-# Copyright (c) 2023 Cisco and/or its affiliates.
+# Copyright (c) 2024 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
"""Implementation of graphs for iterative data.
"""
-
import plotly.graph_objects as go
import pandas as pd
from copy import deepcopy
+from numpy import percentile
from ..utils.constants import Constants as C
from ..utils.utils import get_color, get_hdrh_latencies
test_type = "ndrpdr"
elif itm["testtype"] == "mrr":
test_type = "mrr"
+ elif itm["testtype"] == "soak":
+ test_type = "soak"
elif itm["area"] == "hoststack":
test_type = "hoststack"
df = data.loc[(
return df
-def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
- normalize: bool) -> tuple:
+def graph_iterative(data: pd.DataFrame, sel: list, layout: dict,
+ normalize: bool=False, remove_outliers: bool=False) -> tuple:
"""Generate the statistical box graph with iterative data (MRR, NDR and PDR,
for PDR also Latencies).
:param layout: Layout of plot.ly graph.
:param normalize: If True, the data is normalized to CPU frequency
Constants.NORM_FREQUENCY.
- :param data: pandas.DataFrame
- :param sel: dict
- :param layout: dict
- :param normalize: bool
+ :param remove_outliers: If True the outliers are removed before
+ generating the table.
+ :type data: pandas.DataFrame
+ :type sel: list
+ :type layout: dict
+ :type normalize: bool
+ :type remove_outliers: bool
:returns: Tuple of graphs - throughput and latency.
:rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
- def get_y_values(data, y_data_max, param, norm_factor, release=str()):
+ def get_y_values(data, y_data_max, param, norm_factor, release=str(),
+ remove_outliers=False):
if param == "result_receive_rate_rate_values":
- if release == "rls2402":
+ if release in ("rls2402", "rls2406", "rls2410"):
y_vals_raw = data["result_receive_rate_rate_avg"].to_list()
else:
y_vals_raw = data[param].to_list()[0]
else:
y_vals_raw = data[param].to_list()
y_data = [(y * norm_factor) for y in y_vals_raw]
+
+ if remove_outliers:
+ try:
+ q1 = percentile(y_data, 25, method=C.COMP_PERCENTILE_METHOD)
+ q3 = percentile(y_data, 75, method=C.COMP_PERCENTILE_METHOD)
+ irq = q3 - q1
+ lif = q1 - C.COMP_OUTLIER_TYPE * irq
+ uif = q3 + C.COMP_OUTLIER_TYPE * irq
+ y_data = [i for i in y_data if i >= lif and i <= uif]
+ except TypeError:
+ pass
try:
y_data_max = max(max(y_data), y_data_max)
except TypeError:
y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
y_data, y_tput_max = get_y_values(
- itm_data, y_tput_max, C.VALUE_ITER[ttype], norm_factor, itm["rls"]
+ itm_data,
+ y_tput_max,
+ C.VALUE_ITER[ttype],
+ norm_factor,
+ itm["rls"],
+ remove_outliers
)
nr_of_samples = len(y_data)
)
}
- if itm["testtype"] == "mrr":
- # and itm["rls"] in ("rls2306", "rls2310"):
+ if itm["testtype"] == "mrr" and itm["rls"] == "rls2310":
trial_run = "trial"
metadata["csit-ref"] = (
f"{itm_data['job'].to_list()[0]}/",
trial_run = "run"
for _, row in itm_data.iterrows():
metadata["csit-ref"] = f"{row['job']}/{row['build']}"
+ try:
+ metadata["hosts"] = ", ".join(row["hosts"])
+ except (KeyError, TypeError):
+ pass
customdata.append({"metadata": deepcopy(metadata)})
tput_kwargs = dict(
y=y_data,
)
tput_traces.append(go.Box(**tput_kwargs))
- if ttype in ("ndr", "pdr"):
+ if ttype in C.TESTS_WITH_BANDWIDTH:
y_band, y_band_max = get_y_values(
itm_data,
y_band_max,
C.VALUE_ITER[f"{ttype}-bandwidth"],
- norm_factor
+ norm_factor,
+ remove_outliers=remove_outliers
)
if not all(pd.isna(y_band)):
y_band_units.update(
x_band.append(idx + 1)
band_traces.append(go.Box(**band_kwargs))
- if ttype == "pdr":
+ if ttype in C.TESTS_WITH_LATENCY:
y_lat, y_lat_max = get_y_values(
itm_data,
y_lat_max,
C.VALUE_ITER["latency"],
- 1 / norm_factor
+ 1 / norm_factor,
+ remove_outliers=remove_outliers
)
if not all(pd.isna(y_lat)):
customdata = list()
pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
if y_tput_max:
- pl_tput["yaxis"]["range"] = [0, int(y_tput_max) + 2e6]
+ pl_tput["yaxis"]["range"] = [0, int(y_tput_max) * 1.1]
fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
if band_traces:
pl_band["yaxis"]["title"] = \
f"Bandwidth [{'|'.join(sorted(y_band_units))}]"
if y_band_max:
- pl_band["yaxis"]["range"] = [0, int(y_band_max) + 2e9]
+ pl_band["yaxis"]["range"] = [0, int(y_band_max) * 1.1]
fig_band = go.Figure(data=band_traces, layout=pl_band)
if lat_traces:
Code Review / csit.git / blobdiff