diff options
Diffstat (limited to 'resources/tools/dash/app/pal/trending/graphs.py')
| -rw-r--r-- | resources/tools/dash/app/pal/trending/graphs.py | 120 |
1 files changed, 19 insertions, 101 deletions
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py index a63bebb818..0b4968082f 100644 --- a/resources/tools/dash/app/pal/trending/graphs.py +++ b/resources/tools/dash/app/pal/trending/graphs.py @@ -22,96 +22,14 @@ import hdrh.codec from datetime import datetime -from ..data.utils import classify_anomalies - -_NORM_FREQUENCY = 2.0 # [GHz] -_FREQURENCY = { # [GHz] - "2n-aws": 1.000, - "2n-dnv": 2.000, - "2n-clx": 2.300, - "2n-icx": 2.600, - "2n-skx": 2.500, - "2n-tx2": 2.500, - "2n-zn2": 2.900, - "3n-alt": 3.000, - "3n-aws": 1.000, - "3n-dnv": 2.000, - "3n-icx": 2.600, - "3n-skx": 2.500, - "3n-tsh": 2.200 -} - -_ANOMALY_COLOR = { - "regression": 0.0, - "normal": 0.5, - "progression": 1.0 -} -_COLORSCALE_TPUT = [ - [0.00, "red"], - [0.33, "red"], - [0.33, "white"], - [0.66, "white"], - [0.66, "green"], - [1.00, "green"] -] -_TICK_TEXT_TPUT = ["Regression", "Normal", "Progression"] -_COLORSCALE_LAT = [ - [0.00, "green"], - [0.33, "green"], - [0.33, "white"], - [0.66, "white"], - [0.66, "red"], - [1.00, "red"] -] -_TICK_TEXT_LAT = ["Progression", "Normal", "Regression"] -_VALUE = { - "mrr": "result_receive_rate_rate_avg", - "ndr": "result_ndr_lower_rate_value", - "pdr": "result_pdr_lower_rate_value", - "pdr-lat": "result_latency_forward_pdr_50_avg" -} -_UNIT = { - "mrr": "result_receive_rate_rate_unit", - "ndr": "result_ndr_lower_rate_unit", - "pdr": "result_pdr_lower_rate_unit", - "pdr-lat": "result_latency_forward_pdr_50_unit" -} -_LAT_HDRH = ( # Do not change the order - "result_latency_forward_pdr_0_hdrh", - "result_latency_reverse_pdr_0_hdrh", - "result_latency_forward_pdr_10_hdrh", - "result_latency_reverse_pdr_10_hdrh", - "result_latency_forward_pdr_50_hdrh", - "result_latency_reverse_pdr_50_hdrh", - "result_latency_forward_pdr_90_hdrh", - "result_latency_reverse_pdr_90_hdrh", -) -# This value depends on latency stream rate (9001 pps) and duration (5s). -# Keep it slightly higher to ensure rounding errors to not remove tick mark. -PERCENTILE_MAX = 99.999501 - -_GRAPH_LAT_HDRH_DESC = { - "result_latency_forward_pdr_0_hdrh": "No-load.", - "result_latency_reverse_pdr_0_hdrh": "No-load.", - "result_latency_forward_pdr_10_hdrh": "Low-load, 10% PDR.", - "result_latency_reverse_pdr_10_hdrh": "Low-load, 10% PDR.", - "result_latency_forward_pdr_50_hdrh": "Mid-load, 50% PDR.", - "result_latency_reverse_pdr_50_hdrh": "Mid-load, 50% PDR.", - "result_latency_forward_pdr_90_hdrh": "High-load, 90% PDR.", - "result_latency_reverse_pdr_90_hdrh": "High-load, 90% PDR." -} +from ..utils.constants import Constants as C +from ..utils.utils import classify_anomalies def _get_color(idx: int) -> str: """ """ - _COLORS = ( - "#1A1110", "#DA2647", "#214FC6", "#01786F", "#BD8260", "#FFD12A", - "#A6E7FF", "#738276", "#C95A49", "#FC5A8D", "#CEC8EF", "#391285", - "#6F2DA8", "#FF878D", "#45A27D", "#FFD0B9", "#FD5240", "#DB91EF", - "#44D7A8", "#4F86F7", "#84DE02", "#FFCFF1", "#614051" - ) - return _COLORS[idx % len(_COLORS)] + return C.PLOT_COLORS[idx % len(C.PLOT_COLORS)] def _get_hdrh_latencies(row: pd.Series, name: str) -> dict: @@ -119,7 +37,7 @@ def _get_hdrh_latencies(row: pd.Series, name: str) -> dict: """ latencies = {"name": name} - for key in _LAT_HDRH: + for key in C.LAT_HDRH: try: latencies[key] = row[key] except KeyError: @@ -176,7 +94,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, """ """ - df = df.dropna(subset=[_VALUE[ttype], ]) + df = df.dropna(subset=[C.VALUE[ttype], ]) if df.empty: return list() df = df.loc[((df["start_time"] >= start) & (df["start_time"] <= end))] @@ -185,9 +103,9 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, x_axis = df["start_time"].tolist() if ttype == "pdr-lat": - y_data = [(itm / norm_factor) for itm in df[_VALUE[ttype]].tolist()] + y_data = [(itm / norm_factor) for itm in df[C.VALUE[ttype]].tolist()] else: - y_data = [(itm * norm_factor) for itm in df[_VALUE[ttype]].tolist()] + 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)} @@ -199,7 +117,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, 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[_UNIT[ttype]]}]: {y_data[idx]:,.0f}<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>" @@ -277,7 +195,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, if anomaly in ("regression", "progression"): anomaly_x.append(x_axis[idx]) anomaly_y.append(trend_avg[idx]) - anomaly_color.append(_ANOMALY_COLOR[anomaly]) + 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>" @@ -301,8 +219,8 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, "size": 15, "symbol": "circle-open", "color": anomaly_color, - "colorscale": _COLORSCALE_LAT \ - if ttype == "pdr-lat" else _COLORSCALE_TPUT, + "colorscale": C.COLORSCALE_LAT \ + if ttype == "pdr-lat" else C.COLORSCALE_TPUT, "showscale": True, "line": { "width": 2 @@ -314,8 +232,8 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, "titleside": "right", "tickmode": "array", "tickvals": [0.167, 0.500, 0.833], - "ticktext": _TICK_TEXT_LAT \ - if ttype == "pdr-lat" else _TICK_TEXT_TPUT, + "ticktext": C.TICK_TEXT_LAT \ + if ttype == "pdr-lat" else C.TICK_TEXT_TPUT, "ticks": "", "ticklen": 0, "tickangle": -90, @@ -349,7 +267,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, if normalize: phy = itm["phy"].split("-") topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str() - norm_factor = (_NORM_FREQUENCY / _FREQURENCY[topo_arch]) \ + norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \ if topo_arch else 1.0 else: norm_factor = 1.0 @@ -400,11 +318,11 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: # For 100%, we cut that down to "x_perc" to avoid # infinity. percentile = item.percentile_level_iterated_to - x_perc = min(percentile, PERCENTILE_MAX) + x_perc = min(percentile, C.PERCENTILE_MAX) xaxis.append(previous_x) yaxis.append(item.value_iterated_to) hovertext.append( - f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>" + f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>" f"Direction: {('W-E', 'E-W')[idx % 2]}<br>" f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>" f"Latency: {item.value_iterated_to}uSec" @@ -413,7 +331,7 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: xaxis.append(next_x) yaxis.append(item.value_iterated_to) hovertext.append( - f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>" + f"<b>{C.GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>" f"Direction: {('W-E', 'E-W')[idx % 2]}<br>" f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>" f"Latency: {item.value_iterated_to}uSec" @@ -425,9 +343,9 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure: go.Scatter( x=xaxis, y=yaxis, - name=_GRAPH_LAT_HDRH_DESC[lat_name], + name=C.GRAPH_LAT_HDRH_DESC[lat_name], mode="lines", - legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name], + legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name], showlegend=bool(idx % 2), line=dict( color=_get_color(int(idx/2)), |