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(
name="{0} perfect".format(name),
legendgroup=name,
showlegend=False,
- mode="lines+markers",
+ mode="lines",
line=dict(
color=COLORS[cidx],
width=2,
dash="dash"),
- marker=dict(
- symbol="circle",
- size=10
- ),
text=["perfect: {0:.2f}Mpps".format(y)
for y in val["ideal"]],
hoverinfo="text"