X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=0b4968082fb48a140c863766bd9705ce39279f06;hb=refs%2Fchanges%2F16%2F36716%2F3;hp=d3164a8e45c6e78272d29ffa64195aa66d0f5052;hpb=a6c94c7c5898fb8570f6f9ca6fdc1909d43c5dc0;p=csit.git

diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index d3164a8e45..0b4968082f 100644
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -21,82 +21,15 @@ import hdrh.histogram
 import hdrh.codec
 
 from datetime import datetime
-from numpy import isnan
-
-from ..jumpavg import classify
-
-
-_ANOMALY_COLOR = {
-    u"regression": 0.0,
-    u"normal": 0.5,
-    u"progression": 1.0
-}
-_COLORSCALE_TPUT = [
-    [0.00, u"red"],
-    [0.33, u"red"],
-    [0.33, u"white"],
-    [0.66, u"white"],
-    [0.66, u"green"],
-    [1.00, u"green"]
-]
-_TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"]
-_COLORSCALE_LAT = [
-    [0.00, u"green"],
-    [0.33, u"green"],
-    [0.33, u"white"],
-    [0.66, u"white"],
-    [0.66, u"red"],
-    [1.00, u"red"]
-]
-_TICK_TEXT_LAT = [u"Progression", u"Normal", u"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 = {
-    u"result_latency_forward_pdr_0_hdrh": u"No-load.",
-    u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
-    u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
-    u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
-    u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
-    u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
-    u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
-    u"result_latency_reverse_pdr_90_hdrh": u"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:
@@ -104,7 +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:
@@ -113,56 +46,6 @@ def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
     return latencies
 
 
-def _classify_anomalies(data):
-    """Process the data and return anomalies and trending values.
-
-    Gather data into groups with average as trend value.
-    Decorate values within groups to be normal,
-    the first value of changed average as a regression, or a progression.
-
-    :param data: Full data set with unavailable samples replaced by nan.
-    :type data: OrderedDict
-    :returns: Classification and trend values
-    :rtype: 3-tuple, list of strings, list of floats and list of floats
-    """
-    # NaN means something went wrong.
-    # Use 0.0 to cause that being reported as a severe regression.
-    bare_data = [0.0 if isnan(sample) else sample for sample in data.values()]
-    # TODO: Make BitCountingGroupList a subclass of list again?
-    group_list = classify(bare_data).group_list
-    group_list.reverse()  # Just to use .pop() for FIFO.
-    classification = list()
-    avgs = list()
-    stdevs = list()
-    active_group = None
-    values_left = 0
-    avg = 0.0
-    stdv = 0.0
-    for sample in data.values():
-        if isnan(sample):
-            classification.append(u"outlier")
-            avgs.append(sample)
-            stdevs.append(sample)
-            continue
-        if values_left < 1 or active_group is None:
-            values_left = 0
-            while values_left < 1:  # Ignore empty groups (should not happen).
-                active_group = group_list.pop()
-                values_left = len(active_group.run_list)
-            avg = active_group.stats.avg
-            stdv = active_group.stats.stdev
-            classification.append(active_group.comment)
-            avgs.append(avg)
-            stdevs.append(stdv)
-            values_left -= 1
-            continue
-        classification.append(u"normal")
-        avgs.append(avg)
-        stdevs.append(stdv)
-        values_left -= 1
-    return classification, avgs, stdevs
-
-
 def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
     """
     """
@@ -207,11 +90,11 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
 
 
 def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
-    start: datetime, end: datetime, color: str) -> list:
+    start: datetime, end: datetime, color: str, norm_factor: float) -> list:
     """
     """
 
-    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))]
@@ -219,18 +102,22 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
         return list()
 
     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, df[_VALUE[ttype]])}
+    anomalies, trend_avg, trend_stdev = classify_anomalies(
+        {k: v for k, v in zip(x_axis, y_data)}
     )
 
     hover = list()
     customdata = list()
-    for _, row in df.iterrows():
+    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[_UNIT[ttype]]}]: {row[_VALUE[ttype]]:,.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>"
@@ -268,16 +155,16 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
     traces = [
         go.Scatter(  # Samples
             x=x_axis,
-            y=df[_VALUE[ttype]],
+            y=y_data,
             name=name,
             mode="markers",
             marker={
-                u"size": 5,
-                u"color": color,
-                u"symbol": u"circle",
+                "size": 5,
+                "color": color,
+                "symbol": "circle",
             },
             text=hover,
-            hoverinfo=u"text+name",
+            hoverinfo="text+name",
             showlegend=True,
             legendgroup=name,
             customdata=customdata
@@ -288,12 +175,12 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
             name=name,
             mode="lines",
             line={
-                u"shape": u"linear",
-                u"width": 1,
-                u"color": color,
+                "shape": "linear",
+                "width": 1,
+                "color": color,
             },
             text=hover_trend,
-            hoverinfo=u"text+name",
+            hoverinfo="text+name",
             showlegend=False,
             legendgroup=name,
         )
@@ -305,10 +192,10 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
         anomaly_color = list()
         hover = list()
         for idx, anomaly in enumerate(anomalies):
-            if anomaly in (u"regression", u"progression"):
+            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>"
@@ -322,35 +209,35 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
             go.Scatter(
                 x=anomaly_x,
                 y=anomaly_y,
-                mode=u"markers",
+                mode="markers",
                 text=hover,
-                hoverinfo=u"text+name",
+                hoverinfo="text+name",
                 showlegend=False,
                 legendgroup=name,
                 name=name,
                 marker={
-                    u"size": 15,
-                    u"symbol": u"circle-open",
-                    u"color": anomaly_color,
-                    u"colorscale": _COLORSCALE_LAT \
-                        if ttype == "pdr-lat" else _COLORSCALE_TPUT,
-                    u"showscale": True,
-                    u"line": {
-                        u"width": 2
+                    "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
                     },
-                    u"colorbar": {
-                        u"y": 0.5,
-                        u"len": 0.8,
-                        u"title": u"Circles Marking Data Classification",
-                        u"titleside": u"right",
-                        u"tickmode": u"array",
-                        u"tickvals": [0.167, 0.500, 0.833],
-                        u"ticktext": _TICK_TEXT_LAT \
-                            if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
-                        u"ticks": u"",
-                        u"ticklen": 0,
-                        u"tickangle": -90,
-                        u"thickness": 10
+                    "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
                     }
                 }
             )
@@ -360,7 +247,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
 
 
 def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
-    start: datetime, end: datetime) -> tuple:
+    start: datetime, end: datetime, normalize: bool) -> tuple:
     """
     """
 
@@ -377,8 +264,15 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
 
         name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
             itm["test"], itm["testtype"], ))
+        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]) \
+                if topo_arch else 1.0
+        else:
+            norm_factor = 1.0
         traces = _generate_trending_traces(
-            itm["testtype"], name, df, start, end, _get_color(idx)
+            itm["testtype"], name, df, start, end, _get_color(idx), norm_factor
         )
         if traces:
             if not fig_tput:
@@ -387,7 +281,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
 
         if itm["testtype"] == "pdr":
             traces = _generate_trending_traces(
-                "pdr-lat", name, df, start, end, _get_color(idx)
+                "pdr-lat", name, df, start, end, _get_color(idx), norm_factor
             )
             if traces:
                 if not fig_lat:
@@ -424,12 +318,12 @@ 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"Direction: {(u'W-E', u'E-W')[idx % 2]}<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"
             )
@@ -437,8 +331,8 @@ 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"Direction: {(u'W-E', u'E-W')[idx % 2]}<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"
             )
@@ -449,17 +343,17 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
             go.Scatter(
                 x=xaxis,
                 y=yaxis,
-                name=_GRAPH_LAT_HDRH_DESC[lat_name],
-                mode=u"lines",
-                legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
+                name=C.GRAPH_LAT_HDRH_DESC[lat_name],
+                mode="lines",
+                legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
                 showlegend=bool(idx % 2),
                 line=dict(
                     color=_get_color(int(idx/2)),
-                    dash=u"solid",
+                    dash="solid",
                     width=1 if idx % 2 else 2
                 ),
                 hovertext=hovertext,
-                hoverinfo=u"text"
+                hoverinfo="text"
             )
         )
     if traces: