working benchmark generation

1 files changed, 10 insertions, 10 deletions

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.
     """