"""Implementation of graphs for iterative data.
"""
+
import plotly.graph_objects as go
import pandas as pd
nr_of_samples = len(y_data)
+ customdata = list()
+ metadata = {
+ "csit release": itm["rls"],
+ "dut": itm["dut"],
+ "dut version": itm["dutver"],
+ "infra": itm["phy"],
+ "test": (
+ f"{itm['area']}-{itm['framesize']}-{itm['core']}-"
+ f"{itm['test']}-{itm['testtype']}"
+ )
+ }
+
if itm["testtype"] == "mrr":
- c_data = [
- (
- f"{itm_data['job'].to_list()[0]}/",
- f"{itm_data['build'].to_list()[0]}"
- ),
- ] * nr_of_samples
+ metadata["csit-ref"] = (
+ f"{itm_data['job'].to_list()[0]}/",
+ f"{itm_data['build'].to_list()[0]}"
+ )
+ customdata = [{"metadata": metadata}, ] * nr_of_samples
else:
- c_data = list()
for _, row in itm_data.iterrows():
- c_data.append(f"{row['job']}/{row['build']}")
+ metadata["csit-ref"] = f"{row['job']}/{row['build']}"
+ customdata.append({"metadata": deepcopy(metadata)})
tput_kwargs = dict(
y=y_data,
name=(
boxpoints="all",
jitter=0.3,
marker=dict(color=get_color(idx)),
- customdata=c_data
+ customdata=customdata
)
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']}")
+ hdrh = get_hdrh_latencies(
+ row,
+ f"{metadata['infra']}-{metadata['test']}"
)
+ metadata["csit-ref"] = f"{row['job']}/{row['build']}"
+ customdata.append({
+ "metadata": deepcopy(metadata),
+ "hdrh": hdrh
+ })
y_lat_row = itm_data[C.VALUE_ITER["latency"]].to_list()
y_lat = [(y / norm_factor) for y in y_lat_row]