From 3276e286ce2ee2789ebd947b995870a5abb83222 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Thu, 30 Nov 2017 16:49:35 -0500
Subject: [PATCH 01/52] =?UTF-8?q?=E2=80=A2=20System=20=20=20-=20show=5Fgra?=
 =?UTF-8?q?ph():=20=20=20=20=20fixed=20bug=20producing=20empty=20image=20f?=
 =?UTF-8?q?or=20graphs=20with=20just=20one=20Mechanism=20=20=20=20=20added?=
 =?UTF-8?q?=20auto-recurrent=20projections?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .idea/runConfigurations/AGT_Test_Script.xml                 | 4 ++--
 .idea/runConfigurations/DDM_Learning_Test.xml               | 4 ++--
 .idea/runConfigurations/EVC_Gratton_Script___SEBASTIAN.xml  | 4 ++--
 .../EVC_System_Laming_Validation_Test_Script_DEBUGGING.xml  | 4 ++--
 ...ing_Validation_Test_Script__SPECIFY_ControlSignals__.xml | 4 ++--
 ..._System_Laming_Validation_Test_Script_with_Custom_Fn.xml | 4 ++--
 .../EVC_System_Test_Script_with_Profiler.xml                | 4 ++--
 .idea/runConfigurations/FinalProject.xml                    | 4 ++--
 .idea/runConfigurations/Gating_Mechanism_Test_Script.xml    | 4 ++--
 .idea/runConfigurations/GilzenratModel.xml                  | 4 ++--
 .idea/runConfigurations/Make_HTML.xml                       | 4 ++--
 .../Multilayer_Learning_Test_Script_VARIATIONS.xml          | 4 ++--
 .../Multilayer_Learning_Test_Script__WITH_GATING_.xml       | 4 ++--
 .idea/runConfigurations/NEW_Learning_Markus.xml             | 4 ++--
 .idea/runConfigurations/NetworkX_Examples.xml               | 4 ++--
 .idea/runConfigurations/Profile_EVC_System_Test.xml         | 4 ++--
 .../Stroop_Model_Learning_and_Control_Test_Script.xml       | 4 ++--
 .../Stroop_Model_Test_Script_VARIATIONS.xml                 | 4 ++--
 .idea/runConfigurations/Tests.xml                           | 4 ++--
 .idea/runConfigurations/Two_Layer_Test_Script.xml           | 4 ++--
 .idea/runConfigurations/Xor_Script_1_process_working.xml    | 4 ++--
 .idea/runConfigurations/Xor_Script_2_processes.xml          | 4 ++--
 .idea/runConfigurations/_DDM_Test_Script.xml                | 4 ++--
 .idea/runConfigurations/_DDM_scheduled_simple.xml           | 4 ++--
 .idea/runConfigurations/_Documentation_Examples.xml         | 4 ++--
 .idea/runConfigurations/_EVC_System_DEMO_Script.xml         | 6 +++---
 .../_EVC_System_Laming_Validation_Test_Script.xml           | 4 ++--
 .../runConfigurations/_Integrator_scheduled_timescales.xml  | 4 ++--
 .idea/runConfigurations/_META_TEST.xml                      | 4 ++--
 .idea/runConfigurations/_Mixed_NN___DDM_DEMO_Script.xml     | 6 +++---
 .idea/runConfigurations/_Mixed_Neural_Network_and_DDM.xml   | 4 ++--
 .../runConfigurations/_Multilayer_Learning_Test_Script.xml  | 4 ++--
 .../_Multilayer_Learning_Test_Script_DEMO.xml               | 6 +++---
 .../_Reinforcement_Learning_Test_Script.xml                 | 4 ++--
 .idea/runConfigurations/_Scratch_Pad.xml                    | 4 ++--
 .idea/runConfigurations/_Stroop_Demo_Script.xml             | 4 ++--
 .../_Stroop_Model_Learning_Test_Script.xml                  | 4 ++--
 .idea/runConfigurations/_Stroop_Model_Test.xml              | 4 ++--
 .idea/runConfigurations/_System_Graph_and_Input_Test.xml    | 4 ++--
 .idea/runConfigurations/_System_Learning_Test_Script.xml    | 4 ++--
 .idea/runConfigurations/make_html_and_ghpages_py.xml        | 4 ++--
 ...tuple_from_input_and_output_states_to_gating_signals.xml | 4 ++--
 .../py_test_for_tests_control_test_EVC_test_EVC.xml         | 4 ++--
 ...brary_test_lib_mechanism_docs_test_lc_mechanism_docs.xml | 4 ++--
 ...cationFormats_test_formats_for_control_specification.xml | 4 ++--
 ...icationFormats_test_formats_for_gating_specification.xml | 4 ++--
 ...st_mapping_projection_with_mech_and_state_name_specs.xml | 4 ++--
 .../py_test_in_test_lib_mechanism_docs_py.xml               | 4 ++--
 .idea/runConfigurations/test_system.xml                     | 4 ++--
 .idea/runConfigurations/topo_example.xml                    | 4 ++--
 50 files changed, 103 insertions(+), 103 deletions(-)

diff --git a/.idea/runConfigurations/AGT_Test_Script.xml b/.idea/runConfigurations/AGT_Test_Script.xml
index e8d443a835e..3d4f6b09ec4 100644
--- a/.idea/runConfigurations/AGT_Test_Script.xml
+++ b/.idea/runConfigurations/AGT_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="AGT Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="AGT Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/AGT Test Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/DDM_Learning_Test.xml b/.idea/runConfigurations/DDM_Learning_Test.xml
index 1830c04f79a..f1148b39497 100644
--- a/.idea/runConfigurations/DDM_Learning_Test.xml
+++ b/.idea/runConfigurations/DDM_Learning_Test.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="DDM Learning Test" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="DDM Learning Test" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/MISCELLANEOUS SCRIPTS/TEST SCRIPTS/DDM Learning Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/EVC_Gratton_Script___SEBASTIAN.xml b/.idea/runConfigurations/EVC_Gratton_Script___SEBASTIAN.xml
index d5ebe06c98c..5bbf0a13946 100644
--- a/.idea/runConfigurations/EVC_Gratton_Script___SEBASTIAN.xml
+++ b/.idea/runConfigurations/EVC_Gratton_Script___SEBASTIAN.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="EVC Gratton Script - SEBASTIAN" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="EVC Gratton Script - SEBASTIAN" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/EVC Gratton Script - SEBASTIAN.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_DEBUGGING.xml b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_DEBUGGING.xml
index 068f6e70c3c..cdd65326432 100644
--- a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_DEBUGGING.xml
+++ b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_DEBUGGING.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="EVC System Laming Validation Test Script DEBUGGING" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="EVC System Laming Validation Test Script DEBUGGING" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script DEBUGGING.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml
index 22c4216d7c7..4b53c89b214 100644
--- a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml
+++ b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script__SPECIFY_ControlSignals__.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="EVC System Laming Validation Test Script [SPECIFY ControlSignals]]" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="EVC System Laming Validation Test Script [SPECIFY ControlSignals]]" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/EVC System Laming Validation Test Script [SPECIFY ControlSignals]].py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_with_Custom_Fn.xml b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_with_Custom_Fn.xml
index 29683d7ef3e..6d41f53f9c8 100644
--- a/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_with_Custom_Fn.xml
+++ b/.idea/runConfigurations/EVC_System_Laming_Validation_Test_Script_with_Custom_Fn.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="EVC System Laming Validation Test Script with Custom Fn" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="EVC System Laming Validation Test Script with Custom Fn" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/MISCELLANEOUS SCRIPTS/EVC System Laming Validation Test Script with Custom Fn.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/EVC_System_Test_Script_with_Profiler.xml b/.idea/runConfigurations/EVC_System_Test_Script_with_Profiler.xml
index a132b8e24fa..d5d1fcaaba8 100644
--- a/.idea/runConfigurations/EVC_System_Test_Script_with_Profiler.xml
+++ b/.idea/runConfigurations/EVC_System_Test_Script_with_Profiler.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="EVC System Test Script with Profiler" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="EVC System Test Script with Profiler" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/MISCELLANEOUS SCRIPTS/EVC System Test Script with Profiler.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/FinalProject.xml b/.idea/runConfigurations/FinalProject.xml
index e14524de681..f473fc6af86 100644
--- a/.idea/runConfigurations/FinalProject.xml
+++ b/.idea/runConfigurations/FinalProject.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="FinalProject" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="FinalProject" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/STUDENT SCRIPTS/FinalProject.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Gating_Mechanism_Test_Script.xml b/.idea/runConfigurations/Gating_Mechanism_Test_Script.xml
index 3b434d0b03e..ad6e2aad0cc 100644
--- a/.idea/runConfigurations/Gating_Mechanism_Test_Script.xml
+++ b/.idea/runConfigurations/Gating_Mechanism_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Gating Mechanism Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="Gating Mechanism Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Gating Mechanism Test Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/GilzenratModel.xml b/.idea/runConfigurations/GilzenratModel.xml
index 551bfb71abb..a31c5a68506 100644
--- a/.idea/runConfigurations/GilzenratModel.xml
+++ b/.idea/runConfigurations/GilzenratModel.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="GilzenratModel" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="GilzenratModel" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Models/GilzenratModel.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/Make_HTML.xml b/.idea/runConfigurations/Make_HTML.xml
index 273f929bfe6..eacc86ebae0 100644
--- a/.idea/runConfigurations/Make_HTML.xml
+++ b/.idea/runConfigurations/Make_HTML.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Make HTML" type="docs" factoryName="Sphinx task" singleton="true">
+  <configuration default="false" factoryName="Sphinx task" name="Make HTML" singleton="true" type="docs">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="docutils_input_file" value="$PROJECT_DIR$/docs/source" />
     <option name="docutils_output_file" value="$PROJECT_DIR$/docs/build/html" />
diff --git a/.idea/runConfigurations/Multilayer_Learning_Test_Script_VARIATIONS.xml b/.idea/runConfigurations/Multilayer_Learning_Test_Script_VARIATIONS.xml
index 71e33c5b672..c6d8fea16c6 100644
--- a/.idea/runConfigurations/Multilayer_Learning_Test_Script_VARIATIONS.xml
+++ b/.idea/runConfigurations/Multilayer_Learning_Test_Script_VARIATIONS.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Multilayer Learning Test Script VARIATIONS" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="Multilayer Learning Test Script VARIATIONS" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script VARIATIONS.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml b/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml
index 04e08d34a15..1fda4fd49f3 100644
--- a/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml
+++ b/.idea/runConfigurations/Multilayer_Learning_Test_Script__WITH_GATING_.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Multilayer Learning Test Script [WITH GATING]" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="Multilayer Learning Test Script [WITH GATING]" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/Multilayer Learning Test Script [WITH GATING].py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/NEW_Learning_Markus.xml b/.idea/runConfigurations/NEW_Learning_Markus.xml
index d2e7bece107..cb52aa9c21c 100644
--- a/.idea/runConfigurations/NEW_Learning_Markus.xml
+++ b/.idea/runConfigurations/NEW_Learning_Markus.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="NEW_Learning_Markus" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="NEW_Learning_Markus" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/NEW_Learning_Markus.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/NetworkX_Examples.xml b/.idea/runConfigurations/NetworkX_Examples.xml
index df2774f8a56..305239e043c 100644
--- a/.idea/runConfigurations/NetworkX_Examples.xml
+++ b/.idea/runConfigurations/NetworkX_Examples.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="NetworkX Examples" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="NetworkX Examples" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/PsyNeuLink/Globals/NetworkX tesst.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Profile_EVC_System_Test.xml b/.idea/runConfigurations/Profile_EVC_System_Test.xml
index 13365fb7733..069058f85f0 100644
--- a/.idea/runConfigurations/Profile_EVC_System_Test.xml
+++ b/.idea/runConfigurations/Profile_EVC_System_Test.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Profile EVC System Test" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="Profile EVC System Test" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/MISCELLANEOUS SCRIPTS/Profile EVC System Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Stroop_Model_Learning_and_Control_Test_Script.xml b/.idea/runConfigurations/Stroop_Model_Learning_and_Control_Test_Script.xml
index d110cc35e81..438bd3390ce 100644
--- a/.idea/runConfigurations/Stroop_Model_Learning_and_Control_Test_Script.xml
+++ b/.idea/runConfigurations/Stroop_Model_Learning_and_Control_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Stroop Model Learning and Control Test Script" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="Stroop Model Learning and Control Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Stroop Model Learning and Control Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Stroop_Model_Test_Script_VARIATIONS.xml b/.idea/runConfigurations/Stroop_Model_Test_Script_VARIATIONS.xml
index d3886cc032f..a083b422b07 100644
--- a/.idea/runConfigurations/Stroop_Model_Test_Script_VARIATIONS.xml
+++ b/.idea/runConfigurations/Stroop_Model_Test_Script_VARIATIONS.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Stroop Model Test Script VARIATIONS" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="Stroop Model Test Script VARIATIONS" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEBUGGING SCRIPTS/Stroop Model Test Script VARIATIONS.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Tests.xml b/.idea/runConfigurations/Tests.xml
index 4dba46615a7..a4b442aacdb 100644
--- a/.idea/runConfigurations/Tests.xml
+++ b/.idea/runConfigurations/Tests.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Tests" type="tests" factoryName="py.test" singleton="true">
+  <configuration default="false" factoryName="py.test" name="Tests" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;$PROJECT_DIR$/tests&quot;" />
diff --git a/.idea/runConfigurations/Two_Layer_Test_Script.xml b/.idea/runConfigurations/Two_Layer_Test_Script.xml
index e5dd04359a9..85af3a59b79 100644
--- a/.idea/runConfigurations/Two_Layer_Test_Script.xml
+++ b/.idea/runConfigurations/Two_Layer_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Two Layer Test Script" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="Two Layer Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/MISCELLANEOUS SCRIPTS/Two Layer Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Xor_Script_1_process_working.xml b/.idea/runConfigurations/Xor_Script_1_process_working.xml
index 9c86080c6e5..ae3d7891666 100644
--- a/.idea/runConfigurations/Xor_Script_1_process_working.xml
+++ b/.idea/runConfigurations/Xor_Script_1_process_working.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Xor_Script_1_process_working" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="Xor_Script_1_process_working" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/Xor_Script_Working.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/Xor_Script_2_processes.xml b/.idea/runConfigurations/Xor_Script_2_processes.xml
index a4372c37cc6..9099901b7bf 100644
--- a/.idea/runConfigurations/Xor_Script_2_processes.xml
+++ b/.idea/runConfigurations/Xor_Script_2_processes.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Xor_Script_2_processes" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="Xor_Script_2_processes" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/Xor_Script_2_processes.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_DDM_Test_Script.xml b/.idea/runConfigurations/_DDM_Test_Script.xml
index a07730b20c8..48631c31cc0 100644
--- a/.idea/runConfigurations/_DDM_Test_Script.xml
+++ b/.idea/runConfigurations/_DDM_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• DDM Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; DDM Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/DDM Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_DDM_scheduled_simple.xml b/.idea/runConfigurations/_DDM_scheduled_simple.xml
index db8dfd99dc2..76f1c7fd7a3 100644
--- a/.idea/runConfigurations/_DDM_scheduled_simple.xml
+++ b/.idea/runConfigurations/_DDM_scheduled_simple.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• DDM_scheduled_simple" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="&#8226; DDM_scheduled_simple" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/DDM_scheduled_simple.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Documentation_Examples.xml b/.idea/runConfigurations/_Documentation_Examples.xml
index 021c739c153..e5e2da3904a 100644
--- a/.idea/runConfigurations/_Documentation_Examples.xml
+++ b/.idea/runConfigurations/_Documentation_Examples.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Documentation Examples" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; Documentation Examples" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Documentation Examples Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_EVC_System_DEMO_Script.xml b/.idea/runConfigurations/_EVC_System_DEMO_Script.xml
index 5d2f7244c7c..2119a4e7aa9 100644
--- a/.idea/runConfigurations/_EVC_System_DEMO_Script.xml
+++ b/.idea/runConfigurations/_EVC_System_DEMO_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• EVC System DEMO Script" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="&#8226; EVC System DEMO Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,9 +11,9 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
-    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/• EVC System DEMO Script.py" />
+    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/&#8226; EVC System DEMO Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
     <option name="EMULATE_TERMINAL" value="false" />
diff --git a/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml b/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml
index 8f52a8aa544..3f57422ee34 100644
--- a/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml
+++ b/.idea/runConfigurations/_EVC_System_Laming_Validation_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• EVC System Laming Validation Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; EVC System Laming Validation Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/EVC System Laming Validation Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Integrator_scheduled_timescales.xml b/.idea/runConfigurations/_Integrator_scheduled_timescales.xml
index e8bc21786ce..59fdbefd978 100644
--- a/.idea/runConfigurations/_Integrator_scheduled_timescales.xml
+++ b/.idea/runConfigurations/_Integrator_scheduled_timescales.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Integrator_scheduled_timescales" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="&#8226; Integrator_scheduled_timescales" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/Integrator_scheduled_timescales.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_META_TEST.xml b/.idea/runConfigurations/_META_TEST.xml
index bae196ffa6f..7d6b4e3d70d 100644
--- a/.idea/runConfigurations/_META_TEST.xml
+++ b/.idea/runConfigurations/_META_TEST.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• META TEST" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; META TEST" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/META Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Mixed_NN___DDM_DEMO_Script.xml b/.idea/runConfigurations/_Mixed_NN___DDM_DEMO_Script.xml
index fcc62c27e0e..5c7c7925582 100644
--- a/.idea/runConfigurations/_Mixed_NN___DDM_DEMO_Script.xml
+++ b/.idea/runConfigurations/_Mixed_NN___DDM_DEMO_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Mixed NN &amp; DDM DEMO Script" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="&#8226; Mixed NN &amp; DDM DEMO Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,9 +11,9 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
-    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/• Mixed NN &amp; DDM DEMO Script.py" />
+    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/&#8226; Mixed NN &amp; DDM DEMO Script.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
     <option name="EMULATE_TERMINAL" value="false" />
diff --git a/.idea/runConfigurations/_Mixed_Neural_Network_and_DDM.xml b/.idea/runConfigurations/_Mixed_Neural_Network_and_DDM.xml
index e349065887c..d51b15b5f0b 100644
--- a/.idea/runConfigurations/_Mixed_Neural_Network_and_DDM.xml
+++ b/.idea/runConfigurations/_Mixed_Neural_Network_and_DDM.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Mixed Neural Network and DDM" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; Mixed Neural Network and DDM" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Mixed NN &amp; DDM script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml b/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml
index e6f3df4461a..d12ac2717e1 100644
--- a/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml
+++ b/.idea/runConfigurations/_Multilayer_Learning_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Multilayer Learning Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; Multilayer Learning Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Multilayer-Learning.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Multilayer_Learning_Test_Script_DEMO.xml b/.idea/runConfigurations/_Multilayer_Learning_Test_Script_DEMO.xml
index 7664f86ca9c..14ec01694d2 100644
--- a/.idea/runConfigurations/_Multilayer_Learning_Test_Script_DEMO.xml
+++ b/.idea/runConfigurations/_Multilayer_Learning_Test_Script_DEMO.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Multilayer Learning Test Script DEMO" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="&#8226; Multilayer Learning Test Script DEMO" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,9 +11,9 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
-    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/• Multilayer Learning Test Script DEMO.py" />
+    <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/DEMO SCRIPTS/&#8226; Multilayer Learning Test Script DEMO.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
     <option name="EMULATE_TERMINAL" value="false" />
diff --git a/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml b/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml
index 3b5775ad30b..6c9fc40dc47 100644
--- a/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml
+++ b/.idea/runConfigurations/_Reinforcement_Learning_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Reinforcement Learning Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; Reinforcement Learning Test Script" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Reinforcement Learning Test Script.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Scratch_Pad.xml b/.idea/runConfigurations/_Scratch_Pad.xml
index cb1de20263c..35f487c2a27 100644
--- a/.idea/runConfigurations/_Scratch_Pad.xml
+++ b/.idea/runConfigurations/_Scratch_Pad.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Scratch Pad" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; Scratch Pad" singleton="true" type="PythonConfigurationType">
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@@ -10,7 +10,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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diff --git a/.idea/runConfigurations/_Stroop_Demo_Script.xml b/.idea/runConfigurations/_Stroop_Demo_Script.xml
index 69554f0ec22..145532dcbe7 100644
--- a/.idea/runConfigurations/_Stroop_Demo_Script.xml
+++ b/.idea/runConfigurations/_Stroop_Demo_Script.xml
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 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Stroop Demo Script" type="PythonConfigurationType" factoryName="Python" folderName="DEMO SCRIPTS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="DEMO SCRIPTS" name="&#8226; Stroop Demo Script" singleton="true" type="PythonConfigurationType">
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@@ -11,7 +11,7 @@
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     <option name="ADD_SOURCE_ROOTS" value="true" />
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+    <module name="" />
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diff --git a/.idea/runConfigurations/_Stroop_Model_Learning_Test_Script.xml b/.idea/runConfigurations/_Stroop_Model_Learning_Test_Script.xml
index 6ad30dcbae8..5c7aed239e9 100644
--- a/.idea/runConfigurations/_Stroop_Model_Learning_Test_Script.xml
+++ b/.idea/runConfigurations/_Stroop_Model_Learning_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Stroop Model Learning Test Script" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226;&#160;Stroop Model Learning Test Script" singleton="true" type="PythonConfigurationType">
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@@ -11,7 +11,7 @@
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     <option name="ADD_SOURCE_ROOTS" value="true" />
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+    <module name="" />
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     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/TEST SCRIPTS/Stroop Model Learning Test Script.py" />
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diff --git a/.idea/runConfigurations/_Stroop_Model_Test.xml b/.idea/runConfigurations/_Stroop_Model_Test.xml
index 710857aec7b..31ab27c142d 100644
--- a/.idea/runConfigurations/_Stroop_Model_Test.xml
+++ b/.idea/runConfigurations/_Stroop_Model_Test.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Stroop Model Test" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="&#8226; Stroop Model Test" singleton="true" type="PythonConfigurationType">
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@@ -11,7 +11,7 @@
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     <option name="ADD_SOURCE_ROOTS" value="true" />
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+    <module name="" />
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diff --git a/.idea/runConfigurations/_System_Graph_and_Input_Test.xml b/.idea/runConfigurations/_System_Graph_and_Input_Test.xml
index 060d51af7cc..19277f5ce09 100644
--- a/.idea/runConfigurations/_System_Graph_and_Input_Test.xml
+++ b/.idea/runConfigurations/_System_Graph_and_Input_Test.xml
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@@ -11,7 +11,7 @@
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     <option name="ADD_SOURCE_ROOTS" value="true" />
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diff --git a/.idea/runConfigurations/_System_Learning_Test_Script.xml b/.idea/runConfigurations/_System_Learning_Test_Script.xml
index f53d89841bb..9a08f0af7e6 100644
--- a/.idea/runConfigurations/_System_Learning_Test_Script.xml
+++ b/.idea/runConfigurations/_System_Learning_Test_Script.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• System Learning Test Script" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="&#8226; System Learning Test Script" singleton="true" type="PythonConfigurationType">
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     <option name="PARENT_ENVS" value="true" />
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@@ -10,7 +10,7 @@
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     <option name="ADD_SOURCE_ROOTS" value="true" />
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+    <module name="" />
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diff --git a/.idea/runConfigurations/make_html_and_ghpages_py.xml b/.idea/runConfigurations/make_html_and_ghpages_py.xml
index bef143ce0ac..6d8ea246da1 100644
--- a/.idea/runConfigurations/make_html_and_ghpages_py.xml
+++ b/.idea/runConfigurations/make_html_and_ghpages_py.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="make_html_and_ghpages.py" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="make_html_and_ghpages.py" singleton="true" type="PythonConfigurationType">
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@@ -10,7 +10,7 @@
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     <option name="ADD_SOURCE_ROOTS" value="true" />
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+    <module name="" />
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diff --git a/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml b/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml
index d865da7d558..7483402424d 100644
--- a/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml
+++ b/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml
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+  <configuration default="false" factoryName="py.test" name="py.test for test_projections_specifications.TestProjectionSpecificationFormats.test_2_item_tuple_from_input_and_output_states_to_gating_signals" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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index 33319263978..449a9154266 100644
--- a/.idea/runConfigurations/py_test_for_tests_control_test_EVC_test_EVC.xml
+++ b/.idea/runConfigurations/py_test_for_tests_control_test_EVC_test_EVC.xml
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 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for tests.control.test_EVC.test_EVC" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.control.test_EVC.test_EVC" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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     <option name="_new_target" value="&quot;tests.control.test_EVC.test_EVC&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml b/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml
index 6d11c29fa19..df395bdc12d 100644
--- a/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml
+++ b/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml
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-  <configuration default="false" name="py.test for tests.documentation.library.test_lib_mechanism_docs.test_lc_mechanism_docs" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.documentation.library.test_lib_mechanism_docs.test_lc_mechanism_docs" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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     <option name="_new_target" value="&quot;tests.documentation.library.test_lib_mechanism_docs.test_lc_mechanism_docs&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml
index a90b728db7a..dda9c26a96a 100644
--- a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml
+++ b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml
@@ -1,5 +1,5 @@
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+  <configuration default="false" factoryName="py.test" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_control_specification" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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     <option name="_new_target" value="&quot;tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_control_specification&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml
index e7a0c2147d6..90fc5d81e5e 100644
--- a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml
+++ b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml
@@ -1,5 +1,5 @@
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+  <configuration default="false" factoryName="py.test" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_gating_specification" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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diff --git a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml
index 639ad80991a..c95bfd67e89 100644
--- a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml
+++ b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
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+  <configuration default="false" factoryName="py.test" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_mapping_projection_with_mech_and_state_name_specs" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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     <option name="_new_target" value="&quot;tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_mapping_projection_with_mech_and_state_name_specs&quot;" />
diff --git a/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml b/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml
index 6cafd894296..892f1719fa1 100644
--- a/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml
+++ b/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test in test_lib_mechanism_docs.py" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test in test_lib_mechanism_docs.py" nameIsGenerated="true" singleton="true" type="tests">
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@@ -8,7 +8,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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diff --git a/.idea/runConfigurations/test_system.xml b/.idea/runConfigurations/test_system.xml
index 0442de18fd8..a64b85cfcb7 100644
--- a/.idea/runConfigurations/test_system.xml
+++ b/.idea/runConfigurations/test_system.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="test_system" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="test_system" singleton="true" type="PythonConfigurationType">
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     <option name="PARENT_ENVS" value="true" />
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@@ -11,7 +11,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
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     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/tests/system/test_system.py" />
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diff --git a/.idea/runConfigurations/topo_example.xml b/.idea/runConfigurations/topo_example.xml
index 98674565da0..5f227813b7f 100644
--- a/.idea/runConfigurations/topo_example.xml
+++ b/.idea/runConfigurations/topo_example.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="topo_example" type="PythonConfigurationType" factoryName="Python" folderName="OTHER CONFIGS" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="OTHER CONFIGS" name="topo_example" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
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@@ -10,7 +10,7 @@
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     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
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+    <module name="" />
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     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/PsyNeuLink/Globals/toposort/topo_example.py" />
     <option name="PARAMETERS" value="" />

From bd5f9a097dea7c5440ebcd80d1a5c5be73faac43 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Mon, 4 Dec 2017 22:02:36 -0500
Subject: [PATCH 02/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20added=20numpy?=
 =?UTF-8?q?=5Farray=20output=20method?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Scripts/Scratch Pad.py    |  10 ++-
 psyneulink/globals/log.py | 131 +++++++++++++++++++++++++++++++++++---
 2 files changed, 130 insertions(+), 11 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index b78c9924b2b..55915474c95 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2230,8 +2230,8 @@ def __init__(self, error_value):
 
 
 # Create a Process with two TransferMechanisms:
-my_mech_A = pnl.TransferMechanism(name='mech_A')
-my_mech_B = pnl.TransferMechanism(name='mech_B')
+my_mech_A = pnl.TransferMechanism(name='mech_A', size=2)
+my_mech_B = pnl.TransferMechanism(name='mech_B', size=2)
 my_process = pnl.Process(pathway=[my_mech_A, my_mech_B])
 
 # Print the loggable items for each Mechanism:
@@ -2258,6 +2258,7 @@ def __init__(self, error_value):
 # Execute each Process twice (to generate some values in the logs):
 my_process.execute()
 my_process.execute()
+my_process.execute()
 
 # Print the logged items of each Mechanism:
 print(my_mech_A.logged_items)
@@ -2293,11 +2294,14 @@ def __init__(self, error_value):
 # 'Entry', 'noise'
 # 0,  0.
 # 1,  0.
-print(my_mech_B.log.csv(entries=proj_A_to_B, owner_name=False, quotes=True))
+print(my_mech_B.log.csv(entries=proj_A_to_B, owner_name=True, quotes=True))
 # 'Entry', 'MappingProjection from mech_A to mech_B'
 # 0,  1.
 # 1,  1.
 
+x = my_mech_A.log.numpy_array(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+print(x)
+
 # my_mech_A = pnl.TransferMechanism(name='my_mech_1A')
 # print(my_mech_A.loggable_items)
 # {'InputState-0': 'OFF', 'time_constant': 'OFF', 'intercept': 'OFF', 'noise': 'OFF', 'RESULTS': 'OFF', 'slope': 'OFF'}
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 8f182b0b6bc..d407226b960 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -53,6 +53,12 @@
     * *context* -- a string indicating the context in which the value was recorded;
     * *value* -- the value of the item.
 
+    .. note::
+       Currently the "time" field of the entry is not used, and reports indicate the entry number
+       (corresonding to the number of executions of the Component), which may or may not correspond to the
+       `TIME_STEP` of execution.  This will be corrected in a future release.
+
+
 A Log has several methods that make it easy to manage when it values are recorded and accessing its `entries
 <Log.entries>`:
 
@@ -271,6 +277,7 @@
 import typecheck as tc
 from collections import namedtuple
 from enum import IntEnum
+import numpy as np
 
 from psyneulink.globals.keywords import kwContext, kwTime, kwValue
 
@@ -600,7 +607,7 @@ def print_entries(self, entries=None, csv=False, synch_time=False, *args):
         kwSpacer = ' '
 
 
-        # MODIFIED 12/4/17 OLD:
+        # MODIFIED 12/4/17 OLD: [USES Time]
         # header = "Variable:".ljust(variable_width, kwSpacer)
         # if not args or kwTime in args:
         #     header = header + " " + kwTime.ljust(time_width, kwSpacer)
@@ -609,7 +616,7 @@ def print_entries(self, entries=None, csv=False, synch_time=False, *args):
         # if not args or kwValue in args:
         #     # header = header + "   " + kwValue.rjust(value_width)
         #     header = header + "  " + kwValue
-        # MODIFIED 12/4/17 NEW:
+        # MODIFIED 12/4/17 NEW: [USES entry]
         header = "Logged Item:".ljust(variable_width, kwSpacer)
         if not args or kwTime in args:
             header = "Entry".ljust(time_width, kwSpacer) + header
@@ -619,12 +626,7 @@ def print_entries(self, entries=None, csv=False, synch_time=False, *args):
             header = header + "  " + kwValue
         # MODIFIED 12/4/17 END
 
-
-            # header = header + " " + kwTime.ljust(time_width, kwSpacer)
-
-
         print("\nLog for {0}:".format(self.owner.name))
-
         print('\n'+header+'\n')
 
         # Sort for consistency of reporting
@@ -690,6 +692,10 @@ def csv(self, entries=None, owner_name:bool=False, quotes:tc.optional(tc.any(boo
         The first record (row) begins with "Entry" and is followed by the header for each field (column).
         Subsequent records begin with the record number, and are followed by the value for each entry.
 
+        .. note::
+           Currently only supports reports of entries with the same length.  A future version will allow
+           entries of differing lengths in the same report.
+
         Arguments
         ---------
 
@@ -759,9 +765,118 @@ def csv(self, entries=None, owner_name:bool=False, quotes:tc.optional(tc.any(boo
                                      replace("[[",quotes)).replace("]]",quotes).replace("[",quotes).replace("]",quotes)
         return(csv)
 
+    # def temp(self, csvx):
+    #     from io import StringIO
+    #     import csv
+    #
+    #     csv_file = StringIO()
+    #     csv_file.write(csvx)
+    #     # thingie = np.genfromtxt(csv_file, delimiter=',')
+    #     # assert True
+    #     # csv_file.close()
+    #
+    #     # with open(csv, newline='') as csvfile:
+    #     with csv_file as csvfile:
+    #          spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')
+    #     assert True
+
+
+    @tc.typecheck
+    def numpy_array(self,
+                    entries=None,
+                    header:bool=True,
+                    owner_name:bool=False):
+        """
+        nparray(                 \
+            entries=None,        \
+            header:bool=True,    \
+            owner_name=False):   \
+            )
+
+        Return a 2d numpy array with (optional) headers and values for the specified entries.
+
+        First row (axis 0) is entry number, and subsequent rows are data for each entry, in the ordered listed in
+        the **entries** argument.  If header is `True`, the first item of each row is the header field: in the first
+        row, it is the string "Entry" and in subsequent rows the name of the entry.
+
+        .. note::
+           Currently only supports reports of entries with the same length.  A future version will allow
+           entries of differing lengths in the same report.
+
+        Arguments
+        ---------
+
+        entries : string, Component
+            specifies the entries to be included;  they must be `loggable_items <Log.loggable_items>` of the Log.
+
+        header : bool : default True
+            specifies whether or not to a header row, with the names of the entries.
+
+        owner_name : bool : default False
+            specifies whether or not to include the Log's `owner <Log.owner>` in the header of each field;
+            if it is True, the format of the header for each field is "<Owner name>[<entry name>]";
+            otherwise, it is "<entry name>".
+
+        Returns:
+            2d np.array
+        """
+        from psyneulink.components.component import Component
+
+        # If Log.ALL_LOG_ENTRIES, set entries to all entries in self.entries
+        if entries is ALL_ENTRIES or entries is None:
+            entries = self.entries.keys()
+
+        # If entries is a single entry, put in list for processing below
+        if isinstance(entries, (str, Component)):
+            entries = [entries]
+
+        # Make sure all entries are the names of Components
+        entries = [entry.name if isinstance(entry, Component) else entry for entry in entries ]
+
+        # Validate entries
+        for entry in entries:
+            if entry not in self.loggable_items:
+                raise LogError("{0} is not a loggable attribute of {1}".format(repr(entry), self.owner.name))
+            if entry not in self.entries:
+                raise LogError("{} is not currently being logged by {} (try using log_items)".
+                               format(repr(entry), self.owner.name))
+
+        max_len = max([len(self.entries[e]) for e in entries])
+
+        # Currently only supports entries of the same length
+        if not all(len(self.entries[e])==len(self.entries[entries[0]])for e in entries):
+            raise LogError("CSV output currently only supported for log entries of equal length")
+
+        if owner_name is True:
+            owner_name_str = self.owner.name
+            lb = "["
+            rb = "]"
+        else:
+            owner_name_str = lb = rb = ""
+
+
+        header = 1 if header is True else 0
+
+        npa = np.arange(max_len).reshape(max_len,1).tolist()
+        if header:
+            npa = [[["Entry"]] + npa]
+        else:
+            npa = [npa]
+
+        for i, entry in enumerate(entries):
+            row = [e[2] for e in self.entries[entry]]
+            if header:
+                entry = "{}{}{}{}".format(owner_name_str, lb, entry, rb)
+                row = [entry] + row
+            npa.append(row)
+        npa = np.array(npa)
+
+        return(npa)
+
+
     # ******************************************************************************************************
-    # DEPRECATED OR IN NEED OF REFACTORING:
     # ******************************************************************************************************
+    # DEPRECATED OR IN NEED OF REFACTORING:
 
     def delete_entry(self, entries, confirm=True):
         """Delete entry for attribute from self.entries

From cda732eef4fc274bb3b70cd1a3713d9abb3ffd13 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Thu, 7 Dec 2017 07:15:46 -0500
Subject: [PATCH 03/52] -

---
 psyneulink/library/subsystems/agt/agtcontrolmechanism.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/psyneulink/library/subsystems/agt/agtcontrolmechanism.py b/psyneulink/library/subsystems/agt/agtcontrolmechanism.py
index 7ccb817584c..76ae2f72914 100644
--- a/psyneulink/library/subsystems/agt/agtcontrolmechanism.py
+++ b/psyneulink/library/subsystems/agt/agtcontrolmechanism.py
@@ -148,7 +148,7 @@ def __init__(self, error_value):
 
 class AGTControlMechanism(ControlMechanism):
     """
-    AGTControlMechanism(                       \
+    AGTControlMechanism(                \
         system=None,                    \
         monitored_output_states=None,   \
         function=Linear,                \

From 5603fa6ddc21025ad59ecb4afc76768e90f2d76f Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 09:27:17 -0500
Subject: [PATCH 04/52] -

---
 .idea/runConfigurations/Make_HTML.xml         |  4 +-
 .idea/runConfigurations/Scratch_Pad.xml       |  4 +-
 .idea/runConfigurations/Tests.xml             |  4 +-
 .idea/runConfigurations/_Gating_Mechanism.xml |  4 +-
 .idea/runConfigurations/_GilzenratModel.xml   |  4 +-
 .idea/runConfigurations/_Mixed_NN_and_DDM.xml |  4 +-
 .../_Multilayer_Learning.xml                  |  4 +-
 .../_Reinforcement_Learning.xml               |  4 +-
 .idea/runConfigurations/_Stroop_Simple.xml    |  4 +-
 ...ut_and_output_states_to_gating_signals.xml |  4 +-
 ...st_for_tests_control_test_EVC_test_EVC.xml |  4 +-
 ..._mechanism_docs_test_lc_mechanism_docs.xml |  4 +-
 ...test_formats_for_control_specification.xml |  4 +-
 ..._test_formats_for_gating_specification.xml |  4 +-
 ...jection_with_mech_and_state_name_specs.xml |  4 +-
 .../py_test_in_test_lib_mechanism_docs_py.xml |  4 +-
 psyneulink/globals/log.py                     | 77 +++++++++----------
 tests/log/test_log.py                         |  2 +
 18 files changed, 72 insertions(+), 71 deletions(-)

diff --git a/.idea/runConfigurations/Make_HTML.xml b/.idea/runConfigurations/Make_HTML.xml
index 273f929bfe6..eacc86ebae0 100644
--- a/.idea/runConfigurations/Make_HTML.xml
+++ b/.idea/runConfigurations/Make_HTML.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Make HTML" type="docs" factoryName="Sphinx task" singleton="true">
+  <configuration default="false" factoryName="Sphinx task" name="Make HTML" singleton="true" type="docs">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="docutils_input_file" value="$PROJECT_DIR$/docs/source" />
     <option name="docutils_output_file" value="$PROJECT_DIR$/docs/build/html" />
diff --git a/.idea/runConfigurations/Scratch_Pad.xml b/.idea/runConfigurations/Scratch_Pad.xml
index df8e8e22cab..83220830e54 100644
--- a/.idea/runConfigurations/Scratch_Pad.xml
+++ b/.idea/runConfigurations/Scratch_Pad.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Scratch Pad" type="PythonConfigurationType" factoryName="Python" singleton="true">
+  <configuration default="false" factoryName="Python" name="Scratch Pad" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Scratch Pad.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/Tests.xml b/.idea/runConfigurations/Tests.xml
index 4dba46615a7..a4b442aacdb 100644
--- a/.idea/runConfigurations/Tests.xml
+++ b/.idea/runConfigurations/Tests.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Tests" type="tests" factoryName="py.test" singleton="true">
+  <configuration default="false" factoryName="py.test" name="Tests" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;$PROJECT_DIR$/tests&quot;" />
diff --git a/.idea/runConfigurations/_Gating_Mechanism.xml b/.idea/runConfigurations/_Gating_Mechanism.xml
index 0549f1160e4..db9ae4e5d12 100644
--- a/.idea/runConfigurations/_Gating_Mechanism.xml
+++ b/.idea/runConfigurations/_Gating_Mechanism.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Gating-Mechanism" type="PythonConfigurationType" factoryName="Python" folderName="Examples" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="Examples" name="&#8226; Gating-Mechanism" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Gating-Mechanism.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/_GilzenratModel.xml b/.idea/runConfigurations/_GilzenratModel.xml
index eec2a0eabc0..84f5e01b816 100644
--- a/.idea/runConfigurations/_GilzenratModel.xml
+++ b/.idea/runConfigurations/_GilzenratModel.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• GilzenratModel" type="PythonConfigurationType" factoryName="Python" folderName="Examples" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="Examples" name="&#8226; GilzenratModel" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Models/GilzenratModel.py" />
     <option name="PARAMETERS" value="" />
     <option name="SHOW_COMMAND_LINE" value="false" />
diff --git a/.idea/runConfigurations/_Mixed_NN_and_DDM.xml b/.idea/runConfigurations/_Mixed_NN_and_DDM.xml
index c50deeb8708..7a7c4803c43 100644
--- a/.idea/runConfigurations/_Mixed_NN_and_DDM.xml
+++ b/.idea/runConfigurations/_Mixed_NN_and_DDM.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Mixed-NN-and-DDM" type="PythonConfigurationType" factoryName="Python" folderName="Examples" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="Examples" name="&#8226; Mixed-NN-and-DDM" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Mixed-NN-and-DDM.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Multilayer_Learning.xml b/.idea/runConfigurations/_Multilayer_Learning.xml
index bffabd9f848..8eb201ff359 100644
--- a/.idea/runConfigurations/_Multilayer_Learning.xml
+++ b/.idea/runConfigurations/_Multilayer_Learning.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Multilayer-Learning" type="PythonConfigurationType" factoryName="Python" folderName="Examples" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="Examples" name="&#8226; Multilayer-Learning" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Multilayer-Learning.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Reinforcement_Learning.xml b/.idea/runConfigurations/_Reinforcement_Learning.xml
index 1d67fd4ab48..479c0c6e4a9 100644
--- a/.idea/runConfigurations/_Reinforcement_Learning.xml
+++ b/.idea/runConfigurations/_Reinforcement_Learning.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Reinforcement-Learning" type="PythonConfigurationType" factoryName="Python" folderName="Examples" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="Examples" name="&#8226; Reinforcement-Learning" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -10,7 +10,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Reinforcement-Learning.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/_Stroop_Simple.xml b/.idea/runConfigurations/_Stroop_Simple.xml
index ee474e36bb9..b98ea56475e 100644
--- a/.idea/runConfigurations/_Stroop_Simple.xml
+++ b/.idea/runConfigurations/_Stroop_Simple.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="• Stroop-Simple" type="PythonConfigurationType" factoryName="Python" folderName="Examples" singleton="true">
+  <configuration default="false" factoryName="Python" folderName="Examples" name="&#8226; Stroop-Simple" singleton="true" type="PythonConfigurationType">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs>
@@ -11,7 +11,7 @@
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="SCRIPT_NAME" value="$PROJECT_DIR$/Scripts/Examples/Stroop-Simple.py" />
     <option name="PARAMETERS" value="" />
diff --git a/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml b/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml
index d865da7d558..7483402424d 100644
--- a/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml
+++ b/.idea/runConfigurations/py_test_for_test_projections_specifications_TestProjectionSpecificationFormats_test_2_item_tuple_from_input_and_output_states_to_gating_signals.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for test_projections_specifications.TestProjectionSpecificationFormats.test_2_item_tuple_from_input_and_output_states_to_gating_signals" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for test_projections_specifications.TestProjectionSpecificationFormats.test_2_item_tuple_from_input_and_output_states_to_gating_signals" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;test_projections_specifications.TestProjectionSpecificationFormats.test_2_item_tuple_from_input_and_output_states_to_gating_signals&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_control_test_EVC_test_EVC.xml b/.idea/runConfigurations/py_test_for_tests_control_test_EVC_test_EVC.xml
index 33319263978..449a9154266 100644
--- a/.idea/runConfigurations/py_test_for_tests_control_test_EVC_test_EVC.xml
+++ b/.idea/runConfigurations/py_test_for_tests_control_test_EVC_test_EVC.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for tests.control.test_EVC.test_EVC" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.control.test_EVC.test_EVC" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;tests.control.test_EVC.test_EVC&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml b/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml
index 6d11c29fa19..df395bdc12d 100644
--- a/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml
+++ b/.idea/runConfigurations/py_test_for_tests_documentation_library_test_lib_mechanism_docs_test_lc_mechanism_docs.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for tests.documentation.library.test_lib_mechanism_docs.test_lc_mechanism_docs" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.documentation.library.test_lib_mechanism_docs.test_lc_mechanism_docs" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;tests.documentation.library.test_lib_mechanism_docs.test_lc_mechanism_docs&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml
index a90b728db7a..dda9c26a96a 100644
--- a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml
+++ b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_control_specification.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_control_specification" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_control_specification" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_control_specification&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml
index e7a0c2147d6..90fc5d81e5e 100644
--- a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml
+++ b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_formats_for_gating_specification.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_gating_specification" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_gating_specification" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_formats_for_gating_specification&quot;" />
diff --git a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml
index 639ad80991a..c95bfd67e89 100644
--- a/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml
+++ b/.idea/runConfigurations/py_test_for_tests_projections_test_projections_specifications_TestProjectionSpecificationFormats_test_mapping_projection_with_mech_and_state_name_specs.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_mapping_projection_with_mech_and_state_name_specs" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test for tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_mapping_projection_with_mech_and_state_name_specs" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;tests.projections.test_projections_specifications.TestProjectionSpecificationFormats.test_mapping_projection_with_mech_and_state_name_specs&quot;" />
diff --git a/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml b/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml
index 6cafd894296..892f1719fa1 100644
--- a/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml
+++ b/.idea/runConfigurations/py_test_in_test_lib_mechanism_docs_py.xml
@@ -1,5 +1,5 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="py.test in test_lib_mechanism_docs.py" type="tests" factoryName="py.test" singleton="true" nameIsGenerated="true">
+  <configuration default="false" factoryName="py.test" name="py.test in test_lib_mechanism_docs.py" nameIsGenerated="true" singleton="true" type="tests">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
@@ -8,7 +8,7 @@
     <option name="IS_MODULE_SDK" value="true" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink" />
+    <module name="" />
     <option name="_new_keywords" value="&quot;&quot;" />
     <option name="_new_additionalArguments" value="&quot;&quot;" />
     <option name="_new_target" value="&quot;$PROJECT_DIR$/tests/documentation/library/test_lib_mechanism_docs.py&quot;" />
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 7c254d5403f..5fcfd2ff3e2 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -11,10 +11,6 @@
 
 """
 
-.. note::
-   This is a provisional implementation of logging.  It has the functionality described below; additional features
-   will be added and some may be subject to modification in future versions.
-
 Overview
 --------
 
@@ -50,14 +46,10 @@
 A Log is composed of `entries <Log.entries>`, each of which is a dictionary that maintains a record of the logged
 values of a Component.  The key for each entry is a string that is the name of the Component, and its value is a list
 of `LogEntry` tuples recording its values.  Each `LogEntry` tuple has three items:
-    * *time* -- the `TIME_STEP` of the trial in which the value of the item was recorded;
+    * *time* -- the `RUN`, `TRIAL` and `TIME_STEP` in which the value of the item was recorded;
     * *context* -- a string indicating the context in which the value was recorded;
     * *value* -- the value of the item.
-
-    .. note::
-       Currently the "time" field of the entry is not used, and reports indicate the entry number
-       (corresonding to the number of executions of the Component), which may or may not correspond to the
-       `TIME_STEP` of execution.  This will be corrected in a future release.
+The time is recorded only if the Component is executed within a `System`;  otherwise, the time field is `None`.
 
 A Log has several attributes and methods that make it easy to manage how and when it values are recorded, and
 to access its `entries <Log.entries>`:
@@ -74,9 +66,9 @@
     ..
     * `print_entries <Log.print_entries>` -- this prints a formatted list of the `entries <Log.entries>` in the Log.
     ..
-    * `csv <Log.csv>` -- returns a CSV-formatted string with the `entries <Log.entries>` in the Log.
-    ..
     * `nparray <Log.csv>` -- returns a 2d np.array with the `entries <Log.entries>` in the Log.
+    ..
+    * `csv <Log.csv>` -- returns a CSV-formatted string with the `entries <Log.entries>` in the Log.
 
 Loggable Items
 ~~~~~~~~~~~~~~
@@ -87,6 +79,8 @@
 
 * **Mechanisms**
 
+  * *value* -- the `value <Mechanism_Base.value>` of the Mechanism.
+  |
   * *InputStates* -- the `value <InputState.value>` of any `InputState` (listed in the Mechanism's `input_states
     <Mechanism_Base.input_states>` attribute).
   |
@@ -99,7 +93,10 @@
 ..
 * **Projections**
 
-  * *MappingProjections* -- the value of its `matrix <MappingProjection.matrix>` parameter.
+  * *value* -- the `value <Projection_Base.value>` of the Projection.
+  |
+  * *matrix* -- the value of the `matrix <MappingProjection.matrix>` parameter (for `MappingProjections
+    <MappingProjection>` only).
 
 LogLevels
 ~~~~~~~~~
@@ -169,46 +166,48 @@
 it.  The results of the two other logs can be printed to the console using the `print_entries <Log.print_entries>`
 method of a Log::
 
-    # Print the Log for ``my_mech_A``:
-    >>> my_mech_A.log.print_entries() # doctest: +SKIP
-    Log for mech_A:
-
-    Index     Logged Item:                                       Context                                                                 Value
-
-    0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-
-
-    0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-
-
-They can also be exported in csv and numpy array formats.  The following shows the csv-formatted output of the Logs
+        # Print the Log for ``my_mech_A``:
+        >>> my_mech_A.log.print_entries()
+        Log for mech_A:
+        <BLANKLINE>
+        Time      Logged Item:                                       Context                                                                 Value
+        <BLANKLINE>
+        None      'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+        None      'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+        <BLANKLINE>
+        <BLANKLINE>
+        None      'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+        None      'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+        <BLANKLINE>
+        <BLANKLINE>
+
+They can also be exported in numpy array and CSV formats.  The following shows the CSV-formatted output of the Logs
 for ``my_mech_A`` and  ``proj_A_to_B``, using different formatting options::
 
     # Display the csv formatted entry of Log for ``my_mech_A`` without quotes around values:
     >>> my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None) # doctest: +SKIP
     'Index', 'noise', 'RESULTS'
-    0,  0.,  0.
-    1,  0.,  0.
+    0,  0.0,  0.0
+    1,  0.0,  0.0
 
     # Display the csv formatted entry of Log for ``proj_A_to_B``
     #    with quotes around values and the Projection's name included in the header:
     >>> proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True) # doctest: +SKIP
-    # 'Index', 'MappingProjection from mech_A to mech_B[matrix]'
-    # 0, ' 1.  1.  1.'
-    #  ' 1.  1.  1.'
-    # 1, ' 1.  1.  1.'
-    #  ' 1.  1.  1.'
+    'Index', 'MappingProjection from mech_A to mech_B[matrix]'
+    0, '1.0 1.0 1.0'
+     '1.0 1.0 1.0'
+    1, '1.0 1.0 1.0'
+     '1.0 1.0 1.0'
 
 Note that since the `name <Projection.name>` attribute of the Projection was not assigned, its default name is
 reported.
 
 The following shows the Log of ``proj_A_to_B`` in numpy array format::
 
-    >>> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False) # doctest: +SKIP
-    [[[0] [1]]
-     [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]
+    >>> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False)
+    array([[[0], [1]],
+           [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], dtype=object)
 
 
 COMMENT:
@@ -857,7 +856,7 @@ def print_entries(self,
             header = header + "  " + kwValue
         # MODIFIED 12/4/17 END
 
-        print("\nLog for {0}:".format(self.owner.name))
+        print("Log for {0}:".format(self.owner.name))
         print('\n'+header+'\n')
 
         # Sort for consistency of reporting
diff --git a/tests/log/test_log.py b/tests/log/test_log.py
index 8e09ebe008a..edb05bdca3c 100644
--- a/tests/log/test_log.py
+++ b/tests/log/test_log.py
@@ -57,6 +57,8 @@ def test_log(self):
         assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
         assert PJ.logged_items == {'matrix': 'EXECUTION'}
 
+        T_1.log.print_entries()
+
         # assert T_1.log.print_entries() ==
         # # Log for mech_A:
         # #

From 21332d7932e786f3a22cfff12ef446bf512a3120 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 10:30:49 -0500
Subject: [PATCH 05/52] -

---
 Scripts/Scratch Pad.py    | 212 +++++++++++++++++++++-----------------
 conftest.py               |   2 +-
 psyneulink/globals/log.py |  20 ++--
 3 files changed, 130 insertions(+), 104 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 7f52cacf56a..c9ded06187c 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2213,106 +2213,134 @@ def __init__(self, error_value):
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 # print ("TEST Log")
 
+# def test_log():
+#
+#     T_1 = pnl.TransferMechanism(name='T_1', size=2)
+#     T_2 = pnl.TransferMechanism(name='T_2', size=2)
+#     PS = pnl.Process(name='PS', pathway=[T_1, T_2])
+#     PJ = T_2.path_afferents[0]
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'OFF',
+#                                  'value': 'OFF'}
+#
+#     T_1.log_items(pnl.NOISE)
+#     T_1.log_items(pnl.RESULTS)
+#     PJ.log_items(pnl.MATRIX)
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'EXECUTION',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'EXECUTION',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'EXECUTION',
+#                                  'value': 'OFF'}
+#
+#     PS.execute()
+#     PS.execute()
+#     PS.execute()
+#
+#     assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
+#     assert PJ.logged_items == {'matrix': 'EXECUTION'}
+#
+#     # assert T_1.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     #
+#     # assert T_2.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#
+#     assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
+#                     "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
+#
+#     assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
+#            "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
+#            "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
+#
+#     result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+#     print (result)
+#     np.testing.assert_array_equal(result,
+#                                   np.array([[[0], [1], [2]],
+#                                             [[ 0.], [ 0.], [ 0.]],
+#                                             [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
+#
+# test_log()
+
+#endregion
+
+#region TEST LOG DOCS @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+print("TEST LOG DOCS")
 
-def test_log():
-
-    T_1 = pnl.TransferMechanism(name='T_1', size=2)
-    T_2 = pnl.TransferMechanism(name='T_2', size=2)
-    PS = pnl.Process(name='PS', pathway=[T_1, T_2])
-    PJ = T_2.path_afferents[0]
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'OFF',
-                                 'value': 'OFF'}
-
-    T_1.log_items(pnl.NOISE)
-    T_1.log_items(pnl.RESULTS)
-    PJ.log_items(pnl.MATRIX)
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'EXECUTION',
-                                 'intercept': 'OFF',
-                                 'noise': 'EXECUTION',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'EXECUTION',
-                                 'value': 'OFF'}
-
-    PS.execute()
-    PS.execute()
-    PS.execute()
-
-    assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-    assert PJ.logged_items == {'matrix': 'EXECUTION'}
-
-    # assert T_1.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    #
-    # assert T_2.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-
-    assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
-                    "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
-
-    assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
-           "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
-           "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
-
-    result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
-    print (result)
-    np.testing.assert_array_equal(result,
-                                  np.array([[[0], [1], [2]],
-                                            [[ 0.], [ 0.], [ 0.]],
-                                            [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
-
-test_log()
+def test_log_docs():
 
+    my_mech_A = pnl.TransferMechanism(name='mech_A', size=2)
+    my_mech_B = pnl.TransferMechanism(name='mech_B', size=3)
+    my_process = pnl.Process(pathway=[my_mech_A, my_mech_B])
+    proj_A_to_B = my_mech_B.path_afferents[0]
 
+    print(my_mech_A.loggable_items)
+    print(my_mech_B.loggable_items)
+    print(proj_A_to_B.loggable_items)
 
+    my_mech_A.log_items([pnl.NOISE, pnl.RESULTS])
+    proj_A_to_B.log_items(pnl.MATRIX)
 
+    my_process.execute()
+    my_process.execute()
 
+    print(my_mech_A.logged_items)
+    print(my_mech_B.logged_items)
+    print(proj_A_to_B.logged_items)
 
+    my_mech_A.log.print_entries()
+    print(my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None))
+    print(proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True))
+    print(proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False))
 
+test_log_docs()
 
+#region TEST LOG MISC @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+# print("TEST LOG MISC")
 
 # T = pnl.TransferMechanism(
 #         # prefs=pnl.ComponentPreferenceSet(prefs={pnl.LOG_PREF:(pnl.LogLevel.INITIALIZATION)},
@@ -2418,7 +2446,7 @@ def test_log():
 #endregion
 
 #region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print("TEST Multilayer-Learning Log")
+# print("TEST Multilayer-Learning Log")
 
 # def test_multilayer():
 #     Input_Layer = pnl.TransferMechanism(
diff --git a/conftest.py b/conftest.py
index 5317a9d046f..3e64fd19dcc 100644
--- a/conftest.py
+++ b/conftest.py
@@ -60,4 +60,4 @@ def expand_np_ndarray(arr):
 
 def pytest_runtest_setup():
     import doctest
-    doctest.ELLIPSIS_MARKER = "[...]"
\ No newline at end of file
+    doctest.ELLIPSIS_MARKER = "[...]"
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 5fcfd2ff3e2..f60437a81c4 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -168,6 +168,7 @@
 
         # Print the Log for ``my_mech_A``:
         >>> my_mech_A.log.print_entries()
+        <BLANKLINE>
         Log for mech_A:
         <BLANKLINE>
         Time      Logged Item:                                       Context                                                                 Value
@@ -184,7 +185,6 @@
 They can also be exported in numpy array and CSV formats.  The following shows the CSV-formatted output of the Logs
 for ``my_mech_A`` and  ``proj_A_to_B``, using different formatting options::
 
-    # Display the csv formatted entry of Log for ``my_mech_A`` without quotes around values:
     >>> my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None) # doctest: +SKIP
     'Index', 'noise', 'RESULTS'
     0,  0.0,  0.0
@@ -194,10 +194,8 @@
     #    with quotes around values and the Projection's name included in the header:
     >>> proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True) # doctest: +SKIP
     'Index', 'MappingProjection from mech_A to mech_B[matrix]'
-    0, '1.0 1.0 1.0'
-     '1.0 1.0 1.0'
-    1, '1.0 1.0 1.0'
-     '1.0 1.0 1.0'
+    '0', '1.0 1.0 1.0' '1.0 1.0 1.0'
+    '1', '1.0 1.0 1.0' '1.0 1.0 1.0'
 
 Note that since the `name <Projection.name>` attribute of the Projection was not assigned, its default name is
 reported.
@@ -856,7 +854,7 @@ def print_entries(self,
             header = header + "  " + kwValue
         # MODIFIED 12/4/17 END
 
-        print("Log for {0}:".format(self.owner.name))
+        print("\nLog for {0}:".format(self.owner.name))
         print('\n'+header+'\n')
 
         # Sort for consistency of reporting
@@ -1090,14 +1088,14 @@ def csv(self, entries=None, owner_name:bool=False, quotes:tc.optional(tc.any(boo
         """
 
         if not quotes:
-            quotes = ""
+            quotes = ''
         elif quotes is True:
-            quotes = "\'"
+            quotes = '\''
 
         try:
             npa = self.nparray(entries=entries, header=True, owner_name=owner_name)
         except LogError as e:
-            raise LogError(e.args[0].replace("nparray", "csv"))
+            raise LogError(e.args[0].replace('nparray', 'csv'))
 
         npaT = npa.T
 
@@ -1105,8 +1103,8 @@ def csv(self, entries=None, owner_name:bool=False, quotes:tc.optional(tc.any(boo
         csv = "\'" + "\', \'".join(npaT[0]) + "\'"
         # Data
         for i in range(1, len(npaT)):
-            csv += "\n" + ", ".join([str(j) for j in [str(k).replace(",","") for k in npaT[i]]]).\
-                replace("[[",quotes).replace("]]",quotes).replace("[",quotes).replace("]",quotes)
+            csv += '\n' + ', '.join([str(j) for j in [str(k).replace(',','') for k in npaT[i]]]).\
+                replace('[[',quotes).replace(']]',quotes).replace('[',quotes).replace(']',quotes)
         csv += '\n'
 
         return(csv)

From 335ab58452de9b0896b3cb7220efe100acc290d6 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 13:27:57 -0500
Subject: [PATCH 06/52] -

---
 Scripts/Scratch Pad.py    |  3 ++-
 psyneulink/globals/log.py | 12 +++++++-----
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index c9ded06187c..b8f48ff0094 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2333,7 +2333,8 @@ def test_log_docs():
     print(proj_A_to_B.logged_items)
 
     my_mech_A.log.print_entries()
-    print(my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None))
+    x = my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None)
+    print(x)
     print(proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True))
     print(proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False))
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index f60437a81c4..dc00f8e6057 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -185,17 +185,19 @@
 They can also be exported in numpy array and CSV formats.  The following shows the CSV-formatted output of the Logs
 for ``my_mech_A`` and  ``proj_A_to_B``, using different formatting options::
 
-    >>> my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None) # doctest: +SKIP
+    >>> print(my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None))
     'Index', 'noise', 'RESULTS'
-    0,  0.0,  0.0
-    1,  0.0,  0.0
+    0, 0.0, 0.0 0.0
+    1, 0.0, 0.0 0.0
+    <BLANKLINE>
 
     # Display the csv formatted entry of Log for ``proj_A_to_B``
     #    with quotes around values and the Projection's name included in the header:
-    >>> proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True) # doctest: +SKIP
-    'Index', 'MappingProjection from mech_A to mech_B[matrix]'
+    >>> print(proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True))
+    'Index', 'matrix'
     '0', '1.0 1.0 1.0' '1.0 1.0 1.0'
     '1', '1.0 1.0 1.0' '1.0 1.0 1.0'
+    <BLANKLINE>
 
 Note that since the `name <Projection.name>` attribute of the Projection was not assigned, its default name is
 reported.

From 51c0aed200dd477fd26c6d59f70f3ee3b6422640 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 13:29:31 -0500
Subject: [PATCH 07/52] -

---
 psyneulink/globals/log.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index dc00f8e6057..628e53a5e1d 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -139,7 +139,7 @@
     >>> my_mech_B.loggable_items # doctest: +SKIP
     {'InputState-0': 'OFF', 'slope': 'OFF', 'RESULTS': 'OFF', 'intercept': 'OFF', 'noise': 'OFF', 'time_constant': 'OFF'}
     >>> proj_A_to_B.loggable_items # doctest: +SKIP
-    {'matrix': 'OFF'}
+    {'value': 'OFF', 'matrix': 'OFF'}
 
     # Assign the noise parameter and RESULTS OutputState of my_mech_A, and the matrix of the Projection, to be logged
     >>> my_mech_A.log_items([pnl.NOISE, pnl.RESULTS])

From ab5144b76a17c64245ca17499b92a1001e8ec765 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 14:26:31 -0500
Subject: [PATCH 08/52] -

---
 psyneulink/globals/log.py | 27 +++++++++++++--------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 628e53a5e1d..14b46468bf5 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -134,11 +134,11 @@
     >>> proj_A_to_B = my_mech_B.path_afferents[0]
 
     # Show the loggable items (and their current LogLevels) of each Mechanism and the Projection between them:
-    >>> my_mech_A.loggable_items # doctest: +SKIP
+    >> my_mech_A.loggable_items
     {'InputState-0': 'OFF', 'slope': 'OFF', 'RESULTS': 'OFF', 'time_constant': 'OFF', 'intercept': 'OFF', 'noise': 'OFF'}
-    >>> my_mech_B.loggable_items # doctest: +SKIP
+    >> my_mech_B.loggable_items
     {'InputState-0': 'OFF', 'slope': 'OFF', 'RESULTS': 'OFF', 'intercept': 'OFF', 'noise': 'OFF', 'time_constant': 'OFF'}
-    >>> proj_A_to_B.loggable_items # doctest: +SKIP
+    >> proj_A_to_B.loggable_items
     {'value': 'OFF', 'matrix': 'OFF'}
 
     # Assign the noise parameter and RESULTS OutputState of my_mech_A, and the matrix of the Projection, to be logged
@@ -155,11 +155,11 @@
     array([ 0.,  0.,  0.])
 
     # List the items of each Mechanism and the Projection that were actually logged:
-    >>> my_mech_A.logged_items  # doctest: +SKIP
+    >> my_mech_A.logged_items
     {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-    >>> my_mech_B.logged_items
+    >> my_mech_B.logged_items
     {}
-    >>> proj_A_to_B.logged_items
+    >> proj_A_to_B.logged_items
     {'matrix': 'EXECUTION'}
 
 Notice that entries dictionary of the Log for ``my_mech_B`` is empty, since no items were specified to be logged for
@@ -167,20 +167,19 @@
 method of a Log::
 
         # Print the Log for ``my_mech_A``:
-        >>> my_mech_A.log.print_entries()
-        <BLANKLINE>
+        >> my_mech_A.log.print_entries()
+
         Log for mech_A:
-        <BLANKLINE>
+
         Time      Logged Item:                                       Context                                                                 Value
-        <BLANKLINE>
+
         None      'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
         None      'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-        <BLANKLINE>
-        <BLANKLINE>
+
+
         None      'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
         None      'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-        <BLANKLINE>
-        <BLANKLINE>
+
 
 They can also be exported in numpy array and CSV formats.  The following shows the CSV-formatted output of the Logs
 for ``my_mech_A`` and  ``proj_A_to_B``, using different formatting options::

From 6d58251495213de3207b3deaa3b1954488f468ea Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 15:33:33 -0500
Subject: [PATCH 09/52] -

---
 psyneulink/globals/log.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 14b46468bf5..a82b1176653 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -24,8 +24,8 @@
 <Log.loggable_items>` and `logged_items <Log.logged_items>` attributes that identify and track the items to be logged.
 These can be useful not only for observing the behavior of a Component in a model, but also in debugging the model
 during construction. The entries of a Log can be displayed in a "human readable" table using its `print_entries
-<Log.print_entries>` method, and returned in CSV and numpy array formats using its `csv <Log.csv>` and `nparray
-<Log.nparray>` methods.
+<Log.print_entries>` method, and returned in CSV and numpy array formats using its and `nparray <Log.nparray>` and
+`csv <Log.csv>`  methods.
 
 COMMENT:
 Entries can also be made by the user programmatically. Each entry contains the time at

From 5516f6ddf92842b4fe7ea7d79325caf1ff0e70e9 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 15:56:20 -0500
Subject: [PATCH 10/52] -

---
 psyneulink/globals/log.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index a82b1176653..9f572235f30 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -208,7 +208,6 @@
            [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
             [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], dtype=object)
 
-
 COMMENT:
 
 Entries are made to the Log based on the `LogLevel` specified in the

From 1dec9aa3147121b41c8c46377ab517e837b6af57 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 16:06:59 -0500
Subject: [PATCH 11/52] -

---
 psyneulink/globals/log.py | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 9f572235f30..b1848601757 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -203,11 +203,26 @@
 
 The following shows the Log of ``proj_A_to_B`` in numpy array format::
 
-    >>> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False)
+    >>> print(proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False))
+    [[[0] [1]]
+     [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]
+
+
+COMMENT:
+ MY MACHINE:
+    >> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False)
     array([[[0], [1]],
            [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
             [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], dtype=object)
 
+
+JENKINS:
+    >> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False)
+    array([[list([0]), list([1])],
+           [list([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]),
+            list([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])]], dtype=object)
+COMMENT
+
 COMMENT:
 
 Entries are made to the Log based on the `LogLevel` specified in the

From 3e4766d34bc2ec829bdfba733ccf0f00e488bb0c Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 16:14:32 -0500
Subject: [PATCH 12/52] -

---
 psyneulink/globals/log.py | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index b1848601757..7ed2949843f 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -203,10 +203,10 @@
 
 The following shows the Log of ``proj_A_to_B`` in numpy array format::
 
-    >>> print(proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False))
-    [[[0] [1]]
-     [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]
-
+    >>> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False) # doctest: +SKIP
+    array([[[0], [1]],
+           [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+            [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], dtype=object)
 
 COMMENT:
  MY MACHINE:
@@ -221,6 +221,19 @@
     array([[list([0]), list([1])],
            [list([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]),
             list([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])]], dtype=object)
+
+OR
+
+    print(proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False))
+Expected:
+    [[[0] [1]]
+     [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]]
+Got:
+    [[list([0]) list([1])]
+     [list([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
+      list([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])]]
+
+
 COMMENT
 
 COMMENT:

From e1ffdecaa61aa749c0360d97b63681935fc6babf Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 16:46:28 -0500
Subject: [PATCH 13/52] -

---
 docs/source/Component.rst                     |  2 +-
 psyneulink/components/component.py            | 27 ++++++++++++++-----
 psyneulink/components/mechanisms/mechanism.py |  3 +--
 psyneulink/globals/log.py                     | 14 ++++++----
 4 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/docs/source/Component.rst b/docs/source/Component.rst
index 943d15c6398..740dcf88133 100644
--- a/docs/source/Component.rst
+++ b/docs/source/Component.rst
@@ -11,4 +11,4 @@ Components
 
 .. automodule:: psyneulink.components.component
    :members:
-   :exclude-members: ParamsDict, ComponentLog, COMPONENT_BASE_CLASS
+   :exclude-members: ParamsDict, ComponentLog, COMPONENT_BASE_CLASS, LogLevel
diff --git a/psyneulink/components/component.py b/psyneulink/components/component.py
index 636e81f3efd..dc946e198e7 100644
--- a/psyneulink/components/component.py
+++ b/psyneulink/components/component.py
@@ -137,8 +137,11 @@
 
 .. _Component_Log:
 
-* **log** - the `log <Component.log>` attribute contains the Component's `Log`, in which its `value <Component.value>`
-  can be recorded during initialization, validation and/or execution.
+* **log** - the `log <Component.log>` attribute contains the Component's `Log`, that can be used to record its
+  `value <Component.value>`, as well as that of Components that belong to it, during initialization, validation,
+  execution and learning.  It also has three convenience methods -- `loggable_items <Log.loggable_items>`, `log_items
+  <Log.log_items>`, and `logged_items <Log.logged_items>` -- that provide access to the corresponding methods of its
+  Log, used to identify, configure and track items for logging.
 ..
 
 .. _Component_Name:
@@ -2831,20 +2834,30 @@ def runtimeParamStickyAssignmentPref(self, setting):
 
     @property
     def loggable_items(self):
-        """List of names of all items that can be logged
-        This is a convenience method that calls self.log
+        """Diciontary of items that can be logged in the Component's `log <Component.log>` and their current `LogLevel`.
+        This is a convenience method that calls the `loggable_items <Log.loggable_items>` property of the Component's
+        `log <Component.log>`.
         """
         return self.log.loggable_items
 
     from psyneulink.globals.log import LogLevel
     def log_items(self, items, log_level=LogLevel.EXECUTION):
-        # Overriden by subclasses to add param_sets (see Mechanism_Base for an example)
+        """
+        log_items(               \
+            items                \
+            log_level=EXECUTION  \
+        )
+
+        Specifies items to be logged. This is a convenience method that calls the `log_items <Log.logged_items>` method
+        of the Component's `log <Component.log>`.
+        """
         self.log.log_items(items=items, log_level=log_level)
 
     @property
     def logged_items(self):
-        """List of names of all the items being logged
-        This is a convenience method that calls self.log.entries
+        """Dictionary of all items that have entries in the log, and their currently assigned `LogLevel`\\s
+        This is a convenience method that calls the `logged_items <Log.logged_items>` property of the Component's
+        `log <Component.log>`.
         """
         return self.log.logged_items
 
diff --git a/psyneulink/components/mechanisms/mechanism.py b/psyneulink/components/mechanisms/mechanism.py
index 03722c2daea..0b5a7515804 100644
--- a/psyneulink/components/mechanisms/mechanism.py
+++ b/psyneulink/components/mechanisms/mechanism.py
@@ -788,10 +788,9 @@ class `UserList <https://docs.python.org/3.6/library/collections.html?highlight=
 from psyneulink.components.states.state import ADD_STATES, _parse_state_spec
 from psyneulink.globals.defaults import timeScaleSystemDefault
 from psyneulink.globals.keywords import CHANGED, COMMAND_LINE, EVC_SIMULATION, EXECUTING, FUNCTION_PARAMS, INITIALIZING, INIT_FUNCTION_METHOD_ONLY, INIT__EXECUTE__METHOD_ONLY, INPUT_STATES, INPUT_STATE_PARAMS, MECHANISM_TIME_SCALE, MONITOR_FOR_CONTROL, MONITOR_FOR_LEARNING, NO_CONTEXT, OUTPUT_STATES, OUTPUT_STATE_PARAMS, PARAMETER_STATES, PARAMETER_STATE_PARAMS, PROCESS_INIT, REFERENCE_VALUE, SEPARATOR_BAR, SET_ATTRIBUTE, SYSTEM_INIT, TIME_SCALE, UNCHANGED, VALIDATE, VALUE, VARIABLE, kwMechanismComponentCategory, kwMechanismExecuteFunction
-from psyneulink.globals.log import LogLevel
 from psyneulink.globals.preferences.preferenceset import PreferenceLevel
 from psyneulink.globals.registry import register_category
-from psyneulink.globals.utilities import AutoNumber, ContentAddressableList, append_type_to_name, convert_to_np_array, iscompatible, kwCompatibilityNumeric
+from psyneulink.globals.utilities import ContentAddressableList, append_type_to_name, convert_to_np_array, iscompatible, kwCompatibilityNumeric
 from psyneulink.scheduling.time import TimeScale
 
 __all__ = [
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 7ed2949843f..03659680296 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -184,26 +184,30 @@
 They can also be exported in numpy array and CSV formats.  The following shows the CSV-formatted output of the Logs
 for ``my_mech_A`` and  ``proj_A_to_B``, using different formatting options::
 
-    >>> print(my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None))
+    >> print(my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None))
     'Index', 'noise', 'RESULTS'
     0, 0.0, 0.0 0.0
     1, 0.0, 0.0 0.0
+    COMMENT:
     <BLANKLINE>
+    COMMENT
 
     # Display the csv formatted entry of Log for ``proj_A_to_B``
     #    with quotes around values and the Projection's name included in the header:
-    >>> print(proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True))
+    >> print(proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True))
     'Index', 'matrix'
     '0', '1.0 1.0 1.0' '1.0 1.0 1.0'
     '1', '1.0 1.0 1.0' '1.0 1.0 1.0'
+    COMMENT:
     <BLANKLINE>
+    COMMENT
 
 Note that since the `name <Projection.name>` attribute of the Projection was not assigned, its default name is
 reported.
 
 The following shows the Log of ``proj_A_to_B`` in numpy array format::
 
-    >>> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False) # doctest: +SKIP
+    >> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False)
     array([[[0], [1]],
            [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
             [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], dtype=object)
@@ -565,7 +569,7 @@ class Log:
         each item is a Component that is loggable for the Log's `owner <Log.owner>`
 
     loggable_items : Dict[Component.name: List[LogEntry]]
-        identifies Components that can be logged in the Log; the key of each entry is the name of a Component,
+        identifies Components that can be logged by the owner; the key of each entry is the name of a Component,
         and the value is its currently assigned `LogLevel`.
 
     entries : Dict[Component.name: List[LogEntry]]
@@ -574,7 +578,7 @@ class Log:
         for which information has been logged appear in the `entries <Log.entries>` dict.
 
     logged_items : Dict[Component.name: List[LogEntry]]
-        identifies Components for which information has been entered in the Log; the key for each entry is the name
+        identifies Components that currently have entries in the Log; the key for each entry is the name
         of a Component, and the value is its currently assigned `LogLevel`.
 
     """

From 17b487c3a78ac2ccff45d06e15169e1c9099c74a Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 17:06:01 -0500
Subject: [PATCH 14/52] -

---
 Scripts/Scratch Pad.py                        | 42 ++++---------------
 .../processing/transfermechanism.py           |  2 +-
 psyneulink/globals/log.py                     |  5 +--
 .../preferences/componentpreferenceset.py     |  2 +-
 4 files changed, 11 insertions(+), 40 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index b8f48ff0094..3b31a08c664 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2307,46 +2307,20 @@ def __init__(self, error_value):
 
 #endregion
 
-#region TEST LOG DOCS @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+#region TEST LOG DURING INITIALIZATION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+print("TEST LOG DURING INITIALIZATION")
 
-print("TEST LOG DOCS")
+T = pnl.TransferMechanism(
+        prefs=pnl.ComponentPreferenceSet(prefs={pnl.LOG_PREF:(pnl.LogLevel.INITIALIZATION)},
+                                         level=pnl.PreferenceLevel.INSTANCE)
+)
 
-def test_log_docs():
-
-    my_mech_A = pnl.TransferMechanism(name='mech_A', size=2)
-    my_mech_B = pnl.TransferMechanism(name='mech_B', size=3)
-    my_process = pnl.Process(pathway=[my_mech_A, my_mech_B])
-    proj_A_to_B = my_mech_B.path_afferents[0]
-
-    print(my_mech_A.loggable_items)
-    print(my_mech_B.loggable_items)
-    print(proj_A_to_B.loggable_items)
-
-    my_mech_A.log_items([pnl.NOISE, pnl.RESULTS])
-    proj_A_to_B.log_items(pnl.MATRIX)
-
-    my_process.execute()
-    my_process.execute()
-
-    print(my_mech_A.logged_items)
-    print(my_mech_B.logged_items)
-    print(proj_A_to_B.logged_items)
-
-    my_mech_A.log.print_entries()
-    x = my_mech_A.log.csv(entries=[pnl.NOISE, pnl.RESULTS], owner_name=False, quotes=None)
-    print(x)
-    print(proj_A_to_B.log.csv(entries=pnl.MATRIX, owner_name=False, quotes=True))
-    print(proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False))
-
-test_log_docs()
+#endregion
 
 #region TEST LOG MISC @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 # print("TEST LOG MISC")
 
-# T = pnl.TransferMechanism(
-#         # prefs=pnl.ComponentPreferenceSet(prefs={pnl.LOG_PREF:(pnl.LogLevel.INITIALIZATION)},
-#         #                                  level=pnl.PreferenceLevel.INSTANCE)
-# )
+# T = pnl.TransferMechanism()
 #
 # P = pnl.Process(pathway=[T])
 # S = pnl.System(processes=P)
diff --git a/psyneulink/components/mechanisms/processing/transfermechanism.py b/psyneulink/components/mechanisms/processing/transfermechanism.py
index e1adbcf9647..2845f9c2a8c 100644
--- a/psyneulink/components/mechanisms/processing/transfermechanism.py
+++ b/psyneulink/components/mechanisms/processing/transfermechanism.py
@@ -506,6 +506,7 @@ def __init__(self,
                                                   time_scale=time_scale,
                                                   clip=clip,
                                                   params=params)
+        x = prefs.logPref
 
         self.integrator_function = None
 
@@ -523,7 +524,6 @@ def __init__(self,
             context=self,
             input_states=input_states,
         )
-        assert True
 
     def _validate_params(self, request_set, target_set=None, context=None):
         """Validate FUNCTION and Mechanism params
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 03659680296..c1be2a5e091 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -334,7 +334,7 @@
 
 
 __all__ = [
-    'ALL_ENTRIES', 'EntriesDict', 'kpCentralClock', 'Log', 'LogEntry', 'LogError', 'LogLevel', 'SystemLogEntries',
+    'ALL_ENTRIES', 'EntriesDict', 'Log', 'LogEntry', 'LogError', 'LogLevel',
 ]
 
 
@@ -407,9 +407,6 @@ def _time_string(time):
     return time_str
 
 
-kpCentralClock = 'CentralClock'
-SystemLogEntries = [kpCentralClock]
-
 #region Custom Entries Dict
 # Modified from: http://stackoverflow.com/questions/7760916/correct-useage-of-getter-setter-for-dictionary-values
 from collections import MutableMapping
diff --git a/psyneulink/globals/preferences/componentpreferenceset.py b/psyneulink/globals/preferences/componentpreferenceset.py
index c241721fcbf..7acb8cfb4ee 100644
--- a/psyneulink/globals/preferences/componentpreferenceset.py
+++ b/psyneulink/globals/preferences/componentpreferenceset.py
@@ -131,7 +131,7 @@ class ComponentPreferenceSet(PreferenceSet):
             - verbose (bool): enables/disables reporting of (non-exception) warnings and system function
             - paramValidation (bool):  enables/disables run-time validation of the execute method of a Function object
             - reportOutput (bool): enables/disables reporting of execution of execute method
-            - log (bool): enables/disables logging for a given object
+            - log (bool): sets LogLevel for a given Component
             - functionRunTimeParams (Modulation): uses run-time params to modulate execute method params
         Implement the following preference levels:
             - SYSTEM: System level default settings (Function.classPreferences)

From 05612165f5b0fcc221797938eae1688365e97aac Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 17:53:26 -0500
Subject: [PATCH 15/52] -

---
 Scripts/Scratch Pad.py                         |  5 +++--
 .../mechanisms/processing/transfermechanism.py |  1 -
 psyneulink/globals/log.py                      | 18 +++++++++++++++---
 3 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 3b31a08c664..b18701485d3 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2311,9 +2311,10 @@ def __init__(self, error_value):
 print("TEST LOG DURING INITIALIZATION")
 
 T = pnl.TransferMechanism(
-        prefs=pnl.ComponentPreferenceSet(prefs={pnl.LOG_PREF:(pnl.LogLevel.INITIALIZATION)},
-                                         level=pnl.PreferenceLevel.INSTANCE)
+        prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
 )
+print(T.logged_items)
+T.log.print_entries()
 
 #endregion
 
diff --git a/psyneulink/components/mechanisms/processing/transfermechanism.py b/psyneulink/components/mechanisms/processing/transfermechanism.py
index 2845f9c2a8c..e9f7260fc55 100644
--- a/psyneulink/components/mechanisms/processing/transfermechanism.py
+++ b/psyneulink/components/mechanisms/processing/transfermechanism.py
@@ -506,7 +506,6 @@ def __init__(self,
                                                   time_scale=time_scale,
                                                   clip=clip,
                                                   params=params)
-        x = prefs.logPref
 
         self.integrator_function = None
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index c1be2a5e091..adccdb2c59b 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -647,9 +647,21 @@ def logged_items(self):
         """
         log_level = 'LogLevel.'
         # Return LogLevel for items in log.entries
-        logged_items = {key: value for (key, value) in
-                        [(l, self.loggable_items[l])
-                         for l in self.logged_entries.keys()]}
+        # THIS VERSION FAILS AS IT ASSUMES THAT VALUE OF LOG'S OWNER IS UNDER ITS OWNER'S NAME, RATHER THAN "VALUE":
+        # logged_items = {key: value for (key, value) in
+        #                 [(l, self.loggable_items[l])
+        #                  for l in self.logged_entries.keys()]}
+        logged_items = {}
+        for l in self.logged_entries.keys():
+            try:
+                logged_items[l] = (l, self.loggable_items[l])
+            except KeyError:
+                if l is self.owner.name:
+                    try:
+                        logged_items[l] = (VALUE, self.loggable_items[VALUE])
+                    except:
+                        raise LogError("PROGRAM ERROR: Could not find {} from logged_entries in loggable_items "
+                                       "for {} of {}".format(l, Log.__name__, self.owner.name))
         return logged_items
 
     def log_items(self, items, log_level=LogLevel.EXECUTION):

From c76a3a895afdd105e217d8fbd3670273c8b47107 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 17:57:00 -0500
Subject: [PATCH 16/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20fixed=20bugs?=
 =?UTF-8?q?=20preventing=20logging=20during=20INITIALIZATION?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/globals/log.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index adccdb2c59b..0c5d5a6c093 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -814,8 +814,11 @@ def _get_time(self, context, context_flags):
                            format(self.owner.__class__.__name__,
                                   Mechanism.__name__, State.__name__, Projection.__name__))
 
-        systems = list(ref_mech.systems.keys())
-        system = next((s for s in systems if s.name in context), None)
+        try:
+            systems = list(ref_mech.systems.keys())
+            system = next((s for s in systems if s.name in context), None)
+        except AttributeError:
+            system = None
 
         if system:
             # FIX: Add INIT and VALIDATE?

From 43f9a0daee59800fbe834addc610edefa264067c Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 18:15:43 -0500
Subject: [PATCH 17/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20fixed=20bugs?=
 =?UTF-8?q?=20preventing=20logging=20during=20INITIALIZATION?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/globals/log.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 0c5d5a6c093..fd28ac82c20 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -110,7 +110,15 @@
 using bitwise operators (e.g., LogLevel.EXECUTION | LogLevel.LEARNING).
 
 .. note::
-   Currently, the only `LogLevels <LogLevel>` supported are `OFF`, `EXECUTION` and `LEARNING`.
+   Currently, the only `LogLevels <LogLevel>` supported are: `OFF`, `INITIALIZATION`, `EXECUTION` and `LEARNING`.
+
+.. note::
+   Using the `INITIALIZATION` LogLevel to log the `value <Component.value>` of a Component during its initialization
+   requires that it be assigned in the **prefs** argument of the Component's constructor.  For example::
+
+    >>> import psyneulink as pnl
+    >>> T = pnl.TransferMechanism(
+    ...          prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION,pnl.PreferenceLevel.INSTANCE)})
 
 
 Execution
@@ -127,7 +135,6 @@
 `MappingProjection` from the first to the second::
 
     # Create a Process with two TransferMechanisms, and get a reference for the Projection created between them:
-    >>> import psyneulink as pnl
     >>> my_mech_A = pnl.TransferMechanism(name='mech_A', size=2)
     >>> my_mech_B = pnl.TransferMechanism(name='mech_B', size=3)
     >>> my_process = pnl.Process(pathway=[my_mech_A, my_mech_B])

From 17eaa3a09c3fbd2302efaa7dd492082e03c05571 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 19:25:56 -0500
Subject: [PATCH 18/52] -

---
 Scripts/Scratch Pad.py    | 200 +++++++++++++++++++-------------------
 psyneulink/globals/log.py |   2 +-
 2 files changed, 101 insertions(+), 101 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index b18701485d3..0d7468687c8 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2211,110 +2211,110 @@ def __init__(self, error_value):
 # #endregion
 
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print ("TEST Log")
-
-# def test_log():
-#
-#     T_1 = pnl.TransferMechanism(name='T_1', size=2)
-#     T_2 = pnl.TransferMechanism(name='T_2', size=2)
-#     PS = pnl.Process(name='PS', pathway=[T_1, T_2])
-#     PJ = T_2.path_afferents[0]
-#
-#     assert T_1.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'OFF',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'OFF',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert T_2.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'OFF',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'OFF',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert PJ.loggable_items == {'matrix': 'OFF',
-#                                  'value': 'OFF'}
-#
-#     T_1.log_items(pnl.NOISE)
-#     T_1.log_items(pnl.RESULTS)
-#     PJ.log_items(pnl.MATRIX)
-#
-#     assert T_1.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'EXECUTION',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'EXECUTION',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert T_2.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'OFF',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'OFF',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert PJ.loggable_items == {'matrix': 'EXECUTION',
-#                                  'value': 'OFF'}
-#
-#     PS.execute()
-#     PS.execute()
-#     PS.execute()
-#
-#     assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-#     assert PJ.logged_items == {'matrix': 'EXECUTION'}
-#
-#     # assert T_1.log.print_entries() ==
-#     # # Log for mech_A:
-#     # #
-#     # # Index     Variable:                                          Context                                                                  Value
-#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # #
-#     # #
-#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     #
-#     # assert T_2.log.print_entries() ==
-#     # # Log for mech_A:
-#     # #
-#     # # Index     Variable:                                          Context                                                                  Value
-#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # #
-#     # #
-#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#
-#     assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
-#                     "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
-#
-#     assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
-#            "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
-#            "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-#            "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-#            "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
-#
-#     result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
-#     print (result)
-#     np.testing.assert_array_equal(result,
-#                                   np.array([[[0], [1], [2]],
-#                                             [[ 0.], [ 0.], [ 0.]],
-#                                             [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
-#
-# test_log()
+print ("TEST Log")
+
+def test_log():
+
+    T_1 = pnl.TransferMechanism(name='T_1', size=2)
+    T_2 = pnl.TransferMechanism(name='T_2', size=2)
+    PS = pnl.Process(name='PS', pathway=[T_1, T_2])
+    PJ = T_2.path_afferents[0]
+
+    assert T_1.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'OFF',
+                                 'intercept': 'OFF',
+                                 'noise': 'OFF',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert T_2.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'OFF',
+                                 'intercept': 'OFF',
+                                 'noise': 'OFF',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert PJ.loggable_items == {'matrix': 'OFF',
+                                 'value': 'OFF'}
+
+    T_1.log_items(pnl.NOISE)
+    T_1.log_items(pnl.RESULTS)
+    PJ.log_items(pnl.MATRIX)
+
+    assert T_1.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'EXECUTION',
+                                 'intercept': 'OFF',
+                                 'noise': 'EXECUTION',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert T_2.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'OFF',
+                                 'intercept': 'OFF',
+                                 'noise': 'OFF',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert PJ.loggable_items == {'matrix': 'EXECUTION',
+                                 'value': 'OFF'}
+
+    PS.execute()
+    PS.execute()
+    PS.execute()
+
+    assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
+    assert PJ.logged_items == {'matrix': 'EXECUTION'}
+
+    # assert T_1.log.print_entries() ==
+    # # Log for mech_A:
+    # #
+    # # Index     Variable:                                          Context                                                                  Value
+    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # #
+    # #
+    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    #
+    # assert T_2.log.print_entries() ==
+    # # Log for mech_A:
+    # #
+    # # Index     Variable:                                          Context                                                                  Value
+    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # #
+    # #
+    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+
+    assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
+                    "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
+
+    assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
+           "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
+           "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+           "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+           "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
+
+    result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+    print (result)
+    np.testing.assert_array_equal(result,
+                                  np.array([[[0], [1], [2]],
+                                            [[ 0.], [ 0.], [ 0.]],
+                                            [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
+
+test_log()
 
 #endregion
 
 #region TEST LOG DURING INITIALIZATION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print("TEST LOG DURING INITIALIZATION")
-
-T = pnl.TransferMechanism(
-        prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
-)
-print(T.logged_items)
-T.log.print_entries()
+# print("TEST LOG DURING INITIALIZATION")
+#
+# T = pnl.TransferMechanism(
+#         prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
+# )
+# print(T.logged_items)
+# T.log.print_entries()
 
 #endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index fd28ac82c20..64e531d6c6e 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -661,7 +661,7 @@ def logged_items(self):
         logged_items = {}
         for l in self.logged_entries.keys():
             try:
-                logged_items[l] = (l, self.loggable_items[l])
+                logged_items[l] = self.loggable_items[l]
             except KeyError:
                 if l is self.owner.name:
                     try:

From f4957514ae9066e6fc944d498e7e1e2977be9c73 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 19:31:58 -0500
Subject: [PATCH 19/52] -

---
 Scripts/Scratch Pad.py    | 784 +++++++++++++++++++-------------------
 psyneulink/globals/log.py |   2 +-
 2 files changed, 393 insertions(+), 393 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 0d7468687c8..b1997d8ffc3 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2213,97 +2213,97 @@ def __init__(self, error_value):
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 print ("TEST Log")
 
-def test_log():
-
-    T_1 = pnl.TransferMechanism(name='T_1', size=2)
-    T_2 = pnl.TransferMechanism(name='T_2', size=2)
-    PS = pnl.Process(name='PS', pathway=[T_1, T_2])
-    PJ = T_2.path_afferents[0]
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'OFF',
-                                 'value': 'OFF'}
-
-    T_1.log_items(pnl.NOISE)
-    T_1.log_items(pnl.RESULTS)
-    PJ.log_items(pnl.MATRIX)
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'EXECUTION',
-                                 'intercept': 'OFF',
-                                 'noise': 'EXECUTION',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'EXECUTION',
-                                 'value': 'OFF'}
-
-    PS.execute()
-    PS.execute()
-    PS.execute()
-
-    assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-    assert PJ.logged_items == {'matrix': 'EXECUTION'}
-
-    # assert T_1.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    #
-    # assert T_2.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-
-    assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
-                    "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
-
-    assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
-           "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
-           "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
-
-    result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
-    print (result)
-    np.testing.assert_array_equal(result,
-                                  np.array([[[0], [1], [2]],
-                                            [[ 0.], [ 0.], [ 0.]],
-                                            [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
-
-test_log()
+# def test_log():
+#
+#     T_1 = pnl.TransferMechanism(name='T_1', size=2)
+#     T_2 = pnl.TransferMechanism(name='T_2', size=2)
+#     PS = pnl.Process(name='PS', pathway=[T_1, T_2])
+#     PJ = T_2.path_afferents[0]
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'OFF',
+#                                  'value': 'OFF'}
+#
+#     T_1.log_items(pnl.NOISE)
+#     T_1.log_items(pnl.RESULTS)
+#     PJ.log_items(pnl.MATRIX)
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'EXECUTION',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'EXECUTION',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'EXECUTION',
+#                                  'value': 'OFF'}
+#
+#     PS.execute()
+#     PS.execute()
+#     PS.execute()
+#
+#     assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
+#     assert PJ.logged_items == {'matrix': 'EXECUTION'}
+#
+#     # assert T_1.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     #
+#     # assert T_2.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#
+#     assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
+#                     "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
+#
+#     assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
+#            "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
+#            "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
+#
+#     result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+#     print (result)
+#     np.testing.assert_array_equal(result,
+#                                   np.array([[[0], [1], [2]],
+#                                             [[ 0.], [ 0.], [ 0.]],
+#                                             [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
+#
+# test_log()
 
 #endregion
 
@@ -2422,307 +2422,307 @@ def test_log():
 #endregion
 
 #region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print("TEST Multilayer-Learning Log")
-
-# def test_multilayer():
-#     Input_Layer = pnl.TransferMechanism(
-#         name='Input Layer',
-#         function=pnl.Logistic,
-#         default_variable=np.zeros((2,)),
-#     )
-#
-#     Hidden_Layer_1 = pnl.TransferMechanism(
-#         name='Hidden Layer_1',
-#         function=pnl.Logistic(),
-#         # default_variable=np.zeros((5,)),
-#         size=5
-#     )
-#
-#     Hidden_Layer_2 = pnl.TransferMechanism(
-#         name='Hidden Layer_2',
-#         function=pnl.Logistic(),
-#         default_variable=[0, 0, 0, 0],
-#     )
-#
-#     Output_Layer = pnl.TransferMechanism(
-#         name='Output Layer',
-#         function=pnl.Logistic,
-#         default_variable=[0, 0, 0],
-#     )
-#
-#     Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
-#     Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
-#     Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
-#
-#     # TEST PROCESS.LEARNING WITH:
-#     # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
-#     # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#     # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#
-#     # This projection will be used by the process below by referencing it in the process' pathway;
-#     #    note: sender and receiver args don't need to be specified
-#     Input_Weights = pnl.MappingProjection(
-#         name='Input Weights',
-#         matrix=Input_Weights_matrix,
-#     )
-#
-#     # This projection will be used by the process below by assigning its sender and receiver args
-#     #    to mechanismss in the pathway
-#     Middle_Weights = pnl.MappingProjection(
-#         name='Middle Weights',
-#         sender=Hidden_Layer_1,
-#         receiver=Hidden_Layer_2,
-#         matrix=Middle_Weights_matrix,
-#     )
-#
-#     # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
-#     Output_Weights = pnl.MappingProjection(
-#         name='Output Weights',
-#         sender=Hidden_Layer_2,
-#         receiver=Output_Layer,
-#         matrix=Output_Weights_matrix,
-#     )
-#
-#     p = pnl.Process(
-#         # default_variable=[0, 0],
-#         size=2,
-#         pathway=[
-#             Input_Layer,
-#             # The following reference to Input_Weights is needed to use it in the pathway
-#             #    since it's sender and receiver args are not specified in its declaration above
-#             Input_Weights,
-#             Hidden_Layer_1,
-#             # No projection specification is needed here since the sender arg for Middle_Weights
-#             #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
-#             # Middle_Weights,
-#             Hidden_Layer_2,
-#             # Output_Weights does not need to be listed for the same reason as Middle_Weights
-#             # If Middle_Weights and/or Output_Weights is not declared above, then the process
-#             #    will assign a default for missing projection
-#             # Output_Weights,
-#             Output_Layer
-#         ],
-#         clamp_input=pnl.SOFT_CLAMP,
-#         learning=pnl.LEARNING,
-#         learning_rate=1.0,
-#         target=[0, 0, 1],
-#         prefs={
-#             pnl.VERBOSE_PREF: False,
-#             pnl.REPORT_OUTPUT_PREF: False
-#         },
-#     )
-#
-#     Middle_Weights.log_items(('matrix', pnl.EXECUTION))
-#
-#     stim_list = {Input_Layer: [[-1, 30]]}
-#     target_list = {Output_Layer: [[0, 0, 1]]}
-#
-#     def show_target():
-#         i = s.input
-#         t = s.target_input_states[0].value
-#         print('\nOLD WEIGHTS: \n')
-#         print('- Input Weights: \n', Input_Weights.matrix)
-#         print('- Middle Weights: \n', Middle_Weights.matrix)
-#         print('- Output Weights: \n', Output_Weights.matrix)
-#         print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
-#         print('ACTIVITY FROM OLD WEIGHTS: \n')
-#         print('- Middle 1: \n', Hidden_Layer_1.value)
-#         print('- Middle 2: \n', Hidden_Layer_2.value)
-#         print('- Output:\n', Output_Layer.value)
-#
-#     s = pnl.System(
-#         processes=[p],
-#         targets=[0, 0, 1],
-#         learning_rate=1.0,
-#     )
-#
-#     # s.reportOutputPref = True
-#
-#     results = s.run(
-#         num_trials=10,
-#         inputs=stim_list,
-#         targets=target_list,
-#         # call_after_trial=show_target,
-#     )
-#
-#     objective_output_layer = s.mechanisms[4]
-#
-#     results_list = []
-#     for elem in s.results:
-#         for nested_elem in elem:
-#             nested_elem = nested_elem.tolist()
-#             try:
-#                 iter(nested_elem)
-#             except TypeError:
-#                 nested_elem = [nested_elem]
-#             results_list.extend(nested_elem)
-#
-#     expected_output = [
-#         (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
-#         (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
-#         (Input_Weights.matrix, np.array([
-#             [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
-#             [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
-#         ])),
-#         (Middle_Weights.matrix, np.array([
-#             [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
-#             [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
-#             [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
-#             [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
-#             [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
-#         ])),
-#         (Output_Weights.matrix, np.array([
-#             [-0.74447522, -0.71016859, 0.31575293],
-#             [-0.50885177, -0.47444784, 0.56676582],
-#             [-0.27333719, -0.23912033, 0.8178167 ],
-#             [-0.03767547, -0.00389039, 1.06888608],
-#         ])),
-#         (results, [
-#             [np.array([0.8344837 , 0.87072018, 0.89997433])],
-#             [np.array([0.77970193, 0.83263138, 0.90159627])],
-#             [np.array([0.70218502, 0.7773823 , 0.90307765])],
-#             [np.array([0.60279149, 0.69958079, 0.90453143])],
-#             [np.array([0.4967927 , 0.60030321, 0.90610082])],
-#             [np.array([0.4056202 , 0.49472391, 0.90786617])],
-#             [np.array([0.33763025, 0.40397637, 0.90977675])],
-#             [np.array([0.28892812, 0.33633532, 0.9117193 ])],
-#             [np.array([0.25348771, 0.28791896, 0.9136125 ])],
-#             [np.array([0.22686074, 0.25270212, 0.91542149])]
-#         ]),
-#     ]
-
-#     # Test nparray output of log for Middle_Weights
-#
-#     for i in range(len(expected_output)):
-#         val, expected = expected_output[i]
-#         # setting absolute tolerance to be in accordance with reference_output precision
-#         # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
-#         # which WILL FAIL unless you gather higher precision values to use as reference
-#         np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
-#
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#             [
-#                 [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
-#                 [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
-#                 [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
-#                 [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
-#                    [ 0.25,  0.3 ,  0.35,  0.4 ],
-#                    [ 0.45,  0.5 ,  0.55,  0.6 ],
-#                    [ 0.65,  0.7 ,  0.75,  0.8 ],
-#                    [ 0.85,  0.9 ,  0.95,  1.  ]],
-#                   [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
-#                    [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
-#                    [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
-#                    [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
-#                    [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
-#                   [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
-#                    [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
-#                    [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
-#                    [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
-#                    [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
-#                   [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
-#                    [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
-#                    [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
-#                    [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
-#                    [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
-#                   [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
-#                    [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
-#                    [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
-#                    [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
-#                    [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
-#                   [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
-#                    [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
-#                    [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
-#                    [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
-#                    [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
-#                   [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
-#                    [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
-#                    [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
-#                    [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
-#                    [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
-#                   [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
-#                    [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
-#                    [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
-#                    [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
-#                    [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
-#                   [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
-#                    [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
-#                    [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
-#                    [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
-#                    [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
-#                   [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
-#                    [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
-#                    [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
-#                    [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
-#                    [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
-#             ], dtype=object
-#     )
-#
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-#     Middle_Weights.log.print_entries()
-#
-#     # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
-#     Middle_Weights.log.clear_entries(entries=None, confirm=False)
-#     Middle_Weights.log_items(('matrix', pnl.LEARNING))
-#     s.run(
-#             num_trials=5,
-#             inputs=stim_list,
-#             targets=target_list,
-#     )
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#                 [
-#                     [[0], [0], [0], [0], [0]],
-#                     [[21], [23], [25], [27], [29]],
-#                     [[3], [3], [3], [3], [3]],
-#                     [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
-#                         [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
-#                         [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
-#                         [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
-#                         [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
-#                        [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
-#                         [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
-#                         [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
-#                         [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
-#                         [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
-#                       [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
-#                        [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
-#                        [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
-#                        [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
-#                        [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
-#                       [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
-#                        [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
-#                        [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
-#                        [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
-#                        [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
-#                       [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
-#                        [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
-#                        [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
-#                        [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
-#                        [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
-#         ], dtype=object
-#     )
-#
-#     assert log_val.shape == expected_log_val.shape
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-# test_multilayer()
+print("TEST Multilayer-Learning Log")
+
+def test_multilayer():
+    Input_Layer = pnl.TransferMechanism(
+        name='Input Layer',
+        function=pnl.Logistic,
+        default_variable=np.zeros((2,)),
+    )
+
+    Hidden_Layer_1 = pnl.TransferMechanism(
+        name='Hidden Layer_1',
+        function=pnl.Logistic(),
+        # default_variable=np.zeros((5,)),
+        size=5
+    )
+
+    Hidden_Layer_2 = pnl.TransferMechanism(
+        name='Hidden Layer_2',
+        function=pnl.Logistic(),
+        default_variable=[0, 0, 0, 0],
+    )
+
+    Output_Layer = pnl.TransferMechanism(
+        name='Output Layer',
+        function=pnl.Logistic,
+        default_variable=[0, 0, 0],
+    )
+
+    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+    # TEST PROCESS.LEARNING WITH:
+    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+    # This projection will be used by the process below by referencing it in the process' pathway;
+    #    note: sender and receiver args don't need to be specified
+    Input_Weights = pnl.MappingProjection(
+        name='Input Weights',
+        matrix=Input_Weights_matrix,
+    )
+
+    # This projection will be used by the process below by assigning its sender and receiver args
+    #    to mechanismss in the pathway
+    Middle_Weights = pnl.MappingProjection(
+        name='Middle Weights',
+        sender=Hidden_Layer_1,
+        receiver=Hidden_Layer_2,
+        matrix=Middle_Weights_matrix,
+    )
+
+    # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+    Output_Weights = pnl.MappingProjection(
+        name='Output Weights',
+        sender=Hidden_Layer_2,
+        receiver=Output_Layer,
+        matrix=Output_Weights_matrix,
+    )
+
+    p = pnl.Process(
+        # default_variable=[0, 0],
+        size=2,
+        pathway=[
+            Input_Layer,
+            # The following reference to Input_Weights is needed to use it in the pathway
+            #    since it's sender and receiver args are not specified in its declaration above
+            Input_Weights,
+            Hidden_Layer_1,
+            # No projection specification is needed here since the sender arg for Middle_Weights
+            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+            # Middle_Weights,
+            Hidden_Layer_2,
+            # Output_Weights does not need to be listed for the same reason as Middle_Weights
+            # If Middle_Weights and/or Output_Weights is not declared above, then the process
+            #    will assign a default for missing projection
+            # Output_Weights,
+            Output_Layer
+        ],
+        clamp_input=pnl.SOFT_CLAMP,
+        learning=pnl.LEARNING,
+        learning_rate=1.0,
+        target=[0, 0, 1],
+        prefs={
+            pnl.VERBOSE_PREF: False,
+            pnl.REPORT_OUTPUT_PREF: False
+        },
+    )
+
+    Middle_Weights.log_items(('matrix', pnl.EXECUTION))
+
+    stim_list = {Input_Layer: [[-1, 30]]}
+    target_list = {Output_Layer: [[0, 0, 1]]}
+
+    def show_target():
+        i = s.input
+        t = s.target_input_states[0].value
+        print('\nOLD WEIGHTS: \n')
+        print('- Input Weights: \n', Input_Weights.matrix)
+        print('- Middle Weights: \n', Middle_Weights.matrix)
+        print('- Output Weights: \n', Output_Weights.matrix)
+        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+        print('ACTIVITY FROM OLD WEIGHTS: \n')
+        print('- Middle 1: \n', Hidden_Layer_1.value)
+        print('- Middle 2: \n', Hidden_Layer_2.value)
+        print('- Output:\n', Output_Layer.value)
+
+    s = pnl.System(
+        processes=[p],
+        targets=[0, 0, 1],
+        learning_rate=1.0,
+    )
+
+    # s.reportOutputPref = True
+
+    results = s.run(
+        num_trials=10,
+        inputs=stim_list,
+        targets=target_list,
+        # call_after_trial=show_target,
+    )
+
+    objective_output_layer = s.mechanisms[4]
+
+    results_list = []
+    for elem in s.results:
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+
+    expected_output = [
+        (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
+        (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
+        (Input_Weights.matrix, np.array([
+            [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
+            [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
+        ])),
+        (Middle_Weights.matrix, np.array([
+            [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
+            [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
+            [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
+            [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
+            [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
+        ])),
+        (Output_Weights.matrix, np.array([
+            [-0.74447522, -0.71016859, 0.31575293],
+            [-0.50885177, -0.47444784, 0.56676582],
+            [-0.27333719, -0.23912033, 0.8178167 ],
+            [-0.03767547, -0.00389039, 1.06888608],
+        ])),
+        (results, [
+            [np.array([0.8344837 , 0.87072018, 0.89997433])],
+            [np.array([0.77970193, 0.83263138, 0.90159627])],
+            [np.array([0.70218502, 0.7773823 , 0.90307765])],
+            [np.array([0.60279149, 0.69958079, 0.90453143])],
+            [np.array([0.4967927 , 0.60030321, 0.90610082])],
+            [np.array([0.4056202 , 0.49472391, 0.90786617])],
+            [np.array([0.33763025, 0.40397637, 0.90977675])],
+            [np.array([0.28892812, 0.33633532, 0.9117193 ])],
+            [np.array([0.25348771, 0.28791896, 0.9136125 ])],
+            [np.array([0.22686074, 0.25270212, 0.91542149])]
+        ]),
+    ]
+
+    # Test nparray output of log for Middle_Weights
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+            [
+                [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
+                [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
+                [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
+                [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
+                   [ 0.25,  0.3 ,  0.35,  0.4 ],
+                   [ 0.45,  0.5 ,  0.55,  0.6 ],
+                   [ 0.65,  0.7 ,  0.75,  0.8 ],
+                   [ 0.85,  0.9 ,  0.95,  1.  ]],
+                  [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
+                   [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
+                   [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
+                   [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
+                   [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
+                  [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
+                   [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
+                   [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
+                   [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
+                   [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
+                  [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
+                   [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
+                   [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
+                   [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
+                   [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
+                  [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
+                   [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
+                   [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
+                   [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
+                   [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
+                  [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
+                   [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
+                   [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
+                   [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
+                   [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
+                  [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
+                   [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
+                   [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
+                   [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
+                   [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
+                  [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
+                   [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
+                   [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
+                   [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
+                   [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
+                  [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
+                   [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
+                   [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
+                   [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
+                   [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
+                  [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
+                   [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
+                   [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
+                   [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
+                   [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
+            ], dtype=object
+    )
+
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+    Middle_Weights.log.print_entries()
+
+    # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
+    Middle_Weights.log.clear_entries(entries=None, confirm=False)
+    Middle_Weights.log_items(('matrix', pnl.LEARNING))
+    s.run(
+            num_trials=5,
+            inputs=stim_list,
+            targets=target_list,
+    )
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+                [
+                    [[0], [0], [0], [0], [0]],
+                    [[21], [23], [25], [27], [29]],
+                    [[3], [3], [3], [3], [3]],
+                    [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
+                        [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
+                        [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
+                        [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
+                        [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
+                       [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
+                        [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
+                        [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
+                        [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
+                        [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
+                      [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
+                       [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
+                       [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
+                       [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
+                       [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
+                      [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
+                       [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
+                       [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
+                       [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
+                       [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
+                      [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
+                       [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
+                       [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
+                       [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
+                       [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
+        ], dtype=object
+    )
+
+    assert log_val.shape == expected_log_val.shape
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+test_multilayer()
 
 #endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 64e531d6c6e..14e3fb061db 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -118,7 +118,7 @@
 
     >>> import psyneulink as pnl
     >>> T = pnl.TransferMechanism(
-    ...          prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION,pnl.PreferenceLevel.INSTANCE)})
+    ...          prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)})
 
 
 Execution

From bb4fecd4b9972ce64775b8699d99e2bdfe1c2c16 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 19:34:27 -0500
Subject: [PATCH 20/52] -

---
 Scripts/Scratch Pad.py    | 618 +++++++++++++++++++-------------------
 psyneulink/globals/log.py |  20 +-
 2 files changed, 324 insertions(+), 314 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index b18701485d3..56636854111 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2212,7 +2212,7 @@ def __init__(self, error_value):
 
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
 # print ("TEST Log")
-
+#
 # def test_log():
 #
 #     T_1 = pnl.TransferMechanism(name='T_1', size=2)
@@ -2308,13 +2308,13 @@ def __init__(self, error_value):
 #endregion
 
 #region TEST LOG DURING INITIALIZATION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print("TEST LOG DURING INITIALIZATION")
-
-T = pnl.TransferMechanism(
-        prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
-)
-print(T.logged_items)
-T.log.print_entries()
+# print("TEST LOG DURING INITIALIZATION")
+#
+# T = pnl.TransferMechanism(
+#         prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
+# )
+# print(T.logged_items)
+# T.log.print_entries()
 
 #endregion
 
@@ -2422,307 +2422,307 @@ def __init__(self, error_value):
 #endregion
 
 #region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print("TEST Multilayer-Learning Log")
-
-# def test_multilayer():
-#     Input_Layer = pnl.TransferMechanism(
-#         name='Input Layer',
-#         function=pnl.Logistic,
-#         default_variable=np.zeros((2,)),
-#     )
-#
-#     Hidden_Layer_1 = pnl.TransferMechanism(
-#         name='Hidden Layer_1',
-#         function=pnl.Logistic(),
-#         # default_variable=np.zeros((5,)),
-#         size=5
-#     )
-#
-#     Hidden_Layer_2 = pnl.TransferMechanism(
-#         name='Hidden Layer_2',
-#         function=pnl.Logistic(),
-#         default_variable=[0, 0, 0, 0],
-#     )
-#
-#     Output_Layer = pnl.TransferMechanism(
-#         name='Output Layer',
-#         function=pnl.Logistic,
-#         default_variable=[0, 0, 0],
-#     )
-#
-#     Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
-#     Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
-#     Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
-#
-#     # TEST PROCESS.LEARNING WITH:
-#     # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
-#     # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#     # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#
-#     # This projection will be used by the process below by referencing it in the process' pathway;
-#     #    note: sender and receiver args don't need to be specified
-#     Input_Weights = pnl.MappingProjection(
-#         name='Input Weights',
-#         matrix=Input_Weights_matrix,
-#     )
-#
-#     # This projection will be used by the process below by assigning its sender and receiver args
-#     #    to mechanismss in the pathway
-#     Middle_Weights = pnl.MappingProjection(
-#         name='Middle Weights',
-#         sender=Hidden_Layer_1,
-#         receiver=Hidden_Layer_2,
-#         matrix=Middle_Weights_matrix,
-#     )
-#
-#     # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
-#     Output_Weights = pnl.MappingProjection(
-#         name='Output Weights',
-#         sender=Hidden_Layer_2,
-#         receiver=Output_Layer,
-#         matrix=Output_Weights_matrix,
-#     )
-#
-#     p = pnl.Process(
-#         # default_variable=[0, 0],
-#         size=2,
-#         pathway=[
-#             Input_Layer,
-#             # The following reference to Input_Weights is needed to use it in the pathway
-#             #    since it's sender and receiver args are not specified in its declaration above
-#             Input_Weights,
-#             Hidden_Layer_1,
-#             # No projection specification is needed here since the sender arg for Middle_Weights
-#             #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
-#             # Middle_Weights,
-#             Hidden_Layer_2,
-#             # Output_Weights does not need to be listed for the same reason as Middle_Weights
-#             # If Middle_Weights and/or Output_Weights is not declared above, then the process
-#             #    will assign a default for missing projection
-#             # Output_Weights,
-#             Output_Layer
-#         ],
-#         clamp_input=pnl.SOFT_CLAMP,
-#         learning=pnl.LEARNING,
-#         learning_rate=1.0,
-#         target=[0, 0, 1],
-#         prefs={
-#             pnl.VERBOSE_PREF: False,
-#             pnl.REPORT_OUTPUT_PREF: False
-#         },
-#     )
-#
-#     Middle_Weights.log_items(('matrix', pnl.EXECUTION))
-#
-#     stim_list = {Input_Layer: [[-1, 30]]}
-#     target_list = {Output_Layer: [[0, 0, 1]]}
-#
-#     def show_target():
-#         i = s.input
-#         t = s.target_input_states[0].value
-#         print('\nOLD WEIGHTS: \n')
-#         print('- Input Weights: \n', Input_Weights.matrix)
-#         print('- Middle Weights: \n', Middle_Weights.matrix)
-#         print('- Output Weights: \n', Output_Weights.matrix)
-#         print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
-#         print('ACTIVITY FROM OLD WEIGHTS: \n')
-#         print('- Middle 1: \n', Hidden_Layer_1.value)
-#         print('- Middle 2: \n', Hidden_Layer_2.value)
-#         print('- Output:\n', Output_Layer.value)
-#
-#     s = pnl.System(
-#         processes=[p],
-#         targets=[0, 0, 1],
-#         learning_rate=1.0,
-#     )
-#
-#     # s.reportOutputPref = True
-#
-#     results = s.run(
-#         num_trials=10,
-#         inputs=stim_list,
-#         targets=target_list,
-#         # call_after_trial=show_target,
-#     )
-#
-#     objective_output_layer = s.mechanisms[4]
-#
-#     results_list = []
-#     for elem in s.results:
-#         for nested_elem in elem:
-#             nested_elem = nested_elem.tolist()
-#             try:
-#                 iter(nested_elem)
-#             except TypeError:
-#                 nested_elem = [nested_elem]
-#             results_list.extend(nested_elem)
-#
-#     expected_output = [
-#         (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
-#         (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
-#         (Input_Weights.matrix, np.array([
-#             [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
-#             [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
-#         ])),
-#         (Middle_Weights.matrix, np.array([
-#             [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
-#             [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
-#             [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
-#             [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
-#             [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
-#         ])),
-#         (Output_Weights.matrix, np.array([
-#             [-0.74447522, -0.71016859, 0.31575293],
-#             [-0.50885177, -0.47444784, 0.56676582],
-#             [-0.27333719, -0.23912033, 0.8178167 ],
-#             [-0.03767547, -0.00389039, 1.06888608],
-#         ])),
-#         (results, [
-#             [np.array([0.8344837 , 0.87072018, 0.89997433])],
-#             [np.array([0.77970193, 0.83263138, 0.90159627])],
-#             [np.array([0.70218502, 0.7773823 , 0.90307765])],
-#             [np.array([0.60279149, 0.69958079, 0.90453143])],
-#             [np.array([0.4967927 , 0.60030321, 0.90610082])],
-#             [np.array([0.4056202 , 0.49472391, 0.90786617])],
-#             [np.array([0.33763025, 0.40397637, 0.90977675])],
-#             [np.array([0.28892812, 0.33633532, 0.9117193 ])],
-#             [np.array([0.25348771, 0.28791896, 0.9136125 ])],
-#             [np.array([0.22686074, 0.25270212, 0.91542149])]
-#         ]),
-#     ]
-
-#     # Test nparray output of log for Middle_Weights
-#
-#     for i in range(len(expected_output)):
-#         val, expected = expected_output[i]
-#         # setting absolute tolerance to be in accordance with reference_output precision
-#         # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
-#         # which WILL FAIL unless you gather higher precision values to use as reference
-#         np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
-#
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#             [
-#                 [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
-#                 [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
-#                 [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
-#                 [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
-#                    [ 0.25,  0.3 ,  0.35,  0.4 ],
-#                    [ 0.45,  0.5 ,  0.55,  0.6 ],
-#                    [ 0.65,  0.7 ,  0.75,  0.8 ],
-#                    [ 0.85,  0.9 ,  0.95,  1.  ]],
-#                   [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
-#                    [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
-#                    [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
-#                    [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
-#                    [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
-#                   [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
-#                    [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
-#                    [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
-#                    [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
-#                    [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
-#                   [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
-#                    [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
-#                    [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
-#                    [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
-#                    [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
-#                   [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
-#                    [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
-#                    [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
-#                    [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
-#                    [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
-#                   [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
-#                    [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
-#                    [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
-#                    [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
-#                    [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
-#                   [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
-#                    [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
-#                    [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
-#                    [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
-#                    [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
-#                   [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
-#                    [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
-#                    [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
-#                    [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
-#                    [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
-#                   [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
-#                    [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
-#                    [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
-#                    [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
-#                    [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
-#                   [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
-#                    [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
-#                    [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
-#                    [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
-#                    [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
-#             ], dtype=object
-#     )
-#
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-#     Middle_Weights.log.print_entries()
-#
-#     # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
-#     Middle_Weights.log.clear_entries(entries=None, confirm=False)
-#     Middle_Weights.log_items(('matrix', pnl.LEARNING))
-#     s.run(
-#             num_trials=5,
-#             inputs=stim_list,
-#             targets=target_list,
-#     )
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#                 [
-#                     [[0], [0], [0], [0], [0]],
-#                     [[21], [23], [25], [27], [29]],
-#                     [[3], [3], [3], [3], [3]],
-#                     [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
-#                         [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
-#                         [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
-#                         [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
-#                         [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
-#                        [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
-#                         [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
-#                         [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
-#                         [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
-#                         [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
-#                       [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
-#                        [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
-#                        [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
-#                        [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
-#                        [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
-#                       [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
-#                        [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
-#                        [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
-#                        [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
-#                        [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
-#                       [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
-#                        [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
-#                        [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
-#                        [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
-#                        [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
-#         ], dtype=object
-#     )
-#
-#     assert log_val.shape == expected_log_val.shape
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-# test_multilayer()
+print("TEST Multilayer-Learning Log")
+
+def test_multilayer():
+    Input_Layer = pnl.TransferMechanism(
+        name='Input Layer',
+        function=pnl.Logistic,
+        default_variable=np.zeros((2,)),
+    )
+
+    Hidden_Layer_1 = pnl.TransferMechanism(
+        name='Hidden Layer_1',
+        function=pnl.Logistic(),
+        # default_variable=np.zeros((5,)),
+        size=5
+    )
+
+    Hidden_Layer_2 = pnl.TransferMechanism(
+        name='Hidden Layer_2',
+        function=pnl.Logistic(),
+        default_variable=[0, 0, 0, 0],
+    )
+
+    Output_Layer = pnl.TransferMechanism(
+        name='Output Layer',
+        function=pnl.Logistic,
+        default_variable=[0, 0, 0],
+    )
+
+    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+    # TEST PROCESS.LEARNING WITH:
+    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+    # This projection will be used by the process below by referencing it in the process' pathway;
+    #    note: sender and receiver args don't need to be specified
+    Input_Weights = pnl.MappingProjection(
+        name='Input Weights',
+        matrix=Input_Weights_matrix,
+    )
+
+    # This projection will be used by the process below by assigning its sender and receiver args
+    #    to mechanismss in the pathway
+    Middle_Weights = pnl.MappingProjection(
+        name='Middle Weights',
+        sender=Hidden_Layer_1,
+        receiver=Hidden_Layer_2,
+        matrix=Middle_Weights_matrix,
+    )
+
+    # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+    Output_Weights = pnl.MappingProjection(
+        name='Output Weights',
+        sender=Hidden_Layer_2,
+        receiver=Output_Layer,
+        matrix=Output_Weights_matrix,
+    )
+
+    p = pnl.Process(
+        # default_variable=[0, 0],
+        size=2,
+        pathway=[
+            Input_Layer,
+            # The following reference to Input_Weights is needed to use it in the pathway
+            #    since it's sender and receiver args are not specified in its declaration above
+            Input_Weights,
+            Hidden_Layer_1,
+            # No projection specification is needed here since the sender arg for Middle_Weights
+            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+            # Middle_Weights,
+            Hidden_Layer_2,
+            # Output_Weights does not need to be listed for the same reason as Middle_Weights
+            # If Middle_Weights and/or Output_Weights is not declared above, then the process
+            #    will assign a default for missing projection
+            # Output_Weights,
+            Output_Layer
+        ],
+        clamp_input=pnl.SOFT_CLAMP,
+        learning=pnl.LEARNING,
+        learning_rate=1.0,
+        target=[0, 0, 1],
+        prefs={
+            pnl.VERBOSE_PREF: False,
+            pnl.REPORT_OUTPUT_PREF: False
+        },
+    )
+
+    Middle_Weights.log_items(('matrix', pnl.EXECUTION))
+
+    stim_list = {Input_Layer: [[-1, 30]]}
+    target_list = {Output_Layer: [[0, 0, 1]]}
+
+    def show_target():
+        i = s.input
+        t = s.target_input_states[0].value
+        print('\nOLD WEIGHTS: \n')
+        print('- Input Weights: \n', Input_Weights.matrix)
+        print('- Middle Weights: \n', Middle_Weights.matrix)
+        print('- Output Weights: \n', Output_Weights.matrix)
+        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+        print('ACTIVITY FROM OLD WEIGHTS: \n')
+        print('- Middle 1: \n', Hidden_Layer_1.value)
+        print('- Middle 2: \n', Hidden_Layer_2.value)
+        print('- Output:\n', Output_Layer.value)
+
+    s = pnl.System(
+        processes=[p],
+        targets=[0, 0, 1],
+        learning_rate=1.0,
+    )
+
+    # s.reportOutputPref = True
+
+    results = s.run(
+        num_trials=10,
+        inputs=stim_list,
+        targets=target_list,
+        # call_after_trial=show_target,
+    )
+
+    objective_output_layer = s.mechanisms[4]
+
+    results_list = []
+    for elem in s.results:
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+
+    expected_output = [
+        (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
+        (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
+        (Input_Weights.matrix, np.array([
+            [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
+            [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
+        ])),
+        (Middle_Weights.matrix, np.array([
+            [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
+            [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
+            [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
+            [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
+            [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
+        ])),
+        (Output_Weights.matrix, np.array([
+            [-0.74447522, -0.71016859, 0.31575293],
+            [-0.50885177, -0.47444784, 0.56676582],
+            [-0.27333719, -0.23912033, 0.8178167 ],
+            [-0.03767547, -0.00389039, 1.06888608],
+        ])),
+        (results, [
+            [np.array([0.8344837 , 0.87072018, 0.89997433])],
+            [np.array([0.77970193, 0.83263138, 0.90159627])],
+            [np.array([0.70218502, 0.7773823 , 0.90307765])],
+            [np.array([0.60279149, 0.69958079, 0.90453143])],
+            [np.array([0.4967927 , 0.60030321, 0.90610082])],
+            [np.array([0.4056202 , 0.49472391, 0.90786617])],
+            [np.array([0.33763025, 0.40397637, 0.90977675])],
+            [np.array([0.28892812, 0.33633532, 0.9117193 ])],
+            [np.array([0.25348771, 0.28791896, 0.9136125 ])],
+            [np.array([0.22686074, 0.25270212, 0.91542149])]
+        ]),
+    ]
+
+    # Test nparray output of log for Middle_Weights
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+            [
+                [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
+                [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
+                [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
+                [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
+                   [ 0.25,  0.3 ,  0.35,  0.4 ],
+                   [ 0.45,  0.5 ,  0.55,  0.6 ],
+                   [ 0.65,  0.7 ,  0.75,  0.8 ],
+                   [ 0.85,  0.9 ,  0.95,  1.  ]],
+                  [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
+                   [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
+                   [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
+                   [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
+                   [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
+                  [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
+                   [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
+                   [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
+                   [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
+                   [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
+                  [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
+                   [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
+                   [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
+                   [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
+                   [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
+                  [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
+                   [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
+                   [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
+                   [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
+                   [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
+                  [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
+                   [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
+                   [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
+                   [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
+                   [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
+                  [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
+                   [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
+                   [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
+                   [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
+                   [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
+                  [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
+                   [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
+                   [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
+                   [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
+                   [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
+                  [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
+                   [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
+                   [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
+                   [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
+                   [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
+                  [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
+                   [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
+                   [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
+                   [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
+                   [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
+            ], dtype=object
+    )
+
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+    Middle_Weights.log.print_entries()
+
+    # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
+    Middle_Weights.log.clear_entries(entries=None, confirm=False)
+    Middle_Weights.log_items(('matrix', pnl.LEARNING))
+    s.run(
+            num_trials=5,
+            inputs=stim_list,
+            targets=target_list,
+    )
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+                [
+                    [[0], [0], [0], [0], [0]],
+                    [[21], [23], [25], [27], [29]],
+                    [[3], [3], [3], [3], [3]],
+                    [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
+                        [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
+                        [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
+                        [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
+                        [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
+                       [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
+                        [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
+                        [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
+                        [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
+                        [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
+                      [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
+                       [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
+                       [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
+                       [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
+                       [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
+                      [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
+                       [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
+                       [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
+                       [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
+                       [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
+                      [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
+                       [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
+                       [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
+                       [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
+                       [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
+        ], dtype=object
+    )
+
+    assert log_val.shape == expected_log_val.shape
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+test_multilayer()
 
 #endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index adccdb2c59b..64e531d6c6e 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -110,7 +110,15 @@
 using bitwise operators (e.g., LogLevel.EXECUTION | LogLevel.LEARNING).
 
 .. note::
-   Currently, the only `LogLevels <LogLevel>` supported are `OFF`, `EXECUTION` and `LEARNING`.
+   Currently, the only `LogLevels <LogLevel>` supported are: `OFF`, `INITIALIZATION`, `EXECUTION` and `LEARNING`.
+
+.. note::
+   Using the `INITIALIZATION` LogLevel to log the `value <Component.value>` of a Component during its initialization
+   requires that it be assigned in the **prefs** argument of the Component's constructor.  For example::
+
+    >>> import psyneulink as pnl
+    >>> T = pnl.TransferMechanism(
+    ...          prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION,pnl.PreferenceLevel.INSTANCE)})
 
 
 Execution
@@ -127,7 +135,6 @@
 `MappingProjection` from the first to the second::
 
     # Create a Process with two TransferMechanisms, and get a reference for the Projection created between them:
-    >>> import psyneulink as pnl
     >>> my_mech_A = pnl.TransferMechanism(name='mech_A', size=2)
     >>> my_mech_B = pnl.TransferMechanism(name='mech_B', size=3)
     >>> my_process = pnl.Process(pathway=[my_mech_A, my_mech_B])
@@ -654,7 +661,7 @@ def logged_items(self):
         logged_items = {}
         for l in self.logged_entries.keys():
             try:
-                logged_items[l] = (l, self.loggable_items[l])
+                logged_items[l] = self.loggable_items[l]
             except KeyError:
                 if l is self.owner.name:
                     try:
@@ -814,8 +821,11 @@ def _get_time(self, context, context_flags):
                            format(self.owner.__class__.__name__,
                                   Mechanism.__name__, State.__name__, Projection.__name__))
 
-        systems = list(ref_mech.systems.keys())
-        system = next((s for s in systems if s.name in context), None)
+        try:
+            systems = list(ref_mech.systems.keys())
+            system = next((s for s in systems if s.name in context), None)
+        except AttributeError:
+            system = None
 
         if system:
             # FIX: Add INIT and VALIDATE?

From aa679dd44d7f20f97371431e1bcedd8e660cfd06 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 19:36:29 -0500
Subject: [PATCH 21/52] -

---
 Scripts/Scratch Pad.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 56636854111..3609e7480b8 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2721,7 +2721,6 @@ def show_target():
                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
                                            atol=1e-08,
                                            err_msg='Failed on test of logged values')
-
 test_multilayer()
 
 #endregion

From 73a8013f9064de770437a362445ad2acb3d217ed Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 19:41:23 -0500
Subject: [PATCH 22/52] -

---
 Scripts/Scratch Pad.py | 788 ++++++++++++++++++++---------------------
 1 file changed, 394 insertions(+), 394 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 0d7468687c8..56636854111 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2211,99 +2211,99 @@ def __init__(self, error_value):
 # #endregion
 
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print ("TEST Log")
-
-def test_log():
-
-    T_1 = pnl.TransferMechanism(name='T_1', size=2)
-    T_2 = pnl.TransferMechanism(name='T_2', size=2)
-    PS = pnl.Process(name='PS', pathway=[T_1, T_2])
-    PJ = T_2.path_afferents[0]
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'OFF',
-                                 'value': 'OFF'}
-
-    T_1.log_items(pnl.NOISE)
-    T_1.log_items(pnl.RESULTS)
-    PJ.log_items(pnl.MATRIX)
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'EXECUTION',
-                                 'intercept': 'OFF',
-                                 'noise': 'EXECUTION',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'EXECUTION',
-                                 'value': 'OFF'}
-
-    PS.execute()
-    PS.execute()
-    PS.execute()
-
-    assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-    assert PJ.logged_items == {'matrix': 'EXECUTION'}
-
-    # assert T_1.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    #
-    # assert T_2.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-
-    assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
-                    "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
-
-    assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
-           "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
-           "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
-
-    result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
-    print (result)
-    np.testing.assert_array_equal(result,
-                                  np.array([[[0], [1], [2]],
-                                            [[ 0.], [ 0.], [ 0.]],
-                                            [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
-
-test_log()
+# print ("TEST Log")
+#
+# def test_log():
+#
+#     T_1 = pnl.TransferMechanism(name='T_1', size=2)
+#     T_2 = pnl.TransferMechanism(name='T_2', size=2)
+#     PS = pnl.Process(name='PS', pathway=[T_1, T_2])
+#     PJ = T_2.path_afferents[0]
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'OFF',
+#                                  'value': 'OFF'}
+#
+#     T_1.log_items(pnl.NOISE)
+#     T_1.log_items(pnl.RESULTS)
+#     PJ.log_items(pnl.MATRIX)
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'EXECUTION',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'EXECUTION',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'EXECUTION',
+#                                  'value': 'OFF'}
+#
+#     PS.execute()
+#     PS.execute()
+#     PS.execute()
+#
+#     assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
+#     assert PJ.logged_items == {'matrix': 'EXECUTION'}
+#
+#     # assert T_1.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     #
+#     # assert T_2.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#
+#     assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
+#                     "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
+#
+#     assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
+#            "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
+#            "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
+#
+#     result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+#     print (result)
+#     np.testing.assert_array_equal(result,
+#                                   np.array([[[0], [1], [2]],
+#                                             [[ 0.], [ 0.], [ 0.]],
+#                                             [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
+#
+# test_log()
 
 #endregion
 
@@ -2422,307 +2422,307 @@ def test_log():
 #endregion
 
 #region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print("TEST Multilayer-Learning Log")
-
-# def test_multilayer():
-#     Input_Layer = pnl.TransferMechanism(
-#         name='Input Layer',
-#         function=pnl.Logistic,
-#         default_variable=np.zeros((2,)),
-#     )
-#
-#     Hidden_Layer_1 = pnl.TransferMechanism(
-#         name='Hidden Layer_1',
-#         function=pnl.Logistic(),
-#         # default_variable=np.zeros((5,)),
-#         size=5
-#     )
-#
-#     Hidden_Layer_2 = pnl.TransferMechanism(
-#         name='Hidden Layer_2',
-#         function=pnl.Logistic(),
-#         default_variable=[0, 0, 0, 0],
-#     )
-#
-#     Output_Layer = pnl.TransferMechanism(
-#         name='Output Layer',
-#         function=pnl.Logistic,
-#         default_variable=[0, 0, 0],
-#     )
-#
-#     Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
-#     Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
-#     Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
-#
-#     # TEST PROCESS.LEARNING WITH:
-#     # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
-#     # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#     # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#
-#     # This projection will be used by the process below by referencing it in the process' pathway;
-#     #    note: sender and receiver args don't need to be specified
-#     Input_Weights = pnl.MappingProjection(
-#         name='Input Weights',
-#         matrix=Input_Weights_matrix,
-#     )
-#
-#     # This projection will be used by the process below by assigning its sender and receiver args
-#     #    to mechanismss in the pathway
-#     Middle_Weights = pnl.MappingProjection(
-#         name='Middle Weights',
-#         sender=Hidden_Layer_1,
-#         receiver=Hidden_Layer_2,
-#         matrix=Middle_Weights_matrix,
-#     )
-#
-#     # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
-#     Output_Weights = pnl.MappingProjection(
-#         name='Output Weights',
-#         sender=Hidden_Layer_2,
-#         receiver=Output_Layer,
-#         matrix=Output_Weights_matrix,
-#     )
-#
-#     p = pnl.Process(
-#         # default_variable=[0, 0],
-#         size=2,
-#         pathway=[
-#             Input_Layer,
-#             # The following reference to Input_Weights is needed to use it in the pathway
-#             #    since it's sender and receiver args are not specified in its declaration above
-#             Input_Weights,
-#             Hidden_Layer_1,
-#             # No projection specification is needed here since the sender arg for Middle_Weights
-#             #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
-#             # Middle_Weights,
-#             Hidden_Layer_2,
-#             # Output_Weights does not need to be listed for the same reason as Middle_Weights
-#             # If Middle_Weights and/or Output_Weights is not declared above, then the process
-#             #    will assign a default for missing projection
-#             # Output_Weights,
-#             Output_Layer
-#         ],
-#         clamp_input=pnl.SOFT_CLAMP,
-#         learning=pnl.LEARNING,
-#         learning_rate=1.0,
-#         target=[0, 0, 1],
-#         prefs={
-#             pnl.VERBOSE_PREF: False,
-#             pnl.REPORT_OUTPUT_PREF: False
-#         },
-#     )
-#
-#     Middle_Weights.log_items(('matrix', pnl.EXECUTION))
-#
-#     stim_list = {Input_Layer: [[-1, 30]]}
-#     target_list = {Output_Layer: [[0, 0, 1]]}
-#
-#     def show_target():
-#         i = s.input
-#         t = s.target_input_states[0].value
-#         print('\nOLD WEIGHTS: \n')
-#         print('- Input Weights: \n', Input_Weights.matrix)
-#         print('- Middle Weights: \n', Middle_Weights.matrix)
-#         print('- Output Weights: \n', Output_Weights.matrix)
-#         print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
-#         print('ACTIVITY FROM OLD WEIGHTS: \n')
-#         print('- Middle 1: \n', Hidden_Layer_1.value)
-#         print('- Middle 2: \n', Hidden_Layer_2.value)
-#         print('- Output:\n', Output_Layer.value)
-#
-#     s = pnl.System(
-#         processes=[p],
-#         targets=[0, 0, 1],
-#         learning_rate=1.0,
-#     )
-#
-#     # s.reportOutputPref = True
-#
-#     results = s.run(
-#         num_trials=10,
-#         inputs=stim_list,
-#         targets=target_list,
-#         # call_after_trial=show_target,
-#     )
-#
-#     objective_output_layer = s.mechanisms[4]
-#
-#     results_list = []
-#     for elem in s.results:
-#         for nested_elem in elem:
-#             nested_elem = nested_elem.tolist()
-#             try:
-#                 iter(nested_elem)
-#             except TypeError:
-#                 nested_elem = [nested_elem]
-#             results_list.extend(nested_elem)
-#
-#     expected_output = [
-#         (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
-#         (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
-#         (Input_Weights.matrix, np.array([
-#             [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
-#             [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
-#         ])),
-#         (Middle_Weights.matrix, np.array([
-#             [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
-#             [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
-#             [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
-#             [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
-#             [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
-#         ])),
-#         (Output_Weights.matrix, np.array([
-#             [-0.74447522, -0.71016859, 0.31575293],
-#             [-0.50885177, -0.47444784, 0.56676582],
-#             [-0.27333719, -0.23912033, 0.8178167 ],
-#             [-0.03767547, -0.00389039, 1.06888608],
-#         ])),
-#         (results, [
-#             [np.array([0.8344837 , 0.87072018, 0.89997433])],
-#             [np.array([0.77970193, 0.83263138, 0.90159627])],
-#             [np.array([0.70218502, 0.7773823 , 0.90307765])],
-#             [np.array([0.60279149, 0.69958079, 0.90453143])],
-#             [np.array([0.4967927 , 0.60030321, 0.90610082])],
-#             [np.array([0.4056202 , 0.49472391, 0.90786617])],
-#             [np.array([0.33763025, 0.40397637, 0.90977675])],
-#             [np.array([0.28892812, 0.33633532, 0.9117193 ])],
-#             [np.array([0.25348771, 0.28791896, 0.9136125 ])],
-#             [np.array([0.22686074, 0.25270212, 0.91542149])]
-#         ]),
-#     ]
-
-#     # Test nparray output of log for Middle_Weights
-#
-#     for i in range(len(expected_output)):
-#         val, expected = expected_output[i]
-#         # setting absolute tolerance to be in accordance with reference_output precision
-#         # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
-#         # which WILL FAIL unless you gather higher precision values to use as reference
-#         np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
-#
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#             [
-#                 [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
-#                 [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
-#                 [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
-#                 [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
-#                    [ 0.25,  0.3 ,  0.35,  0.4 ],
-#                    [ 0.45,  0.5 ,  0.55,  0.6 ],
-#                    [ 0.65,  0.7 ,  0.75,  0.8 ],
-#                    [ 0.85,  0.9 ,  0.95,  1.  ]],
-#                   [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
-#                    [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
-#                    [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
-#                    [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
-#                    [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
-#                   [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
-#                    [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
-#                    [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
-#                    [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
-#                    [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
-#                   [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
-#                    [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
-#                    [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
-#                    [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
-#                    [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
-#                   [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
-#                    [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
-#                    [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
-#                    [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
-#                    [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
-#                   [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
-#                    [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
-#                    [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
-#                    [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
-#                    [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
-#                   [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
-#                    [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
-#                    [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
-#                    [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
-#                    [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
-#                   [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
-#                    [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
-#                    [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
-#                    [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
-#                    [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
-#                   [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
-#                    [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
-#                    [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
-#                    [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
-#                    [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
-#                   [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
-#                    [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
-#                    [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
-#                    [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
-#                    [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
-#             ], dtype=object
-#     )
-#
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-#     Middle_Weights.log.print_entries()
-#
-#     # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
-#     Middle_Weights.log.clear_entries(entries=None, confirm=False)
-#     Middle_Weights.log_items(('matrix', pnl.LEARNING))
-#     s.run(
-#             num_trials=5,
-#             inputs=stim_list,
-#             targets=target_list,
-#     )
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#                 [
-#                     [[0], [0], [0], [0], [0]],
-#                     [[21], [23], [25], [27], [29]],
-#                     [[3], [3], [3], [3], [3]],
-#                     [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
-#                         [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
-#                         [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
-#                         [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
-#                         [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
-#                        [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
-#                         [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
-#                         [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
-#                         [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
-#                         [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
-#                       [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
-#                        [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
-#                        [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
-#                        [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
-#                        [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
-#                       [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
-#                        [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
-#                        [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
-#                        [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
-#                        [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
-#                       [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
-#                        [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
-#                        [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
-#                        [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
-#                        [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
-#         ], dtype=object
-#     )
-#
-#     assert log_val.shape == expected_log_val.shape
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-# test_multilayer()
+print("TEST Multilayer-Learning Log")
+
+def test_multilayer():
+    Input_Layer = pnl.TransferMechanism(
+        name='Input Layer',
+        function=pnl.Logistic,
+        default_variable=np.zeros((2,)),
+    )
+
+    Hidden_Layer_1 = pnl.TransferMechanism(
+        name='Hidden Layer_1',
+        function=pnl.Logistic(),
+        # default_variable=np.zeros((5,)),
+        size=5
+    )
+
+    Hidden_Layer_2 = pnl.TransferMechanism(
+        name='Hidden Layer_2',
+        function=pnl.Logistic(),
+        default_variable=[0, 0, 0, 0],
+    )
+
+    Output_Layer = pnl.TransferMechanism(
+        name='Output Layer',
+        function=pnl.Logistic,
+        default_variable=[0, 0, 0],
+    )
+
+    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+    # TEST PROCESS.LEARNING WITH:
+    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+    # This projection will be used by the process below by referencing it in the process' pathway;
+    #    note: sender and receiver args don't need to be specified
+    Input_Weights = pnl.MappingProjection(
+        name='Input Weights',
+        matrix=Input_Weights_matrix,
+    )
+
+    # This projection will be used by the process below by assigning its sender and receiver args
+    #    to mechanismss in the pathway
+    Middle_Weights = pnl.MappingProjection(
+        name='Middle Weights',
+        sender=Hidden_Layer_1,
+        receiver=Hidden_Layer_2,
+        matrix=Middle_Weights_matrix,
+    )
+
+    # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+    Output_Weights = pnl.MappingProjection(
+        name='Output Weights',
+        sender=Hidden_Layer_2,
+        receiver=Output_Layer,
+        matrix=Output_Weights_matrix,
+    )
+
+    p = pnl.Process(
+        # default_variable=[0, 0],
+        size=2,
+        pathway=[
+            Input_Layer,
+            # The following reference to Input_Weights is needed to use it in the pathway
+            #    since it's sender and receiver args are not specified in its declaration above
+            Input_Weights,
+            Hidden_Layer_1,
+            # No projection specification is needed here since the sender arg for Middle_Weights
+            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+            # Middle_Weights,
+            Hidden_Layer_2,
+            # Output_Weights does not need to be listed for the same reason as Middle_Weights
+            # If Middle_Weights and/or Output_Weights is not declared above, then the process
+            #    will assign a default for missing projection
+            # Output_Weights,
+            Output_Layer
+        ],
+        clamp_input=pnl.SOFT_CLAMP,
+        learning=pnl.LEARNING,
+        learning_rate=1.0,
+        target=[0, 0, 1],
+        prefs={
+            pnl.VERBOSE_PREF: False,
+            pnl.REPORT_OUTPUT_PREF: False
+        },
+    )
+
+    Middle_Weights.log_items(('matrix', pnl.EXECUTION))
+
+    stim_list = {Input_Layer: [[-1, 30]]}
+    target_list = {Output_Layer: [[0, 0, 1]]}
+
+    def show_target():
+        i = s.input
+        t = s.target_input_states[0].value
+        print('\nOLD WEIGHTS: \n')
+        print('- Input Weights: \n', Input_Weights.matrix)
+        print('- Middle Weights: \n', Middle_Weights.matrix)
+        print('- Output Weights: \n', Output_Weights.matrix)
+        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+        print('ACTIVITY FROM OLD WEIGHTS: \n')
+        print('- Middle 1: \n', Hidden_Layer_1.value)
+        print('- Middle 2: \n', Hidden_Layer_2.value)
+        print('- Output:\n', Output_Layer.value)
+
+    s = pnl.System(
+        processes=[p],
+        targets=[0, 0, 1],
+        learning_rate=1.0,
+    )
+
+    # s.reportOutputPref = True
+
+    results = s.run(
+        num_trials=10,
+        inputs=stim_list,
+        targets=target_list,
+        # call_after_trial=show_target,
+    )
+
+    objective_output_layer = s.mechanisms[4]
+
+    results_list = []
+    for elem in s.results:
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+
+    expected_output = [
+        (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
+        (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
+        (Input_Weights.matrix, np.array([
+            [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
+            [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
+        ])),
+        (Middle_Weights.matrix, np.array([
+            [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
+            [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
+            [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
+            [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
+            [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
+        ])),
+        (Output_Weights.matrix, np.array([
+            [-0.74447522, -0.71016859, 0.31575293],
+            [-0.50885177, -0.47444784, 0.56676582],
+            [-0.27333719, -0.23912033, 0.8178167 ],
+            [-0.03767547, -0.00389039, 1.06888608],
+        ])),
+        (results, [
+            [np.array([0.8344837 , 0.87072018, 0.89997433])],
+            [np.array([0.77970193, 0.83263138, 0.90159627])],
+            [np.array([0.70218502, 0.7773823 , 0.90307765])],
+            [np.array([0.60279149, 0.69958079, 0.90453143])],
+            [np.array([0.4967927 , 0.60030321, 0.90610082])],
+            [np.array([0.4056202 , 0.49472391, 0.90786617])],
+            [np.array([0.33763025, 0.40397637, 0.90977675])],
+            [np.array([0.28892812, 0.33633532, 0.9117193 ])],
+            [np.array([0.25348771, 0.28791896, 0.9136125 ])],
+            [np.array([0.22686074, 0.25270212, 0.91542149])]
+        ]),
+    ]
+
+    # Test nparray output of log for Middle_Weights
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+            [
+                [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
+                [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
+                [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
+                [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
+                   [ 0.25,  0.3 ,  0.35,  0.4 ],
+                   [ 0.45,  0.5 ,  0.55,  0.6 ],
+                   [ 0.65,  0.7 ,  0.75,  0.8 ],
+                   [ 0.85,  0.9 ,  0.95,  1.  ]],
+                  [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
+                   [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
+                   [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
+                   [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
+                   [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
+                  [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
+                   [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
+                   [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
+                   [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
+                   [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
+                  [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
+                   [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
+                   [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
+                   [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
+                   [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
+                  [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
+                   [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
+                   [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
+                   [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
+                   [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
+                  [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
+                   [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
+                   [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
+                   [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
+                   [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
+                  [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
+                   [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
+                   [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
+                   [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
+                   [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
+                  [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
+                   [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
+                   [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
+                   [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
+                   [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
+                  [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
+                   [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
+                   [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
+                   [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
+                   [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
+                  [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
+                   [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
+                   [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
+                   [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
+                   [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
+            ], dtype=object
+    )
+
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+    Middle_Weights.log.print_entries()
+
+    # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
+    Middle_Weights.log.clear_entries(entries=None, confirm=False)
+    Middle_Weights.log_items(('matrix', pnl.LEARNING))
+    s.run(
+            num_trials=5,
+            inputs=stim_list,
+            targets=target_list,
+    )
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+                [
+                    [[0], [0], [0], [0], [0]],
+                    [[21], [23], [25], [27], [29]],
+                    [[3], [3], [3], [3], [3]],
+                    [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
+                        [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
+                        [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
+                        [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
+                        [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
+                       [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
+                        [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
+                        [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
+                        [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
+                        [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
+                      [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
+                       [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
+                       [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
+                       [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
+                       [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
+                      [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
+                       [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
+                       [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
+                       [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
+                       [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
+                      [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
+                       [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
+                       [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
+                       [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
+                       [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
+        ], dtype=object
+    )
+
+    assert log_val.shape == expected_log_val.shape
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+test_multilayer()
 
 #endregion
 

From 6416865ca8750cb5290cba4c066178c65285606a Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 20:15:59 -0500
Subject: [PATCH 23/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20logged=5Fitem?=
 =?UTF-8?q?,=20print=5Fentries:=20=20corrected=20to=20use=20'value'=20rath?=
 =?UTF-8?q?er=20than=20owner's=20name=20in=20reports?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Scripts/Scratch Pad.py    | 617 +++++++++++++++++++-------------------
 psyneulink/globals/log.py |  34 ++-
 2 files changed, 330 insertions(+), 321 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 3609e7480b8..910726954d4 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2308,13 +2308,16 @@ def __init__(self, error_value):
 #endregion
 
 #region TEST LOG DURING INITIALIZATION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print("TEST LOG DURING INITIALIZATION")
-#
-# T = pnl.TransferMechanism(
-#         prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
-# )
-# print(T.logged_items)
-# T.log.print_entries()
+print("TEST LOG DURING INITIALIZATION")
+
+def test_log_initialization():
+    T = pnl.TransferMechanism(
+            prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
+    )
+    print(T.logged_items)
+    T.log.print_entries()
+
+test_log_initialization()
 
 #endregion
 
@@ -2422,306 +2425,306 @@ def __init__(self, error_value):
 #endregion
 
 #region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print("TEST Multilayer-Learning Log")
-
-def test_multilayer():
-    Input_Layer = pnl.TransferMechanism(
-        name='Input Layer',
-        function=pnl.Logistic,
-        default_variable=np.zeros((2,)),
-    )
-
-    Hidden_Layer_1 = pnl.TransferMechanism(
-        name='Hidden Layer_1',
-        function=pnl.Logistic(),
-        # default_variable=np.zeros((5,)),
-        size=5
-    )
-
-    Hidden_Layer_2 = pnl.TransferMechanism(
-        name='Hidden Layer_2',
-        function=pnl.Logistic(),
-        default_variable=[0, 0, 0, 0],
-    )
-
-    Output_Layer = pnl.TransferMechanism(
-        name='Output Layer',
-        function=pnl.Logistic,
-        default_variable=[0, 0, 0],
-    )
-
-    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
-    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
-    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
-
-    # TEST PROCESS.LEARNING WITH:
-    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
-    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-
-    # This projection will be used by the process below by referencing it in the process' pathway;
-    #    note: sender and receiver args don't need to be specified
-    Input_Weights = pnl.MappingProjection(
-        name='Input Weights',
-        matrix=Input_Weights_matrix,
-    )
-
-    # This projection will be used by the process below by assigning its sender and receiver args
-    #    to mechanismss in the pathway
-    Middle_Weights = pnl.MappingProjection(
-        name='Middle Weights',
-        sender=Hidden_Layer_1,
-        receiver=Hidden_Layer_2,
-        matrix=Middle_Weights_matrix,
-    )
-
-    # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
-    Output_Weights = pnl.MappingProjection(
-        name='Output Weights',
-        sender=Hidden_Layer_2,
-        receiver=Output_Layer,
-        matrix=Output_Weights_matrix,
-    )
-
-    p = pnl.Process(
-        # default_variable=[0, 0],
-        size=2,
-        pathway=[
-            Input_Layer,
-            # The following reference to Input_Weights is needed to use it in the pathway
-            #    since it's sender and receiver args are not specified in its declaration above
-            Input_Weights,
-            Hidden_Layer_1,
-            # No projection specification is needed here since the sender arg for Middle_Weights
-            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
-            # Middle_Weights,
-            Hidden_Layer_2,
-            # Output_Weights does not need to be listed for the same reason as Middle_Weights
-            # If Middle_Weights and/or Output_Weights is not declared above, then the process
-            #    will assign a default for missing projection
-            # Output_Weights,
-            Output_Layer
-        ],
-        clamp_input=pnl.SOFT_CLAMP,
-        learning=pnl.LEARNING,
-        learning_rate=1.0,
-        target=[0, 0, 1],
-        prefs={
-            pnl.VERBOSE_PREF: False,
-            pnl.REPORT_OUTPUT_PREF: False
-        },
-    )
-
-    Middle_Weights.log_items(('matrix', pnl.EXECUTION))
-
-    stim_list = {Input_Layer: [[-1, 30]]}
-    target_list = {Output_Layer: [[0, 0, 1]]}
-
-    def show_target():
-        i = s.input
-        t = s.target_input_states[0].value
-        print('\nOLD WEIGHTS: \n')
-        print('- Input Weights: \n', Input_Weights.matrix)
-        print('- Middle Weights: \n', Middle_Weights.matrix)
-        print('- Output Weights: \n', Output_Weights.matrix)
-        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
-        print('ACTIVITY FROM OLD WEIGHTS: \n')
-        print('- Middle 1: \n', Hidden_Layer_1.value)
-        print('- Middle 2: \n', Hidden_Layer_2.value)
-        print('- Output:\n', Output_Layer.value)
-
-    s = pnl.System(
-        processes=[p],
-        targets=[0, 0, 1],
-        learning_rate=1.0,
-    )
-
-    # s.reportOutputPref = True
-
-    results = s.run(
-        num_trials=10,
-        inputs=stim_list,
-        targets=target_list,
-        # call_after_trial=show_target,
-    )
-
-    objective_output_layer = s.mechanisms[4]
-
-    results_list = []
-    for elem in s.results:
-        for nested_elem in elem:
-            nested_elem = nested_elem.tolist()
-            try:
-                iter(nested_elem)
-            except TypeError:
-                nested_elem = [nested_elem]
-            results_list.extend(nested_elem)
-
-    expected_output = [
-        (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
-        (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
-        (Input_Weights.matrix, np.array([
-            [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
-            [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
-        ])),
-        (Middle_Weights.matrix, np.array([
-            [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
-            [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
-            [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
-            [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
-            [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
-        ])),
-        (Output_Weights.matrix, np.array([
-            [-0.74447522, -0.71016859, 0.31575293],
-            [-0.50885177, -0.47444784, 0.56676582],
-            [-0.27333719, -0.23912033, 0.8178167 ],
-            [-0.03767547, -0.00389039, 1.06888608],
-        ])),
-        (results, [
-            [np.array([0.8344837 , 0.87072018, 0.89997433])],
-            [np.array([0.77970193, 0.83263138, 0.90159627])],
-            [np.array([0.70218502, 0.7773823 , 0.90307765])],
-            [np.array([0.60279149, 0.69958079, 0.90453143])],
-            [np.array([0.4967927 , 0.60030321, 0.90610082])],
-            [np.array([0.4056202 , 0.49472391, 0.90786617])],
-            [np.array([0.33763025, 0.40397637, 0.90977675])],
-            [np.array([0.28892812, 0.33633532, 0.9117193 ])],
-            [np.array([0.25348771, 0.28791896, 0.9136125 ])],
-            [np.array([0.22686074, 0.25270212, 0.91542149])]
-        ]),
-    ]
-
-    # Test nparray output of log for Middle_Weights
-
-    for i in range(len(expected_output)):
-        val, expected = expected_output[i]
-        # setting absolute tolerance to be in accordance with reference_output precision
-        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
-        # which WILL FAIL unless you gather higher precision values to use as reference
-        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
-
-    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-    expected_log_val = np.array(
-            [
-                [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
-                [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
-                [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
-                [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
-                   [ 0.25,  0.3 ,  0.35,  0.4 ],
-                   [ 0.45,  0.5 ,  0.55,  0.6 ],
-                   [ 0.65,  0.7 ,  0.75,  0.8 ],
-                   [ 0.85,  0.9 ,  0.95,  1.  ]],
-                  [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
-                   [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
-                   [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
-                   [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
-                   [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
-                  [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
-                   [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
-                   [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
-                   [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
-                   [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
-                  [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
-                   [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
-                   [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
-                   [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
-                   [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
-                  [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
-                   [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
-                   [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
-                   [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
-                   [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
-                  [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
-                   [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
-                   [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
-                   [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
-                   [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
-                  [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
-                   [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
-                   [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
-                   [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
-                   [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
-                  [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
-                   [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
-                   [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
-                   [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
-                   [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
-                  [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
-                   [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
-                   [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
-                   [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
-                   [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
-                  [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
-                   [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
-                   [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
-                   [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
-                   [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
-            ], dtype=object
-    )
-
-    for i in range(len(log_val)):
-        try:
-            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-        except:
-            for j in range(len(log_val[i])):
-                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-                                           atol=1e-08,
-                                           err_msg='Failed on test of logged values')
-
-    Middle_Weights.log.print_entries()
-
-    # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
-    Middle_Weights.log.clear_entries(entries=None, confirm=False)
-    Middle_Weights.log_items(('matrix', pnl.LEARNING))
-    s.run(
-            num_trials=5,
-            inputs=stim_list,
-            targets=target_list,
-    )
-    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-    expected_log_val = np.array(
-                [
-                    [[0], [0], [0], [0], [0]],
-                    [[21], [23], [25], [27], [29]],
-                    [[3], [3], [3], [3], [3]],
-                    [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
-                        [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
-                        [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
-                        [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
-                        [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
-                       [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
-                        [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
-                        [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
-                        [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
-                        [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
-                      [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
-                       [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
-                       [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
-                       [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
-                       [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
-                      [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
-                       [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
-                       [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
-                       [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
-                       [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
-                      [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
-                       [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
-                       [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
-                       [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
-                       [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
-        ], dtype=object
-    )
-
-    assert log_val.shape == expected_log_val.shape
-    for i in range(len(log_val)):
-        try:
-            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-        except:
-            for j in range(len(log_val[i])):
-                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-                                           atol=1e-08,
-                                           err_msg='Failed on test of logged values')
-test_multilayer()
+# print("TEST Multilayer-Learning Log")
+#
+# def test_multilayer():
+#     Input_Layer = pnl.TransferMechanism(
+#         name='Input Layer',
+#         function=pnl.Logistic,
+#         default_variable=np.zeros((2,)),
+#     )
+#
+#     Hidden_Layer_1 = pnl.TransferMechanism(
+#         name='Hidden Layer_1',
+#         function=pnl.Logistic(),
+#         # default_variable=np.zeros((5,)),
+#         size=5
+#     )
+#
+#     Hidden_Layer_2 = pnl.TransferMechanism(
+#         name='Hidden Layer_2',
+#         function=pnl.Logistic(),
+#         default_variable=[0, 0, 0, 0],
+#     )
+#
+#     Output_Layer = pnl.TransferMechanism(
+#         name='Output Layer',
+#         function=pnl.Logistic,
+#         default_variable=[0, 0, 0],
+#     )
+#
+#     Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+#     Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+#     Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+#
+#     # TEST PROCESS.LEARNING WITH:
+#     # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+#     # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+#     # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+#
+#     # This projection will be used by the process below by referencing it in the process' pathway;
+#     #    note: sender and receiver args don't need to be specified
+#     Input_Weights = pnl.MappingProjection(
+#         name='Input Weights',
+#         matrix=Input_Weights_matrix,
+#     )
+#
+#     # This projection will be used by the process below by assigning its sender and receiver args
+#     #    to mechanismss in the pathway
+#     Middle_Weights = pnl.MappingProjection(
+#         name='Middle Weights',
+#         sender=Hidden_Layer_1,
+#         receiver=Hidden_Layer_2,
+#         matrix=Middle_Weights_matrix,
+#     )
+#
+#     # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+#     Output_Weights = pnl.MappingProjection(
+#         name='Output Weights',
+#         sender=Hidden_Layer_2,
+#         receiver=Output_Layer,
+#         matrix=Output_Weights_matrix,
+#     )
+#
+#     p = pnl.Process(
+#         # default_variable=[0, 0],
+#         size=2,
+#         pathway=[
+#             Input_Layer,
+#             # The following reference to Input_Weights is needed to use it in the pathway
+#             #    since it's sender and receiver args are not specified in its declaration above
+#             Input_Weights,
+#             Hidden_Layer_1,
+#             # No projection specification is needed here since the sender arg for Middle_Weights
+#             #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+#             # Middle_Weights,
+#             Hidden_Layer_2,
+#             # Output_Weights does not need to be listed for the same reason as Middle_Weights
+#             # If Middle_Weights and/or Output_Weights is not declared above, then the process
+#             #    will assign a default for missing projection
+#             # Output_Weights,
+#             Output_Layer
+#         ],
+#         clamp_input=pnl.SOFT_CLAMP,
+#         learning=pnl.LEARNING,
+#         learning_rate=1.0,
+#         target=[0, 0, 1],
+#         prefs={
+#             pnl.VERBOSE_PREF: False,
+#             pnl.REPORT_OUTPUT_PREF: False
+#         },
+#     )
+#
+#     Middle_Weights.log_items(('matrix', pnl.EXECUTION))
+#
+#     stim_list = {Input_Layer: [[-1, 30]]}
+#     target_list = {Output_Layer: [[0, 0, 1]]}
+#
+#     def show_target():
+#         i = s.input
+#         t = s.target_input_states[0].value
+#         print('\nOLD WEIGHTS: \n')
+#         print('- Input Weights: \n', Input_Weights.matrix)
+#         print('- Middle Weights: \n', Middle_Weights.matrix)
+#         print('- Output Weights: \n', Output_Weights.matrix)
+#         print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+#         print('ACTIVITY FROM OLD WEIGHTS: \n')
+#         print('- Middle 1: \n', Hidden_Layer_1.value)
+#         print('- Middle 2: \n', Hidden_Layer_2.value)
+#         print('- Output:\n', Output_Layer.value)
+#
+#     s = pnl.System(
+#         processes=[p],
+#         targets=[0, 0, 1],
+#         learning_rate=1.0,
+#     )
+#
+#     # s.reportOutputPref = True
+#
+#     results = s.run(
+#         num_trials=10,
+#         inputs=stim_list,
+#         targets=target_list,
+#         # call_after_trial=show_target,
+#     )
+#
+#     objective_output_layer = s.mechanisms[4]
+#
+#     results_list = []
+#     for elem in s.results:
+#         for nested_elem in elem:
+#             nested_elem = nested_elem.tolist()
+#             try:
+#                 iter(nested_elem)
+#             except TypeError:
+#                 nested_elem = [nested_elem]
+#             results_list.extend(nested_elem)
+#
+#     expected_output = [
+#         (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
+#         (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
+#         (Input_Weights.matrix, np.array([
+#             [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
+#             [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
+#         ])),
+#         (Middle_Weights.matrix, np.array([
+#             [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
+#             [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
+#             [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
+#             [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
+#             [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
+#         ])),
+#         (Output_Weights.matrix, np.array([
+#             [-0.74447522, -0.71016859, 0.31575293],
+#             [-0.50885177, -0.47444784, 0.56676582],
+#             [-0.27333719, -0.23912033, 0.8178167 ],
+#             [-0.03767547, -0.00389039, 1.06888608],
+#         ])),
+#         (results, [
+#             [np.array([0.8344837 , 0.87072018, 0.89997433])],
+#             [np.array([0.77970193, 0.83263138, 0.90159627])],
+#             [np.array([0.70218502, 0.7773823 , 0.90307765])],
+#             [np.array([0.60279149, 0.69958079, 0.90453143])],
+#             [np.array([0.4967927 , 0.60030321, 0.90610082])],
+#             [np.array([0.4056202 , 0.49472391, 0.90786617])],
+#             [np.array([0.33763025, 0.40397637, 0.90977675])],
+#             [np.array([0.28892812, 0.33633532, 0.9117193 ])],
+#             [np.array([0.25348771, 0.28791896, 0.9136125 ])],
+#             [np.array([0.22686074, 0.25270212, 0.91542149])]
+#         ]),
+#     ]
+#
+#     # Test nparray output of log for Middle_Weights
+#
+#     for i in range(len(expected_output)):
+#         val, expected = expected_output[i]
+#         # setting absolute tolerance to be in accordance with reference_output precision
+#         # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+#         # which WILL FAIL unless you gather higher precision values to use as reference
+#         np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+#
+#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+#     expected_log_val = np.array(
+#             [
+#                 [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
+#                 [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
+#                 [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
+#                 [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
+#                    [ 0.25,  0.3 ,  0.35,  0.4 ],
+#                    [ 0.45,  0.5 ,  0.55,  0.6 ],
+#                    [ 0.65,  0.7 ,  0.75,  0.8 ],
+#                    [ 0.85,  0.9 ,  0.95,  1.  ]],
+#                   [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
+#                    [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
+#                    [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
+#                    [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
+#                    [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
+#                   [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
+#                    [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
+#                    [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
+#                    [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
+#                    [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
+#                   [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
+#                    [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
+#                    [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
+#                    [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
+#                    [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
+#                   [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
+#                    [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
+#                    [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
+#                    [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
+#                    [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
+#                   [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
+#                    [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
+#                    [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
+#                    [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
+#                    [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
+#                   [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
+#                    [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
+#                    [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
+#                    [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
+#                    [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
+#                   [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
+#                    [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
+#                    [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
+#                    [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
+#                    [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
+#                   [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
+#                    [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
+#                    [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
+#                    [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
+#                    [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
+#                   [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
+#                    [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
+#                    [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
+#                    [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
+#                    [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
+#             ], dtype=object
+#     )
+#
+#     for i in range(len(log_val)):
+#         try:
+#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+#         except:
+#             for j in range(len(log_val[i])):
+#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+#                                            atol=1e-08,
+#                                            err_msg='Failed on test of logged values')
+#
+#     Middle_Weights.log.print_entries()
+#
+#     # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
+#     Middle_Weights.log.clear_entries(entries=None, confirm=False)
+#     Middle_Weights.log_items(('matrix', pnl.LEARNING))
+#     s.run(
+#             num_trials=5,
+#             inputs=stim_list,
+#             targets=target_list,
+#     )
+#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+#     expected_log_val = np.array(
+#                 [
+#                     [[0], [0], [0], [0], [0]],
+#                     [[21], [23], [25], [27], [29]],
+#                     [[3], [3], [3], [3], [3]],
+#                     [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
+#                         [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
+#                         [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
+#                         [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
+#                         [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
+#                        [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
+#                         [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
+#                         [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
+#                         [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
+#                         [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
+#                       [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
+#                        [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
+#                        [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
+#                        [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
+#                        [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
+#                       [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
+#                        [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
+#                        [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
+#                        [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
+#                        [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
+#                       [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
+#                        [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
+#                        [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
+#                        [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
+#                        [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
+#         ], dtype=object
+#     )
+#
+#     assert log_val.shape == expected_log_val.shape
+#     for i in range(len(log_val)):
+#         try:
+#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+#         except:
+#             for j in range(len(log_val[i])):
+#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+#                                            atol=1e-08,
+#                                            err_msg='Failed on test of logged values')
+# test_multilayer()
 
 #endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 64e531d6c6e..670c264b655 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -336,7 +336,7 @@
 
 from psyneulink.scheduling.time import TimeScale
 from psyneulink.globals.utilities import ContentAddressableList, AutoNumber
-from psyneulink.globals.keywords import INITIALIZING, EXECUTING, VALIDATE, LEARNING, CONTROL, VALUE, \
+from psyneulink.globals.keywords import INITIALIZING, EXECUTING, VALIDATE, LEARNING, VALUE, \
     kwContext, kwTime, kwValue
 
 
@@ -594,11 +594,8 @@ def __init__(self, owner, entries=None):
 
         Each item of the entries list should be a string designating a Component to be logged;
         Initialize self.entries dict, each entry of which has a:
-        - key corresponding to an attribute of the object to be logged
-        - value that is a list of sequentially logged values
-
-        :parameter owner: (object in Function hierarchy) - parent object that owns the Log object)
-        :parameter entries: (list) - list of keypaths used as keys for entries in the Log dict
+            - key corresponding to a State of the Component to which the Log belongs
+            - value that is a list of sequentially logged LogEntry items
         """
 
         self.owner = owner
@@ -608,13 +605,11 @@ def __init__(self, owner, entries=None):
         if entries is None:
             return
 
-        # self.add_entries(entries)
-
     @property
     def loggable_items(self):
-        """Return dict of loggable items
+        """Return dict of loggable items.
 
-        Keys are names of the items, values the items themselves
+        Keys are names of the Components, values their LogLevels
         """
         # FIX: The following crashes during init as prefs have not all been assigned
         # return {key: value for (key, value) in [(c.name, c.logPref.name) for c in self.loggable_components]}
@@ -637,7 +632,8 @@ def loggable_items(self):
     def loggable_components(self):
         """Return a list of owner's Components that are loggable
 
-        The loggable items of a Component are specified in the _logagble_items property of its class
+        The loggable items of a Component are the Components (typically States) specified in the _logagble_items
+        property of its class, and its own `value <Component.value>` attribute.
         """
         from psyneulink.components.component import Component
 
@@ -654,10 +650,18 @@ def logged_items(self):
         """
         log_level = 'LogLevel.'
         # Return LogLevel for items in log.entries
-        # THIS VERSION FAILS AS IT ASSUMES THAT VALUE OF LOG'S OWNER IS UNDER ITS OWNER'S NAME, RATHER THAN "VALUE":
+
+        # # IMPLEMENTATION NOTE:
+        # #    THIS VERSION, THOUGH SUCCINCT, FAILS AS IT ASSUMES THAT VALUE OF LOG'S OWNER IS UNDER ITS OWNER'S NAME,
+        # #    RATHER THAN "VALUE" in loggable_items
         # logged_items = {key: value for (key, value) in
-        #                 [(l, self.loggable_items[l])
+        #                 [(l, self.loggable_components[l].logPref.name)
         #                  for l in self.logged_entries.keys()]}
+
+
+        # IMPLEMENTATION NOTE:  Need to expand for loop (rather than use list comprehension) is that the
+        #                       the owner Component's name (from logged.logged_entries.keys() needs to be
+        #                       aliases as *VALUE* when searching loggable_items
         logged_items = {}
         for l in self.logged_entries.keys():
             try:
@@ -665,10 +669,11 @@ def logged_items(self):
             except KeyError:
                 if l is self.owner.name:
                     try:
-                        logged_items[l] = (VALUE, self.loggable_items[VALUE])
+                        logged_items[VALUE] = self.loggable_items[VALUE]
                     except:
                         raise LogError("PROGRAM ERROR: Could not find {} from logged_entries in loggable_items "
                                        "for {} of {}".format(l, Log.__name__, self.owner.name))
+
         return logged_items
 
     def log_items(self, items, log_level=LogLevel.EXECUTION):
@@ -925,6 +930,7 @@ def print_entries(self,
                     if isinstance(value, np.ndarray):
                         value = value[0]
                     time_str = _time_string(time)
+                    attrib_name = attrib_name if attrib_name != self.owner.name else VALUE
                     data_str = repr(attrib_name).ljust(variable_width, kwSpacer)
                     if not args or kwTime in args:
                         data_str = time_str.ljust(time_width) + data_str

From e200f88908997c85b50718e6be0006db51d17a75 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 20:23:09 -0500
Subject: [PATCH 24/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20logged=5Fitem?=
 =?UTF-8?q?,=20print=5Fentries:=20=20corrected=20to=20use=20'value'=20rath?=
 =?UTF-8?q?er=20than=20owner's=20name=20in=20reports?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Tests
  test_log:  added test_log_initialization
---
 Scripts/Scratch Pad.py | 3 ++-
 tests/log/test_log.py  | 6 ++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 910726954d4..5e07ec92213 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2315,7 +2315,8 @@ def test_log_initialization():
             prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
     )
     print(T.logged_items)
-    T.log.print_entries()
+    x= T.log.print_entries()
+    assert True
 
 test_log_initialization()
 
diff --git a/tests/log/test_log.py b/tests/log/test_log.py
index edb05bdca3c..e312442e306 100644
--- a/tests/log/test_log.py
+++ b/tests/log/test_log.py
@@ -97,6 +97,12 @@ def test_log(self):
                                                 [[ 0.], [ 0.], [ 0.]],
                                                 [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
 
+    def test_log_initialization(self):
+        T = pnl.TransferMechanism(
+                prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
+        )
+        assert T.logged_items == {'value': 'INITIALIZATION'}
+
 
 
 

From e5a388168c895c4de8a37001deefaa1484c27512 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 21:05:55 -0500
Subject: [PATCH 25/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20=5Falias=5Fow?=
 =?UTF-8?q?ner=5Fname:=20=20added=20to=20used=20'value'=20rather=20than=20?=
 =?UTF-8?q?owner's=20name=20in=20reports=20=20=20-=20nparray:=20bug=20fix?=
 =?UTF-8?q?=20to=20handle=20None=20values?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Tests
  test_log:  added test_log_initialization
---
 Scripts/Scratch Pad.py    | 209 +++++++++++++++++++-------------------
 psyneulink/globals/log.py |  53 +++++-----
 2 files changed, 128 insertions(+), 134 deletions(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 5e07ec92213..ffcfe72b152 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2211,114 +2211,115 @@ def __init__(self, error_value):
 # #endregion
 
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print ("TEST Log")
-#
-# def test_log():
-#
-#     T_1 = pnl.TransferMechanism(name='T_1', size=2)
-#     T_2 = pnl.TransferMechanism(name='T_2', size=2)
-#     PS = pnl.Process(name='PS', pathway=[T_1, T_2])
-#     PJ = T_2.path_afferents[0]
-#
-#     assert T_1.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'OFF',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'OFF',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert T_2.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'OFF',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'OFF',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert PJ.loggable_items == {'matrix': 'OFF',
-#                                  'value': 'OFF'}
-#
-#     T_1.log_items(pnl.NOISE)
-#     T_1.log_items(pnl.RESULTS)
-#     PJ.log_items(pnl.MATRIX)
-#
-#     assert T_1.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'EXECUTION',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'EXECUTION',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert T_2.loggable_items == {'InputState-0': 'OFF',
-#                                  'slope': 'OFF',
-#                                  'RESULTS': 'OFF',
-#                                  'intercept': 'OFF',
-#                                  'noise': 'OFF',
-#                                  'time_constant': 'OFF',
-#                                  'value': 'OFF'}
-#     assert PJ.loggable_items == {'matrix': 'EXECUTION',
-#                                  'value': 'OFF'}
-#
-#     PS.execute()
-#     PS.execute()
-#     PS.execute()
-#
-#     assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-#     assert PJ.logged_items == {'matrix': 'EXECUTION'}
-#
-#     # assert T_1.log.print_entries() ==
-#     # # Log for mech_A:
-#     # #
-#     # # Index     Variable:                                          Context                                                                  Value
-#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # #
-#     # #
-#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     #
-#     # assert T_2.log.print_entries() ==
-#     # # Log for mech_A:
-#     # #
-#     # # Index     Variable:                                          Context                                                                  Value
-#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # #
-#     # #
-#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-#
-#     assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
-#                     "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
-#
-#     assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
-#            "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
-#            "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-#            "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-#            "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
-#
-#     result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
-#     print (result)
-#     np.testing.assert_array_equal(result,
-#                                   np.array([[[0], [1], [2]],
-#                                             [[ 0.], [ 0.], [ 0.]],
-#                                             [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
-#
-# test_log()
+print ("TEST Log")
+
+def test_log():
+
+    T_1 = pnl.TransferMechanism(name='T_1', size=2)
+    T_2 = pnl.TransferMechanism(name='T_2', size=2)
+    PS = pnl.Process(name='PS', pathway=[T_1, T_2])
+    PJ = T_2.path_afferents[0]
+
+    assert T_1.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'OFF',
+                                 'intercept': 'OFF',
+                                 'noise': 'OFF',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert T_2.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'OFF',
+                                 'intercept': 'OFF',
+                                 'noise': 'OFF',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert PJ.loggable_items == {'matrix': 'OFF',
+                                 'value': 'OFF'}
+
+    T_1.log_items(pnl.NOISE)
+    T_1.log_items(pnl.RESULTS)
+    PJ.log_items(pnl.MATRIX)
+
+    assert T_1.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'EXECUTION',
+                                 'intercept': 'OFF',
+                                 'noise': 'EXECUTION',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert T_2.loggable_items == {'InputState-0': 'OFF',
+                                 'slope': 'OFF',
+                                 'RESULTS': 'OFF',
+                                 'intercept': 'OFF',
+                                 'noise': 'OFF',
+                                 'time_constant': 'OFF',
+                                 'value': 'OFF'}
+    assert PJ.loggable_items == {'matrix': 'EXECUTION',
+                                 'value': 'OFF'}
+
+    PS.execute()
+    PS.execute()
+    PS.execute()
+
+    assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
+    assert PJ.logged_items == {'matrix': 'EXECUTION'}
+
+    # assert T_1.log.print_entries() ==
+    # # Log for mech_A:
+    # #
+    # # Index     Variable:                                          Context                                                                  Value
+    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # #
+    # #
+    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    #
+    # assert T_2.log.print_entries() ==
+    # # Log for mech_A:
+    # #
+    # # Index     Variable:                                          Context                                                                  Value
+    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # #
+    # #
+    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+
+    assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
+                    "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
+
+    assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
+           "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
+           "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+           "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+           "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
+
+    result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+    print (result)
+    np.testing.assert_array_equal(result,
+                                  np.array([[[0], [1], [2]],
+                                            [[ 0.], [ 0.], [ 0.]],
+                                            [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
+
+test_log()
 
 #endregion
 
 #region TEST LOG DURING INITIALIZATION @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print("TEST LOG DURING INITIALIZATION")
-
-def test_log_initialization():
-    T = pnl.TransferMechanism(
-            prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
-    )
-    print(T.logged_items)
-    x= T.log.print_entries()
-    assert True
-
-test_log_initialization()
+# print("TEST LOG DURING INITIALIZATION")
+#
+# def test_log_initialization():
+#     T = pnl.TransferMechanism(
+#             prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogLevel.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)}
+#     )
+#     print(T.logged_items)
+#     print(T.log.nparray())
+#     T.log.print_entries()
+#     assert True
+#
+# test_log_initialization()
 
 #endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 670c264b655..5a512d5c587 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -249,6 +249,9 @@
 
 COMMENT:
 
+IMPLEMENTATION NOTE: Name of owner Component is aliases to VALUE in loggable_items and logged_items,
+but is the Component's actual name in log_entries
+
 Entries are made to the Log based on the `LogLevel` specified in the
 `logPref` item of the component's `prefs <Component.prefs>` attribute.
 
@@ -492,6 +495,10 @@ class Log:
     """Maintain a Log for an object, which contains a dictionary of logged value(s).
 
     COMMENT:
+
+    IMPLEMENTATION NOTE: Name of owner Component is aliases to VALUE in loggable_items and logged_items,
+    but is the Component's actual name in log_entries
+
     Description:
         Log maintains a dict (self.entries), with an entry for each attribute of the owner object being logged
         Each entry of self.entries has:
@@ -605,6 +612,12 @@ def __init__(self, owner, entries=None):
         if entries is None:
             return
 
+    def _alias_owner_name(self, name):
+        """Alias name of owner Component to VALUE in loggable_items and logged_items
+        Component's actual name is preserved and used in log_entries (i.e., as entered by _log_value)
+        """
+        return VALUE if name is self.owner.name else name
+
     @property
     def loggable_items(self):
         """Return dict of loggable items.
@@ -616,10 +629,7 @@ def loggable_items(self):
 
         loggable_items = {}
         for c in self.loggable_components:
-            if c is self.owner:
-                name = VALUE
-            else:
-                name = c.name
+            name = self._alias_owner_name(c.name)
             try:
                 log_pref = c.logPref.name
             except:
@@ -651,28 +661,10 @@ def logged_items(self):
         log_level = 'LogLevel.'
         # Return LogLevel for items in log.entries
 
-        # # IMPLEMENTATION NOTE:
-        # #    THIS VERSION, THOUGH SUCCINCT, FAILS AS IT ASSUMES THAT VALUE OF LOG'S OWNER IS UNDER ITS OWNER'S NAME,
-        # #    RATHER THAN "VALUE" in loggable_items
-        # logged_items = {key: value for (key, value) in
-        #                 [(l, self.loggable_components[l].logPref.name)
-        #                  for l in self.logged_entries.keys()]}
-
-
-        # IMPLEMENTATION NOTE:  Need to expand for loop (rather than use list comprehension) is that the
-        #                       the owner Component's name (from logged.logged_entries.keys() needs to be
-        #                       aliases as *VALUE* when searching loggable_items
-        logged_items = {}
-        for l in self.logged_entries.keys():
-            try:
-                logged_items[l] = self.loggable_items[l]
-            except KeyError:
-                if l is self.owner.name:
-                    try:
-                        logged_items[VALUE] = self.loggable_items[VALUE]
-                    except:
-                        raise LogError("PROGRAM ERROR: Could not find {} from logged_entries in loggable_items "
-                                       "for {} of {}".format(l, Log.__name__, self.owner.name))
+        logged_items = {key: value for (key, value) in
+                        # [(l, self.loggable_components[l].logPref.name)
+                        [(self._alias_owner_name(l), self.loggable_items[self._alias_owner_name(l)])
+                         for l in self.logged_entries.keys()]}
 
         return logged_items
 
@@ -930,7 +922,7 @@ def print_entries(self,
                     if isinstance(value, np.ndarray):
                         value = value[0]
                     time_str = _time_string(time)
-                    attrib_name = attrib_name if attrib_name != self.owner.name else VALUE
+                    attrib_name = self._alias_owner_name(attrib_name)
                     data_str = repr(attrib_name).ljust(variable_width, kwSpacer)
                     if not args or kwTime in args:
                         data_str = time_str.ljust(time_width) + data_str
@@ -1023,7 +1015,7 @@ def nparray(self,
 
         # Validate entries
         for entry in entries:
-            if entry not in self.loggable_items:
+            if self._alias_owner_name(entry) not in self.loggable_items:
                 raise LogError("{0} is not a loggable attribute of {1}".format(repr(entry), self.owner.name))
             if entry not in self.logged_entries:
                 raise LogError("{} is not currently being logged by {} (try using log_items)".
@@ -1084,9 +1076,10 @@ def nparray(self,
                 if time_values:
                     while datum.time != next(time_col,None):
                         row.append(None)
-                row.append(datum.value.tolist())
+                value = None if datum.value is None else datum.value.tolist()
+                row.append(value)
             if header:
-                entry_header = "{}{}{}{}".format(owner_name_str, lb, entry, rb)
+                entry_header = "{}{}{}{}".format(owner_name_str, lb, self._alias_owner_name(entry), rb)
                 row = [entry_header] + row
             npa.append(row)
 

From 5065c1d9db53d9679594203da9633422f49005d1 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 23:04:01 -0500
Subject: [PATCH 26/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20-=20log=5Fvalue:?=
 =?UTF-8?q?=20added?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Tests
  - test_multilayer: added test of log_value
---
 .idea/runConfigurations/Make_HTML.xml |   6 +-
 Scripts/Scratch Pad.py                | 809 +++++++++++++-------------
 psyneulink/globals/log.py             | 327 ++++++-----
 tests/learning/test_multilayer.py     |  22 +-
 4 files changed, 618 insertions(+), 546 deletions(-)

diff --git a/.idea/runConfigurations/Make_HTML.xml b/.idea/runConfigurations/Make_HTML.xml
index eacc86ebae0..cad024aa603 100644
--- a/.idea/runConfigurations/Make_HTML.xml
+++ b/.idea/runConfigurations/Make_HTML.xml
@@ -1,14 +1,14 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" factoryName="Sphinx task" name="Make HTML" singleton="true" type="docs">
+  <configuration default="false" name="Make HTML" type="docs" factoryName="Sphinx task" singleton="true">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
-    <option name="SDK_HOME" value="" />
+    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/docs" />
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="" />
+    <module name="PsyNeuLink (Dropbox)" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="docutils_input_file" value="$PROJECT_DIR$/docs/source" />
     <option name="docutils_output_file" value="$PROJECT_DIR$/docs/build/html" />
diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index ffcfe72b152..4fb65fdc932 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2211,99 +2211,99 @@ def __init__(self, error_value):
 # #endregion
 
 #region TEST Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-print ("TEST Log")
-
-def test_log():
-
-    T_1 = pnl.TransferMechanism(name='T_1', size=2)
-    T_2 = pnl.TransferMechanism(name='T_2', size=2)
-    PS = pnl.Process(name='PS', pathway=[T_1, T_2])
-    PJ = T_2.path_afferents[0]
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'OFF',
-                                 'value': 'OFF'}
-
-    T_1.log_items(pnl.NOISE)
-    T_1.log_items(pnl.RESULTS)
-    PJ.log_items(pnl.MATRIX)
-
-    assert T_1.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'EXECUTION',
-                                 'intercept': 'OFF',
-                                 'noise': 'EXECUTION',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert T_2.loggable_items == {'InputState-0': 'OFF',
-                                 'slope': 'OFF',
-                                 'RESULTS': 'OFF',
-                                 'intercept': 'OFF',
-                                 'noise': 'OFF',
-                                 'time_constant': 'OFF',
-                                 'value': 'OFF'}
-    assert PJ.loggable_items == {'matrix': 'EXECUTION',
-                                 'value': 'OFF'}
-
-    PS.execute()
-    PS.execute()
-    PS.execute()
-
-    assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
-    assert PJ.logged_items == {'matrix': 'EXECUTION'}
-
-    # assert T_1.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    #
-    # assert T_2.log.print_entries() ==
-    # # Log for mech_A:
-    # #
-    # # Index     Variable:                                          Context                                                                  Value
-    # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # #
-    # #
-    # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-    # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
-
-    assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
-                    "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
-
-    assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
-           "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
-           "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
-           "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
-
-    result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
-    print (result)
-    np.testing.assert_array_equal(result,
-                                  np.array([[[0], [1], [2]],
-                                            [[ 0.], [ 0.], [ 0.]],
-                                            [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
-
-test_log()
+# print ("TEST Log")
+#
+# def test_log():
+#
+#     T_1 = pnl.TransferMechanism(name='T_1', size=2)
+#     T_2 = pnl.TransferMechanism(name='T_2', size=2)
+#     PS = pnl.Process(name='PS', pathway=[T_1, T_2])
+#     PJ = T_2.path_afferents[0]
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'OFF',
+#                                  'value': 'OFF'}
+#
+#     T_1.log_items(pnl.NOISE)
+#     T_1.log_items(pnl.RESULTS)
+#     PJ.log_items(pnl.MATRIX)
+#
+#     assert T_1.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'EXECUTION',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'EXECUTION',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert T_2.loggable_items == {'InputState-0': 'OFF',
+#                                  'slope': 'OFF',
+#                                  'RESULTS': 'OFF',
+#                                  'intercept': 'OFF',
+#                                  'noise': 'OFF',
+#                                  'time_constant': 'OFF',
+#                                  'value': 'OFF'}
+#     assert PJ.loggable_items == {'matrix': 'EXECUTION',
+#                                  'value': 'OFF'}
+#
+#     PS.execute()
+#     PS.execute()
+#     PS.execute()
+#
+#     assert T_1.logged_items == {'RESULTS': 'EXECUTION', 'noise': 'EXECUTION'}
+#     assert PJ.logged_items == {'matrix': 'EXECUTION'}
+#
+#     # assert T_1.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     #
+#     # assert T_2.log.print_entries() ==
+#     # # Log for mech_A:
+#     # #
+#     # # Index     Variable:                                          Context                                                                  Value
+#     # # 0         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'RESULTS'.........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # #
+#     # #
+#     # # 0         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#     # # 1         'noise'...........................................' EXECUTING  PROCESS Process-0'.......................................    0.0
+#
+#     assert T_1.log.csv(entries=['noise', 'RESULTS'], owner_name=False, quotes=None) == \
+#                     "\'Index\', \'noise\', \'RESULTS\'\n0, 0.0, 0.0 0.0\n1, 0.0, 0.0 0.0\n2, 0.0, 0.0 0.0\n"
+#
+#     assert PJ.log.csv(entries='matrix', owner_name=True, quotes=True) == \
+#            "\'Index\', \'MappingProjection from T_1 to T_2[matrix]\'\n" \
+#            "\'0\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'1\', \'1.0 0.0\' \'0.0 1.0\'\n" \
+#            "\'2\', \'1.0 0.0\' \'0.0 1.0\'\n"
+#
+#     result = T_1.log.nparray(entries=['noise', 'RESULTS'], header=False, owner_name=True)
+#     print (result)
+#     np.testing.assert_array_equal(result,
+#                                   np.array([[[0], [1], [2]],
+#                                             [[ 0.], [ 0.], [ 0.]],
+#                                             [[ 0.,  0.], [ 0.,  0.],[ 0., 0.]]]))
+#
+# test_log()
 
 #endregion
 
@@ -2426,307 +2426,328 @@ def test_log():
 
 #endregion
 
-#region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-# print("TEST Multilayer-Learning Log")
-#
-# def test_multilayer():
-#     Input_Layer = pnl.TransferMechanism(
-#         name='Input Layer',
-#         function=pnl.Logistic,
-#         default_variable=np.zeros((2,)),
-#     )
-#
-#     Hidden_Layer_1 = pnl.TransferMechanism(
-#         name='Hidden Layer_1',
-#         function=pnl.Logistic(),
-#         # default_variable=np.zeros((5,)),
-#         size=5
-#     )
-#
-#     Hidden_Layer_2 = pnl.TransferMechanism(
-#         name='Hidden Layer_2',
-#         function=pnl.Logistic(),
-#         default_variable=[0, 0, 0, 0],
-#     )
-#
-#     Output_Layer = pnl.TransferMechanism(
-#         name='Output Layer',
-#         function=pnl.Logistic,
-#         default_variable=[0, 0, 0],
-#     )
-#
-#     Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
-#     Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
-#     Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
-#
-#     # TEST PROCESS.LEARNING WITH:
-#     # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
-#     # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#     # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
-#
-#     # This projection will be used by the process below by referencing it in the process' pathway;
-#     #    note: sender and receiver args don't need to be specified
-#     Input_Weights = pnl.MappingProjection(
-#         name='Input Weights',
-#         matrix=Input_Weights_matrix,
-#     )
-#
-#     # This projection will be used by the process below by assigning its sender and receiver args
-#     #    to mechanismss in the pathway
-#     Middle_Weights = pnl.MappingProjection(
-#         name='Middle Weights',
-#         sender=Hidden_Layer_1,
-#         receiver=Hidden_Layer_2,
-#         matrix=Middle_Weights_matrix,
-#     )
-#
-#     # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
-#     Output_Weights = pnl.MappingProjection(
-#         name='Output Weights',
-#         sender=Hidden_Layer_2,
-#         receiver=Output_Layer,
-#         matrix=Output_Weights_matrix,
-#     )
-#
-#     p = pnl.Process(
-#         # default_variable=[0, 0],
-#         size=2,
-#         pathway=[
-#             Input_Layer,
-#             # The following reference to Input_Weights is needed to use it in the pathway
-#             #    since it's sender and receiver args are not specified in its declaration above
-#             Input_Weights,
-#             Hidden_Layer_1,
-#             # No projection specification is needed here since the sender arg for Middle_Weights
-#             #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
-#             # Middle_Weights,
-#             Hidden_Layer_2,
-#             # Output_Weights does not need to be listed for the same reason as Middle_Weights
-#             # If Middle_Weights and/or Output_Weights is not declared above, then the process
-#             #    will assign a default for missing projection
-#             # Output_Weights,
-#             Output_Layer
-#         ],
-#         clamp_input=pnl.SOFT_CLAMP,
-#         learning=pnl.LEARNING,
-#         learning_rate=1.0,
-#         target=[0, 0, 1],
-#         prefs={
-#             pnl.VERBOSE_PREF: False,
-#             pnl.REPORT_OUTPUT_PREF: False
-#         },
-#     )
-#
-#     Middle_Weights.log_items(('matrix', pnl.EXECUTION))
-#
-#     stim_list = {Input_Layer: [[-1, 30]]}
-#     target_list = {Output_Layer: [[0, 0, 1]]}
-#
-#     def show_target():
-#         i = s.input
-#         t = s.target_input_states[0].value
-#         print('\nOLD WEIGHTS: \n')
-#         print('- Input Weights: \n', Input_Weights.matrix)
-#         print('- Middle Weights: \n', Middle_Weights.matrix)
-#         print('- Output Weights: \n', Output_Weights.matrix)
-#         print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
-#         print('ACTIVITY FROM OLD WEIGHTS: \n')
-#         print('- Middle 1: \n', Hidden_Layer_1.value)
-#         print('- Middle 2: \n', Hidden_Layer_2.value)
-#         print('- Output:\n', Output_Layer.value)
-#
-#     s = pnl.System(
-#         processes=[p],
-#         targets=[0, 0, 1],
-#         learning_rate=1.0,
-#     )
-#
-#     # s.reportOutputPref = True
-#
-#     results = s.run(
-#         num_trials=10,
-#         inputs=stim_list,
-#         targets=target_list,
-#         # call_after_trial=show_target,
-#     )
-#
-#     objective_output_layer = s.mechanisms[4]
-#
-#     results_list = []
-#     for elem in s.results:
-#         for nested_elem in elem:
-#             nested_elem = nested_elem.tolist()
-#             try:
-#                 iter(nested_elem)
-#             except TypeError:
-#                 nested_elem = [nested_elem]
-#             results_list.extend(nested_elem)
-#
-#     expected_output = [
-#         (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
-#         (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
-#         (Input_Weights.matrix, np.array([
-#             [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
-#             [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
-#         ])),
-#         (Middle_Weights.matrix, np.array([
-#             [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
-#             [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
-#             [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
-#             [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
-#             [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
-#         ])),
-#         (Output_Weights.matrix, np.array([
-#             [-0.74447522, -0.71016859, 0.31575293],
-#             [-0.50885177, -0.47444784, 0.56676582],
-#             [-0.27333719, -0.23912033, 0.8178167 ],
-#             [-0.03767547, -0.00389039, 1.06888608],
-#         ])),
-#         (results, [
-#             [np.array([0.8344837 , 0.87072018, 0.89997433])],
-#             [np.array([0.77970193, 0.83263138, 0.90159627])],
-#             [np.array([0.70218502, 0.7773823 , 0.90307765])],
-#             [np.array([0.60279149, 0.69958079, 0.90453143])],
-#             [np.array([0.4967927 , 0.60030321, 0.90610082])],
-#             [np.array([0.4056202 , 0.49472391, 0.90786617])],
-#             [np.array([0.33763025, 0.40397637, 0.90977675])],
-#             [np.array([0.28892812, 0.33633532, 0.9117193 ])],
-#             [np.array([0.25348771, 0.28791896, 0.9136125 ])],
-#             [np.array([0.22686074, 0.25270212, 0.91542149])]
-#         ]),
-#     ]
-#
-#     # Test nparray output of log for Middle_Weights
-#
-#     for i in range(len(expected_output)):
-#         val, expected = expected_output[i]
-#         # setting absolute tolerance to be in accordance with reference_output precision
-#         # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
-#         # which WILL FAIL unless you gather higher precision values to use as reference
-#         np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
-#
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#             [
-#                 [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
-#                 [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
-#                 [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
-#                 [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
-#                    [ 0.25,  0.3 ,  0.35,  0.4 ],
-#                    [ 0.45,  0.5 ,  0.55,  0.6 ],
-#                    [ 0.65,  0.7 ,  0.75,  0.8 ],
-#                    [ 0.85,  0.9 ,  0.95,  1.  ]],
-#                   [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
-#                    [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
-#                    [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
-#                    [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
-#                    [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
-#                   [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
-#                    [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
-#                    [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
-#                    [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
-#                    [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
-#                   [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
-#                    [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
-#                    [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
-#                    [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
-#                    [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
-#                   [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
-#                    [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
-#                    [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
-#                    [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
-#                    [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
-#                   [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
-#                    [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
-#                    [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
-#                    [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
-#                    [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
-#                   [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
-#                    [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
-#                    [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
-#                    [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
-#                    [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
-#                   [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
-#                    [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
-#                    [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
-#                    [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
-#                    [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
-#                   [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
-#                    [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
-#                    [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
-#                    [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
-#                    [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
-#                   [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
-#                    [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
-#                    [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
-#                    [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
-#                    [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
-#             ], dtype=object
-#     )
-#
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-#
-#     Middle_Weights.log.print_entries()
-#
-#     # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
-#     Middle_Weights.log.clear_entries(entries=None, confirm=False)
-#     Middle_Weights.log_items(('matrix', pnl.LEARNING))
-#     s.run(
-#             num_trials=5,
-#             inputs=stim_list,
-#             targets=target_list,
-#     )
-#     log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
-#     expected_log_val = np.array(
-#                 [
-#                     [[0], [0], [0], [0], [0]],
-#                     [[21], [23], [25], [27], [29]],
-#                     [[3], [3], [3], [3], [3]],
-#                     [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
-#                         [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
-#                         [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
-#                         [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
-#                         [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
-#                        [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
-#                         [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
-#                         [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
-#                         [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
-#                         [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
-#                       [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
-#                        [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
-#                        [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
-#                        [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
-#                        [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
-#                       [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
-#                        [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
-#                        [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
-#                        [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
-#                        [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
-#                       [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
-#                        [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
-#                        [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
-#                        [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
-#                        [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
-#         ], dtype=object
-#     )
-#
-#     assert log_val.shape == expected_log_val.shape
-#     for i in range(len(log_val)):
-#         try:
-#             np.testing.assert_array_equal(log_val[i], expected_log_val[i])
-#         except:
-#             for j in range(len(log_val[i])):
-#                 np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
-#                                            atol=1e-08,
-#                                            err_msg='Failed on test of logged values')
-# test_multilayer()
+# region TEST Multilayer-Learning Log @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+print("TEST Multilayer-Learning Log")
+
+def test_multilayer():
+    Input_Layer = pnl.TransferMechanism(
+        name='Input Layer',
+        function=pnl.Logistic,
+        default_variable=np.zeros((2,)),
+    )
+
+    Hidden_Layer_1 = pnl.TransferMechanism(
+        name='Hidden Layer_1',
+        function=pnl.Logistic(),
+        # default_variable=np.zeros((5,)),
+        size=5
+    )
+
+    Hidden_Layer_2 = pnl.TransferMechanism(
+        name='Hidden Layer_2',
+        function=pnl.Logistic(),
+        default_variable=[0, 0, 0, 0],
+    )
+
+    Output_Layer = pnl.TransferMechanism(
+        name='Output Layer',
+        function=pnl.Logistic,
+        default_variable=[0, 0, 0],
+    )
+
+    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
+    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
+    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
+
+    # TEST PROCESS.LEARNING WITH:
+    # CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
+    # INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+    # NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
+
+    # This projection will be used by the process below by referencing it in the process' pathway;
+    #    note: sender and receiver args don't need to be specified
+    Input_Weights = pnl.MappingProjection(
+        name='Input Weights',
+        matrix=Input_Weights_matrix,
+    )
+
+    # This projection will be used by the process below by assigning its sender and receiver args
+    #    to mechanismss in the pathway
+    Middle_Weights = pnl.MappingProjection(
+        name='Middle Weights',
+        sender=Hidden_Layer_1,
+        receiver=Hidden_Layer_2,
+        matrix=Middle_Weights_matrix,
+    )
+
+    # Commented lines in this projection illustrate variety of ways in which matrix and learning signals can be specified
+    Output_Weights = pnl.MappingProjection(
+        name='Output Weights',
+        sender=Hidden_Layer_2,
+        receiver=Output_Layer,
+        matrix=Output_Weights_matrix,
+    )
+
+    p = pnl.Process(
+        # default_variable=[0, 0],
+        size=2,
+        pathway=[
+            Input_Layer,
+            # The following reference to Input_Weights is needed to use it in the pathway
+            #    since it's sender and receiver args are not specified in its declaration above
+            Input_Weights,
+            Hidden_Layer_1,
+            # No projection specification is needed here since the sender arg for Middle_Weights
+            #    is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
+            # Middle_Weights,
+            Hidden_Layer_2,
+            # Output_Weights does not need to be listed for the same reason as Middle_Weights
+            # If Middle_Weights and/or Output_Weights is not declared above, then the process
+            #    will assign a default for missing projection
+            # Output_Weights,
+            Output_Layer
+        ],
+        clamp_input=pnl.SOFT_CLAMP,
+        learning=pnl.LEARNING,
+        learning_rate=1.0,
+        target=[0, 0, 1],
+        prefs={
+            pnl.VERBOSE_PREF: False,
+            pnl.REPORT_OUTPUT_PREF: False
+        },
+    )
+
+    Middle_Weights.log_items(('matrix', pnl.EXECUTION))
+
+    stim_list = {Input_Layer: [[-1, 30]]}
+    target_list = {Output_Layer: [[0, 0, 1]]}
+
+    def show_target():
+        i = s.input
+        t = s.target_input_states[0].value
+        print('\nOLD WEIGHTS: \n')
+        print('- Input Weights: \n', Input_Weights.matrix)
+        print('- Middle Weights: \n', Middle_Weights.matrix)
+        print('- Output Weights: \n', Output_Weights.matrix)
+        print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
+        print('ACTIVITY FROM OLD WEIGHTS: \n')
+        print('- Middle 1: \n', Hidden_Layer_1.value)
+        print('- Middle 2: \n', Hidden_Layer_2.value)
+        print('- Output:\n', Output_Layer.value)
+
+    s = pnl.System(
+        processes=[p],
+        targets=[0, 0, 1],
+        learning_rate=1.0,
+    )
+
+    # s.reportOutputPref = True
+
+    results = s.run(
+        num_trials=10,
+        inputs=stim_list,
+        targets=target_list,
+        # call_after_trial=show_target,
+    )
+
+    objective_output_layer = s.mechanisms[4]
+
+    results_list = []
+    for elem in s.results:
+        for nested_elem in elem:
+            nested_elem = nested_elem.tolist()
+            try:
+                iter(nested_elem)
+            except TypeError:
+                nested_elem = [nested_elem]
+            results_list.extend(nested_elem)
+
+    expected_output = [
+        (Output_Layer.output_states.values, [np.array([0.22686074, 0.25270212, 0.91542149])]),
+        (objective_output_layer.output_states[pnl.MSE].value, np.array(0.04082589331852094)),
+        (Input_Weights.matrix, np.array([
+            [ 0.09900247, 0.19839653, 0.29785764, 0.39739191, 0.49700232],
+            [ 0.59629092, 0.69403786, 0.79203411, 0.89030237, 0.98885379],
+        ])),
+        (Middle_Weights.matrix, np.array([
+            [ 0.09490249, 0.10488719, 0.12074013, 0.1428774 ],
+            [ 0.29677354, 0.30507726, 0.31949676, 0.3404652 ],
+            [ 0.49857336, 0.50526254, 0.51830509, 0.53815062],
+            [ 0.70029406, 0.70544225, 0.71717037, 0.73594383],
+            [ 0.90192903, 0.90561554, 0.91609668, 0.93385292],
+        ])),
+        (Output_Weights.matrix, np.array([
+            [-0.74447522, -0.71016859, 0.31575293],
+            [-0.50885177, -0.47444784, 0.56676582],
+            [-0.27333719, -0.23912033, 0.8178167 ],
+            [-0.03767547, -0.00389039, 1.06888608],
+        ])),
+        (results, [
+            [np.array([0.8344837 , 0.87072018, 0.89997433])],
+            [np.array([0.77970193, 0.83263138, 0.90159627])],
+            [np.array([0.70218502, 0.7773823 , 0.90307765])],
+            [np.array([0.60279149, 0.69958079, 0.90453143])],
+            [np.array([0.4967927 , 0.60030321, 0.90610082])],
+            [np.array([0.4056202 , 0.49472391, 0.90786617])],
+            [np.array([0.33763025, 0.40397637, 0.90977675])],
+            [np.array([0.28892812, 0.33633532, 0.9117193 ])],
+            [np.array([0.25348771, 0.28791896, 0.9136125 ])],
+            [np.array([0.22686074, 0.25270212, 0.91542149])]
+        ]),
+    ]
+
+    # Test nparray output of log for Middle_Weights
+
+    for i in range(len(expected_output)):
+        val, expected = expected_output[i]
+        # setting absolute tolerance to be in accordance with reference_output precision
+        # if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
+        # which WILL FAIL unless you gather higher precision values to use as reference
+        np.testing.assert_allclose(val, expected, atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(i))
+
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+            [
+                [[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
+                [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]],
+                [[2], [2], [2], [2], [2], [2], [2], [2], [2], [2]],
+                [ [[ 0.05,  0.1 ,  0.15,  0.2 ],
+                   [ 0.25,  0.3 ,  0.35,  0.4 ],
+                   [ 0.45,  0.5 ,  0.55,  0.6 ],
+                   [ 0.65,  0.7 ,  0.75,  0.8 ],
+                   [ 0.85,  0.9 ,  0.95,  1.  ]],
+                  [[ 0.04789907,  0.09413833,  0.14134241,  0.18938924],
+                   [ 0.24780811,  0.29388455,  0.34096758,  0.38892985],
+                   [ 0.44772121,  0.49364209,  0.54060947,  0.58849095],
+                   [ 0.64763875,  0.69341202,  0.74026967,  0.78807449],
+                   [ 0.84756101,  0.89319513,  0.93994932,  0.98768187]],
+                  [[ 0.04738148,  0.08891106,  0.13248753,  0.177898  ],
+                   [ 0.24726841,  0.28843403,  0.33173452,  0.37694783],
+                   [ 0.44716034,  0.48797777,  0.53101423,  0.57603893],
+                   [ 0.64705774,  0.6875443 ,  0.73032986,  0.77517531],
+                   [ 0.84696096,  0.88713512,  0.92968378,  0.97435998]],
+                  [[ 0.04937771,  0.08530344,  0.12439361,  0.16640433],
+                   [ 0.24934878,  0.28467436,  0.32329947,  0.36496974],
+                   [ 0.44932147,  0.48407216,  0.52225175,  0.56359587],
+                   [ 0.64929589,  0.68349948,  0.72125508,  0.76228876],
+                   [ 0.84927212,  0.88295836,  0.92031297,  0.96105307]],
+                  [[ 0.05440291,  0.08430585,  0.1183739 ,  0.15641064],
+                   [ 0.25458348,  0.28363519,  0.3170288 ,  0.35455942],
+                   [ 0.45475764,  0.48299299,  0.51573974,  0.55278488],
+                   [ 0.65492462,  0.68238209,  0.7145124 ,  0.75109483],
+                   [ 0.85508376,  0.88180465,  0.91335119,  0.94949538]],
+                  [[ 0.06177218,  0.0860581 ,  0.11525064,  0.14926369],
+                   [ 0.26225812,  0.28546004,  0.31377611,  0.34711631],
+                   [ 0.46272625,  0.48488774,  0.51236246,  0.54505667],
+                   [ 0.66317453,  0.68434373,  0.7110159 ,  0.74309381],
+                   [ 0.86360121,  0.88382991,  0.9097413 ,  0.94123489]],
+                  [[ 0.06989398,  0.08959148,  0.11465594,  0.14513241],
+                   [ 0.27071639,  0.2891398 ,  0.31315677,  0.34281389],
+                   [ 0.47150846,  0.48870843,  0.5117194 ,  0.54058946],
+                   [ 0.67226675,  0.68829929,  0.71035014,  0.73846891],
+                   [ 0.87298831,  0.88791376,  0.90905395,  0.93646   ]],
+                  [[ 0.07750784,  0.09371987,  0.11555569,  0.143181  ],
+                   [ 0.27864693,  0.29343991,  0.31409396,  0.3407813 ],
+                   [ 0.47974374,  0.49317377,  0.5126926 ,  0.53847878],
+                   [ 0.68079346,  0.69292265,  0.71135777,  0.73628353],
+                   [ 0.88179203,  0.89268732,  0.91009431,  0.93420362]],
+                  [[ 0.0841765 ,  0.09776672,  0.11711835,  0.14249779],
+                   [ 0.28559463,  0.29765609,  0.31572199,  0.34006951],
+                   [ 0.48695967,  0.49755273,  0.51438349,  0.5377395 ],
+                   [ 0.68826567,  0.69745713,  0.71310872,  0.735518  ],
+                   [ 0.88950757,  0.89736946,  0.91190228,  0.93341316]],
+                  [[ 0.08992499,  0.10150104,  0.11891032,  0.14250149],
+                   [ 0.29158517,  0.30154765,  0.31758943,  0.34007336],
+                   [ 0.49318268,  0.50159531,  0.51632339,  0.5377435 ],
+                   [ 0.69471052,  0.70164382,  0.71511777,  0.73552215],
+                   [ 0.8961628 ,  0.90169281,  0.91397691,  0.93341744]]]
+            ], dtype=object
+    )
+
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+    Middle_Weights.log.print_entries()
+
+    # Test Programatic logging
+    # Test Programatic logging
+    Hidden_Layer_2.log.log_value(pnl.VALUE)
+    log_val = Hidden_Layer_2.log.nparray(header=False)
+    expected_log_val = np.array(
+            [
+                [[0]],
+                [[[0.8565238418942037, 0.8601053239957609, 0.8662098921116546, 0.8746933736954071]]]
+            ], dtype=object
+    )
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+
+    Hidden_Layer_2.log.print_entries()
+
+    # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
+    Middle_Weights.log.clear_entries(entries=None, confirm=False)
+    Middle_Weights.log_items(('matrix', pnl.LEARNING))
+    s.run(
+            num_trials=5,
+            inputs=stim_list,
+            targets=target_list,
+    )
+    log_val = Middle_Weights.log.nparray(entries='matrix', header=False)
+    expected_log_val = np.array(
+                [
+                    [[0], [0], [0], [0], [0]],
+                    [[21], [23], [25], [27], [29]],
+                    [[3], [3], [3], [3], [3]],
+                    [  [[0.09925812411381937, 0.1079522130303428, 0.12252820028789306, 0.14345816973727732],
+                        [0.30131473371328343, 0.30827285172236585, 0.3213609999139731, 0.3410707131678078],
+                        [0.5032924245149345, 0.5085833053183328, 0.5202423523987703, 0.5387798509126243],
+                        [0.70518251216691, 0.7088822116145151, 0.7191771716324874, 0.7365956448426355],
+                        [0.9069777724600303, 0.9091682860319945, 0.9181692763668221, 0.93452610920817]],
+                       [[0.103113468050986, 0.11073719161508278, 0.12424368674464399, 0.14415219181047598],
+                        [0.3053351724284921, 0.3111770895557729, 0.3231499474835138, 0.341794454877438],
+                        [0.5074709829757806, 0.5116017638574931, 0.5221016574478528, 0.5395320566440044],
+                        [0.7095115080472698, 0.7120093413898914, 0.7211034158081356, 0.7373749316571768],
+                        [0.9114489813353512, 0.9123981459792809, 0.9201588001021687, 0.935330996581107]],
+                      [[0.10656261740658036, 0.11328192907953168, 0.12587702586370172, 0.14490737831188183],
+                       [0.30893272045369513, 0.31383131362555394, 0.32485356055342113, 0.3425821330631872],
+                       [0.5112105492674988, 0.5143607671543178, 0.5238725230390068, 0.5403508295336265],
+                       [0.7133860755337162, 0.7148679468096026, 0.7229382109974996, 0.7382232628724675],
+                       [0.9154510531345043, 0.9153508224199809, 0.9220539747533424, 0.936207244690072]],
+                      [[0.10967776822419642, 0.11562091141141007, 0.12742795007904037, 0.14569308665620523],
+                       [0.3121824816018084, 0.316271366885665, 0.3264715025259811, 0.34340179304134666],
+                       [0.5145890402653069, 0.5168974760377518, 0.5255545550838675, 0.5412029579613059],
+                       [0.7168868378231593, 0.7174964619674593, 0.7246811176253708, 0.7391062307617761],
+                       [0.9190671994078436, 0.9180659725806082, 0.923854327015523, 0.9371193149131859]],
+                      [[0.11251466428344682, 0.11778293740676549, 0.12890014813698167, 0.14649079441816393],
+                       [0.31514245505635713, 0.3185271913574249, 0.328007571201157, 0.3442341089776976],
+                       [0.5176666356203712, 0.5192429413004418, 0.5271516632648602, 0.5420683480396268],
+                       [0.7200760707077265, 0.7199270072739019, 0.7263361597421493, 0.7400030122347587],
+                       [0.922361699102421, 0.9205767427437028, 0.9255639970037588, 0.9380456963960624]]]
+        ], dtype=object
+    )
+
+    assert log_val.shape == expected_log_val.shape
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+test_multilayer()
 
 #endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 5a512d5c587..65564dec69e 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -339,7 +339,7 @@
 
 from psyneulink.scheduling.time import TimeScale
 from psyneulink.globals.utilities import ContentAddressableList, AutoNumber
-from psyneulink.globals.keywords import INITIALIZING, EXECUTING, VALIDATE, LEARNING, VALUE, \
+from psyneulink.globals.keywords import INITIALIZING, EXECUTING, VALIDATE, LEARNING, COMMAND_LINE, VALUE, \
     kwContext, kwTime, kwValue
 
 
@@ -348,6 +348,7 @@
 ]
 
 
+# FIX: REPLACE WITH Flags and auto IF/WHEN MOVE TO Python 3.6
 class LogLevel(IntEnum):
     """Specifies levels of logging, as descrdibed below."""
     OFF = 0
@@ -370,10 +371,16 @@ class LogLevel(IntEnum):
     """Record final value assignments during Composition execution."""
     FINAL = 1<<8                    # 256
     """Synonym of VALUE_ASSIGNMENT."""
+    COMMAND_LINE = 1 << 9           # 512
     ALL_ASSIGNMENTS = \
         INITIALIZATION | VALIDATION | EXECUTION | PROCESSING | LEARNING | CONTROL | VALUE_ASSIGNMENT | FINAL
     """Record all value assignments."""
 
+    # @classmethod
+    # def _log_level_max(cls):
+    #     return max([cls[i].value for i in list(cls.__members__) if cls[i] is not LogLevel.ALL_ASSIGNMENTS])
+
+
 LogEntry = namedtuple('LogEntry', 'time, context, value')
 
 ALL_ENTRIES = 'all entries'
@@ -390,6 +397,8 @@ def _get_log_context(context):
         context_flag |= LogLevel.EXECUTION
     if LEARNING in context:
         context_flag |= LogLevel.LEARNING
+    if COMMAND_LINE in context:
+        context_flag |= LogLevel.COMMAND_LINE
     return context_flag
 
 
@@ -612,62 +621,6 @@ def __init__(self, owner, entries=None):
         if entries is None:
             return
 
-    def _alias_owner_name(self, name):
-        """Alias name of owner Component to VALUE in loggable_items and logged_items
-        Component's actual name is preserved and used in log_entries (i.e., as entered by _log_value)
-        """
-        return VALUE if name is self.owner.name else name
-
-    @property
-    def loggable_items(self):
-        """Return dict of loggable items.
-
-        Keys are names of the Components, values their LogLevels
-        """
-        # FIX: The following crashes during init as prefs have not all been assigned
-        # return {key: value for (key, value) in [(c.name, c.logPref.name) for c in self.loggable_components]}
-
-        loggable_items = {}
-        for c in self.loggable_components:
-            name = self._alias_owner_name(c.name)
-            try:
-                log_pref = c.logPref.name
-            except:
-                log_pref = None
-            loggable_items[name] = log_pref
-        return loggable_items
-
-
-    @property
-    def loggable_components(self):
-        """Return a list of owner's Components that are loggable
-
-        The loggable items of a Component are the Components (typically States) specified in the _logagble_items
-        property of its class, and its own `value <Component.value>` attribute.
-        """
-        from psyneulink.components.component import Component
-
-        try:
-            loggable_items = ContentAddressableList(component_type=Component, list=self.owner._loggable_items)
-            loggable_items[self.owner.name] = self.owner
-        except AttributeError:
-            return []
-        return loggable_items
-
-    @property
-    def logged_items(self):
-        """Dict of items that have logged `entries <Log.entries>`, indicating their specified `LogLevel`.
-        """
-        log_level = 'LogLevel.'
-        # Return LogLevel for items in log.entries
-
-        logged_items = {key: value for (key, value) in
-                        # [(l, self.loggable_components[l].logPref.name)
-                        [(self._alias_owner_name(l), self.loggable_items[self._alias_owner_name(l)])
-                         for l in self.logged_entries.keys()]}
-
-        return logged_items
-
     def log_items(self, items, log_level=LogLevel.EXECUTION):
         """Specifies items to be logged at the specified `LogLevel`\\(s).
 
@@ -737,6 +690,8 @@ def _log_value(self, value, context=None):
         Identifies the context in which the call is being made, which is assigned to the context field of the
         `LogEntry`, along with the current time stamp and value itself
 
+        If value is None, uses owner's `value <Component.value>` attribute.
+
         .. note::
             Since _log_value is usually called by the setter for the `value <Component.value>` property of a Component
             (which doesn't/can't receive a context argument), it does not pass a **context** argument to _log_value;
@@ -745,8 +700,16 @@ def _log_value(self, value, context=None):
 
         """
         from psyneulink.components.component import Component
+        programmatic = False
+
 
-        # Get context
+        if context is COMMAND_LINE:
+            # If _log_value is being called programmatically,
+            #    flag for later and set context to None to get context from the stack
+            programmatic = True
+            context = None
+
+        # Get context from the stack
         if context is None:
             curr_frame = inspect.currentframe()
             prev_frame = inspect.getouterframes(curr_frame, 2)
@@ -764,23 +727,27 @@ def _log_value(self, value, context=None):
                 else:
                     break
 
-        if context is None:
-            # No context was specified in any frame
-            raise LogError("PROGRAM ERROR: No context specification found in any frame")
-
         # If context is a Component object, it must be during its initialization, so assign accordingly:
         if isinstance(context, Component):
             context = "{} of {}".format(INITIALIZING, context.name)
 
+        # No context was specified in any frame
+        if context is None:
+            raise LogError("PROGRAM ERROR: No context specification found in any frame")
+
         if not isinstance(context, str):
             raise LogError("PROGRAM ERROR: Unrecognized context specification ({})".format(context))
 
+        # Context is an empty string, but called programatically
+        if not context and programmatic:
+            context = COMMAND_LINE
+
         context_flags = _get_log_context(context)
 
         log_pref = self.owner.prefs.logPref if self.owner.prefs else None
 
-        # Log value if logging condition is satisfied
-        if log_pref and log_pref == context_flags:
+        # Log value if logging condition is satisfied or called for programmatically
+        if (log_pref and log_pref == context_flags) or context_flags & LogLevel.COMMAND_LINE:
         # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
         # if log_pref and log_pref | context_flags:
 
@@ -846,6 +813,75 @@ def _get_time(self, context, context_flags):
 
         return time or no_time
 
+    @tc.typecheck
+    def log_value(self, entries):
+        """Log the value of a Component.
+
+        Arguments
+        ---------
+
+        entries : string, Component or list of them : default None
+            specifies the Components, the current `value <Component.value>`\\s of which should be added to the Log.
+            they must be `loggable_items <Log.loggable_items>` of the owner's Log. If **entries** is `ALL`, `None`
+            or omitted, then the `value <Component.value> of all `loggable_items <Log.loggable_items>` are logged.
+        """
+        entries = self._validate_entries_arg(entries)
+
+        # Validate the Component field of each LogEntry
+        for entry in entries:
+            self._log_value(self.loggable_components[self._dealias_owner_name(entry)].value, context=COMMAND_LINE)
+
+    def clear_entries(self, entries=ALL_LOG_ENTRIES, delete_entry=True, confirm=False):
+        """Clear one or more entries either by deleting the entry or just removing its data.
+
+        Arguments
+        ---------
+
+        entries : string, Component or list of them : default None
+            specifies the entries of the Log to be cleared;  they must be `loggable_items
+            <Log.loggable_items>` of the Log that have been logged (i.e., are also `logged_items <Log.logged_items>`).
+            If **entries** is `ALL`, `None` or omitted, then all `logged_items <Log.logged_items>` are cleared.
+
+        delete_entry : bool : default True
+            specifies whether to delete the entry (if `True`) from the log to which it belongs, or just
+            delete the data, but leave the entry itself (if `False`).
+
+            .. note::
+                This option is included for generality and potential future features, but is not advised;
+                the Log interface (e.g., the `logged_items <Log.logged_items>` interface generally assumes that
+                the only `entries <Log.entries>` in a log are ones with data.
+
+        confirm : bool : default False
+            specifies whether user confirmation is required before clearing the entries.
+
+            .. note::
+                If **confirm** is `True`, only a single confirmation will occur for a list or Log.ALL_LOG_ENTRIES
+
+        """
+
+        entries = self._validate_entries_arg(entries)
+
+        # If any entries remain
+        if entries:
+            if confirm:
+                delete = input("\nAll data will be deleted from {0} in the Log for {1}.  Proceed? (y/n)".
+                               format(entries,self.owner.name))
+                while delete != 'y' and delete != 'y':
+                    input("\nDelete all data from entries? (y/n)")
+                if delete == 'n':
+                    return
+
+            # Reset entries
+            for entry in entries:
+                self.logged_entries[entry]=[]
+                if delete_entry:
+                # Delete the entire entry from the log to which it belongs
+                    del self.loggable_components[entry].log.entries[entry]
+                else:
+                    # Delete the data for the entry but leave the entry itself in the log to which it belongs
+                    del self.logged_entries[entry][0:]
+                assert True
+
     def print_entries(self,
                       entries=None,
                       csv=False,
@@ -863,13 +899,7 @@ def print_entries(self,
         Issue a warning if an entry is not in the Log dict
         """
 
-        # If Log.ALL_LOG_ENTRIES, set entries to all entries in self.logged_entries
-        if entries is ALL_ENTRIES or entries is None:
-            entries = self.logged_entries.keys()
-
-        # If entries is a single entry, put in list for processing below
-        if isinstance(entries, str):
-            entries = [entries]
+        entries = self._validate_entries_arg(entries, logged=True)
 
         if csv is True:
             print(self.csv(entries))
@@ -1000,26 +1030,8 @@ def nparray(self,
         Returns:
             2d np.array
         """
-        from psyneulink.components.component import Component
 
-        # If Log.ALL_LOG_ENTRIES, set entries to all entries in self.logged_entries
-        if entries is ALL_ENTRIES or entries is None:
-            entries = self.logged_entries.keys()
-
-        # If entries is a single entry, put in list for processing below
-        if isinstance(entries, (str, Component)):
-            entries = [entries]
-
-        # Make sure all entries are the names of Components
-        entries = [entry.name if isinstance(entry, Component) else entry for entry in entries ]
-
-        # Validate entries
-        for entry in entries:
-            if self._alias_owner_name(entry) not in self.loggable_items:
-                raise LogError("{0} is not a loggable attribute of {1}".format(repr(entry), self.owner.name))
-            if entry not in self.logged_entries:
-                raise LogError("{} is not currently being logged by {} (try using log_items)".
-                               format(repr(entry), self.owner.name))
+        entries = self._validate_entries_arg(entries, logged=True)
 
         if owner_name is True:
             owner_name_str = self.owner.name
@@ -1159,79 +1171,98 @@ def csv(self, entries=None, owner_name:bool=False, quotes:tc.optional(tc.any(boo
 
         return(csv)
 
-    @property
-    def logged_entries(self):
-        entries = {}
-        for e in self.loggable_components:
-            entries.update(e.log.entries)
+    def _validate_entries_arg(self, entries, loggable=True, logged=False):
+        from psyneulink.components.component import Component
+
+        # If Log.ALL_LOG_ENTRIES, set entries to all entries in self.logged_entries
+        if entries is ALL_ENTRIES or entries is None:
+            entries = self.logged_entries.keys()
+
+        # If entries is a single entry, put in list for processing below
+        if isinstance(entries, (str, Component)):
+            entries = [entries]
+
+        # Make sure all entries are the names of Components
+        entries = [entry.name if isinstance(entry, Component) else entry for entry in entries ]
+
+        # Validate entries
+        for entry in entries:
+            if loggable:
+                if self._alias_owner_name(entry) not in self.loggable_items:
+                    raise LogError("{0} is not a loggable attribute of {1}".format(repr(entry), self.owner.name))
+            if logged:
+                if entry not in self.logged_entries:
+                    raise LogError("{} is not currently being logged by {} (try using log_items)".
+                                   format(repr(entry), self.owner.name))
         return entries
 
-    def clear_entries(self, entries=ALL_LOG_ENTRIES, delete_entry=True, confirm=False):
-        """Clear one or more entries either by deleting the entry or just removing its data.
+    def _alias_owner_name(self, name):
+        """Alias name of owner Component to VALUE in loggable_items and logged_items
+        Component's actual name is preserved and used in log_entries (i.e., as entered by _log_value)
+        """
+        return VALUE if name is self.owner.name else name
 
-        Arguments
-        ---------
 
-        entries : string, Component or list of them : default None
-            specifies the entries of the Log to be cleared;  they must be `loggable_items
-            <Log.loggable_items>` of the Log that have been logged (i.e., are also `logged_items <Log.logged_items>`).
-            If **entries** is `ALL`, `None` or omitted, then all `logged_items <Log.logged_items>` are cleared.
+    def _dealias_owner_name(self, name):
+        """De-alias VALUE to name of owner
+        """
+        return self.owner.name if name is VALUE else name
 
-        delete_entry : bool : default True
-            specifies whether to delete the entry (if `True`) from the log to which it belongs, or just
-            delete the data, but leave the entry itself (if `False`).
+    @property
+    def loggable_items(self):
+        """Return dict of loggable items.
 
-            .. note::
-                This option is included for generality and potential future features, but is not advised;
-                the Log interface (e.g., the `logged_items <Log.logged_items>` interface generally assumes that
-                the only `entries <Log.entries>` in a log are ones with data.
+        Keys are names of the Components, values their LogLevels
+        """
+        # FIX: The following crashes during init as prefs have not all been assigned
+        # return {key: value for (key, value) in [(c.name, c.logPref.name) for c in self.loggable_components]}
 
-        confirm : bool : default False
-            specifies whether user confirmation is required before clearing the entries.
+        loggable_items = {}
+        for c in self.loggable_components:
+            name = self._alias_owner_name(c.name)
+            try:
+                log_pref = c.logPref.name
+            except:
+                log_pref = None
+            loggable_items[name] = log_pref
+        return loggable_items
 
-            .. note::
-                If **confirm** is `True`, only a single confirmation will occur for a list or Log.ALL_LOG_ENTRIES
+    @property
+    def loggable_components(self):
+        """Return a list of owner's Components that are loggable
 
+        The loggable items of a Component are the Components (typically States) specified in the _logagble_items
+        property of its class, and its own `value <Component.value>` attribute.
         """
+        from psyneulink.components.component import Component
 
-        # If Log.ALL_LOG_ENTRIES, set entries to all entries in self.entries
-        if entries in {None, Log.ALL_LOG_ENTRIES}:
-            entries = self.logged_items.keys()
+        try:
+            loggable_items = ContentAddressableList(component_type=Component, list=self.owner._loggable_items)
+            loggable_items[self.owner.name] = self.owner
+        except AttributeError:
+            return []
+        return loggable_items
 
-        # If entries is a single entry, put in list for processing below
-        if isinstance(entries, str):
-            entries = [entries]
+    @property
+    def logged_items(self):
+        """Dict of items that have logged `entries <Log.entries>`, indicating their specified `LogLevel`.
+        """
+        log_level = 'LogLevel.'
+        # Return LogLevel for items in log.entries
 
-        # Validate each entry and delete bad ones from entries
-        for entry in entries:
-            try:
-                self.logged_items[entry]
-            except KeyError:
-                if self.owner.verbosePref:
-                    warnings.warn("Warning: {0} is not an entry in Log of {1}".
-                                  format(entry,self.owner.name))
-                del(entries, entry)
+        logged_items = {key: value for (key, value) in
+                        # [(l, self.loggable_components[l].logPref.name)
+                        [(self._alias_owner_name(l), self.loggable_items[self._alias_owner_name(l)])
+                         for l in self.logged_entries.keys()]}
 
-        # If any entries remain
-        if entries:
-            if confirm:
-                delete = input("\nAll data will be deleted from {0} in the Log for {1}.  Proceed? (y/n)".
-                               format(entries,self.owner.name))
-                while delete != 'y' and delete != 'y':
-                    input("\nDelete all data from entries? (y/n)")
-                if delete == 'n':
-                    return
+        return logged_items
 
-            # Reset entries
-            for entry in entries:
-                self.logged_entries[entry]=[]
-                if delete_entry:
-                # Delete the entire entry from the log to which it belongs
-                    del self.loggable_components[entry].log.entries[entry]
-                else:
-                    # Delete the data for the entry but leave the entry itself in the log to which it belongs
-                    del self.logged_entries[entry][0:]
-                assert True
+    @property
+    def logged_entries(self):
+        entries = {}
+        for e in self.loggable_components:
+            entries.update(e.log.entries)
+        return entries
 
     # def save_log(self):
     #     print("Saved")
diff --git a/tests/learning/test_multilayer.py b/tests/learning/test_multilayer.py
index 490f843d1a8..12b4c943530 100644
--- a/tests/learning/test_multilayer.py
+++ b/tests/learning/test_multilayer.py
@@ -5,7 +5,7 @@
 from psyneulink.components.process import Process
 from psyneulink.components.projections.pathway.mappingprojection import MappingProjection
 from psyneulink.components.system import System
-from psyneulink.globals.keywords import LEARNING, SOFT_CLAMP, EXECUTION, LEARNING
+from psyneulink.globals.keywords import SOFT_CLAMP, EXECUTION, LEARNING, VALUE
 from psyneulink.globals.preferences.componentpreferenceset import REPORT_OUTPUT_PREF, VERBOSE_PREF
 from psyneulink.library.mechanisms.processing.objective.comparatormechanism import MSE
 
@@ -256,6 +256,26 @@ def show_target():
 
     Middle_Weights.log.print_entries()
 
+    # Test Programatic logging
+    Hidden_Layer_2.log.log_value(VALUE)
+    log_val = Hidden_Layer_2.log.nparray(header=False)
+    expected_log_val = np.array(
+            [
+                [[0]],
+                [[[0.8565238418942037, 0.8601053239957609, 0.8662098921116546, 0.8746933736954071]]]
+            ], dtype=object
+    )
+    for i in range(len(log_val)):
+        try:
+            np.testing.assert_array_equal(log_val[i], expected_log_val[i])
+        except:
+            for j in range(len(log_val[i])):
+                np.testing.assert_allclose(np.array(log_val[i][j]), np.array(expected_log_val[i][j]),
+                                           atol=1e-08,
+                                           err_msg='Failed on test of logged values')
+    Hidden_Layer_2.log.print_entries()
+
+
     # Clear log and test with logging of weights set to LEARNING for another 5 trials of learning
     Middle_Weights.log.clear_entries(entries=None, confirm=False)
     Middle_Weights.log_items(('matrix', LEARNING))

From 36c0988abc2e9b790c9456e8ac2b6dfb2f0177ac Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 23:09:48 -0500
Subject: [PATCH 27/52] =?UTF-8?q?=E2=80=A2=20Log=20=C2=A0=20-=20log=5Fvalu?=
 =?UTF-8?q?e:=20implemented=20=C2=A0=20-=20logged=5Fitem,=20print=5Fentrie?=
 =?UTF-8?q?s:=C2=A0=20corrected=20to=20use=20'value'=20rather=20than=20own?=
 =?UTF-8?q?er's=20name=20in=20reports?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Tests
  - test_multilayer: added test of log_value
---
 psyneulink/globals/log.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 65564dec69e..329ff81d1b9 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -949,8 +949,8 @@ def print_entries(self,
                 import numpy as np
                 for i, item in enumerate(datum):
                     time, context, value = item
-                    if isinstance(value, np.ndarray):
-                        value = value[0]
+                    # if isinstance(value, np.ndarray):
+                    #     value = value[0]
                     time_str = _time_string(time)
                     attrib_name = self._alias_owner_name(attrib_name)
                     data_str = repr(attrib_name).ljust(variable_width, kwSpacer)

From 3a295403d7eff9f9ed006ba5eaf216ccaa8360bf Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 10 Dec 2017 23:20:40 -0500
Subject: [PATCH 28/52] =?UTF-8?q?=E2=80=A2=20Log=20=C2=A0=20-=20log=5Fvalu?=
 =?UTF-8?q?e:=20implemented=20=C2=A0=20-=20logged=5Fitem,=20print=5Fentrie?=
 =?UTF-8?q?s:=C2=A0=20corrected=20to=20use=20'value'=20rather=20than=20own?=
 =?UTF-8?q?er's=20name=20in=20reports?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Tests
  - test_multilayer: added test of log_value
---
 Scripts/Scratch Pad.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Scripts/Scratch Pad.py b/Scripts/Scratch Pad.py
index 4fb65fdc932..93867847d0f 100644
--- a/Scripts/Scratch Pad.py	
+++ b/Scripts/Scratch Pad.py	
@@ -2752,7 +2752,6 @@ def show_target():
 #endregion
 
 #region TEST OVER-WRITING OF LOG @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
-#
 
 # class a:
 #

From 5f043a0ad77701a8c2cd3c36457172678d3bffc0 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Mon, 11 Dec 2017 23:07:22 -0500
Subject: [PATCH 29/52] -

---
 .idea/runConfigurations/Make_HTML.xml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.idea/runConfigurations/Make_HTML.xml b/.idea/runConfigurations/Make_HTML.xml
index cad024aa603..eacc86ebae0 100644
--- a/.idea/runConfigurations/Make_HTML.xml
+++ b/.idea/runConfigurations/Make_HTML.xml
@@ -1,14 +1,14 @@
 <component name="ProjectRunConfigurationManager">
-  <configuration default="false" name="Make HTML" type="docs" factoryName="Sphinx task" singleton="true">
+  <configuration default="false" factoryName="Sphinx task" name="Make HTML" singleton="true" type="docs">
     <option name="INTERPRETER_OPTIONS" value="" />
     <option name="PARENT_ENVS" value="true" />
     <envs />
-    <option name="SDK_HOME" value="$USER_HOME$/anaconda/envs/python35/bin/python" />
+    <option name="SDK_HOME" value="" />
     <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/docs" />
     <option name="IS_MODULE_SDK" value="false" />
     <option name="ADD_CONTENT_ROOTS" value="true" />
     <option name="ADD_SOURCE_ROOTS" value="true" />
-    <module name="PsyNeuLink (Dropbox)" />
+    <module name="" />
     <EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" runner="coverage.py" sample_coverage="true" />
     <option name="docutils_input_file" value="$PROJECT_DIR$/docs/source" />
     <option name="docutils_output_file" value="$PROJECT_DIR$/docs/build/html" />

From 7baddeaa43b4a5ca4308e74a562b287fdb7e1936 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Tue, 12 Dec 2017 18:17:24 -0500
Subject: [PATCH 30/52] =?UTF-8?q?=E2=80=A2=20Component=20=20=20-=20moved?=
 =?UTF-8?q?=20value=20property=20to=20Component=20(from=20Mechanism,=20Pro?=
 =?UTF-8?q?jection=20and=20State)=20=20=20=20=20(left=20an=20override=20on?=
 =?UTF-8?q?=20ControlSignal=20that=20needs=20it=20for=20the=20getter)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/globals/log.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 50b328b4183..b6bd7cc5e1e 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -267,7 +267,6 @@
            [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
             [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], dtype=object)
 
-
 JENKINS:
     >> proj_A_to_B.log.nparray(entries=[pnl.MATRIX], owner_name=False, header=False)
     array([[list([0]), list([1])],

From 979a9eac7ffedeb7e8436ba481ef4f9ac93a591a Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Wed, 13 Dec 2017 16:42:39 -0500
Subject: [PATCH 31/52] =?UTF-8?q?=E2=80=A2=C2=A0Log=20=20=20-=20docstring:?=
 =?UTF-8?q?=20added=20hint=20about=20using=20call=5Fbefore=5Ftrial=20and?=
 =?UTF-8?q?=20call=5Fafter=5Ftrial=20=20=20=20=20to=20log=20values?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .idea/runConfigurations/scratch_1.xml | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 .idea/runConfigurations/scratch_1.xml

diff --git a/.idea/runConfigurations/scratch_1.xml b/.idea/runConfigurations/scratch_1.xml
new file mode 100644
index 00000000000..aacc9cfbe83
--- /dev/null
+++ b/.idea/runConfigurations/scratch_1.xml
@@ -0,0 +1,21 @@
+<component name="ProjectRunConfigurationManager">
+  <configuration default="false" factoryName="Python" name="scratch_1" singleton="true" type="PythonConfigurationType">
+    <option name="INTERPRETER_OPTIONS" value="" />
+    <option name="PARENT_ENVS" value="true" />
+    <envs>
+      <env name="PYTHONUNBUFFERED" value="1" />
+    </envs>
+    <option name="SDK_HOME" value="" />
+    <option name="WORKING_DIRECTORY" value="$APPLICATION_CONFIG_DIR$/scratches" />
+    <option name="IS_MODULE_SDK" value="false" />
+    <option name="ADD_CONTENT_ROOTS" value="true" />
+    <option name="ADD_SOURCE_ROOTS" value="true" />
+    <module name="" />
+    <option name="SCRIPT_NAME" value="$APPLICATION_CONFIG_DIR$/scratches/scratch_1.py" />
+    <option name="PARAMETERS" value="" />
+    <option name="SHOW_COMMAND_LINE" value="false" />
+    <option name="EMULATE_TERMINAL" value="false" />
+    <option name="MODULE_MODE" value="false" />
+    <method />
+  </configuration>
+</component>
\ No newline at end of file

From dd84cade9183923d72fa1de7cba0759dcfe3a9f5 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Thu, 14 Dec 2017 18:05:12 -0500
Subject: [PATCH 32/52] =?UTF-8?q?=E2=80=A2=20Mechanism=20=20=20execute():?=
 =?UTF-8?q?=20added=20value=20as=20local=20buffer=20variable=20before=20as?=
 =?UTF-8?q?signing=20to=20self.value=20=20=20=20=20=20=20=20=20=20=20=20?=
 =?UTF-8?q?=20=20to=20avoid=20multiple=20calls=20(and=20log=20entries)=20o?=
 =?UTF-8?q?f=20self.value=20property?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/components/mechanisms/mechanism.py | 39 +++++++------------
 1 file changed, 13 insertions(+), 26 deletions(-)

diff --git a/psyneulink/components/mechanisms/mechanism.py b/psyneulink/components/mechanisms/mechanism.py
index a72733457d3..aef3d8c5075 100644
--- a/psyneulink/components/mechanisms/mechanism.py
+++ b/psyneulink/components/mechanisms/mechanism.py
@@ -2009,51 +2009,38 @@ def execute(self,
 
         #region CALL SUBCLASS _execute method AND ASSIGN RESULT TO self.value
 
-        self.value = self._execute(
-            variable=variable,
-            runtime_params=runtime_params,
-            time_scale=time_scale,
-            context=context,
-        )
+        value = self._execute(variable=variable,
+                                   runtime_params=runtime_params,
+                                   time_scale=time_scale,
+                                   context=context,
+                                   )
 
-        # # MODIFIED 3/3/17 OLD:
-        # self.value = np.atleast_2d(self.value)
-        # # MODIFIED 3/3/17 NEW:
-        # converted_to_2d = np.atleast_2d(self.value)
-        # # If self.value is a list of heterogenous elements, leave as is;
-        # # Otherwise, use converted value (which is a genuine 2d array)
-        # if converted_to_2d.dtype != object:
-        #     self.value = converted_to_2d
-        # MODIFIED 3/8/17 NEWER:
         # IMPLEMENTATION NOTE:  THIS IS HERE BECAUSE IF return_value IS A LIST, AND THE LENGTH OF ALL OF ITS
         #                       ELEMENTS ALONG ALL DIMENSIONS ARE EQUAL (E.G., A 2X2 MATRIX PAIRED WITH AN
         #                       ARRAY OF LENGTH 2), np.array (AS WELL AS np.atleast_2d) GENERATES A ValueError
-        if (isinstance(self.value, list) and
-            (all(isinstance(item, np.ndarray) for item in self.value) and
+        if (isinstance(value, list) and
+            (all(isinstance(item, np.ndarray) for item in value) and
                 all(
-                        all(item.shape[i]==self.value[0].shape[0]
+                        all(item.shape[i]==value[0].shape[0]
                             for i in range(len(item.shape)))
-                        for item in self.value))):
-                # return self.value
+                        for item in value))):
                 pass
         else:
-            converted_to_2d = np.atleast_2d(self.value)
+            converted_to_2d = np.atleast_2d(value)
             # If return_value is a list of heterogenous elements, return as is
             #     (satisfies requirement that return_value be an array of possibly multidimensional values)
             if converted_to_2d.dtype == object:
-                # return self.value
                 pass
             # Otherwise, return value converted to 2d np.array
             else:
                 # return converted_to_2d
-                self.value = converted_to_2d
-        # MODIFIED 3/3/17 END
+                value = converted_to_2d
 
         # Set status based on whether self.value has changed
-        self.status = self.value
-
+        self.status = value
         #endregion
 
+        self.value = value
 
         #region UPDATE OUTPUT STATE(S)
         self._update_output_states(runtime_params=runtime_params, time_scale=time_scale, context=context)

From 1dab5f87b4bf962b3d38cd60e6b012f97e790c5d Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Thu, 14 Dec 2017 18:32:36 -0500
Subject: [PATCH 33/52] =?UTF-8?q?=E2=80=A2=20Mechanism=20=20=20execute():?=
 =?UTF-8?q?=20added=20value=20as=20local=20buffer=20variable=20before=20as?=
 =?UTF-8?q?signing=20to=20self.value=20=20=20=20=20=20=20=20=20=20=20=20?=
 =?UTF-8?q?=20=20to=20avoid=20multiple=20calls=20(and=20log=20entries)=20o?=
 =?UTF-8?q?f=20self.value=20property?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• Tests
  - test_matrix_spec_and_learning: added
---
 Scripts/MARKUS TEST SCRIPT.py                 | 58 +++++--------------
 psyneulink/components/mechanisms/mechanism.py |  2 +
 .../learning/test_matrix_spec_and_learning.py | 17 ++++++
 3 files changed, 32 insertions(+), 45 deletions(-)
 create mode 100644 tests/learning/test_matrix_spec_and_learning.py

diff --git a/Scripts/MARKUS TEST SCRIPT.py b/Scripts/MARKUS TEST SCRIPT.py
index 5d913cddaad..284f923e3b8 100644
--- a/Scripts/MARKUS TEST SCRIPT.py	
+++ b/Scripts/MARKUS TEST SCRIPT.py	
@@ -1,48 +1,16 @@
 import psyneulink as pnl
 import numpy as np
 
-# Built 2 layer network
-
-# Create inpute and output layer
-input_layer = pnl.TransferMechanism(size = 2,
-                                    initial_value= [[0.0,0.0]],
-                                    name = 'INPUT LAYER')
-input_layer.loggable_items
-input_layer.set_log_conditions('value')
-
-
-output_layer = pnl.TransferMechanism(size= 1,
-                                   function = pnl.Logistic,
-                                   name = 'OUTPUT LAYER')
-
-# Create weights from input to output and from bias to output layer
-#First set the
-weight1 = 0.1 #np.random.uniform(0, 0.1)
-weight2 = 0.1 #np.random.uniform(0, 0.1)
-input_output_weights = np.array([[weight1],
-                               [weight2]])
-
-# Create Process from input layer to output layer
-model_process = pnl.Process(pathway=[input_layer,
-                                    input_output_weights,
-                                    output_layer],
-                           learning = pnl.ENABLED,
-                           target= 1.0
-                          )
-
-# Create System by putting the process in the System.
-new_system = pnl.System(processes = [model_process])
-stim_list_dict = {input_layer:[1,0]}
-target_list_dict = {output_layer:[[1]]}
-
-new_system.run(stim_list_dict,
-              targets =target_list_dict,
-              num_trials= 2,
-              learning = True)
-
-print(new_system.results)
-# new_system.show_graph(show_learning=True)
-
-input_layer.log.nparray()
-
-#
\ No newline at end of file
+# Create input and output layer
+T1 = pnl.TransferMechanism(size = 2,
+                           initial_value= [[0.0,0.0]],
+                           name = 'INPUT LAYER')
+T2 = pnl.TransferMechanism(size= 1,
+                           function = pnl.Logistic,
+                           name = 'OUTPUT LAYER')
+W = np.array([[0.1],[0.2]])
+P = pnl.Process(pathway=[T1, W, T2], learning = pnl.ENABLED, target= 1.0)
+S = pnl.System(processes = [P])
+IN = {T1:[1,0]}
+TARG = {T2:[[1]]}
+S.run(IN, targets=TARG, num_trials= 2, learning = True)
diff --git a/psyneulink/components/mechanisms/mechanism.py b/psyneulink/components/mechanisms/mechanism.py
index aef3d8c5075..be584be6c25 100644
--- a/psyneulink/components/mechanisms/mechanism.py
+++ b/psyneulink/components/mechanisms/mechanism.py
@@ -2009,6 +2009,8 @@ def execute(self,
 
         #region CALL SUBCLASS _execute method AND ASSIGN RESULT TO self.value
 
+        # IMPLEMENTATION NOTE: use value as buffer variable until it has been fully processed
+        #                      to avoid multiple calls to (and potential log entries for) self.value property
         value = self._execute(variable=variable,
                                    runtime_params=runtime_params,
                                    time_scale=time_scale,
diff --git a/tests/learning/test_matrix_spec_and_learning.py b/tests/learning/test_matrix_spec_and_learning.py
new file mode 100644
index 00000000000..8cd654d1ed9
--- /dev/null
+++ b/tests/learning/test_matrix_spec_and_learning.py
@@ -0,0 +1,17 @@
+import psyneulink as pnl
+import numpy as np
+
+# Test matrix specification as np.array with learning enabled
+def test_matrix_spec_and_learning():
+    T1 = pnl.TransferMechanism(size = 2,
+                               initial_value= [[0.0,0.0]],
+                               name = 'INPUT LAYER')
+    T2 = pnl.TransferMechanism(size= 1,
+                               function = pnl.Logistic,
+                               name = 'OUTPUT LAYER')
+    W = np.array([[0.1],[0.2]])
+    P = pnl.Process(pathway=[T1, W, T2], learning = pnl.ENABLED, target= 1.0)
+    S = pnl.System(processes = [P])
+    IN = {T1:[1,0]}
+    TARG = {T2:[[1]]}
+    S.run(IN, targets=TARG, num_trials= 2, learning = True)

From d22e5b8a817d0aaeb09992c60bd599ea6f92dd01 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Thu, 14 Dec 2017 23:09:46 -0500
Subject: [PATCH 34/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20=5Flog=5Ftrials?=
 =?UTF-8?q?=5Fand=5Fruns():=20implemented?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Scripts/MARKUS TEST SCRIPT.py |  16 -----
 psyneulink/globals/log.py     | 113 +++++++++++++++++++---------------
 tests/control/test_EVC.py     |  38 +++++++++++-
 3 files changed, 101 insertions(+), 66 deletions(-)
 delete mode 100644 Scripts/MARKUS TEST SCRIPT.py

diff --git a/Scripts/MARKUS TEST SCRIPT.py b/Scripts/MARKUS TEST SCRIPT.py
deleted file mode 100644
index 284f923e3b8..00000000000
--- a/Scripts/MARKUS TEST SCRIPT.py	
+++ /dev/null
@@ -1,16 +0,0 @@
-import psyneulink as pnl
-import numpy as np
-
-# Create input and output layer
-T1 = pnl.TransferMechanism(size = 2,
-                           initial_value= [[0.0,0.0]],
-                           name = 'INPUT LAYER')
-T2 = pnl.TransferMechanism(size= 1,
-                           function = pnl.Logistic,
-                           name = 'OUTPUT LAYER')
-W = np.array([[0.1],[0.2]])
-P = pnl.Process(pathway=[T1, W, T2], learning = pnl.ENABLED, target= 1.0)
-S = pnl.System(processes = [P])
-IN = {T1:[1,0]}
-TARG = {T2:[[1]]}
-S.run(IN, targets=TARG, num_trials= 2, learning = True)
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index d7bcab88547..7ca972ad795 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -746,57 +746,61 @@ def _log_value(self, value, context=None):
         from psyneulink.components.component import Component
         programmatic = False
 
+        if isinstance(value, LogEntry):
+            self.entries[self.owner.name] = value
 
-        if context is COMMAND_LINE:
-            # If _log_value is being called programmatically,
-            #    flag for later and set context to None to get context from the stack
-            programmatic = True
-            context = None
-
-        # Get context from the stack
-        if context is None:
-            curr_frame = inspect.currentframe()
-            prev_frame = inspect.getouterframes(curr_frame, 2)
-            i = 1
-            # Search stack for first frame (most recent call) with a context specification
-            while context is None:
-                try:
-                    context = inspect.getargvalues(prev_frame[i][0]).locals['context']
-                except KeyError:
-                    # Try earlier frame
-                    i += 1
-                except IndexError:
-                    # Ran out of frames, so just set context to empty string
-                    context = ""
-                else:
-                    break
-
-        # If context is a Component object, it must be during its initialization, so assign accordingly:
-        if isinstance(context, Component):
-            context = "{} of {}".format(INITIALIZING, context.name)
-
-        # No context was specified in any frame
-        if context is None:
-            raise LogError("PROGRAM ERROR: No context specification found in any frame")
-
-        if not isinstance(context, str):
-            raise LogError("PROGRAM ERROR: Unrecognized context specification ({})".format(context))
-
-        # Context is an empty string, but called programatically
-        if not context and programmatic:
-            context = COMMAND_LINE
-            # context = self.owner.prev_context + "FROM " + COMMAND_LINE
-            # context = self.owner.prev_context
-
-        context_flags = _get_log_context(context)
-
-        log_pref = self.owner.prefs.logPref if self.owner.prefs else None
+        else:
 
-        # Log value if logging condition is satisfied or called for programmatically
-        if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
-        # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-        # if log_pref and log_pref | context_flags:
-            self.entries[self.owner.name] = LogEntry(self._get_time(context, context_flags), context, value)
+            if context is COMMAND_LINE:
+                # If _log_value is being called programmatically,
+                #    flag for later and set context to None to get context from the stack
+                programmatic = True
+                context = None
+
+            # Get context from the stack
+            if context is None:
+                curr_frame = inspect.currentframe()
+                prev_frame = inspect.getouterframes(curr_frame, 2)
+                i = 1
+                # Search stack for first frame (most recent call) with a context specification
+                while context is None:
+                    try:
+                        context = inspect.getargvalues(prev_frame[i][0]).locals['context']
+                    except KeyError:
+                        # Try earlier frame
+                        i += 1
+                    except IndexError:
+                        # Ran out of frames, so just set context to empty string
+                        context = ""
+                    else:
+                        break
+
+            # If context is a Component object, it must be during its initialization, so assign accordingly:
+            if isinstance(context, Component):
+                context = "{} of {}".format(INITIALIZING, context.name)
+
+            # No context was specified in any frame
+            if context is None:
+                raise LogError("PROGRAM ERROR: No context specification found in any frame")
+
+            if not isinstance(context, str):
+                raise LogError("PROGRAM ERROR: Unrecognized context specification ({})".format(context))
+
+            # Context is an empty string, but called programatically
+            if not context and programmatic:
+                context = COMMAND_LINE
+                # context = self.owner.prev_context + "FROM " + COMMAND_LINE
+                # context = self.owner.prev_context
+
+            context_flags = _get_log_context(context)
+
+            log_pref = self.owner.prefs.logPref if self.owner.prefs else None
+
+            # Log value if logging condition is satisfied or called for programmatically
+            if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
+            # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
+            # if log_pref and log_pref | context_flags:
+                self.entries[self.owner.name] = LogEntry(self._get_time(context, context_flags), context, value)
 
         if context is not COMMAND_LINE:
             self.owner.prev_context = context
@@ -1437,3 +1441,14 @@ def logged_entries(self):
 
     # def save_log(self):
     #     print("Saved")
+
+def _log_trials_and_runs(self, composition, curr_condition:tc.enum(LogCondition.TRIAL, LogCondition.RUN), context):
+    for mech in composition.mechanisms:
+        for component, condition in mech.loggable_components:
+            if condition is curr_condition:
+                value = LogEntry((composition.simple_time.run,
+                                  composition.simple_time.trial,
+                                  composition.simple_time.time_step),
+                                 context,
+                                 component.value)
+                component.log._log_value(value, context)
diff --git a/tests/control/test_EVC.py b/tests/control/test_EVC.py
index 12cc8fecf25..624f4a8a31d 100644
--- a/tests/control/test_EVC.py
+++ b/tests/control/test_EVC.py
@@ -6,7 +6,7 @@
 from psyneulink.components.process import Process
 from psyneulink.components.projections.modulatory.controlprojection import ControlProjection
 from psyneulink.components.system import System
-from psyneulink.globals.keywords import ALLOCATION_SAMPLES, IDENTITY_MATRIX, MEAN, RESULT, VARIANCE
+from psyneulink.globals.keywords import ALLOCATION_SAMPLES, IDENTITY_MATRIX, MEAN, RESULT, VARIANCE, SLOPE, CONTROL
 from psyneulink.globals.preferences.componentpreferenceset import ComponentPreferenceSet, kpReportOutputPref, kpVerbosePref
 from psyneulink.globals.preferences.preferenceset import PreferenceEntry, PreferenceLevel
 from psyneulink.library.mechanisms.processing.integrator.ddm import DDM, DECISION_VARIABLE, PROBABILITY_UPPER_THRESHOLD, RESPONSE_TIME
@@ -467,6 +467,8 @@ def test_outcome_function(**kwargs):
         0.2645,  0.28289958,  0.98320731, 100.,
     ]
 
+    Flanker_Rep.set_log_conditions((SLOPE, CONTROL))
+
     mySystem.run(
         num_trials=nTrials,
         inputs=stim_list_dict,
@@ -479,6 +481,40 @@ def test_outcome_function(**kwargs):
         verbose=True,
     )
 
+    # log_val = Flanker_Rep.log.nparray(SLOPE, header=False)
+    # trial_vals = [[1], [2], [3], [4], [5],
+    #               [6], [7], [8], [9], [10],
+    #               [11], [12], [13], [14], [15],
+    #               [16], [17], [18], [19], [20],
+    #               [21], [22], [23], [24], [25],
+    #               [27], [28], [29], [30], [31],
+    #               [32], [33], [34], [35], [36],
+    #               [37], [38], [39], [40], [41],
+    #               [42], [43], [44], [45], [46],
+    #               [47], [48], [49], [50], [51],
+    #               [53], [54], [55], [56], [57],
+    #               [58], [59], [60], [61], [62],
+    #               [63], [64], [65], [66], [67],
+    #               [68], [69], [70], [71], [72],
+    #               [73], [74], [75], [76], [77]]
+    # slope_vals = [[1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8],
+    #               [1.0], [1.2], [1.4], [1.6], [1.8]]
+    # np.testing.assert_allclose(pytest.helpers.expand_np_nd(log_val[1][0:]), trial_vals, atol=1e-08, verbose=True)
+    # np.testing.assert_allclose(pytest.helpers.expand_np_nd(log_val[3][0:]), slope_vals, atol=1e-08, verbose=True)
+
 
 def test_laming_validation_specify_control_signals():
     # Mechanisms:

From 38ffc9da9e9958463472c228be4021d503bebffa Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Fri, 15 Dec 2017 07:38:27 -0800
Subject: [PATCH 35/52] -

---
 Scripts/Nieuwenhuis_documented.py |   2 +-
 psyneulink/globals/log.py         | 120 ++++++++++++++++++------------
 2 files changed, 74 insertions(+), 48 deletions(-)

diff --git a/Scripts/Nieuwenhuis_documented.py b/Scripts/Nieuwenhuis_documented.py
index e9611be3bed..5e2db449a08 100644
--- a/Scripts/Nieuwenhuis_documented.py
+++ b/Scripts/Nieuwenhuis_documented.py
@@ -303,4 +303,4 @@ def record_trial():
 LC.log.nparray()
 
 
-# decision_layer.log.nparray()
\ No newline at end of file
+decision_layer.log.nparray()
\ No newline at end of file
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 7ca972ad795..edd62cf17e9 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -425,6 +425,20 @@ class LogCondition(IntEnum):
     # def _log_level_max(cls):
     #     return max([cls[i].value for i in list(cls.__members__) if cls[i] is not LogCondition.ALL_ASSIGNMENTS])
 
+    @classmethod
+    def _get_condition_string(cls, condition, string=None):
+        """Return string with the names of all flags that are set in **condition**, prepended by **string**"""
+        if string:
+            string += ": "
+        else:
+            string = ""
+        flagged_items = []
+        for c in list(cls.__members__):
+            if LogCondition[c] & condition:
+               flagged_items.append(c)
+        string += ", ".join(flagged_items)
+        return string
+
 
 LogEntry = namedtuple('LogEntry', 'time, context, value')
 
@@ -728,12 +742,14 @@ def assign_log_level(item, level):
                 # self.add_entries(item[0])
                 assign_log_level(item[0], item[1])
 
-    def _log_value(self, value, context=None):
+    def _log_value(self, value, time=None, context=None):
         """Add LogEntry to an entry in the Log
 
-        Identifies the context in which the call is being made, which is assigned to the context field of the
-        `LogEntry`, along with the current time stamp and value itself
-
+        If **value** is a LogEntry, it is assigned to the entry
+        If **context** is a LogCondition, it is used to determine whether the entry should be made;
+           **time** must be passed;  the name of the LogCondition(s) specified are assigned to the context of LogEntry
+        Otherwise, uses string (or Component) passed in **context**, or searches stack (see note) to determine the
+        context, and uses that to determine the scheduler and, from that, the time;
         If value is None, uses owner's `value <Component.value>` attribute.
 
         .. note::
@@ -751,48 +767,57 @@ def _log_value(self, value, context=None):
 
         else:
 
-            if context is COMMAND_LINE:
-                # If _log_value is being called programmatically,
-                #    flag for later and set context to None to get context from the stack
-                programmatic = True
-                context = None
-
-            # Get context from the stack
-            if context is None:
-                curr_frame = inspect.currentframe()
-                prev_frame = inspect.getouterframes(curr_frame, 2)
-                i = 1
-                # Search stack for first frame (most recent call) with a context specification
-                while context is None:
-                    try:
-                        context = inspect.getargvalues(prev_frame[i][0]).locals['context']
-                    except KeyError:
-                        # Try earlier frame
-                        i += 1
-                    except IndexError:
-                        # Ran out of frames, so just set context to empty string
-                        context = ""
-                    else:
-                        break
-
-            # If context is a Component object, it must be during its initialization, so assign accordingly:
-            if isinstance(context, Component):
-                context = "{} of {}".format(INITIALIZING, context.name)
-
-            # No context was specified in any frame
-            if context is None:
-                raise LogError("PROGRAM ERROR: No context specification found in any frame")
-
-            if not isinstance(context, str):
-                raise LogError("PROGRAM ERROR: Unrecognized context specification ({})".format(context))
-
-            # Context is an empty string, but called programatically
-            if not context and programmatic:
-                context = COMMAND_LINE
-                # context = self.owner.prev_context + "FROM " + COMMAND_LINE
-                # context = self.owner.prev_context
-
-            context_flags = _get_log_context(context)
+            if isinstance(context, LogCondition):
+                context_flags = context
+                context = LogCondition._get_condition_string(context)
+                if not time:
+                    raise LogError("Use of LogCondition ({}) by {} to specify context requires specificatiom of time".
+                                   format(context.name, self.owner.name ))
+
+            # Get context
+            else:
+                if context is COMMAND_LINE:
+                    # If _log_value is being called programmatically,
+                    #    flag for later and set context to None to get context from the stack
+                    programmatic = True
+                    context = None
+
+                # Get context from the stack
+                if context is None:
+                    curr_frame = inspect.currentframe()
+                    prev_frame = inspect.getouterframes(curr_frame, 2)
+                    i = 1
+                    # Search stack for first frame (most recent call) with a context specification
+                    while context is None:
+                        try:
+                            context = inspect.getargvalues(prev_frame[i][0]).locals['context']
+                        except KeyError:
+                            # Try earlier frame
+                            i += 1
+                        except IndexError:
+                            # Ran out of frames, so just set context to empty string
+                            context = ""
+                        else:
+                            break
+
+                # If context is a Component object, it must be during its initialization, so assign accordingly:
+                if isinstance(context, Component):
+                    context = "{} of {}".format(INITIALIZING, context.name)
+
+                # No context was specified in any frame
+                if context is None:
+                    raise LogError("PROGRAM ERROR: No context specification found in any frame")
+
+                if not isinstance(context, str):
+                    raise LogError("PROGRAM ERROR: Unrecognized context specification ({})".format(context))
+
+                # Context is an empty string, but called programatically
+                if not context and programmatic:
+                    context = COMMAND_LINE
+                    # context = self.owner.prev_context + "FROM " + COMMAND_LINE
+                    # context = self.owner.prev_context
+
+                context_flags = _get_log_context(context)
 
             log_pref = self.owner.prefs.logPref if self.owner.prefs else None
 
@@ -800,7 +825,8 @@ def _log_value(self, value, context=None):
             if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
             # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
             # if log_pref and log_pref | context_flags:
-                self.entries[self.owner.name] = LogEntry(self._get_time(context, context_flags), context, value)
+                time = time or self._get_time(context, context_flags)
+                self.entries[self.owner.name] = LogEntry(time, context, value)
 
         if context is not COMMAND_LINE:
             self.owner.prev_context = context

From 5132edb4cae9f52663b6321d46aa080e45b733a9 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Fri, 15 Dec 2017 18:04:01 -0800
Subject: [PATCH 36/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20print=5Fentries()?=
 =?UTF-8?q?:=20context=5Fwidth=20adaptively=20set=20to=20minimum=20context?=
 =?UTF-8?q?=20width?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/globals/log.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index edd62cf17e9..f66e3358506 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -1082,7 +1082,14 @@ class options(IntEnum):
         value_spacer = " ".ljust(value_spacer_width)
         base_width = item_name_width
 
-        # FIX: COULD "ALGORITHMIZE" THIS:
+        # Get max width of context and set context_width to that + 4
+        if option_flags & options.CONTEXT:
+            for entry in entries:
+                for datum in self.logged_entries[entry]:
+                    context_width = min(context_width, len(datum.context))
+
+        # Set other widths based on options:
+        # FIX: "ALGORITHMIZE" THIS:
         if option_flags == options.TIME:
             pass
         elif option_flags == options.CONTEXT:

From f77dcb48ee2e2b05fcd188b5422c2d697e3cad22 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 14:51:18 -0800
Subject: [PATCH 37/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20print=5Fentries()?=
 =?UTF-8?q?:=20context=5Fwidth=20adaptively=20set=20to=20minimum=20context?=
 =?UTF-8?q?=20width?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/globals/log.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index f66e3358506..e96b53c5842 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -433,7 +433,14 @@ def _get_condition_string(cls, condition, string=None):
         else:
             string = ""
         flagged_items = []
+        # If ALL_ASSIGNMENTS, just return that
+        if condition is LogCondition.ALL_ASSIGNMENTS:
+            return LogCondition.ALL_ASSIGNMENTS.name
+        # Otherwise, append each flag's name to the string
         for c in list(cls.__members__):
+            # Don't include ALL_ASSIGNMENTS:
+            if c is LogCondition.ALL_ASSIGNMENTS.name:
+                continue
             if LogCondition[c] & condition:
                flagged_items.append(c)
         string += ", ".join(flagged_items)
@@ -1015,12 +1022,14 @@ def print_entries(self,
                       entries:tc.optional(tc.any(str, list, is_component))=ALL,
                       width:int=120,
                       display:tc.any(tc.enum(TIME, CONTEXT, VALUE, ALL), list)=ALL,
+                      long_context=False
                       ):
         """
         print_entries(          \
               entries=ALL,      \
               width=120,        \
               display=None      \
+              long_context=Full \
             )
 
         Print summary of the Log's entries in a (human-readable) table format.
@@ -1043,6 +1052,10 @@ def print_entries(self,
             specified items;  the widths of the columns for the items is dynamically adjusted, based on how many
             are specified, allowing more information about one to be shown by omitting others (this is useful 
             if the context strings are long and/or the values are arrays).
+
+        full_context : bool : default False
+            specifies the use of the full context string in the display;  this can be informative, but also take up
+            more space in each line of the display.
             
         """
 
@@ -1086,7 +1099,11 @@ class options(IntEnum):
         if option_flags & options.CONTEXT:
             for entry in entries:
                 for datum in self.logged_entries[entry]:
-                    context_width = min(context_width, len(datum.context))
+                    if long_context:
+                        context = LogCondition._get_condition_string(_get_log_context(context))
+                    else:
+                        context = datum.context
+                    context_width = min(context_width, len(context))
 
         # Set other widths based on options:
         # FIX: "ALGORITHMIZE" THIS:
@@ -1146,7 +1163,13 @@ class options(IntEnum):
                     if options.TIME & option_flags:
                         data_str = data_str + time_str.ljust(time_width)
                     if options.CONTEXT & option_flags:
-                        data_str = data_str + repr(context).ljust(context_width, spacer)
+                        if long_context:
+                            # Use context from LogEntry
+                            context = repr(context)
+                        else:
+                            # Get names of LogCondition flag(s) from parse of context string
+                            context = LogCondition._get_condition_string(_get_log_context(context))
+                        data_str = data_str + context.ljust(context_width, spacer)
                     if options.VALUE & option_flags:
                         format_str = "{{:2.{0}}}".format(value_width)
                         data_str = data_str + value_spacer + format_str.format(value).ljust(value_width)

From 6e60b06370d5399739932b6bc67a18ca97108884 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 15:08:59 -0800
Subject: [PATCH 38/52] =?UTF-8?q?=E2=80=A2=20Projection=20=20=20-=20added?=
 =?UTF-8?q?=20explicit=20tests=20for=20"LEARNING"=20in=20context?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/components/functions/function.py   | 27 ++++++++++++-------
 psyneulink/components/mechanisms/mechanism.py |  8 +++---
 psyneulink/components/process.py              |  4 +--
 psyneulink/components/system.py               |  5 +++-
 4 files changed, 29 insertions(+), 15 deletions(-)

diff --git a/psyneulink/components/functions/function.py b/psyneulink/components/functions/function.py
index de40e95c690..88d3df4a7e8 100644
--- a/psyneulink/components/functions/function.py
+++ b/psyneulink/components/functions/function.py
@@ -192,7 +192,8 @@
     EXPONENTIAL_DIST_FUNCTION, EXPONENTIAL_FUNCTION, EXPONENTS, FHN_INTEGRATOR_FUNCTION, FULL_CONNECTIVITY_MATRIX, \
     FUNCTION, FUNCTION_OUTPUT_TYPE, FUNCTION_OUTPUT_TYPE_CONVERSION, FUNCTION_PARAMS, GAIN, GAMMA_DIST_FUNCTION, \
     HEBBIAN_FUNCTION, HIGH, HOLLOW_MATRIX, IDENTITY_MATRIX, INCREMENT, INITIALIZER, \
-    INITIALIZING, INPUT_STATES, INTEGRATOR_FUNCTION, INTEGRATOR_FUNCTION_TYPE, INTERCEPT, LEARNING_FUNCTION_TYPE, \
+    INITIALIZING, INPUT_STATES, INTEGRATOR_FUNCTION, INTEGRATOR_FUNCTION_TYPE, INTERCEPT, \
+    LEARNING, LEARNING_FUNCTION_TYPE, \
     LEARNING_RATE, LINEAR_COMBINATION_FUNCTION, LINEAR_FUNCTION, LINEAR_MATRIX_FUNCTION, LOGISTIC_FUNCTION, LOW, MATRIX, \
     MATRIX_KEYWORD_NAMES, MATRIX_KEYWORD_VALUES, MAX_INDICATOR, MAX_VAL, NOISE, NORMAL_DIST_FUNCTION, \
     OBJECTIVE_FUNCTION_TYPE, OFFSET, OPERATION, ORNSTEIN_UHLENBECK_INTEGRATOR_FUNCTION, OUTPUT_STATES, OUTPUT_TYPE, \
@@ -1557,14 +1558,16 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if any(c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[WEIGHTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
                                         format(len(target_set[WEIGHTS]), len(self.instance_defaults.variable.shape)))
 
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[EXPONENTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
                                         format(len(target_set[EXPONENTS]), len(self.instance_defaults.variable.shape)))
@@ -1578,7 +1581,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(SCALE, self.name, scale))
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(scale, np.ndarray) and
                         (scale.size != self.instance_defaults.variable.size or
                          scale.shape != self.instance_defaults.variable.shape)):
@@ -1596,7 +1600,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(OFFSET, self.name, offset))
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(offset, np.ndarray) and
                         (offset.size != self.instance_defaults.variable.size or
                          offset.shape != self.instance_defaults.variable.shape)):
@@ -1996,14 +2001,16 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[WEIGHTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
                                         format(len(target_set[WEIGHTS]), len(self.instance_defaults.variable.shape)))
 
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[EXPONENTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
                                         format(len(target_set[EXPONENTS]), len(self.instance_defaults.variable.shape)))
@@ -2017,7 +2024,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(SCALE, self.name, scale))
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(scale, np.ndarray) and
                         (scale.size != self.instance_defaults.variable.size or
                          scale.shape != self.instance_defaults.variable.shape)):
@@ -2035,7 +2043,8 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(OFFSET, self.name, offset))
-            if EXECUTING in context:
+            # if EXECUTING in context:
+            if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(offset, np.ndarray) and
                         (offset.size != self.instance_defaults.variable.size or
                          offset.shape != self.instance_defaults.variable.shape)):
diff --git a/psyneulink/components/mechanisms/mechanism.py b/psyneulink/components/mechanisms/mechanism.py
index be584be6c25..3818aeae6a4 100644
--- a/psyneulink/components/mechanisms/mechanism.py
+++ b/psyneulink/components/mechanisms/mechanism.py
@@ -787,7 +787,7 @@ class `UserList <https://docs.python.org/3.6/library/collections.html?highlight=
 from psyneulink.components.states.parameterstate import ParameterState
 from psyneulink.components.states.state import ADD_STATES, _parse_state_spec
 from psyneulink.globals.defaults import timeScaleSystemDefault
-from psyneulink.globals.keywords import CHANGED, COMMAND_LINE, EVC_SIMULATION, EXECUTING, FUNCTION_PARAMS, INITIALIZING, INIT_FUNCTION_METHOD_ONLY, INIT__EXECUTE__METHOD_ONLY, INPUT_STATES, INPUT_STATE_PARAMS, MECHANISM_TIME_SCALE, MONITOR_FOR_CONTROL, MONITOR_FOR_LEARNING, NO_CONTEXT, OUTPUT_STATES, OUTPUT_STATE_PARAMS, PARAMETER_STATES, PARAMETER_STATE_PARAMS, PROCESS_INIT, REFERENCE_VALUE, SEPARATOR_BAR, SET_ATTRIBUTE, SYSTEM_INIT, TIME_SCALE, UNCHANGED, VALIDATE, VALUE, VARIABLE, kwMechanismComponentCategory, kwMechanismExecuteFunction
+from psyneulink.globals.keywords import CHANGED, COMMAND_LINE, EVC_SIMULATION, EXECUTING, FUNCTION_PARAMS, INITIALIZING, INIT_FUNCTION_METHOD_ONLY, INIT__EXECUTE__METHOD_ONLY, INPUT_STATES, INPUT_STATE_PARAMS, LEARNING, MECHANISM_TIME_SCALE, MONITOR_FOR_CONTROL, MONITOR_FOR_LEARNING, NO_CONTEXT, OUTPUT_STATES, OUTPUT_STATE_PARAMS, PARAMETER_STATES, PARAMETER_STATE_PARAMS, PROCESS_INIT, REFERENCE_VALUE, SEPARATOR_BAR, SET_ATTRIBUTE, SYSTEM_INIT, TIME_SCALE, UNCHANGED, VALIDATE, VALUE, VARIABLE, kwMechanismComponentCategory, kwMechanismExecuteFunction
 from psyneulink.globals.preferences.preferenceset import PreferenceLevel
 from psyneulink.globals.registry import register_category
 from psyneulink.globals.utilities import ContentAddressableList, append_type_to_name, convert_to_np_array, iscompatible, kwCompatibilityNumeric
@@ -1986,7 +1986,8 @@ def execute(self,
         # Executing or simulating Process or System, get input by updating input_states
 
         if (input is None
-            and (EXECUTING in context or EVC_SIMULATION in context)
+            # and (EXECUTING in context or EVC_SIMULATION in context)
+            and (c in context for c in {EXECUTING, LEARNING, EVC_SIMULATION})
             and (self.input_state.path_afferents != [])):
             variable = self._update_variable(self._update_input_states(runtime_params=runtime_params,
                                                                        time_scale=time_scale,
@@ -2049,7 +2050,8 @@ def execute(self,
         #endregion
 
         #region REPORT EXECUTION
-        if self.prefs.reportOutputPref and context and EXECUTING in context:
+        # if self.prefs.reportOutputPref and context and EXECUTING in context:
+        if self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING, LEARNING}):
             self._report_mechanism_execution(self.input_values, self.user_params, self.output_state.value)
         #endregion
 
diff --git a/psyneulink/components/process.py b/psyneulink/components/process.py
index e7c53659690..eda9a8e1d61 100644
--- a/psyneulink/components/process.py
+++ b/psyneulink/components/process.py
@@ -2153,8 +2153,8 @@ def execute(
             mech._execution_id = self._execution_id
 
         # Report output if reporting preference is on and this is not an initialization run
-        report_output = self.prefs.reportOutputPref and context and EXECUTING in context
-
+        # report_output = self.prefs.reportOutputPref and context and EXECUTING in context
+        report_output = self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING, LEARNING})
 
         # FIX: CONSOLIDATE/REARRANGE _assign_input_values, _check_args, AND ASSIGNMENT OF input TO variable
         # FIX: (SO THAT assign_input_value DOESN'T HAVE TO RETURN input
diff --git a/psyneulink/components/system.py b/psyneulink/components/system.py
index 473d5df0bbd..8a7fc428e63 100644
--- a/psyneulink/components/system.py
+++ b/psyneulink/components/system.py
@@ -2459,7 +2459,10 @@ def execute(self,
                     for projection in state.all_afferents:
                         projection.sender.owner._execution_id = self._execution_id
 
-        self._report_system_output = self.prefs.reportOutputPref and context and EXECUTING in context
+        # self._report_system_output = self.prefs.reportOutputPref and context and EXECUTING in context
+        self._report_system_output = self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING,
+                                                                                                         LEARNING})
+
         if self._report_system_output:
             self._report_process_output = any(process.reportOutputPref for process in self.processes)
 

From ef3db0959141c8c0c9bd32c496204eb4313959ce Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 15:21:47 -0800
Subject: [PATCH 39/52] =?UTF-8?q?=E2=80=A2=20Projection=20=20=20-=20added?=
 =?UTF-8?q?=20explicit=20tests=20for=20"LEARNING"=20in=20context?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• System, Process
  - execute<_learning>): replace "EXECUTING" with "LEARNING" in context
---
 psyneulink/components/process.py | 4 ++--
 psyneulink/components/system.py  | 4 ++--
 psyneulink/globals/log.py        | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/psyneulink/components/process.py b/psyneulink/components/process.py
index eda9a8e1d61..813e72ca3bd 100644
--- a/psyneulink/components/process.py
+++ b/psyneulink/components/process.py
@@ -2278,9 +2278,9 @@ def _execute_learning(self, target=None, context=None):
                             # Call parameter_state.update with LEARNING in context to update LearningSignals
                             # Note: do this rather just calling LearningSignals directly
                             #       since parameter_state.update() handles parsing of LearningProjection-specific params
-                            context = context + SEPARATOR_BAR + LEARNING
+                            # context = context + SEPARATOR_BAR + LEARNING
                             # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-                            # context = context.replace(EXECUTING, LEARNING + ' ')
+                            context = context.replace(EXECUTING, LEARNING + ' ')
 
                             # NOTE: This will need to be updated when runtime params are re-enabled
                             # parameter_state.update(params=params, time_scale=TimeScale.TRIAL, context=context)
diff --git a/psyneulink/components/system.py b/psyneulink/components/system.py
index 8a7fc428e63..c409c1d562d 100644
--- a/psyneulink/components/system.py
+++ b/psyneulink/components/system.py
@@ -2542,9 +2542,9 @@ def execute(self,
 
         # Don't execute learning for simulation runs
         if not EVC_SIMULATION in context and self.learning:
-            self._execute_learning(context=context + SEPARATOR_BAR + LEARNING)
+            # self._execute_learning(context=context + SEPARATOR_BAR + LEARNING)
             # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-            # self._execute_learning(clock=clock, context=context.replace(EXECUTING, LEARNING + ' '))
+            self._execute_learning(context=context.replace(EXECUTING, LEARNING + ' '))
         # endregion
 
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index e96b53c5842..7960f460b24 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -402,8 +402,8 @@ class LogCondition(IntEnum):
     PROCESSING =         1<<4       # 16
     """Record all value assignments during processing phase of Composition execution."""
     # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-    # LEARNING =         1<<5       # 32
-    LEARNING = (1<<5) + EXECUTION   # 40
+    LEARNING =         1<<5       # 32
+    # LEARNING = (1<<5) + EXECUTION   # 40
     """Record all value assignments during learning phase of Composition execution."""
     CONTROL =            1<<6       # 64
     """Record all value assignments during control phase of Composition execution."""

From 523d1097dd137b7797b98be6f48346867661ba3e Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 16:16:37 -0800
Subject: [PATCH 40/52] =?UTF-8?q?=E2=80=A2=20Projection=20=20=20-=20added?=
 =?UTF-8?q?=20explicit=20tests=20for=20"LEARNING"=20in=20context?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

• System, Process
  - execute<_learning>): replace "EXECUTING" with "LEARNING" in context

• Log
  - print_entries():
     long_context option implemented
     fixed bug preventing use of full width of value when possible
---
 psyneulink/globals/log.py | 35 ++++++++++++++++++++++-------------
 1 file changed, 22 insertions(+), 13 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 7960f460b24..2ce0359e49b 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -402,7 +402,7 @@ class LogCondition(IntEnum):
     PROCESSING =         1<<4       # 16
     """Record all value assignments during processing phase of Composition execution."""
     # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-    LEARNING =         1<<5       # 32
+    LEARNING =           1<<5       # 32
     # LEARNING = (1<<5) + EXECUTION   # 40
     """Record all value assignments during learning phase of Composition execution."""
     CONTROL =            1<<6       # 64
@@ -829,9 +829,9 @@ def _log_value(self, value, time=None, context=None):
             log_pref = self.owner.prefs.logPref if self.owner.prefs else None
 
             # Log value if logging condition is satisfied or called for programmatically
-            if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
             # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-            # if log_pref and log_pref | context_flags:
+            if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
+            # if (log_pref and log_pref | context_flags) or context_flags & LogCondition.COMMAND_LINE:
                 time = time or self._get_time(context, context_flags)
                 self.entries[self.owner.name] = LogEntry(time, context, value)
 
@@ -1075,6 +1075,7 @@ class options(IntEnum):
         if not isinstance(display, list):
             display = [display]
 
+        # Set option_flags for specified options
         option_flags = options.NONE
         if TIME in display:
             option_flags |= options.TIME
@@ -1085,6 +1086,7 @@ class options(IntEnum):
         if ALL in display:
             option_flags = options.ALL
 
+        # Default widths
         full_width = width
         item_name_width = 15
         time_width = 10
@@ -1095,14 +1097,14 @@ class options(IntEnum):
         value_spacer = " ".ljust(value_spacer_width)
         base_width = item_name_width
 
-        # Get max width of context and set context_width to that + 4
+        # Set context_width based on long_context option (length of context string) or context flags
         if option_flags & options.CONTEXT:
             for entry in entries:
                 for datum in self.logged_entries[entry]:
                     if long_context:
-                        context = LogCondition._get_condition_string(_get_log_context(context))
-                    else:
                         context = datum.context
+                    else:
+                        context = LogCondition._get_condition_string(_get_log_context(datum.context))
                     context_width = min(context_width, len(context))
 
         # Set other widths based on options:
@@ -1121,7 +1123,7 @@ class options(IntEnum):
             context_width = full_width - value_width
             value_width = full_width - context_width
         elif option_flags == options.ALL:
-            pass
+            value_width = full_width - context_width
         else:
             raise LogError("PROGRAM ERROR:  unrecognized state of option_flags: {}".format(option_flags))
 
@@ -1151,17 +1153,16 @@ class options(IntEnum):
             else:
                 import numpy as np
                 for i, item in enumerate(datum):
+
                     time, context, value = item
-                    if len(context) > context_width:
-                        context = context[:context_width-3] + "..."
-                    value = str(value).replace('\n',',')
-                    if len(value) > value_width:
-                        value = value[:value_width-3].rstrip() + "..."
-                    time_str = _time_string(time)
+
                     entry_name = self._alias_owner_name(entry_name)
                     data_str = repr(entry_name).ljust(item_name_width, spacer)
+
                     if options.TIME & option_flags:
+                        time_str = _time_string(time)
                         data_str = data_str + time_str.ljust(time_width)
+
                     if options.CONTEXT & option_flags:
                         if long_context:
                             # Use context from LogEntry
@@ -1169,11 +1170,19 @@ class options(IntEnum):
                         else:
                             # Get names of LogCondition flag(s) from parse of context string
                             context = LogCondition._get_condition_string(_get_log_context(context))
+                        if len(context) > context_width:
+                            context = context[:context_width-3] + "..."
                         data_str = data_str + context.ljust(context_width, spacer)
+
                     if options.VALUE & option_flags:
+                        value = str(value).replace('\n',',')
+                        if len(value) > value_width:
+                            value = value[:value_width-3].rstrip() + "..."
                         format_str = "{{:2.{0}}}".format(value_width)
                         data_str = data_str + value_spacer + format_str.format(value).ljust(value_width)
+
                     print(data_str)
+
                 if len(datum) > 1:
                     print("\n")
 

From 8d8be2f95dac28cafeed7943a13ba22d697a3a74 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 16:20:58 -0800
Subject: [PATCH 41/52] -

---
 psyneulink/components/functions/function.py   | 8 --------
 psyneulink/components/mechanisms/mechanism.py | 1 -
 psyneulink/components/process.py              | 1 -
 psyneulink/components/system.py               | 1 -
 4 files changed, 11 deletions(-)

diff --git a/psyneulink/components/functions/function.py b/psyneulink/components/functions/function.py
index 88d3df4a7e8..717c55ab233 100644
--- a/psyneulink/components/functions/function.py
+++ b/psyneulink/components/functions/function.py
@@ -1558,7 +1558,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
-            # if EXECUTING in context:
             if any(c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[WEIGHTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
@@ -1566,7 +1565,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[EXPONENTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
@@ -1581,7 +1579,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(SCALE, self.name, scale))
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(scale, np.ndarray) and
                         (scale.size != self.instance_defaults.variable.size or
@@ -1600,7 +1597,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(OFFSET, self.name, offset))
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(offset, np.ndarray) and
                         (offset.size != self.instance_defaults.variable.size or
@@ -2001,7 +1997,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if WEIGHTS in target_set and target_set[WEIGHTS] is not None:
             target_set[WEIGHTS] = np.atleast_2d(target_set[WEIGHTS]).reshape(-1, 1)
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[WEIGHTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of weights ({0}) is not equal to number of items in variable ({1})".
@@ -2009,7 +2004,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
 
         if EXPONENTS in target_set and target_set[EXPONENTS] is not None:
             target_set[EXPONENTS] = np.atleast_2d(target_set[EXPONENTS]).reshape(-1, 1)
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if len(target_set[EXPONENTS]) != len(self.instance_defaults.variable):
                     raise FunctionError("Number of exponents ({0}) does not equal number of items in variable ({1})".
@@ -2024,7 +2018,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(SCALE, self.name, scale))
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(scale, np.ndarray) and
                         (scale.size != self.instance_defaults.variable.size or
@@ -2043,7 +2036,6 @@ def _validate_params(self, request_set, target_set=None, context=None):
             else:
                 raise FunctionError("{} param of {} ({}) must be a scalar or an np.ndarray".
                                     format(OFFSET, self.name, offset))
-            # if EXECUTING in context:
             if (c in context for c in {EXECUTING, LEARNING}):
                 if (isinstance(offset, np.ndarray) and
                         (offset.size != self.instance_defaults.variable.size or
diff --git a/psyneulink/components/mechanisms/mechanism.py b/psyneulink/components/mechanisms/mechanism.py
index 3818aeae6a4..a6065ead21a 100644
--- a/psyneulink/components/mechanisms/mechanism.py
+++ b/psyneulink/components/mechanisms/mechanism.py
@@ -1986,7 +1986,6 @@ def execute(self,
         # Executing or simulating Process or System, get input by updating input_states
 
         if (input is None
-            # and (EXECUTING in context or EVC_SIMULATION in context)
             and (c in context for c in {EXECUTING, LEARNING, EVC_SIMULATION})
             and (self.input_state.path_afferents != [])):
             variable = self._update_variable(self._update_input_states(runtime_params=runtime_params,
diff --git a/psyneulink/components/process.py b/psyneulink/components/process.py
index 813e72ca3bd..3b9ce3428de 100644
--- a/psyneulink/components/process.py
+++ b/psyneulink/components/process.py
@@ -2153,7 +2153,6 @@ def execute(
             mech._execution_id = self._execution_id
 
         # Report output if reporting preference is on and this is not an initialization run
-        # report_output = self.prefs.reportOutputPref and context and EXECUTING in context
         report_output = self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING, LEARNING})
 
         # FIX: CONSOLIDATE/REARRANGE _assign_input_values, _check_args, AND ASSIGNMENT OF input TO variable
diff --git a/psyneulink/components/system.py b/psyneulink/components/system.py
index c409c1d562d..f0d244cdf1d 100644
--- a/psyneulink/components/system.py
+++ b/psyneulink/components/system.py
@@ -2459,7 +2459,6 @@ def execute(self,
                     for projection in state.all_afferents:
                         projection.sender.owner._execution_id = self._execution_id
 
-        # self._report_system_output = self.prefs.reportOutputPref and context and EXECUTING in context
         self._report_system_output = self.prefs.reportOutputPref and context and (c in context for c in {EXECUTING,
                                                                                                          LEARNING})
 

From 48a06147a8f4473732c4040e12a5f171fa7830e6 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 16:48:05 -0800
Subject: [PATCH 42/52] -

---
 psyneulink/globals/log.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 2ce0359e49b..70b7bdd895e 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -830,8 +830,8 @@ def _log_value(self, value, time=None, context=None):
 
             # Log value if logging condition is satisfied or called for programmatically
             # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-            if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
-            # if (log_pref and log_pref | context_flags) or context_flags & LogCondition.COMMAND_LINE:
+            # if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
+            if (log_pref and log_pref & context_flags) or context_flags & LogCondition.COMMAND_LINE:
                 time = time or self._get_time(context, context_flags)
                 self.entries[self.owner.name] = LogEntry(time, context, value)
 

From a56e9c1a0d6adf4b25779afb8bf66ce395337ad7 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 16:49:24 -0800
Subject: [PATCH 43/52] =?UTF-8?q?=E2=80=A2=20Log=20=20=20=5Flog=5Fvalue:?=
 =?UTF-8?q?=20replaced=20log=5Fpref=20=3D=3D=20context=20to=20log=5Fpref?=
 =?UTF-8?q?=20&=20context?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/globals/log.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 70b7bdd895e..8ef88abfe60 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -830,7 +830,6 @@ def _log_value(self, value, time=None, context=None):
 
             # Log value if logging condition is satisfied or called for programmatically
             # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
-            # if (log_pref and log_pref == context_flags) or context_flags & LogCondition.COMMAND_LINE:
             if (log_pref and log_pref & context_flags) or context_flags & LogCondition.COMMAND_LINE:
                 time = time or self._get_time(context, context_flags)
                 self.entries[self.owner.name] = LogEntry(time, context, value)

From a76c57d905496e32e1164ae103484d54b0cc047c Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 16:50:49 -0800
Subject: [PATCH 44/52] =?UTF-8?q?=E2=80=A2=20Project:=20=20=20Got=20rid=20?=
 =?UTF-8?q?of=20"FIX:=20IMPLEMENT=20EXECUTION+LEARNING=20CONDITION"?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 psyneulink/components/process.py | 2 --
 psyneulink/components/system.py  | 2 --
 psyneulink/globals/log.py        | 2 --
 3 files changed, 6 deletions(-)

diff --git a/psyneulink/components/process.py b/psyneulink/components/process.py
index 3b9ce3428de..385be55e5b8 100644
--- a/psyneulink/components/process.py
+++ b/psyneulink/components/process.py
@@ -2277,8 +2277,6 @@ def _execute_learning(self, target=None, context=None):
                             # Call parameter_state.update with LEARNING in context to update LearningSignals
                             # Note: do this rather just calling LearningSignals directly
                             #       since parameter_state.update() handles parsing of LearningProjection-specific params
-                            # context = context + SEPARATOR_BAR + LEARNING
-                            # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
                             context = context.replace(EXECUTING, LEARNING + ' ')
 
                             # NOTE: This will need to be updated when runtime params are re-enabled
diff --git a/psyneulink/components/system.py b/psyneulink/components/system.py
index f0d244cdf1d..92de952e53e 100644
--- a/psyneulink/components/system.py
+++ b/psyneulink/components/system.py
@@ -2541,8 +2541,6 @@ def execute(self,
 
         # Don't execute learning for simulation runs
         if not EVC_SIMULATION in context and self.learning:
-            # self._execute_learning(context=context + SEPARATOR_BAR + LEARNING)
-            # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
             self._execute_learning(context=context.replace(EXECUTING, LEARNING + ' '))
         # endregion
 
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 8ef88abfe60..97886b1b88a 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -401,9 +401,7 @@ class LogCondition(IntEnum):
     """Record all value assignments during any execution of the Component."""
     PROCESSING =         1<<4       # 16
     """Record all value assignments during processing phase of Composition execution."""
-    # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
     LEARNING =           1<<5       # 32
-    # LEARNING = (1<<5) + EXECUTION   # 40
     """Record all value assignments during learning phase of Composition execution."""
     CONTROL =            1<<6       # 64
     """Record all value assignments during control phase of Composition execution."""

From 55620859cc24341874958a94b59b9a76db4fedb5 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sat, 16 Dec 2017 16:51:19 -0800
Subject: [PATCH 45/52] -

---
 psyneulink/globals/log.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 97886b1b88a..11571cbb392 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -826,8 +826,7 @@ def _log_value(self, value, time=None, context=None):
 
             log_pref = self.owner.prefs.logPref if self.owner.prefs else None
 
-            # Log value if logging condition is satisfied or called for programmatically
-            # FIX: IMPLEMENT EXECUTION+LEARNING CONDITION
+            # Get time and log value if logging condition is satisfied or called for programmatically
             if (log_pref and log_pref & context_flags) or context_flags & LogCondition.COMMAND_LINE:
                 time = time or self._get_time(context, context_flags)
                 self.entries[self.owner.name] = LogEntry(time, context, value)

From 9394d30c0ddd87495a878a9c3736683f2e1f08d7 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 07:10:13 -0800
Subject: [PATCH 46/52] -

---
 psyneulink/globals/log.py | 43 +++++++++++++++++++++------------------
 1 file changed, 23 insertions(+), 20 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 11571cbb392..62bfd1000bb 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -1095,13 +1095,15 @@ class options(IntEnum):
 
         # Set context_width based on long_context option (length of context string) or context flags
         if option_flags & options.CONTEXT:
+            c_width = 0
             for entry in entries:
                 for datum in self.logged_entries[entry]:
                     if long_context:
                         context = datum.context
                     else:
                         context = LogCondition._get_condition_string(_get_log_context(datum.context))
-                    context_width = min(context_width, len(context))
+                    c_width = max(c_width, len(context))
+            context_width = min(context_width, c_width)
 
         # Set other widths based on options:
         # FIX: "ALGORITHMIZE" THIS:
@@ -1302,25 +1304,26 @@ def nparray(self,
             time_col = iter(time_values)
             for datum in self.logged_entries[entry]:
                 if time_values:
-                # MODIFIED 12/14/17 OLD:
-                    while datum.time != next(time_col,None):
-                        row.append(None)
-                value = None if datum.value is None else np.array(datum.value).tolist()
-                row.append(value)
-                # # MODIFIED 12/14/17 NEW:
-                #     for i in range(len(time_values)):
-                #         time = next(time_col,None)
-                #         if time is None:
-                #             break
-                #         if datum.time != time:
-                #             row.append(None)
-                #             continue
-                #         value = None if datum.value is None else datum.value.tolist()
-                #         row.append(value)
-                #         break
-                # else:
-                #     value = None if datum.value is None else datum.value.tolist()
-                #     row.append(value)
+                    # time_col = iter(time_values)
+                # # MODIFIED 12/14/17 OLD:
+                #     while datum.time != next(time_col,None):
+                #         row.append(None)
+                # value = None if datum.value is None else np.array(datum.value).tolist()
+                # row.append(value)
+                # MODIFIED 12/14/17 NEW:
+                    for i in range(len(time_values)):
+                        time = next(time_col,None)
+                        if time is None:
+                            break
+                        if datum.time != time:
+                            row.append(None)
+                            continue
+                        value = None if datum.value is None else datum.value.tolist()
+                        row.append(value)
+                        break
+                else:
+                    value = None if datum.value is None else datum.value.tolist()
+                    row.append(value)
                 # MODIFIED 12/14/17 END
 
             if header:

From a8119fce7bfda394aca09aa89ff29b65b46dd340 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 07:19:01 -0800
Subject: [PATCH 47/52] -

---
 psyneulink/globals/log.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 62bfd1000bb..4e55140d484 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -713,11 +713,17 @@ def set_log_conditions(self, items, log_condition=LogCondition.EXECUTION):
 
         def assign_log_level(item, level):
 
-            try:
-                level = LogCondition[level] if isinstance(level, str) else level
-            except KeyError:
-                raise LogError("\'{}\' is not a value of {}".
-                               format(level, LogCondition.__name__))
+            if not isinstance(level, list):
+                level = [level]
+            levels = LogCondition.OFF
+            for l in level:
+                try:
+                    l = LogCondition[l] if isinstance(l, str) else l
+                except KeyError:
+                    raise LogError("\'{}\' is not a value of {}".
+                                   format(l, LogCondition.__name__))
+                levels |= l
+            level = levels
 
             if not item in self.loggable_items:
                 raise LogError("\'{0}\' is not a loggable item for {1} (try using \'{1}.log.add_entries()\')".
@@ -739,12 +745,10 @@ def assign_log_level(item, level):
 
         for item in items:
             if isinstance(item, (str, Component)):
-                # self.add_entries(item)
                 if isinstance(item, Component):
                     item = item.name
                 assign_log_level(item, log_condition)
             else:
-                # self.add_entries(item[0])
                 assign_log_level(item[0], item[1])
 
     def _log_value(self, value, time=None, context=None):

From bf743e4ceb5cb0ffd2253bb0007febde455b020e Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 08:25:29 -0800
Subject: [PATCH 48/52] -

---
 psyneulink/globals/log.py | 42 +++++++++++++++++++++++++--------------
 1 file changed, 27 insertions(+), 15 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 4e55140d484..ef04d9152d9 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -56,9 +56,10 @@
     * `loggable_items <Log.loggable_items>` -- a dictionary with the items that can be logged in a Component's `log
       <Component.log>`;  the key for each entry is the name of a Component,  and the value is it current `LogCondition`.
     ..
-    * `set_log_conditions <Log.set_log_conditions>` -- used to assign the LogCondition for one or more Components.  Components can be
-      specified by their names, a reference to the Component object, in a tuple that specifies the `LogCondition` to
-      assign to that Component, or in a list with a `LogCondition` to be applied to multiple items at once.
+    * `set_log_conditions <Log.set_log_conditions>` -- used to assign the LogCondition for one or more Components.
+      Components can be specified by their names, a reference to the Component object, in a tuple that specifies the
+      `LogCondition` to assign to that Component, or in a list with a `LogCondition` to be applied to multiple items
+      at once.
     ..
     * `log_values <Log.log_values>` -- used to the `value <Component.value>` of one or more Components in the Log
       programmatically ("manually").  Components can be specified by their names or references to the objects.
@@ -108,12 +109,21 @@
 ~~~~~~~~~~~~~
 
 Configuring a Component to be logged is done using a `LogCondition`, that specifies the conditions under which its
-`value <Component.value>` should be entered in its Log.  These can be specified in the `set_log_conditions <Log.set_log_conditions>`
-method of a Log, or directly by specifying a LogCondition for the value a Component's `logPref  <Compnent.logPref>` item
-of its `prefs <Component.prefs>` attribute.  The former is easier, and allows multiple Components to be specied at
-once, while the latter affords more control over the specification (see `Preferences`).  LogConditions are treated as
-binary "flags", and can be combined to permit logging under more than one condition, using bitwise operators on
-LogConditions (e.g., LogCondition.EXECUTION | LogCondition.LEARNING).
+`value <Component.value>` should be entered in its Log.  These can be specified in the `set_log_conditions
+<Log.set_log_conditions>` method of a Log, or directly by specifying a LogCondition for the value a Component's
+`logPref  <Compnent.logPref>` item of its `prefs <Component.prefs>` attribute.  The former is easier, and allows
+multiple Components to be specied at once, while the latter affords more control over the specification (see
+`Preferences`).  LogConditions are treated as binary "flags", and can be combined to permit logging under more than
+one condition using bitwise operators on LogConditions.  For convenience, they can also be referred to by their
+names, and combined by specifying a list.  For example, all of the following specify that the `matrix
+<MappingProjection.matrix>` of ``my_projection`` be logged both during execution and learning::
+
+    >>> import psyneulink as pnl
+    >>> my_projection = pnl.MappingProjection()
+    >>> my_projection.set_log_condition('matrix', pnl.LogCondition.EXECUTION | pnl.LogCondition.LEARNING)
+    >>> my_projection.set_log_condition('matrix', pnl.LogCondition.EXECUTION + pnl.LogCondition.LEARNING)
+    >>> my_projection.set_log_condition('matrix', [pnl.EXECUTION, LEARNING])
+
 
 .. note::
    Currently, the `VALIDATION` `LogCondition` is not implemented.
@@ -128,7 +138,6 @@
    COMMENT:
    FIX: THIS EXAMPLE CAN'T CURRENTLY BE EXECUTED AS IT PERMANENTLY SETS THE LogPref FOR ALL TransferMechanism
    COMMENT
-    >>> import psyneulink as pnl
     >>> T = pnl.TransferMechanism(
     ...        prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogCondition.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)})
 
@@ -432,8 +441,9 @@ def _get_condition_string(cls, condition, string=None):
             string = ""
         flagged_items = []
         # If ALL_ASSIGNMENTS, just return that
-        if condition is LogCondition.ALL_ASSIGNMENTS:
-            return LogCondition.ALL_ASSIGNMENTS.name
+        if condition in (LogCondition.ALL_ASSIGNMENTS, LogCondition.OFF):
+            # return LogCondition.ALL_ASSIGNMENTS.name
+            return condition.name
         # Otherwise, append each flag's name to the string
         for c in list(cls.__members__):
             # Don't include ALL_ASSIGNMENTS:
@@ -1464,10 +1474,12 @@ def loggable_items(self):
         for c in self.loggable_components:
             name = self._alias_owner_name(c.name)
             try:
-                log_pref = c.logPref.name
+                # log_pref_names = c.logPref.name
+                log_pref_names = LogCondition._get_condition_string(c.logPref)
             except:
-                log_pref = None
-            loggable_items[name] = log_pref
+                log_pref_names = None
+                # log_pref_names = LogCondition._get_condition_string(c.logPref)
+            loggable_items[name] = log_pref_names
         return loggable_items
 
     @property

From 8586828be78359c242f5e7bdecd8fa73ba5ffb6a Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 08:26:47 -0800
Subject: [PATCH 49/52] -

---
 psyneulink/globals/log.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index ef04d9152d9..fd07c51459c 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -440,13 +440,12 @@ def _get_condition_string(cls, condition, string=None):
         else:
             string = ""
         flagged_items = []
-        # If ALL_ASSIGNMENTS, just return that
+        # If OFF or ALL_ASSIGNMENTS, just return that
         if condition in (LogCondition.ALL_ASSIGNMENTS, LogCondition.OFF):
-            # return LogCondition.ALL_ASSIGNMENTS.name
             return condition.name
         # Otherwise, append each flag's name to the string
         for c in list(cls.__members__):
-            # Don't include ALL_ASSIGNMENTS:
+            # Skip ALL_ASSIGNMENTS (handled above)
             if c is LogCondition.ALL_ASSIGNMENTS.name:
                 continue
             if LogCondition[c] & condition:
@@ -723,6 +722,7 @@ def set_log_conditions(self, items, log_condition=LogCondition.EXECUTION):
 
         def assign_log_level(item, level):
 
+            # Handle multiple level assignments (as LogConditions or strings in a list)
             if not isinstance(level, list):
                 level = [level]
             levels = LogCondition.OFF

From bd0abf464be71f1c527973dfb2fb7c31278dc566 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 10:48:04 -0800
Subject: [PATCH 50/52] -

---
 psyneulink/components/component.py | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/psyneulink/components/component.py b/psyneulink/components/component.py
index b97273a5e5f..54eb4119691 100644
--- a/psyneulink/components/component.py
+++ b/psyneulink/components/component.py
@@ -358,7 +358,8 @@ class `UserList <https://docs.python.org/3.6/library/collections.html?highlight=
 from psyneulink.globals.registry import register_category
 from psyneulink.globals.keywords import COMMAND_LINE, DEFERRED_INITIALIZATION, DEFERRED_DEFAULT_NAME, COMPONENT_INIT, \
     CONTEXT, CONTROL, CONTROL_PROJECTION, FUNCTION, FUNCTION_CHECK_ARGS, FUNCTION_PARAMS, INITIALIZING, LOG_ENTRIES, \
-    INIT_FULL_EXECUTE_METHOD, INPUT_STATES, LEARNING, LEARNING_PROJECTION, MAPPING_PROJECTION, NAME, OUTPUT_STATES, \
+    INIT_FULL_EXECUTE_METHOD, INPUT_STATES, LEARNING, LEARNING_PROJECTION, MAPPING_PROJECTION, NAME, \
+    OUTPUT_STATES, \
     PARAMS, PARAMS_CURRENT, PARAM_CLASS_DEFAULTS, PARAM_INSTANCE_DEFAULTS, PREFS_ARG, SEPARATOR_BAR, SET_ATTRIBUTE, \
     SIZE, USER_PARAMS, VALUE, VARIABLE, MODULATORY_SPEC_KEYWORDS, kwComponentCategory
 # from psyneulink.globals.log import Log, LogCondition
@@ -841,14 +842,12 @@ def __init__(self,
             self.prefs = ComponentPreferenceSet(owner=self, prefs=prefs, context=context)
 
         # ASSIGN LOG
-
         from psyneulink.globals.log import Log
         self.log = Log(owner=self)
         self.recording = False
         # Used by run to store return value of execute
         self.results = []
 
-
         # ENFORCE REQUIRED CLASS DEFAULTS
 
         # All subclasses must implement self.ClassDefaults.variable
@@ -936,6 +935,20 @@ def __init__(self,
 
         self.init_status = InitStatus.INITIALIZED
 
+    def __getattr__(self, key):
+        if self.init_status is InitStatus.DEFERRED_INITIALIZATION and key in {'log'}:
+            raise ComponentError("Initialization of {} is deferred; try assigning {} after it is complete".
+                                 format(self.name, key))
+        else:
+            raise AttributeError
+
+    # def __setattr__(self, key, value):
+    #     if self.init_status is InitStatus.DEFERRED_INITIALIZATION:
+    #         raise ComponentError("Initialization of {} is deferred; try assigning {} after it is complete".
+    #                              format(self.name, key))
+    #     else:
+    #         raise AttributeError
+
     def __repr__(self):
         return '({0} {1})'.format(type(self).__name__, self.name)
         #return '{1}'.format(type(self).__name__, self.name)

From b096c811102aafaf590cb183b8ea3561a812df65 Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 11:43:10 -0800
Subject: [PATCH 51/52] -

---
 psyneulink/components/component.py | 29 +++++++++++++++--------------
 psyneulink/globals/log.py          | 14 +++++++-------
 2 files changed, 22 insertions(+), 21 deletions(-)

diff --git a/psyneulink/components/component.py b/psyneulink/components/component.py
index 54eb4119691..160f60a2f6f 100644
--- a/psyneulink/components/component.py
+++ b/psyneulink/components/component.py
@@ -935,20 +935,6 @@ def __init__(self,
 
         self.init_status = InitStatus.INITIALIZED
 
-    def __getattr__(self, key):
-        if self.init_status is InitStatus.DEFERRED_INITIALIZATION and key in {'log'}:
-            raise ComponentError("Initialization of {} is deferred; try assigning {} after it is complete".
-                                 format(self.name, key))
-        else:
-            raise AttributeError
-
-    # def __setattr__(self, key, value):
-    #     if self.init_status is InitStatus.DEFERRED_INITIALIZATION:
-    #         raise ComponentError("Initialization of {} is deferred; try assigning {} after it is complete".
-    #                              format(self.name, key))
-    #     else:
-    #         raise AttributeError
-
     def __repr__(self):
         return '({0} {1})'.format(type(self).__name__, self.name)
         #return '{1}'.format(type(self).__name__, self.name)
@@ -2854,6 +2840,21 @@ def runtimeParamStickyAssignmentPref(self):
     def runtimeParamStickyAssignmentPref(self, setting):
         self.prefs.runtimeParamStickyAssignmentPref = setting
 
+    @property
+    def log(self):
+        try:
+            return self._log
+        except AttributeError:
+            if self.init_status is InitStatus.DEFERRED_INITIALIZATION:
+                raise ComponentError("Initialization of {} is deferred; try assigning {} after it is complete".
+                                     format(self.name, 'log'))
+            else:
+                raise AttributeError
+
+    @log.setter
+    def log(self, log):
+        self._log = log
+
     @property
     def loggable_items(self):
         """Diciontary of items that can be logged in the Component's `log <Component.log>` and their current `LogCondition`.
diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index fd07c51459c..63efc8059f0 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -115,14 +115,14 @@
 multiple Components to be specied at once, while the latter affords more control over the specification (see
 `Preferences`).  LogConditions are treated as binary "flags", and can be combined to permit logging under more than
 one condition using bitwise operators on LogConditions.  For convenience, they can also be referred to by their
-names, and combined by specifying a list.  For example, all of the following specify that the `matrix
-<MappingProjection.matrix>` of ``my_projection`` be logged both during execution and learning::
+names, and combined by specifying a list.  For example, all of the following specify that the `value
+<Mechanism_Base.value>` of ``my_mech`` be logged both during execution and learning::
 
     >>> import psyneulink as pnl
-    >>> my_projection = pnl.MappingProjection()
-    >>> my_projection.set_log_condition('matrix', pnl.LogCondition.EXECUTION | pnl.LogCondition.LEARNING)
-    >>> my_projection.set_log_condition('matrix', pnl.LogCondition.EXECUTION + pnl.LogCondition.LEARNING)
-    >>> my_projection.set_log_condition('matrix', [pnl.EXECUTION, LEARNING])
+    >>> my_mech = pnl.TransferMechanism()
+    >>> my_mech.set_log_conditions('value', pnl.LogCondition.EXECUTION | pnl.LogCondition.LEARNING)
+    >>> my_mech.set_log_conditions('value', pnl.LogCondition.EXECUTION + pnl.LogCondition.LEARNING)
+    >>> my_mech.set_log_conditions('value', [pnl.EXECUTION, LEARNING])
 
 
 .. note::
@@ -138,7 +138,7 @@
    COMMENT:
    FIX: THIS EXAMPLE CAN'T CURRENTLY BE EXECUTED AS IT PERMANENTLY SETS THE LogPref FOR ALL TransferMechanism
    COMMENT
-    >>> T = pnl.TransferMechanism(
+    >>> my_mech = pnl.TransferMechanism(
     ...        prefs={pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogCondition.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)})
 
 .. hint::

From 467c54f02e3e70526bb8a1799b702786c94cc9ee Mon Sep 17 00:00:00 2001
From: "jdc@princeton.edu" <jdc@princeton.edu>
Date: Sun, 17 Dec 2017 11:46:37 -0800
Subject: [PATCH 52/52] -

---
 psyneulink/globals/log.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/psyneulink/globals/log.py b/psyneulink/globals/log.py
index 63efc8059f0..54850f36162 100644
--- a/psyneulink/globals/log.py
+++ b/psyneulink/globals/log.py
@@ -1063,8 +1063,8 @@ def print_entries(self,
             are specified, allowing more information about one to be shown by omitting others (this is useful 
             if the context strings are long and/or the values are arrays).
 
-        full_context : bool : default False
-            specifies the use of the full context string in the display;  this can be informative, but also take up
+        long_context : bool : default False
+            specifies the use of the full context string in the display;  this can be informative, but can also take up
             more space in each line of the display.
             
         """