diff options
Diffstat (limited to 'csit.infra.dash')
| -rw-r--r-- | csit.infra.dash/app/cdash/trending/graphs.py | 71 |
1 files changed, 38 insertions, 33 deletions
diff --git a/csit.infra.dash/app/cdash/trending/graphs.py b/csit.infra.dash/app/cdash/trending/graphs.py index fa60ffdd77..57fc165cb3 100644 --- a/csit.infra.dash/app/cdash/trending/graphs.py +++ b/csit.infra.dash/app/cdash/trending/graphs.py @@ -98,7 +98,7 @@ def graph_trending( name: str, df: pd.DataFrame, color: str, - norm_factor: float + nf: float ) -> list: """Generate the trending traces for the trending graph. @@ -106,13 +106,13 @@ def graph_trending( :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 + :param nf: 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 + :type nf: float :returns: Traces (samples, trending line, anomalies) :rtype: list """ @@ -121,88 +121,78 @@ def graph_trending( if df.empty: return list(), list() - x_axis = df["start_time"].tolist() - if ttype == "latency": - y_data = [(v / norm_factor) for v in df[C.VALUE[ttype]].tolist()] - else: - y_data = [(v * norm_factor) for v in df[C.VALUE[ttype]].tolist()] - units = df[C.UNIT[ttype]].unique().tolist() - - try: - anomalies, trend_avg, trend_stdev = classify_anomalies( - {k: v for k, v in zip(x_axis, y_data)} - ) - except ValueError as err: - logging.error(err) - return list(), list() - hover = list() customdata = list() customdata_samples = list() name_lst = name.split("-") - for idx, (_, row) in enumerate(df.iterrows()): + for _, row in df.iterrows(): h_tput, h_band, h_lat = str(), str(), str() if ttype in ("mrr", "mrr-bandwidth"): h_tput = ( f"tput avg [{row['result_receive_rate_rate_unit']}]: " - f"{row['result_receive_rate_rate_avg']:,.0f}<br>" + f"{row['result_receive_rate_rate_avg'] * nf:,.0f}<br>" f"tput stdev [{row['result_receive_rate_rate_unit']}]: " - f"{row['result_receive_rate_rate_stdev']:,.0f}<br>" + f"{row['result_receive_rate_rate_stdev'] * nf:,.0f}<br>" ) if pd.notna(row["result_receive_rate_bandwidth_avg"]): h_band = ( f"bandwidth avg " f"[{row['result_receive_rate_bandwidth_unit']}]: " - f"{row['result_receive_rate_bandwidth_avg']:,.0f}<br>" + f"{row['result_receive_rate_bandwidth_avg'] * nf:,.0f}" + "<br>" f"bandwidth stdev " f"[{row['result_receive_rate_bandwidth_unit']}]: " - f"{row['result_receive_rate_bandwidth_stdev']:,.0f}<br>" + f"{row['result_receive_rate_bandwidth_stdev']* nf:,.0f}" + "<br>" ) elif ttype in ("ndr", "ndr-bandwidth"): h_tput = ( f"tput [{row['result_ndr_lower_rate_unit']}]: " - f"{row['result_ndr_lower_rate_value']:,.0f}<br>" + f"{row['result_ndr_lower_rate_value'] * nf:,.0f}<br>" ) if pd.notna(row["result_ndr_lower_bandwidth_value"]): h_band = ( f"bandwidth [{row['result_ndr_lower_bandwidth_unit']}]:" - f" {row['result_ndr_lower_bandwidth_value']:,.0f}<br>" + f" {row['result_ndr_lower_bandwidth_value'] * nf:,.0f}" + "<br>" ) elif ttype in ("pdr", "pdr-bandwidth", "latency"): h_tput = ( f"tput [{row['result_pdr_lower_rate_unit']}]: " - f"{row['result_pdr_lower_rate_value']:,.0f}<br>" + f"{row['result_pdr_lower_rate_value'] * nf:,.0f}<br>" ) if pd.notna(row["result_pdr_lower_bandwidth_value"]): h_band = ( f"bandwidth [{row['result_pdr_lower_bandwidth_unit']}]:" - f" {row['result_pdr_lower_bandwidth_value']:,.0f}<br>" + f" {row['result_pdr_lower_bandwidth_value'] * nf:,.0f}" + "<br>" ) if pd.notna(row["result_latency_forward_pdr_50_avg"]): h_lat = ( f"latency " f"[{row['result_latency_forward_pdr_50_unit']}]: " - f"{row['result_latency_forward_pdr_50_avg']:,.0f}<br>" + f"{row['result_latency_forward_pdr_50_avg'] / nf:,.0f}" + "<br>" ) elif ttype in ("hoststack-cps", "hoststack-rps", "hoststack-cps-bandwidth", "hoststack-rps-bandwidth", "hoststack-latency"): h_tput = ( f"tput [{row['result_rate_unit']}]: " - f"{row['result_rate_value']:,.0f}<br>" + f"{row['result_rate_value'] * nf:,.0f}<br>" ) h_band = ( f"bandwidth [{row['result_bandwidth_unit']}]: " - f"{row['result_bandwidth_value']:,.0f}<br>" + f"{row['result_bandwidth_value'] * nf:,.0f}<br>" ) h_lat = ( f"latency [{row['result_latency_unit']}]: " - f"{row['result_latency_value']:,.0f}<br>" + f"{row['result_latency_value'] / nf:,.0f}<br>" ) elif ttype in ("hoststack-bps", ): h_band = ( f"bandwidth [{row['result_bandwidth_unit']}]: " - f"{row['result_bandwidth_value']:,.0f}<br>" + f"{row['result_bandwidth_value'] * nf:,.0f}<br>" ) hover_itm = ( f"dut: {name_lst[0]}<br>" @@ -224,6 +214,21 @@ def graph_trending( ) customdata.append({"name": name}) + x_axis = df["start_time"].tolist() + if "latency" in ttype: + y_data = [(v / nf) for v in df[C.VALUE[ttype]].tolist()] + else: + y_data = [(v * nf) for v in df[C.VALUE[ttype]].tolist()] + units = df[C.UNIT[ttype]].unique().tolist() + + try: + anomalies, trend_avg, trend_stdev = classify_anomalies( + {k: v for k, v in zip(x_axis, y_data)} + ) + except ValueError as err: + logging.error(err) + return list(), list() + hover_trend = list() for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()): hover_itm = ( @@ -352,7 +357,7 @@ def graph_trending( if normalize: phy = itm["phy"].split("-") topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str() - norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \ + norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY.get(topo_arch, 1.0)) \ if topo_arch else 1.0 else: norm_factor = 1.0 |