rule_file_validator update

rule_file_validator.py now will display both the changes in affect and any time an affect or modifier is changed along A*'s path

some fixes to csv file printouts to have them display more information

tools/response_curve.csv

@@ -0,0 +1,9 @@
+count,init_affect,prev_affect,curr_affect,curr_action,curr_modifier
+0,joy,joy,joy,resting,neutral
+180,joy,joy,joy,closed_flow,tempo_up
+360,joy,joy,sadness,closed_flow,tempo_up
+540,joy,sadness,sadness,open_flow,tempo_down
+720,joy,sadness,sadness,closed_flow,tempo_up
+900,joy,sadness,sadness,open_flow,tempo_down
+1080,joy,sadness,fear,resting,tempo_down
+1260,joy,fear,fear,resting,tempo_down

tools/rule_file_validator.py

@@ -78,20 +78,34 @@ def main():
     response_curve = th.find_all_affect_changes(action_path)
     with open('response_curve.csv', 'w', newline = '') as csvfile:
         response_writer = csv.writer(csvfile, delimiter = ',', quotechar = '|', quoting = csv.QUOTE_MINIMAL)
+
+        start_state, start_action, start_mod, start_affect = action_path[len(action_path) - 1]
+
+        response_writer.writerow(['count', 'init_affect', 'prev_affect', 'curr_affect', 'curr_action', 'curr_modifier'])
+        response_writer.writerow([0, start_affect, start_affect, start_affect, start_action, start_mod]) # add initial state to file
 
-        response_writer.writerow(['count', 'init_affect', 'prev_affect', 'curr_affect'])
-        response_writer.writerow([0, response_curve[0].init_affect, response_curve[0].init_affect, response_curve[0].init_affect]) # add initial state to file
+        affect_step_cache = 0
 
         for i, node in enumerate(response_curve):
             step_diff = node.count
             if i > 0:
                 step_diff = node.count - response_curve[i - 1].count
 
-            print('\nnumber of steps to display a new affect: ', step_diff, '\nprevious affect: ', node.prev_affect, '\nnew affect: ', node.curr_affect, '\nit will take ', step_diff / 60, ' seconds for the player to see a new expression in a 60hz update loop')
+            if node.prev_affect != node.curr_affect:
+                if affect_step_cache == 0:
+                    step_diff = node.count
+                else:
+                    step_diff = node.count - affect_step_cache
+
+                print('\nnumber of steps to display a new affect: ', step_diff, '\nprevious affect: ', node.prev_affect, '\nnew affect: ', node.curr_affect, '\nit will take ', step_diff / 60, ' seconds for the player to see a new expression in a 60hz update loop', '\naction: ', node.curr_action, '\nmodifier: ', node.curr_mod)
+                affect_step_cache = node.count
+            else:
+                print('\nnumber of steps for a new action or modifier to be used: ', step_diff, '\naction: ', node.curr_action, '\nmodifier: ', node.curr_mod)
 
-            response_writer.writerow([node.count, node.init_affect, node.prev_affect, node.curr_affect])
+            response_writer.writerow([node.count, node.init_affect, node.prev_affect, node.curr_affect, node.curr_action, node.curr_mod])
         last_index = len(response_curve) - 1
-        response_writer.writerow([len(action_path), response_curve[last_index].init_affect, response_curve[last_index].curr_affect, response_curve[last_index].curr_affect]) # add the last step to the file
+        end = response_curve[last_index]
+        response_writer.writerow([len(action_path), end.init_affect, end.curr_affect, end.curr_affect, end.curr_action, end.curr_mod]) # add the last step to the file
 
     th.apply_print_path(action_path, character_test, character_av, step_value, verbose)    
     print('\nfinal affect vector: ', character_av)

tools/test_helper.py

@@ -61,22 +61,35 @@ def find_first_affect_change(path):
 #   - the previous expressed affect
 #   - the currently expressed affect
 def find_all_affect_changes(path):
-    delta_info = collections.namedtuple('delta_info', ['count', 'init_affect', 'prev_affect', 'curr_affect'])
+    delta_info = collections.namedtuple('delta_info', ['count', 'init_affect', 'prev_affect', 'curr_affect', 'curr_action', 'curr_mod'])
 
     start_node = path[len(path) - 1]
     init_affect = start_node[3]
     prev_affect = init_affect
+    prev_action = start_node[1]
+    prev_mod = start_node[2]
     count = 0
     curr_affect = None
 
     affect_changes = []
 
     for node in reversed(path):
+        curr_action = node[1]
+        curr_mod = node[2]
         curr_affect = node[3]
         if curr_affect != prev_affect:
-            temp_node = delta_info._make((count, init_affect, prev_affect, curr_affect))
+            temp_node = delta_info._make((count, init_affect, prev_affect, curr_affect, curr_action, curr_mod))
             affect_changes.append(temp_node)
             prev_affect = curr_affect
+            prev_action = curr_action
+            prev_mod = curr_mod
+
+        elif curr_action != prev_action or curr_mod != prev_mod:
+            temp_node = delta_info._make((count, init_affect, prev_affect, curr_affect, curr_action, curr_mod))
+            affect_changes.append(temp_node)
+            prev_affect = curr_affect
+            prev_action = curr_action
+            prev_mod = curr_mod
 
         count += 1