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
"""
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
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
Code Review / csit.git / blobdiff