functions/MemoryFunction: Use np.asarray instead of asfarray

Rely on numpy type detection and promotion instead of explicitly converting to float64. Signed-off-by: Jan Vesely <[email protected]>
jvesely · Jan 9, 2025 · c1effb2 · c1effb2
1 parent 9ffe61d
commit c1effb2
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/psyneulink/core/components/functions/stateful/memoryfunctions.py b/psyneulink/core/components/functions/stateful/memoryfunctions.py
@@ -1289,7 +1289,7 @@ def _parse_selection_function_variable(self, variable, context=None, distance_re
         distance_result = self.distance_function(self._parse_distance_function_variable(variable), context=context)
         # TEST PRINT:
         # print(distance_result, self.distance_function.defaults.value)
-        return np.asfarray([
+        return np.asarray([
             distance_result if i == 0 else np.zeros_like(distance_result)
             for i in range(self.defaults.max_entries)
         ])
@@ -1328,7 +1328,7 @@ def _validate(self, context=None):
 
             # Default to full memory
             selection_function = self.selection_function
-            test_var = np.asfarray([
+            test_var = np.asarray([
                 distance_result if i == 0 else np.zeros_like(distance_result)
                 for i in range(self._get_current_parameter_value('max_entries', context))
             ])
@@ -2313,7 +2313,7 @@ def _parse_selection_function_variable(self, variable, context=None):
         distance_result = self.distance_function.parameters.value._get(context)
         # TEST PRINT:
         # print(distance_result, self.distance_function.defaults.value)
-        return np.asfarray([
+        return np.asarray([
             distance_result if i == 0 else np.zeros_like(distance_result)
             for i in range(self.defaults.max_entries)
         ])
@@ -2553,7 +2553,7 @@ def _validate(self, context=None):
 
         # Default to full memory
         selection_function = self.selection_function
-        test_var = np.asfarray([distance_result if i==0
+        test_var = np.asarray([distance_result if i==0
                                 else np.zeros_like(distance_result)
                                 for i in range(self._get_current_parameter_value('max_entries', context))])
         if isinstance(selection_function, type):
@@ -2709,12 +2709,12 @@ def _function(self,
         # Store variable to dict:
         rate = self._get_current_parameter_value(RATE, context)
         if rate is not None:
-            key = np.asfarray(key) * np.asfarray(rate)
+            key = np.asarray(key) * np.asarray(rate)
             assert len(key) == len(variable[KEYS]), "{} vs. {}".format(key, variable[KEYS])
 
         if noise is not None:
             # TODO: does val need noise?
-            key = np.asfarray(key) + np.asfarray(noise)[KEYS]
+            key = np.asarray(key) + np.asarray(noise)[KEYS]
             assert len(key) == len(variable[KEYS]), "{} vs. {}".format(key, variable[KEYS])
 
         if storage_prob == 1.0 or (storage_prob > 0.0 and storage_prob > random_state.uniform()):