X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=06bea25466cf4c123aa7b528dcc0145a7fee7e45;hb=d2ddfd1ead021f1dd520271d763e1789954e32d9;hp=895055816635a9d93d76a30ea7889c1ba14976c9;hpb=2f6295d7c63b7e231b0198ee055468b2fc54fa94;p=csit.git
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index 8950558166..06bea25466 100644
--- 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
@@ -22,107 +21,24 @@ import hdrh.histogram
import hdrh.codec
from datetime import datetime
-from numpy import isnan
-
-from ..jumpavg import classify
-
-
-_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."
-}
-
-
-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)]
+
+from ..utils.constants import Constants as C
+from ..utils.utils import classify_anomalies, get_color
def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
- """
+ """Get the HDRH latencies from the test data.
+
+ :param row: A row fron the data frame with test data.
+ :param name: The test name to be displayed as the graph title.
+ :type row: pandas.Series
+ :type name: str
+ :returns: Dictionary with HDRH latencies.
+ :rtype: dict
"""
latencies = {"name": name}
- for key in _LAT_HDRH:
+ for key in C.LAT_HDRH:
try:
latencies[key] = row[key]
except KeyError:
@@ -131,58 +47,16 @@ 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:
- """
+ """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
+ :returns: A data frame with selected data.
+ :rtype: pandas.DataFrame
"""
phy = itm["phy"].split("-")
@@ -226,10 +100,28 @@ 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, 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 start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :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 start: datetime.datetime
+ :type end: datetime.datetime
+ :type color: str
+ :type norm_factor: float
+ :returns: Traces (samples, trending line, anomalies)
+ :rtype: 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))]
@@ -238,11 +130,11 @@ 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(
+ anomalies, trend_avg, trend_stdev = classify_anomalies(
{k: v for k, v in zip(x_axis, y_data)}
)
@@ -252,7 +144,7 @@ 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')}
"
- f" [{row[_UNIT[ttype]]}]: {y_data[idx]:,.0f}
"
+ f" [{row[C.UNIT[ttype]]}]: {y_data[idx]:,.0f}
"
f""
f"{d_type}-ref: {row['dut_version']}
"
f"csit-ref: {row['job']}/{row['build']}
"
@@ -330,7 +222,7 @@ 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')}
"
f"trend [pps]: {trend_avg[idx]:,.0f}
"
@@ -354,8 +246,8 @@ 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
@@ -367,8 +259,8 @@ 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,
@@ -383,7 +275,24 @@ 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, 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 start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :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 start: datetime.datetime
+ :type end: datetype.datetype
+ :type normalize: bool
+ :returns: Trending graph(s)
+ :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
if not sel:
@@ -402,12 +311,12 @@ 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
traces = _generate_trending_traces(
- itm["testtype"], name, df, start, end, _get_color(idx), norm_factor
+ itm["testtype"], name, df, start, end, get_color(idx), norm_factor
)
if traces:
if not fig_tput:
@@ -416,7 +325,7 @@ 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), norm_factor
+ "pdr-lat", name, df, start, end, get_color(idx), norm_factor
)
if traces:
if not fig_lat:
@@ -432,7 +341,14 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
- """
+ """Generate HDR Latency histogram graphs.
+
+ :param data: HDRH data.
+ :param layout: Layout of plot.ly graph.
+ :type data: dict
+ :type layout: dict
+ :returns: HDR latency Histogram.
+ :rtype: plotly.graph_objects.Figure
"""
fig = None
@@ -453,11 +369,11 @@ 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"{_GRAPH_LAT_HDRH_DESC[lat_name]}
"
+ f"{C.GRAPH_LAT_HDRH_DESC[lat_name]}
"
f"Direction: {('W-E', 'E-W')[idx % 2]}
"
f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
"
f"Latency: {item.value_iterated_to}uSec"
@@ -466,7 +382,7 @@ def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
xaxis.append(next_x)
yaxis.append(item.value_iterated_to)
hovertext.append(
- f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
"
+ f"{C.GRAPH_LAT_HDRH_DESC[lat_name]}
"
f"Direction: {('W-E', 'E-W')[idx % 2]}
"
f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
"
f"Latency: {item.value_iterated_to}uSec"
@@ -478,12 +394,12 @@ 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)),
+ color=get_color(int(idx/2)),
dash="solid",
width=1 if idx % 2 else 2
),