move running_mean helper from dfatool to utils

8 files changed, 22 insertions, 26 deletions

diff --git a/bin/cal-hist b/bin/cal-hist
index 96c4469..f615c57 100755
--- a/bin/cal-hist
+++ b/bin/cal-hist
@@ -7,7 +7,8 @@ import struct
 import sys
 import tarfile
 import matplotlib.pyplot as plt
-from dfatool import running_mean, MIMOSA
+from dfatool import MIMOSA
+from utils import running_mean
 
 voltage = float(sys.argv[1])
 shunt = float(sys.argv[2])
diff --git a/bin/gradient b/bin/gradient
index e54f10f..eb79b25 100755
--- a/bin/gradient
+++ b/bin/gradient
@@ -7,7 +7,8 @@ import struct
 import sys
 import tarfile
 import matplotlib.pyplot as plt
-from dfatool import running_mean, MIMOSA
+from dfatool import MIMOSA
+from utils import running_mean
 
 voltage = float(sys.argv[1])
 shunt = float(sys.argv[2])
diff --git a/bin/mim-vs-keysight.py b/bin/mim-vs-keysight.py
index d017c30..df37d8b 100755
--- a/bin/mim-vs-keysight.py
+++ b/bin/mim-vs-keysight.py
@@ -1,13 +1,10 @@
 #!/usr/bin/env python3
 
-import csv
 import numpy as np
-import os
-import struct
 import sys
-import tarfile
 import matplotlib.pyplot as plt
-from dfatool import running_mean, MIMOSA, KeysightCSV
+from dfatool import MIMOSA, KeysightCSV
+from utils import running_mean
 
 voltage = float(sys.argv[1])
 shunt = float(sys.argv[2])
diff --git a/bin/mimosa-etv b/bin/mimosa-etv
index 8e8ba44..417f1cf 100755
--- a/bin/mimosa-etv
+++ b/bin/mimosa-etv
@@ -8,7 +8,8 @@ import numpy as np
 import os
 import re
 import sys
-from dfatool import aggregate_measures, running_mean, MIMOSA
+from dfatool import aggregate_measures, MIMOSA
+from utils import running_mean
 
 opt = dict()
 
diff --git a/bin/mimplot b/bin/mimplot
index 60da5c0..cd71a4f 100755
--- a/bin/mimplot
+++ b/bin/mimplot
@@ -9,7 +9,8 @@ import struct
 import sys
 import tarfile
 import matplotlib.pyplot as plt
-from dfatool import running_mean, MIMOSA
+from dfatool import MIMOSA
+from utils import running_mean
 
 opt = dict()
 
diff --git a/bin/workload.py b/bin/workload.py
index c0a03c7..c78e9a8 100755
--- a/bin/workload.py
+++ b/bin/workload.py
@@ -7,11 +7,6 @@ from lex import TimedSequence, TimedWord, Workload
 
 args = sys.argv[1:]
 
-# TODO loops im raw_word:
-# init(); repeat { foo(); sleep(5m); bar(); ... } o.ä.
-# - Zeitangaben mit Einheit in sleep
-# - Ausgabe in Gesamt, Init und Schleifeninhalt aufdröseln
-
 loops = dict()
 ptafiles = list()
 loop_names = set()
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: