diff options
Diffstat (limited to 'csit.infra.dash/app/cdash/trending/graphs.py')
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/graphs.py | 547 |
1 files changed, 368 insertions, 179 deletions
diff --git a/csit.infra.dash/app/cdash/trending/graphs.py b/csit.infra.dash/app/cdash/trending/graphs.py index fdad73b8c3..79e2697f54 100644 --- a/csit.infra.dash/app/cdash/trending/graphs.py +++ b/csit.infra.dash/app/cdash/trending/graphs.py @@ -45,14 +45,14 @@ def _get_hdrh_latencies(row: pd.Series, name: str) -> dict: return latencies -def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: +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 + :type itm: dict :returns: A data frame with selected data. :rtype: pandas.DataFrame """ @@ -84,206 +84,217 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: return df -def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, - color: str, norm_factor: float) -> list: - """Generate the trending traces for the trending graph. +def graph_trending( + data: pd.DataFrame, + sel: dict, + layout: dict, + normalize: bool + ) -> tuple: + """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences + (result_latency_forward_pdr_50_avg). - :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 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 color: str - :type norm_factor: float - :returns: Traces (samples, trending line, anomalies) - :rtype: list + :param data: Data frame with test results. + :param sel: Selected tests. + :param layout: Layout of plot.ly graph. + :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 normalize: bool + :returns: Trending graph(s) + :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure) """ - df = df.dropna(subset=[C.VALUE[ttype], ]) - if df.empty: - return list() + if not sel: + return None, None - 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, y_data)} - ) - - hover = list() - customdata = list() - customdata_samples = list() - 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[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>" - f"hosts: {', '.join(row['hosts'])}" - ) - if ttype == "mrr": - stdev = ( - f"stdev [{row['result_receive_rate_rate_unit']}]: " - f"{row['result_receive_rate_rate_stdev']:,.0f}<br>" - ) - else: - stdev = "" - hover_itm = hover_itm.replace( - "<prop>", "latency" if ttype == "pdr-lat" else "average" - ).replace("<stdev>", stdev) - hover.append(hover_itm) + + def _generate_trending_traces( + ttype: str, + name: str, + df: pd.DataFrame, + 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 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 color: str + :type norm_factor: float + :returns: Traces (samples, trending line, anomalies) + :rtype: list + """ + + df = df.dropna(subset=[C.VALUE[ttype], ]) + if df.empty: + return list() + + x_axis = df["start_time"].tolist() if ttype == "pdr-lat": - customdata_samples.append(_get_hdrh_latencies(row, name)) - customdata.append({"name": name}) + y_data = [(v / norm_factor) for v in df[C.VALUE[ttype]].tolist()] else: - customdata_samples.append({"name": name, "show_telemetry": True}) - customdata.append({"name": name}) - - 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('%Y-%m-%d %H:%M:%S')}<br>" - f"trend [pps]: {avg:,.0f}<br>" - f"stdev [pps]: {stdev:,.0f}<br>" - f"{d_type}-ref: {row['dut_version']}<br>" - f"csit-ref: {row['job']}/{row['build']}<br>" - f"hosts: {', '.join(row['hosts'])}" - ) - if ttype == "pdr-lat": - hover_itm = hover_itm.replace("[pps]", "[us]") - hover_trend.append(hover_itm) - - traces = [ - go.Scatter( # Samples - x=x_axis, - y=y_data, - name=name, - mode="markers", - marker={ - "size": 5, - "color": color, - "symbol": "circle", - }, - text=hover, - hoverinfo="text+name", - showlegend=True, - legendgroup=name, - customdata=customdata_samples - ), - go.Scatter( # Trend line - x=x_axis, - y=trend_avg, - name=name, - mode="lines", - line={ - "shape": "linear", - "width": 1, - "color": color, - }, - text=hover_trend, - hoverinfo="text+name", - showlegend=False, - legendgroup=name, - customdata=customdata + y_data = [(v * norm_factor) for v in df[C.VALUE[ttype]].tolist()] + + anomalies, trend_avg, trend_stdev = classify_anomalies( + {k: v for k, v in zip(x_axis, y_data)} ) - ] - if anomalies: - anomaly_x = list() - anomaly_y = list() - anomaly_color = list() hover = list() - for idx, anomaly in enumerate(anomalies): - if anomaly in ("regression", "progression"): - anomaly_x.append(x_axis[idx]) - anomaly_y.append(trend_avg[idx]) - 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>" - f"classification: {anomaly}" + customdata = list() + customdata_samples = list() + 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[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>" + f"hosts: {', '.join(row['hosts'])}" + ) + if ttype == "mrr": + stdev = ( + f"stdev [{row['result_receive_rate_rate_unit']}]: " + f"{row['result_receive_rate_rate_stdev']:,.0f}<br>" ) - if ttype == "pdr-lat": - hover_itm = hover_itm.replace("[pps]", "[us]") - hover.append(hover_itm) - anomaly_color.extend([0.0, 0.5, 1.0]) - traces.append( - go.Scatter( - x=anomaly_x, - y=anomaly_y, + else: + stdev = "" + hover_itm = hover_itm.replace( + "<prop>", "latency" if ttype == "pdr-lat" else "average" + ).replace("<stdev>", stdev) + hover.append(hover_itm) + if ttype == "pdr-lat": + customdata_samples.append(_get_hdrh_latencies(row, name)) + customdata.append({"name": name}) + else: + customdata_samples.append( + {"name": name, "show_telemetry": True} + ) + customdata.append({"name": name}) + + 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('%Y-%m-%d %H:%M:%S')}<br>" + f"trend [pps]: {avg:,.0f}<br>" + f"stdev [pps]: {stdev:,.0f}<br>" + f"{d_type}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}<br>" + f"hosts: {', '.join(row['hosts'])}" + ) + if ttype == "pdr-lat": + hover_itm = hover_itm.replace("[pps]", "[us]") + hover_trend.append(hover_itm) + + traces = [ + go.Scatter( # Samples + x=x_axis, + y=y_data, + name=name, mode="markers", + marker={ + "size": 5, + "color": color, + "symbol": "circle", + }, text=hover, hoverinfo="text+name", - showlegend=False, + showlegend=True, legendgroup=name, + customdata=customdata_samples + ), + go.Scatter( # Trend line + x=x_axis, + y=trend_avg, name=name, - customdata=customdata, - marker={ - "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 - }, - "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 + mode="lines", + line={ + "shape": "linear", + "width": 1, + "color": color, + }, + text=hover_trend, + hoverinfo="text+name", + showlegend=False, + legendgroup=name, + customdata=customdata + ) + ] + + if anomalies: + anomaly_x = list() + anomaly_y = list() + anomaly_color = list() + hover = list() + for idx, anomaly in enumerate(anomalies): + if anomaly in ("regression", "progression"): + anomaly_x.append(x_axis[idx]) + anomaly_y.append(trend_avg[idx]) + 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>" + f"classification: {anomaly}" + ) + if ttype == "pdr-lat": + hover_itm = hover_itm.replace("[pps]", "[us]") + hover.append(hover_itm) + anomaly_color.extend([0.0, 0.5, 1.0]) + traces.append( + go.Scatter( + x=anomaly_x, + y=anomaly_y, + mode="markers", + text=hover, + hoverinfo="text+name", + showlegend=False, + legendgroup=name, + name=name, + customdata=customdata, + marker={ + "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 + }, + "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 + } } - } + ) ) - ) - return traces + return traces -def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, - 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 normalize: If True, the data is normalized to CPU frquency - Constants.NORM_FREQUENCY. - :type data: pandas.DataFrame - :type sel: dict - :type layout: dict - :type normalize: bool - :returns: Trending graph(s) - :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure) - """ - - if not sel: - return None, None - fig_tput = None fig_lat = None for idx, itm in enumerate(sel): - df = select_trending_data(data, itm) if df is None or df.empty: continue @@ -393,3 +404,181 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: fig.update_layout(layout_hdrh) return fig + + +def graph_tm_trending(data: pd.DataFrame, layout: dict) -> list: + """Generates one trending graph per test, each graph includes all selected + metrics. + + :param data: Data frame with telemetry data. + :param layout: Layout of plot.ly graph. + :type data: pandas.DataFrame + :type layout: dict + :returns: List of generated graphs together with test names. + list(tuple(plotly.graph_objects.Figure(), str()), tuple(...), ...) + :rtype: list + """ + + + def _generate_graph( + data: pd.DataFrame, + test: str, + layout: dict + ) -> go.Figure: + """Generates a trending graph for given test with all metrics. + + :param data: Data frame with telemetry data for the given test. + :param test: The name of the test. + :param layout: Layout of plot.ly graph. + :type data: pandas.DataFrame + :type test: str + :type layout: dict + :returns: A trending graph. + :rtype: plotly.graph_objects.Figure + """ + graph = None + traces = list() + for idx, metric in enumerate(data.tm_metric.unique()): + if "-pdr" in test and "='pdr'" not in metric: + continue + if "-ndr" in test and "='ndr'" not in metric: + continue + + df = data.loc[(data["tm_metric"] == metric)] + x_axis = df["start_time"].tolist() + y_data = [float(itm) for itm in df["tm_value"].tolist()] + hover = list() + for i, (_, row) in enumerate(df.iterrows()): + hover.append( + f"date: " + f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"value: {y_data[i]:,.0f}<br>" + f"{row['dut_type']}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}<br>" + ) + if any(y_data): + anomalies, trend_avg, trend_stdev = classify_anomalies( + {k: v for k, v in zip(x_axis, y_data)} + ) + hover_trend = list() + for avg, stdev, (_, row) in \ + zip(trend_avg, trend_stdev, df.iterrows()): + hover_trend.append( + f"date: " + f"{row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>" + f"trend: {avg:,.0f}<br>" + f"stdev: {stdev:,.0f}<br>" + f"{row['dut_type']}-ref: {row['dut_version']}<br>" + f"csit-ref: {row['job']}/{row['build']}" + ) + else: + anomalies = None + color = get_color(idx) + traces.append( + go.Scatter( # Samples + x=x_axis, + y=y_data, + name=metric, + mode="markers", + marker={ + "size": 5, + "color": color, + "symbol": "circle", + }, + text=hover, + hoverinfo="text+name", + showlegend=True, + legendgroup=metric + ) + ) + if anomalies: + traces.append( + go.Scatter( # Trend line + x=x_axis, + y=trend_avg, + name=metric, + mode="lines", + line={ + "shape": "linear", + "width": 1, + "color": color, + }, + text=hover_trend, + hoverinfo="text+name", + showlegend=False, + legendgroup=metric + ) + ) + + anomaly_x = list() + anomaly_y = list() + anomaly_color = list() + hover = list() + for idx, anomaly in enumerate(anomalies): + if anomaly in ("regression", "progression"): + anomaly_x.append(x_axis[idx]) + anomaly_y.append(trend_avg[idx]) + anomaly_color.append(C.ANOMALY_COLOR[anomaly]) + hover_itm = ( + f"date: {x_axis[idx].strftime('%Y-%m-%d %H:%M:%S')}" + f"<br>trend: {trend_avg[idx]:,.0f}" + f"<br>classification: {anomaly}" + ) + hover.append(hover_itm) + anomaly_color.extend([0.0, 0.5, 1.0]) + traces.append( + go.Scatter( + x=anomaly_x, + y=anomaly_y, + mode="markers", + text=hover, + hoverinfo="text+name", + showlegend=False, + legendgroup=metric, + name=metric, + marker={ + "size": 15, + "symbol": "circle-open", + "color": anomaly_color, + "colorscale": C.COLORSCALE_TPUT, + "showscale": True, + "line": { + "width": 2 + }, + "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_TPUT, + "ticks": "", + "ticklen": 0, + "tickangle": -90, + "thickness": 10 + } + } + ) + ) + + if traces: + graph = go.Figure() + graph.add_traces(traces) + graph.update_layout(layout.get("plot-trending-telemetry", dict())) + + return graph + + + tm_trending_graphs = list() + + if data.empty: + return tm_trending_graphs + + for test in data.test_name.unique(): + df = data.loc[(data["test_name"] == test)] + graph = _generate_graph(df, test, layout) + if graph: + tm_trending_graphs.append((graph, test, )) + + return tm_trending_graphs |