from copy import deepcopy
from ..utils.constants import Constants as C
-from ..utils.utils import get_color
+from ..utils.utils import get_color, get_hdrh_latencies
def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
y_tput_max = max(max(y_data), y_tput_max)
nr_of_samples = len(y_data)
+
+ if itm["testtype"] == "mrr":
+ c_data = [
+ (
+ f"{itm_data['job'].to_list()[0]}/",
+ f"{itm_data['build'].to_list()[0]}"
+ ),
+ ] * nr_of_samples
+ else:
+ c_data = list()
+ for _, row in itm_data.iterrows():
+ c_data.append(f"{row['job']}/{row['build']}")
tput_kwargs = dict(
y=y_data,
name=(
hoverinfo=u"y+name",
boxpoints="all",
jitter=0.3,
- marker=dict(color=get_color(idx))
+ marker=dict(color=get_color(idx)),
+ customdata=c_data
)
tput_traces.append(go.Box(**tput_kwargs))
show_tput = True
if ttype == "pdr":
+ customdata = list()
+ for _, row in itm_data.iterrows():
+ customdata.append(
+ get_hdrh_latencies(row, f"{row['job']}/{row['build']}")
+ )
+
y_lat_row = itm_data[C.VALUE_ITER["latency"]].to_list()
y_lat = [(y / norm_factor) for y in y_lat_row]
if y_lat:
hoverinfo="all",
boxpoints="all",
jitter=0.3,
- marker=dict(color=get_color(idx))
+ marker=dict(color=get_color(idx)),
+ customdata=customdata
)
x_lat.append(idx + 1)
lat_traces.append(go.Box(**lat_kwargs))