move running_mean helper from dfatool to utils

2 files changed, 12 insertions, 12 deletions

diff --git a/lib/dfatool.py b/lib/dfatool.py
index 607028e..b206250 100644
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -16,7 +16,7 @@ from functions import analytic
 from functions import AnalyticFunction
 from parameters import ParamStats
 from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, remove_index_from_tuple
-from utils import by_name_to_by_param, match_parameter_values
+from utils import by_name_to_by_param, match_parameter_values, running_mean
 
 try:
     from pubcode import Code128
@@ -29,17 +29,6 @@ except ImportError:
 arg_support_enabled = True
 
 
-def running_mean(x: np.ndarray, N: int) -> np.ndarray:
-    """
-    Compute `N` elements wide running average over `x`.
-
-    :param x: 1-Dimensional NumPy array
-    :param N: how many items to average
-    """
-    cumsum = np.cumsum(np.insert(x, 0, 0))
-    return (cumsum[N:] - cumsum[:-N]) / N
-
-
 def gplearn_to_function(function_str: str):
     """
     Convert gplearn-style function string to Python function.
diff --git a/lib/utils.py b/lib/utils.py
index a582d6b..26a591e 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -14,6 +14,17 @@ def vprint(verbose, string):
         print(string)
 
 
+def running_mean(x: np.ndarray, N: int) -> np.ndarray:
+    """
+    Compute `N` elements wide running average over `x`.
+
+    :param x: 1-Dimensional NumPy array
+    :param N: how many items to average
+    """
+    cumsum = np.cumsum(np.insert(x, 0, 0))
+    return (cumsum[N:] - cumsum[:-N]) / N
+
+
 def human_readable(value, unit):
     for prefix, factor in (('p', 1e-12), ('n', 1e-9), (u'µ', 1e-6), ('m', 1e-3), ('', 1), ('k', 1e3)):
         if value < 1e3 * factor: