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
|
# Copyright (c) 2023 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.
"""Implementation of graphs for iterative data.
"""
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 select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
"""Select the data for graphs and tables from the provided data frame.
:param data: Data frame with data for graphs and tables.
: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("-")
if len(phy) == 4:
topo, arch, nic, drv = phy
if drv == "dpdk":
drv = ""
else:
drv += "-"
drv = drv.replace("_", "-")
else:
return None
if itm["testtype"] in ("ndr", "pdr"):
test_type = "ndrpdr"
elif itm["testtype"] == "mrr":
test_type = "mrr"
elif itm["area"] == "hoststack":
test_type = "hoststack"
df = data.loc[(
(data["release"] == itm["rls"]) &
(data["test_type"] == test_type) &
(data["passed"] == True)
)]
core = str() if itm["dut"] == "trex" else f"{itm['core']}"
ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
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:
"""Generate the statistical box graph with iterative data (MRR, NDR and PDR,
for PDR also Latencies).
:param data: Data frame with iterative data.
:param sel: Selected tests.
: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
:returns: Tuple of graphs - throughput and latency.
:rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
fig_tput = None
fig_lat = None
tput_traces = list()
y_tput_max = 0
lat_traces = list()
y_lat_max = 0
x_lat = list()
y_units = set()
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["area"] == "hoststack":
ttype = f"hoststack-{itm['testtype']}"
else:
ttype = itm["testtype"]
y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
if itm["testtype"] == "mrr":
y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()[0]
else:
y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()
y_data = [(y * norm_factor) for y in y_data_raw]
if y_data:
y_tput_max = max(max(y_data), 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 ttype == "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:
try:
y_lat_max = max(max(y_lat), y_lat_max)
except TypeError:
continue
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))]
pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
if y_tput_max:
pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 2) * 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
|
