From 9e289e3b167f006d4e704e5fdb6c59ed7ff51e52 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Wed, 26 Aug 2026 13:01:41 +0200
Subject: [PATCH 01/11] rename patch with crop in dataset and predict module

---
 climanet/dataset.py | 154 ++++++++++++++++++++++----------------------
 climanet/predict.py |   6 +-
 2 files changed, 80 insertions(+), 80 deletions(-)

diff --git a/climanet/dataset.py b/climanet/dataset.py
index 6dddec4..086111b 100644
--- a/climanet/dataset.py
+++ b/climanet/dataset.py
@@ -27,14 +27,14 @@ class DataLoaderConfig:
 
 
 class STDataset(Dataset):
-    """Dataset for spatiotemporal patches.
+    """Dataset for spatiotemporal data crops.
 
     This class provides a PyTorch Dataset interface for spatiotemporal data,
-    allowing for the extraction of patches from daily/hourly and monthly data
+    allowing for the extraction of data crops from daily/hourly and monthly data
     arrays. The `input_da` is expected to be a daily or hourly data array, while
-    the `monthly_da` is a monthly data array. To extract monthly patches, the
+    the `monthly_da` is a monthly data array. To extract monthly data crops, the
     `input_da` and `monthly_da` are reshaped internally to have a month
-    dimension, and the patches are extracted accordingly.
+    dimension, and the data crops are extracted accordingly.
     """
 
     def __init__(
@@ -46,7 +46,7 @@ def __init__(
         time_features: xr.DataArray,
         land_mask: xr.DataArray = None,
         spatial_dims: tuple[str, str] = ("lat", "lon"),
-        patch_size: tuple[int, int, int] = (1, 16, 16),  # (Month, lat, lon)
+        crop_size: tuple[int, int, int] = (1, 16, 16),  # (Month, lat, lon)
         stride: tuple[int, int] = None,
         sh_embed_dim: int = 96,  # sh_embed_dim should <= (sh_order_L + 1)**2
         sh_order_L: int = 10,
@@ -62,14 +62,14 @@ def __init__(
             padded_days_mask: xarray DataArray with padded days mask for input_da (time, H, W)
             land_mask: Optional xarray DataArray with land mask (H, W) or (1, H, W)
             spatial_dims: Tuple of (lat_dim, lon_dim) names in the input data
-            patch_size: Tuple of (patch_time, patch_height, patch_width) in time
+            crop_size: Tuple of (crop_time, crop_height, crop_width) in time
                 unit and pixels in monthly data. For example, (1, 16, 16) means
                 1 month, 16 pixels height, 16 pixels width. For this, the
                 spatial resolution of `input_da` and `monthly_da` must match. To
-                extract monthly patches, the `input_da` and `monthly_da` are
-                reshaped internally to have a month dimension, and the patches are
+                extract monthly data crops, the `input_da` and `monthly_da` are
+                reshaped internally to have a month dimension, and the data crops are
                 extracted accordingly.
-            stride: Tuple of (stride_height, stride_width) in pixels. If None, defaults to patch_size (non-overlapping patches).
+            stride: Tuple of (stride_height, stride_width) in pixels. If None, defaults to crop_size (non-overlapping data crops).
             sh_pos_table: Optional path to precomputed spherical harmonics position embeddings.
             sh_embed_dim: Dimension of the spherical harmonics embedding.
             sh_order_L: Order of the spherical harmonics.
@@ -77,7 +77,7 @@ def __init__(
             load_lazy: If True, use data lazily with zarr backend. This may slow down getitem but saves memory.
         """
         self.spatial_dims = spatial_dims
-        self.patch_size = patch_size
+        self.crop_size = crop_size
         self.input_da = input_da
         self.input_da_nan_mask = input_da_nan_mask
         self.monthly_da = monthly_da
@@ -85,7 +85,7 @@ def __init__(
         self.time_features = time_features
         self.land_mask = land_mask
 
-        self.stride = stride if stride is not None else (patch_size[1], patch_size[2])
+        self.stride = stride if stride is not None else (crop_size[1], crop_size[2])
 
         self.sh_embed_dim = sh_embed_dim
         self.sh_order_L = sh_order_L
@@ -98,11 +98,11 @@ def __init__(
                 raise ValueError(f"Spatial dimension '{dim}' not found in input data")
 
         if (
-            patch_size[1] > input_da.sizes[spatial_dims[0]]
-            or patch_size[2] > input_da.sizes[spatial_dims[1]]
+            crop_size[1] > input_da.sizes[spatial_dims[0]]
+            or crop_size[2] > input_da.sizes[spatial_dims[1]]
         ):
             raise ValueError(
-                f"Patch size {patch_size} is larger than data dimensions {input_da.sizes}"
+                f"Crop size {crop_size} is larger than data dimensions {input_da.sizes}"
             )
 
         # Materialize data arrays to contiguous tensors for efficient access
@@ -136,12 +136,12 @@ def __init__(
         self.lon_coords = torch.from_numpy(input_da[spatial_dims[1]].to_numpy().copy())
 
         # Pre-build zero land tensor for the no-mask case
-        _, ph, pw = self.patch_size
+        _, ph, pw = self.crop_size
         self._zero_land = torch.zeros(ph, pw, dtype=torch.bool)
 
-        # Precompute lazy index mapping for patches
+        # Precompute lazy index mapping for data crops
         M, H, W = self.input_da.shape[0], self.input_da.shape[2], self.input_da.shape[3]
-        self.patch_indices = self._compute_patch_indices(M, H, W)
+        self.crop_indices = self._compute_crop_indices(M, H, W)
 
         self.sh_embed_dim_t = torch.tensor(self.sh_embed_dim)
         self.harmonic_order_t = torch.tensor(self.sh_order_L)
@@ -150,40 +150,40 @@ def __init__(
             self.lat_coords, self.lon_coords, self.sh_order_L, self.sh_embed_dim
         )
 
-    def _compute_patch_indices(self, M: int, H: int, W: int) -> list:
-        """Generate patch start indices with coverage warning (overlap support)."""
-        pm, ph, pw = self.patch_size
+    def _compute_crop_indices(self, M: int, H: int, W: int) -> list:
+        """Generate crop start indices with coverage warning (overlap support)."""
+        pm, ph, pw = self.crop_size
         sh, sw = self.stride
 
-        # validate temporal patch size
+        # validate temporal crop size
         if pm > M:
             raise ValueError(
-                f"Temporal patch size {pm} is larger than available months {M}."
+                f"Temporal crop size {pm} is larger than available months {M}."
             )
         if pm < 1:
-            raise ValueError(f"Temporal patch size {pm} must be at least 1.")
+            raise ValueError(f"Temporal crop size {pm} must be at least 1.")
 
         # Validate stride
         if sh > ph or sw > pw:
             warnings.warn(
-                f"Stride {self.stride} is larger than patch size {self.patch_size}. "
-                f"This will leave gaps between patches.",
+                f"Stride {self.stride} is larger than crop size {self.crop_size}. "
+                f"This will leave gaps between data crops.",
                 UserWarning,
             )
 
-        # Compute patch start indices using stride
+        # Compute crop start indices using stride
         # Ensure we don't go out of bounds
         m_starts = list(
             range(0, M - pm + 1, pm)
-        )  # Temporal patches are non-overlapping
+        )  # Temporal data crops are non-overlapping
         i_starts = list(range(0, H - ph + 1, sh))
         j_starts = list(range(0, W - pw + 1, sw))
 
         # Warn if there's incomplete coverage at the edges
         if not i_starts or not j_starts or not m_starts:
             raise ValueError(
-                f"No valid patches can be extracted. Image size ({M}, {H}, {W}) "
-                f"is smaller than patch size {self.patch_size}."
+                f"No valid data crops can be extracted. Image size ({M}, {H}, {W}) "
+                f"is smaller than crop size {self.crop_size}."
             )
 
         # Check edge coverage
@@ -192,9 +192,9 @@ def _compute_patch_indices(self, M: int, H: int, W: int) -> list:
         last_j = j_starts[-1] + pw
         if last_m < M or last_i < H or last_j < W:
             warnings.warn(
-                f"Patches do not fully cover the image. "
+                f"Data crops do not fully cover the image. "
                 f"Uncovered pixels: {M - last_m} in month, {H - last_i} in height, {W - last_j} in width. "
-                f"Consider adjusting stride or adding edge patches.",
+                f"Consider adjusting stride or adding edge data crops.",
                 UserWarning,
             )
 
@@ -207,7 +207,7 @@ def _compute_patch_indices(self, M: int, H: int, W: int) -> list:
         if self.verbose:
             print("Creating dataset:")
             print(
-                f"Patch grid (m x i x j): {len_m} x {len_i} x {len_j} = {len_m * len_i * len_j} patches"
+                f"Crop grid (m x i x j): {len_m} x {len_i} x {len_j} = {len_m * len_i * len_j} data crops"
             )
             print(f"Overlap: {overlap_h} pixels (height), {overlap_w} pixels (width)")
 
@@ -224,124 +224,124 @@ def _prepare_land_mask(self, land_mask):
         return lm
 
     def __len__(self):
-        return len(self.patch_indices)
+        return len(self.crop_indices)
 
     def __getitem__(self, idx):
-        """Get a spatiotemporal patch sample based on the index."""
+        """Get a spatiotemporal crop sample based on the index."""
 
-        if idx < 0 or idx >= len(self.patch_indices):
+        if idx < 0 or idx >= len(self.crop_indices):
             raise IndexError("Index out of range")
 
-        m, i, j = self.patch_indices[idx]
-        pm, ph, pw = self.patch_size
+        m, i, j = self.crop_indices[idx]
+        pm, ph, pw = self.crop_size
 
         if self.load_lazy:
-            daily_t_patch = self.input_da.isel(
+            daily_t_crop = self.input_da.isel(
                 M=slice(m, m + pm),
                 **{
                     self.spatial_dims[0]: slice(i, i + ph),
                     self.spatial_dims[1]: slice(j, j + pw),
                 },
             )
-            daily_t_patch = (
-                torch.from_numpy(daily_t_patch.to_numpy()).contiguous().unsqueeze(0)
+            daily_t_crop = (
+                torch.from_numpy(daily_t_crop.to_numpy()).contiguous().unsqueeze(0)
             )
 
-            daily_nan_mask_t_patch = self.input_da_nan_mask.isel(
+            daily_nan_mask_t_crop = self.input_da_nan_mask.isel(
                 M=slice(m, m + pm),
                 **{
                     self.spatial_dims[0]: slice(i, i + ph),
                     self.spatial_dims[1]: slice(j, j + pw),
                 },
             )
-            daily_nan_mask_t_patch = (
-                torch.from_numpy(daily_nan_mask_t_patch.to_numpy())
+            daily_nan_mask_t_crop = (
+                torch.from_numpy(daily_nan_mask_t_crop.to_numpy())
                 .contiguous()
                 .unsqueeze(0)
             )
 
-            monthly_t_patch = self.monthly_da.isel(
+            monthly_t_crop = self.monthly_da.isel(
                 M=slice(m, m + pm),
                 **{
                     self.spatial_dims[0]: slice(i, i + ph),
                     self.spatial_dims[1]: slice(j, j + pw),
                 },
             )
-            monthly_t_patch = torch.from_numpy(monthly_t_patch.to_numpy()).contiguous()
+            monthly_t_crop = torch.from_numpy(monthly_t_crop.to_numpy()).contiguous()
 
             if self.land_mask is not None:
-                land_t_patch = self.land_mask.isel(
+                land_t_crop = self.land_mask.isel(
                     **{
                         self.spatial_dims[0]: slice(i, i + ph),
                         self.spatial_dims[1]: slice(j, j + pw),
                     },
                 )
-                land_t_patch = self._prepare_land_mask(land_t_patch)
+                land_t_crop = self._prepare_land_mask(land_t_crop)
 
-            daily_timef_patch = self.time_features.isel(M=slice(m, m + pm))
-            daily_timef_patch = torch.from_numpy(
-                daily_timef_patch.to_numpy().astype(np.float32, copy=False)
+            daily_timef_crop = self.time_features.isel(M=slice(m, m + pm))
+            daily_timef_crop = torch.from_numpy(
+                daily_timef_crop.to_numpy().astype(np.float32, copy=False)
             ).contiguous()
 
-            padded_days_mask_patch = self.padded_days_mask.isel(M=slice(m, m + pm))
-            padded_days_mask_patch = torch.from_numpy(
-                padded_days_mask_patch.to_numpy()
+            padded_days_mask_crop = self.padded_days_mask.isel(M=slice(m, m + pm))
+            padded_days_mask_crop = torch.from_numpy(
+                padded_days_mask_crop.to_numpy()
             ).bool()
         else:
-            # Extract the patch data
-            daily_t_patch = self.daily_data_t[
+            # Extract the crop data
+            daily_t_crop = self.daily_data_t[
                 m : m + pm, :, i : i + ph, j : j + pw
             ].unsqueeze(0)  # (1, pm, T, pH, pW)
 
-            daily_nan_mask_t_patch = self.daily_nan_mask_t[
+            daily_nan_mask_t_crop = self.daily_nan_mask_t[
                 m : m + pm, :, i : i + ph, j : j + pw
             ].unsqueeze(0)  # (1, pm, T, pH, pW)
 
-            monthly_t_patch = self.monthly_data_t[m : m + pm, i : i + ph, j : j + pw]
+            monthly_t_crop = self.monthly_data_t[m : m + pm, i : i + ph, j : j + pw]
 
             if self.land_mask_t is not None:
-                land_t_patch = self.land_mask_t[i : i + ph, j : j + pw]
+                land_t_crop = self.land_mask_t[i : i + ph, j : j + pw]
             else:
-                land_t_patch = self._zero_land
+                land_t_crop = self._zero_land
 
-            daily_timef_patch = self.daily_timef_t[m : m + pm]
-            padded_days_mask_patch = self.padded_days_t[m : m + pm]
+            daily_timef_crop = self.daily_timef_t[m : m + pm]
+            padded_days_mask_crop = self.padded_days_t[m : m + pm]
 
         # daily_mask: NaN locations that are NOT land
         # Reshape land_tensor for broadcasting: (pH, pW) → (1, 1, 1, pH, pW)
-        daily_mask_t_patch = daily_nan_mask_t_patch & (
-            ~land_t_patch.unsqueeze(0).unsqueeze(0).unsqueeze(0)
+        daily_mask_t_crop = daily_nan_mask_t_crop & (
+            ~land_t_crop.unsqueeze(0).unsqueeze(0).unsqueeze(0)
         )
 
-        # Extract lat/lon coordinates for this patch
-        lat_patch = self.lat_coords[i : i + ph]  # (H,) -> (pH,)
-        lon_patch = self.lon_coords[j : j + pw]  # (W,) -> (pW,)
+        # Extract lat/lon coordinates for this crop
+        lat_crop = self.lat_coords[i : i + ph]  # (H,) -> (pH,)
+        lon_crop = self.lon_coords[j : j + pw]  # (W,) -> (pW,)
 
         geo_pos_embedding_t = compute_patch_geo_pos_embedding(
             self.geo_pos_t[i : i + ph, j : j + pw],
-            lat_patch,
+            lat_crop,
         )
 
         scale_feature_t = compute_patch_scale_features(
-            lat_patch,
-            lon_patch,
+            lat_crop,
+            lon_crop,
         )
 
         # Convert to dictionary
         return {
-            "daily_patch": daily_t_patch,  # (C=1, pm, T=31, pH, pW)
-            "monthly_patch": monthly_t_patch,  # (pm, pH, pW)
-            "daily_mask_patch": daily_mask_t_patch,  # (C=1, pm, T=31, pH, pW)
-            "land_mask_patch": land_t_patch,  # (pH,pW) True=Land
-            "daily_timef_patch": daily_timef_patch,  # (pm, T=31, 3)
-            "padded_days_mask": padded_days_mask_patch,  # (pm, T=31) True=padded
+            "daily_patch": daily_t_crop,  # (C=1, pm, T=31, pH, pW)
+            "monthly_patch": monthly_t_crop,  # (pm, pH, pW)
+            "daily_mask_patch": daily_mask_t_crop,  # (C=1, pm, T=31, pH, pW)
+            "land_mask_patch": land_t_crop,  # (pH,pW) True=Land
+            "daily_timef_patch": daily_timef_crop,  # (pm, T=31, 3)
+            "padded_days_mask": padded_days_mask_crop,  # (pm, T=31) True=padded
             "scale_feature_patch": scale_feature_t,  # (10,)
             "geo_pos_embedding_patch": geo_pos_embedding_t,  # (sh_embed_dim,)
             "sh_embed_dim": self.sh_embed_dim_t,
             "harmonic_order": self.harmonic_order_t,
             "coords": torch.tensor([m, i, j]),
-            "lat_patch": lat_patch,  # (pH,)
-            "lon_patch": lon_patch,  # (pW,)
+            "lat_patch": lat_crop,  # (pH,)
+            "lon_patch": lon_crop,  # (pW,)
         }
 
     def __getitems__(self, indices):
diff --git a/climanet/predict.py b/climanet/predict.py
index b7dddd9..c7b6005 100644
--- a/climanet/predict.py
+++ b/climanet/predict.py
@@ -44,8 +44,8 @@ def _save_netcdf(
     full_predictions = np.full(
         (len(times), len(lats), len(lons)), np.nan, dtype=predictions.dtype
     )
-    for i, patch_idx in enumerate(indices):
-        month_start, lat_start, lon_start = base_dataset.patch_indices[patch_idx]
+    for i, data_idx in enumerate(indices):
+        month_start, lat_start, lon_start = base_dataset.crop_indices[data_idx]
         full_predictions[
             month_start : month_start + M,
             lat_start : lat_start + H,
@@ -143,7 +143,7 @@ def predict_monthly_var(
     # Initialize an empty list to store predictions
     base_dataset = dataset.dataset if hasattr(dataset, "dataset") else dataset
 
-    M, H, W = base_dataset.patch_size
+    M, H, W = base_dataset.crop_size
     all_predictions = torch.empty(len(dataset), M, H, W, device=device)
 
     # Set up logging

From 9c523e5e63524d2d712058e6f69b643d30b64c56 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Wed, 26 Aug 2026 13:32:24 +0200
Subject: [PATCH 02/11] fix geo_embeding in the model

---
 climanet/dataset.py            | 60 +++++++++++++++++++++++++++++-----
 climanet/st_encoder_decoder.py | 16 +++++----
 2 files changed, 61 insertions(+), 15 deletions(-)

diff --git a/climanet/dataset.py b/climanet/dataset.py
index 086111b..b7d73c8 100644
--- a/climanet/dataset.py
+++ b/climanet/dataset.py
@@ -45,6 +45,7 @@ def __init__(
         padded_days_mask: xr.DataArray,
         time_features: xr.DataArray,
         land_mask: xr.DataArray = None,
+        model_patch_size: tuple[int, int, int] = (1, 4, 4),  # (Month, lat, lon)
         spatial_dims: tuple[str, str] = ("lat", "lon"),
         crop_size: tuple[int, int, int] = (1, 16, 16),  # (Month, lat, lon)
         stride: tuple[int, int] = None,
@@ -84,6 +85,7 @@ def __init__(
         self.padded_days_mask = padded_days_mask
         self.time_features = time_features
         self.land_mask = land_mask
+        self.model_patch_size = model_patch_size
 
         self.stride = stride if stride is not None else (crop_size[1], crop_size[2])
 
@@ -317,15 +319,9 @@ def __getitem__(self, idx):
         lat_crop = self.lat_coords[i : i + ph]  # (H,) -> (pH,)
         lon_crop = self.lon_coords[j : j + pw]  # (W,) -> (pW,)
 
-        geo_pos_embedding_t = compute_patch_geo_pos_embedding(
-            self.geo_pos_t[i : i + ph, j : j + pw],
-            lat_crop,
-        )
-
-        scale_feature_t = compute_patch_scale_features(
-            lat_crop,
-            lon_crop,
-        )
+        # compute geo_pos_embedding and scale_feature for each model patch within the dataset crop
+        geo_pos_embedding_t = self._compute_patch_geo_pos_embedding(i, j)  # (Hp*Wp, sh_embed_dim)
+        scale_feature_t = self._compute_patch_scale_features(i, j)  # (Hp*Wp, scale_dim)
 
         # Convert to dictionary
         return {
@@ -346,3 +342,49 @@ def __getitem__(self, idx):
 
     def __getitems__(self, indices):
         return [self.__getitem__(i) for i in indices]
+
+    def _compute_patch_geo_pos_embedding(self, i: int, j: int):
+        pm, ph, pw = self.crop_size
+        model_patch_m, model_patch_h, model_patch_w = self.model_patch_size
+        Hp = ph // model_patch_h
+        Wp = pw // model_patch_w
+
+        geo_pos_embeddings_list = []
+        for hi in range(Hp):
+            for wi in range(Wp):
+                # Geo embedding for this model patch within the dataset patch
+                h_start = i + hi * model_patch_h
+                h_end = h_start + model_patch_h
+                w_start = j + wi * model_patch_w
+                w_end = w_start + model_patch_w
+
+                patch_geo = compute_patch_geo_pos_embedding(
+                    self.geo_pos_t[h_start:h_end, w_start:w_end],
+                    self.lat_coords[h_start:h_end],
+                )
+                geo_pos_embeddings_list.append(patch_geo)
+
+        # Shape: (Hp*Wp, sh_embed_dim)
+        return torch.stack(geo_pos_embeddings_list)
+
+    def _compute_patch_scale_features(self, i: int, j: int):
+        pm, ph, pw = self.crop_size
+        model_patch_m, model_patch_h, model_patch_w = self.model_patch_size
+        Hp = ph // model_patch_h
+        Wp = pw // model_patch_w
+
+        scale_features_list = []
+        for hi in range(Hp):
+            for wi in range(Wp):
+                h_start = i + hi * model_patch_h
+                h_end = h_start + model_patch_h
+                w_start = j + wi * model_patch_w
+                w_end = w_start + model_patch_w
+
+                patch_scale = compute_patch_scale_features(
+                    self.lat_coords[h_start:h_end],
+                    self.lon_coords[w_start:w_end],
+                )
+                scale_features_list.append(patch_scale)
+
+        return torch.stack(scale_features_list)  # (Hp*Wp, scale_dim)
\ No newline at end of file
diff --git a/climanet/st_encoder_decoder.py b/climanet/st_encoder_decoder.py
index de10794..b0773ac 100644
--- a/climanet/st_encoder_decoder.py
+++ b/climanet/st_encoder_decoder.py
@@ -787,20 +787,24 @@ def forward(
             )  # (B, M, Hp*Wp, embed_dim)
 
         # Step 3: Add geo position and scale encodings
+        B = geo_pos_embedding_patch.shape[0]
+
         if self.use_checkpoint:
-            geo_emb = checkpoint(
+            geo_emb_flat = checkpoint(
                 self.geo_embedding,
-                geo_pos_embedding_patch,
-                scale_feature_patch,
+                geo_pos_embedding_patch.view(B * Hp * Wp, -1),
+                scale_feature_patch.view(B * Hp * Wp, -1),
                 use_reentrant=False,
             )[:, None, None, :]  # (B,1,1,E)
         else:
-            geo_emb = self.geo_embedding(geo_pos_embedding_patch, scale_feature_patch)[
-                :, None, None, :
-            ]  # (B,1,1,E)
+            geo_emb_flat = self.geo_embedding(
+                geo_pos_embedding_patch.view(B * Hp * Wp, -1),
+                scale_feature_patch.view(B * Hp * Wp, -1)
+            )  # (B*Hp*Wp, E)
 
         # Broadcasting: same geo embedding for all M months at each Hp*Wp location
         # we use weighted mean patch embedding, see `geo_embedding_utils.py`
+        geo_emb = geo_emb_flat.view(B, 1, Hp * Wp, embed_dim)  # (B, 1, Hp*Wp, E)
         x = agg_latent + geo_emb  # (B, M, Hp*Wp, E)
 
         # Step 4: Spatial mixing with Transformer

From 83a0a201cced8f1ecee7f5afd381b43e07466edc Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Wed, 26 Aug 2026 14:21:32 +0200
Subject: [PATCH 03/11] remove spatial transfomer from the model

---
 climanet/st_encoder_decoder.py | 71 +---------------------------------
 1 file changed, 2 insertions(+), 69 deletions(-)

diff --git a/climanet/st_encoder_decoder.py b/climanet/st_encoder_decoder.py
index b0773ac..0004bc3 100644
--- a/climanet/st_encoder_decoder.py
+++ b/climanet/st_encoder_decoder.py
@@ -435,6 +435,7 @@ def __init__(
             nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
             nn.GroupNorm(num_groups=8, num_channels=out_channels),
             nn.GELU(),
+
             nn.Dropout2d(dropout),
             nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
             nn.GroupNorm(num_groups=8, num_channels=out_channels),
@@ -568,56 +569,6 @@ def forward(
         return geo_emb
 
 
-class SpatialTransformer(nn.Module):
-    """Spatial Transformer for spatial feature mixing.
-
-    This module applies a standard Transformer encoder to a sequence of spatial tokens
-    (patch embeddings), allowing information to be mixed across all spatial locations.
-
-    Key points:
-        - Uses multi-head self-attention and feedforward layers.
-        - Designed to operate on flattened spatial tokens.
-    """
-
-    def __init__(self, embed_dim=128, depth=2, num_heads=4, mlp_ratio=4.0, dropout=0.0):
-        """Initialize the spatial transformer.
-        Args:
-            embed_dim: Dimension of the embedding. Default is 128.
-                The embedding dimensions are multiples of 64 (e.g., 64, 128,
-                256). This can be tuned.
-            depth: Number of transformer encoder layers. Default is 2. This can be
-                increased for more complex spatial mixing.
-            num_heads: Number of attention heads in each layer. Default is 4.
-                When embed_dim is 128, 4 heads is a common choice.
-            mlp_ratio: Ratio of feedforward hidden dimension to embed_dim. Default is 4.0.
-            dropout: Dropout rate applied to attention and feedforward layers. Default is 0.0.
-        """
-        super().__init__()
-
-        # a single Transformer encoder block that
-        # performs self-attention and feedforward processing
-        encoder_layer = nn.TransformerEncoderLayer(
-            d_model=embed_dim,
-            nhead=num_heads,
-            dim_feedforward=int(embed_dim * mlp_ratio),
-            batch_first=True,
-            dropout=dropout,
-            activation="gelu",
-        )
-        # stack multiple layers to form the full encoder
-        self.enc = nn.TransformerEncoder(encoder_layer, num_layers=depth)
-
-    def forward(self, x):
-        """Forward pass of the spatial transformer.
-        Args:
-            x: Input tensor of shape (B, N, C), where N = number of spatial tokens (H'*W') and
-                C = embedding dimension
-        Returns:
-            Tensor of shape (B, N, C) with spatially mixed features across patches
-        """
-        return self.enc(x)
-
-
 class SpatioTemporalModel(nn.Module):
     """Spatio-Temporal Model for Monthly Prediction.
 
@@ -645,8 +596,6 @@ def __init__(
         patch_size=(1, 4, 4),
         hidden=256,
         overlap=1,
-        spatial_depth=2,
-        spatial_heads=4,
         dropout=0.0,
         sh_dim=96,
         scale_dim=10,
@@ -662,8 +611,6 @@ def __init__(
             overlap: Overlap for deconvolution in the decoder
             max_H: Maximum spatial height for 2D positional encoding
             max_W: Maximum spatial width for 2D positional encoding
-            spatial_depth: Number of layers in the spatial Transformer
-            spatial_heads: Number of attention heads in the spatial Transformer
             dropout: Dropout rate for regularization in various components. Increase it if there is overfitting.
             sh_dim: Dimension of spherical harmonics based pca of geo-position
             scale_dim: Dimension of patch-level patch-scale features
@@ -691,12 +638,7 @@ def __init__(
             scale_dim=scale_dim,
             embed_dim=embed_dim,
         )
-        self.spatial_tr = SpatialTransformer(
-            embed_dim=embed_dim,
-            depth=spatial_depth,
-            num_heads=spatial_heads,
-            dropout=dropout,
-        )
+
         self.decoder = MonthlyConvDecoder(
             embed_dim=embed_dim,
             patch_h=patch_size[1],
@@ -807,15 +749,6 @@ def forward(
         geo_emb = geo_emb_flat.view(B, 1, Hp * Wp, embed_dim)  # (B, 1, Hp*Wp, E)
         x = agg_latent + geo_emb  # (B, M, Hp*Wp, E)
 
-        # Step 4: Spatial mixing with Transformer
-        # spatial transformer input shape = (B, N, C), output shape = (B, N, C) C: embedding dimension
-        # M is folded in B.
-
-        C = x.shape[-1]
-        x = x.reshape(B * M, Hp * Wp, C)
-        x = self.spatial_tr(x)
-        x = x.view(B, M, Hp * Wp, C)
-
         # Step 5: Decode to full-resolution 2D map
         # decoder input shape is (B, M*Hp*Wp, C), C: embedding dimension
         # decoder output shape is (B, M, H, W)

From 63ba353c02a41abe6e5b3bfc5e053114e950b4d0 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Thu, 27 Aug 2026 12:56:39 +0200
Subject: [PATCH 04/11] add some model improvemnets

---
 climanet/st_encoder_decoder.py | 53 +++++++++++++---------------------
 1 file changed, 20 insertions(+), 33 deletions(-)

diff --git a/climanet/st_encoder_decoder.py b/climanet/st_encoder_decoder.py
index 0004bc3..737cbd5 100644
--- a/climanet/st_encoder_decoder.py
+++ b/climanet/st_encoder_decoder.py
@@ -41,9 +41,6 @@ def __init__(self, in_chans=1, embed_dim=128, patch_size=(1, 4, 4)):
             2 * in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
         )
 
-        # norm is LayerNorm over the embedding dimension to normalize patch embeddings
-        self.norm = nn.LayerNorm(embed_dim)
-
     def forward(self, x, mask):
         """Forward pass with masking support via an additional validity channel.
         Args:
@@ -70,7 +67,6 @@ def forward(self, x, mask):
         x = self.proj(x)
         x = x.flatten(2).transpose(1, 2)
 
-        x = self.norm(x)
         return x
 
 
@@ -267,11 +263,11 @@ def __init__(self, embed_dim=128, dropout=0.0, chunk_size=1024):
 
         # Day scorer (within each month)
         self.day_scorer = nn.Sequential(
-            nn.LayerNorm(embed_dim),  # normalizing features
-            nn.Linear(embed_dim, embed_dim),  # learns temporal feature transformation
-            nn.GELU(),  # adds non-linearity to capture complex temporal patterns
+            nn.LayerNorm(2 * embed_dim),
+            nn.Linear(2 * embed_dim, embed_dim),
+            nn.GELU(),
             nn.Dropout(dropout),
-            nn.Linear(embed_dim, 1),  # project to a single score
+            nn.Linear(embed_dim, 1),
         )
 
         # Cross month mixing
@@ -330,13 +326,19 @@ def forward(self, x, M, time_features, padded_days_mask=None):
         month_emb = self.month_embed(time_features)
         token_emb = temp_emb + month_emb
 
-        day_logits = self.day_scorer(token_emb).squeeze(-1)
+        # Make day attention depend on both the actual daily data
+        # and the temporal/calendar information.
+        token_emb_seq = token_emb.unsqueeze(1)  # (B, 1, M, Tp, C)
+        day_score_input = torch.cat(
+            [seq,token_emb_seq.expand(-1, HW, -1, -1, -1)], dim=-1,
+        )
+
+        day_logits = self.day_scorer(day_score_input).squeeze(-1)
 
         if padded_days_mask is not None:
-            day_logits = day_logits.masked_fill(padded_days_mask, float("-inf"))
+            day_logits = day_logits.masked_fill(padded_days_mask.unsqueeze(1), float("-inf"))
 
-        day_w = torch.softmax(day_logits, dim=-1)
-        day_w = day_w.unsqueeze(1).unsqueeze(-1)
+        day_w = torch.softmax(day_logits, dim=-1).unsqueeze(-1)
 
         # avoid implicit expand copies
         seq = seq.reshape(B, HW, M, Tp, C)
@@ -346,7 +348,6 @@ def forward(self, x, M, time_features, padded_days_mask=None):
 
         # avoid broadcast materialization
         aggregated_month_embed = (token_emb_seq * day_w).sum(dim=3)
-
         month_tokens = month_tokens + aggregated_month_embed
 
         z = month_tokens.reshape(B * HW, M, C)
@@ -379,7 +380,6 @@ def __init__(
         patch_h=4,
         patch_w=4,
         hidden=128,
-        overlap=1,
         dropout=0.0,
     ):
         """
@@ -391,14 +391,11 @@ def __init__(
             patch_w: Patch width
             hidden: Hidden dimension in the decoder for mixing channel features.
                 The default is 128, which can be tuned.
-            overlap: Overlap size for deconvolution. It creates smooth blending
-                between adjacent upsampled patches. Default is 1, no overlap at edges.
             dropout: Dropout rate for regularization in the refinement block. Default is 0.0.
         """
         super().__init__()
         self.patch_h = patch_h
         self.patch_w = patch_w
-        self.overlap = overlap
 
         # Mix channel features on the patch grid (Hp, Wp)
         # Input shape: (B, embed_dim, Hp, Wp) → Output shape: (B, hidden, Hp, Wp)
@@ -408,21 +405,13 @@ def __init__(
         self.proj = nn.Conv2d(in_channels, out_channels, kernel_size=1)
 
         # Upsample to full resolution
-        # With kernel = stride + 2*overlap and padding=overlap,
-        # output size is exact: H = Hp*patch_h, W = Wp*patch_w (no output_padding needed).
-        k_h = patch_h + 2 * overlap
-        k_w = patch_w + 2 * overlap
-        # As spatial size increases, channel count decreases to keep computation
-        # manageable; here  hidden // 2 is a design choice.
         in_channels, out_channels = hidden, hidden // 2
         self.deconv = nn.ConvTranspose2d(
             in_channels,
             out_channels,
-            kernel_size=(k_h, k_w),
+            kernel_size=(patch_h, patch_w),
             stride=(patch_h, patch_w),
-            padding=overlap,
-            output_padding=0,
-            bias=True,
+            padding=0,
         )
 
         # Final conv head to get single channel output kernel_size=3 is the most
@@ -433,12 +422,11 @@ def __init__(
         # Refinement block: a small conv layers to smooth patch boundaries
         self.refine = nn.Sequential(
             nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
-            nn.GroupNorm(num_groups=8, num_channels=out_channels),
+            # nn.GroupNorm(num_groups=8, num_channels=out_channels),
             nn.GELU(),
-
             nn.Dropout2d(dropout),
             nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
-            nn.GroupNorm(num_groups=8, num_channels=out_channels),
+            # nn.GroupNorm(num_groups=8, num_channels=out_channels),
             nn.GELU(),
         )
 
@@ -475,7 +463,9 @@ def forward(self, latent, M, out_H, out_W, land_mask=None):
         out = self.deconv(out)  # (B*M, hidden//2, H, W)
 
         # Refinement CNN to smooth boundaries
+        refine_input = out
         out = self.refine(out)  # (B*M, hidden//2, H, W)
+        out = out + refine_input  # Residual connection to preserve original features
 
         # Apply final conv head to get single channel output
         out = self.head(out)  # (B*M, 1, H, W)
@@ -595,7 +585,6 @@ def __init__(
         embed_dim=128,
         patch_size=(1, 4, 4),
         hidden=256,
-        overlap=1,
         dropout=0.0,
         sh_dim=96,
         scale_dim=10,
@@ -608,7 +597,6 @@ def __init__(
             embed_dim: Dimension of the patch embedding
             patch_size: Tuple of (T, H, W) patch sizes for temporal and spatial patching
             hidden: Hidden dimension used in the decoder
-            overlap: Overlap for deconvolution in the decoder
             max_H: Maximum spatial height for 2D positional encoding
             max_W: Maximum spatial width for 2D positional encoding
             dropout: Dropout rate for regularization in various components. Increase it if there is overfitting.
@@ -644,7 +632,6 @@ def __init__(
             patch_h=patch_size[1],
             patch_w=patch_size[2],
             hidden=hidden,
-            overlap=overlap,
             dropout=dropout,
         )
         self.patch_size = patch_size

From 222f2e591c4310d5c8e63a83cdeb314e8ea47e3e Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Thu, 27 Aug 2026 15:49:42 +0200
Subject: [PATCH 05/11] uncomment groupnorm in decoder

---
 climanet/st_encoder_decoder.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/climanet/st_encoder_decoder.py b/climanet/st_encoder_decoder.py
index 737cbd5..39ca4d7 100644
--- a/climanet/st_encoder_decoder.py
+++ b/climanet/st_encoder_decoder.py
@@ -422,11 +422,11 @@ def __init__(
         # Refinement block: a small conv layers to smooth patch boundaries
         self.refine = nn.Sequential(
             nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
-            # nn.GroupNorm(num_groups=8, num_channels=out_channels),
+            nn.GroupNorm(num_groups=8, num_channels=out_channels),
             nn.GELU(),
             nn.Dropout2d(dropout),
             nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
-            # nn.GroupNorm(num_groups=8, num_channels=out_channels),
+            nn.GroupNorm(num_groups=8, num_channels=out_channels),
             nn.GELU(),
         )
 

From 0ff075fc2bcc4bea9dee9fa9409278f9a59e6ac3 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Fri, 28 Aug 2026 15:13:30 +0200
Subject: [PATCH 06/11] improve docstring

---
 climanet/st_encoder_decoder.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/climanet/st_encoder_decoder.py b/climanet/st_encoder_decoder.py
index 39ca4d7..384cfb3 100644
--- a/climanet/st_encoder_decoder.py
+++ b/climanet/st_encoder_decoder.py
@@ -572,7 +572,7 @@ class SpatioTemporalModel(nn.Module):
     The model pipeline:
         1. Encode spatio-temporal patches using VideoEncoder.
         2. Aggregate temporal information for each spatial patch via TemporalAttentionAggregator.
-        3. Add 2D spatial positional encodings and mix spatial features with SpatialTransformer.
+        3. Add 2D spatial positional encodings.
         4. Decode aggregated tokens into a full-resolution 2D map using MonthlyConvDecoder.
 
     Output:
@@ -736,7 +736,7 @@ def forward(
         geo_emb = geo_emb_flat.view(B, 1, Hp * Wp, embed_dim)  # (B, 1, Hp*Wp, E)
         x = agg_latent + geo_emb  # (B, M, Hp*Wp, E)
 
-        # Step 5: Decode to full-resolution 2D map
+        # Step 4: Decode to full-resolution 2D map
         # decoder input shape is (B, M*Hp*Wp, C), C: embedding dimension
         # decoder output shape is (B, M, H, W)
         if self.use_checkpoint:

From 1fb8ba073438b2b87b0d15e5a1e585435d7e5c50 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Fri, 28 Aug 2026 15:25:36 +0200
Subject: [PATCH 07/11] remove patch from dataset, adjust api in source modules

---
 climanet/dataset.py | 18 +++++++++---------
 climanet/predict.py | 14 +++++++-------
 climanet/train.py   | 14 +++++++-------
 climanet/tune.py    |  9 ++-------
 4 files changed, 25 insertions(+), 30 deletions(-)

diff --git a/climanet/dataset.py b/climanet/dataset.py
index b7d73c8..53f89d4 100644
--- a/climanet/dataset.py
+++ b/climanet/dataset.py
@@ -325,19 +325,19 @@ def __getitem__(self, idx):
 
         # Convert to dictionary
         return {
-            "daily_patch": daily_t_crop,  # (C=1, pm, T=31, pH, pW)
-            "monthly_patch": monthly_t_crop,  # (pm, pH, pW)
-            "daily_mask_patch": daily_mask_t_crop,  # (C=1, pm, T=31, pH, pW)
-            "land_mask_patch": land_t_crop,  # (pH,pW) True=Land
-            "daily_timef_patch": daily_timef_crop,  # (pm, T=31, 3)
+            "input_data": daily_t_crop,  # (C=1, pm, T=31, pH, pW)
+            "monthly_data": monthly_t_crop,  # (pm, pH, pW)
+            "input_data_mask": daily_mask_t_crop,  # (C=1, pm, T=31, pH, pW)
+            "land_mask": land_t_crop,  # (pH,pW) True=Land
+            "input_data_timef": daily_timef_crop,  # (pm, T=31, 3)
             "padded_days_mask": padded_days_mask_crop,  # (pm, T=31) True=padded
-            "scale_feature_patch": scale_feature_t,  # (10,)
-            "geo_pos_embedding_patch": geo_pos_embedding_t,  # (sh_embed_dim,)
+            "scale_feature": scale_feature_t,  # (10,)
+            "geo_pos_embedding": geo_pos_embedding_t,  # (sh_embed_dim,)
             "sh_embed_dim": self.sh_embed_dim_t,
             "harmonic_order": self.harmonic_order_t,
             "coords": torch.tensor([m, i, j]),
-            "lat_patch": lat_crop,  # (pH,)
-            "lon_patch": lon_crop,  # (pW,)
+            "lat": lat_crop,  # (pH,)
+            "lon": lon_crop,  # (pW,)
         }
 
     def __getitems__(self, indices):
diff --git a/climanet/predict.py b/climanet/predict.py
index c7b6005..f959870 100644
--- a/climanet/predict.py
+++ b/climanet/predict.py
@@ -82,17 +82,17 @@ def _move_batch_to_device(batch: dict, device: str):
 def _run_one_batch(model: torch.nn.Module, batch: dict, device: str):
     batch = _move_batch_to_device(batch, device)
     pred = model(
-        batch["daily_patch"],
-        batch["daily_mask_patch"],
-        batch["daily_timef_patch"],
-        batch["land_mask_patch"],
-        batch["geo_pos_embedding_patch"],
-        batch["scale_feature_patch"],
+        batch["input_data"],
+        batch["input_data_mask"],
+        batch["input_data_timef"],
+        batch["land_mask"],
+        batch["geo_pos_embedding"],
+        batch["scale_feature"],
         batch["padded_days_mask"],
     )  # (B, M, H, W)
 
     # Compute masked loss
-    loss = compute_masked_loss(pred, batch["monthly_patch"], batch["land_mask_patch"])
+    loss = compute_masked_loss(pred, batch["monthly_data"], batch["land_mask"])
     return loss, pred
 
 
diff --git a/climanet/train.py b/climanet/train.py
index 682133f..61758e7 100644
--- a/climanet/train.py
+++ b/climanet/train.py
@@ -41,17 +41,17 @@ def _move_batch_to_device(batch: dict, device: str):
 def _run_one_batch(model: torch.nn.Module, batch: dict, device):
     batch = _move_batch_to_device(batch, device)
     pred = model(
-        batch["daily_patch"],
-        batch["daily_mask_patch"],
-        batch["daily_timef_patch"],
-        batch["land_mask_patch"],
-        batch["geo_pos_embedding_patch"],
-        batch["scale_feature_patch"],
+        batch["input_data"],
+        batch["input_data_mask"],
+        batch["input_data_timef"],
+        batch["land_mask"],
+        batch["geo_pos_embedding"],
+        batch["scale_feature"],
         batch["padded_days_mask"],
     )  # (B, M, H, W)
 
     # Compute masked loss
-    return compute_masked_loss(pred, batch["monthly_patch"], batch["land_mask_patch"])
+    return compute_masked_loss(pred, batch["monthly_data"], batch["land_mask"])
 
 
 def _load_checkpoint(model, optimizer, loaded_checkpoint):
diff --git a/climanet/tune.py b/climanet/tune.py
index 6e071f3..7c99799 100644
--- a/climanet/tune.py
+++ b/climanet/tune.py
@@ -26,8 +26,9 @@ def _tune_data_preparation(data_config):
         padded_days_mask=padded_days_mask,
         time_features=time_features,
         land_mask=ray.get(data_config["land_mask_data"]),
-        patch_size=data_config["patch_size"],  # based on the patch_size in model
+        crop_size=data_config["crop_size"],  # based on the patch_size in model
         stride=data_config["stride"],
+        model_patch_size=data_config["model_patch_size"],
         sh_embed_dim=96,
         sh_order_L=10,
         verbose=False,
@@ -73,20 +74,14 @@ def _train(tune_config, static_args):
     set_seed()
 
     patch_size = tune_config["patch_size"]
-    overlap = tune_config["overlap"]
     embed_dim = tune_config["embed_dim"]
     dropout = tune_config["dropout"]
     hidden = tune_config["hidden"]
-    spatial_depth = tune_config["spatial_depth"]
-    spatial_heads = tune_config["spatial_heads"]
     model = SpatioTemporalModel(
         patch_size=(1, patch_size, patch_size),
-        overlap=overlap,
         embed_dim=embed_dim,
         dropout=dropout,
         hidden=hidden,
-        spatial_depth=spatial_depth,
-        spatial_heads=spatial_heads,
     )
 
     _ = train_monthly_model(

From 09c55db3cc5df06e21c05a87aa1ed6b451972fb4 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Fri, 28 Aug 2026 16:20:07 +0200
Subject: [PATCH 08/11] fix linters

---
 climanet/dataset.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/climanet/dataset.py b/climanet/dataset.py
index 53f89d4..9636e3a 100644
--- a/climanet/dataset.py
+++ b/climanet/dataset.py
@@ -344,8 +344,8 @@ def __getitems__(self, indices):
         return [self.__getitem__(i) for i in indices]
 
     def _compute_patch_geo_pos_embedding(self, i: int, j: int):
-        pm, ph, pw = self.crop_size
-        model_patch_m, model_patch_h, model_patch_w = self.model_patch_size
+        _, ph, pw = self.crop_size
+        _, model_patch_h, model_patch_w = self.model_patch_size
         Hp = ph // model_patch_h
         Wp = pw // model_patch_w
 
@@ -368,8 +368,8 @@ def _compute_patch_geo_pos_embedding(self, i: int, j: int):
         return torch.stack(geo_pos_embeddings_list)
 
     def _compute_patch_scale_features(self, i: int, j: int):
-        pm, ph, pw = self.crop_size
-        model_patch_m, model_patch_h, model_patch_w = self.model_patch_size
+        _, ph, pw = self.crop_size
+        _, model_patch_h, model_patch_w = self.model_patch_size
         Hp = ph // model_patch_h
         Wp = pw // model_patch_w
 

From 814f1515c0503bcd33abe534bb45d47ac1214ed0 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Fri, 28 Aug 2026 16:37:13 +0200
Subject: [PATCH 09/11] fix tests

---
 tests/test_dataset.py | 32 ++++++++++++++++++--------------
 tests/test_train.py   | 22 +++++++---------------
 2 files changed, 25 insertions(+), 29 deletions(-)

diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 9d9dd18..2a01ae3 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -64,21 +64,22 @@ def test_len_and_shapes():
         time_features=time_features,
         monthly_da=monthly_da,
         land_mask=land_mask,
-        patch_size=(1, 2, 2),
+        crop_size=(1, 2, 2),
+        model_patch_size=(1, 1, 1),
     )
 
     assert len(dataset) == 4
 
     sample = dataset[0]
     assert torch.equal(sample["coords"], torch.tensor([0, 0, 0]))
-    assert sample["daily_patch"].shape == (1, 1, 31, 2, 2)
-    assert sample["monthly_patch"].shape == (1, 2, 2)
-    assert sample["daily_mask_patch"].shape == (1, 1, 31, 2, 2)
-    assert sample["daily_timef_patch"].shape == (1, 31, 3)
-    assert sample["daily_patch"].dtype == torch.float32
-    assert sample["monthly_patch"].dtype == torch.float32
-    assert sample["daily_mask_patch"].dtype == torch.bool
-    assert sample["daily_timef_patch"].dtype == torch.float32
+    assert sample["input_data"].shape == (1, 1, 31, 2, 2)
+    assert sample["monthly_data"].shape == (1, 2, 2)
+    assert sample["input_data_mask"].shape == (1, 1, 31, 2, 2)
+    assert sample["input_data_timef"].shape == (1, 31, 3)
+    assert sample["input_data"].dtype == torch.float32
+    assert sample["monthly_data"].dtype == torch.float32
+    assert sample["input_data_mask"].dtype == torch.bool
+    assert sample["input_data_timef"].dtype == torch.float32
 
 
 def test_index_bounds():
@@ -93,7 +94,8 @@ def test_index_bounds():
         time_features=time_features,
         monthly_da=monthly_da,
         land_mask=land_mask,
-        patch_size=(1, 2, 2),
+        crop_size=(1, 2, 2),
+        model_patch_size=(1, 1, 1),
     )
 
     with pytest.raises(IndexError):
@@ -115,14 +117,15 @@ def test_index_mapping_and_mask_values():
         time_features=time_features,
         monthly_da=monthly_da,
         land_mask=land_mask,
-        patch_size=(1, 2, 2),
+        crop_size=(1, 2, 2),
+        model_patch_size=(1, 1, 1),
     )
 
     sample = dataset[3]
     assert torch.equal(sample["coords"], torch.tensor([0, 2, 2]))
 
     expected_mask = land_mask.isel(lat=slice(2, 4), lon=slice(2, 4)).to_numpy()
-    assert torch.equal(sample["land_mask_patch"], torch.from_numpy(expected_mask))
+    assert torch.equal(sample["land_mask"], torch.from_numpy(expected_mask))
 
 
 def test_time_feature_generation():
@@ -137,11 +140,12 @@ def test_time_feature_generation():
         time_features=time_features,
         monthly_da=monthly_da,
         land_mask=land_mask,
-        patch_size=(1, 2, 2),
+        crop_size=(1, 2, 2),
+        model_patch_size=(1, 1, 1),
     )
 
     sample = dataset[0]
     expected_time_feature = torch.tensor(
         [np.float32(0.), np.float32(2 * np.pi * 6 / 365.24), np.float32(0.0)]
     )
-    assert torch.equal(sample["daily_timef_patch"][0, 5, :], expected_time_feature)
+    assert torch.equal(sample["input_data_timef"][0, 5, :], expected_time_feature)
diff --git a/tests/test_train.py b/tests/test_train.py
index bf6da45..1691d77 100644
--- a/tests/test_train.py
+++ b/tests/test_train.py
@@ -10,20 +10,14 @@
 def dummy_batch():
     # a dummy batch for testing
     return {
-        "daily_patch": torch.rand(1, 1, 2, 31, 40, 40),
-        "monthly_patch": torch.rand(1, 2, 40, 40),
-        "daily_mask_patch": torch.rand(1, 1, 2, 31, 40, 40) > 0.5,  # boolean mask
-        "land_mask_patch": torch.rand(1, 40, 40) > 0.5,  # boolean mask
-        "daily_timef_patch": torch.rand(1, 2, 31, 3),
+        "input_data": torch.rand(1, 1, 2, 31, 40, 40),
+        "monthly_data": torch.rand(1, 2, 40, 40),
+        "input_data_mask": torch.rand(1, 1, 2, 31, 40, 40) > 0.5,  # boolean mask
+        "land_mask": torch.rand(1, 40, 40) > 0.5,  # boolean mask
+        "input_data_timef": torch.rand(1, 2, 31, 3),
         "padded_days_mask": torch.rand(1, 2, 31) > 0.5,  # boolean mask
-        "scale_feature_patch": torch.rand(1, 10),
-        "geo_pos_embedding_patch": torch.rand(1, 96),
-        "sh_embed_dim": torch.rand(1),
-        "harmonic_order": torch.rand(1),
-        "scale_f_dim": torch.rand(1),
-        "coords": torch.rand(1, 2),
-        "lat_patch": torch.rand(1, 40),
-        "lon_patch": torch.rand(1, 40),
+        "scale_feature": torch.rand(1, 100, 10),
+        "geo_pos_embedding": torch.rand(1, 100, 96),
     }
 
 
@@ -36,7 +30,6 @@ def test_model_meta_device(dummy_batch):
     """
     model = SpatioTemporalModel(
         patch_size=(1, 4, 4),
-        overlap=2,
         embed_dim=64,
         dropout=0.2,
         hidden=64,
@@ -64,7 +57,6 @@ def test_model_fake_tensor(dummy_batch):
     """
     model = SpatioTemporalModel(
         patch_size=(1, 4, 4),
-        overlap=2,
         embed_dim=64,
         dropout=0.2,
         hidden=64,

From 76842bd90c87a361ffc2169cefdc5c2c0aa2917e Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Fri, 28 Aug 2026 16:37:48 +0200
Subject: [PATCH 10/11] fix docstring and comments

---
 climanet/dataset.py            | 4 ++--
 climanet/st_encoder_decoder.py | 2 ++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/climanet/dataset.py b/climanet/dataset.py
index 9636e3a..4053cfc 100644
--- a/climanet/dataset.py
+++ b/climanet/dataset.py
@@ -331,8 +331,8 @@ def __getitem__(self, idx):
             "land_mask": land_t_crop,  # (pH,pW) True=Land
             "input_data_timef": daily_timef_crop,  # (pm, T=31, 3)
             "padded_days_mask": padded_days_mask_crop,  # (pm, T=31) True=padded
-            "scale_feature": scale_feature_t,  # (10,)
-            "geo_pos_embedding": geo_pos_embedding_t,  # (sh_embed_dim,)
+            "scale_feature": scale_feature_t,  # (Hp*Wp, scale_dim)
+            "geo_pos_embedding": geo_pos_embedding_t,  # (Hp*Wp, sh_embed_dim)
             "sh_embed_dim": self.sh_embed_dim_t,
             "harmonic_order": self.harmonic_order_t,
             "coords": torch.tensor([m, i, j]),
diff --git a/climanet/st_encoder_decoder.py b/climanet/st_encoder_decoder.py
index 384cfb3..32f76c4 100644
--- a/climanet/st_encoder_decoder.py
+++ b/climanet/st_encoder_decoder.py
@@ -655,6 +655,8 @@ def forward(
             daily_timef: Tensor of shape (B, M, T, 2) containing the cyclically phase encoded day-of-year
                 and hour-of-day information for the daily data
             land_mask_patch: Boolean tensor of shape (B, H, W) to mask land areas in the output
+            geo_pos_embedding_patch: Tensor of shape (B, Hp*Wp, sh_embed_dim) containing the geo-positional embeddings for each model patch within the dataset patch
+            scale_feature_patch: Tensor of shape (B, Hp*Wp, scale_dim) containing the scale features for each model patch within the dataset patch
             padded_days_mask: Optional boolean tensor of shape (B, M, T) indicating which day tokens are padded
                  (True for padded tokens). Used to mask out padded tokens in temporal attention.
         Returns:

From e9cdd56760196ad86b4a51b487aaac9654dbed16 Mon Sep 17 00:00:00 2001
From: SarahAlidoost <f.alidoost@esciencecenter.nl>
Date: Fri, 28 Aug 2026 16:43:01 +0200
Subject: [PATCH 11/11] fix nbs

---
 notebooks/example_daily.ipynb  | 235 ++++++++++++---------------------
 notebooks/example_hourly.ipynb |  56 ++++----
 2 files changed, 112 insertions(+), 179 deletions(-)

diff --git a/notebooks/example_daily.ipynb b/notebooks/example_daily.ipynb
index 044d83f..e07c011 100644
--- a/notebooks/example_daily.ipynb
+++ b/notebooks/example_daily.ipynb
@@ -43,13 +43,13 @@
     "var_name = \"tos\"\n",
     "\n",
     "# 1 month train, 1 month validation and test\n",
-    "daily_data = xr.open_mfdataset(data_folder / f\"202101_day_ERA5dc_masked_{var_name}.nc\")\n",
-    "daily_data_validation = xr.open_mfdataset(data_folder / f\"202102_day_ERA5dc_masked_{var_name}.nc\")\n",
-    "daily_data_test = xr.open_mfdataset(data_folder / f\"202103_day_ERA5dc_masked_{var_name}.nc\")\n",
+    "daily_data = xr.open_mfdataset(data_folder / f\"202001_day_ERA5dc_masked_{var_name}.nc\")\n",
+    "daily_data_validation = xr.open_mfdataset(data_folder / f\"202101_day_ERA5dc_masked_{var_name}.nc\")\n",
+    "daily_data_test = xr.open_mfdataset(data_folder / f\"202201_day_ERA5dc_masked_{var_name}.nc\")\n",
     "\n",
-    "monthly_data = xr.open_mfdataset(data_folder / f\"202101_mon_ERA5dc_full_{var_name}.nc\")\n",
-    "monthly_data_validation = xr.open_mfdataset(data_folder / f\"202102_mon_ERA5dc_full_{var_name}.nc\")\n",
-    "monthly_data_test = xr.open_mfdataset(data_folder / f\"202103_mon_ERA5dc_full_{var_name}.nc\")\n",
+    "monthly_data = xr.open_mfdataset(data_folder / f\"202001_mon_ERA5dc_full_{var_name}.nc\")\n",
+    "monthly_data_validation = xr.open_mfdataset(data_folder / f\"202101_mon_ERA5dc_full_{var_name}.nc\")\n",
+    "monthly_data_test = xr.open_mfdataset(data_folder / f\"202201_mon_ERA5dc_full_{var_name}.nc\")\n",
     "\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 = xr.open_dataset(file_name)\n",
@@ -74,14 +74,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(31, 160, 400) (1, 160, 400)\n"
+      "(31, 40, 40) (1, 40, 40)\n"
      ]
     }
    ],
    "source": [
     "# coordinates of subset\n",
-    "lon_subset = slice(-50, 50)  # one lon -179.9 is nan, check data\n",
-    "lat_subset = slice(-30, 10)\n",
+    "lon_subset = slice(-0, 10)  # one lon -179.9 is nan, check data\n",
+    "lat_subset = slice(-5, 5)\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",
@@ -125,18 +125,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "id": "1ceb7756-f274-4823-bded-87c0f8f16a79",
    "metadata": {},
    "outputs": [],
    "source": [
     "patch_size = (1, 4, 4)\n",
-    "model = SpatioTemporalModel(patch_size=patch_size, overlap=2, embed_dim=64, dropout=0.2, hidden=64)"
+    "model = SpatioTemporalModel(patch_size=patch_size, embed_dim=64, dropout=0.1, hidden=64)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "id": "5840197d-3a4f-4942-ac42-f56e0fdaed0f",
    "metadata": {},
    "outputs": [],
@@ -144,7 +144,7 @@
     "# Device and resources\n",
     "device = \"cpu\"\n",
     "compute_threads = 4\n",
-    "dataloader_num_workers = 1\n",
+    "dataloader_num_workers = 0\n",
     "model = configure_compute_resources(model, device=device, compute_threads=compute_threads, dataloader_num_workers=dataloader_num_workers)"
    ]
   },
@@ -173,6 +173,7 @@
    },
    "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",
@@ -204,17 +205,17 @@
      "output_type": "stream",
      "text": [
       "Creating dataset:\n",
-      "Patch grid (m x i x j): 1 x 16 x 46 = 736 patches\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",
-      "736\n"
+      "1\n"
      ]
     }
    ],
    "source": [
     "# create dataset config\n",
-    "num_patches = (10, 10)\n",
-    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
-    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\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",
@@ -223,12 +224,13 @@
     "    padded_days_mask=padded_days_mask,\n",
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\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",
+    "    load_lazy=False,   # set it to true when data is large\n",
     ")\n",
     "print(len(dataset_train))"
    ]
@@ -248,6 +250,7 @@
    "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",
@@ -277,16 +280,16 @@
      "output_type": "stream",
      "text": [
       "Creating dataset:\n",
-      "Patch grid (m x i x j): 1 x 16 x 46 = 736 patches\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",
-      "736\n"
+      "1\n"
      ]
     }
    ],
    "source": [
-    "num_patches = (10, 10)\n",
-    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
-    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\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_validation = STDataset(\n",
     "    input_da=input_da,\n",
@@ -295,12 +298,13 @@
     "    padded_days_mask=padded_days_mask,\n",
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\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",
+    "    load_lazy=False,  # set it to true when data is large\n",
     ")\n",
     "print(len(dataset_validation))"
    ]
@@ -314,7 +318,7 @@
     {
      "data": {
       "text/plain": [
-       "DataLoaderConfig(batch_size=10, shuffle=True, num_workers=1, pin_memory=False, persistent_workers=True, device='cpu', multiprocessing_context='spawn')"
+       "DataLoaderConfig(batch_size=10, shuffle=True, num_workers=0, pin_memory=False, persistent_workers=False, device='cpu', multiprocessing_context=None)"
       ]
      },
      "execution_count": 13,
@@ -329,26 +333,26 @@
     "    shuffle=True,\n",
     "    num_workers= dataloader_num_workers,\n",
     "    pin_memory=False,\n",
-    "    persistent_workers=True,\n",
+    "    persistent_workers=False,  # set it to true when num_workers >0 and lazy data\n",
     "    device=device,\n",
-    "    multiprocessing_context=\"spawn\", # keep this when num_workers >0 and lazy data\n",
+    "    multiprocessing_context=None, # set it to \"spawn\" when num_workers >0 and lazy data\n",
     ")\n",
     "dataloader_config"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "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, device='cpu', verbose=True, verbose_epoch_interval=20, tune_checkpoint=False, store_model=True)"
+       "TrainConfig(calculate_residuals=True, num_epoch=100, patience=10, accumulation_steps=2, optimizer_lr=0.001, device='cpu', verbose=True, verbose_epoch_interval=20, tune_checkpoint=False, store_model=True, store_logs=True)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "id": "b9742e67-165f-4b5a-bab0-27b8b0d7070d",
    "metadata": {},
    "outputs": [
@@ -388,20 +392,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0: gap between train and val loss: -0.043650\n",
-      "Epoch 0: best_loss = 0.027357\n",
-      "Epoch 20: gap between train and val loss: 0.007366\n",
-      "Epoch 20: best_loss = 0.022358\n",
-      "Epoch 40: gap between train and val loss: 0.008106\n",
-      "Epoch 40: best_loss = 0.022358\n",
-      "Epoch 60: gap between train and val loss: 0.008217\n",
-      "Epoch 60: best_loss = 0.022358\n",
-      "Epoch 80: gap between train and val loss: 0.008329\n",
-      "Epoch 80: best_loss = 0.022358\n",
-      "Training complete. Best loss: 0.022358\n",
-      "Model saved to runs_daily/best_model.pth\n",
-      "CPU times: user 1h 19min 40s, sys: 45.8 s, total: 1h 20min 26s\n",
-      "Wall time: 1h 12min 59s\n"
+      "Epoch 0: gap between train and val loss: 0.051503\n",
+      "Epoch 0: best_loss = 0.302883\n",
+      "Epoch 20: gap between train and val loss: 0.002308\n",
+      "Epoch 20: best_loss = 0.046616\n",
+      "Epoch 40: gap between train and val loss: 0.010280\n",
+      "Epoch 40: best_loss = 0.030662\n",
+      "Epoch 60: gap between train and val loss: 0.015593\n",
+      "Epoch 60: best_loss = 0.029539\n",
+      "Epoch 80: gap between train and val loss: 0.016490\n",
+      "Epoch 80: best_loss = 0.029539\n",
+      "Training complete. Best loss: 0.029539\n",
+      "Model saved to /home/sarah/GitHub/ClimaNet/notebooks/runs_daily/best_model.pth\n",
+      "CPU times: user 30.4 s, sys: 281 ms, total: 30.7 s\n",
+      "Wall time: 15.6 s\n"
      ]
     }
    ],
@@ -413,7 +417,7 @@
     "    dataset_train=dataset_train,\n",
     "    dataloader_config=dataloader_config,\n",
     "    training_config=training_config,\n",
-    "    dataset_validation=dataset_validation,\n",
+    "    dataset_validation=dataset_validation,  # here validation dataset is used to improve model generalization\n",
     "    run_dir=run_dir,\n",
     ")"
    ]
@@ -428,13 +432,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "id": "a2b81805",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -457,11 +461,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "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",
@@ -470,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "id": "4c30511a-49fc-4a4d-8bfc-61ec3857fde0",
    "metadata": {},
    "outputs": [],
@@ -482,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "id": "ac99fdf5-dfd8-44a0-957b-dea177ac92a1",
    "metadata": {},
    "outputs": [
@@ -491,16 +496,14 @@
      "output_type": "stream",
      "text": [
       "Creating dataset:\n",
-      "Patch grid (m x i x j): 1 x 16 x 46 = 736 patches\n",
-      "Overlap: 32 pixels (height), 32 pixels (width)\n",
-      "736\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": [
-    "num_patches = (10, 10)\n",
-    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
-    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\n",
+    "spatial_patch_size = monthly_da.shape[1:]  # the whole dataset \n",
     "\n",
     "dataset_test = STDataset(\n",
     "    input_da=input_da,\n",
@@ -509,8 +512,9 @@
     "    padded_days_mask=padded_days_mask,\n",
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\n",
-    "    stride=stride,\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",
@@ -521,17 +525,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 21,
    "id": "c3dadb35-a6a8-413c-869b-1fa90adfec7b",
    "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)"
+       "DataLoaderConfig(batch_size=10, shuffle=False, num_workers=0, pin_memory=False, persistent_workers=False, device='cpu', multiprocessing_context=None)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -540,7 +544,7 @@
     "# create dataloader config\n",
     "dataloader_config = DataLoaderConfig(\n",
     "    batch_size=10,\n",
-    "    shuffle=True,\n",
+    "    shuffle=False,  # no shuffling in inference\n",
     "    num_workers=0,\n",
     "    pin_memory=False,\n",
     "    persistent_workers=False,\n",
@@ -552,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "id": "dc9bffb1-5db9-46d0-bd0a-987772321451",
    "metadata": {},
    "outputs": [],
@@ -570,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "id": "7c2deb40-bee8-4973-80f0-9d9485eabf0c",
    "metadata": {},
    "outputs": [
@@ -578,81 +582,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Processed batch 1/74, with loss: 0.0303\n",
-      "Processed batch 2/74, with loss: 0.0230\n",
-      "Processed batch 3/74, with loss: 0.0260\n",
-      "Processed batch 4/74, with loss: 0.0476\n",
-      "Processed batch 5/74, with loss: 0.0320\n",
-      "Processed batch 6/74, with loss: 0.0272\n",
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-      "Processed batch 64/74, with loss: 0.0188\n",
-      "Processed batch 65/74, with loss: 0.0266\n",
-      "Processed batch 66/74, with loss: 0.0409\n",
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-      "Processed batch 69/74, with loss: 0.0195\n",
-      "Processed batch 70/74, with loss: 0.0396\n",
-      "Processed batch 71/74, with loss: 0.0398\n",
-      "Processed batch 72/74, with loss: 0.0265\n",
-      "Processed batch 73/74, with loss: 0.0293\n",
-      "Processed batch 74/74, with loss: 0.0206\n",
-      "Average loss over all batches: 0.0308\n"
+      "Processed batch 1/1, with loss: 0.0545\n",
+      "Average loss over all batches: 0.0545\n"
      ]
     }
    ],
@@ -669,17 +600,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "id": "370bf4d4-0f97-418e-ada3-9e958248d386",
    "metadata": {},
    "outputs": [],
    "source": [
-    "predictions_res = xr.open_dataset(f\"{run_dir}/202103_{var_name}_prediction_residual.nc\")"
+    "predictions_res = xr.open_dataset(f\"{run_dir}/202201_{var_name}_prediction_residual.nc\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "id": "76a795e8-b77c-4fb3-8c55-43d9996e2c5d",
    "metadata": {},
    "outputs": [],
@@ -692,13 +623,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "id": "14789287-c63f-49ad-bb45-e5fe4a7f039f",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 3 Axes>"
       ]
@@ -716,13 +647,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 27,
    "id": "7ba5d204-aefc-4103-8475-c802c603ad3f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 3 Axes>"
       ]
@@ -744,13 +675,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 28,
    "id": "a7420dc7-fe1b-4f34-a62e-5914091f04b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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dO35sPeZkvz/u7u5WZX/99ZchyXj33Xdz9GWLChUqGJKMjRs3Wsqyx9q104lmz56d4/tQr149Izg42Dh37pxVTC4uLkbv3r0tZdnHsCeeeMKq/4cfftgoU6aMVVlB08BsaSOvY8u1j4KOk+fOnTOCg4ONZs2aWZXXrl3buPfee3PU37VrV67HyqulpaUZtWrVMipVqmRkZGTkWud6poFlj7urj4fZRo0aZUiyOvYYhmHExsZavR+tW7c2jh07ZnOf2fKbBjZs2DDLMbJdu3bGl19+aUydOtUoVaqUUaVKlTynFOPWwTQw3JJmzpyp5cuX53jUqVOnwH39/f21a9cu7d+/3+5+f/rpJ7m6umrYsGFW5c8995wMw9DPP/8sSZZpF9l/ccw2dOjQPNv+z3/+k6Ps6r9epaam6uzZs7rnnnskSdu2bctRf8CAAZb/d3V1VcOGDWUYhtUVVfz9/VW9enWrKQu5+eabb2Q2m9WtWzedPXvW8ggNDVW1atW0evVqq/oeHh65TkOSpC5duigoKCjf/vJSsWJFy+f7888/a/r06UpMTFT79u2trmBz9XuVkZGhc+fOqWrVqvL397d6rwr6/P/991+tWrVK3bp108WLFy2v+9y5c4qJidH+/ft18uRJu19H9+7dFRAQYHme/Rf17M/hRvu99957FRgYaHVlovPnz2v58uXq3r271es/ceJErlPQ8vPVV1+pZs2aqlGjhtV4uPfeeyUpx3ho0aKFatWqlWtbAwcOlKurq139Z/voo48UFBSk4OBgRUVFacOGDRoxYoSGDx9uVa9Pnz5WY2L79u3av3+/Hn30UZ07d84Sf0pKilq3bq1169bJbDYrKytLy5YtU6dOnaymytSsWVMxMTEFxvf111+rbt26uZ4FK+hqSLmx9ZiTrU2bNlZnGurUqSNfX98Cv+/5qVWrlqKjoy3Po6KiJF0Zc1e/R9nl2X2dPn1a27dvV9++fa3OptapU0f33Xdfrn/Jv/Y42KxZM507d84yjc8WtrTx5ptv5vpvyLWP559/Ps9+zGazHnvsMV24cEHvvvuu1bbLly/Lw8Mjxz6enp6W7XkZMmSIdu/erRkzZlhNdb1R2X3aE1fPnj21fPlyLViwwHLmNb/Yr0dycrKkK9N7f/zxR3Xr1k0jR47Uhx9+qIMHD9o0RRU3N6aB4ZbUqFGjXE9lBwQEFHiN/ZdfflkPPfSQ7rzzTt11111q166devXqZVOic/ToUZUrV04+Pj5W5TVr1rRsz/6vi4uLKlWqZFXv2qkqV7u2rnTlB+ykSZP0xRdfKD4+3mpbYmJijvpX/3CQJD8/P3l6euaYSuDn55dj3cu19u/fL8MwVK1atVy3Z09dyhYWFmY1/ehqub02W3l7e6tNmzaW5+3atVPTpk3VsGFDvfbaa5b1N5cvX1ZsbKzmzJmjkydPWs3nv/q9KujzP3DggAzD0EsvvaSXXnop15ji4+MVFhZm1+u49rPJTlzOnz/vkH7d3NzUpUsXLViwQGlpafLw8NA333yjjIwMq2TlhRde0IoVK9SoUSNVrVpVbdu21aOPPmo1pS43+/fv1549e/JMOq8dn/l95jcyHh566CENGTJEJpNJPj4+ql27try9vQvsIzs57dOnT55tJyYmKi0tTZcvX8513FevXj3XH9hXO3jwoLp06WLLS7GJrcecbNeOM+nKWMseZ9cjt+OKpBzTmLLLs/vKjq169eo52qxZs6aWLVumlJQUq88vv+/JtVOqbI03tzYiIyNtais/Q4cO1dKlS/XJJ5+obt26Vtu8vLxyrOWSrvzRKXt7bqZOnaoPP/xQkydP1v33339dcSUnJ1sSAOnKH66CgoIsfdoTV4UKFSzrdHr27KlBgwapTZs22rdvn7y8vPLsyx7ZfXbr1s3qynpdu3ZVr169tHHjRqs/xOHWQ7ICXKN58+Y6ePCglixZol9++UX/+9//9Pbbb2vWrFnFekDM7R+vbt26aePGjRo1apTq1aunUqVKyWw2q127drle3jS3v1bn9Rds45rFudcym80ymUz6+eefc23j2nnc+c1hduT8ZunKDw0/Pz+tW7fOUjZ06FDNmTNHw4cPV3R0tPz8/GQymdSjRw+r96qgzz+77siRI/P8S3p+SWdeCvocHNFvjx49NHv2bP3888/q1KmTFi5cqBo1alj9kKpZs6b27dunH374QUuXLtXXX3+t9957T+PHj89x6dWrmc1mRURE6K233sp1+7U/XAtrPJQvX94qebW1j+z3d+rUqTkuF5ytVKlSuf6Qu5lc7/f9etosyr7sadOWNv7991+bFol7eXlZkrCrTZo0Se+9955ee+019erVK8f2smXL5nom9PTp05KkcuXK5dg2d+5cvfDCC/rPf/6jcePGFRhbXqZNm2b1Xa5QoYKOHDmismXLWsVwbVylS5fO9azL1R555BF9+OGHWrdunWJiYvLsyx7Z78W1a8JcXV1VpkyZG0q0cXMgWQFykX3lpH79+ik5OVnNmzfXxIkTLclKXtM1KlSooBUrVujixYtWf+ncu3evZXv2f81msw4fPmz1F9oDBw7YHOP58+e1cuVKTZo0SePHj7eUX8/0tetRpUoVGYahSpUq6c477yySPu2RlZVl9Re9RYsWqU+fPlZXOktNTdWFCxdy7Jvf51+5cmVJV84c2fKj2FEc0W/z5s1VtmxZffnll2ratKlWrVqlF198MUc9b29vde/eXd27d1d6ero6d+6sV155RWPGjLFMB7lWlSpV9Ndff6l169bXNZ2puGVPjfL19c33/c3+C3Ru37N9+/bZ1M/OnTvzrWPP+2frMccZZceW2/u2d+9eBQYG5npWrCCOGH+dO3fW2rVrC6zXp08fy5XasmXfg2v48OF64YUXct2vXr16Wr16tZKSkqzOCG3evNmy/WpLlizRgAED1LlzZ8vVKK9X7969LVeUlP5/4h4WFqagoKBcb6r8+++/55nEXy17Clj22eq8+rJH9lmua5O79PR0nT179rqnEOPmwZoV4BrXTn8qVaqUqlatavUX1ex/QK/9oXv//fcrKytLM2bMsCp/++23ZTKZLDely/7L+HvvvWdV79p5zfnJ/uvgtX9RnD59us1t3IjOnTvL1dVVkyZNyhGDYRgFTiMrTKtXr1ZycrLVGQNXV9cccb777rs57gJd0OcfHBysli1bavbs2bn+BfLaOz07iiP6dXFx0SOPPKLvv/9en376qTIzM62mgEk5X7+7u7tq1aolwzDyvTpSt27ddPLkSX344Yc5tmVfMciZRUZGqkqVKpo2bZpVkpst+/11dXVVTEyMFi9erGPHjlm279mzx6ab8HXp0kV//fWXvv322xzbssdnXseX3Nh6zHFGZcuWVb169TRv3jyr17pz50798ssv1z3Nydvb26b3Lj/Xu2blyy+/1LBhw/TYY4/leZZRunIGIisrSx988IGlLC0tTXPmzFFUVJTVmch169apR48eat68uebPn2/3TUavVblyZbVp08byuHqKZ5cuXfTDDz9YXX5+5cqV+ueff9S1a1dLWV7Hm48++kgmk0kNGjQosC9btWzZUsHBwZo/f75lOpp05UxTVlaW5SqNuHVxZgW4Rq1atdSyZUtFRkaqdOnS2rJlixYtWqQhQ4ZY6mT/pWfYsGGKiYmRq6urevTooQ4dOqhVq1Z68cUXdeTIEdWtW1e//PKLlixZouHDh1v+ehsZGakuXbpo+vTpOnfunOXSxdl38LXlL4O+vr5q3ry53njjDWVkZCgsLEy//PKL5X4Sha1KlSqaMmWKxowZoyNHjqhTp07y8fHR4cOH9e2332rQoEFW9+4oLImJifrss88kSZmZmdq3b5/ef/99eXl5afTo0ZZ6Dz74oD799FP5+fmpVq1a2rRpk1asWJHjcq22fP4zZ85U06ZNFRERoYEDB6py5co6c+aMNm3apBMnTuivv/4qlNfqiH67d++ud999VxMmTFBERIRlbUO2tm3bKjQ0VE2aNFFISIj27NmjGTNm6IEHHsixLuJqvXr10sKFC/Wf//xHq1evVpMmTZSVlaW9e/dq4cKFWrZsWa7ryJyFi4uL/ve//6l9+/aqXbu2+vXrp7CwMJ08eVKrV6+Wr6+vvv/+e0lXpvgsXbpUzZo109NPP63MzEzLvWn+/vvvfPsZNWqUFi1apK5du+qJJ55QZGSk/v33X3333XeaNWuW6tatqypVqsjf31+zZs2Sj4+PvL29FRUVletaHluPOfYymUxq0aKF5X4phWXq1Klq3769oqOj1b9/f8uli/38/KzuN2WPyMhIrVixQm+99ZbKlSunSpUqWRb329OGvX7//Xf17t1bZcqUUevWrTV//nyr7Y0bN7acIY2KilLXrl01ZswYxcfHq2rVqpo3b56OHDmijz76yLLP0aNH1bFjR5lMJj3yyCP66quvrNqsU6eO1XrKTz/9VEePHrXcN2bdunWaMmWKpCvf0YLOtI0dO1ZfffWVWrVqpWeeeUbJycmaOnWqIiIirC6Q8sorr2jDhg1q166d7rjjDv3777/6+uuv9ccff2jo0KE2TYU9evSo5fL/2WdzsmOtUKGCZfqch4eHpk6dqj59+qh58+bq1auXjh07pv/+979q1qyZOnfuXGBfuMkV7cXHgMJ1PXewv/bSolOmTDEaNWpk+Pv7G15eXkaNGjWMV155xUhPT7fUyczMNIYOHWoEBQUZJpPJ6pKLFy9eNJ599lmjXLlyRokSJYxq1aoZU6dOtbocqmFcuYP74MGDjdKlSxulSpUyOnXqZOzbt8+QZHUp4fzuunzixAnj4YcfNvz9/Q0/Pz+ja9euxqlTp/K8/PG1bfTp08fw9va26X3Ky9dff200bdrU8Pb2Nry9vY0aNWoYgwcPNvbt21dge9mX7b36Ls72uPbyoiaTyShdurTRsWNHY+vWrVZ1z58/b/Tr188IDAw0SpUqZcTExBh79+69rs/fMAzj4MGDRu/evY3Q0FCjRIkSRlhYmPHggw8aixYtstSx59LFub0H136OtvabH7PZbISHh+d6uVvDuHJ52ebNmxtlypQxPDw8jCpVqhijRo0yEhMTC2w7PT3deP31143atWsbHh4eRkBAgBEZGWlMmjTJan/lcqd5wyj4+1uQvNq9WvZn8tVXX+W6/c8//zQ6d+5sef0VKlQwunXrZqxcudKq3tq1a43IyEjD3d3dqFy5sjFr1qxc72B/7fgyjCuXsx0yZIgRFhZmuLu7G+XLlzf69OljdXfuJUuWGLVq1TLc3NysLmN87fgxDNuPOXm9P9fGePHiRUOS0aNHj1zfo2v3ze1S37n1lddYX7FihdGkSRPDy8vL8PX1NTp06GDs3r3bqk5ex7DsMZN953bDMIy9e/cazZs3N7y8vKwuL2xPG9ejoEvnX3sp6suXLxsjR440QkNDDQ8PD+Puu+82li5dalUne7zm9bj2+JDfJZevPg7lZ+fOnUbbtm2NkiVLGv7+/sZjjz1mxMXFWdX55ZdfjAcffNAy5nx8fIwmTZoYc+bMyTHu8pLfa2vRokWO+p9//rlRt25dw8PDwwgJCTGGDBliJCUl2dQXbm4mw7iBlW4AHGr79u2qX7++PvvsMz322GPFHQ6A29BPP/2kBx98UH/99ZciIiKKOxwAtznWrADFJLdr0U+fPl0uLi4F3jkeAArL6tWr1aNHDxIVAE6BMytAMZk0aZK2bt2qVq1ayc3NTT///LN+/vlnDRo0SLNnzy7u8IpVQkJCjoXvV3N3d7e6iRxuXVlZWQVePKBUqVI5LpUNALg1kKwAxWT58uWaNGmSdu/ereTkZN1xxx3q1auXXnzxRYfelfhmVLFixRw3s7taUSz8hXM4cuRIgTeJnDBhwnUvxgYAODeSFQBOZ8OGDblOk8sWEBDgkDtMw/mlpqZq/fr1+dapXLmy5SpLAIBbC8kKAAAAAKfEAnsAAAAATun2nhifB7PZrFOnTsnHx8emm/MBAAAAsJ1hGLp48aLKlSsnF5e8z5+QrOTi1KlTCg8PL+4wAAAAgFva8ePHVb58+Ty3k6zkwsfHR9KVN8/X17eYo4Ejmc1mJSQkKCgoKN8sHnAkxh2KA+MORY0xB3skJSUpPDzc8rs7LyQrucie+uXr60uycosxm81KTU2Vr68vB1IUGcYdigPjDkWNMYfrUdCSC0YSAAAAAKdEsgIAAADAKZGsAAAAAHBKrFkBAABArgzDUGZmprKysgqsazablZGRodTUVNasQK6urnJzc7vh24CQrAAAACCH9PR0nT59WpcuXbKpvmEYMpvNunjxIvepgySpZMmSKlu2rNzd3a+7DZIVAAAAWDGbzTp8+LBcXV1Vrlw5ubu7F5iAZJ+FccRf03FzMwxD6enpSkhI0OHDh1WtWrXrPttGsgIAAAAr6enpMpvNCg8PV8mSJW3ah2QFV/Py8lKJEiV09OhRpaeny9PT87raYUIhAAAAcsXaE9wIR4wfRiAAAAAAp0SyAgAAAMApsWYFAAAANunQIf/tZrOrHDlz7PvvHdfWrejIkSOqVKmS/vzzT9WrV09r1qxRq1atdP78efn7+xd3eA7BmRUAAADAThUrVpTJZJLJZLJcNa1///46f/58scXUuHFjnT59Wn5+fsUWg6NxZgUAANywgv7ibi/+oo6iYBiGsrKy5OZm/ZM4PT3dpnuDvPzyyxo4cKCysrL0zz//aNCgQRo2bJg+/fTTwgo5X+7u7goNDS2WvgsLZ1YAAABwyzCbzYqNjVWlSpXk5eWlunXratGiRZKkNWvWyGQy6eeff1ZkZKQ8PDy0fv16tWzZUkOGDNHw4cMVGBiomJgYm/ry8fFRaGiowsLC1KpVK/Xp00fbtm2zbD937px69uypsLAwlSxZUhEREfr888+t2li0aJEiIiLk5eWlMmXKqE2bNkpJSbFs/9///qeaNWvK09NTNWrU0HvvvZdnPNmv78KFC5KkuXPnyt/fX8uWLVPNmjVVqlQptWvXTqdPn7baz54+ihpnVgAAAHDLiI2N1WeffaZZs2apWrVqWrdunR5//HEFBQVZ6owePVrTpk1T5cqVFRAQIEmaN2+ennrqKW3YsOG6+j158qS+//57RUVFWcpSU1MVGRmpF154Qb6+vvrxxx/Vq1cvValSRY0aNdLp06fVs2dPvfHGG3r44Yd18eJF/frrrzIMQ5I0f/58jR8/XjNmzFD9+vX1559/auDAgfL29lafPn1siuvSpUuaNm2aPv30U7m4uOjxxx/XyJEjNX/+fIf1UZhIVgAAAHBLSEtL06uvvqoVK1YoOjpaklS5cmWtX79es2fP1qBBgyRdmb513333We1brVo1vfHGG3b198ILL2jcuHHKyspSamqqoqKi9NZbb1m2h4WFaeTIkZbnQ4cO1bJly7Rw4UJLspKZmanOnTurQoUKkqSIiAhL/QkTJujNN99U586dJUmVKlXS7t27NXv2bJsTiYyMDM2aNUtVqlSRJA0ZMkQvv/yyQ/soTCQrAAAAuCUcOHBAly5dypGIpKenq379+pbnDRs2zLFvZGSk3f2NGjVKffv2lWEYOn78uMaOHasHHnhA69atk6urq7KysvTqq69q4cKFOnnypNLT05WWlqaSJUtKkurWravWrVsrIiJCMTExatu2rR555BEFBAQoJSVFBw8eVP/+/TVw4EBLn5mZmXYtoC9ZsqQlUZGksmXLKj4+XpIc1kdhIlkBAADALSE5OVmS9OOPPyosLMxqm4eHhw4ePChJ8vb2zrFvbmUFCQwMVNWqVSVdOTMzffp0RUdHa/Xq1WrTpo2mTp2q//73v5o+fboiIiLk7e2t4cOHKz09XZLk6uqq5cuXa+PGjfrll1/07rvv6sUXX9TmzZstCc2HH35oNbUsez9blShRwuq5yWSyTDPLfr9utI/CRLICAACAW0KtWrXk4eGhY8eOqUWLFjm2ZycrhSX7B/7ly5clSRs2bNBDDz2kxx9/XNKVxf///POPatWqZdnHZDKpSZMmatKkicaPH68KFSro22+/1YgRI1SuXDkdOnRIjz32WKHEGxISUuh93CiSFQAAANwSfHx8NHLkSD377LMym81q2rSpEhMTtWHDBvn6+lrWhTjKxYsXFRcXZ5kG9vzzzysoKEiNGzeWdOVsy6JFi7Rx40YFBATorbfe0pkzZyzJyubNm7Vy5Uq1bdtWwcHB2rx5sxISElSzZk1J0qRJkzRs2DD5+fmpXbt2SktL05YtW3T+/HmNGDHCIa+hKPq4ESQrAAAAsEl+978xDCkz88o9S0ymoovpWpMnT1ZQUJBiY2N16NAh+fv7q0GDBho7dqzMZrND+xo/frzGjx8vSQoKCtLdd9+tX375RWXKlJEkjRs3TocOHVJMTIxKliypQYMGqVOnTkpMTJQk+fr6at26dZo+fbqSkpJUoUIFvfnmm2rfvr0kacCAASpZsqSmTp2qUaNGydvbWxERERo+fLjDXkNR9HEjTEb2pDVYJCUlyc/PT4mJifL19S3ucOBAZrNZ8fHxCg4OlosLtxlC0WDcoTgU9bjjppC3ltTUVB0+fFiVKlWSp6enTfsYhqHMzMz/S1aKMVuB08hvHNn6e5t/NQEAAAA4JZIVAAAA4Crz589XqVKlcn3Url27uMO7rbBmBQAAALhKx44dc1zKN9u1lwJG4SJZAQAAAK7i4+MjHx+f4g4DYhoYAAAAACdFsgIAAADAKZGsAAAAAHBKxZqsrFu3Th06dFC5cuVkMpm0ePFiq+0mkynXx9SpU/Nsc+LEiTnq16hRo5BfCQAAAABHK9ZkJSUlRXXr1tXMmTNz3X769Gmrx8cffyyTyaQuXbrk227t2rWt9lu/fn1hhA8AAACgEBVrstK+fXtNmTJFDz/8cK7bQ0NDrR5LlixRq1atVLly5XzbdXNzs9ovMDCwMMIHAAAACl3FihU1ffp0y/PcZiTZyxFtFIWb5tLFZ86c0Y8//qh58+YVWHf//v0qV66cPD09FR0drdjYWN1xxx151k9LS1NaWprleVJSkiTJbDbLbDbfePBwGmazWYZh8LmiSDHuUByKetyZTI5tj69L8coeP9kPi7Ud893P1TBLJhcZ+dayQ4vvHNWSw1WqVElHjx6VJJUsWVLVq1fX6NGj1bVr10Lp7+rP4tSpUwoICLD+bPIwceJELVmyRH/++adVuT1tXK/smHP7TW3rsemmSVbmzZsnHx8fde7cOd96UVFRmjt3rqpXr67Tp09r0qRJatasmXbu3Jnn9bJjY2M1adKkHOUJCQlKTU11SPxwDmazWYmJiTIMQy4uXF8CRYNxh+JQ1OMuPNyx7cXHO7Y92CcjI0Nms1mZmZnKzMy0lLsa+f/AvPLD13GZZtZVfRcGwzCUlZUlNzfrn8Tp6elyd3cvcP8JEyaof//+SkpK0vTp09WjRw+FhoYqOjo6R11b28xL9uchyTJrKNOG9yc78by2rj1tXK/MzEyZzWadO3cux800L168aFMbN02y8vHHH+uxxx6Tp6dnvvXat29v+f86deooKipKFSpU0MKFC9W/f/9c9xkzZoxGjBhheZ6UlKTw8HAFBQXJ19fXMS8ATsFsNstkMikoKIgfjSgyjDsUh6Ied8ePO7a94GDHtgf7pKam6uLFi3Jzc7P+IW8qaCyZZSqwju2uTSJsYTab9frrr+vDDz9UXFyc7rzzTo0bN06PPPKI1qxZo3vvvVc//vijXnrpJe3YsUPLli3TpEmTVLt2bbm5uWn+/PmKiIjQqlWrCuzLz89P5cuXlyS99957WrBggX766Sc1a9ZMlSpV0hNPPKEDBw5o8eLF6ty5s+bMmaP169dr7Nix2rJliwIDA9WpUyfFxsbK29tbkhQfH68BAwZoxYoVCg0N1eTJkyVJLi4ulvfDxcVF33zzjTp16iRJOnHihJ5//nktW7ZMaWlpqlmzpmbMmKE9e/ZoypQpkmRJlD7++GP17ds3Rxs7duzQ8OHDtWnTJpUsWVKdO3fWW2+9pVKlSkmS+vXrpwsXLqhJkyZ66623lJ6eru7du2v69Ok5EpGrPz8XFxeVKVMmx2/4gn7TW9qwqVYx+/XXX7Vv3z59+eWXdu/r7++vO++8UwcOHMizjoeHhzw8PHKUu7i48MPiFmQymfhsUeQYdygORTnuHD2ThK9K8XJxcbG6sqotrh4CDpsVeB3zC1977TV99tlnmjVrlqpVq6Z169apV69eCg4OtryWMWPGaNq0aapcubICAgIkSZ988omeeuopbdiw4f+6Lrjvq9+fEiVKqESJEsrIyLCUvfnmmxo/frwmTJggSTp06JBlzfbHH3+shIQEDRkyREOHDtWcOXMkXUkKTp06pdWrV6tEiRIaNmyY4uPjc3wW2c+Tk5PVsmVLhYWF6bvvvlNoaKi2bdsmwzDUo0cP7dq1S0uXLtWKFSskXUmwstvJbiMlJUXt2rVTdHS0/vjjD0vCNHToUM2dO9fS5+rVq1W2bFmtXr1aBw4cUPfu3VW/fn0NHDgw3/cnt+OQrcelmyJZ+eijjxQZGam6devavW9ycrIOHjyoXr16FUJkAAAAcBZpaWl69dVXtWLFCstUrMqVK2v9+vWaPXu2Bg0aJEl6+eWXdd9991ntW61aNb3xxhvX1W96errefPNNJSYm6t5777WU33vvvXruuecszwcMGKDHHntMw4cPt/T5zjvvqEWLFnr//fd17Ngx/fzzz/r999919913S7ryO7hmzZp59r1gwQIlJCTojz/+UOnSpSVJVatWtWwvVaqU5eJT+bWRmpqqTz75xHKGZ8aMGerQoYNef/11hYSESJICAgI0Y8YMubq6qkaNGnrggQe0cuXKPJMVRyjWZCU5OdnqjMfhw4e1fft2lS5d2rIgPikpSV999ZXefPPNXNto3bq1Hn74YQ0ZMkSSNHLkSHXo0EEVKlTQqVOnNGHCBLm6uqpnz56F/4IAAABQbA4cOKBLly7lSETS09NVv359y/OGDRvm2DcyMtLu/l544QWNGzdOqampKlWqlF577TU98MADefbz119/6e+//9b8+fMtZdkL0A8fPqx//vlHbm5uVrHUqFFD/v7+ecawfft21a9f35KoXI89e/aobt26lkRFkpo0aSKz2ax9+/ZZkpXatWvL1dXVUqds2bLasWPHdfdri2JNVrZs2aJWrVpZnmevG+nTp4/llNMXX3whwzDyTDYOHjyos2fPWp6fOHFCPXv21Llz5xQUFKSmTZvqt99+U1BQUOG9EAAAABS75ORkSdKPP/6osLAwq20eHh46ePCgJFn9KM+WW1lBRo0apb59+6pUqVIKCQnJMXXs2jaTk5P15JNPatiwYTnauuOOO/TPP//YHYOXl5fd+1yva9emmEymQr/iYLEmKy1btizwcmmDBg2ynLLLzZEjR6yef/HFF44IDQAAADeZWrVqycPDQ8eOHVOLFi1ybM9OVhwlMDDQaspVQRo0aKDdu3fnuU+NGjWUmZmprVu3WqaB7du3TxcuXMizzTp16uh///uf/v3331zPrri7uysrKyvfuGrWrKm5c+cqJSXFkmBt2LBBLi4uql69uo2vrnCwfA0AAAC3BB8fH40cOVLPPvus5s2bp4MHD2rbtm169913bbpXX2F74YUXtHHjRg0ZMkTbt2/X/v37tWTJEstyhurVq6tdu3Z68skntXnzZm3dulUDBgzI9+xJz549FRoaqk6dOmnDhg06dOiQvv76a23atEnSlRtKZi+1OHv2rNW9BbNlX3G3T58+2rlzp1avXq2hQ4eqV69elilgxeWmWGAPAAAAJ9Dy+7y3GYayMjOvXF7X0XcJtcPkyZMVFBSk2NhYHTp0SP7+/mrQoIHGjh1b7DfnrVOnjtauXasXX3xRzZo1k2EYqlKlirp3726pM2fOHA0YMEAtWrRQSEiIpkyZopdeeinPNt3d3fXLL7/oueee0/3336/MzEzVqlVLM2fOlCR16dJF33zzjVq1aqULFy5ozpw56tu3r1UbJUuW1LJly/TMM8/o7rvvVsmSJdWlSxe99dZbhfI+2MNkFOZtK29SSUlJ8vPzU2JiIvdZucWYzWbFx8crODiYS8iiyDDuUByKetx16ODY9r7P5zcxCl9qaqoOHz6sSpUq2Xw/jOwbD7q5udl8uWPc2vIbR7b+3uZfTQAAAABOiWQFAAAAuMr8+fNVqlSpXB+1a9cu7vBuK6xZAQAAAK7SsWNHRUVF5brt2sv3onCRrAAAAABX8fHxkY+PT3GHATENDAAAAHngOky4EY4YPyQrAAAAsJI91enSpUvFHAluZtnj50amzjENDAAAAFZcXV3l7++v+Ph4SVfuw1HQ5Yi5dDGyGYahS5cuKT4+Xv7+/nJ1db3utkhWAAAAkENoaKgkWRKWghiGIbPZLBcXF5IVSJL8/f0t4+h6kawAAAAgB5PJpLJlyyo4OFgZGRkF1jebzTp37pzKlCnDDXChEiVK3NAZlWwkKwAAAMiTq6urTT86zWazSpQoIU9PT5IVOAwjCQAAAIBTIlkBAAAA4JRIVgAAAAA4JZIVAAAAAE6JZAUAAACAUyJZAQAAAOCUSFYAAAAAOCWSFQAAAABOiWQFAAAAgFMiWQEAAADglEhWAAAAADglkhUAAAAATolkBQAAAIBTIlkBAAAA4JRIVgAAAAA4JZIVAAAAAE6JZAUAAACAUyJZAQAAAOCUijVZWbdunTp06KBy5crJZDJp8eLFVtv79u0rk8lk9WjXrl2B7c6cOVMVK1aUp6enoqKi9PvvvxfSKwAAAABQWIo1WUlJSVHdunU1c+bMPOu0a9dOp0+ftjw+//zzfNv88ssvNWLECE2YMEHbtm1T3bp1FRMTo/j4eEeHDwAAAKAQuRVn5+3bt1f79u3zrePh4aHQ0FCb23zrrbc0cOBA9evXT5I0a9Ys/fjjj/r44481evToXPdJS0tTWlqa5XlSUpIkyWw2y2w229w3nJ/ZbJZhGHyuKFKMOxSHoh53JpNj2+PrcvPhWAd72DpOijVZscWaNWsUHBysgIAA3XvvvZoyZYrKlCmTa9309HRt3bpVY8aMsZS5uLioTZs22rRpU559xMbGatKkSTnKExISlJqaeuMvAk7DbDYrMTFRhmHIxYUlWygajDsUh6Ied+Hhjm2PCRE3H451sMfFixdtqufUyUq7du3UuXNnVapUSQcPHtTYsWPVvn17bdq0Sa6urjnqnz17VllZWQoJCbEqDwkJ0d69e/PsZ8yYMRoxYoTleVJSksLDwxUUFCRfX1/HvSAUO7PZLJPJpKCgIA6kKDKMOxSHoh53x487tr3gYMe2h8LHsQ728PT0tKmeUycrPXr0sPx/RESE6tSpoypVqmjNmjVq3bq1w/rx8PCQh4dHjnIXFxe+bLcgk8nEZ4six7hDcSjKcWcYjm2Pr8rNiWMdbGXrGLmpRlLlypUVGBioAwcO5Lo9MDBQrq6uOnPmjFX5mTNn7Fr3AgAAAKD43VTJyokTJ3Tu3DmVLVs21+3u7u6KjIzUypUrLWVms1krV65UdHR0UYUJAAAAwAGKNVlJTk7W9u3btX37dknS4cOHtX37dh07dkzJyckaNWqUfvvtNx05ckQrV67UQw89pKpVqyomJsbSRuvWrTVjxgzL8xEjRujDDz/UvHnztGfPHj311FNKSUmxXB0MAAAAwM2hWNesbNmyRa1atbI8z17k3qdPH73//vv6+++/NW/ePF24cEHlypVT27ZtNXnyZKv1JQcPHtTZs2ctz7t3766EhASNHz9ecXFxqlevnpYuXZpj0T0AAAAA51asyUrLli1l5LMib9myZQW2ceTIkRxlQ4YM0ZAhQ24kNAAAAADF7KZaswIAAADg9kGyAgAAAMApkawAAAAAcEokKwAAAACcEskKAAAAAKdEsgIAAADAKZGsAAAAAHBKJCsAAAAAnBLJCgAAAACnRLICAAAAwCmRrAAAAABwSiQrAAAAAJwSyQoAAAAAp0SyAgAAAMAp2Z2szJkzR5cuXSqMWAAAAADAwu5kZfTo0QoNDVX//v21cePGwogJAAAAAOxPVk6ePKl58+bp7NmzatmypWrUqKHXX39dcXFxhREfAAAAgNuU3cmKm5ubHn74YS1ZskTHjx/XwIEDNX/+fN1xxx3q2LGjlixZIrPZXBixAgAAALiN3NAC+5CQEDVt2lTR0dFycXHRjh071KdPH1WpUkVr1qxxUIgAAAAAbkfXlaycOXNG06ZNU+3atdWyZUslJSXphx9+0OHDh3Xy5El169ZNffr0cXSsAAAAAG4jdicrHTp0UHh4uObOnauBAwfq5MmT+vzzz9WmTRtJkre3t5577jkdP37c4cECAAAAuH242btDcHCw1q5dq+jo6DzrBAUF6fDhwzcUGAAAAIDbm91nVlq0aKEGDRrkKE9PT9cnn3wiSTKZTKpQocKNRwcAAADgtmV3stKvXz8lJibmKL948aL69evnkKAAAAAAwO5kxTAMmUymHOUnTpyQn5+fQ4ICAAAAAJvXrNSvX18mk0kmk0mtW7eWm9v/3zUrK0uHDx9Wu3btCiVIAAAAALcfm5OVTp06SZK2b9+umJgYlSpVyrLN3d1dFStWVJcuXRweIAAAAIDbk83JyoQJEyRJFStWVPfu3eXp6VloQQEAAACA3Zcu5maPAAAAAIqCTclK6dKl9c8//ygwMFABAQG5LrDP9u+//zosOAAAAAC3L5uSlbfffls+Pj6W/88vWbHHunXrNHXqVG3dulWnT5/Wt99+a1kbk5GRoXHjxumnn37SoUOH5OfnpzZt2ui1115TuXLl8mxz4sSJmjRpklVZ9erVtXfvXofEDAAAAKBo2JSsXD31q2/fvg7rPCUlRXXr1tUTTzyhzp07W227dOmStm3bppdeekl169bV+fPn9cwzz6hjx47asmVLvu3Wrl1bK1assDy/+splAAAAAG4Odv+K37Ztm0qUKKGIiAhJ0pIlSzRnzhzVqlVLEydOlLu7u81ttW/fXu3bt891m5+fn5YvX25VNmPGDDVq1EjHjh3THXfckWe7bm5uCg0NtTkOAAAAAM7H7mTlySef1OjRoxUREaFDhw6pe/fu6ty5s7766itdunRJ06dPL4Qwr0hMTJTJZJK/v3++9fbv369y5crJ09NT0dHRio2NzTe5SUtLU1pamuV5UlKSJMlsNstsNjskdjgHs9kswzD4XFGkGHcoDkU97hw0Q9yCr8vNh2Md7GHrOLE7Wfnnn39Ur149SdJXX32lFi1aaMGCBdqwYYN69OhRaMlKamqqXnjhBfXs2VO+vr551ouKitLcuXNVvXp1nT59WpMmTVKzZs20c+dOy7qba8XGxuZY5yJJCQkJSk1NddhrQPEzm81KTEyUYRhycXEp7nBwm2DcoTgU9bgLD3dse/Hxjm0PhY9jHexx8eJFm+rZnaxcnTGvWLFCDz74oCQpPDxcZ8+etbc5m2RkZKhbt24yDEPvv/9+vnWvnlZWp04dRUVFqUKFClq4cKH69++f6z5jxozRiBEjLM+TkpIUHh6uoKCgfBMj3HzMZrNMJpOCgoI4kKLIMO5QHIp63B0/7tj2goMd2x4KH8c62MPWezbanaw0bNhQU6ZMUZs2bbR27VpL8nD48GGFhITY21yBshOVo0ePatWqVXYnD/7+/rrzzjt14MCBPOt4eHjIw8MjR7mLiwtftluQyWTis0WRY9yhOBTluDMMx7bHV+XmxLEOtrJ1jNg9kqZPn65t27ZpyJAhevHFF1W1alVJ0qJFi9S4cWN7m8tXdqKyf/9+rVixQmXKlLG7jeTkZB08eFBly5Z1aGwAAAAACpfdZ1bq1KmjHTt25CifOnWqXF1d7WorOTnZ6ozH4cOHtX37dpUuXVply5bVI488om3btumHH35QVlaW4uLiJF25SWX2Vcdat26thx9+WEOGDJEkjRw5Uh06dFCFChV06tQpTZgwQa6ururZs6e9LxUAAABAMbruG5Ckp6crPj4+x0r+/K66da0tW7aoVatWlufZ60b69OmjiRMn6rvvvpMky4L+bKtXr1bLli0lSQcPHrRaK3PixAn17NlT586dU1BQkJo2barffvtNQUFB9rw8AAAAAMXsuq4G1r9/f23cuNGq3DAMmUwmZWVl2dxWy5YtZeQzyTW/bdmOHDli9fyLL76wuX8AAAAAzsvuZKVfv35yc3PTDz/8oLJly8rk6AurAwAAAICuI1nZvn27tm7dqho1ahRGPAAAAAAg6TquBlarVq1Cu58KAAAAAGSzO1l5/fXX9fzzz2vNmjU6d+6ckpKSrB4AAAAA4Ah2TwNr06aNpCuXDL7a9SywBwAAAIC82J2srF69ujDiAAAAAAArdicrLVq0KIw4AAAAAMCK3WtWJOnXX3/V448/rsaNG+vkyZOSpE8//VTr1693aHAAAAAAbl92Jytff/21YmJi5OXlpW3btiktLU2SlJiYqFdffdXhAQIAAAC4PdmdrEyZMkWzZs3Shx9+qBIlSljKmzRpom3btjk0OAAAAAC3L7uTlX379ql58+Y5yv38/HThwgVHxAQAAAAA9icroaGhOnDgQI7y9evXq3Llyg4JCgAAAADsTlYGDhyoZ555Rps3b5bJZNKpU6c0f/58jRw5Uk899VRhxAgAAADgNmT3pYtHjx4ts9ms1q1b69KlS2revLk8PDw0cuRIDR06tDBiBAAAAHAbsjtZMZlMevHFFzVq1CgdOHBAycnJqlWrlkqVKlUY8QEAAAC4TdmdrEiSYRhKSkpSSEiIatWq5eiYAAAAAMC+NStxcXHq3bu3AgICFBISouDgYAUEBOiJJ57QmTNnCitGAAAAALchm8+sJCUlqXHjxkpOTla/fv1Uo0YNGYah3bt36/PPP9f69eu1bds2poMBAAAAcAibk5X//ve/cnV11a5duxQUFGS1bdy4cWrSpIneeecdjR071uFBAgAAALj92DwN7Mcff9TYsWNzJCqSFBwcrDFjxuj77793aHAAAAAAbl82Jyv//POPGjdunOf2xo0ba9++fQ4JCgAAAABsTlaSkpLk7++f53Z/f38lJSU5IiYAAAAAsD1ZMQxDLi55VzeZTDIMwyFBAQAAAIDNC+wNw9Cdd94pk8mU53YAAHBz6NChuCMAgILZnKzMmTOnMOMAAAAAACs2Jyt9+vQpzDgAAAAAwIpdd7AHAAAAgKJCsgIAAADAKZGsAAAAAHBKJCsAAAAAnJLdycrq1asd1vm6devUoUMHlStXTiaTSYsXL7babhiGxo8fr7Jly8rLy0tt2rTR/v37C2x35syZqlixojw9PRUVFaXff//dYTEDAAAAKBp2Jyvt2rVTlSpVNGXKFB0/fvyGOk9JSVHdunU1c+bMXLe/8cYbeueddzRr1ixt3rxZ3t7eiomJUWpqap5tfvnllxoxYoQmTJigbdu2qW7duoqJiVF8fPwNxQoAAACgaNmdrJw8eVJDhgzRokWLVLlyZcXExGjhwoVKT0+3u/P27dtrypQpevjhh3NsMwxD06dP17hx4/TQQw+pTp06+uSTT3Tq1KkcZ2Cu9tZbb2ngwIHq16+fatWqpVmzZqlkyZL6+OOP7Y4PAAAAQPGx+T4r2QIDA/Xss8/q2Wef1bZt2zRnzhw9/fTTevrpp/Xoo4+qf//+qlu37g0HdvjwYcXFxalNmzaWMj8/P0VFRWnTpk3q0aNHjn3S09O1detWjRkzxlLm4uKiNm3aaNOmTXn2lZaWprS0NMvzpKQkSZLZbJbZbL7h1wLnYTabZRgGnyuKFOMOxaGgcWcyFXFAduLrcvPhWAd72DpO7E5WrtagQQOFhoaqTJkyeu211/Txxx/rvffeU3R0tGbNmqXatWtfd9txcXGSpJCQEKvykJAQy7ZrnT17VllZWbnus3fv3jz7io2N1aRJk3KUJyQk5DvlDDcfs9msxMREGYYhFxeuL4GiwbhDcSho3IWHF0NQdmD29s2HYx3scfHiRZvqXVeykpGRoSVLlujjjz/W8uXL1bBhQ82YMUM9e/ZUQkKCxo0bp65du2r37t3X03yRGzNmjEaMGGF5npSUpPDwcAUFBcnX17cYI4Ojmc1mmUwmBQUFcSBFkWHcoTgUNO5ucNlpoQsOLu4IYC+OdbCHp6enTfXsTlaGDh2qzz//XIZhqFevXnrjjTd01113WbZ7e3tr2rRpKleunL1NWwkNDZUknTlzRmXLlrWUnzlzRvXq1ct1n8DAQLm6uurMmTNW5WfOnLG0lxsPDw95eHjkKHdxceHLdgsymUx8tihyjDsUh/zGnWEUQ0B24Ktyc+JYB1vZOkbsHkm7d+/Wu+++q1OnTmn69OlWiUq2wMDAG77EcaVKlRQaGqqVK1daypKSkrR582ZFR0fnuo+7u7siIyOt9jGbzVq5cmWe+wAAAABwTnYnKxMmTFDXrl1znInIzMzUunXrJElubm5q0aJFgW0lJydr+/bt2r59u6Qri+q3b9+uY8eOyWQyafjw4ZoyZYq+++477dixQ71791a5cuXUqVMnSxutW7fWjBkzLM9HjBihDz/8UPPmzdOePXv01FNPKSUlRf369bP3pQIAAAAoRnZPA2vVqpVOnz6t4GsmkyYmJqpVq1bKysqyua0tW7aoVatWlufZ60b69OmjuXPn6vnnn1dKSooGDRqkCxcuqGnTplq6dKnVHLeDBw/q7Nmzlufdu3dXQkKCxo8fr7i4ONWrV09Lly7NsegeAAAAgHMzGYZ9s1ZdXFx05swZBQUFWZX/888/atiwoeWyvzezpKQk+fn5KTExkQX2txiz2az4+HgFBwcznxZFhnGH4lDQuOvQoRiCssP33xd3BLAXxzrYw9bf2zafWencubOkKwun+vbtazUNLCsrS3///bcaN258AyEDAHCbWVMIGUNLfuUDuHXYnKz4+flJunJneR8fH3l5eVm2ubu765577tHAgQMdHyEAAACA25LNycqcOXMkSRUrVtTIkSPl7e1daEEBAAAAgN0L7CdMmFAYcQAAAACAFZuSlQYNGmjlypUKCAhQ/fr1ZTKZ8qy7bds2hwUHAAAA4PZlU7Ly0EMPWRbUX32PEwAAAAAoLDYlK1dP/WIaGAAAAICiwEWwAQAAADglm86sBAQE5LtO5Wr//vvvDQUEAAAAAJKNycr06dMLOQwAAAAAsGZTstKnT5/CjgMAAAAArNiUrCQlJcnX19fy//nJrgcAAAAAN8LmNSunT59WcHCw/P39c12/YhiGTCaTsrKyHB4kAAAAgNuPTcnKqlWrVLp0aUnS6tWrCzUgAAAAAJBsTFZatGiR6/8DAAAAQGGxKVm51vnz5/XRRx9pz549kqRatWqpX79+lrMvAADg1vFS8w4Ob3Pyuu8d3iaAW4/dN4Vct26dKlasqHfeeUfnz5/X+fPn9c4776hSpUpat25dYcQIAAAA4DZk95mVwYMHq3v37nr//ffl6uoqScrKytLTTz+twYMHa8eOHQ4PEgAAAMDtx+4zKwcOHNBzzz1nSVQkydXVVSNGjNCBAwccGhwAAACA25fdyUqDBg0sa1WutmfPHtWtW9chQQEAAACATdPA/v77b8v/Dxs2TM8884wOHDige+65R5L022+/aebMmXrttdcKJ0oAAAAAtx2bkpV69erJZDLJMAxL2fPPP5+j3qOPPqru3bs7LjoAAAAAty2bkpXDhw8XdhwAAAAAYMWmZKVChQqFHQcAAAAAWLmum0JK0u7du3Xs2DGlp6dblXfs2PGGgwIAAAAAu5OVQ4cO6eGHH9aOHTus1rGYTCZJV+65AgAAAAA3yu5LFz/zzDOqVKmS4uPjVbJkSe3atUvr1q1Tw4YNtWbNmkIIEQAAAMDtyO4zK5s2bdKqVasUGBgoFxcXubi4qGnTpoqNjdWwYcP0559/FkacAAAAAG4zdp9ZycrKko+PjyQpMDBQp06dknRlEf6+ffscGx0AAACA25bdZ1buuusu/fXXX6pUqZKioqL0xhtvyN3dXR988IEqV65cGDECAAAAuA3ZnayMGzdOKSkpkqSXX35ZDz74oJo1a6YyZcroyy+/dHiAAAAAAG5Pdk8Di4mJUefOnSVJVatW1d69e3X27FnFx8fr3nvvdXiAFStWlMlkyvEYPHhwrvXnzp2bo66np6fD4wIAAABQuK77PiuSdPz4cUlSeHi4Q4LJzR9//GF1OeSdO3fqvvvuU9euXfPcx9fX12r9TPZllQEAAADcPOw+s5KZmamXXnpJfn5+qlixoipWrCg/Pz+NGzdOGRkZDg8wKChIoaGhlscPP/ygKlWqqEWLFnnuYzKZrPYJCQlxeFwAAAAACpfdZ1aGDh2qb775Rm+88Yaio6MlXbmc8cSJE3Xu3Dm9//77Dg8yW3p6uj777DONGDEi37MlycnJqlChgsxmsxo0aKBXX31VtWvXzrN+Wlqa0tLSLM+TkpIkSWazWWaz2XEvAMXObDbLMAw+VxQpxh3yZBTCmf//G2cFjTt7Jh0YcnycJlP+3we+LjcfjnWwh63jxO5kZcGCBfriiy/Uvn17S1mdOnUUHh6unj17FmqysnjxYl24cEF9+/bNs0716tX18ccfq06dOkpMTNS0adPUuHFj7dq1S+XLl891n9jYWE2aNClHeUJCglJTUx0VPpyA2WxWYmKiDMOQi4vdJxaB68K4Q54yC2EadXy8pILHnT0zuNO8HR9neHh8vtvj898MJ8SxDva4ePGiTfXsTlY8PDxUsWLFHOWVKlWSu7u7vc3Z5aOPPlL79u1Vrly5POtER0dbzvhIUuPGjVWzZk3Nnj1bkydPznWfMWPGaMSIEZbnSUlJCg8PV1BQkHx9fR33AlDszGazTCaTgoKCOJCiyDDukCe3445vMzhYUsHj7rgdXXtUdHycx48H57s9OP/NcEIc62APWy+AZXeyMmTIEE2ePFlz5syRh4eHpCvTqF555RUNGTLE3uZsdvToUa1YsULffPONXfuVKFFC9evX14EDB/Ks4+HhYXktV3NxceHLdgsymUx8tihyjDvkymQ4vs2rxlh+486wo2uTHB+nYeT/XeCrcnPiWAdb2TpGbEpWsi9VnG3FihUqX7686tatK0n666+/lJ6ertatW9sZpu3mzJmj4OBgPfDAA3btl5WVpR07duj+++8vpMgAAAAAFAabkhU/Pz+r5126dLF6XpiXLpaunFacM2eO+vTpIzc365B79+6tsLAwxcbGSrpyo8p77rlHVatW1YULFzR16lQdPXpUAwYMKNQYAQAAADiWTcnKnDlzCjuOfK1YsULHjh3TE088kWPbsWPHrE4jnT9/XgMHDlRcXJwCAgIUGRmpjRs3qlatWkUZMgAAAIAbdN03hUxISLDceLF69eoKCgpyWFDXatu2rYw8JteuWbPG6vnbb7+tt99+u9BiAQAAAFA07F79lJKSoieeeEJly5ZV8+bN1bx5c5UrV079+/fXpUuXCiNGAAAAALchu5OVESNGaO3atfr+++914cIFXbhwQUuWLNHatWv13HPPFUaMAAAAAG5Ddk8D+/rrr7Vo0SK1bNnSUnb//ffLy8tL3bp1K9SbQgIAAAC4fdh9ZuXSpUsKCQnJUR4cHMw0MAAAAAAOY3eyEh0drQkTJig1NdVSdvnyZU2aNMnqzvEAAAAAcCPsngY2ffp0tWvXLsdNIT09PbVs2TKHBwgAAADg9mR3shIREaH9+/dr/vz52rt3rySpZ8+eeuyxx+Tl5eXwAAEAAADcnuxKVjIyMlSjRg398MMPGjhwYGHFBAAArteaDlf+a5ikzHDJ7bhkynmvspeaF3FcAHAd7FqzUqJECau1KgAAAABQWOxeYD948GC9/vrryszMLIx4AAAAAEDSdaxZ+eOPP7Ry5Ur98ssvioiIkLe3t9X2b775xmHBAQAAALh92Z2s+Pv7q0uXLoURCwAAAABY2J2szJkzpzDiAAAAAAArNq9ZMZvNev3119WkSRPdfffdGj16tC5fvlyYsQEAAAC4jdmcrLzyyisaO3asSpUqpbCwMP33v//V4MGDCzM2AAAAALcxm5OVTz75RO+9956WLVumxYsX6/vvv9f8+fNlNpsLMz4AAAAAtymbk5Vjx47p/vvvtzxv06aNTCaTTp06VSiBAQAAALi92ZysZGZmytPT06qsRIkSysjIcHhQAAAAAGDz1cAMw1Dfvn3l4eFhKUtNTdV//vMfq3utcJ8VAAAAAI5gc7LSp0+fHGWPP/64Q4MBAAAAgGw2JyvcXwUAAABAUbJ5zQoAAAAAFCWSFQAAAABOiWQFAAAAgFMiWQEAAADglEhWAAAAADglkhUAAAAATolkBQAAAIBTIlkBAAAA4JRIVgAAAAA4JadOViZOnCiTyWT1qFGjRr77fPXVV6pRo4Y8PT0VERGhn376qYiiBQAAAOBITp2sSFLt2rV1+vRpy2P9+vV51t24caN69uyp/v37688//1SnTp3UqVMn7dy5swgjBgAAAOAITp+suLm5KTQ01PIIDAzMs+5///tftWvXTqNGjVLNmjU1efJkNWjQQDNmzCjCiAEAAAA4gltxB1CQ/fv3q1y5cvL09FR0dLRiY2N1xx135Fp306ZNGjFihFVZTEyMFi9enG8faWlpSktLszxPSkqSJJnNZpnN5ht7AXAqZrNZhmHwuaJIMe6QJ8NUaE2bDZMMwyRzHn0YhdazbUym/L8PfF1uPhzrYA9bx4lTJytRUVGaO3euqlevrtOnT2vSpElq1qyZdu7cKR8fnxz14+LiFBISYlUWEhKiuLi4fPuJjY3VpEmTcpQnJCQoNTX1xl4EnIrZbFZiYqIMw5CLi9OfWMQtgnGHPGWGF1rTZkNKzAqUIUMuueQrad6F1rVNwsPj890en/9mOCGOdbDHxYsXbarn1MlK+/btLf9fp04dRUVFqUKFClq4cKH69+/vsH7GjBljdUYmKSlJ4eHhCgoKkq+vr8P6QfEzm80ymUwKCgriQIoiw7hDntyOF1rTZsMkk0wKcjshF1PO8yhHUwqta5scPx6c7/bg/DfDCXGsgz08PT1tqufUycq1/P39deedd+rAgQO5bg8NDdWZM2esys6cOaPQ0NB82/Xw8JCHh0eOchcXF75styCTycRniyLHuEOuckkiHNq8yZDL/z1ybCvUngtmGPl/F/iq3Jw41sFWto6Rm2okJScn6+DBgypbtmyu26Ojo7Vy5UqrsuXLlys6OroowgMAAADgQE59ZmXkyJHq0KGDKlSooFOnTmnChAlydXVVz549JUm9e/dWWFiYYmNjJUnPPPOMWrRooTfffFMPPPCAvvjiC23ZskUffPBBcb4MAMCtYE2H4o4AAG47Tp2snDhxQj179tS5c+cUFBSkpk2b6rffflNQUJAk6dixY1ankBo3bqwFCxZo3LhxGjt2rKpVq6bFixfrrrvuKq6XAAAAAOA6OXWy8sUXX+S7fc2aNTnKunbtqq5duxZSRAAAAACKyk21ZgUAAADA7YNkBQAAAIBTIlkBAAAA4JRIVgAAAAA4JZIVAAAAAE6JZAUAAACAUyJZAQAAAOCUSFYAAAAAOCWSFQAAAABOiWQFAAAAgFMiWQEAAADglEhWAAAAADglkhUAAAAATolkBQAAAIBTIlkBAAAA4JRIVgAAAAA4JbfiDgAAAOBaHTo4rq3vv3dcWwCKFmdWAAAAADglkhUAAAAATolkBQAAAIBTIlkBAAAA4JRYYA8AAIrcS80duIL+/0xex0p64FbDmRUAAAAATolkBQAAAIBTIlkBAAAA4JRIVgAAAAA4JZIVAAAAAE6Jq4EBAHCT+P0P2+saktK8paMpkqnQIgKAwsWZFQAAAABOiWQFAAAAgFMiWQEAAADglJw6WYmNjdXdd98tHx8fBQcHq1OnTtq3b1+++8ydO1cmk8nq4enpWUQRAwAAAHAUp05W1q5dq8GDB+u3337T8uXLlZGRobZt2yolJSXf/Xx9fXX69GnL4+jRo0UUMQAAAABHceqrgS1dutTq+dy5cxUcHKytW7eqefPmee5nMpkUGhpqcz9paWlKS0uzPE9KSpIkmc1mmc1mO6OGMzObzTIMg88VRYpxd4swiv+aWoZddU2Wx+3CZMr9O8ZXr2hwrIM9bB0nTp2sXCsxMVGSVLp06XzrJScnq0KFCjKbzWrQoIFeffVV1a5dO8/6sbGxmjRpUo7yhIQEpaam3ljQcCpms1mJiYkyDEMuLk59YhG3EMbdLSIzvLgjUJq37XUNSRmegdJtlK6Eh8fnWh6fezEcjGMd7HHx4kWb6pkMw7DnDzXFxmw2q2PHjrpw4YLWr1+fZ71NmzZp//79qlOnjhITEzVt2jStW7dOu3btUvny5XPdJ7czK+Hh4Tp//rx8fX0d/lpQfMxmsxISEhQUFMSBFEWGcXeLWNupuCPQH1tsr2vIpDTvcHmkHJfJrnMyN69Xfl2ca/ni3IvhYBzrYI+kpCQFBAQoMTEx39/bN82ZlcGDB2vnzp35JiqSFB0drejoaMvzxo0bq2bNmpo9e7YmT56c6z4eHh7y8PDIUe7i4sKX7RZkMpn4bFHkGHe3AFPx/+C39wyJ6arJYLcDw8j9+8XXruhwrIOtbB0jN0WyMmTIEP3www9at25dnmdH8lKiRAnVr19fBw4cKKToAAAAABQGp057DcPQkCFD9O2332rVqlWqVKmS3W1kZWVpx44dKlu2bCFECAAAAKCwOPWZlcGDB2vBggVasmSJfHx8FBcXJ0ny8/OTl5eXJKl3794KCwtTbGysJOnll1/WPffco6pVq+rChQuaOnWqjh49qgEDBhTb6wAAAABgP6dOVt5//31JUsuWLa3K58yZo759+0qSjh07ZjXn7fz58xo4cKDi4uIUEBCgyMhIbdy4UbVq1SqqsAEAAAA4gFMnK7ZcqGzNmjVWz99++229/fbbhRQRAAAAgKLi1GtWAAAAANy+SFYAAAAAOCWSFQAAAABOiWQFAAAAgFMiWQEAAADglJz6amAAAFyXNR2KOwIAgANwZgUAAACAUyJZAQAAAOCUSFYAAAAAOCWSFQAAAABOiWQFAAAAgFMiWQEAAADglLh0MQAAuKV1cPCVrL//3rHtAcgbZ1YAAAAAOCWSFQAAAABOiWQFAAAAgFMiWQEAAADglEhWAAAAADglkhUAAAAATolLFwMAUEh+/6O4IwCAmxtnVgAAAAA4Jc6sAACAW8JLzR1890dJk9dxB0igOHFmBQAAAIBT4swKAKB4rXH8X8MBALcGzqwAAAAAcEqcWQEAALBDBwefDPyeZTFAnjizAgAAAMApcWYFAAAgD1xhDChenFkBAAAA4JQ4swIAwP/hjvMoDqyBAfJ2UyQrM2fO1NSpUxUXF6e6devq3XffVaNGjfKs/9VXX+mll17SkSNHVK1aNb3++uu6//77izBiALhFcZlh4IYV9tQyRyY/JD4obk6frHz55ZcaMWKEZs2apaioKE2fPl0xMTHat2+fgoODc9TfuHGjevbsqdjYWD344INasGCBOnXqpG3btumuu+4qhlcAAABQuFhbg1uVyTAMo7iDyE9UVJTuvvtuzZgxQ5JkNpsVHh6uoUOHavTo0Tnqd+/eXSkpKfrhhx8sZffcc4/q1aunWbNm2dRnUlKS/Pz8lJiYKF9fX8e8EDgFs9ms+Ph4BQcHy8WFJVsoGsU27m6TsyBM3cqdIZPSvMPlkXJcJjn1P/VwUvYmKyaTWeHh8Tp+PFiGkfuxjjM1yGbr722nPrOSnp6urVu3asyYMZYyFxcXtWnTRps2bcp1n02bNmnEiBFWZTExMVq8eHGe/aSlpSktLc3yPDExUZJ04cIFmc3mG3gFcDZms1lJSUlyd3d33mRlfU/Ht9n0c8e3eTvHaSezYVJSZnm5u52Qi6lofjRu+7NIuoETM2RSmkuGMlIzSVZwXZ5t1N6u+oZMSitZXh6hJ/Icc6smOyIya1M3FsK/HQ7wuXOG5TSSkpIkSQWdN3HqZOXs2bPKyspSSEiIVXlISIj27t2b6z5xcXG51o+Li8uzn9jYWE2aNClHeYUKFa4jasAZBRR3ADa6WeIEADgP5/y3I8A5w3I6Fy9elJ+fX57bnTpZKSpjxoyxOhtjNpv177//qkyZMjKZTMUYGRwtKSlJ4eHhOn78OFP8UGQYdygOjDsUNcYc7GEYhi5evKhy5crlW8+pk5XAwEC5urrqzJkzVuVnzpxRaGhorvuEhobaVV+SPDw85OHhYVXm7+9/fUHjpuDr68uBFEWOcYfiwLhDUWPMwVb5nVHJ5qST9q9wd3dXZGSkVq5caSkzm81auXKloqOjc90nOjraqr4kLV++PM/6AAAAAJyTU59ZkaQRI0aoT58+atiwoRo1aqTp06crJSVF/fr1kyT17t1bYWFhio2NlSQ988wzatGihd5880098MAD+uKLL7RlyxZ98MEHxfkyAAAAANjJ6ZOV7t27KyEhQePHj1dcXJzq1aunpUuXWhbRHzt2zOqqTo0bN9aCBQs0btw4jR07VtWqVdPixYu5xwokXZnyN2HChBzT/oDCxLhDcWDcoagx5lAYnP4+KwAAAABuT069ZgUAAADA7YtkBQAAAIBTIlkBAAAA4JRIVgAAAAA4JZIV3PL+/fdfPfbYY/L19ZW/v7/69++v5ORkm/Y1DEPt27eXyWTS4sWLCzdQ3FLsHXf//vuvhg4dqurVq8vLy0t33HGHhg0bpsTExCKMGjebmTNnqmLFivL09FRUVJR+//33fOt/9dVXqlGjhjw9PRUREaGffvqpiCLFrcKeMffhhx+qWbNmCggIUEBAgNq0aVPgGAWuRbKCW95jjz2mXbt2afny5frhhx+0bt06DRo0yKZ9p0+fLpPJVMgR4lZk77g7deqUTp06pWnTpmnnzp2aO3euli5dqv79+xdh1LiZfPnllxoxYoQmTJigbdu2qW7duoqJiVF8fHyu9Tdu3KiePXuqf//++vPPP9WpUyd16tRJO3fuLOLIcbOyd8ytWbNGPXv21OrVq7Vp0yaFh4erbdu2OnnyZBFHjpuaAdzCdu/ebUgy/vjjD0vZzz//bJhMJuPkyZP57vvnn38aYWFhxunTpw1JxrffflvI0eJWcSPj7moLFy403N3djYyMjMIIEze5Ro0aGYMHD7Y8z8rKMsqVK2fExsbmWr9bt27GAw88YFUWFRVlPPnkk4UaJ24d9o65a2VmZho+Pj7GvHnzCitE3II4s4Jb2qZNm+Tv76+GDRtaytq0aSMXFxdt3rw5z/0uXbqkRx99VDNnzlRoaGhRhIpbyPWOu2slJibK19dXbm5Of/9eFLH09HRt3bpVbdq0sZS5uLioTZs22rRpU677bNq0yaq+JMXExORZH7ja9Yy5a126dEkZGRkqXbp0YYWJWxDJCm5pcXFxCg4Otipzc3NT6dKlFRcXl+d+zz77rBo3bqyHHnqosEPELeh6x93Vzp49q8mTJ9s8ZRG3l7NnzyorK0shISFW5SEhIXmOsbi4OLvqA1e7njF3rRdeeEHlypXLkTQD+SFZwU1p9OjRMplM+T727t17XW1/9913WrVqlaZPn+7YoHHTK8xxd7WkpCQ98MADqlWrliZOnHjjgQNAMXvttdf0xRdf6Ntvv5Wnp2dxh4ObCHMLcFN67rnn1Ldv33zrVK5cWaGhoTkW/mVmZurff//Nc3rXqlWrdPDgQfn7+1uVd+nSRc2aNdOaNWtuIHLczApz3GW7ePGi2rVrJx8fH3377bcqUaLEjYaNW1BgYKBcXV115swZq/IzZ87kOcZCQ0Ptqg9c7XrGXLZp06bptdde04oVK1SnTp3CDBO3IJIV3JSCgoIUFBRUYL3o6GhduHBBW7duVWRkpKQryYjZbFZUVFSu+4wePVoDBgywKouIiNDbb7+tDh063HjwuGkV5riTrpxRiYmJkYeHh7777jv++og8ubu7KzIyUitXrlSnTp0kSWazWStXrtSQIUNy3Sc6OlorV67U8OHDLWXLly9XdHR0EUSMm931jDlJeuONN/TKK69o2bJlVuv4AJsV9wp/oLC1a9fOqF+/vrF582Zj/fr1RrVq1YyePXtatp84ccKoXr26sXnz5jzbEFcDg53sHXeJiYlGVFSUERERYRw4cMA4ffq05ZGZmVlcLwNO7IsvvjA8PDyMuXPnGrt37zYGDRpk+Pv7G3FxcYZhGEavXr2M0aNHW+pv2LDBcHNzM6ZNm2bs2bPHmDBhglGiRAljx44dxfUScJOxd8y99tprhru7u7Fo0SKrY9rFixeL6yXgJsSZFdzy5s+fryFDhqh169ZycXFRly5d9M4771i2Z2RkaN++fbp06VIxRolbjb3jbtu2bZYrhVWtWtWqrcOHD6tixYpFFjtuDt27d1dCQoLGjx+vuLg41atXT0uXLrUsgD527JhcXP7/0tTGjRtrwYIFGjdunMaOHatq1app8eLFuuuuu4rrJeAmY++Ye//995Wenq5HHnnEqp0JEyawHg82MxmGYRR3EAAAAABwLa4GBgAAAMApkawAAAAAcEokKwAAAACcEskKAAAAAKdEsgIAAADAKZGsAAAAAHBKJCsAAAAAnBLJCgAAAACnRLICAAAAwCmRrAAAbnsTJ05UvXr1rMp+/fVX+fv7a/jw4TIMo3gCA4DbHMkKAMBpGYahzMzMHOXp6enX1Z6t+/3444+KiYnRiBEjNH36dJlMpuvqDwBwY0hWAABFxmw2KzY2VpUqVZKXl5fq1q2rRYsWWbavWbNGJpNJP//8syIjI+Xh4aH169erZcuWGjJkiIYPH67AwEDFxMRIktauXatGjRrJw8NDZcuW1ejRo62Sm7z2y8+CBQvUuXNnvfHGGxo/frzj3wQAgM3cijsAAMDtIzY2Vp999plmzZqlatWqad26dXr88ccVFBSkFi1aWOqNHj1a06ZNU+XKlRUQECBJmjdvnp566ilt2LBBknTy5Endf//96tu3rz755BPt3btXAwcOlKenpyZOnGhp69r98jNz5kyNGDFCH3/8sR577DHHvngAgN1MBhNxAQBFIC0tTaVLl9aKFSsUHR1tKR8wYIAuXbqkBQsWaM2aNWrVqpUWL16shx56yFKnZcuWSkpK0rZt2yxlL774or7++mvt2bPHMk3rvffe0wsvvKDExES5uLjkul9uJk6cqNjYWKWnp+ujjz7SE0884eBXDwC4HpxZAQAUiQMHDujSpUu67777rMrT09NVv359q7KGDRvm2D8yMtLq+Z49exQdHW21nqRJkyZKTk7WiRMndMcdd+S6X17Kly8vf39/TZ06Ve3bt1fZsmVt2g8AUHhIVgAARSI5OVnSlcXrYWFhVts8PDysnnt7e+fYP7cyW9i6n4+Pj1asWKH77rtPrVq10urVq0lYAKCYscAeAFAkatWqJQ8PDx07dkxVq1a1eoSHh9vdXs2aNbVp0yarywpv2LBBPj4+Kl++/HXFGBAQoBUrVsjX11ctW7bUqVOnrqsdAIBjkKwAAIqEj4+PRo4cqWeffVbz5s3TwYMHtW3bNr377ruaN2+e3e09/fTTOn78uIYOHaq9e/dqyZIlmjBhgkaMGCEXl+v/583f31/Lly9XQEAACQsAFDOmgQEAiszkyZMVFBSk2NhYHTp0SP7+/mrQoIHGjh1rd1thYWH66aefNGrUKNWtW1elS5dW//79NW7cuBuO08/PT7/88ovatWunFi1aaM2aNTmmrgEACh9XAwMAAADglJgGBgAAAMApkawAAAAAcEokKwAAAACcEskKAAAAAKdEsgIAAADAKZGsAAAAAHBKJCsAAAAAnBLJCgAAAACnRLICAAAAwCmRrAAAAABwSiQrAAAAAJzS/wMLdv5LgSGAOAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
diff --git a/notebooks/example_hourly.ipynb b/notebooks/example_hourly.ipynb
index 6d265b0..a67abaa 100644
--- a/notebooks/example_hourly.ipynb
+++ b/notebooks/example_hourly.ipynb
@@ -44,13 +44,13 @@
     "var_name = \"tos\"\n",
     "\n",
     "# 1 month train, 1 month validation and test\n",
-    "hourly_data = xr.open_mfdataset(data_folder / f\"202101_hr_ERA5dc_masked_{var_name}.nc\")\n",
-    "hourly_data_validation = xr.open_mfdataset(data_folder / f\"202102_hr_ERA5dc_masked_{var_name}.nc\")\n",
-    "hourly_data_test = xr.open_mfdataset(data_folder / f\"202103_hr_ERA5dc_masked_{var_name}.nc\")\n",
+    "hourly_data = xr.open_mfdataset(data_folder / f\"202001_hr_ERA5dc_masked_{var_name}.nc\")\n",
+    "hourly_data_validation = xr.open_mfdataset(data_folder / f\"202101_hr_ERA5dc_masked_{var_name}.nc\")\n",
+    "hourly_data_test = xr.open_mfdataset(data_folder / f\"202201_hr_ERA5dc_masked_{var_name}.nc\")\n",
     "\n",
-    "monthly_data = xr.open_mfdataset(data_folder / f\"202101_mon_ERA5dc_full_{var_name}.nc\")\n",
-    "monthly_data_validation = xr.open_mfdataset(data_folder / f\"202102_mon_ERA5dc_full_{var_name}.nc\")\n",
-    "monthly_data_test = xr.open_mfdataset(data_folder / f\"202103_mon_ERA5dc_full_{var_name}.nc\")\n",
+    "monthly_data = xr.open_mfdataset(data_folder / f\"202001_mon_ERA5dc_full_{var_name}.nc\")\n",
+    "monthly_data_validation = xr.open_mfdataset(data_folder / f\"202101_mon_ERA5dc_full_{var_name}.nc\")\n",
+    "monthly_data_test = xr.open_mfdataset(data_folder / f\"202001_mon_ERA5dc_full_{var_name}.nc\")\n",
     "\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 = xr.open_dataset(file_name)"
@@ -115,7 +115,7 @@
     "set_seed()\n",
     "\n",
     "patch_size = (1, 4, 4)\n",
-    "model = SpatioTemporalModel(patch_size=patch_size, overlap=2, embed_dim=64, dropout=0.2, hidden=64)"
+    "model = SpatioTemporalModel(patch_size=patch_size, embed_dim=64, dropout=0.2, hidden=64)"
    ]
   },
   {
@@ -155,6 +155,7 @@
    "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",
@@ -192,9 +193,9 @@
    ],
    "source": [
     "# create dataset config\n",
-    "num_patches = (10, 10)\n",
-    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
-    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\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",
@@ -203,7 +204,8 @@
     "    padded_days_mask=padded_days_mask,\n",
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\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",
@@ -228,6 +230,7 @@
    "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",
@@ -264,9 +267,9 @@
     }
    ],
    "source": [
-    "num_patches = (10, 10)\n",
-    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
-    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\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_validation = STDataset(\n",
     "    input_da=input_da,\n",
@@ -275,7 +278,8 @@
     "    padded_days_mask=padded_days_mask,\n",
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\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",
@@ -428,11 +432,9 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 16,
+   "cell_type": "markdown",
    "id": "59fe46f2-12f2-45ea-81af-e2669cf2c21b",
    "metadata": {},
-   "outputs": [],
    "source": [
     "#### Prepare the test data"
    ]
@@ -444,6 +446,7 @@
    "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",
     "    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",
@@ -480,9 +483,7 @@
     }
    ],
    "source": [
-    "num_patches = (10, 10)\n",
-    "spatial_patch_size = (patch_size[1]*num_patches[0], patch_size[2]*num_patches[1])\n",
-    "stride = (spatial_patch_size[0] // 5, spatial_patch_size[1] // 5)\n",
+    "spatial_patch_size = monthly_da.shape[1:]  # the whole dataset \n",
     "\n",
     "dataset_test = STDataset(\n",
     "    input_da=input_da,\n",
@@ -491,8 +492,9 @@
     "    padded_days_mask=padded_days_mask,\n",
     "    time_features=time_features,\n",
     "    land_mask=lsm_subset[\"lsm\"],\n",
-    "    patch_size=(1, *spatial_patch_size),  # based on the patch_size in model\n",
-    "    stride=stride,\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",
@@ -522,7 +524,7 @@
     "# create dataloader config\n",
     "dataloader_config = DataLoaderConfig(\n",
     "    batch_size=10,\n",
-    "    shuffle=True,\n",
+    "    shuffle=False,   # no shuffling in inference\n",
     "    num_workers=0,\n",
     "    pin_memory=True,\n",
     "    persistent_workers=False,\n",
@@ -675,9 +677,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "climanet",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "climanet"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -689,7 +691,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.6"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,