working benchmark generation

3 files changed, 19 insertions, 19 deletions

diff --git a/lib/automata.py b/lib/automata.py
index df8363f..94b3717 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -259,7 +259,7 @@ class PTA:
     def __init__(self, state_names: list = [],
             accepting_states: list = None,
             parameters: list = [], initial_param_values: list = None,
-            instance: str = None, header: str = None):
+            codegen: dict = {}):
         """
         Return a new PTA object.
 
@@ -275,8 +275,7 @@ class PTA:
         self.state = dict([[state_name, State(state_name)] for state_name in state_names])
         self.accepting_states = accepting_states.copy() if accepting_states else None
         self.parameters = parameters.copy()
-        self.instance = instance
-        self.header = header
+        self.codegen = codegen
         if initial_param_values:
             self.initial_param_values = initial_param_values.copy()
         else:
@@ -385,11 +384,8 @@ class PTA:
         if 'states' in yaml_input:
             kwargs['state_names'] = yaml_input['states']
 
-        if 'instance' in yaml_input:
-            kwargs['instance'] = yaml_input['instance']
-
-        if 'header' in yaml_input:
-            kwargs['header'] = yaml_input['header']
+        if 'codegen' in yaml_input:
+            kwargs['codegen'] = yaml_input['codegen']
 
         pta = cls(**kwargs)
 
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 38e140d..a089c1d 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -19,7 +19,7 @@ from utils import *
 
 arg_support_enabled = True
 
-def running_mean(x, N):
+def running_mean(x: np.ndarray, N: int) -> np.ndarray:
     """
     Compute running average.
 
@@ -44,7 +44,7 @@ def soft_cast_int(n):
     except ValueError:
         return n
 
-def vprint(verbose, string):
+def vprint(verbose: bool, string: str):
     """
     Print string if verbose.
 
@@ -69,7 +69,7 @@ def vprint(verbose, string):
             return x / y
         return 1.
 
-def gplearn_to_function(function_str):
+def gplearn_to_function(function_str: str):
     """
     Convert gplearn-style function string to Python function.
 
@@ -109,7 +109,7 @@ def gplearn_to_function(function_str):
     print(eval_str)
     return eval(eval_str, eval_globals)
 
-def _elem_param_and_arg_list(elem):
+def _elem_param_and_arg_list(elem: dict):
     param_dict = elem['parameter']
     paramkeys = sorted(param_dict.keys())
     paramvalue = [soft_cast_int(param_dict[x]) for x in paramkeys]
@@ -117,10 +117,10 @@ def _elem_param_and_arg_list(elem):
         paramvalue.extend(map(soft_cast_int, elem['args']))
     return paramvalue
 
-def _arg_name(arg_index):
+def _arg_name(arg_index: int) -> str:
     return '~arg{:02}'.format(arg_index)
 
-def append_if_set(aggregate, data, key):
+def append_if_set(aggregate: dict, data: dict, key: str):
     """Append data[key] to aggregate if key in data."""
     if key in data:
         aggregate.append(data[key])
@@ -131,7 +131,7 @@ def mean_or_none(arr):
         return np.mean(arr)
     return -1
 
-def aggregate_measures(aggregate, actual):
+def aggregate_measures(aggregate: float, actual: list) -> dict:
     """
     Calculate error measures for model value on data list.
 
@@ -145,7 +145,7 @@ def aggregate_measures(aggregate, actual):
     aggregate_array = np.array([aggregate] * len(actual))
     return regression_measures(aggregate_array, np.array(actual))
 
-def regression_measures(predicted, actual):
+def regression_measures(predicted: np.ndarray, actual: np.ndarray):
     """
     Calculate error measures by comparing model values to reference values.
 
@@ -204,7 +204,7 @@ class KeysightCSV:
         """Create a new KeysightCSV object."""
         pass
 
-    def load_data(self, filename):
+    def load_data(self, filename: str):
         """
         Load log data from filename, return timestamps and currents.
 
@@ -225,7 +225,7 @@ class KeysightCSV:
                 currents[i] = float(row[2]) * -1
         return timestamps, currents
 
-def by_name_to_by_param(by_name):
+def by_name_to_by_param(by_name: dict):
     """
     Convert aggregation by name to aggregation by name and parameter values.
     """
diff --git a/lib/harness.py b/lib/harness.py
index 3f8d93c..67a9f7d 100644
--- a/lib/harness.py
+++ b/lib/harness.py
@@ -22,7 +22,11 @@ class OnboardTimerHarness:
         return ret
 
     def start_benchmark(self):
-        return 'ptalog.startBenchmark(0);\n'
+        ret = 'counter.start();\n'
+        ret += 'counter.stop();\n'
+        ret += 'ptalog.passNop(counter);\n'
+        ret += 'ptalog.startBenchmark(0);\n'
+        return ret
 
     def start_run(self):
         return 'ptalog.reset();\n'