UTI: Add regressions and progressions

[csit.git] / resources / tools / dash / app / pal / trending / graphs.py

diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index 150b705..a63bebb 100644 (file)
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -14,7 +14,6 @@
 """
 """
 
-import logging
 import plotly.graph_objects as go
 import pandas as pd
 
@@ -22,10 +21,8 @@ import hdrh.histogram
 import hdrh.codec
 
 from datetime import datetime
-from numpy import isnan
-
-from ..jumpavg import classify
 
+from ..data.utils import classify_anomalies
 
 _NORM_FREQUENCY = 2.0  # [GHz]
 _FREQURENCY = {  # [GHz]
@@ -131,56 +128,6 @@ 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("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("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:
     """
     """
@@ -237,19 +184,22 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
         return list()
 
     x_axis = df["start_time"].tolist()
-    y_data = [itm * norm_factor for itm in df[_VALUE[ttype]].tolist()]
+    if ttype == "pdr-lat":
+        y_data = [(itm / norm_factor) for itm in df[_VALUE[ttype]].tolist()]
+    else:
+        y_data = [(itm * norm_factor) for itm in df[_VALUE[ttype]].tolist()]
 
-    anomalies, trend_avg, trend_stdev = _classify_anomalies(
+    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[_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>"