Refactor/log/entry management (#579)

* • System
  - show_graph():
    fixed bug producing empty image for graphs with just one Mechanism
    added auto-recurrent projections

* • Log
  - added numpy_array output method

* • Log
  - docstring corrections

* • Log
  - changed all references to LogEntry to use field names
  - EntryDict: added valiation of assignments as LogEntry in setter

* • Log
  - changed all references to LogEntry to use field names
  - EntryDict: added valiation of assignments as LogEntry in setter

* • Log
  - changed all references to LogEntry to use field names
  - EntryDict: added valiation of assignments as LogEntry in setter

• Tests
  - added TestLog

* • Component:
  - make_property (Line 2883): changed return value

* • Component:
  - make_property (Line 2883): changed return value

* -

* -

* • Log
  - refactored to keep data for a Compoent in its *own* Log,
    and have others that require that data to reference it

* • Log
  - refactored to keep data for a Compoent in its *own* Log,
    and have others that require that data to reference it

* -

* -

Scripts/Examples/Multilayer-Learning.py

@@ -104,7 +104,8 @@ def show_target(system):
 )
 
 # Log Middle_Weights of MappingProjection to Hidden_Layer_2
-Hidden_Layer_2.log_items('Middle Weights')
+# Hidden_Layer_2.log_items('Middle Weights')
+Middle_Weights.log_items('matrix')
 
 mySystem.reportOutputPref = True
 # Shows graph will full information:
@@ -125,4 +126,5 @@ def show_target(system):
 )
 
 # Print out logged weights for Middle_Weights
-print('\nMiddle Weights (to Hidden_Layer_2): \n', Hidden_Layer_2.log.csv(entries='Middle Weights'))
+# print('\nMiddle Weights (to Hidden_Layer_2): \n', Hidden_Layer_2.log.nparray(entries='Middle Weights', header=False))
+print('\nMiddle Weights (to Hidden_Layer_2): \n', Middle_Weights.log.nparray(entries='matrix', header=False))

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

@@ -446,7 +446,7 @@ class ControlMechanism(AdaptiveMechanism_Base):
         specifies the default form of modulation used by the ControlMechanism's `ControlSignals <ControlSignal>`,
         unless they are `individually specified <ControlSignal_Specification>`.
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters
         for the Mechanism, parameters for its function, and/or a custom function and its parameters. Values
         specified for parameters in the dictionary override any assigned to those parameters in arguments of the

psyneulink/components/mechanisms/adaptive/gating/gatingmechanism.py

@@ -280,7 +280,7 @@ class GatingMechanism(AdaptiveMechanism_Base):
         specifies the default form of modulation used by the GatingMechanism's `GatingSignals <GatingSignal>`,
         unless they are `individually specified <GatingSignal_Specification>`.
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters
         for the Mechanism, parameters for its function, and/or a custom function and its parameters. Values
         specified for parameters in the dictionary override any assigned to those parameters in arguments of the

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

@@ -708,7 +708,7 @@ class LearningMechanism(AdaptiveMechanism_Base):
         specifies the learning rate for the LearningMechanism (see `learning_rate <LearningMechanism.learning_rate>`
         for details).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that specifies the parameters for the
         Projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
         the Projection's default `function <LearningProjection.function>` and parameter assignments.  Values specified

psyneulink/components/mechanisms/mechanism.py

@@ -1123,13 +1123,14 @@ class ClassDefaults(Mechanism.ClassDefaults):
     # Class-specific loggable items
     @property
     def _loggable_items(self):
-        # States and afferent Projections are loggable for a Mechanism
+        # States, afferent Projections are loggable for a Mechanism
         #     - this allows the value of InputStates and OutputStates to be logged
         #     - for MappingProjections, this logs the value of the Projection's matrix parameter
         #     - for ModulatoryProjections, this logs the value of the Projection
-        # IMPLEMENTATION NOTE: this needs to be a property as projections may be added after initialization
+        # IMPLEMENTATION NOTE: this needs to be a property as Projections may be added after instantiation
         try:
-            return list(self.states) + list(self.afferents)
+            # return list(self.states) + list(self.afferents)
+            return list(self.states)
         except:
             return []
 

psyneulink/components/mechanisms/processing/compositioninterfacemechanism.py

@@ -90,7 +90,7 @@ class CompositionInterfaceMechanism(ProcessingMechanism_Base):
         specifies the function used to integrate the input.  Must take a single numeric value, or a list or np.array
         of values, and return one of the same form.
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specifying_Parameters>` that can be used to specify the parameters for
         the `Mechanism <Mechanism>`, parameters for its `function <CompositionInterfaceMechanism.function>`, and/or a
         custom function and its parameters.  Values specified for parameters in the dictionary override any assigned

psyneulink/components/mechanisms/processing/integratormechanism.py

@@ -144,7 +144,7 @@ class IntegratorMechanism(ProcessingMechanism_Base):
         specifies the function used to integrate the input.  Must take a single numeric value, or a list or np.array
         of values, and return one of the same form.
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for
         the Mechanism, parameters for its `function <IntegratorMechanism.function>`, and/or a custom function and its
         parameters.  Values specified for parameters in the dictionary override any assigned to those parameters in

psyneulink/components/mechanisms/processing/objectivemechanism.py

@@ -455,7 +455,7 @@ class ObjectiveMechanism(ProcessingMechanism_Base):
     role: Optional[LEARNING, CONTROL]
         specifies if the ObjectiveMechanism is being used for learning or control (see `role` for details).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for the
         Mechanism, its `function <Mechanism_Base.function>`, and/or a custom function and its parameters. Values
         specified for parameters in the dictionary override any assigned to those parameters in arguments of the

psyneulink/components/mechanisms/processing/transfermechanism.py

@@ -353,7 +353,7 @@ class TransferMechanism(ProcessingMechanism_Base):
         specified in **input_states**;  see `note <TransferMechanism_OutputStates_Note>`, and `output_states
         <TransferMechanism.output_states>` for additional details).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for
         the Mechanism, its `function <Mechanism_Base.function>`, and/or a custom function and its parameters.  Values
         specified for parameters in the dictionary override any assigned to those parameters in arguments of the

psyneulink/components/process.py

@@ -2111,7 +2111,7 @@ def execute(self,
         time_scale : TimeScale :  default TimeScale.TRIAL
             specifies whether Mechanisms are executed for a single time step or a trial.
 
-        params : Dict[param keyword, param value] :  default None
+        params : Dict[param keyword: param value] :  default None
             a `parameter dictionary <ParameterState_Specification>` that can include any of the parameters used
             as arguments to instantiate the object. Use parameter's name as the keyword for its entry.  Values specified
             for parameters in the dictionary override any assigned to those parameters in arguments of the constructor.

psyneulink/components/projections/modulatory/controlprojection.py

@@ -199,12 +199,12 @@ class ControlProjection(ModulatoryProjection_Base):
        specifies the value by which to exponentiate the ControlProjection's `value <ControlProjection.value>`
        before combining it with others (see `exponent <ControlProjection.exponent>` for additional details).
 
-    control_signal_params pip install --updgrade Sphinx==1.6.2 sphinx-rtd-theme==0.2.4 sphinxcontrib-websupport==1.0.1: Dict[param keyword, param value]
+    control_signal_params pip install --updgrade Sphinx==1.6.2 sphinx-rtd-theme==0.2.4 sphinxcontrib-websupport==1.0.1: Dict[param keyword: param value]
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for the
         ControlProjection's `sender <ControlProjection.sender>` (see `ControlSignal_Structure` for a description
         of ControlSignal parameters).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for
         the ControlProjection, its `function <ControlProjection.function>`, and/or a custom function and its parameters.
         Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the

psyneulink/components/projections/modulatory/gatingprojection.py

@@ -185,12 +185,12 @@ class GatingProjection(ModulatoryProjection_Base):
        specifies the value by which to exponentiate the GatingProjection's `value <GatingProjection.value>`
        before combining it with others (see `exponent <GatingProjection.exponent>` for additional details).
 
-    gating_signal_params : Dict[param keyword, param value]
+    gating_signal_params : Dict[param keyword: param value]
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for the
         GatingProjection's `sender <ControlProjection.sender>` (see `GatingSignal_Structure` for a description
         of GatingSignal parameters).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for
         the GatingProjection, its `function <GatingProjection.function>`, and/or a custom function and its parameters.
         Values specified for parameters in the dictionary override any assigned to those parameters in arguments of the

psyneulink/components/projections/modulatory/learningprojection.py

@@ -292,7 +292,7 @@ class LearningProjection(ModulatoryProjection_Base):
        specifies the value by which to exponentiate the LearningProjection's `value <LearningProjection.value>`
        before combining it with others (see `exponent <LearningProjection.exponent>` for additional details).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that specifies the parameters for the
         Projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
         the Projection's default `function <LearningProjection.function>` and parameter assignments.  Values specified

psyneulink/components/projections/modulatory/modulatoryprojection.py

@@ -137,7 +137,7 @@ class ModulatoryProjection_Base(Projection_Base):
        specifies the value by which to exponentiate the ModulatoryProjection's `value <ModulatoryProjection.value>`
        before combining it with others (see `exponent <ModulatoryProjection.exponent>` for additional details).
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that specifies the parameters for the
         ModulatoryProjection, its `function <ModulatoryProject.function>`, and/or a custom function and its parameters.
         By default, it contains an entry for the ModulatoryProjection's default `function <ModulatoryProject.function>`
@@ -244,43 +244,3 @@ def _assign_default_projection_name(self, state=None, sender_name=None, receiver
         else:
             raise ModulatoryProjectionError("PROGRAM ERROR: {} has unrecognized InitStatus ({})".
                                             format(self, self.init_status))
-
-    @property
-    def value(self):
-        return self._value
-
-    @value.setter
-    def value(self, assignment):
-
-        # MODIFIED 7/8/17 OLD:
-        # from math import isnan
-        # if isinstance(assignment, np.ndarray) and assignment.ndim == 2 and isnan(assignment[0][0]):
-        #             TEST = True
-        # MODIFIED 7/8/17 END
-
-        self._value = assignment
-
-        # STORE value IN log IF SPECIFIED
-
-        # Get context
-        try:
-            curr_frame = inspect.currentframe()
-            prev_frame = inspect.getouterframes(curr_frame, 2)
-            context = inspect.getargvalues(prev_frame[1][0]).locals['context']
-        except KeyError:
-            context = ""
-        if not isinstance(context, str):
-            context = ""
-
-        # Get logPref
-        log_pref = self.prefs.logPref if self.prefs else None
-        sender_log_pref = self.sender.owner.prefs.logPref if self.sender.owner.prefs else None
-
-        # If context is consistent with log_pref, record value to log
-        if (log_pref is LogLevel.ALL_ASSIGNMENTS or
-                (INITIALIZING in context and LogLevel.INITIALIZATION in {log_pref, sender_log_pref}) or
-                (EXECUTING in context and log_pref is LogLevel.EXECUTION) or
-                (all(c in context for c in {EXECUTING, kwAssign}) and log_pref is LogLevel.VALUE_ASSIGNMENT)
-        ):
-            self.owner.log.entries[self.name] = LogEntry(CurrentTime(), context, assignment)
-            # self.owner.log.entries[self.name] = LogEntry(CentralClock, context, assignment)

psyneulink/components/projections/pathway/mappingprojection.py

@@ -365,7 +365,7 @@ class MappingProjection(PathwayProjection_Base):
         value of the `sender <MappingProjection.sender>` into a form suitable for the `variable <InputState.variable>`
         of its `receiver <MappingProjection.receiver>`.
 
-    params : Dict[param keyword, param value] : default None
+    params : Dict[param keyword: param value] : default None
         a `parameter dictionary <ParameterState_Specification>` that can be used to specify the parameters for
         the Projection, its function, and/or a custom function and its parameters. By default, it contains an entry for
         the Projection's default assignment (`LinearCombination`).  Values specified for parameters in the dictionary
@@ -516,9 +516,11 @@ def _instantiate_parameter_states(self, context=None):
                                                                             initializer=matrix,
                                                                             # rate=initial_rate
                                                                                )
-
         self._parameter_states[MATRIX]._function = self._parameter_states[MATRIX].function_object.function
 
+        # # Assign ParameterState the same Log as the MappingProjection, so that its entries are accessible to Mechanisms
+        # self._parameter_states[MATRIX].log = self.log
+
     def _instantiate_receiver(self, context=None):
         """Determine matrix needed to map from sender to receiver
 
@@ -656,42 +658,26 @@ def matrix(self, matrix):
 
         self.function_object.matrix = matrix
 
-        # Log matrix value if specified by owner, sender, or sender's owner
-
-        # Get context
-        try:
-            curr_frame = inspect.currentframe()
-            prev_frame = inspect.getouterframes(curr_frame, 2)
-            context = inspect.getargvalues(prev_frame[2][0]).locals['context']
-        except KeyError:
-            context = ""
-
-        # Get logPref
-        self_log_pref = self.prefs.logPref if self.prefs else None
-
-        loggers = [
-
-            # Log to self for logPref
-            (self.name, self.log, self_log_pref),
-
-            # Use self.logPref to log to receiver's log if name of self is in receiver's log.entries;
-            #    otherwise use receiver's logPref
-            (self.receiver.name, self.receiver.log,
-             self_log_pref if self.name in self.receiver.log.loggable_items else self.receiver.logPref),
-
-            # Use self.logPref to log to receiver owner's log if name of self is in receiver owner's log.entries
-            #    otherwise use receiver owner's logPref
-            (self.receiver.owner.name, self.receiver.owner.log,
-             self_log_pref if self.name in self.receiver.owner.log.loggable_items else self.receiver.owner.logPref)
-        ]
-
-        # Go through loggers, and if context is consistent with log_pref of logger, record value to logger's log
-        for log_name, log, log_pref in loggers:
-            if (log_pref is LogLevel.ALL_ASSIGNMENTS or
-                    (INITIALIZING in context and log_pref is LogLevel.INITIALIZATION) or
-                    (EXECUTING in context and log_pref is LogLevel.EXECUTION) or
-                    (all(c in context for c in {EXECUTING, kwAssign}) and log_pref is LogLevel.VALUE_ASSIGNMENT)):
-                log.entries[self.name] = LogEntry(CurrentTime(), context, matrix)
+        # # Log matrix value if specified by owner, sender, or sender's owner
+        #
+        # # Get context
+        # try:
+        #     curr_frame = inspect.currentframe()
+        #     prev_frame = inspect.getouterframes(curr_frame, 2)
+        #     context = inspect.getargvalues(prev_frame[2][0]).locals['context']
+        # except KeyError:
+        #     context = ""
+
+        # # Get logPref
+        # self_log_pref = self.prefs.logPref if self.prefs else None
+        #
+        # # Go through loggers, and if context is consistent with log_pref of logger, record value to logger's log
+        # for log_name, log, log_pref in loggers:
+        #     if (log_pref is LogLevel.ALL_ASSIGNMENTS or
+        #             (INITIALIZING in context and log_pref is LogLevel.INITIALIZATION) or
+        #             (EXECUTING in context and log_pref is LogLevel.EXECUTION) or
+        #             (all(c in context for c in {EXECUTING, kwAssign}) and log_pref is LogLevel.VALUE_ASSIGNMENT)):
+        #         log.entries[self.name] = LogEntry(CurrentTime(), context, matrix)
 
 
     @property
@@ -726,3 +712,13 @@ def _matrix_spec(self, value):
 
         else:
             self.paramsCurrent[FUNCTION_PARAMS].__additem__(MATRIX, value)
+
+    @property
+    def logPref(self):
+        return self.prefs.logPref
+
+    # Always assign matrix Parameter state the same logPref as the MappingProjection
+    @logPref.setter
+    def logPref(self, setting):
+        self.prefs.logPref = setting
+        self.parameter_states[MATRIX].logPref = setting