1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
|
# Copyright (c) 2022 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:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
"""
import re
import plotly.graph_objects as go
import pandas as pd
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$)"),
string=version
)
if groups:
try:
s_version = \
f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
replace("release", "rls")
except IndexError:
pass
return s_version
def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
"""
"""
phy = itm["phy"].split("-")
if len(phy) == 4:
topo, arch, nic, drv = phy
if drv == "dpdk":
drv = ""
else:
drv += "-"
drv = drv.replace("_", "-")
else:
return None
core = str() if itm["dut"] == "trex" else f"{itm['core']}"
ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
dut_v101 = itm["dut"]
df = data.loc[(
(data["release"] == itm["rls"]) &
(
(
(data["version"] == "1.0.0") &
(data["dut_type"].str.lower() == dut_v100)
) |
(
(data["version"] == "1.0.1") &
(data["dut_type"].str.lower() == dut_v101)
)
) &
(data["test_type"] == ttype) &
(data["passed"] == True)
)]
regex_test = \
f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
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,
normalize: bool) -> tuple:
"""
"""
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)
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,
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 pd.DataFrame() #table
|
