X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=150b7056ba7d680e0992b96d4c802a920299b38f;hp=3b81cf39c4551cbc70723768605ecc6b2f23e2e6;hb=06d3f7331f9f10d99baa334b1808dfdc9c6fc8be;hpb=650d20f1fc6bdea669982f2a549744fcdcce5a37 diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py index 3b81cf39c4..150b7056ba 100644 --- a/resources/tools/dash/app/pal/trending/graphs.py +++ b/resources/tools/dash/app/pal/trending/graphs.py @@ -14,9 +14,9 @@ """ """ +import logging import plotly.graph_objects as go import pandas as pd -import re import hdrh.histogram import hdrh.codec @@ -27,35 +27,46 @@ 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" -) +_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 = { - u"regression": 0.0, - u"normal": 0.5, - u"progression": 1.0 + "regression": 0.0, + "normal": 0.5, + "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"] + [0.00, "red"], + [0.33, "red"], + [0.33, "white"], + [0.66, "white"], + [0.66, "green"], + [1.00, "green"] ] -_TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"] +_TICK_TEXT_TPUT = ["Regression", "Normal", "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"] + [0.00, "green"], + [0.33, "green"], + [0.33, "white"], + [0.66, "white"], + [0.66, "red"], + [1.00, "red"] ] -_TICK_TEXT_LAT = [u"Progression", u"Normal", u"Regression"] +_TICK_TEXT_LAT = ["Progression", "Normal", "Regression"] _VALUE = { "mrr": "result_receive_rate_rate_avg", "ndr": "result_ndr_lower_rate_value", @@ -83,17 +94,29 @@ _LAT_HDRH = ( # Do not change the order 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." + "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." } +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)] + + def _get_hdrh_latencies(row: pd.Series, name: str) -> dict: """ """ @@ -135,7 +158,7 @@ def _classify_anomalies(data): stdv = 0.0 for sample in data.values(): if isnan(sample): - classification.append(u"outlier") + classification.append("outlier") avgs.append(sample) stdevs.append(sample) continue @@ -151,7 +174,7 @@ def _classify_anomalies(data): stdevs.append(stdv) values_left -= 1 continue - classification.append(u"normal") + classification.append("normal") avgs.append(avg) stdevs.append(stdv) values_left -= 1 @@ -175,10 +198,20 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: 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) )] @@ -192,7 +225,7 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: 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: """ """ @@ -204,26 +237,28 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, return list() x_axis = df["start_time"].tolist() + y_data = [itm * norm_factor for itm in df[_VALUE[ttype]].tolist()] anomalies, trend_avg, trend_stdev = _classify_anomalies( - {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])} + {k: v for k, v in zip(x_axis, y_data)} ) hover = list() customdata = list() for _, row in df.iterrows(): + d_type = "trex" if row["dut_type"] == "none" else row["dut_type"] hover_itm = ( - f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
" - f" [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]}
" + f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}
" + f" [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]:,.0f}
" f"" - f"{row['dut_type']}-ref: {row['dut_version']}
" + f"{d_type}-ref: {row['dut_version']}
" f"csit-ref: {row['job']}/{row['build']}
" f"hosts: {', '.join(row['hosts'])}" ) if ttype == "mrr": stdev = ( f"stdev [{row['result_receive_rate_rate_unit']}]: " - f"{row['result_receive_rate_rate_stdev']}
" + f"{row['result_receive_rate_rate_stdev']:,.0f}
" ) else: stdev = "" @@ -236,11 +271,12 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, hover_trend = list() for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()): + d_type = "trex" if row["dut_type"] == "none" else row["dut_type"] hover_itm = ( - f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
" - f"trend [pps]: {avg}
" - f"stdev [pps]: {stdev}
" - f"{row['dut_type']}-ref: {row['dut_version']}
" + f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}
" + f"trend [pps]: {avg:,.0f}
" + f"stdev [pps]: {stdev:,.0f}
" + f"{d_type}-ref: {row['dut_version']}
" f"csit-ref: {row['job']}/{row['build']}
" f"hosts: {', '.join(row['hosts'])}" ) @@ -251,16 +287,16 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, 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 @@ -271,12 +307,12 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, 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, ) @@ -288,13 +324,13 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, 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]) hover_itm = ( - f"date: {x_axis[idx].strftime('%d-%m-%Y %H:%M:%S')}
" - f"trend [pps]: {trend_avg[idx]}
" + f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}
" + f"trend [pps]: {trend_avg[idx]:,.0f}
" f"classification: {anomaly}" ) if ttype == "pdr-lat": @@ -305,35 +341,35 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, 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 \ + "size": 15, + "symbol": "circle-open", + "color": anomaly_color, + "colorscale": _COLORSCALE_LAT \ if ttype == "pdr-lat" else _COLORSCALE_TPUT, - u"showscale": True, - u"line": { - u"width": 2 + "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 \ + "colorbar": { + "y": 0.5, + "len": 0.8, + "title": "Circles Marking Data Classification", + "titleside": "right", + "tickmode": "array", + "tickvals": [0.167, 0.500, 0.833], + "ticktext": _TICK_TEXT_LAT \ if ttype == "pdr-lat" else _TICK_TEXT_TPUT, - u"ticks": u"", - u"ticklen": 0, - u"tickangle": -90, - u"thickness": 10 + "ticks": "", + "ticklen": 0, + "tickangle": -90, + "thickness": 10 } } ) @@ -343,7 +379,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, - start: datetime, end: datetime) -> tuple: + start: datetime, end: datetime, normalize: bool) -> tuple: """ """ @@ -360,8 +396,15 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, 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 = (_NORM_FREQUENCY / _FREQURENCY[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: @@ -370,7 +413,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, 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: @@ -412,7 +455,7 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: yaxis.append(item.value_iterated_to) hovertext.append( f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
" - f"Direction: {(u'W-E', u'E-W')[idx % 2]}
" + f"Direction: {('W-E', 'E-W')[idx % 2]}
" f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
" f"Latency: {item.value_iterated_to}uSec" ) @@ -421,7 +464,7 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: yaxis.append(item.value_iterated_to) hovertext.append( f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
" - f"Direction: {(u'W-E', u'E-W')[idx % 2]}
" + f"Direction: {('W-E', 'E-W')[idx % 2]}
" f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
" f"Latency: {item.value_iterated_to}uSec" ) @@ -433,16 +476,16 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: x=xaxis, y=yaxis, name=_GRAPH_LAT_HDRH_DESC[lat_name], - mode=u"lines", + mode="lines", legendgroup=_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: