"""
"""
+import re
import plotly.graph_objects as go
import pandas as pd
-import hdrh.histogram
-import hdrh.codec
-
-
-_COLORS = (
- u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
- u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
- u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
- u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
-)
-_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 copy import deepcopy
+
+from ..utils.constants import Constants as C
+from ..utils.utils import get_color
+
+
+def get_short_version(version: str, dut_type: str="vpp") -> str:
+ """
+ """
+
+ if dut_type in ("trex", "dpdk"):
+ return version
+
+ s_version = str()
+ groups = re.search(
+ pattern=re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)"),
+ string=version
+ )
+ if groups:
+ try:
+ s_version = \
+ f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
+ replace("release", "rls")
+ except IndexError:
+ pass
+
+ return s_version
def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
core = str() if itm["dut"] == "trex" else f"{itm['core']}"
ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
- dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
+ dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+ dut_v101 = itm["dut"]
df = data.loc[(
- (data["dut_type"] == dut) &
+ (data["release"] == itm["rls"]) &
+ (
+ (
+ (data["version"] == "1.0.0") &
+ (data["dut_type"].str.lower() == dut_v100)
+ ) |
+ (
+ (data["version"] == "1.0.1") &
+ (data["dut_type"].str.lower() == dut_v101)
+ )
+ ) &
(data["test_type"] == ttype) &
(data["passed"] == True)
)]
- df = df[df.job.str.endswith(f"{topo}-{arch}")]
- df = df[df.test_id.str.contains(
- f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
- regex=True
- )].sort_values(by="start_time", ignore_index=True)
+ regex_test = \
+ f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
+ df = df[
+ (df.job.str.endswith(f"{topo}-{arch}")) &
+ (df.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
+ replace("rls", "release"))) &
+ (df.test_id.str.contains(regex_test, regex=True))
+ ]
return df
-def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict) -> tuple:
+def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
+ normalize: bool) -> tuple:
"""
"""
- fig_tput = go.Figure()
- fig_tsa = go.Figure()
+ fig_tput = None
+ fig_lat = None
+
+ tput_traces = list()
+ y_tput_max = 0
+ lat_traces = list()
+ y_lat_max = 0
+ x_lat = list()
+ show_latency = False
+ show_tput = False
+ for idx, itm in enumerate(sel):
+ itm_data = select_iterative_data(data, itm)
+ if itm_data.empty:
+ continue
+ 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 normalize else 1.0
+ if itm["testtype"] == "mrr":
+ y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()[0]
+ y_data = [(y * norm_factor) for y in y_data_raw]
+ if len(y_data) > 0:
+ y_tput_max = \
+ max(y_data) if max(y_data) > y_tput_max else y_tput_max
+ else:
+ y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()
+ y_data = [(y * norm_factor) for y in y_data_raw]
+ if y_data:
+ y_tput_max = \
+ max(y_data) if max(y_data) > y_tput_max else y_tput_max
+ nr_of_samples = len(y_data)
+ tput_kwargs = dict(
+ y=y_data,
+ name=(
+ f"{idx + 1}. "
+ f"({nr_of_samples:02d} "
+ f"run{'s' if nr_of_samples > 1 else ''}) "
+ f"{itm['id']}"
+ ),
+ hoverinfo=u"y+name",
+ boxpoints="all",
+ jitter=0.3,
+ marker=dict(color=get_color(idx))
+ )
+ tput_traces.append(go.Box(**tput_kwargs))
+ show_tput = True
+
+ if itm["testtype"] == "pdr":
+ y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
+ y_lat = [(y / norm_factor) for y in y_lat_row]
+ if y_lat:
+ y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max
+ nr_of_samples = len(y_lat)
+ lat_kwargs = dict(
+ y=y_lat,
+ name=(
+ f"{idx + 1}. "
+ f"({nr_of_samples:02d} "
+ f"run{u's' if nr_of_samples > 1 else u''}) "
+ f"{itm['id']}"
+ ),
+ hoverinfo="all",
+ boxpoints="all",
+ jitter=0.3,
+ marker=dict(color=get_color(idx))
+ )
+ x_lat.append(idx + 1)
+ lat_traces.append(go.Box(**lat_kwargs))
+ show_latency = True
+ else:
+ lat_traces.append(go.Box())
+
+ if show_tput:
+ pl_tput = deepcopy(layout["plot-throughput"])
+ pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
+ pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
+ if y_tput_max:
+ pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 1) * 1e6]
+ fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
- return fig_tput, fig_tsa
+ if show_latency:
+ pl_lat = deepcopy(layout["plot-latency"])
+ pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
+ pl_lat["xaxis"]["ticktext"] = x_lat
+ if y_lat_max:
+ pl_lat["yaxis"]["range"] = [0, (int(y_lat_max / 10) + 1) * 10]
+ fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
+ return fig_tput, fig_lat
-def table_comparison(data: pd.DataFrame, sel:dict) -> pd.DataFrame:
+
+def table_comparison(data: pd.DataFrame, sel:dict,
+ normalize: bool) -> pd.DataFrame:
"""
"""
table = pd.DataFrame(
- {
- "Test Case": [
- "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
- "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
- "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
- "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
- "2106.0-8": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2110.0-8": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2110.0-9": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2202.0-9": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2110.0-9 vs 2110.0-8": [
- "-0.23 +- 0.62",
- "-1.37 +- 1.3",
- "+0.08 +- 0.2",
- "-2.16 +- 0.83"],
- "2202.0-9 vs 2110.0-9": [
- "+6.95 +- 0.72",
- "+5.35 +- 1.26",
- "+4.48 +- 1.48",
- "+4.09 +- 0.95"]
- }
-)
-
- return table
-
-
-def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
- """
- """
-
- fig = None
-
- traces = list()
- for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
- try:
- decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
- except (hdrh.codec.HdrLengthException, TypeError) as err:
- continue
- previous_x = 0.0
- prev_perc = 0.0
- xaxis = list()
- yaxis = list()
- hovertext = list()
- for item in decoded.get_recorded_iterator():
- # The real value is "percentile".
- # For 100%, we cut that down to "x_perc" to avoid
- # infinity.
- percentile = item.percentile_level_iterated_to
- x_perc = min(percentile, 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"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- next_x = 100.0 / (100.0 - x_perc)
- 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"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- previous_x = next_x
- prev_perc = percentile
-
- traces.append(
- go.Scatter(
- x=xaxis,
- y=yaxis,
- name=_GRAPH_LAT_HDRH_DESC[lat_name],
- mode=u"lines",
- legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
- showlegend=bool(idx % 2),
- line=dict(
- color=_COLORS[int(idx/2)],
- dash=u"solid",
- width=1 if idx % 2 else 2
- ),
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- if traces:
- fig = go.Figure()
- fig.add_traces(traces)
- layout_hdrh = layout.get("plot-hdrh-latency", None)
- if lat_hdrh:
- fig.update_layout(layout_hdrh)
-
- return fig
+ {
+ "Test Case": [
+ "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
+ "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
+ "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
+ "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
+ "2106.0-8": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2110.0-8": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2110.0-9": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2202.0-9": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2110.0-9 vs 2110.0-8": [
+ "-0.23 +- 0.62",
+ "-1.37 +- 1.3",
+ "+0.08 +- 0.2",
+ "-2.16 +- 0.83"],
+ "2202.0-9 vs 2110.0-9": [
+ "+6.95 +- 0.72",
+ "+5.35 +- 1.26",
+ "+4.48 +- 1.48",
+ "+4.09 +- 0.95"]
+ }
+ )
+
+ return pd.DataFrame() #table
Code Review / csit.git / blobdiff