address montonic and prediction size issues

deepsensor/model/model.py

@@ -918,29 +918,38 @@ def stitch_clipped_predictions(patch_preds, patch_overlap, patches_per_row, x1_a
                         """
                         if patch_x2_index[0] == data_x2_index[0]:
                             b_x2_min = 0
+                            # This line, as well as 933, 940 and 950 address the different shapes between the input and prediction
+                            # TODO: Try to resolve this issue in data/loader.py by ensuring patches are perfectly square. 
+                            b_x2_max = b_x2_max + 1
                         elif patch_x2_index[1] == data_x2_index[1]:
                             b_x2_max = 0
                             patch_row_prev = preds[i-1]
                             if x2_ascend:
-                                prev_patch_x2_max = get_index(int(patch_row_prev[var_name].coords[orig_x2_name].max()), x1 = False)
+                                prev_patch_x2_max = get_index(patch_row_prev[var_name].coords[orig_x2_name].max(), x1 = False)
                                 b_x2_min = (prev_patch_x2_max - patch_x2_index[0])-patch_overlap[1]
                             else:
-                                prev_patch_x2_min = get_index(int(patch_row_prev[var_name].coords[orig_x2_name].min()), x1 = False)
+                                prev_patch_x2_min = get_index(patch_row_prev[var_name].coords[orig_x2_name].min(), x1 = False)
                                 b_x2_min = (patch_x2_index[0] -prev_patch_x2_min)-patch_overlap[1]
-
+                        else:
+                            b_x2_max = b_x2_max + 1
+
+
                         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 + 1
                             patch_prev = preds[i-patches_per_row]
                             if x1_ascend:
-                                prev_patch_x1_max = get_index(int(patch_prev[var_name].coords[orig_x1_name].max()), x1 = True)
+                                prev_patch_x1_max = get_index(patch_prev[var_name].coords[orig_x1_name].max(), x1 = True)
                                 b_x1_min = (prev_patch_x1_max - patch_x1_index[0])- patch_overlap[0]
                             else:
-                                prev_patch_x1_min = get_index(int(patch_prev[var_name].coords[orig_x1_name].min()), x1 = True)
+                                prev_patch_x1_min = get_index(patch_prev[var_name].coords[orig_x1_name].min(), x1 = True)
 
                                 b_x1_min = (prev_patch_x1_min- patch_x1_index[0])- patch_overlap[0]
-
+                        else:
+                            b_x1_max = b_x1_max + 1
 
                         patch_clip_x1_min = int(b_x1_min)
                         patch_clip_x1_max = int(data_array.sizes[orig_x1_name] - b_x1_max)