name: str,
df: pd.DataFrame,
color: str,
- norm_factor: float
+ nf: float
) -> list:
"""Generate the trending traces for the trending graph.
:param name: The test name to be displayed as the graph title.
:param df: Data frame with test data.
:param color: The color of the trace (samples and trend line).
- :param norm_factor: The factor used for normalization of the results to
+ :param nf: The factor used for normalization of the results to
CPU frequency set to Constants.NORM_FREQUENCY.
:type ttype: str
:type name: str
:type df: pandas.DataFrame
:type color: str
- :type norm_factor: float
+ :type nf: float
:returns: Traces (samples, trending line, anomalies)
:rtype: list
"""
if df.empty:
return list(), list()
- x_axis = df["start_time"].tolist()
- if ttype == "latency":
- y_data = [(v / norm_factor) for v in df[C.VALUE[ttype]].tolist()]
- else:
- y_data = [(v * norm_factor) for v in df[C.VALUE[ttype]].tolist()]
- units = df[C.UNIT[ttype]].unique().tolist()
-
- try:
- anomalies, trend_avg, trend_stdev = classify_anomalies(
- {k: v for k, v in zip(x_axis, y_data)}
- )
- except ValueError as err:
- logging.error(err)
- return list(), list()
-
hover = list()
customdata = list()
customdata_samples = list()
name_lst = name.split("-")
- for idx, (_, row) in enumerate(df.iterrows()):
+ for _, row in df.iterrows():
h_tput, h_band, h_lat = str(), str(), str()
if ttype in ("mrr", "mrr-bandwidth"):
h_tput = (
f"tput avg [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_avg']:,.0f}<br>"
+ f"{row['result_receive_rate_rate_avg'] * nf:,.0f}<br>"
f"tput stdev [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_stdev']:,.0f}<br>"
+ f"{row['result_receive_rate_rate_stdev'] * nf:,.0f}<br>"
)
if pd.notna(row["result_receive_rate_bandwidth_avg"]):
h_band = (
f"bandwidth avg "
f"[{row['result_receive_rate_bandwidth_unit']}]: "
- f"{row['result_receive_rate_bandwidth_avg']:,.0f}<br>"
+ f"{row['result_receive_rate_bandwidth_avg'] * nf:,.0f}"
+ "<br>"
f"bandwidth stdev "
f"[{row['result_receive_rate_bandwidth_unit']}]: "
- f"{row['result_receive_rate_bandwidth_stdev']:,.0f}<br>"
+ f"{row['result_receive_rate_bandwidth_stdev']* nf:,.0f}"
+ "<br>"
)
elif ttype in ("ndr", "ndr-bandwidth"):
h_tput = (
f"tput [{row['result_ndr_lower_rate_unit']}]: "
- f"{row['result_ndr_lower_rate_value']:,.0f}<br>"
+ f"{row['result_ndr_lower_rate_value'] * nf:,.0f}<br>"
)
if pd.notna(row["result_ndr_lower_bandwidth_value"]):
h_band = (
f"bandwidth [{row['result_ndr_lower_bandwidth_unit']}]:"
- f" {row['result_ndr_lower_bandwidth_value']:,.0f}<br>"
+ f" {row['result_ndr_lower_bandwidth_value'] * nf:,.0f}"
+ "<br>"
)
elif ttype in ("pdr", "pdr-bandwidth", "latency"):
h_tput = (
f"tput [{row['result_pdr_lower_rate_unit']}]: "
- f"{row['result_pdr_lower_rate_value']:,.0f}<br>"
+ f"{row['result_pdr_lower_rate_value'] * nf:,.0f}<br>"
)
if pd.notna(row["result_pdr_lower_bandwidth_value"]):
h_band = (
f"bandwidth [{row['result_pdr_lower_bandwidth_unit']}]:"
- f" {row['result_pdr_lower_bandwidth_value']:,.0f}<br>"
+ f" {row['result_pdr_lower_bandwidth_value'] * nf:,.0f}"
+ "<br>"
)
if pd.notna(row["result_latency_forward_pdr_50_avg"]):
h_lat = (
f"latency "
f"[{row['result_latency_forward_pdr_50_unit']}]: "
- f"{row['result_latency_forward_pdr_50_avg']:,.0f}<br>"
+ f"{row['result_latency_forward_pdr_50_avg'] / nf:,.0f}"
+ "<br>"
)
elif ttype in ("hoststack-cps", "hoststack-rps",
"hoststack-cps-bandwidth",
"hoststack-rps-bandwidth", "hoststack-latency"):
h_tput = (
f"tput [{row['result_rate_unit']}]: "
- f"{row['result_rate_value']:,.0f}<br>"
+ f"{row['result_rate_value'] * nf:,.0f}<br>"
)
h_band = (
f"bandwidth [{row['result_bandwidth_unit']}]: "
- f"{row['result_bandwidth_value']:,.0f}<br>"
+ f"{row['result_bandwidth_value'] * nf:,.0f}<br>"
)
h_lat = (
f"latency [{row['result_latency_unit']}]: "
- f"{row['result_latency_value']:,.0f}<br>"
+ f"{row['result_latency_value'] / nf:,.0f}<br>"
)
elif ttype in ("hoststack-bps", ):
h_band = (
f"bandwidth [{row['result_bandwidth_unit']}]: "
- f"{row['result_bandwidth_value']:,.0f}<br>"
+ f"{row['result_bandwidth_value'] * nf:,.0f}<br>"
)
hover_itm = (
f"dut: {name_lst[0]}<br>"
)
customdata.append({"name": name})
+ x_axis = df["start_time"].tolist()
+ if "latency" in ttype:
+ y_data = [(v / nf) for v in df[C.VALUE[ttype]].tolist()]
+ else:
+ y_data = [(v * nf) for v in df[C.VALUE[ttype]].tolist()]
+ units = df[C.UNIT[ttype]].unique().tolist()
+
+ try:
+ anomalies, trend_avg, trend_stdev = classify_anomalies(
+ {k: v for k, v in zip(x_axis, y_data)}
+ )
+ except ValueError as err:
+ logging.error(err)
+ return list(), list()
+
hover_trend = list()
for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
hover_itm = (
if normalize:
phy = itm["phy"].split("-")
topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
- norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
+ norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY.get(topo_arch, 1.0)) \
if topo_arch else 1.0
else:
norm_factor = 1.0
Code Review / csit.git / blobdiff