initial update for uncertainty perturbation workflow notebook

esat/data/datahandler.py

@@ -271,11 +271,12 @@ def _determine_optimal_block(self, input_data=None):
         if input_data is None:
             input_data = self.input_data.to_numpy()
         try:
-            optimal_blocks = optimal_block_length(input_data)
-            optimal_block = []
-            for opt in optimal_blocks:
-                optimal_block.append(opt.b_star_cb)
-            self.optimal_block = math.floor(np.mean(optimal_block))
+            with np.errstate(divide='ignore', invalid='ignore'):
+                optimal_blocks = optimal_block_length(input_data)
+                optimal_block = []
+                for opt in optimal_blocks:
+                    optimal_block.append(opt.b_star_cb)
+                self.optimal_block = math.floor(np.mean(optimal_block))
         except ValueError as e:
             self.optimal_block = int(input_data.shape[1]/5)
             logger.error(f"Unable to determine optimal block size. Setting default to {self.optimal_block}")

esat/model/sa.py

@@ -323,11 +323,14 @@ def initialize(self,
                              f"of factors.")
 
         if prior_H is not None:
-            _p_i = prior_H.shape[0]
-            _H = H[_p_i:]
-            _H = (_H / _H.sum(axis=0)) * (1.0 - H_ratio)
-            _prior_H = (prior_H / prior_H.sum(axis=0)) * H_ratio
-            H = np.vstack((_prior_H, _H))
+            for i in range(prior_H.shape[0]):
+                H[i] = prior_H[i]
+            H = H / H.sum(axis=0)
+            # _p_i = prior_H.shape[0]
+            # _H = H[_p_i:]
+            # _H = (_H / _H.sum(axis=0)) * (1.0 - H_ratio)
+            # _prior_H = (prior_H / prior_H.sum(axis=0)) * H_ratio
+            # H = np.vstack((_prior_H, _H))
         H[H <= 0.0] = 1e-12
         self.H = H
         self.W = W