import hdrh.codec
from datetime import datetime
-from numpy import isnan
-
-from ..jumpavg import classify
-
-
-_COLORS = (
- u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
- u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
- u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
- u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
-)
-_ANOMALY_COLOR = {
- u"regression": 0.0,
- u"normal": 0.5,
- u"progression": 1.0
-}
-_COLORSCALE_TPUT = [
- [0.00, u"red"],
- [0.33, u"red"],
- [0.33, u"white"],
- [0.66, u"white"],
- [0.66, u"green"],
- [1.00, u"green"]
-]
-_TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"]
-_COLORSCALE_LAT = [
- [0.00, u"green"],
- [0.33, u"green"],
- [0.33, u"white"],
- [0.66, u"white"],
- [0.66, u"red"],
- [1.00, u"red"]
-]
-_TICK_TEXT_LAT = [u"Progression", u"Normal", u"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 = {
- u"result_latency_forward_pdr_0_hdrh": u"No-load.",
- u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
- u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
- u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
- u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
- u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
- u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
- u"result_latency_reverse_pdr_90_hdrh": u"High-load, 90% PDR."
-}
+
+from ..utils.constants import Constants as C
+from ..utils.utils import classify_anomalies, get_color
def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
- """
+ """Get the HDRH latencies from the test data.
+
+ :param row: A row fron the data frame with test data.
+ :param name: The test name to be displayed as the graph title.
+ :type row: pandas.Series
+ :type name: str
+ :returns: Dictionary with HDRH latencies.
+ :rtype: dict
"""
latencies = {"name": name}
- for key in _LAT_HDRH:
+ for key in C.LAT_HDRH:
try:
latencies[key] = row[key]
except KeyError:
return latencies
-def _classify_anomalies(data):
- """Process the data and return anomalies and trending values.
-
- Gather data into groups with average as trend value.
- Decorate values within groups to be normal,
- the first value of changed average as a regression, or a progression.
-
- :param data: Full data set with unavailable samples replaced by nan.
- :type data: OrderedDict
- :returns: Classification and trend values
- :rtype: 3-tuple, list of strings, list of floats and list of floats
- """
- # NaN means something went wrong.
- # Use 0.0 to cause that being reported as a severe regression.
- bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
- # TODO: Make BitCountingGroupList a subclass of list again?
- group_list = classify(bare_data).group_list
- group_list.reverse() # Just to use .pop() for FIFO.
- classification = list()
- avgs = list()
- stdevs = list()
- active_group = None
- values_left = 0
- avg = 0.0
- stdv = 0.0
- for sample in data.values():
- if isnan(sample):
- classification.append(u"outlier")
- avgs.append(sample)
- stdevs.append(sample)
- continue
- if values_left < 1 or active_group is None:
- values_left = 0
- while values_left < 1: # Ignore empty groups (should not happen).
- active_group = group_list.pop()
- values_left = len(active_group.run_list)
- avg = active_group.stats.avg
- stdv = active_group.stats.stdev
- classification.append(active_group.comment)
- avgs.append(avg)
- stdevs.append(stdv)
- values_left -= 1
- continue
- classification.append(u"normal")
- avgs.append(avg)
- stdevs.append(stdv)
- values_left -= 1
- return classification, avgs, stdevs
-
-
def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
- """
+ """Select the data for graphs from the provided data frame.
+
+ :param data: Data frame with data for graphs.
+ :param itm: Item (in this case job name) which data will be selected from
+ the input data frame.
+ :type data: pandas.DataFrame
+ :type itm: str
+ :returns: A data frame with selected data.
+ :rtype: pandas.DataFrame
"""
phy = itm["phy"].split("-")
core = str() if itm["dut"] == "trex" else f"{itm['core']}"
ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
- dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
+ dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+ dut_v101 = itm["dut"]
df = data.loc[(
- (data["dut_type"] == dut) &
+ (
+ (
+ (data["version"] == "1.0.0") &
+ (data["dut_type"].str.lower() == dut_v100)
+ ) |
+ (
+ (data["version"] == "1.0.1") &
+ (data["dut_type"].str.lower() == dut_v101)
+ )
+ ) &
(data["test_type"] == ttype) &
(data["passed"] == True)
)]
def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
- start: datetime, end: datetime, color: str) -> list:
- """
+ start: datetime, end: datetime, color: str, norm_factor: float) -> list:
+ """Generate the trending traces for the trending graph.
+
+ :param ttype: Test type (MRR, NDR, PDR).
+ :param name: The test name to be displayed as the graph title.
+ :param df: Data frame with test data.
+ :param start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :param color: The color of the trace (samples and trend line).
+ :param norm_factor: 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 start: datetime.datetime
+ :type end: datetime.datetime
+ :type color: str
+ :type norm_factor: float
+ :returns: Traces (samples, trending line, anomalies)
+ :rtype: list
"""
- 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))]
return list()
x_axis = df["start_time"].tolist()
+ if ttype == "pdr-lat":
+ y_data = [(itm / norm_factor) for itm in df[C.VALUE[ttype]].tolist()]
+ else:
+ 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, df[_VALUE[ttype]])}
+ anomalies, trend_avg, trend_stdev = classify_anomalies(
+ {k: v for k, v in zip(x_axis, y_data)}
)
hover = list()
customdata = list()
- for _, row in df.iterrows():
+ for idx, (_, row) in enumerate(df.iterrows()):
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]]}]: {row[_VALUE[ttype]]:,.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>"
traces = [
go.Scatter( # Samples
x=x_axis,
- y=df[_VALUE[ttype]],
+ y=y_data,
name=name,
mode="markers",
marker={
- u"size": 5,
- u"color": color,
- u"symbol": u"circle",
+ "size": 5,
+ "color": color,
+ "symbol": "circle",
},
text=hover,
- hoverinfo=u"text+name",
+ hoverinfo="text+name",
showlegend=True,
legendgroup=name,
customdata=customdata
name=name,
mode="lines",
line={
- u"shape": u"linear",
- u"width": 1,
- u"color": color,
+ "shape": "linear",
+ "width": 1,
+ "color": color,
},
text=hover_trend,
- hoverinfo=u"text+name",
+ hoverinfo="text+name",
showlegend=False,
legendgroup=name,
)
anomaly_color = list()
hover = list()
for idx, anomaly in enumerate(anomalies):
- if anomaly in (u"regression", u"progression"):
+ 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>"
go.Scatter(
x=anomaly_x,
y=anomaly_y,
- mode=u"markers",
+ mode="markers",
text=hover,
- hoverinfo=u"text+name",
+ hoverinfo="text+name",
showlegend=False,
legendgroup=name,
name=name,
marker={
- u"size": 15,
- u"symbol": u"circle-open",
- u"color": anomaly_color,
- u"colorscale": _COLORSCALE_LAT \
- if ttype == "pdr-lat" else _COLORSCALE_TPUT,
- u"showscale": True,
- u"line": {
- u"width": 2
+ "size": 15,
+ "symbol": "circle-open",
+ "color": anomaly_color,
+ "colorscale": C.COLORSCALE_LAT \
+ if ttype == "pdr-lat" else C.COLORSCALE_TPUT,
+ "showscale": True,
+ "line": {
+ "width": 2
},
- u"colorbar": {
- u"y": 0.5,
- u"len": 0.8,
- u"title": u"Circles Marking Data Classification",
- u"titleside": u"right",
- u"tickmode": u"array",
- u"tickvals": [0.167, 0.500, 0.833],
- u"ticktext": _TICK_TEXT_LAT \
- if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
- u"ticks": u"",
- u"ticklen": 0,
- u"tickangle": -90,
- u"thickness": 10
+ "colorbar": {
+ "y": 0.5,
+ "len": 0.8,
+ "title": "Circles Marking Data Classification",
+ "titleside": "right",
+ "tickmode": "array",
+ "tickvals": [0.167, 0.500, 0.833],
+ "ticktext": C.TICK_TEXT_LAT \
+ if ttype == "pdr-lat" else C.TICK_TEXT_TPUT,
+ "ticks": "",
+ "ticklen": 0,
+ "tickangle": -90,
+ "thickness": 10
}
}
)
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
- start: datetime, end: datetime) -> tuple:
- """
+ start: datetime, end: datetime, normalize: bool) -> tuple:
+ """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences
+ (result_latency_forward_pdr_50_avg).
+
+ :param data: Data frame with test results.
+ :param sel: Selected tests.
+ :param layout: Layout of plot.ly graph.
+ :param start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :param normalize: If True, the data is normalized to CPU frquency
+ Constants.NORM_FREQUENCY.
+ :type data: pandas.DataFrame
+ :type sel: dict
+ :type layout: dict
+ :type start: datetime.datetime
+ :type end: datetype.datetype
+ :type normalize: bool
+ :returns: Trending graph(s)
+ :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
if not sel:
name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
itm["test"], itm["testtype"], ))
+ 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]) \
+ if topo_arch else 1.0
+ else:
+ norm_factor = 1.0
traces = _generate_trending_traces(
- itm["testtype"], name, df, start, end, _COLORS[idx % len(_COLORS)]
+ itm["testtype"], name, df, start, end, get_color(idx), norm_factor
)
if traces:
if not fig_tput:
if itm["testtype"] == "pdr":
traces = _generate_trending_traces(
- "pdr-lat", name, df, start, end, _COLORS[idx % len(_COLORS)]
+ "pdr-lat", name, df, start, end, get_color(idx), norm_factor
)
if traces:
if not fig_lat:
def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
- """
+ """Generate HDR Latency histogram graphs.
+
+ :param data: HDRH data.
+ :param layout: Layout of plot.ly graph.
+ :type data: dict
+ :type layout: dict
+ :returns: HDR latency Histogram.
+ :rtype: plotly.graph_objects.Figure
"""
fig = None
# 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"Direction: {(u'W-E', u'E-W')[idx % 2]}<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"Direction: {(u'W-E', u'E-W')[idx % 2]}<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],
- mode=u"lines",
- legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
+ name=C.GRAPH_LAT_HDRH_DESC[lat_name],
+ mode="lines",
+ legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
showlegend=bool(idx % 2),
line=dict(
- color=_COLORS[int(idx/2)],
- dash=u"solid",
+ color=get_color(int(idx/2)),
+ dash="solid",
width=1 if idx % 2 else 2
),
hovertext=hovertext,
- hoverinfo=u"text"
+ hoverinfo="text"
)
)
if traces: