y=[y / 1000000 if y else None for y in df[col]],
name=name,
**plot["traces"]))
- val_max = max(df[col])
+ try:
+ val_max = max(df[col])
+ except ValueError as err:
+ logging.error(err)
+ continue
if val_max:
y_max.append(int(val_max / 1000000) + 1)
y_sorted = OrderedDict()
y_tags_l = {s: [t.lower() for t in ts] for s, ts in y_tags.items()}
for tag in order:
+ logging.info(tag)
for suite, tags in y_tags_l.items():
- if tag.lower() in tags:
- try:
- y_sorted[suite] = y_tmp_vals.pop(suite)
- y_tags_l.pop(suite)
- except KeyError as err:
- logging.error("Not found: {0}".format(err))
- finally:
- break
+ if "not " in tag:
+ tag = tag.split(" ")[-1]
+ if tag.lower() in tags:
+ continue
+ else:
+ if tag.lower() not in tags:
+ continue
+ try:
+ y_sorted[suite] = y_tmp_vals.pop(suite)
+ y_tags_l.pop(suite)
+ logging.info(suite)
+ except KeyError as err:
+ logging.error("Not found: {0}".format(err))
+ finally:
+ break
else:
y_sorted = y_tmp_vals
vals[name]["diff"] = \
[(y_val_1 - y_1c_max[test_name]) * 100 / y_val_1, None, None]
- val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
+ try:
+ val_max = max(max(vals[name]["val"], vals[name]["ideal"]))
+ except ValueError as err:
+ logging.error(err)
+ continue
if val_max:
y_max.append(int((val_max / 10) + 1) * 10)
x_vals = [1, 2, 4]
# Limits:
- threshold = 1.1 * max(y_max) # 10%
-
+ try:
+ threshold = 1.1 * max(y_max) # 10%
+ except ValueError as err:
+ logging.error(err)
+ return
nic_limit /= 1000000.0
if nic_limit < threshold:
traces.append(plgo.Scatter(