Merge pull request #19 from davidwilby/simplify_stitching

Simplify stitching process

Author: davidwyld

deepsensor/model/model.py

@@ -810,6 +810,51 @@ def overlap_index(
                 ),
             )
 
+        def get_coordinate_extent(ds: Union[xr.DataArray, xr.Dataset], x1_ascend: bool, x2_ascend: bool) -> tuple:
+            """
+            Get coordinate extent of dataset. This method is applied to either X_t or patchwise predictions. 
+
+            Parameters
+            ----------
+            ds : Data object
+                The dataset or data array to determine coordinate extent for.
+                Refer to `X_t` in `predict_patchwise()` for supported types.
+
+            x1_ascend : bool
+                Whether the x1 coordinates ascend (increase) from top to bottom.
+
+            x2_ascend : bool
+                Whether the x2 coordinates ascend (increase) from left to right.
+
+            Returns
+            -------
+            tuple of tuples:
+                Extents of x1 and x2 coordinates as ((min_x1, max_x1), (min_x2, max_x2)).
+            """
+            
+            if x1_ascend:
+                ds_x1_coords = (
+                    ds.coords[orig_x1_name].min().values,
+                    ds.coords[orig_x1_name].max().values,
+                )
+            else:
+                ds_x1_coords = (
+                    ds.coords[orig_x1_name].max().values,
+                    ds.coords[orig_x1_name].min().values,
+                )
+            if x2_ascend:
+                ds_x2_coords = (
+                    ds.coords[orig_x2_name].min().values,
+                    ds.coords[orig_x2_name].max().values,
+                )
+            else:
+                ds_x2_coords = (
+                    ds.coords[orig_x2_name].max().values,
+                    ds.coords[orig_x2_name].min().values,
+                )
+            return ds_x1_coords, ds_x2_coords
+
+        
         def get_index(*args, x1=True) -> Union[int, Tuple[List[int], List[int]]]:
             """Convert coordinates into pixel row/column (index).
 
@@ -886,74 +931,38 @@ def stitch_clipped_predictions(
             combined: dict
                 Dictionary object containing the stitched model predictions.
             """
-            # Get row/col index values of X_t. Order depends on whether coordinate is ascending or descending.
-            if x1_ascend:
-                data_x1 = (
-                    X_t.coords[orig_x1_name].min().values,
-                    X_t.coords[orig_x1_name].max().values,
-                )
-            else:
-                data_x1 = (
-                    X_t.coords[orig_x1_name].max().values,
-                    X_t.coords[orig_x1_name].min().values,
-                )
-            if x2_ascend:
-                data_x2 = (
-                    X_t.coords[orig_x2_name].min().values,
-                    X_t.coords[orig_x2_name].max().values,
-                )
-            else:
-                data_x2 = (
-                    X_t.coords[orig_x2_name].max().values,
-                    X_t.coords[orig_x2_name].min().values,
-                )
 
-            data_x1_index, data_x2_index = get_index(data_x1, data_x2)
-            patches_clipped = {var_name: [] for var_name in patch_preds[0].keys()}
+            # Get row/col index values of X_t.            
+            data_x1_coords, data_x2_coords= get_coordinate_extent(X_t, x1_ascend, x2_ascend)
+            data_x1_index, data_x2_index = get_index(data_x1_coords, data_x2_coords)
 
+            # Iterate through patchwise predictions and slice edges prior to stitchin.
+            patches_clipped = {var_name: [] for var_name in patch_preds[0].keys()}
             for i, patch_pred in enumerate(patch_preds):
                 for var_name, data_array in patch_pred.items():
                     if var_name in patch_pred:
-                        # Get row/col index values of each patch. Order depends on whether coordinate is ascending or descending.
-                        if x1_ascend:
-                            patch_x1 = (
-                                data_array.coords[orig_x1_name].min().values,
-                                data_array.coords[orig_x1_name].max().values,
-                            )
-                        else:
-                            patch_x1 = (
-                                data_array.coords[orig_x1_name].max().values,
-                                data_array.coords[orig_x1_name].min().values,
-                            )
-                        if x2_ascend:
-                            patch_x2 = (
-                                data_array.coords[orig_x2_name].min().values,
-                                data_array.coords[orig_x2_name].max().values,
-                            )
-                        else:
-                            patch_x2 = (
-                                data_array.coords[orig_x2_name].max().values,
-                                data_array.coords[orig_x2_name].min().values,
-                            )
-                        patch_x1_index, patch_x2_index = get_index(patch_x1, patch_x2)
+                        
+                        # Get row/col index values of each patch.
+                        patch_x1_coords, patch_x2_coords= get_coordinate_extent(data_array, x1_ascend, x2_ascend)
+                        patch_x1_index, patch_x2_index = get_index(patch_x1_coords, patch_x2_coords)
 
+                        # Calculate size of border to slice of each edge of patchwise predictions.
+                        # Initially set the size of all borders to the size of the overlap. 
                         b_x1_min, b_x1_max = patch_overlap[0], patch_overlap[0]
                         b_x2_min, b_x2_max = patch_overlap[1], patch_overlap[1]
 
                         # Do not remove border for the patches along top and left of dataset and change overlap size for last patch in each row and column.
                         if patch_x2_index[0] == data_x2_index[0]:
                             b_x2_min = 0
-                            # TODO: Try to resolve this issue in data/loader.py by ensuring patches are perfectly square.
                             b_x2_max = b_x2_max
 
-                        # At end of row (when patch_x2_index = data_x2_index), to calculate the number of pixels to remove from left hand side of patch:
+                        # At end of row (when patch_x2_index = data_x2_index), calculate the number of pixels to remove from left hand side of patch.
                         elif patch_x2_index[1] == data_x2_index[1]:
                             b_x2_max = 0
                             patch_row_prev = preds[i - 1]
 
                             # If x2 is ascending, subtract previous patch x2 max value from current patch x2 min value to get bespoke overlap in column pixels.
                             # To account for the clipping done to the previous patch, then subtract patch_overlap value in pixels
-                            # to get the number of pixels to remove from left hand side of patch.
                             if x2_ascend:
                                 prev_patch_x2_max = get_index(
                                     patch_row_prev[var_name].coords[orig_x2_name].max(),
@@ -963,9 +972,8 @@ def stitch_clipped_predictions(
                                     prev_patch_x2_max - patch_x2_index[0]
                                 ) - patch_overlap[1]
 
-                            # If x2 is descending. Subtract current patch max x2 value from previous patch min x2 value to get bespoke overlap in column pixels.
+                            # If x2 is descending, subtract current patch max x2 value from previous patch min x2 value to get bespoke overlap in column pixels.
                             # To account for the clipping done to the previous patch, then subtract patch_overlap value in pixels
-                            # to get the number of pixels to remove from left hand side of patch.
                             else:
                                 prev_patch_x2_min = get_index(
                                     patch_row_prev[var_name].coords[orig_x2_name].min(),
@@ -977,9 +985,10 @@ def stitch_clipped_predictions(
                         else:
                             b_x2_max = b_x2_max
 
+                        # Repeat process as above for x1 coordinates. 
                         if patch_x1_index[0] == data_x1_index[0]:
                             b_x1_min = 0
-                        # TODO: ensure this elif statement is robust to multiple patch sizes.
+                        
                         elif abs(patch_x1_index[1] - data_x1_index[1]) < 2:
                             b_x1_max = 0
                             b_x1_max = b_x1_max
@@ -1013,6 +1022,7 @@ def stitch_clipped_predictions(
                             data_array.sizes[orig_x2_name] - b_x2_max
                         )
 
+                        # Slice patchwise predictions
                         patch_clip = data_array.isel(
                             **{
                                 orig_x1_name: slice(
@@ -1025,51 +1035,47 @@ def stitch_clipped_predictions(
                         )
 
                         patches_clipped[var_name].append(patch_clip)
-
-            # Create blank prediction
-            combined_dataset = copy.deepcopy(patches_clipped)
-
-            # Generate new blank DeepSensor.prediction object with same extent and coordinate system as X_t.
-            for var, data_array_list in combined_dataset.items():
-                first_patchwise_pred = data_array_list[0]
-
-                # Define coordinate extent and time
-                blank_pred = xr.Dataset(
+            
+            # Create blank prediction object to stitch prediction values onto.
+            stitched_prediction = copy.deepcopy(patch_preds[0])
+            # Set prediction object extent to the same as X_t.
+            for var_name, data_array in stitched_prediction.items():
+                blank_ds= xr.Dataset(
                     coords={
                         orig_x1_name: X_t[orig_x1_name],
                         orig_x2_name: X_t[orig_x2_name],
-                        "time": first_patchwise_pred["time"],
+                        "time": stitched_prediction[0]["time"],
                     }
                 )
 
-                # Set variable names to those in patched predictions, set values to Nan.
-                for param in first_patchwise_pred.data_vars:
-                    blank_pred[param] = first_patchwise_pred[param]
-                    blank_pred[param][:] = np.nan
-                combined_dataset[var] = blank_pred
-
-            # Merge patchwise predictions to create final combined dataset.
+                # Set data variable names e.g. mean, std to those in patched prediction. Make all values Nan.
+                for data_var in data_array.data_vars:
+                    blank_ds[data_var] = data_array[data_var]
+                    blank_ds[data_var][:] = np.nan
+                stitched_prediction[var_name] = blank_ds
+            
+            # Merge patchwise predictions to create final stiched prediction.
             # Iterate over each variable (key) in the prediction dictionary
             for var_name, patches in patches_clipped.items():
                 # Retrieve the blank dataset for the current variable
-                combined_array = combined_dataset[var_name]
+                prediction_array = stitched_prediction[var_name]
 
                 # Merge each patch into the combined dataset
                 for patch in patches:
                     for var in patch.data_vars:
                         # Reindex the patch to catch any slight rounding errors and misalignment with the combined dataset
                         reindexed_patch = patch[var].reindex_like(
-                            combined_array[var], method="nearest", tolerance=1e-6
+                            prediction_array[var], method="nearest", tolerance=1e-6
                         )
 
                         # Combine data, prioritizing non-NaN values from patches
-                        combined_array[var] = combined_array[var].where(
+                        prediction_array[var] = prediction_array[var].where(
                             np.isnan(reindexed_patch), reindexed_patch
                         )
 
                 # Update the dictionary with the merged dataset
-                combined_dataset[var_name] = combined_array
-            return combined_dataset
+                stitched_prediction[var_name] = prediction_array
+            return stitched_prediction
 
         # load patch_size and stride from task
         patch_size = tasks[0]["patch_size"]
@@ -1164,34 +1170,10 @@ def stitch_clipped_predictions(
         )
 
         patches_per_row = get_patches_per_row(preds)
-        stitched_prediction = stitch_clipped_predictions(
+        prediction = stitch_clipped_predictions(
             preds, patch_overlap_unnorm, patches_per_row, x1_ascending, x2_ascending
         )
 
-        ## Cast prediction into DeepSensor.Prediction object.
-        # TODO make this into seperate method.
-        prediction = copy.deepcopy(preds[0])
-
-        # Generate new blank DeepSensor.prediction object in original coordinate system.
-        for var_name_copy, data_array_copy in prediction.items():
-            # set x and y coords
-            stitched_preds = xr.Dataset(
-                coords={
-                    orig_x1_name: X_t[orig_x1_name],
-                    orig_x2_name: X_t[orig_x2_name],
-                }
-            )
-
-            # Set time to same as patched prediction
-            stitched_preds["time"] = data_array_copy["time"]
-
-            # set variable names to those in patched prediction, make values blank
-            for var_name_i in data_array_copy.data_vars:
-                stitched_preds[var_name_i] = data_array_copy[var_name_i]
-                stitched_preds[var_name_i][:] = np.nan
-            prediction[var_name_copy] = stitched_preds
-            prediction[var_name_copy] = stitched_prediction[var_name_copy]
-
         return prediction