diff options
Diffstat (limited to 'resources/tools/dash/app/pal/trending')
| -rw-r--r-- | resources/tools/dash/app/pal/trending/graphs.py | 250 | ||||
| -rw-r--r-- | resources/tools/dash/app/pal/trending/layout.py | 2 |
2 files changed, 218 insertions, 34 deletions
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py index a20ce8efd4..da528a9a08 100644 --- a/resources/tools/dash/app/pal/trending/graphs.py +++ b/resources/tools/dash/app/pal/trending/graphs.py @@ -15,14 +15,117 @@ """ +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): @@ -32,30 +135,26 @@ def trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: 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']}" ) @@ -67,20 +166,102 @@ def trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: datetime, 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 @@ -88,6 +269,7 @@ def trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: datetime, drv = "" else: drv += "-" + drv = drv.replace("_", "-") else: continue cadence = \ @@ -111,7 +293,9 @@ def trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: datetime, 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", diff --git a/resources/tools/dash/app/pal/trending/layout.py b/resources/tools/dash/app/pal/trending/layout.py index 081f977852..6369a027cf 100644 --- a/resources/tools/dash/app/pal/trending/layout.py +++ b/resources/tools/dash/app/pal/trending/layout.py @@ -520,7 +520,7 @@ class Layout: for framesize in framesizes: for ttype in testtypes: tid = ( - f"{phy}-" + f"{phy.replace('af_xdp', 'af-xdp')}-" f"{area}-" f"{framesize.lower()}-" f"{core.lower()}-" |