzZs3R6dOnRAXF+d2EM/4RkRElSGERqvTi8rXcbu65Qfxffv2Rd++fctdbtmyZbDb7XjqqacqtN+ioiKUlJS4TbdYLNIvT0RERO507twZnTt3Lne5LVu24MKFC5g4cWKF9ltYWMj4RkREHqP1I+aorNvuEXOlvvnmGwwbNszl0v/Lli3DP//5TwBASUkJfv31V6f0JUuWIDU1FQ8++GCVtJWIiEitb775Bt27d0dkZGSZtC1btmDatGlOr2+0fft27N+/n/GNiIjIi92Wg/hDhw4hPj7e7VTB3377DcuWLQNwfaGCjz/+GJ07d8a4cePQt29fTJ48GbNnz+YK9UREdEtJS0vDjz/+6Da+7du3D1999ZXj9erVq9GmTRuMHj0aDz/8MB566CFMmTKlwrPUiIiIZKrjOfF3mlt+On1F2O12fPHFF+jVq5fL9FGjRjmm6BsMBmzcuBFHjhxBfHw8atWqhe+++87psQJERES3guzsbHzyySd4/PHHXaY/9NBDqFu3ruP1ggULkJSUhL1798LX1xfz5s1Do0aNqqq5RER0B+J0es+7LQfx7du3R/v27d2m9+nTp8w2NY8kICIiqk5NmzbFlClT3KZ37NgRHTt2dNrWrFkz6bPkiYiIyHvcloN4IiIiIiIiqnrCbtfoSrxdg9bcnjiIJyIiIiIiIk3Y7TZAg0G8ndPp3botF7YjIiIiIiIiuh3xSjwRERERERFpggvbeR4H8RrLvJwLg0VUuLxOp1NM1+uV09Ww2yvevvK0Q6cij8GgPBnEaDZI6zCalOswWeSHuZ+Pcp5Aq1laR4CvSTG9pmQfAOCr4v2ajcrvV00dPpJ+txjldVgk7TCp+fwlefSSvwc1+zEZ5HX4SN4LAFgMyn3iKzkOr+9HuS0Wg/wYMRuUj7Ma/v7SOgwlBYrpuuI8aR16ay35fvzrKKfXClZMN1/Lle6Dqk5Weh4MFtdpQsjjitGk/DckVMQmveRvVU0dasjqURPfZHnMKmKTXnL+skjiDiCPb3X9feR1qGirLMbJzveAvK2y+AcAVslxJot/AOArqUMNk2Q/KkKTNM6qoUV8q2GW12GUfL4+GsQ3v4DKxzdAHuP0vgHSOoyBdRXTRXa6YrrpNo9vHMR7HqfTExEREREREXkJXoknIiIiIiIibdhsEHoNrqLbeCXeHQ7iiYiIiIiISBNCaLM6vRAcxLvD6fREREREREREXoJX4omIiIiIiEgTwm7X5kq83a5Ba25PHMQTERERERGRJoRdo+n0XJ3eLU6nJyIiIiIiIvISvBJPREREREREmrg+nb7yU+E5nd49DuKJiIiIiIhIE5xO73kcxGssMzUTenNRhcvr9IZKt0GLOvR6nQbtkNeh0ynnUdMOo0n5/Zp85P2RKanjsq9JWofZR/nPyVeSDgA1VeTxNSvnMRvld8n4St6vRU0dZuU6DBocQ2rI9qOqPyTvBQB8DMr1+PvIj5EAi/JnV9Mib4ef5PP3VfF+rSbltvqaAuV1+NaS5tEX5iim66zKddh9lMtT1cpKz4XeLFym2UvkcU8Wm9TELi1ikxpq4peMLDapYZCcd9TEtxzJuSn9Sr60DouvivhlVd6PmvhmNiq/HzXnc7Okz9S0QxZX1MQ3LepQQ1aPmvhWUxJX/FTUESA5ztTUUUOSJ7tIflW2hiS+AYCvOVAx3UdNfCvIUkyXxTeRw/hGlcNBPBEREREREWmCV+I9j4N4IiIiIiIi0oTdboOOg3iP4ur0RERERERERF6CV+KJiIiIiIhIE8JmB3QaXIm3cXV6dziIJyIiIiIiIk0IodE98YLT6d3hdHoiIiIiIiIiL8Er8URERERERKQJYbdpM52eC9u5xUE8ERERERERaYKDeM/jdHoiIiIiIiIiL8Er8URERERERKQJUVygzVV0W3Hl67hNcRCvsZyLJ6EzWjxWv05vkObRm8yVrsNgrHwdWuWpbB0Gi6+0Dr3k/RpN8nZafE2SdPmfW4ZFnkfWFoNRJ61Dp1fOYzDIJ+kYjcp5DJJ9qFFVdVgk7wUA/HyUP9/aNZSPIQAI8lPOU0dFHf6SdtSxKqdfz6O8H18V/ZGn4jjzM/sppvtYaiqm22CV7oOqTvZfx6Az+lS4vBbn+6qIb2rqkcUMNVTFSIPkfG+Wxzd5zJD/vcviGwBkS2KcmjhqNCm3Ra/ifK6XxC+zWcUxIouRKtpRVfHLKMnja5Z/r/CTfPeoLYldgDy+BUpiFwDUkhxnauJbgEWexyKJXz4Geb9bTf6K6b6+gYrpJboa0n14I7PZjNDQUKT8uVKzOkNDQ2E2V/6ce7vhIJ6IiIiIiIgqxcfHB2fOnEFRUZFmdZrNZvj4VPwH5NsVB/FERERERERUaT4+Phx0VwEubEdERERERETkJTiIJyIiIiIiIvISHMQTEREREREReQkO4omIiIiIiIi8BAfxRERERERERF6Cg3giIiIiIiIiL8FBPBEREREREZGX4CCeiIiIiIiIyEsYq7sBt5vc9L+gM5g9Vr9Ob5DmMZh9ldNN8vbpjcp59EaTtA41bdWraEtl96PLU9EOWR0q3kuBj59iutFXOR0AzBb5n6TeqPzbm16nk9ah0yvnMUj2cb0O5XSDQU0dknaoqEPWH2roJe0AAJNF+Rj4S8VnV9ffopgeFqD8twsAoYE+iuklNru0jsIS5TwBPvL3UlPF+y2wCcX0Giblzy6nUP5eqOpcSzvvNr6pOUfKqIoZkthktMj/htTsR6eXnGcl7QAAg6QtBhV1yKh7L5WPb3kq+tXkY1VOV3HOkJ3zZTFDTR6j5LyjhroYKWuHmu8m8vcrbYeK7wSy+OZnlX/nC5TkURPfwusoH0N5xTZpHQGWEmkeP7PysRjoKz9W80uU45tVkp6TL38vREp4JZ6IiIiIiIjIS3AQT0REREREROQlOIgnIiIiIiIi8hIcxBMRERERERF5Ca8cxF+6dAlxcXHIyspymyc5ORn79+9Hdna26norUoaIiEgrGRkZiIuLQ1pamts8qamp2LdvH9LT01XXW5EyREREdGvyqkH8/v37MXz4cLRt2xY9evRAfHx8mTwFBQUYNmwYWrRogaeeegqhoaGYN2+eYr0VKUNERKSVpKQkjBs3Dvfccw969OiBjRs3lskjhMDzzz+PiIgIjB07FvXr18f06dMV661IGSIiIrq1edUgPjExESNGjMDevXvd5nn33Xfx3//+F6dOncLRo0exbNkyvPjii5qXISIi0srRo0fRo0cPnDp1CgaD68c9ff3111i6dCkOHDiAP/74Azt37sSnn36KlStXuq23ImWIiIjo1uZVg/ixY8fiscceg8nk/lmUixYtwsSJExEWFgYAGDJkCO655x4sWrRI0zJERERaeeSRRzB+/Hj4+rp/lvK3336LYcOGITIyEgDQuXNn9O/fH99++62mZYiIiOjWZqzuBmjp4sWLSE1NRYcOHZy2d+7c2eXU+4qWAYDCwkIUFhY6Xpfeny9sxRVtvjrC9RUapywlynmETsjrgHIeAbu0DujlvxHZ1dQjodMrv19ZOgBAWoeK9yKpw26Q12HTyf8khU25HqHTSevQ6SV5JPu4Xockg0GDdqjpd2Plf4vUy9oBQCf52zPY5Z9dkUn5/FBosknrKJDUkYciaR0Wm/sfQgHAWCx/L7oi+d9VieSzkX1213JyAFyfEn6nE0Lg8OHDeOqpp5y2d+7cGfPnz9esDFDB+KYiNkmpiW/SKlScD1TFBMl5VkXsskvOKzqhRfxTc66ufIxUEVZglzTFpuYrp+Tzk8YMFXl0soaqYVPRDlmn2eT9riY2VbodkMe3Yp1yzACAIsn3FzXxLT9XOU+e3Sytw6QmfpmU8xgl36MBoERyrNoY38jDqnUQn5SUhNTUVMU8HTt2hI+Pj6r6MjIyAAC1a9d22l67dm1HmhZlAGD27Nl49913y2y3/Vn9UxQ9/DMCEVGVunLlCgICAqq7GeVy/vx5nD9/XjFP69atVb+v3NxcFBYWlitWVaQMcGvHNyKi24k3xje6NVTrIH7VqlUuF++50YoVK1CvXj1V9ZVOsy8oKHDanp+fD7PZ9a93FSkDAK+//jpeeeUVx+vMzExERETg/Pnzt/QfY3Z2Nho0aIDk5GT4+/tXd3MUsa2e4S1t9ZZ2Amyrp2RlZSE8PBxBQUHV3ZRy++WXX6RT1ufOnYuOHTuqqo/xTc6bjm221TO8pa3e0k6AbfUUb45vdGuo1kH8G2+8gTfeeEOz+ho0aAC9Xo8LFy44bb9w4QLCw8M1KwMAFosFFoulzPaAgIBb/sQBAP7+/l7RToBt9RRvaau3tBNgWz1Fr2LK8K1m/PjxGD9+vGb1WSwWhISElCtWVaRMaTnGt6rBtnqGt7TVW9oJsK2e4o3xjW4Nt9WRY7Va0b17d/zwww+Obbm5ufjll1/Qr18/x7aTJ0867ndXW4aIiKg69evXDzExMY7XNpsNP/74o1OsSk5Oxu+//16uMkRERORdvGphu7S0NJw4cQKXL18GACQkJMBoNCI8PNxxVWHWrFno168fXn/9dXTr1g3z5s1D3bp18cwzzzjqmTNnDvbs2YPExETVZYiIiDwlKysLCQkJjtdJSUmIi4tDSEgImjVrBgB466230KlTJ0ycOBGDBw/GsmXLkJGRgVdffdVRbvHixZgzZw6uXbumugwRERF5F6+6En/gwAFMnz4dH3/8MaKiorBixQpMnz4dv/zyiyNPr169sG3bNpw7dw6fffYZIiMjERcXBz8/P0eeZs2aoX379uUqI2OxWDBjxgyXUxBvJd7SToBt9RRvaau3tBNgWz3Fm9paWadPn8b06dMxffp0dO3aFTt27MD06dOxatUqR54WLVrg999/h91ux9y5c1GzZk3s2bMHDRo0cOQJDw9H9+7dy1VGxls+B29pJ8C2eoq3tNVb2gmwrZ7iTW2lW5NO8NkGRERERERERF7Bq67EExEREREREd3JOIgnIiIiIiIi8hIcxBMRERERERF5Ca9anf5WcO3aNRw+fBgRERGoX7++yzxXrlzB6dOn0aBBA4SGhqqqtyJl1EpOTsa5c+dcpnXt2hVGY9nDoLCwEPv27SuzPTIyErVq1dK0fTcqfQLBzbp37y59lqYn+9Adm82G48ePw2w2o2HDhi778kZJSUlITU112lazZk20bdtWszYJIfDnn3+ipKQEkZGR0jZVtIwWMjIycO7cOURERCAoKEgxb1FREf773/+W2d6qVStp2cq4fPkyjh8/XmZ7t27dYDAYFMtmZGTg1KlTqF+/PsLCwjzVRIfdu3fDbreX2R4WFoYmTZq4LHPy5EmkpKQ4bfPz88O9996reftKz5/h4eFuF1arSJ9VdT/frgoLC3Hw4EHUrVvX7fGSk5OD48ePIzg4GBEREarqrUgZtdz9fQLAvffe63aB2ri4uDLbmjRp4tHj5+YnEJRq3749rFarYllP9qE7QgicPHkSNpsNjRs3htlsVsx//vx5nD9/3mmbyWRCly5dNG1XUlIScnJy0KpVK/j4+HisTGVlZ2fj1KlTqFevHurWrSvNz2NS2YEDB5Cfn19me1BQEFq1auWyTFUdk4B3nj/JywlSJTk5WTz33HMiNDRUmEwmMXv2bJf53nnnHWGxWESrVq2ExWIREyZMEDabTbHuipQpj8WLF4uoqCinf8HBwcJqtYr8/HyXZc6cOSMAiA4dOjiV27Vrl2btcmXRokXCbDaXaW9eXp5iOU/34c1sNpuYOXOmqFu3rrj77rtFeHi4CA8PF5s3b1YsN2bMGBEcHOz03iZMmKBZu44fPy5atmwp6tatKxo0aCDq1asndu/erXmZyjp48KB44IEHRO3atcW9994rrFarePLJJ90ej0Jc/xsEINq3b+/Ufzt37vRoW5cuXSpMJlOZYzInJ0ex3Lvvvut0TI4dO1aUlJR4tK3333+/Uxs7duwoAIi3337bbZkJEyaIOnXqOJUbO3aspu26cOGCeP7550VYWJgwm83iH//4h8t8Femz6ujn283Vq1fFa6+9JurVqydq1KghJk2a5DLfl19+KaxWq2jZsqWwWq3i4YcfFrm5uYp1V6RMeWzZsqXM32aDBg2ETqcTZ8+edVsOgLjnnnucyq1Zs0azdrmybds2AaBMe0+fPq1YztN96Mr8+fNFgwYNRLNmzUSzZs1EnTp1xJIlSxTLzJgxQ/j7+zu9t4EDB2rWptTUVNG1a1cRGBgomjZtKmrVqiU2bNigeZnKSkpKEo8++qgIDAwU7dq1E35+fmLAgAHiypUriuV4TCobMWKEUxu7d+8uAIhx48a5LePpY1II7z5/knfjIF6l7du3i/nz54usrCxRr149l4P49evXC5PJ5BjoHjt2TAQEBIjPPvvMbb0VKVNZdrtdNGzYUPHEVzqIT0pK8lg7XFm0aJGoV69eucpURx/m5eWJGTNmiKtXrwohrvfp9OnThZ+fn0hPT3dbbsyYMWLUqFEea1e7du3EoEGDHIOYSZMmibvuuktxcFyRMpW1YsUK8csvvzhenzt3ToSFhYlXX33VbZnSQfzRo0c91i5Xli5dKkJCQspV5scffxRGo9HxA8OJEydEYGCg+Pjjjz3RRLeWLFkidDqd4t/xhAkTxIgRIzzajp07d4p///vfIjMzU0RERLgcxFekz26VfvZ2iYmJYvbs2SI1NVVERUW5/BIaHx8vdDqdWLlypRDi+uAoPDxcTJ061W29FSmjhZ49e4o+ffoo5gEgfv75Z4+242alA6bi4mLVZaqrD2fOnCkuXLjgeP3FF18Ig8Eg/vjjD7dlZsyYIaKiojzWpiFDhoiOHTs6BjGzZ88WVqtVXLp0SdMylbV582axdu1aYbfbhRBCpKeni1atWomRI0cqluMxWT6//fabACB27NjhNo+nj0khbr/zJ3kPDuIrwN0g/pFHHhEPPfSQ07aJEyeKtm3buq2rImUq6+effxYAFK+2lg7if/75Z3Hw4EGRnZ3tsfbcaNGiRSIsLEwkJCSIxMREUVhYKC1THX3oytmzZwUA8euvv7rNM2bMGDF06FCxf/9+cfr0aU1nCxw8eFAAEHv27HFsS05OFjqdTqxbt06zMp4yceJE0a1bN7fppYP4zZs3iwMHDoisrKwqadfSpUtF3bp1RWJiokhMTBQFBQXSMkOHDhUPPPCA07bJkyeLyMhITzXTpV69eon7779fMc+ECRPE4MGDxf79+8WpU6c8OoNFCOF2EF+RPrtV+vl24u5L6IsvvihatmzptG3WrFmiVq1abo+ZipSprKSkJAFALFu2TDEfALFkyRKxf/9+6RVSrZQOmBITE8Xhw4dVXVGrjj50paSkRBgMBrFw4UK3eWbMmCE6d+4s4uPjxfHjx8s1MJS5fPmy0Ov1Ijo62rEtPz9f1KxZU3z66aealfGUmTNnigYNGijm4TFZPqNGjRLNmzdXzOPJY9IVbz9/knfhwnYaio+PR4cOHZy2de7cGYmJiSguLtasTGUtXLgQkZGR6NatmzTv6NGj8T//8z+oXbs2Jk6ciLy8PI+06UaXLl3CsGHD8PDDDyMoKAiffvqpYv7q6ENXStcQcHcvVKmYmBiMHz8enTt3RuPGjfHzzz9rsv/4+HgA1+9lK1V6j3BpmhZlPEEIgQMHDqBp06bSvGPHjsXo0aNRp04djBs3Drm5uR5vX1paGh599FEMGjQIQUFB+OijjxTzuzsmjx49isLCQk821eHkyZPYuXMnnn76aWnejRs3Yty4cejatSsaNWqELVu2VEELnVWkz26Ffr5TuOvrq1evul1zpSJlKmvhwoUICgrC0KFDpXmnTp2K8ePH46677sKQIUNw+fJlj7TpZg8//DCGDx+OWrVqYfr06S7XsShVHX3orh02m016jt6/fz9GjRqFPn36ICwsDMuWLdNk/0eOHIHdbnfqCx8fH7Ru3dptrKpIGU/Zt2+fqvjGY1KdzMxMrF27VlV889QxWR7ecv4k73LHLmyXkpKCkydPKuZp0aIFgoODVdeZkZGB2rVrO22rXbs2bDYbsrOzy6RVtEx+fj4OHDig2JbQ0FCXASMjIwPr1q3DBx98oFje19cX69atw5AhQwAAf/zxB/r06YMaNWrgs88+Uyx7o/L2c7NmzZCYmIjIyEgAwMqVK/HEE0+gYcOGePTRR12Wr0gfunL8+HFpwOzcubPLxX1SUlLw8ssvY/To0YoLjwwaNAgff/wxateujZKSEkybNg3Dhg1DYmIiwsPDVbXTnYyMDPj7+8NkMjltr127NjIyMjQr4wkffPABjh49isWLF7vN4+Pjg9WrV2PYsGEAgKNHj6JPnz74+9//js8//9xjbWvSpAmOHDmC1q1bAwDWrFmDxx57DBEREXjsscdclnF3TNrtdmRmZiIkJMRj7S317bffIigoyO3fTakBAwZg9uzZCA4ORklJCV577TXHMdmwYUOPt7NURfrsVujnW1FmZiYSExMV80RERLhdXNCVjIwMtGvXzmlbad9nZGSgUaNGmpQRQmDXrl2KbQkMDMQ999xTZrvNZsPixYsxevRoWCwWxTq+/vprTJw4ETqdDufPn8eDDz6IiRMnYsOGDYrlblTefq5bty5iY2Nx3333AQBiY2PRv39/hISEYOrUqS7LV6QPXTl37hySk5MV87Rp0wb+/v5ltufm5mLixIno1asXevTo4bZ8ly5dcObMGYSHh0MIgY8//hijR49Gs2bN0KlTJ1XtdKc0Hrn6e1eKb+Ut4wnfffcdNm3ahK1btyrm0+KYLK/qPCYrY9myZSgpKcGYMWMU83nymCyPqjp/0p3ljh3E7927Fx9++KFinnfeeQf9+/dXXafJZEJBQYHTttKVNN2t6lqRMpcvX8b06dMV2zJo0CC89tprZbZ///33AICnnnpKsXxISIhjAA9cX5X+xRdfxLx588o1iC9vP0dFRTmlPf744/j6668RHR3tdjBSkT50Zfny5fjll18U86xbt67MDzsZGRl48MEH0bhxY3zxxReK5UsHoABgNBrx0UcfYcGCBfjpp5/w7LPPqm6rK676AbjeF+U5/mRltLZo0SK88847WLZsmWOg7EqdOnWc+u/uu+/Gyy+/jA8//NCjg/ibZ6wMGzYM/fv3R3R0tNtBvFbHZEWVZzBz4xVLo9GIDz74AF9//TViYmLwwgsveLqpDlV1/rwTHD9+XBojnnnmGYwePVp1nVX1+RQXF0vb3qlTJ5cztDZu3IhLly5h4sSJiuUBOF3BCw8Px5tvvonRo0cjNzcXNWrUkJYHyt/PN6+g3aNHD4waNQrR0dFuB0xaHeObNm3Cd999p5jniy++KHMOLigowJAhQ1BUVISVK1dCp9O5Lf+3v/3N8X+dTof//d//xVdffYXVq1dXesBU+kOzq75w9cNDRctobdOmTZgwYQLmzp2Lvn37KubV4pgsr+o8Jitj4cKFGDJkiPRCmyePyfJgfCNPuGMH8YMHD8bgwYM1rTMiIgIXLlxw2nbhwgUEBgaiZs2ampUJDw93+SgSNRYuXIhhw4ZV6JFcISEhSEtLQ0lJiepHkGnRzyEhIYpThyrSh67MnDkTM2fOLFfbrl69igceeAA1a9bExo0bpY9kuZnJZEKtWrXKtL8iIiIiUFRUhPT0dNSpUwfA9cFcamqq26v8FSmjpSVLlmDSpElYsmQJhg8fXu7yISEhyMjIQEFBQZU9Nqh0v+4eawW4PyZr1qzp0Uc0ltq0aRMuXryoajBzM6PRiKCgIE2OyfKoSJ9Vdz/fqrp06VLhGOGOu74G4PaKfkXKmM3mSsW37t27O2ZylUdISAiEELh06ZKqac+ANv0cEhKCzZs3u02vSB+6MnnyZEyePLlcbSssLMSQIUOQnJyM7du3q3pM2s3q1q2rWXwDrr/3Gx+5duHCBZezMipaRkubN2/G0KFDMWfOnAr9IFqRY1ILVXVMVtShQ4dw8OBBzJkzp0LltTomy6Oqzp90Z+E98Rrq168fNm7cCJvN5ti2YcMG9OvXz/E6NTUVcXFxjvuN1JTRyv79+3H48GGX9xAVFBQgLi4OV69eBQCX9xlv3boVLVq08OgzxG/eb35+PuLi4pwCbnX24Y1KB/BWqxWbN292+TziEydO4PDhwwCuD45v/lX1jz/+wMWLFzX5QtGzZ0+YzWb88MMPjm2//fYbcnJynPpi3759OHv2bLnKeMLSpUvx9NNPY/HixXjiiSfKpBcWFiIuLs4x7dHdMdmkSROPDuBv3m9BQQFiY2OdPrO0tDTExcU5jsF+/fph06ZNKCkpceTZsGEDHnjgAY+180bffPMNoqKiXD47NykpCYcOHQIA2O32Ms/dPXbsGJKTk6vkS+6N1PTZrdbPd5J+/fph+/btyMnJcWzbsGEDOnXqhMDAQADXnzkdFxfnWDtFTRmtpKSk4KeffnJ7j2xcXBwuXboEwP25pGbNmh798fLm/Qoh8Msvvzj9rVVnH96odAB/9uxZbNu2DaGhoWXynD9/Hnv37nW8vvn9paam4siRI5qcS1q3bo2QkBCnWHXixAkcPXrUKVYlJCTg2LFj5SrjCVu3bsWjjz6K999/3+0VbR6TFfPNN9+gYcOGLs/zVXlMlsetfv4kL1V9a+p5l7y8PBEbGytiY2NFcHCwmDx5soiNjRWJiYmOPBcvXhR169YVw4cPFz/88IOYNGmSsFqtIiEhwZFnwYIFAoDjGdNqymhl8uTJolmzZo7HntyodEXfmJgYIcT1566PHTtWREdHi5iYGDF27FhhMpnE+vXrNW/XjQYMGCDefPNNERMTI1asWCGioqJE3bp1nZ5ZWp19WCo/P1906tRJ3HXXXWLTpk2OYyM2NlakpaU58o0aNUp06NBBCCFETk6OiIyMFJ9++qnYvHmz+PLLL0WDBg1E9+7dRVFRkSbtevvtt0VgYKBYsGCB+P7770X9+vXF6NGjnfLUq1dPTJs2rVxltLZ27VphMBjElClTnPpu3759jjylT0goXSX/vffeE6NHjxbLly8XMTExYsKECcJoNHr8ObqDBg0Sr7/+uvjhhx/EypUrRY8ePURwcLA4efKkI8+iRYsEAMcjB1NSUkRoaKgYOnSo+OGHH8Szzz4rfH19xaFDhzza1tJ9G41G8Z///Mdl+pgxYxxPbsjLyxOtWrUSn3zyidi8ebP46quvRHh4uOjSpYuqJ0OolZ+f7/iMQ0NDxdNPPy1iY2Od/kbV9Nmt1M+3E7vd7vh8WrduLQYPHixiY2PFgQMHHHlyc3NFixYtRJ8+fcT69evFW2+9JQwGg9i6dasjT+mTT0o/VzVltDJnzhzh7+/vcnXt4uJiAUDMmzdPCCHEN998I4YMGSKWLFkiNm7cKKZOnSqMRqOYP3++5u260YQJE8RLL70k1q5dK9auXSsGDRokatSoIfbu3evIU519eKNBgwaJmjVrilWrVjmdo8+fP+/I8+abb4ratWs7Xnfo0EH885//FBs3bhSLFy8WkZGRonnz5o6/18pauHChMJvN4pNPPhGrV68WrVu3Fr169XL6ThMVFSWGDRtWrjJai4uLE76+vuLxxx936ru4uDhHHh6TFZOfny9q1aolZs2a5TK9qo9JIW6P8yd5pzt2On153XgfevPmzZGQkIDp06eja9eujpWqw8LCsGfPHvzrX//Cp59+ivDwcOzevdvpF7/Q0FBERUXBYDCoLqMFIQSSk5Px2muvubynzdfXF1FRUY5p9jNnzsTKlSuxYcMGZGZmOt5zixYtNG3XzVatWoUvv/wSCxYsgMFgQL9+/RATE+M0Nba6+vBGOTk5MJvNaNSoEWbNmuWUduM9/i1atHBcJfbz88PmzZsxf/58bN68GbVr18Zbb72F8ePHaza74d1330WjRo2wZs0alJSU4JVXXsGUKVOc8nTu3NlpQRQ1ZbR26tQpdO3aFfHx8U6rBIeFhWHVqlUAri9kFxUV5VjI5a233sLq1auxfv16XL16Fc2aNUNCQgJatmzp0bauWLECX375JRYuXAidTof7778f69evd7olJSQkBFFRUY7PMSQkBHv27MEHH3yAuXPnon79+ti1axfatm3r0bYCwO7du9GjRw+39+s3b97ccQ7w9fXF1q1bMX/+fGzduhW1atXCG2+8gQkTJmg64+bKlSuO82eTJk3w559/Yvr06U73Nqvps1upn28nN96H7u/vj/T0dEyfPh0RERGOdVSsVitiY2MxZ84c/Pvf/0ZwcDB+/fVX9OrVy1FPYGAgoqKiHPfvqimjlePHj+PVV191eUuTTqdDVFQU7rrrLgDAhAkTUL9+fSxfvhwpKSlo1KgRdu/e7fF7ZL/88kssWrQI33//PYqKitCqVSt88cUXqFevniNPdfbhjbKzs9GmTRvMnTvXafuN9/hHRESga9eujrTSc8n8+fNRo0YNjBs3Ds899xx8fX01adP48eMRFBSEJUuWICcnB0OHDsWrr77q9J2mTZs2TgvZqSmjtVOnTqF9+/a4cOGC05oJRqMR27dvB8BjsqL279+PyMhIjBs3zmV6VR+TwO1x/iTvpBNCiOpuBBERERERERHJ8Z54IiIiIiIiIi/BQTwRERERERGRl+AgnoiIiIiIiMhLcBBPRERERERE5CU4iCciIiIiIiLyEhzEExEREREREXkJDuKJiIiIiIiIvISxuhtA5C0OHTqErKws9OrVS/O6ExMTkZaWhvvvv7/Cdfz+++84d+4cAOD+++9H3bp1PbYvum7Lli24evUqAGDIkCHw8fGp5hYREZVfUlISkpKSMGDAAM3rPnPmDBITEzFo0KAK13HkyBH8+eefAIAuXbqgUaNGHtsXXRcXF4e//voLANCvXz/Url27mltERDfilXi6paWnpyM6OhrXrl2r7qbgu+++w+zZsx2v4+PjERsbq0ndq1evxnvvvVepOj777DO8+eabWL9+PdLT0z26rztRTEwMTp8+7bRt27ZtWLx4MUaOHInMzMzqaRgReaX8/HxER0cjNTW1upuCLVu24JVXXnG8PnHiBDZv3qxJ3Tt27MALL7xQqTqWLVuGl19+GevXr8f58+c9uq870W+//YaEhASnbXv37sXq1asxcuRIJCUlVVPLiMgdDuLplnbs2DGMHDkSKSkp1d0UtGvXDr1793a8Xrx4MT744IPqa5ALffr0QXR0NFq1alXdTbntvPDCC/jtt9+cts2ZMwcffvhhNbWIiLzZlStXMHLkyDKDp+rQvHlzDBw40PF648aN+N///d9qbFFZ99xzD6Kjoz0yG+5O995772HFihVO26ZNm4b//Oc/1dMgIpLidHryena7Hf/9739x8eJFNGnSBG3btnVK379/PwoLC9G+fXvHlPjOnTsjKCjIKV9hYSF27twJvV6Pdu3a4eLFi07TziMjI1G/fn0AwJ9//okTJ04gJSUF0dHRAICePXvizJkzsFgs6Nixo6PehIQEXL582Wn6elFREXbu3AmdTod27dq5fW9//PEHkpKSUK9ePbRr1w5GY/n/ZLXal6x/Svv53nvvxe7du1FQUOCY0lhcXIw9e/YgMzMTd999N5o2bVpm/2ryKJGV//XXX3H58mXodDqEhYXh3nvvhb+/f5l64uPjkZycjKZNmzp+DNm6dStyc3Oxb98++Pn5Qa/X4/HHHy9X+4iIKiI+Ph5nzpxB/fr10alTJ+h0Okfa0aNHkZycjD59+uDw4cNITU1F+/btERYW5lSHzWbDrl27kJeXh3vvvRf5+flO084bNWqEvn37AgDOnTuH+Ph4ZGVlOeJbhw4dkJubW+aWstOnT+OPP/5wmr5ut9uxa9cu5Obm4t5773X7vk6fPo2EhAQEBwejffv2FbodSat9yfqntJ979+6N33//HampqXjssceg0+lgt9uxb98+pKSkoEmTJrjnnntctlOWR/Y+lcrv2bMHZ8+ehU6nQ506ddC2bVvUqVOnTD1Hjx5FUlISwsPD0aZNG+j1euzatQtpaWn4888/HZ/34MGD4evrW642ElHV4iCevNqVK1fw0EMP4dKlS2jdujX27t2Lnj17YtWqVTCZTACAb775Brt370ZJSQnCw8ORlpaG8+fPY9u2bWjdujUA4OLFi+jduzeKiopw9913IzExEQ0bNoTBYHAMUr/77jskJiaiV69eOH78OE6dOoVr165h/fr1AIAWLVrg008/RZ06dZwG8StWrEBcXJyjntTUVPTp0we5ublo1aoVEhIS0KhRIxgMBkeZvLw8jBw5Evv370eHDh1w8uRJWCwWxMTEOH5IUEOrfanpn9J+LiwsRMOGDdG0aVMMGjQICQkJGDx4MAICAtCgQQP8/vvvGDJkCL7++mvHl1E1eZSoKR8bG4tjx45BCIEzZ87gzJkzWL16teMLaVFREQYOHIhjx46hQ4cOOHPmDBo1aoS1a9dix44dyMvLc3yxNRgMHMQTkUcVFBTg0Ucfxb59+9C5c2fEx8ejcePG+OmnnxAYGAgA2LBhA7744gsEBwcjICAARUVFOHjwIDZs2IAHHngAAHDt2jX069cPZ86cQfv27XHkyBG0bNkSJ0+edAxSt2zZgvnz52PAgAFITk5GQkICcnJyHPEtKCgIW7dudcTAUr/99htmzZrlqCcvLw8PPfQQjh8/jg4dOuDw4cO4++67nd6X3W7H5MmTsW7dOnTp0gWXLl3C1atXsX79erRp00Z1/2i1LzX9U9rPQUFBCAgIQGhoKIYPH46//voLjzzyCPLz89G8eXPEx8ejbdu2WLNmDSwWCwAgOTlZmkeJmvIHDhxw3N538eJFHDp0CN98841TnBozZgx++uknREVF4eLFi7Barfjhhx+wb98+pKen4/jx447Pu3///hzEE93qBNEtLDY2VgAQSUlJLtMnT54s2rRpI7Kzs4UQQpw7d04EBQWJzz77zJFn0qRJwmw2i8OHDzu2/e1vfxNPPvmk4/X48eNFp06dRF5enhBCiOPHjwtfX1/Rq1cvR55p06aJBx980PH6pZdeEgMHDnRqz7Bhw8SkSZOctr355ptO9Tz99NOiU6dOIjc3VwghxNGjR4WPj49TnhdffFH07dtXFBQUCCGEsNvt4sknnxTDhg1z21cjRowQEyZMcNqm1b7U9M+kSZOEXq8Xe/fudWwrLi4WTZo0ER9++KFjW3p6uqhXr55YunSp6jxKKlr+gw8+EC1btnS83rx5s/D39xc5OTmObTExMaKoqEgIIURERIRYsGBBmXoSEhIEAHHp0iVpW4mISiUnJwsA4ueff3aZPmfOHBEWFiYuXrwohBAiIyNDNG/eXEydOtWRZ/bs2QKA2LRpk2Pb5MmTRffu3R2v33vvPRERESHS09OFEEKkpKSIu+66S0RERDjyzJs3T7Ro0cLx+tNPPxWRkZFO7bk5BgohxIIFC5zqef/990VERIS4fPmyEEKIS5cuidDQUKc8n3zyiYiMjBSZmZmObdOnTxcdOnRw2Q9CCPHaa6+Jvn37Om3Tal9q+qe0n1etWuXUhp49e4opU6YIu90uhBAiLy9PtGvXTvzjH/8oVx4lFSm/YsUKERQU5IjZx44dEwDEuXPnHHl2797tiFu9evUSb775Zpl6cnJyBADx+++/q2orEVUd3hNPXm3FihWYMmUKatasCQAIDw/H6NGjHVPCSkVFRTn9wl96Nb3UmjVr8Pzzzzt+eW7evDkGDx7skTavWrUKzz//PKxWKwCgZcuWeOSRRxzpdrsdixcvxj333IOYmBisWrUKq1atQlhYGLZt21Yt+1LbP126dEHnzp0dr3fu3InTp08jNDQUq1evxqpVq/Drr7+iadOmjvrV5FFSnvLJycnYunUrVqxYAaPRiGPHjiE7OxsA4Ovri6KiIhw9etSR/+GHH3bM6CAiqkrR0dEYN26cY2p8rVq18Nxzz5WJb40aNcJDDz3keN27d2+n+LZ69WqMGzfOsbp4SEgIRo8e7ZE2r1y5EuPGjXNM5Q4NDS2zr0WLFqFNmzb4+eefsWrVKqxcuRKBgYE4cOCA43xclftS2z9hYWEYPny44/Xp06exc+dONGnSBGvWrMGqVasQExODJk2aOGKPmjxKylM+LS0Nv/76K1asWIHCwkJkZGTg5MmTAAAfHx/odDocOXLEkb9bt24IDQ2VtoGIbk2cTk9eKzs7G1evXkXjxo2dtjdp0gQrV6502nbz/e8WiwUFBQWOerKystCwYUOnPA0bNsSlS5c0b3NmZmaZfTVq1MixQnFmZiaysrKQkJBQZkG/fv36wW63Q6+X//6m1b6uXbumun9uvg/z7NmzMBqN+PHHH522h4aGokWLFqrzKFFbfurUqViwYAE6deqEOnXqOD7/y5cvw9/fHz179sS0adPQv39/BAcHo2/fvnj66afRvn17aRuIiLR27tw5l/Ht0qVLKCoqgtlsBqAc34DrP166On97wvnz513GnBudPXsWVqsVq1evdto+YsQI5Ofnu1yrxJP7Uts/ruIbcP1WrRt/7DUYDI64oSaPErXlP/74Y8yYMQNt27ZFaGioI29aWhoAICIiAvPmzcOECRNgsVjQp08fjB07Fn369JG2gYhuTRzEk9eqWbMmfHx8kJGR4bQ9IyPD5YIu7vj5+cFsNpd5RFjp87/LQ6/Xw263O2278cuUn58fLBZLmbpvfF2jRg0YDAaMGTOmUldLtNpXefrn5vvX/f39UVxcjK+++goBAQEu61eTR4ma8keOHMHcuXNx/PhxNG/eHMD1hfh+/PFHCCEc+WbNmoWZM2fiwIEDWL58Obp06YIDBw6U6z5NIiIt1KlTx2V88/f3dwzg1QgKCqqS+AYAtWvXVow5wPVz9oABA/DOO++Uuw2e2Jfa/nEV3wBgxowZbmOEmjxK1JTPzMzEq6++ii1btqBfv34Arj+ed8WKFU7x7fnnn8ezzz6LhIQErF+/Hv3798f69eudnkpARN6D0+nJa+l0OnTr1g1r1651bLPb7Vi7di3uu+8+1fXo9Xp07doVGzZscGwrLi7Gpk2bFMv5+fmV+QJTr149x/Q1ABBCYMeOHU776tatm2PxGOD6qu8//fST47XFYkHv3r3x9ddfl/nCdOHChXK9Ly32VdH+Aa7ftuDj44OvvvrKabvNZnPMBlCTp7L7SElJgcVicbpKc/OVmZSUFNhsNhiNRnTp0gVz585FUFAQ9u/fD8D1501E5Cn33Xcf1q1b5zQQW716dbniG3D9drIbz98A8MMPPyiWcRffTp065dSem6d033fffU4xRwiBdevWOeV56KGHsHjxYuTn5zttL09803JfFekfAI6r3l9++WWZtIsXL6rOU9l9pKWlQQjhNPPs5vh25coVFBQUQK/Xo23btpgxYwbat2+PvXv3AmB8I/JGvBJPXuGnn35CSEiI07bBgwfjww8/RI8ePTB27Fh0794d69atQ0pKCt5+++1y1T9r1iz07dsXZrMZ7du3x/Lly5GXl6e4MnrHjh3x2Wef4fPPP0ft2rXRs2dPjBo1CvPmzcPUqVMRGRmJ9evX49y5c07PbX///ffRu3dvmM1mdOjQAd9//z0KCwud6p43bx769OmDXr164YknnkBxcTG2bduGunXrYsGCBarfl1b7qkj/AEBwcDDmzZuHZ599FidOnEC3bt1w4cIFrFmzBu+99x4GDx6sKk9l99GlSxfUqlULw4cPx+DBg/Hf//4Xq1atcqpn7969ePvttzF8+HCEh4dj586dKC4udjx2qWPHjli0aBH8/f3h4+PD1emJSBPbt29Henq607YHHngAM2fORPv27TF48GAMGjQI27Ztw7Zt27B79+5y1f/WW2+hffv2ePzxx9GvXz9s2rQJSUlJ8PPzc1umY8eOOHPmDP71r38hPDwcHTp0wLBhw/DGG29gzJgx6NWrF7Zv347Y2FjHmis37mv48OHo378/fvzxR5w5cwY1atRw5Hn//ffRo0cPdO7cGePHj4fZbMbvv/+O9PR0bN68udzvq7L7qkj/AIDJZMLChQsxbNgwXL58Gf3790d6ejpiYmLw5JNPYsqUKaryVHYfpY+cGzVqFMaNG4djx45h8eLFTvWcOXMGo0aNwmOPPYZmzZrhyJEjSEhIwPz58wFc/7yXLFmCVq1awWq18hFzRF6AV+LplhYcHIwRI0bg999/x/r1653+5efno0OHDoiPj0dISAji4uLQtWtXHDp0yGmxlk6dOpW5ctGiRQv87W9/c7zu0aMH4uLioNfrceTIETz77LN44oknHAvmAUC7du3Qu3dvx+vBgwfj888/x+HDh7F+/XqkpqaiY8eOiI2Nhc1mw5EjRzBp0iTMnz/f6Rnx3bt3x+7du2EymZCQkIApU6bgiy++cMpz9913448//sCQIUOwb98+JCcn45lnnpEO4E+fPo3o6GjHfXBa7UtN/7jqZwCYOHEi9u/fj+DgYOzcuRNCCKxYscJpcK4mjxJZ+YCAAOzZswctW7bEjh07UK9ePezcuRMjRoxwvIfBgwdj5cqVKCkpwc6dO9GwYUMcOnQIERERAIBPP/0UQ4cOxa+//uq4arNly5ZyfekkIipltVoxYsQInDx5skx8u3LlCsLDw3H48GG0a9cOsbGxiIiIQHx8vOPRqADQqlUrPPjgg0711q9f32kBtmbNmmHfvn2oV68eDhw4gIEDB+Lvf/+70/m7efPmTtOq7733Xqxbtw5nzpzBhg0bHM+p37dvH4KDg3HgwAE8+OCDWL58udMz4ps0aYJ9+/YhPDwcBw8edJxXb8wTEhKC+Ph4PPfcc0hMTMTx48fx8MMPY+PGjYr9lZqaiujoaJw5c0bTfanpH1f9DAADBgxAYmIiIiMjERcXh2vXrmHu3LlOg3M1eZTIyhsMBmzbtg19+/bFjh07YDKZsGfPHowYMcLxXahjx4749ddfYbVasWPHDvj4+ODAgQPo1KkTAOC1117DCy+8gLi4OMf3q7i4OKxZs0ZVG4mo6unEjfOiiO5Qubm5MJlMjvsMS0pK0Lp1azzxxBOYMWNGNbdOnX//+9+OKzTvvPOO09X/yrod+scTpk+f7lh4qKL39BMReZLdbkdOTo7T+alfv36oX78+Fi1aVI0tU2/58uWOH0+fffZZp2fVV9bt0D+e8PHHH2Pfvn0Ars/Ga9q0aTW3iIhuxEE8EYC//voLQ4cOxeOPPw6r1Yrly5fj3Llz2LdvX5lp/Hei6uyfCxcuIDY21mWawWDAY4895tH9ExF5s+LiYnTu3BnDhw9HcHAwYmJiEBcXh7i4OERGRlZ386pddfZPZmam4myuIUOGwMfHx6NtICLvxEE80f/zxx9/YNmyZUhLS0PLli3x9NNPq37UzZ2guvpn//79+Oijj1ymmc1mLFmyxONtICLyZufPn8d//vMfnD9/Ho0bN3Z6/jxVX/+cO3cOr732mtt0zvAiInc4iCciIiIiIiLyElzYjoiIiIiIiMhLcBBPRERERERE5CU4iCciIiIiIiLyEhzEExEREREREXkJDuKJiIiIiIiIvAQH8URERERERERegoN4IiIiIiIiIi/BQTwRERERERGRl+AgnoiIiIiIiMhL/H8AeN/1xBpsNAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ocean = ~lsm_subset[\"lsm\"].values\n",
+    "masked_target = monthly_test_subset[var_name].where(ocean)\n",
+    "masked_pred = predictions.where(ocean)\n",
+    "plot_results(masked_target, masked_pred, label=\"Water Vapour kg/m2\", title=(\"Target\", \"Prediction\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7ba5d204-aefc-4103-8475-c802c603ad3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "err_baseline = (monthly_test_subset[var_name] - input_data_averaged[var_name])   # M - mean(D)\n",
+    "err_predictions = (monthly_test_subset[var_name] - predictions)  # M - M_hat\n",
+    "\n",
+    "ocean = ~lsm_subset[\"lsm\"].values\n",
+    "masked_err_baseline = err_baseline.where(ocean)\n",
+    "masked_err_predictions = err_predictions.where(ocean) \n",
+    "\n",
+    "plot_results(masked_err_baseline, masked_err_predictions, label=\"error kg/m2\", title=(\"err_Baseline\", \"err_Prediction\"), error=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a7420dc7-fe1b-4f34-a62e-5914091f04b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_histograms(masked_err_baseline, masked_err_predictions, label=\"error kg/m2\", legend_labels=(\"err_Baseline\", \"err_Prediction\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ClimaNet (3.14.0)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/watervapor/training_hourly_watervapor.ipynb b/notebooks/watervapor/training_hourly_watervapor.ipynb
new file mode 100755
index 0000000..1804b8e
--- /dev/null
+++ b/notebooks/watervapor/training_hourly_watervapor.ipynb
@@ -0,0 +1,1260 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7e6644bb-2d85-47ec-af53-f0960f9314e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import xarray as xr\n",
+    "import torch\n",
+    "import torch.nn.functional\n",
+    "from climanet.st_encoder_decoder import SpatioTemporalModel\n",
+    "from climanet.utils import set_seed, configure_compute_resources, plot_results, plot_histograms, plot_loss, data_preparation, read_st_data, coarsen_land_mask\n",
+    "from climanet.train import train_monthly_model, TrainConfig\n",
+    "from climanet.predict import predict_monthly_var, PredictionConfig\n",
+    "from climanet.dataset import STDataset, DataLoaderConfig\n",
+    "\n",
+    "from tbparse import SummaryReader\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0f5c8e2-693b-4f6e-8e88-e3c002001b24",
+   "metadata": {},
+   "source": [
+    "## Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f383c4c8-0199-4092-be12-07be525fdec1",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
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+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 268kB\n",
+       "Dimensions:  (time: 1, lat: 360, lon: 720)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2013-11-29\n",
+       "  * lat      (lat) float64 3kB -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
+       "  * lon      (lon) float64 6kB -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "Data variables:\n",
+       "    lsm      (time, lat, lon) bool 259kB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5d249ecb-e3bf-4f1e-beb5-0ef401a69057' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-5d249ecb-e3bf-4f1e-beb5-0ef401a69057' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 360</li><li><span class='xr-has-index'>lon</span>: 720</li></ul></div></li><li class='xr-section-item'><input id='section-cf4accbf-d881-43eb-8a40-d1989102f66b' class='xr-section-summary-in' type='checkbox' checked /><label for='section-cf4accbf-d881-43eb-8a40-d1989102f66b' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-11-29</div><input id='attrs-1827edfb-c2a0-4334-9df9-872da9ef9a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1827edfb-c2a0-4334-9df9-872da9ef9a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8ee3a9b-8cc5-4431-b99d-1286bee853bf' class='xr-var-data-in' type='checkbox'><label for='data-b8ee3a9b-8cc5-4431-b99d-1286bee853bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-11-29T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.75 -89.25 ... 89.25 89.75</div><input id='attrs-7f87fc0c-8fcd-4608-9820-ec43e266cf3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f87fc0c-8fcd-4608-9820-ec43e266cf3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f31af5b8-f974-4d74-ba8b-3bc983855ead' class='xr-var-data-in' type='checkbox'><label for='data-f31af5b8-f974-4d74-ba8b-3bc983855ead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.75, -89.25, -88.75, ...,  88.75,  89.25,  89.75], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-ea86dfd8-2ed4-4d01-90e5-51237994ba24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea86dfd8-2ed4-4d01-90e5-51237994ba24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc7fe467-246a-4a05-bc24-e1444f90da33' class='xr-var-data-in' type='checkbox'><label for='data-bc7fe467-246a-4a05-bc24-e1444f90da33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ...,  178.75,  179.25,  179.75], shape=(720,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b9d99dbf-bf22-4ee8-b9b0-2078f9a7f738' class='xr-section-summary-in' type='checkbox' checked /><label for='section-b9d99dbf-bf22-4ee8-b9b0-2078f9a7f738' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0c4ae54-f834-437d-8730-282ff9bfcaf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0c4ae54-f834-437d-8730-282ff9bfcaf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebfd7d6d-a71c-4328-aa49-84e30088da79' class='xr-var-data-in' type='checkbox'><label for='data-ebfd7d6d-a71c-4328-aa49-84e30088da79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd></dl></div><div class='xr-var-data'><pre>[259200 values with dtype=bool]</pre></div></li></ul></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 268kB\n",
+       "Dimensions:  (time: 1, lat: 360, lon: 720)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2013-11-29\n",
+       "  * lat      (lat) float64 3kB -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75\n",
+       "  * lon      (lon) float64 6kB -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
+       "Data variables:\n",
+       "    lsm      (time, lat, lon) bool 259kB ..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# without diurnal cycle\n",
+    "data_folder = Path(\"../eso4clima/watervapor\")\n",
+    "run_dir = Path(\"./runs_hourly\").resolve()\n",
+    "var_name = \"prw\"\n",
+    "\n",
+    "# 1 month train, 1 month validation and test\n",
+    "hourly_data = xr.open_mfdataset(data_folder / f\"202001_hr_ERA5_masked_{var_name}.nc\")\n",
+    "hourly_data_validation = xr.open_mfdataset(data_folder / f\"202101_hr_ERA5_masked_{var_name}.nc\")\n",
+    "hourly_data_test = xr.open_mfdataset(data_folder / f\"202201_hr_ERA5_masked_{var_name}.nc\")\n",
+    "\n",
+    "monthly_data = xr.open_mfdataset(data_folder / f\"202001_mon_ERA5_full_{var_name}.nc\")\n",
+    "monthly_data_validation = xr.open_mfdataset(data_folder / f\"202101_mon_ERA5_full_{var_name}.nc\")\n",
+    "monthly_data_test = xr.open_mfdataset(data_folder / f\"202201_mon_ERA5_full_{var_name}.nc\")\n",
+    "\n",
+    "# Make land-sea mask for watervapor from sst mask\n",
+    "file_name = data_folder / \"era5_lsm_bool.nc\"  # downloded from era5 and regridded using the function `regrid_to_boundary_centered_grid`\n",
+    "lsm_mask_025 = xr.open_dataset(file_name) # land-sea mask at 0.25 degree resolution\n",
+    "lsm_mask = coarsen_land_mask(lsm_mask_025) # Lower resolution to 0.5 degree to match the watervapor data\n",
+    "lsm_mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "58568e58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort in lat dimension to make it ascending\n",
+    "hourly_data = hourly_data.sortby(\"lat\")\n",
+    "hourly_data_validation = hourly_data_validation.sortby(\"lat\")\n",
+    "hourly_data_test = hourly_data_test.sortby(\"lat\")\n",
+    "\n",
+    "monthly_data = monthly_data.sortby(\"lat\")\n",
+    "monthly_data_validation = monthly_data_validation.sortby(\"lat\")\n",
+    "monthly_data_test = monthly_data_test.sortby(\"lat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4be672b-a2c1-4a6f-89a1-2c22cc6cde02",
+   "metadata": {},
+   "source": [
+    "## Subset data (for fast example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c61aee7b-6083-4466-883c-19aa9a03cfca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(744, 80, 200) (1, 80, 200)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# coordinates of subset\n",
+    "lon_subset = slice(-50, 50)\n",
+    "lat_subset = slice(-30, 10)\n",
+    "\n",
+    "hourly_subset = hourly_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_subset = monthly_data.sel(lon=lon_subset, lat=lat_subset)\n",
+    "lsm_subset = lsm_mask.sel(lon=lon_subset, lat=lat_subset)  # True=Land\n",
+    "\n",
+    "hourly_validation_subset = hourly_data_validation.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_validation_subset = monthly_data_validation.sel(lon=lon_subset, lat=lat_subset)\n",
+    "\n",
+    "hourly_test_subset = hourly_data_test.sel(lon=lon_subset, lat=lat_subset)\n",
+    "monthly_test_subset = monthly_data_test.sel(lon=lon_subset, lat=lat_subset)\n",
+    "\n",
+    "print(hourly_subset[var_name].shape, monthly_subset[var_name].shape)  # (time, lat, lon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b0cccab",
+   "metadata": {},
+   "source": [
+    "## Train the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1ceb7756-f274-4823-bded-87c0f8f16a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the model (small)\n",
+    "set_seed()\n",
+    "\n",
+    "patch_size = (1, 4, 4)\n",
+    "model = SpatioTemporalModel(patch_size=patch_size, embed_dim=64, dropout=0.2, hidden=64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a9d056b8-2cb1-42af-94d6-9fb58586dcbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Device and resources\n",
+    "compute_threads = 4\n",
+    "dataloader_num_workers = 2\n",
+    "device = \"cuda\"\n",
+    "model = configure_compute_resources(model, device=device, compute_threads=compute_threads, dataloader_num_workers=dataloader_num_workers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "082e4c9e",
+   "metadata": {},
+   "source": [
+    "### Build the datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "945ec311-53b3-4cac-a1e3-d16f4508e7a7",
+   "metadata": {},
+   "source": [
+    "#### Prepare the train data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c17c2f1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this should be done once\n",
+    "data_dir = Path(f\"{run_dir}/data_train\")\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(\n",
+    "    hourly_subset[var_name], monthly_subset[var_name], calculate_residuals=True, is_hourly=True, save_to_zarr=True, run_dir=data_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "67b469b6-5d61-4ff1-a54f-f077965b8979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read data\n",
+    "data_dir = f\"{run_dir}/data_train\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b9742e67-165f-4b5a-bab0-27b8b0d7070d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset:\n",
+      "Crop grid (m x i x j): 1 x 6 x 21 = 126 data crops\n",
+      "Overlap: 32 pixels (height), 32 pixels (width)\n",
+      "126\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create dataset config\n",
+    "num_subsets = (10, 10)\n",
+    "spatial_crop_size = (patch_size[1]*num_subsets[0], patch_size[2]*num_subsets[1])\n",
+    "stride = (spatial_crop_size[0] // 5, spatial_crop_size[1] // 5)  # for data agumentation\n",
+    "\n",
+    "dataset_train = STDataset(\n",
+    "    input_da=input_da,\n",
+    "    input_da_nan_mask=input_da_nan_mask,\n",
+    "    monthly_da=monthly_da,\n",
+    "    padded_days_mask=padded_days_mask,\n",
+    "    time_features=time_features,\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    model_patch_size=patch_size,\n",
+    "    crop_size=(1, *spatial_crop_size),   # based on the patch_size in model\n",
+    "    stride=stride,\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    verbose=True,\n",
+    "    load_lazy=True,\n",
+    ")\n",
+    "print(len(dataset_train))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e27a96aa-5560-47b6-91eb-dc4530dca090",
+   "metadata": {},
+   "source": [
+    "#### Prepare validation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ff6159b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this should be done once\n",
+    "data_dir = f\"{run_dir}/data_validation\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(\n",
+    "    hourly_validation_subset[var_name], monthly_validation_subset[var_name], calculate_residuals=True, is_hourly=True, save_to_zarr=True, run_dir=data_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "343c5dce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read data\n",
+    "data_dir = f\"{run_dir}/data_validation\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7c2deb40-bee8-4973-80f0-9d9485eabf0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset:\n",
+      "Crop grid (m x i x j): 1 x 6 x 21 = 126 data crops\n",
+      "Overlap: 32 pixels (height), 32 pixels (width)\n",
+      "126\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_subsets = (10, 10)\n",
+    "spatial_crop_size = (patch_size[1]*num_subsets[0], patch_size[2]*num_subsets[1])\n",
+    "stride = (spatial_crop_size[0] // 5, spatial_crop_size[1] // 5)  # for data agumentation\n",
+    "\n",
+    "dataset_validation = STDataset(\n",
+    "    input_da=input_da,\n",
+    "    input_da_nan_mask=input_da_nan_mask,\n",
+    "    monthly_da=monthly_da,\n",
+    "    padded_days_mask=padded_days_mask,\n",
+    "    time_features=time_features,\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    model_patch_size=patch_size,\n",
+    "    crop_size=(1, *spatial_crop_size),   # based on the patch_size in model\n",
+    "    stride=stride,\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L = 10,\n",
+    "    verbose=True,\n",
+    "    load_lazy=True,\n",
+    ")\n",
+    "print(len(dataset_validation))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "200264a1-c3cb-42d2-95eb-887259ed63cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DataLoaderConfig(batch_size=10, shuffle=True, num_workers=2, pin_memory=True, persistent_workers=True, device='cuda', multiprocessing_context='spawn')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create dataloader config\n",
+    "dataloader_config = DataLoaderConfig(\n",
+    "    batch_size=10,\n",
+    "    shuffle=True,\n",
+    "    num_workers=dataloader_num_workers,\n",
+    "    pin_memory=True,\n",
+    "    persistent_workers=True,\n",
+    "    device=device,\n",
+    "    multiprocessing_context=\"spawn\", # keep this when num_workers >0 and lazy data\n",
+    ")\n",
+    "dataloader_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "74c0cfd5-4ebf-4df6-b788-19563a76155b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TrainConfig(calculate_residuals=True, num_epoch=100, patience=10, accumulation_steps=2, optimizer_lr=0.001, optimizer_weight_decay=0.01, scheduler_lr_factor=0.1, scheduler_min_lr=1e-05, device='cuda', verbose=True, verbose_epoch_interval=20, tune_checkpoint=False, store_model=True, store_logs=True)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create training config\n",
+    "training_config = TrainConfig(\n",
+    "    calculate_residuals=True,\n",
+    "    num_epoch=100,\n",
+    "    patience=10,\n",
+    "    accumulation_steps=2,\n",
+    "    optimizer_lr=1e-3,\n",
+    "    device=device,\n",
+    "    verbose=True,\n",
+    "    verbose_epoch_interval=20,\n",
+    "    tune_checkpoint=False,\n",
+    "    store_model=True,\n",
+    ")\n",
+    "training_config"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0afcf1c1",
+   "metadata": {},
+   "source": [
+    "### Start training loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3b0e3f24-c9d0-48e8-858a-48deb8d000d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: gap between train and val loss: -0.056109\n",
+      "Epoch 0: best_loss = 0.169462\n",
+      "Early stopping triggered at epoch 13. Best loss: 0.163902\n",
+      "Training complete. Best loss: 0.163902\n",
+      "Model saved to /home/b/b383704/ClimaNet/notebooks/watervapor/runs_hourly/best_model.pth\n",
+      "CPU times: user 12.2 s, sys: 3.28 s, total: 15.5 s\n",
+      "Wall time: 3min 31s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# verbose is True\n",
+    "trained_model = train_monthly_model(\n",
+    "    model=model,\n",
+    "    dataset_train=dataset_train,\n",
+    "    dataloader_config=dataloader_config,\n",
+    "    training_config=training_config,\n",
+    "    dataset_validation=dataset_validation,\n",
+    "    run_dir=run_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f93e14f",
+   "metadata": {},
+   "source": [
+    "## Inspect results and compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "51784fe8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_loss(run_dir, list_loss_var=[\"Loss/train\", \"Loss/validation\"], unit=\"kg/m2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57d94c9f",
+   "metadata": {},
+   "source": [
+    "#### Prepare the test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "029d9552",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this should be done once\n",
+    "data_dir = f\"{run_dir}/data_test\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(\n",
+    "    hourly_test_subset[var_name], monthly_test_subset[var_name], calculate_residuals=True, is_hourly=True, save_to_zarr=True, run_dir=data_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e5a4be23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read data\n",
+    "data_dir = f\"{run_dir}/data_test\"\n",
+    "input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = read_st_data(data_path=data_dir, var_name=var_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "54bdaeeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating dataset:\n",
+      "Crop grid (m x i x j): 1 x 1 x 1 = 1 data crops\n",
+      "Overlap: 0 pixels (height), 0 pixels (width)\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "spatial_crop_size = monthly_da.shape[1:]  # the whole dataset\n",
+    "\n",
+    "dataset_test = STDataset(\n",
+    "    input_da=input_da,\n",
+    "    input_da_nan_mask=input_da_nan_mask,\n",
+    "    monthly_da=monthly_da,\n",
+    "    padded_days_mask=padded_days_mask,\n",
+    "    time_features=time_features,\n",
+    "    land_mask=lsm_subset[\"lsm\"],\n",
+    "    model_patch_size=patch_size,\n",
+    "    crop_size=(1, *spatial_crop_size),\n",
+    "    stride=None,  # no stride in inference\n",
+    "    sh_embed_dim=96,\n",
+    "    sh_order_L=10,\n",
+    "    verbose=True,\n",
+    "    load_lazy=False,\n",
+    ")\n",
+    "print(len(dataset_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "94a12d1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create dataloader config for inference\n",
+    "dataloader_config = DataLoaderConfig(\n",
+    "    batch_size=10,\n",
+    "    shuffle=False,\n",
+    "    num_workers=0,\n",
+    "    pin_memory=False,\n",
+    "    persistent_workers=False,\n",
+    "    device=device,\n",
+    "    multiprocessing_context=None,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "95c6068f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create prediction config\n",
+    "prediction_config = PredictionConfig(\n",
+    "    calculate_residuals=True,\n",
+    "    return_numpy=True,\n",
+    "    save_predictions=True,\n",
+    "    return_loss=False,\n",
+    "    device=device,\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4b8f947e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inference on test data, verbose is True\n",
+    "predictions = predict_monthly_var(\n",
+    "    model=f\"{run_dir}/best_model.pth\",\n",
+    "    dataset=dataset_test,  \n",
+    "    dataloader_config=dataloader_config,\n",
+    "    prediction_config=prediction_config,\n",
+    "    run_dir=run_dir,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e823452b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to monthly average\n",
+    "predictions_res = xr.open_dataset(f\"{run_dir}/202201_{var_name}_prediction_residual.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d521af83-7120-4ce4-9f08-0c5d1567d9aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to monthly average\n",
+    "input_data_averaged = hourly_test_subset.resample({\"time\": \"MS\"}).mean(skipna=True)\n",
+    "input_data_averaged[\"time\"] = monthly_test_subset[\"time\"]\n",
+    "predictions = input_data_averaged[var_name] + predictions_res[var_name]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9bf2dc57-80c4-4c93-bd0a-f1c77f140573",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ocean = ~lsm_subset[\"lsm\"].values\n",
+    "masked_target = monthly_test_subset[var_name].where(ocean)\n",
+    "masked_pred = predictions.where(ocean)\n",
+    "plot_results(masked_target, masked_pred, label=\"Water Vapour kg/m2\", title=(\"Target\", \"Prediction\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "0683310d-4424-4397-992e-74caaff69d11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "err_baseline = monthly_test_subset[var_name] - input_data_averaged[var_name]  # M - mean(D)\n",
+    "err_predictions = monthly_test_subset[var_name] - predictions  # M - M_hat\n",
+    "\n",
+    "masked_err_baseline = err_baseline.where(ocean)\n",
+    "masked_err_predictions = err_predictions.where(ocean)\n",
+    "plot_results(masked_err_baseline, masked_err_predictions, label=\"error kg/m2\", title=(\"err_Baseline\", \"err_Prediction\"), error=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a7420dc7-fe1b-4f34-a62e-5914091f04b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_histograms(masked_err_baseline, masked_err_predictions, label=\"error kg/m2\", legend_labels=(\"err_Baseline\", \"err_Prediction\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ClimaNet (3.14.0)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/tuning.py b/scripts/tuning.py
index af0234c..3790486 100644
--- a/scripts/tuning.py
+++ b/scripts/tuning.py
@@ -96,9 +96,11 @@
         "dropout": tune.choice([0.0, 0.1, 0.2]),
         "hidden": tune.choice([32, 64, 128]),
         "optimizer_lr": tune.loguniform(1e-3, 1e-1),
-        "batch_config": tune.grid_search([
-            {"batch_size": 100, "accumulation_steps": 2},
-        ]),
+        "batch_config": tune.grid_search(
+            [
+                {"batch_size": 100, "accumulation_steps": 2},
+            ]
+        ),
     }
 
     # Start Ray Tune for distributed training on several nodes
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 5b28806..6c68a12 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -2,7 +2,7 @@
 import xarray as xr
 from tbparse import SummaryReader
 
-from climanet.utils import data_preparation, setup_logging
+from climanet.utils import coarsen_land_mask, data_preparation, setup_logging
 
 
 def test_setup_logging(tmp_path):
@@ -22,7 +22,6 @@ def test_setup_logging(tmp_path):
     assert reader.scalars["value"].iloc[0] == 42  # check scalar
 
 
-
 def _make_datasets():
     # 4x4 dataset with, 6 days in one month
     lat = 4
@@ -67,12 +66,14 @@ def _make_datasets():
 def test_data_preparation():
     daily_da, monthly_da = _make_datasets()
 
-    input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = data_preparation(
-        daily_da, monthly_da, calculate_residuals=False, save_to_zarr=False
+    input_da, input_da_nan_mask, monthly_da, padded_days_mask, time_features = (
+        data_preparation(
+            daily_da, monthly_da, calculate_residuals=False, save_to_zarr=False
+        )
     )
     assert input_da.shape == (1, 31, 4, 4)  # (M, T=31, H=4, W=4)
     assert isinstance(input_da, xr.DataArray)
-    assert input_da_nan_mask[0, 1, 1, 1] == True  # check that the NaN mask is correctly set
+    assert input_da_nan_mask[0, 1, 1, 1]  # check that the NaN mask is correctly set
     assert isinstance(input_da_nan_mask, xr.DataArray)
     assert monthly_da.shape == (1, 4, 4)  # (M, H=4, W=4)
     assert isinstance(monthly_da, xr.DataArray)
@@ -86,7 +87,11 @@ def test_data_preparation_to_zarr(tmp_path):
     daily_da, monthly_da = _make_datasets()
 
     _ = data_preparation(
-        daily_da, monthly_da, run_dir=tmp_path, calculate_residuals=False, save_to_zarr=True
+        daily_da,
+        monthly_da,
+        run_dir=tmp_path,
+        calculate_residuals=False,
+        save_to_zarr=True,
     )
     assert (tmp_path / "input_da.zarr").exists()
     assert (tmp_path / "input_da_nan_mask.zarr").exists()
@@ -99,7 +104,11 @@ def test_data_preparation_from_zarr(tmp_path):
     daily_da, monthly_da = _make_datasets()
 
     _ = data_preparation(
-        daily_da, monthly_da, run_dir=tmp_path, calculate_residuals=False, save_to_zarr=True
+        daily_da,
+        monthly_da,
+        run_dir=tmp_path,
+        calculate_residuals=False,
+        save_to_zarr=True,
     )
 
     # Now load from zarr
@@ -111,11 +120,44 @@ def test_data_preparation_from_zarr(tmp_path):
 
     assert input_da["tos"].shape == (1, 31, 4, 4)  # (M, T=31, H=4, W=4)
     assert isinstance(input_da, xr.Dataset)
-    assert input_da_nan_mask["tos"][0, 1, 1, 1] == True  # check that the NaN mask is correctly set
+    assert input_da_nan_mask["tos"][
+        0, 1, 1, 1
+    ]  # check that the NaN mask is correctly set
     assert isinstance(input_da_nan_mask, xr.Dataset)
     assert monthly_da["tos"].shape == (1, 4, 4)  # (M, H=4, W=4)
     assert isinstance(monthly_da, xr.Dataset)
     assert padded_days_mask["tos"].shape == (1, 31)  # (M, T=31)
     assert isinstance(padded_days_mask, xr.Dataset)
-    assert time_features["tos"].shape == (1, 31, 3)  # (M, T=31, 2) for month and day features
+    assert time_features["tos"].shape == (
+        1,
+        31,
+        3,
+    )  # (M, T=31, 2) for month and day features
     assert isinstance(time_features, xr.Dataset)
+
+
+def test_coarsen_land_mask():
+    """Downsample a mock lsm with a factor of 2."""
+    mask_values = np.zeros((1, 8, 4))  # Mock values for the land-sea mask
+    mask_values[0, 0:2, 0:2] = 0.7  # Set 4 values to be above 0.5
+    mask_values[0, 2:4, 2:4] = 0.2  # Set another 4 values to be below 0.5
+    lsm_mask_025_mock = xr.DataArray(
+        mask_values,
+        dims=["time", "lat", "lon"],
+        coords={
+            "time": [0],
+            "lat": np.linspace(-2.0, 2.0, 8),
+            "lon": np.linspace(-1.0, 1.0, 4),
+        },
+        name="lsm",
+    )
+    lsm_mask_05 = coarsen_land_mask(lsm_mask_025_mock)
+
+    assert np.all(
+        lsm_mask_05.values[0, 0:1, 0:1]  # after downsampling 0:2 -> 0:1
+    )  # all values above 0.5 should be True
+    assert not np.any(
+        lsm_mask_05.values[0, 1:2, 1:2]  # after downsampling 2:4 -> 1:2
+    )  # all values below 0.5 should be False
+    assert lsm_mask_05.shape == (1, 4, 2)  # By default downsample with a factor of 2
+    assert lsm_mask_05.dtype == bool  # Mask should be of boolean type