return results
-def _generate_trending_traces(in_data, period, moving_win_size=10,
+def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
fill_missing=True, use_first=False,
show_moving_median=True, name="", color=""):
"""Generate the trending traces:
- outliers, regress, progress
:param in_data: Full data set.
+ :param build_info: Information about the builds.
:param period: Sampling period.
:param moving_win_size: Window size.
:param fill_missing: If the chosen sample is missing in the full set, its
:param name: Name of the plot
:param color: Name of the color for the plot.
:type in_data: OrderedDict
+ :type build_info: dict
:type period: int
:type moving_win_size: int
:type fill_missing: bool
in_data = _select_data(in_data, period,
fill_missing=fill_missing,
use_first=use_first)
+ try:
+ data_x = ["{0}/{1}".format(key, build_info[str(key)][1].split("~")[-1])
+ for key in in_data.keys()]
+ except KeyError:
+ data_x = [key for key in in_data.keys()]
+ # hover_text = ["vpp-build: {0}".format(x[1].split("~")[-1])
+ # for x in build_info.values()]
+ # data_x = [key for key in in_data.keys()]
- data_x = [key for key in in_data.keys()]
data_y = [val for val in in_data.values()]
data_pd = pd.Series(data_y, index=data_x)
anomalies = pd.Series()
anomalies_res = list()
for idx, item in enumerate(in_data.items()):
- item_pd = pd.Series([item[1], ], index=[item[0], ])
+ item_pd = pd.Series([item[1], ],
+ index=["{0}/{1}".
+ format(item[0],
+ build_info[str(item[0])][1].split("~")[-1]),
+ ])
+ #item_pd = pd.Series([item[1], ], index=[item[0], ])
if item[0] in outliers.keys():
anomalies = anomalies.append(item_pd)
anomalies_res.append(0.0)
"color": color,
"symbol": "circle",
},
+ # text=hover_text,
+ # hoverinfo="x+y+text+name"
)
traces = [trace_samples, ]
"color": anomalies_res,
"colorscale": color_scale,
"showscale": True,
-
+ "line": {
+ "width": 2
+ },
"colorbar": {
"y": 0.5,
"len": 0.8,
- "title": "Results Clasification",
+ "title": "Circles Marking Data Classification",
"titleside": 'right',
"titlefont": {
"size": 14
},
"tickmode": 'array',
"tickvals": [0.125, 0.375, 0.625, 0.875],
- "ticktext": ["Outlier", "Regress", "Normal", "Progress"],
- "ticks": 'outside',
+ "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
+ "ticks": "",
"ticklen": 0,
"tickangle": -90,
"thickness": 10
if show_moving_median:
data_mean_y = pd.Series(data_y).rolling(
- window=moving_win_size).median()
+ window=moving_win_size, min_periods=2).median()
trace_median = plgo.Scatter(
x=data_x,
y=data_mean_y,
"width": 1,
"color": color,
},
- name='{name}-trend'.format(name=name, size=moving_win_size)
+ name='{name}-trend'.format(name=name)
)
traces.append(trace_median)
:type input_data: InputData
"""
- csv_table = list()
+ job_name = spec.cpta["data"].keys()[0]
+
+ builds_lst = list()
+ for build in spec.input["builds"][job_name]:
+ status = build["status"]
+ if status != "failed" and status != "not found":
+ builds_lst.append(str(build["build"]))
+
+ # Get "build ID": "date" dict:
+ build_info = dict()
+ for build in builds_lst:
+ try:
+ build_info[build] = (
+ input_data.metadata(job_name, build)["generated"][:14],
+ input_data.metadata(job_name, build)["version"]
+ )
+ except KeyError:
+ build_info[build] = ("", "")
+
# Create the header:
- builds = spec.cpta["data"].values()[0]
- builds_lst = [str(build) for build in range(builds[0], builds[-1] + 1)]
+ csv_table = list()
header = "Build Number:," + ",".join(builds_lst) + '\n'
csv_table.append(header)
+ build_dates = [x[0] for x in build_info.values()]
+ header = "Build Date:," + ",".join(build_dates) + '\n'
+ csv_table.append(header)
+ vpp_versions = [x[1] for x in build_info.values()]
+ header = "VPP Version:," + ",".join(vpp_versions) + '\n'
+ csv_table.append(header)
results = list()
for chart in spec.cpta["plots"]:
chart_data[test_name][int(idx)] = \
test["result"]["throughput"]
except (KeyError, TypeError):
- chart_data[test_name][int(idx)] = None
+ pass
# Add items to the csv table:
for tst_name, tst_data in chart_data.items():
test_name = test_name.split('.')[-1]
trace, result = _generate_trending_traces(
test_data,
+ build_info=build_info,
period=period,
moving_win_size=win_size,
fill_missing=True,
idx += 1
# Generate the chart:
- period_name = "Daily" if period == 1 else \
- "Weekly" if period < 20 else "Monthly"
- chart["layout"]["title"] = chart["title"].format(period=period_name)
+ chart["layout"]["xaxis"]["title"] = \
+ chart["layout"]["xaxis"]["title"].format(job=job_name)
_generate_chart(traces,
chart["layout"],
file_name="{0}-{1}-{2}{3}".format(
txt_table = None
with open("{0}.csv".format(file_name), 'rb') as csv_file:
csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ line_nr = 0
for row in csv_content:
if txt_table is None:
txt_table = prettytable.PrettyTable(row)
else:
- txt_table.add_row(row)
+ if line_nr > 1:
+ for idx, item in enumerate(row):
+ try:
+ row[idx] = str(round(float(item) / 1000000, 2))
+ except ValueError:
+ pass
+ try:
+ txt_table.add_row(row)
+ except Exception as err:
+ logging.warning("Error occurred while generating TXT table:"
+ "\n{0}".format(err))
+ line_nr += 1
txt_table.align["Build Number:"] = "l"
with open("{0}.txt".format(file_name), "w") as txt_file:
txt_file.write(str(txt_table))
Code Review / csit.git / blobdiff