diff options
Diffstat (limited to 'csit.infra.dash/app')
| -rw-r--r-- | csit.infra.dash/app/cdash/data/data.py | 7 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/data/data.yaml | 2 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/news/layout.py | 6 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/report/layout.py | 5 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/stats/layout.py | 6 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/graphs.py | 547 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/layout.py | 654 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/layout.yaml | 45 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/constants.py | 5 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/control_panel.py | 4 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/telemetry_data.py | 330 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/trigger.py | 6 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/url_processing.py | 4 | ||||
| -rw-r--r-- | csit.infra.dash/app/cdash/utils/utils.py | 25 |
14 files changed, 1366 insertions, 280 deletions
diff --git a/csit.infra.dash/app/cdash/data/data.py b/csit.infra.dash/app/cdash/data/data.py index 8d2ae965dd..7ddb44311a 100644 --- a/csit.infra.dash/app/cdash/data/data.py +++ b/csit.infra.dash/app/cdash/data/data.py @@ -135,8 +135,9 @@ class Data: :type last_modified_end: datetime, optional :type days: integer, optional :returns: List of file names. - :rtype: List + :rtype: list """ + file_list = list() if days: last_modified_begin = datetime.now(tz=UTC) - timedelta(days=days) try: @@ -215,9 +216,7 @@ class Data: if self._debug: df.info(verbose=True, memory_usage='deep') logging.info( - u"\n" - f"Creation of dataframe {path} took: {time() - start}" - u"\n" + f"\nCreation of dataframe {path} took: {time() - start}\n" ) except NoFilesFound as err: logging.error(f"No parquets found.\n{err}") diff --git a/csit.infra.dash/app/cdash/data/data.yaml b/csit.infra.dash/app/cdash/data/data.yaml index 396f1b1638..ec7f7ef1dd 100644 --- a/csit.infra.dash/app/cdash/data/data.yaml +++ b/csit.infra.dash/app/cdash/data/data.yaml @@ -51,6 +51,7 @@ trending-mrr: - result_receive_rate_rate_avg - result_receive_rate_rate_stdev - result_receive_rate_rate_unit + - telemetry trending-ndrpdr: path: s3://fdio-docs-s3-cloudfront-index/csit/parquet/trending columns: @@ -77,6 +78,7 @@ trending-ndrpdr: - result_latency_forward_pdr_50_unit - result_latency_forward_pdr_10_hdrh - result_latency_forward_pdr_0_hdrh + - telemetry iterative-mrr: path: s3://fdio-docs-s3-cloudfront-index/csit/parquet/iterative_{release} columns: diff --git a/csit.infra.dash/app/cdash/news/layout.py b/csit.infra.dash/app/cdash/news/layout.py index 31712d6902..11151d727a 100644 --- a/csit.infra.dash/app/cdash/news/layout.py +++ b/csit.infra.dash/app/cdash/news/layout.py @@ -71,7 +71,11 @@ class Layout: debug=True ).read_stats(days=C.NEWS_TIME_PERIOD) - df_tst_info = pd.concat([data_mrr, data_ndrpdr], ignore_index=True) + df_tst_info = pd.concat( + [data_mrr, data_ndrpdr], + ignore_index=True, + copy=False + ) # Prepare information for the control panel: self._jobs = sorted(list(df_tst_info["job"].unique())) diff --git a/csit.infra.dash/app/cdash/report/layout.py b/csit.infra.dash/app/cdash/report/layout.py index 64e3b8bcde..50cf092ae1 100644 --- a/csit.infra.dash/app/cdash/report/layout.py +++ b/csit.infra.dash/app/cdash/report/layout.py @@ -122,7 +122,8 @@ class Layout: data_ndrpdr["release"] = rls self._data = pd.concat( [self._data, data_mrr, data_ndrpdr], - ignore_index=True + ignore_index=True, + copy=False ) # Get structure of tests: @@ -1251,7 +1252,7 @@ class Layout: if on_draw: if store_sel: - lg_selected = get_list_group_items(store_sel) + lg_selected = get_list_group_items(store_sel, "sel-cl") plotting_area = self._get_plotting_area( store_sel, bool(normalize), diff --git a/csit.infra.dash/app/cdash/stats/layout.py b/csit.infra.dash/app/cdash/stats/layout.py index 2b88caaf04..116185d62c 100644 --- a/csit.infra.dash/app/cdash/stats/layout.py +++ b/csit.infra.dash/app/cdash/stats/layout.py @@ -83,7 +83,11 @@ class Layout: debug=True ).read_stats(days=self._time_period) - df_tst_info = pd.concat([data_mrr, data_ndrpdr], ignore_index=True) + df_tst_info = pd.concat( + [data_mrr, data_ndrpdr], + ignore_index=True, + copy=False + ) # Pre-process the data: data_stats = data_stats[~data_stats.job.str.contains("-verify-")] 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 diff --git a/csit.infra.dash/app/cdash/trending/layout.py b/csit.infra.dash/app/cdash/trending/layout.py index 48b480193b..1866183da0 100644 --- a/csit.infra.dash/app/cdash/trending/layout.py +++ b/csit.infra.dash/app/cdash/trending/layout.py @@ -27,15 +27,18 @@ from dash import Input, Output, State from dash.exceptions import PreventUpdate from yaml import load, FullLoader, YAMLError from ast import literal_eval +from copy import deepcopy from ..utils.constants import Constants as C from ..utils.control_panel import ControlPanel from ..utils.trigger import Trigger +from ..utils.telemetry_data import TelemetryData from ..utils.utils import show_tooltip, label, sync_checklists, gen_new_url, \ generate_options, get_list_group_items from ..utils.url_processing import url_decode from ..data.data import Data -from .graphs import graph_trending, graph_hdrh_latency, select_trending_data +from .graphs import graph_trending, graph_hdrh_latency, select_trending_data, \ + graph_tm_trending # Control panel partameters and their default values. @@ -119,7 +122,11 @@ class Layout: debug=True ).read_trending_ndrpdr(days=self._time_period) - self._data = pd.concat([data_mrr, data_ndrpdr], ignore_index=True) + self._data = pd.concat( + [data_mrr, data_ndrpdr], + ignore_index=True, + copy=False + ) # Get structure of tests: tbs = dict() @@ -251,6 +258,8 @@ class Layout: children=[ dcc.Store(id="store-selected-tests"), dcc.Store(id="store-control-panel"), + dcc.Store(id="store-telemetry-data"), + dcc.Store(id="store-telemetry-user"), dcc.Location(id="url", refresh=False), dbc.Row( id="row-navbar", @@ -278,7 +287,8 @@ class Layout: dbc.Row(id="metadata-tput-lat"), dbc.Row(id="metadata-hdrh-graph") ] - ) + ), + delay_show=C.SPINNER_DELAY ) ] ) @@ -333,30 +343,6 @@ class Layout: ) ]) - def _add_plotting_col(self) -> dbc.Col: - """Add column with plots and tables. It is placed on the right side. - - :returns: Column with tables. - :rtype: dbc.Col - """ - return dbc.Col( - id="col-plotting-area", - children=[ - dbc.Spinner( - children=[ - dbc.Row( - id="plotting-area", - class_name="g-0 p-0", - children=[ - C.PLACEHOLDER - ] - ) - ] - ) - ], - width=9 - ) - def _add_ctrl_panel(self) -> list: """Add control panel. @@ -671,13 +657,167 @@ class Layout: ) ] - def _get_plotting_area( + def _add_plotting_col(self) -> dbc.Col: + """Add column with plots. It is placed on the right side. + + :returns: Column with plots. + :rtype: dbc.Col + """ + return dbc.Col( + id="col-plotting-area", + children=[ + dbc.Spinner( + dbc.Row( + id="plotting-area-trending", + class_name="g-0 p-0", + children=C.PLACEHOLDER + ), + delay_show=C.SPINNER_DELAY + ), + dbc.Row( + id="plotting-area-telemetry", + class_name="g-0 p-0", + children=C.PLACEHOLDER + ), + dbc.Row( + id="plotting-area-buttons", + class_name="g-0 p-0", + children=C.PLACEHOLDER + ) + ], + width=9 + ) + + def _get_plotting_area_buttons(self) -> dbc.Col: + """Add buttons and modals to the plotting area. + + :returns: A column with buttons and modals for telemetry. + :rtype: dbc.Col + """ + return dbc.Col([ + html.Div( + [ + dbc.Button( + id={"type": "telemetry-btn", "index": "open"}, + children="Add Panel with Telemetry", + class_name="me-1", + color="info", + style={ + "text-transform": "none", + "padding": "0rem 1rem" + } + ), + dbc.Modal( + [ + dbc.ModalHeader( + dbc.ModalTitle( + "Select a Metric" + ), + close_button=False + ), + dbc.Spinner( + dbc.ModalBody( + id="plot-mod-telemetry-body-1", + children=self._get_telemetry_step_1() + ), + delay_show=2*C.SPINNER_DELAY + ), + dbc.ModalFooter([ + dbc.Button( + "Select", + id={ + "type": "telemetry-btn", + "index": "select" + }, + disabled=True + ), + dbc.Button( + "Cancel", + id={ + "type": "telemetry-btn", + "index": "cancel" + }, + disabled=False + ) + ]) + ], + id="plot-mod-telemetry-1", + size="lg", + is_open=False, + scrollable=False, + backdrop="static", + keyboard=False + ), + dbc.Modal( + [ + dbc.ModalHeader( + dbc.ModalTitle( + "Select Labels" + ), + close_button=False + ), + dbc.Spinner( + dbc.ModalBody( + id="plot-mod-telemetry-body-2", + children=self._get_telemetry_step_2() + ), + delay_show=2*C.SPINNER_DELAY + ), + dbc.ModalFooter([ + dbc.Button( + "Back", + id={ + "type": "telemetry-btn", + "index": "back" + }, + disabled=False + ), + dbc.Button( + "Add Telemetry", + id={ + "type": "telemetry-btn", + "index": "add" + }, + disabled=True + ), + dbc.Button( + "Cancel", + id={ + "type": "telemetry-btn", + "index": "cancel" + }, + disabled=False + ) + ]) + ], + id="plot-mod-telemetry-2", + size="xl", + is_open=False, + scrollable=False, + backdrop="static", + keyboard=False + ) + ], + className="d-grid gap-0 d-md-flex justify-content-md-end" + ) + ]) + + def _get_plotting_area_trending( self, tests: list, normalize: bool, url: str - ) -> list: + ) -> dbc.Col: """Generate the plotting area with all its content. + + :param tests: A list of tests to be displayed in the trending graphs. + :param normalize: If True, the data in graphs is normalized. + :param url: An URL to be displayed in the modal window. + :type tests: list + :type normalize: bool + :type url: str + :returns: A collumn with trending graphs (tput and latency) in tabs. + :rtype: dbc.Col """ if not tests: return C.PLACEHOLDER @@ -711,13 +851,13 @@ class Layout: ) trending = [ - dbc.Row( - children=dbc.Tabs( + dbc.Row(children=[ + dbc.Tabs( children=tab_items, id="tabs", active_tab="tab-tput", ) - ), + ]), dbc.Row( [ dbc.Col([html.Div( @@ -762,12 +902,43 @@ class Layout: ) ] - acc_items = [ - dbc.AccordionItem( - title="Trending", - children=trending + return dbc.Col( + children=[ + dbc.Row( + dbc.Accordion( + children=[ + dbc.AccordionItem( + title="Trending", + children=trending + ) + ], + class_name="g-0 p-1", + start_collapsed=False, + always_open=True, + active_item=["item-0", ] + ), + class_name="g-0 p-0", + ) + ] + ) + + def _get_plotting_area_telemetry(self, graphs: list) -> dbc.Col: + """Generate the plotting area with telemetry. + """ + if not graphs: + return C.PLACEHOLDER + + acc_items = list() + for graph in graphs: + acc_items.append( + dbc.AccordionItem( + title=f"Telemetry: {graph[1]}", + children=dcc.Graph( + id={"type": "graph-telemetry", "index": graph[1]}, + figure=graph[0] + ) + ) ) - ] return dbc.Col( children=[ @@ -780,45 +951,88 @@ class Layout: active_item=[f"item-{i}" for i in range(len(acc_items))] ), class_name="g-0 p-0", - ), - # dbc.Row( - # dbc.Col([html.Div( - # [ - # dbc.Button( - # id="btn-add-telemetry", - # children="Add Panel with Telemetry", - # class_name="me-1", - # color="info", - # style={ - # "text-transform": "none", - # "padding": "0rem 1rem" - # } - # ) - # ], - # className=\ - # "d-grid gap-0 d-md-flex justify-content-md-end" - # )]), - # class_name="g-0 p-0" - # ) + ) ] ) + @staticmethod + def _get_telemetry_step_1() -> list: + """Return the content of the modal window used in the step 1 of metrics + selection. + + :returns: A list of dbc rows with 'input' and 'search output'. + :rtype: list + """ + return [ + dbc.Row( + class_name="g-0 p-1", + children=[ + dbc.Input( + id="telemetry-search-in", + placeholder="Start typing a metric name...", + type="text" + ) + ] + ), + dbc.Row( + class_name="g-0 p-1", + children=[ + dbc.ListGroup( + class_name="overflow-auto p-0", + id="telemetry-search-out", + children=[], + style={"max-height": "14em"}, + flush=True + ) + ] + ) + ] + + @staticmethod + def _get_telemetry_step_2() -> list: + """Return the content of the modal window used in the step 2 of metrics + selection. + + :returns: A list of dbc rows with 'container with dynamic dropdowns' and + 'search output'. + :rtype: list + """ + return [ + dbc.Row( + id="telemetry-dd", + class_name="g-0 p-1", + children=["Add content here."] + ), + dbc.Row( + class_name="g-0 p-1", + children=[ + dbc.Textarea( + id="telemetry-list-metrics", + rows=20, + size="sm", + wrap="off", + readonly=True + ) + ] + ) + ] + def callbacks(self, app): """Callbacks for the whole application. :param app: The application. :type app: Flask """ - + @app.callback( [ Output("store-control-panel", "data"), Output("store-selected-tests", "data"), - Output("plotting-area", "children"), + Output("plotting-area-trending", "children"), + Output("plotting-area-buttons", "children"), Output("row-card-sel-tests", "style"), Output("row-btns-sel-tests", "style"), Output("lg-selected", "children"), - Output({"type": "ctrl-dd", "index": "dut"}, "value"), Output({"type": "ctrl-dd", "index": "phy"}, "options"), Output({"type": "ctrl-dd", "index": "phy"}, "disabled"), @@ -852,11 +1066,11 @@ class Layout: [ Input("url", "href"), Input("normalize", "value"), - Input({"type": "ctrl-dd", "index": ALL}, "value"), Input({"type": "ctrl-cl", "index": ALL}, "value"), Input({"type": "ctrl-btn", "index": ALL}, "n_clicks") - ] + ], + prevent_initial_call=True ) def _update_application( control_panel: dict, @@ -879,11 +1093,6 @@ class Layout: else: url_params = None - plotting_area = no_update - row_card_sel_tests = no_update - row_btns_sel_tests = no_update - lg_selected = no_update - trigger = Trigger(callback_context.triggered) if trigger.type == "url" and url_params: @@ -1124,11 +1333,11 @@ class Layout: store_sel = new_store_sel elif trigger.idx == "rm-test-all": store_sel = list() - + if on_draw: if store_sel: - lg_selected = get_list_group_items(store_sel) - plotting_area = self._get_plotting_area( + lg_selected = get_list_group_items(store_sel, "sel-cl") + plotting_area_trending = self._get_plotting_area_trending( store_sel, bool(normalize), gen_new_url( @@ -1136,18 +1345,28 @@ class Layout: {"store_sel": store_sel, "norm": normalize} ) ) + plotting_area_buttons = self._get_plotting_area_buttons() row_card_sel_tests = C.STYLE_ENABLED row_btns_sel_tests = C.STYLE_ENABLED else: - plotting_area = C.PLACEHOLDER + plotting_area_trending = C.PLACEHOLDER + plotting_area_buttons = C.PLACEHOLDER row_card_sel_tests = C.STYLE_DISABLED row_btns_sel_tests = C.STYLE_DISABLED + lg_selected = no_update store_sel = list() + else: + plotting_area_trending = no_update + plotting_area_buttons = no_update + row_card_sel_tests = no_update + row_btns_sel_tests = no_update + lg_selected = no_update ret_val = [ ctrl_panel.panel, store_sel, - plotting_area, + plotting_area_trending, + plotting_area_buttons, row_card_sel_tests, row_btns_sel_tests, lg_selected @@ -1157,8 +1376,8 @@ class Layout: @app.callback( Output("plot-mod-url", "is_open"), - [Input("plot-btn-url", "n_clicks")], - [State("plot-mod-url", "is_open")], + Input("plot-btn-url", "n_clicks"), + State("plot-mod-url", "is_open") ) def toggle_plot_mod_url(n, is_open): """Toggle the modal window with url. @@ -1168,6 +1387,289 @@ class Layout: return is_open @app.callback( + Output("store-telemetry-data", "data"), + Output("store-telemetry-user", "data"), + Output("telemetry-search-in", "value"), + Output("telemetry-search-out", "children"), + Output("telemetry-list-metrics", "value"), + Output("telemetry-dd", "children"), + Output("plotting-area-telemetry", "children"), + Output("plot-mod-telemetry-1", "is_open"), + Output("plot-mod-telemetry-2", "is_open"), + Output({"type": "telemetry-btn", "index": "select"}, "disabled"), + Output({"type": "telemetry-btn", "index": "add"}, "disabled"), + State("store-telemetry-data", "data"), + State("store-telemetry-user", "data"), + State("store-selected-tests", "data"), + Input({"type": "tele-cl", "index": ALL}, "value"), + Input("telemetry-search-in", "value"), + Input({"type": "telemetry-btn", "index": ALL}, "n_clicks"), + Input({"type": "tm-dd", "index": ALL}, "value"), + prevent_initial_call=True + ) + def _update_plot_mod_telemetry( + tm_data: dict, + tm_user: dict, + store_sel: list, + cl_metrics: list, + search_in: str, + n_clicks: list, + tm_dd_in: list + ) -> tuple: + """Toggle the modal window with telemetry. + """ + + if not any(n_clicks): + raise PreventUpdate + + if tm_user is None: + # Telemetry user data + # The data provided by user or result of user action + tm_user = { + # List of unique metrics: + "unique_metrics": list(), + # List of metrics selected by user: + "selected_metrics": list(), + # Labels from metrics selected by user (key: label name, + # value: list of all possible values): + "unique_labels": dict(), + # Labels selected by the user (subset of 'unique_labels'): + "selected_labels": dict(), + # All unique metrics with labels (output from the step 1) + # converted from pandas dataframe to dictionary. + "unique_metrics_with_labels": dict(), + # Metrics with labels selected by the user using dropdowns. + "selected_metrics_with_labels": dict() + } + + tm = TelemetryData(tests=store_sel) + tm_json = no_update + search_out = no_update + list_metrics = no_update + tm_dd = no_update + plotting_area_telemetry = no_update + is_open = (False, False) + is_btn_disabled = (True, True) + + trigger = Trigger(callback_context.triggered) + if trigger.type == "telemetry-btn": + if trigger.idx in ("open", "back"): + tm.from_dataframe(self._data) + tm_json = tm.to_json() + tm_user["unique_metrics"] = tm.unique_metrics + tm_user["selected_metrics"] = list() + tm_user["unique_labels"] = dict() + tm_user["selected_labels"] = dict() + search_in = str() + search_out = get_list_group_items( + tm_user["unique_metrics"], + "tele-cl", + False + ) + is_open = (True, False) + elif trigger.idx == "select": + tm.from_json(tm_data) + if any(cl_metrics): + if not tm_user["selected_metrics"]: + tm_user["selected_metrics"] = \ + tm_user["unique_metrics"] + metrics = [a for a, b in \ + zip(tm_user["selected_metrics"], cl_metrics) if b] + tm_user["selected_metrics"] = metrics + tm_user["unique_labels"] = \ + tm.get_selected_labels(metrics) + tm_user["unique_metrics_with_labels"] = \ + tm.unique_metrics_with_labels + list_metrics = tm.str_metrics + tm_dd = _get_dd_container(tm_user["unique_labels"]) + if list_metrics: + is_btn_disabled = (True, False) + is_open = (False, True) + else: + tm_user = None + is_open = (False, False) + elif trigger.idx == "add": + tm.from_json(tm_data) + plotting_area_telemetry = self._get_plotting_area_telemetry( + graph_tm_trending( + tm.select_tm_trending_data( + tm_user["selected_metrics_with_labels"] + ), + self._graph_layout) + ) + tm_user = None + is_open = (False, False) + elif trigger.idx == "cancel": + tm_user = None + is_open = (False, False) + elif trigger.type == "telemetry-search-in": + tm.from_metrics(tm_user["unique_metrics"]) + tm_user["selected_metrics"] = \ + tm.search_unique_metrics(search_in) + search_out = get_list_group_items( + tm_user["selected_metrics"], + type="tele-cl", + colorize=False + ) + is_open = (True, False) + elif trigger.type == "tele-cl": + if any(cl_metrics): + is_btn_disabled = (False, True) + is_open = (True, False) + elif trigger.type == "tm-dd": + tm.from_metrics_with_labels( + tm_user["unique_metrics_with_labels"] + ) + selected = dict() + previous_itm = None + for itm in tm_dd_in: + if itm is None: + show_new = True + elif isinstance(itm, str): + show_new = False + selected[itm] = list() + elif isinstance(itm, list): + if previous_itm is not None: + selected[previous_itm] = itm + show_new = True + previous_itm = itm + + tm_dd = _get_dd_container( + tm_user["unique_labels"], + selected, + show_new + ) + sel_metrics = tm.filter_selected_metrics_by_labels(selected) + tm_user["selected_metrics_with_labels"] = sel_metrics.to_dict() + if not sel_metrics.empty: + list_metrics = tm.metrics_to_str(sel_metrics) + else: + list_metrics = str() + if list_metrics: + is_btn_disabled = (True, False) + is_open = (False, True) + + # Return values: + ret_val = [ + tm_json, + tm_user, + search_in, + search_out, + list_metrics, + tm_dd, + plotting_area_telemetry + ] + ret_val.extend(is_open) + ret_val.extend(is_btn_disabled) + return ret_val + + def _get_dd_container( + all_labels: dict, + selected_labels: dict=dict(), + show_new=True + ) -> list: + """Generate a container with dropdown selection boxes depenting on + the input data. + + :param all_labels: A dictionary with unique labels and their + possible values. + :param selected_labels: A dictionalry with user selected lables and + their values. + :param show_new: If True, a dropdown selection box to add a new + label is displayed. + :type all_labels: dict + :type selected_labels: dict + :type show_new: bool + :returns: A list of dbc rows with dropdown selection boxes. + :rtype: list + """ + + def _row( + id: str, + lopts: list=list(), + lval: str=str(), + vopts: list=list(), + vvals: list=list() + ) -> dbc.Row: + """Generates a dbc row with dropdown boxes. + + :param id: A string added to the dropdown ID. + :param lopts: A list of options for 'label' dropdown. + :param lval: Value of 'label' dropdown. + :param vopts: A list of options for 'value' dropdown. + :param vvals: A list of values for 'value' dropdown. + :type id: str + :type lopts: list + :type lval: str + :type vopts: list + :type vvals: list + :returns: dbc row with dropdown boxes. + :rtype: dbc.Row + """ + children = list() + if lopts: + children.append( + dbc.Col( + width=6, + children=[ + dcc.Dropdown( + id={ + "type": "tm-dd", + "index": f"label-{id}" + }, + placeholder="Select a label...", + optionHeight=20, + multi=False, + options=lopts, + value=lval if lval else None + ) + ] + ) + ) + if vopts: + children.append( + dbc.Col( + width=6, + children=[ + dcc.Dropdown( + id={ + "type": "tm-dd", + "index": f"value-{id}" + }, + placeholder="Select a value...", + optionHeight=20, + multi=True, + options=vopts, + value=vvals if vvals else None + ) + ] + ) + ) + + return dbc.Row(class_name="g-0 p-1", children=children) + + container = list() + + # Display rows with items in 'selected_labels'; label on the left, + # values on the right: + keys_left = list(all_labels.keys()) + for idx, label in enumerate(selected_labels.keys()): + container.append(_row( + id=idx, + lopts=deepcopy(keys_left), + lval=label, + vopts=all_labels[label], + vvals=selected_labels[label] + )) + keys_left.remove(label) + + # Display row with dd with labels on the left, right side is empty: + if show_new and keys_left: + container.append(_row(id="new", lopts=keys_left)) + + return container + + @app.callback( Output("metadata-tput-lat", "children"), Output("metadata-hdrh-graph", "children"), Output("offcanvas-metadata", "is_open"), @@ -1253,7 +1755,7 @@ class Layout: Input("plot-btn-download", "n_clicks"), prevent_initial_call=True ) - def _download_trending_data(store_sel, _): + def _download_trending_data(store_sel: list, _) -> dict: """Download the data :param store_sel: List of tests selected by user stored in the @@ -1272,6 +1774,6 @@ class Layout: sel_data = select_trending_data(self._data, itm) if sel_data is None: continue - df = pd.concat([df, sel_data], ignore_index=True) + df = pd.concat([df, sel_data], ignore_index=True, copy=False) return dcc.send_data_frame(df.to_csv, C.TREND_DOWNLOAD_FILE_NAME) diff --git a/csit.infra.dash/app/cdash/trending/layout.yaml b/csit.infra.dash/app/cdash/trending/layout.yaml index 0eada51fe3..bc11dde61f 100644 --- a/csit.infra.dash/app/cdash/trending/layout.yaml +++ b/csit.infra.dash/app/cdash/trending/layout.yaml @@ -115,3 +115,48 @@ plot-hdrh-latency: autosize: True paper_bgcolor: "white" plot_bgcolor: "white" + +plot-trending-telemetry: + autosize: True + showlegend: True + yaxis: + showticklabels: True + tickformat: ".3s" + title: "Metric" + hoverformat: ".5s" + gridcolor: "rgb(238, 238, 238)" + linecolor: "rgb(238, 238, 238)" + showline: True + zeroline: False + tickcolor: "rgb(238, 238, 238)" + linewidth: 1 + showgrid: True + xaxis: + title: 'Date [MMDD]' + type: "date" + autorange: True + fixedrange: False + showgrid: True + gridcolor: "rgb(238, 238, 238)" + showline: True + linecolor: "rgb(238, 238, 238)" + zeroline: False + linewidth: 1 + showticklabels: True + tickcolor: "rgb(238, 238, 238)" + tickmode: "auto" + tickformat: "%m%d" + margin: + r: 20 + b: 0 + t: 5 + l: 70 + paper_bgcolor: "#fff" + plot_bgcolor: "#fff" + hoverlabel: + namelength: 50 + legend: + orientation: "h" + y: -0.2 + font: + size: 12 diff --git a/csit.infra.dash/app/cdash/utils/constants.py b/csit.infra.dash/app/cdash/utils/constants.py index 135f06f4d4..95acc07c47 100644 --- a/csit.infra.dash/app/cdash/utils/constants.py +++ b/csit.infra.dash/app/cdash/utils/constants.py @@ -63,7 +63,7 @@ class Constants: # Maximal value of TIME_PERIOD for data read from the parquets in days. # Do not change without a good reason. - MAX_TIME_PERIOD = 180 + MAX_TIME_PERIOD = 150 # 180 # It defines the time period for data read from the parquets in days from # now back to the past. @@ -79,6 +79,9 @@ class Constants: ############################################################################ # General, application wide, layout affecting constants. + # Add a time delay (in ms) to the spinner being shown + SPINNER_DELAY = 500 + # If True, clear all inputs in control panel when button "ADD SELECTED" is # pressed. CLEAR_ALL_INPUTS = False diff --git a/csit.infra.dash/app/cdash/utils/control_panel.py b/csit.infra.dash/app/cdash/utils/control_panel.py index 723f404313..a81495e30c 100644 --- a/csit.infra.dash/app/cdash/utils/control_panel.py +++ b/csit.infra.dash/app/cdash/utils/control_panel.py @@ -15,7 +15,7 @@ """ from copy import deepcopy - +from typing import Any class ControlPanel: """A class representing the control panel. @@ -74,7 +74,7 @@ class ControlPanel: else: raise KeyError(f"The key {key} is not defined.") - def get(self, key: str) -> any: + def get(self, key: str) -> Any: """Returns the value of a key from the Control panel. :param key: The key which value should be returned. diff --git a/csit.infra.dash/app/cdash/utils/telemetry_data.py b/csit.infra.dash/app/cdash/utils/telemetry_data.py new file mode 100644 index 0000000000..e88b8eed06 --- /dev/null +++ b/csit.infra.dash/app/cdash/utils/telemetry_data.py @@ -0,0 +1,330 @@ +# Copyright (c) 2023 Cisco and/or its affiliates. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""A module implementing the parsing of OpenMetrics data and elementary +operations with it. +""" + + +import pandas as pd + +from ..trending.graphs import select_trending_data + + +class TelemetryData: + """A class to store and manipulate the telemetry data. + """ + + def __init__(self, tests: list=list()) -> None: + """Initialize the object. + + :param in_data: Input data. + :param tests: List of selected tests. + :type in_data: pandas.DataFrame + :type tests: list + """ + + self._tests = tests + self._data = None + self._unique_metrics = list() + self._unique_metrics_labels = pd.DataFrame() + self._selected_metrics_labels = pd.DataFrame() + + def from_dataframe(self, in_data: pd.DataFrame=pd.DataFrame()) -> None: + """Read the input from pandas DataFrame. + + This method must be call at the begining to create all data structures. + """ + + if in_data.empty: + return + + df = pd.DataFrame() + metrics = set() # A set of unique metrics + + # Create a dataframe with metrics for selected tests: + for itm in self._tests: + sel_data = select_trending_data(in_data, itm) + if sel_data is not None: + sel_data["test_name"] = itm["id"] + df = pd.concat([df, sel_data], ignore_index=True, copy=False) + # Use only neccessary data: + df = df[[ + "job", + "build", + "dut_type", + "dut_version", + "start_time", + "passed", + "test_name", + "test_type", + "result_receive_rate_rate_avg", + "result_receive_rate_rate_stdev", + "result_receive_rate_rate_unit", + "result_pdr_lower_rate_value", + "result_pdr_lower_rate_unit", + "result_ndr_lower_rate_value", + "result_ndr_lower_rate_unit", + "telemetry" + ]] + # Transform metrics from strings to dataframes: + lst_telemetry = list() + for _, row in df.iterrows(): + d_telemetry = { + "metric": list(), + "labels": list(), # list of tuple(label, value) + "value": list(), + "timestamp": list() + } + if row["telemetry"] is not None and \ + not isinstance(row["telemetry"], float): + for itm in row["telemetry"]: + itm_lst = itm.replace("'", "").rsplit(" ", maxsplit=2) + metric, labels = itm_lst[0].split("{") + d_telemetry["metric"].append(metric) + d_telemetry["labels"].append( + [tuple(x.split("=")) for x in labels[:-1].split(",")] + ) + d_telemetry["value"].append(itm_lst[1]) + d_telemetry["timestamp"].append(itm_lst[2]) + metrics.update(d_telemetry["metric"]) + lst_telemetry.append(pd.DataFrame(data=d_telemetry)) + df["telemetry"] = lst_telemetry + + self._data = df + self._unique_metrics = sorted(metrics) + + def from_json(self, in_data: dict) -> None: + """Read the input data from json. + """ + + df = pd.read_json(in_data) + lst_telemetry = list() + metrics = set() # A set of unique metrics + for _, row in df.iterrows(): + telemetry = pd.DataFrame(row["telemetry"]) + lst_telemetry.append(telemetry) + metrics.update(telemetry["metric"].to_list()) + df["telemetry"] = lst_telemetry + + self._data = df + self._unique_metrics = sorted(metrics) + + def from_metrics(self, in_data: set) -> None: + """Read only the metrics. + """ + self._unique_metrics = in_data + + def from_metrics_with_labels(self, in_data: dict) -> None: + """Read only metrics with labels. + """ + self._unique_metrics_labels = pd.DataFrame.from_dict(in_data) + + def to_json(self) -> str: + """Return the data transformed from dataframe to json. + + :returns: Telemetry data transformed to a json structure. + :rtype: dict + """ + return self._data.to_json() + + @property + def unique_metrics(self) -> list: + """Return a set of unique metrics. + + :returns: A set of unique metrics. + :rtype: set + """ + return self._unique_metrics + + @property + def unique_metrics_with_labels(self) -> dict: + """ + """ + return self._unique_metrics_labels.to_dict() + + def get_selected_labels(self, metrics: list) -> dict: + """Return a dictionary with labels (keys) and all their possible values + (values) for all selected 'metrics'. + + :param metrics: List of metrics we are interested in. + :type metrics: list + :returns: A dictionary with labels and all their possible values. + :rtype: dict + """ + + df_labels = pd.DataFrame() + tmp_labels = dict() + for _, row in self._data.iterrows(): + telemetry = row["telemetry"] + for itm in metrics: + df = telemetry.loc[(telemetry["metric"] == itm)] + df_labels = pd.concat( + [df_labels, df], + ignore_index=True, + copy=False + ) + for _, tm in df.iterrows(): + for label in tm["labels"]: + if label[0] not in tmp_labels: + tmp_labels[label[0]] = set() + tmp_labels[label[0]].add(label[1]) + + selected_labels = dict() + for key in sorted(tmp_labels): + selected_labels[key] = sorted(tmp_labels[key]) + + self._unique_metrics_labels = df_labels[["metric", "labels"]].\ + loc[df_labels[["metric", "labels"]].astype(str).\ + drop_duplicates().index] + + return selected_labels + + @property + def str_metrics(self) -> str: + """Returns all unique metrics as a string. + """ + return TelemetryData.metrics_to_str(self._unique_metrics_labels) + + @staticmethod + def metrics_to_str(in_data: pd.DataFrame) -> str: + """Convert metrics from pandas dataframe to string. Metrics in string + are separated by '\n'. + + :param in_data: Metrics to be converted to a string. + :type in_data: pandas.DataFrame + :returns: Metrics as a string. + :rtype: str + """ + metrics = str() + for _, row in in_data.iterrows(): + labels = ','.join([f"{itm[0]}='{itm[1]}'" for itm in row["labels"]]) + metrics += f"{row['metric']}{{{labels}}}\n" + return metrics[:-1] + + def search_unique_metrics(self, string: str) -> list: + """Return a list of metrics which name includes the given string. + + :param string: A string which must be in the name of metric. + :type string: str + :returns: A list of metrics which name includes the given string. + :rtype: list + """ + return [itm for itm in self._unique_metrics if string in itm] + + def filter_selected_metrics_by_labels( + self, + selection: dict + ) -> pd.DataFrame: + """Filter selected unique metrics by labels and their values. + + :param selection: Labels and their values specified by the user. + :type selection: dict + :returns: Pandas dataframe with filtered metrics. + :rtype: pandas.DataFrame + """ + + def _is_selected(labels: list, sel: dict) -> bool: + """Check if the provided 'labels' are selected by the user. + + :param labels: List of labels and their values from a metric. The + items in this lists are two-item-lists whre the first item is + the label and the second one is its value. + :param sel: User selection. The keys are the selected lables and the + values are lists with label values. + :type labels: list + :type sel: dict + :returns: True if the 'labels' are selected by the user. + :rtype: bool + """ + passed = list() + labels = dict(labels) + for key in sel.keys(): + if key in list(labels.keys()): + if sel[key]: + passed.append(labels[key] in sel[key]) + else: + passed.append(True) + else: + passed.append(False) + return bool(passed and all(passed)) + + self._selected_metrics_labels = pd.DataFrame() + for _, row in self._unique_metrics_labels.iterrows(): + if _is_selected(row["labels"], selection): + self._selected_metrics_labels = pd.concat( + [self._selected_metrics_labels, row.to_frame().T], + ignore_index=True, + axis=0, + copy=False + ) + return self._selected_metrics_labels + + def select_tm_trending_data(self, selection: dict) -> pd.DataFrame: + """Select telemetry data for trending based on user's 'selection'. + + The output dataframe includes these columns: + - "job", + - "build", + - "dut_type", + - "dut_version", + - "start_time", + - "passed", + - "test_name", + - "test_id", + - "test_type", + - "result_receive_rate_rate_avg", + - "result_receive_rate_rate_stdev", + - "result_receive_rate_rate_unit", + - "result_pdr_lower_rate_value", + - "result_pdr_lower_rate_unit", + - "result_ndr_lower_rate_value", + - "result_ndr_lower_rate_unit", + - "tm_metric", + - "tm_value". + + :param selection: User's selection (metrics and labels). + :type selection: dict + :returns: Dataframe with selected data. + :rtype: pandas.DataFrame + """ + + df = pd.DataFrame() + + if self._data is None: + return df + if self._data.empty: + return df + if not selection: + return df + + df_sel = pd.DataFrame.from_dict(selection) + for _, row in self._data.iterrows(): + tm_row = row["telemetry"] + for _, tm_sel in df_sel.iterrows(): + df_tmp = tm_row.loc[tm_row["metric"] == tm_sel["metric"]] + for _, tm in df_tmp.iterrows(): + if tm["labels"] == tm_sel["labels"]: + labels = ','.join( + [f"{itm[0]}='{itm[1]}'" for itm in tm["labels"]] + ) + row["tm_metric"] = f"{tm['metric']}{{{labels}}}" + row["tm_value"] = tm["value"] + new_row = row.drop(labels=["telemetry", ]) + df = pd.concat( + [df, new_row.to_frame().T], + ignore_index=True, + axis=0, + copy=False + ) + return df diff --git a/csit.infra.dash/app/cdash/utils/trigger.py b/csit.infra.dash/app/cdash/utils/trigger.py index 60ef9a3f91..ac303b6b0b 100644 --- a/csit.infra.dash/app/cdash/utils/trigger.py +++ b/csit.infra.dash/app/cdash/utils/trigger.py @@ -14,6 +14,8 @@ """A module implementing the processing of a trigger. """ +from typing import Any + from json import loads, JSONDecodeError @@ -51,7 +53,7 @@ class Trigger: return self._id["type"] @property - def idx(self) -> any: + def idx(self) -> Any: return self._id["index"] @property @@ -59,5 +61,5 @@ class Trigger: return self._param @property - def value(self) -> any: + def value(self) -> Any: return self._val diff --git a/csit.infra.dash/app/cdash/utils/url_processing.py b/csit.infra.dash/app/cdash/utils/url_processing.py index 7f0121ef34..c90c54c41f 100644 --- a/csit.infra.dash/app/cdash/utils/url_processing.py +++ b/csit.infra.dash/app/cdash/utils/url_processing.py @@ -69,7 +69,7 @@ def url_decode(url: str) -> dict: parsed_url = urlparse(url) except ValueError as err: logging.warning(f"\nThe url {url} is not valid, ignoring.\n{repr(err)}") - return None + return dict() if parsed_url.fragment: try: @@ -85,7 +85,7 @@ def url_decode(url: str) -> dict: f"\nEncoded parameters: '{parsed_url.fragment}'" f"\n{repr(err)}" ) - return None + return dict() else: params = None diff --git a/csit.infra.dash/app/cdash/utils/utils.py b/csit.infra.dash/app/cdash/utils/utils.py index 8584dee067..63e13ce141 100644 --- a/csit.infra.dash/app/cdash/utils/utils.py +++ b/csit.infra.dash/app/cdash/utils/utils.py @@ -346,29 +346,34 @@ def set_job_params(df: pd.DataFrame, job: str) -> dict: } -def get_list_group_items(tests: list) -> list: - """Generate list of ListGroupItems with checkboxes with selected tests. - - :param tests: List of tests to be displayed in the ListGroup. - :type tests: list - :returns: List of ListGroupItems with checkboxes with selected tests. +def get_list_group_items(items: list, type: str, colorize: bool=True) -> list: + """Generate list of ListGroupItems with checkboxes with selected items. + + :param items: List of items to be displayed in the ListGroup. + :param type: The type part of an element ID. + :param colorize: If True, the color of labels is set, otherwise the default + color is used. + :type items: list + :type type: str + :type colorize: bool + :returns: List of ListGroupItems with checkboxes with selected items. :rtype: list """ return [ dbc.ListGroupItem( children=[ dbc.Checkbox( - id={"type": "sel-cl", "index": i}, - label=l["id"], + id={"type": type, "index": i}, + label=l["id"] if isinstance(l, dict) else l, value=False, label_class_name="m-0 p-0", label_style={ "font-size": ".875em", - "color": get_color(i) + "color": get_color(i) if colorize else "#55595c" }, class_name="info" ) ], class_name="p-0" - ) for i, l in enumerate(tests) + ) for i, l in enumerate(items) ] |