+
+
+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