add checks and update docs

docs/Examples/01.AdaSTEM_demo.ipynb

@@ -743,7 +743,7 @@
     "    ),                                      # hurdel model for zero-inflated problem (e.g., count)\n",
     "    save_gridding_plot = True,\n",
     "    ensemble_fold=10,                       # data are modeled 10 times, each time with jitter and rotation in Quadtree algo\n",
-    "    min_ensemble_required=7,                # Only points covered by > 7 stixels will be predicted\n",
+    "    min_ensemble_required=7,                # Only points covered by > 7 ensembles will be predicted\n",
     "    grid_len_upper_threshold=25,            # force splitting if the grid length exceeds 25\n",
     "    grid_len_lower_threshold=5,             # stop splitting if the grid length fall short 5         \n",
     "    temporal_start=1,                       # The next 4 params define the temporal sliding window\n",

docs/index.md

@@ -135,7 +135,7 @@ model = AdaSTEMRegressor(
     ),                                      # hurdel model for zero-inflated problem (e.g., count)
     save_gridding_plot = True,
     ensemble_fold=10,                       # data are modeled 10 times, each time with jitter and rotation in Quadtree algo
-    min_ensemble_required=7,                # Only points covered by > 7 stixels will be predicted
+    min_ensemble_required=7,                # Only points covered by > 7 ensembles will be predicted
     grid_len_upper_threshold=25,            # force splitting if the grid length exceeds 25
     grid_len_lower_threshold=5,             # stop splitting if the grid length fall short 5         
     temporal_start=1,                       # The next 4 params define the temporal sliding window

stemflow/model/AdaSTEM.py

@@ -205,6 +205,9 @@ def __init__(
         self.Spatio2 = Spatio2
 
         # 3. Gridding params
+        if min_ensemble_required > ensemble_fold:
+            raise ValueError("Not satisfied: min_ensemble_required <= ensemble_fold")
+
         self.ensemble_fold = ensemble_fold
         self.min_ensemble_required = min_ensemble_required
         self.grid_len_upper_threshold = (
@@ -607,6 +610,11 @@ def SAC_predict(
             )
 
         # pred = pred.reset_index(drop=False)
+        if len(pred) == 0:
+            raise ValueError(
+                "All samples are not predictable based on current settings!\nTry adjusting the 'points_lower_threshold', increase the grid size, or increase sample size!"
+            )
+
         pred = pred.droplevel(1, axis=0).reset_index(drop=False)
         pred = pred.pivot_table(index="index", columns="ensemble_index", values="pred")
         return pred