Name/project/replace assign index calculate (#711)

* • ControlMechanism
  assign_as_controller:  fixed bug (assignment of self.allocation)

* • Project
  - Delete ASSIGN everywhere except in OutputState and Keywords

* • LearningMechanism failing

* • LearningMechanism
  _validate_params:
      replaced try and except for learning_signal[PARAMS][PROJECTIONS]
      with if-else

• OutputState:
  _parse_state_specific_specs:
      refactored for VARIABLE in place of INDEX in 3-item tuple

* • LearningMechanism
  _validate_params:
      replaced try and except for learning_signal[PARAMS][PROJECTIONS]
      with if-else

• OutputState:
  _parse_state_specific_specs:
      refactored for VARIABLE in place of INDEX in 3-item tuple
  docstring revised per above

* -

* -

Scripts/Examples/EVC-Gratton.py

@@ -74,10 +74,8 @@
         pnl.PROBABILITY_UPPER_THRESHOLD,
         {
             pnl.NAME: 'OFFSET RT',
-            pnl.INDEX: 2,
-            pnl.ASSIGN: pnl.Linear(0, slope=0.3, intercept=1)
-            # pnl.VARIABLE:[(pnl.OWNER_VALUE,2)],
-            # pnl.FUNCTION: pnl.Linear(0, slope=0.3, intercept=1)
+            pnl.VARIABLE: (pnl.OWNER_VALUE, 2),
+            pnl.FUNCTION: pnl.Linear(0, slope=0.3, intercept=1)
         }
     ],
 )

Scripts/Examples/EVC_MARKUS_control.py

@@ -46,8 +46,8 @@
         pnl.PROBABILITY_UPPER_THRESHOLD,
         {
             pnl.NAME: 'OFFSET RT',
-            pnl.INDEX: 2,
-            pnl.ASSIGN: pnl.Linear(0, slope=1.0, intercept=1)
+            pnl.VARIABLE: (pnl.OWNER_VALUE, 2),
+            pnl.FUNCTION: pnl.Linear(0, slope=1.0, intercept=1)
         }
     ],) #drift_rate=(1.0),threshold=(0.2645),noise=(0.5),starting_point=(0), t0=0.15
 Decision.set_log_conditions('DECISION_VARIABLE')

psyneulink/components/mechanisms/adaptive/control/controlmechanism.py

@@ -311,7 +311,7 @@
 from psyneulink.components.mechanisms.mechanism import Mechanism_Base
 from psyneulink.components.shellclasses import System_Base
 from psyneulink.components.states.modulatorysignals.controlsignal import ControlSignal
-from psyneulink.components.states.outputstate import INDEX, SEQUENTIAL
+from psyneulink.components.states.outputstate import SEQUENTIAL
 from psyneulink.globals.defaults import defaultControlAllocation
 from psyneulink.globals.keywords import AUTO_ASSIGN_MATRIX, COMMAND_LINE, CONTROL, CONTROLLER, CONTROL_PROJECTION, \
     CONTROL_PROJECTIONS, CONTROL_SIGNAL, CONTROL_SIGNALS, EXPONENT, INIT__EXECUTE__METHOD_ONLY, NAME, \
@@ -886,19 +886,14 @@ def _instantiate_control_signal(self, control_signal, context=None):
         self.value = self.instance_defaults.value
 
         # Assign ControlSignal's variable to appended item of owner's value
-        # control_signal._variable = (OWNER_VALUE, len(self.instance_defaults.value) - 1)
         if control_signal.owner_value_index is None:
             control_signal._variable = [(OWNER_VALUE, len(self.instance_defaults.value) - 1)]
         if not isinstance(control_signal.owner_value_index, int):
             raise ControlMechanismError(
-                "PROGRAM ERROR: {} attribute of {} for {} is not {} or an int".format(
-                    INDEX, ControlSignal.__name__, SEQUENTIAL, self.name
-                )
-            )
-
+                    "PROGRAM ERROR: The \'owner_value_index\' attribute for {} of {} ({})is not an int."
+                        .format(control_signal.name, self.name, control_signal.owner_value_index))
         # Validate index
         try:
-
             self.value[control_signal.owner_value_index]
         except IndexError:
             raise ControlMechanismError(

psyneulink/components/mechanisms/adaptive/learning/learningmechanism.py

@@ -549,10 +549,10 @@
 from psyneulink.components.states.inputstate import InputState
 from psyneulink.components.states.parameterstate import ParameterState
 from psyneulink.components.states.modulatorysignals.learningsignal import LearningSignal
-from psyneulink.globals.keywords import ASSERT, CONTROL_PROJECTIONS, ENABLED, IDENTITY_MATRIX, INDEX, INITIALIZING, \
+from psyneulink.globals.keywords import ASSERT, CONTROL_PROJECTIONS, ENABLED, IDENTITY_MATRIX, INITIALIZING, \
     INPUT_STATES, LEARNED_PARAM, LEARNING, LEARNING_MECHANISM, LEARNING_PROJECTION, LEARNING_SIGNAL, LEARNING_SIGNALS, \
     MATRIX, MATRIX_KEYWORD_SET, NAME, OUTPUT_STATE, OUTPUT_STATES, OWNER_VALUE, PARAMS, \
-    PROJECTIONS, SAMPLE, STATE_TYPE, TARGET
+    PROJECTIONS, SAMPLE, STATE_TYPE, TARGET, VARIABLE
 from psyneulink.globals.preferences.componentpreferenceset import is_pref_set
 from psyneulink.globals.preferences.preferenceset import PreferenceLevel
 from psyneulink.globals.utilities import ContentAddressableList, is_numeric, parameter_spec
@@ -893,9 +893,9 @@ class LearningMechanism(AdaptiveMechanism_Base):
         INPUT_STATES:input_state_names,
         OUTPUT_STATES:[{NAME:ERROR_SIGNAL,
                         STATE_TYPE:OUTPUT_STATE,
-                        INDEX:1},
+                        VARIABLE: (OWNER_VALUE, 1)},
                        {NAME:LEARNING_SIGNAL,  # NOTE: This is the default, but is overridden by any LearningSignal arg
-                        INDEX:0}
+                        VARIABLE: (OWNER_VALUE, 0)}
                        ]})
 
     @tc.typecheck
@@ -1017,17 +1017,16 @@ def _validate_params(self, request_set, target_set=None, context=None):
             for spec in target_set[LEARNING_SIGNALS]:
                 learning_signal = _parse_state_spec(state_type=LearningSignal, owner=self, state_spec=spec)
 
-
                 # Validate that the receiver of the LearningProjection (if specified)
                 #     is a MappingProjection and in the same System as self (if specified)
-                try:
+                if learning_signal[PARAMS] and PROJECTIONS in learning_signal[PARAMS]:
                     for learning_projection in  learning_signal[PARAMS][PROJECTIONS]:
                         _validate_receiver(sender_mech=self,
                                            projection=learning_projection,
                                            expected_owner_type=MappingProjection,
                                            spec_type=LEARNING_SIGNAL,
                                            context=context)
-                except KeyError:
+                else:
                     pass
 
     def _instantiate_attributes_before_function(self, context=None):

psyneulink/components/mechanisms/processing/transfermechanism.py

@@ -311,9 +311,9 @@
 from psyneulink.components.mechanisms.processing.processingmechanism import ProcessingMechanism_Base
 from psyneulink.components.states.inputstate import InputState
 from psyneulink.components.states.outputstate import OutputState, PRIMARY, StandardOutputStates, standard_output_states
-from psyneulink.globals.keywords import FUNCTION, INDEX, INITIALIZER, INITIALIZING, \
+from psyneulink.globals.keywords import FUNCTION, INITIALIZER, INITIALIZING, OWNER_VALUE, \
     MAX_VAL, MAX_ABS_VAL, MAX_INDICATOR, MAX_ABS_INDICATOR, MEAN, MEDIAN, NAME, NOISE, NORMALIZING_FUNCTION_TYPE, \
-    PROB,RATE, RESULT, RESULTS, STANDARD_DEVIATION, TRANSFER_FUNCTION_TYPE, TRANSFER_MECHANISM, VARIANCE, \
+    PROB,RATE, RESULT, RESULTS, STANDARD_DEVIATION, TRANSFER_FUNCTION_TYPE, TRANSFER_MECHANISM, VARIABLE, VARIANCE, \
     kwPreferenceSetName
 from psyneulink.globals.preferences.componentpreferenceset import is_pref_set, \
     kpReportOutputPref, kpRuntimeParamStickyAssignmentPref
@@ -871,7 +871,7 @@ def _instantiate_output_states(self, context=None):
         if len(self.instance_defaults.variable) > 1 and len(self.output_states) == 1 and self.output_states[0] == RESULTS:
             self.output_states = []
             for i, item in enumerate(self.instance_defaults.variable):
-                self.output_states.append({NAME: RESULT, INDEX: i})
+                self.output_states.append({NAME: RESULT, VARIABLE: (OWNER_VALUE, i)})
         super()._instantiate_output_states(context=context)
 
     def _execute(self,

psyneulink/components/states/modulatorysignals/gatingsignal.py

@@ -66,9 +66,9 @@
 
     The dictionary can also contain entries for any other GatingSignal attributes to be specified
     (e.g., a *MODULATION* entry, the value of which determines how the GatingSignal modulates the
-    `value <State_Base.value>` of the State(s) that it gates; or an *INDEX* entry specifying which item
+    `value <State_Base.value>` of the State(s) that it gates; or a *VARIABLE* entry specifying which item
     of the GatingMechanism's `gating_policy <GatingMechanism.gating_policy>` it should use as its `value
-    <GatingSignal,value>`).
+    <GatingSignal,value>`;  see `OutputState_Customization`).
   ..
   * **2-item tuple** -- the 1st item must be the name of the State (or list of State names), and the 2nd item the
     Mechanism to which it (they) belong(s); this is a convenience format, which is simpler to use than a specification