requirements: update sphinx requirement from <4.2.1 to <8.1.1

doc_requirements.txt

@@ -1,3 +1,3 @@
 psyneulink-sphinx-theme<1.2.4.1
-sphinx<4.2.1
+sphinx<8.1.1
 sphinx_autodoc_typehints<1.16.0

psyneulink/core/components/functions/function.py

@@ -375,7 +375,7 @@ def _random_state_getter(self, owning_component, context, modulated=False):
     # 'has_modulation' indicates that seed has an active modulatory projection
     # 'modulated' indicates that the modulated value is requested
     if has_modulation and modulated:
-        seed_value = [int(owning_component._get_current_parameter_value(seed_param, context))]
+        seed_value = [int(owning_component._get_current_parameter_value(seed_param, context).item())]
     else:
         seed_value = [int(seed_param._get(context=context))]
 
@@ -832,7 +832,7 @@ 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.array(float(value))
+                value = np.asfarray(value)
             else:
                 raise FunctionError(f"Can't convert value ({value}) with more than a single number to a raw number.")
 

psyneulink/core/components/functions/nonstateful/combinationfunctions.py

@@ -2191,7 +2191,7 @@ def _function(self,
         delta values : 1d np.array
 
         """
-        gamma = self._get_current_parameter_value(GAMMA, context)
+        gamma = self._get_current_parameter_value(GAMMA, context).item()
         sample = variable[0]
         reward = variable[1]
         delta = np.zeros(sample.shape)

psyneulink/core/components/functions/nonstateful/distributionfunctions.py

@@ -1225,13 +1225,13 @@ def _function(self,
 
         """
 
-        attentional_drift_rate = float(self._get_current_parameter_value(DRIFT_RATE, context))
-        stimulus_drift_rate = float(variable)
+        attentional_drift_rate = self._get_current_parameter_value(DRIFT_RATE, context).item()
+        stimulus_drift_rate = variable.item()
         drift_rate = attentional_drift_rate * stimulus_drift_rate
         threshold = self._get_current_parameter_value(THRESHOLD, context)
-        starting_value = float(self._get_current_parameter_value(STARTING_VALUE, context))
-        noise = float(self._get_current_parameter_value(NOISE, context))
-        non_decision_time = float(self._get_current_parameter_value(NON_DECISION_TIME, context))
+        starting_value = self._get_current_parameter_value(STARTING_VALUE, context).item()
+        noise = self._get_current_parameter_value(NOISE, context).item()
+        non_decision_time = self._get_current_parameter_value(NON_DECISION_TIME, context).item()
 
         # drift_rate = float(self.drift_rate) * float(variable)
         # threshold = float(self.threshold)

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

@@ -396,10 +396,10 @@ def _distance_field_weights_setter(value, owning_component=None, context=None):
     # NOTE: need the following to accommodate various forms of specification (single value, None's, etc)
     #       that are resolved elsewhere
     # FIX: STANDARDIZE FORMAT FOR FIELDWEIGHTS HERE (AS LIST OF INTS) AND GET RID OF THE FOLLOWING
-    test_val = np.array([int(val) if val else 0 for val in value])
-    test_val = np.full(len(variable),test_val) if len(test_val) == 1 else test_val
-    test_curr_field_weights = np.array([int(val) if val else 0 for val in current_field_weights])
-    test_curr_field_weights = (np.full(len(variable),test_curr_field_weights) if len(variable) == 1
+    test_val = np.array([int(np.array(val).item()) if val else 0 for val in value])
+    test_val = np.full(len(variable), test_val) if len(test_val) == 1 else test_val
+    test_curr_field_weights = np.array([int(np.array(val).item()) if val else 0 for val in current_field_weights])
+    test_curr_field_weights = (np.full(len(variable), test_curr_field_weights) if len(variable) == 1
                                else test_curr_field_weights)
     if np.all(test_curr_field_weights == test_val) and not owning_component.is_initializing:
         pass
@@ -2777,8 +2777,8 @@ def get_memory(self, query_key:Union[list, np.ndarray], context=None):
         indices_of_selected_items = np.flatnonzero(selection_array)
 
         # Single key identified
-        if len(indices_of_selected_items)==1:
-            index_of_selected_item = int(np.flatnonzero(selection_array))
+        if len(indices_of_selected_items) == 1:
+            index_of_selected_item = int(np.flatnonzero(selection_array).item())
         # More than one key identified
         else:
             selected_keys = _memory[KEYS]

psyneulink/core/components/ports/modulatorysignals/controlsignal.py

@@ -1112,6 +1112,6 @@ def compute_costs(self, intensity, context=None):
         all_costs = [[intensity_cost, adjustment_cost, duration_cost]]
 
         # Combine the costs. Convert to a float because reRedcu
-        combined_cost = float(self.combine_costs_function(all_costs, context=context))
+        combined_cost = float(self.combine_costs_function(all_costs, context=context).item())
 
         return max(0.0, combined_cost)

psyneulink/core/compositions/report.py

@@ -1380,9 +1380,9 @@ def node_execution_report(self,
         # FIX: kmantel: previous version would fail on anything but iterables of things that can be cast to floats
         #      if you want more specific output, you can add conditional tests here
         try:
-            input_string = [float("{:0.3}".format(float(i))) for i in input_val].__str__().strip("[]")
+            input_string = ", ".join([np.format_float_positional(i.item(), precision=2, trim='0') for i in input_val])
             # input_string = re.sub(r'[\[,\],\n]', '', str([float("{:0.3}".format(float(i))) for i in input_val]))
-        except TypeError:
+        except ValueError:
             input_string = node.parameters.variable.get(context)
 
         input_report = f"input: {input_string}"

psyneulink/core/scheduling/condition.py

@@ -324,7 +324,7 @@ def func(threshold, comparator, indices, atol, rtol, execution_id):
                 for i in indices:
                     param_value = param_value[i]
 
-            param_value = float(param_value)
+            param_value = float(np.array(param_value).item())
 
             if comparator == '==':
                 return np.isclose(param_value, threshold, atol=atol, rtol=rtol)

psyneulink/library/components/mechanisms/processing/integrator/ddm.py

@@ -1110,7 +1110,7 @@ def _execute(
                                format(self.function.name, self.name))
 
             # Convert ER to decision variable:
-            threshold = float(self.function._get_current_parameter_value(THRESHOLD, context))
+            threshold = float(self.function._get_current_parameter_value(THRESHOLD, context).item())
             random_state = self._get_current_parameter_value(self.parameters.random_state, context)
             if random_state.uniform() < return_value[self.PROBABILITY_LOWER_THRESHOLD_INDEX]:
                 return_value[self.DECISION_VARIABLE_INDEX] = np.atleast_1d(-1 * threshold)

psyneulink/library/compositions/regressioncfa.py

@@ -638,7 +638,7 @@ def compute_terms(self, control_allocation, context=None):
             # Compute value of each control_signal from its variable
             c = np.zeros((len(control_allocation), ))
             for i, var in enumerate(control_allocation):
-                c[i] = self.control_signal_functions[i](var, context=context)
+                c[i] = self.control_signal_functions[i](var, context=context).item()
             computed_terms[PV.C] = c
 
             # Compute costs for new control_signal values