"""
+import logging
import plotly.graph_objects as go
import pandas as pd
import re
from datetime import datetime
-
+from numpy import isnan
from dash import no_update
+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 = [
+ [0.00, u"red"],
+ [0.33, u"red"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"green"],
+ [1.00, u"green"]
+]
+_VALUE = {
+ "mrr": "result_receive_rate_rate_avg",
+ "ndr": "result_ndr_lower_rate_value",
+ "pdr": "result_pdr_lower_rate_value"
+}
+_UNIT = {
+ "mrr": "result_receive_rate_rate_unit",
+ "ndr": "result_ndr_lower_rate_unit",
+ "pdr": "result_pdr_lower_rate_unit"
+}
+
+
+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 trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: datetime,
end: datetime):
if not sel:
return no_update, no_update
- def _generate_trace(ttype: str, name: str, df: pd.DataFrame,
- start: datetime, end: datetime):
-
- value = {
- "mrr": "result_receive_rate_rate_avg",
- "ndr": "result_ndr_lower_rate_value",
- "pdr": "result_pdr_lower_rate_value"
- }
- unit = {
- "mrr": "result_receive_rate_rate_unit",
- "ndr": "result_ndr_lower_rate_unit",
- "pdr": "result_pdr_lower_rate_unit"
- }
-
- x_axis = [
- d for d in df["start_time"] if d >= start and d <= end
- ]
- hover_txt = list()
+ def _generate_traces(ttype: str, name: str, df: pd.DataFrame,
+ start: datetime, end: datetime, color: str):
+
+ df = df.dropna(subset=[_VALUE[ttype], ])
+ if df.empty:
+ return list()
+
+ x_axis = [d for d in df["start_time"] if d >= start and d <= end]
+
+ anomalies, trend_avg, trend_stdev = _classify_anomalies(
+ {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
+ )
+
+ hover = list()
for _, row in df.iterrows():
hover_itm = (
f"date: "
f"{row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}<br>"
- f"average [{row[unit[ttype]]}]: "
- f"{row[value[ttype]]}<br>"
+ f"average [{row[_UNIT[ttype]]}]: "
+ f"{row[_VALUE[ttype]]}<br>"
f"{row['dut_type']}-ref: {row['dut_version']}<br>"
f"csit-ref: {row['job']}/{row['build']}"
)
else:
stdev = ""
hover_itm = hover_itm.replace("<stdev>", stdev)
- hover_txt.append(hover_itm)
-
- return go.Scatter(
- x=x_axis,
- y=df[value[ttype]],
- name=name,
- mode="markers+lines",
- text=hover_txt,
- hoverinfo=u"text+name"
- )
+ hover.append(hover_itm)
+
+ hover_trend = list()
+ for avg, stdev in zip(trend_avg, trend_stdev):
+ hover_trend.append(
+ f"trend [pps]: {avg}<br>"
+ f"stdev [pps]: {stdev}"
+ )
+
+ traces = [
+ go.Scatter( # Samples
+ x=x_axis,
+ y=df[_VALUE[ttype]],
+ name=name,
+ mode="markers",
+ marker={
+ u"size": 5,
+ u"color": color,
+ u"symbol": u"circle",
+ },
+ text=hover,
+ hoverinfo=u"text+name",
+ showlegend=True,
+ legendgroup=name,
+ ),
+ go.Scatter( # Trend line
+ x=x_axis,
+ y=trend_avg,
+ name=name,
+ mode="lines",
+ line={
+ u"shape": u"linear",
+ u"width": 1,
+ u"color": color,
+ },
+ text=hover_trend,
+ hoverinfo=u"text+name",
+ showlegend=False,
+ legendgroup=name,
+ )
+ ]
+
+ if anomalies:
+ anomaly_x = list()
+ anomaly_y = list()
+ anomaly_color = list()
+ ticktext = [u"Regression", u"Normal", u"Progression"]
+ for idx, anomaly in enumerate(anomalies):
+ if anomaly in (u"regression", u"progression"):
+ anomaly_x.append(x_axis[idx])
+ anomaly_y.append(trend_avg[idx])
+ anomaly_color.append(_ANOMALY_COLOR[anomaly])
+ anomaly_color.append([0.0, 1.0])
+ traces.append(
+ go.Scatter(
+ x=anomaly_x,
+ y=anomaly_y,
+ mode=u"markers",
+ hoverinfo=u"none",
+ showlegend=False,
+ legendgroup=name,
+ name=f"{name}-anomalies",
+ marker={
+ u"size": 15,
+ u"symbol": u"circle-open",
+ u"color": anomaly_color,
+ u"colorscale": _COLORSCALE,
+ u"showscale": True,
+ u"line": {
+ u"width": 2
+ },
+ u"colorbar": {
+ u"y": 0.5,
+ u"len": 0.8,
+ u"title": u"Circles Marking Data Classification",
+ u"titleside": u"right",
+ u"titlefont": {
+ u"size": 14
+ },
+ u"tickmode": u"array",
+ u"tickvals": [0.167, 0.500, 0.833],
+ u"ticktext": ticktext,
+ u"ticks": u"",
+ u"ticklen": 0,
+ u"tickangle": -90,
+ u"thickness": 10
+ }
+ }
+ )
+ )
+
+ return traces
# Generate graph:
fig = go.Figure()
- for itm in sel:
+ for idx, itm in enumerate(sel):
phy = itm["phy"].split("-")
if len(phy) == 4:
topo, arch, nic, drv = phy
drv = ""
else:
drv += "-"
+ drv = drv.replace("_", "-")
else:
continue
cadence = \
f"{itm['phy']}-{itm['framesize']}-{itm['core']}-"
f"{itm['test']}-{itm['testtype']}"
)
- fig.add_trace(_generate_trace(itm['testtype'], name, df, start, end))
+ for trace in _generate_traces(itm['testtype'], name, df, start, end,
+ _COLORS[idx % len(_COLORS)]):
+ fig.add_trace(trace)
style={
"vertical-align": "top",
Code Review / csit.git / commitdiff