df.head()
y_max = list()
for i, col in enumerate(df.columns):
- name = "{0}. {1} ({2} run{3})".\
- format(i + 1,
- col.lower().replace('-ndrpdr', ''),
- nr_of_samples[i],
- 's' if nr_of_samples[i] > 1 else '')
+ name = "{nr}. ({samples:02d} run{plural}) {name}".\
+ format(nr=(i + 1),
+ samples=nr_of_samples[i],
+ plural='s' if nr_of_samples[i] > 1 else '',
+ name=col.lower().replace('-ndrpdr', ''))
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br> "
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+
logging.debug(name)
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=[y / 1000000 if y else None for y in df[col]],
y_maxs = list()
nr_of_samples = list()
for key, val in y_sorted.items():
- key = "-".join(key.split("-")[1:-1])
- x_vals.append(key) # dir 1
+ name = "-".join(key.split("-")[1:-1])
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br>"
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+ x_vals.append(name) # dir 1
y_vals.append(mean(val[1]) if val[1] else None)
y_mins.append(mean(val[0]) if val[0] else None)
y_maxs.append(mean(val[2]) if val[2] else None)
nr_of_samples.append(len(val[1]) if val[1] else 0)
- x_vals.append(key) # dir 2
+ x_vals.append(name) # dir 2
y_vals.append(mean(val[4]) if val[4] else None)
y_mins.append(mean(val[3]) if val[3] else None)
y_maxs.append(mean(val[5]) if val[5] else None)
for idx in range(len(x_vals)):
if not bool(int(idx % 2)):
- direction = "West - East"
+ direction = "West-East"
else:
- direction = "East - West"
- hovertext = ("Test: {test}<br>"
- "Direction: {dir}<br>"
- "No. of Runs: {nr}<br>".format(test=x_vals[idx],
- dir=direction,
- nr=nr_of_samples[idx]))
+ direction = "East-West"
+ hovertext = ("No. of Runs: {nr}<br>"
+ "Test: {test}<br>"
+ "Direction: {dir}<br>".format(test=x_vals[idx],
+ dir=direction,
+ nr=nr_of_samples[idx]))
if isinstance(y_maxs[idx], float):
hovertext += "Max: {max:.2f}uSec<br>".format(max=y_maxs[idx])
if isinstance(y_vals[idx], float):
- hovertext += "Avg: {avg:.2f}uSec<br>".format(avg=y_vals[idx])
+ hovertext += "Mean: {avg:.2f}uSec<br>".format(avg=y_vals[idx])
if isinstance(y_mins[idx], float):
hovertext += "Min: {min:.2f}uSec".format(min=y_mins[idx])
try:
if test_vals["1"][1]:
name = "-".join(test_name.split('-')[1:-1])
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br>"
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
vals[name] = dict()
y_val_1 = test_vals["1"][0] / 1000000.0
limit = plot["limits"]["nic"]["xxv710"]
elif "xl710" in test_name:
limit = plot["limits"]["nic"]["xl710"]
+ elif "x553" in test_name:
+ limit = plot["limits"]["nic"]["x553"]
else:
limit = 0
if limit > nic_limit:
for idx in range(len(val["val"])):
htext = ""
if isinstance(val["val"][idx], float):
- htext += "Value: {0:.2f}Mpps<br>" \
- "No. of Runs: {1}<br>".format(val["val"][idx],
- val["count"][idx])
+ htext += "No. of Runs: {1}<br>" \
+ "Mean: {0:.2f}Mpps<br>".format(val["val"][idx],
+ val["count"][idx])
if isinstance(val["diff"][idx], float):
htext += "Diff: {0:.0f}%<br>".format(round(val["diff"][idx]))
if isinstance(val["rel"][idx], float):
color=COLORS[cidx],
width=2,
dash="dash"),
- text=["perfect: {0:.2f}Mpps".format(y)
+ text=["Perfect: {0:.2f}Mpps".format(y)
for y in val["ideal"]],
hoverinfo="text"
))
df = pd.DataFrame(y_vals)
df.head()
for i, col in enumerate(df.columns):
- name = "{0}. {1} ({2} run{3})".\
- format(i + 1,
- col.lower().replace('-cps', '').replace('-rps', ''),
- nr_of_samples[i],
- 's' if nr_of_samples[i] > 1 else '')
+ name = "{nr}. ({samples:02d} run{plural}) {name}".\
+ format(nr=(i + 1),
+ samples=nr_of_samples[i],
+ plural='s' if nr_of_samples[i] > 1 else '',
+ name=col.lower().replace('-ndrpdr', ''))
+ if len(name) > 50:
+ name_lst = name.split('-')
+ name = ""
+ split_name = True
+ for segment in name_lst:
+ if (len(name) + len(segment) + 1) > 50 and split_name:
+ name += "<br> "
+ split_name = False
+ name += segment + '-'
+ name = name[:-1]
+
traces.append(plgo.Box(x=[str(i + 1) + '.'] * len(df[col]),
y=df[col],
name=name,