from datetime import datetime
-from ..data.utils import classify_anomalies
-
-_NORM_FREQUENCY = 2.0 # [GHz]
-_FREQURENCY = { # [GHz]
- "2n-aws": 1.000,
- "2n-dnv": 2.000,
- "2n-clx": 2.300,
- "2n-icx": 2.600,
- "2n-skx": 2.500,
- "2n-tx2": 2.500,
- "2n-zn2": 2.900,
- "3n-alt": 3.000,
- "3n-aws": 1.000,
- "3n-dnv": 2.000,
- "3n-icx": 2.600,
- "3n-skx": 2.500,
- "3n-tsh": 2.200
-}
-
-_ANOMALY_COLOR = {
- "regression": 0.0,
- "normal": 0.5,
- "progression": 1.0
-}
-_COLORSCALE_TPUT = [
- [0.00, "red"],
- [0.33, "red"],
- [0.33, "white"],
- [0.66, "white"],
- [0.66, "green"],
- [1.00, "green"]
-]
-_TICK_TEXT_TPUT = ["Regression", "Normal", "Progression"]
-_COLORSCALE_LAT = [
- [0.00, "green"],
- [0.33, "green"],
- [0.33, "white"],
- [0.66, "white"],
- [0.66, "red"],
- [1.00, "red"]
-]
-_TICK_TEXT_LAT = ["Progression", "Normal", "Regression"]
-_VALUE = {
- "mrr": "result_receive_rate_rate_avg",
- "ndr": "result_ndr_lower_rate_value",
- "pdr": "result_pdr_lower_rate_value",
- "pdr-lat": "result_latency_forward_pdr_50_avg"
-}
-_UNIT = {
- "mrr": "result_receive_rate_rate_unit",
- "ndr": "result_ndr_lower_rate_unit",
- "pdr": "result_pdr_lower_rate_unit",
- "pdr-lat": "result_latency_forward_pdr_50_unit"
-}
-_LAT_HDRH = ( # Do not change the order
- "result_latency_forward_pdr_0_hdrh",
- "result_latency_reverse_pdr_0_hdrh",
- "result_latency_forward_pdr_10_hdrh",
- "result_latency_reverse_pdr_10_hdrh",
- "result_latency_forward_pdr_50_hdrh",
- "result_latency_reverse_pdr_50_hdrh",
- "result_latency_forward_pdr_90_hdrh",
- "result_latency_reverse_pdr_90_hdrh",
-)
-# This value depends on latency stream rate (9001 pps) and duration (5s).
-# Keep it slightly higher to ensure rounding errors to not remove tick mark.
-PERCENTILE_MAX = 99.999501
-
-_GRAPH_LAT_HDRH_DESC = {
- "result_latency_forward_pdr_0_hdrh": "No-load.",
- "result_latency_reverse_pdr_0_hdrh": "No-load.",
- "result_latency_forward_pdr_10_hdrh": "Low-load, 10% PDR.",
- "result_latency_reverse_pdr_10_hdrh": "Low-load, 10% PDR.",
- "result_latency_forward_pdr_50_hdrh": "Mid-load, 50% PDR.",
- "result_latency_reverse_pdr_50_hdrh": "Mid-load, 50% PDR.",
- "result_latency_forward_pdr_90_hdrh": "High-load, 90% PDR.",
- "result_latency_reverse_pdr_90_hdrh": "High-load, 90% PDR."
-}
+from ..utils.constants import Constants as C
+from ..utils.utils import classify_anomalies
def _get_color(idx: int) -> str:
"""
"""
- _COLORS = (
- "#1A1110", "#DA2647", "#214FC6", "#01786F", "#BD8260", "#FFD12A",
- "#A6E7FF", "#738276", "#C95A49", "#FC5A8D", "#CEC8EF", "#391285",
- "#6F2DA8", "#FF878D", "#45A27D", "#FFD0B9", "#FD5240", "#DB91EF",
- "#44D7A8", "#4F86F7", "#84DE02", "#FFCFF1", "#614051"
- )
- return _COLORS[idx % len(_COLORS)]
+ return C.PLOT_COLORS[idx % len(C.PLOT_COLORS)]
def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
"""
latencies = {"name": name}
- for key in _LAT_HDRH:
+ for key in C.LAT_HDRH:
try:
latencies[key] = row[key]
except KeyError:
"""
"""
- df = df.dropna(subset=[_VALUE[ttype], ])
+ df = df.dropna(subset=[C.VALUE[ttype], ])
if df.empty:
return list()
df = df.loc[((df["start_time"] >= start) & (df["start_time"] <= end))]
x_axis = df["start_time"].tolist()
if ttype == "pdr-lat":
- y_data = [(itm / norm_factor) for itm in df[_VALUE[ttype]].tolist()]
+ y_data = [(itm / norm_factor) for itm in df[C.VALUE[ttype]].tolist()]
else:
- y_data = [(itm * norm_factor) for itm in df[_VALUE[ttype]].tolist()]
+ y_data = [(itm * norm_factor) for itm in df[C.VALUE[ttype]].tolist()]
anomalies, trend_avg, trend_stdev = classify_anomalies(
{k: v for k, v in zip(x_axis, y_data)}
d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
hover_itm = (
f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
- f"<prop> [{row[_UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
+ f"<prop> [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}<br>"
f"<stdev>"
f"{d_type}-ref: {row['dut_version']}<br>"
f"csit-ref: {row['job']}/{row['build']}<br>"
if anomaly in ("regression", "progression"):
anomaly_x.append(x_axis[idx])
anomaly_y.append(trend_avg[idx])
- anomaly_color.append(_ANOMALY_COLOR[anomaly])
+ anomaly_color.append(C.ANOMALY_COLOR[anomaly])
hover_itm = (
f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}<br>"
f"trend [pps]: {trend_avg[idx]:,.0f}<br>"
"size": 15,
"symbol": "circle-open",
"color": anomaly_color,
- "colorscale": _COLORSCALE_LAT \
- if ttype == "pdr-lat" else _COLORSCALE_TPUT,
+ "colorscale": C.COLORSCALE_LAT \
+ if ttype == "pdr-lat" else C.COLORSCALE_TPUT,
"showscale": True,
"line": {
"width": 2
"titleside": "right",
"tickmode": "array",
"tickvals": [0.167, 0.500, 0.833],
- "ticktext": _TICK_TEXT_LAT \
- if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
+ "ticktext": C.TICK_TEXT_LAT \
+ if ttype == "pdr-lat" else C.TICK_TEXT_TPUT,
"ticks": "",
"ticklen": 0,
"tickangle": -90,
if normalize:
phy = itm["phy"].split("-")
topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
- norm_factor = (_NORM_FREQUENCY / _FREQURENCY[topo_arch]) \
+ norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
if topo_arch else 1.0
else:
norm_factor = 1.0
# For 100%, we cut that down to "x_perc" to avoid
# infinity.
percentile = item.percentile_level_iterated_to
- x_perc = min(percentile, PERCENTILE_MAX)
+ x_perc = min(percentile, C.PERCENTILE_MAX)
xaxis.append(previous_x)
yaxis.append(item.value_iterated_to)
hovertext.append(
- f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
+ f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
f"Latency: {item.value_iterated_to}uSec"
xaxis.append(next_x)
yaxis.append(item.value_iterated_to)
hovertext.append(
- f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
+ f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
f"Direction: {('W-E', 'E-W')[idx % 2]}<br>"
f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
f"Latency: {item.value_iterated_to}uSec"
go.Scatter(
x=xaxis,
y=yaxis,
- name=_GRAPH_LAT_HDRH_DESC[lat_name],
+ name=C.GRAPH_LAT_HDRH_DESC[lat_name],
mode="lines",
- legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
+ legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
showlegend=bool(idx % 2),
line=dict(
color=_get_color(int(idx/2)),
Code Review / csit.git / blobdiff