treewide: Use asarray(dtype=float) instead of asfarray

asfarray is not present in Numpy 2+

Signed-off-by: Jan Vesely <[email protected]>

psyneulink/core/components/component.py

@@ -1425,7 +1425,7 @@ def _get_state_initializer(self, context):
         def _convert(p):
             x = p.get(context)
             if p.name == 'matrix': # Flatten matrix
-                val = tuple(np.asfarray(x).flatten())
+                val = tuple(np.asarray(x, dtype=float).ravel())
             elif isinstance(x, np.random.RandomState):
                 state = x.get_state(legacy=False)
 
@@ -1626,7 +1626,7 @@ def _get_values(p):
             elif p.name == 'num_trials_per_estimate': # Should always be int
                 return 0 if param is None else int(param)
             elif p.name == 'matrix': # Flatten matrix
-                return tuple(np.asfarray(param).flatten())
+                return tuple(np.asarray(param, dtype=float).ravel())
             return _convert(param)
 
         return tuple(map(_get_values, self._get_compilation_params()))

psyneulink/core/components/functions/function.py

@@ -835,7 +835,7 @@ def convert_output_type(self, value, output_type=None):
         # Note: if 2D or 1D array has more than two items, generate exception
         elif output_type is FunctionOutputType.NP_0D_ARRAY:
             if object_has_single_value(value):
-                value = np.asfarray(value)
+                value = np.asarray(value, dtype=float)
             else:
                 raise FunctionError(f"Can't convert value ({value}) with more than a single number to a raw number.")
 

psyneulink/core/components/functions/stateful/memoryfunctions.py

@@ -1289,10 +1289,10 @@ 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)
-        ])
+        ], dtype=float)
 
     def _validate(self, context=None):
         """Validate distance_function, selection_function and memory store"""
@@ -1328,10 +1328,10 @@ 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))
-            ])
+            ], dtype=float)
             try:
                 result = np.asarray(selection_function(test_var, context=context))
             except Exception as e:
@@ -2313,10 +2313,10 @@ 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)
-        ])
+        ], dtype=float)
 
     def _get_state_ids(self):
         return super()._get_state_ids() + ["ring_memory"]
@@ -2553,9 +2553,10 @@ def _validate(self, context=None):
 
         # Default to full memory
         selection_function = self.selection_function
-        test_var = np.asfarray([distance_result if i==0
-                                else np.zeros_like(distance_result)
-                                for i in range(self._get_current_parameter_value('max_entries', context))])
+        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))],
+                              dtype=float)
+
         if isinstance(selection_function, type):
             selection_function = selection_function(default_variable=test_var, context=context)
             fct_string = 'Function type'
@@ -2709,12 +2710,12 @@ 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, dtype=float) * np.asarray(rate, dtype=float)
             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, dtype=float) + np.asarray(noise, dtype=float)[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()):

psyneulink/core/llvm/builder_context.py

@@ -485,7 +485,7 @@ def _param_struct(p):
             if isinstance(val, ContentAddressableList):
                 return ir.LiteralStructType(self.get_param_struct_type(x) for x in val)
             elif p.name == 'matrix':   # Flatten matrix
-                val = np.asfarray(val).flatten()
+                val = np.asarray(val, dtype=float).ravel()
             elif p.name == 'num_trials_per_estimate':  # Should always be int
                 val = np.int32(0) if val is None else np.int32(val)
             elif np.ndim(val) == 0 and component._is_param_modulated(p):
@@ -508,7 +508,7 @@ def _state_struct(p):
             if isinstance(val, ContentAddressableList):
                 return ir.LiteralStructType(self.get_state_struct_type(x) for x in val)
             if p.name == 'matrix':   # Flatten matrix
-                val = np.asfarray(val).flatten()
+                val = np.asarray(val, dtype=float).ravel()
             struct = self.convert_python_struct_to_llvm_ir(val)
             return ir.ArrayType(struct, p.history_min_length + 1)