import datetime
import logging
+import csv
+import prettytable
import plotly.offline as ploff
import plotly.graph_objs as plgo
import plotly.exceptions as plerr
if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
- results = [0.0, ] * win_size
- median = in_data.rolling(window=win_size).median()
+ results = [0.66, ]
+ median = in_data.rolling(window=win_size, min_periods=2).median()
stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
- m_vals = median.values
- s_vals = stdev_t.values
- d_vals = in_data.values
- for day in range(win_size, in_data.size):
- if np.isnan(m_vals[day - 1]) or np.isnan(s_vals[day - 1]):
+
+ first = True
+ for build_nr, value in in_data.iteritems():
+ if first:
+ first = False
+ continue
+ if np.isnan(trimmed_data[build_nr]) \
+ or np.isnan(median[build_nr]) \
+ or np.isnan(stdev_t[build_nr]) \
+ or np.isnan(value):
results.append(0.0)
- elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]):
+ elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
results.append(0.33)
- elif (m_vals[day - 1] - 3 * s_vals[day - 1]) <= d_vals[day] <= \
- (m_vals[day - 1] + 3 * s_vals[day - 1]):
- results.append(0.66)
- else:
+ elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
results.append(1.0)
+ else:
+ results.append(0.66)
else:
results = [0.0, ]
try:
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
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)
- t_data, outliers = find_outliers(data_pd)
+ hover_text = list()
+ for idx in data_x:
+ hover_text.append("vpp-build: {0}".
+ format(build_info[str(idx)][1].split("~")[-1]))
+
+ data_pd = pd.Series(data_y, index=data_x)
+ t_data, outliers = find_outliers(data_pd, outlier_const=1.5)
results = _evaluate_results(data_pd, t_data, window=moving_win_size)
anomalies = pd.Series()
"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
"""
+ 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 = OrderedDict()
+ 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] = ("", "")
+ logging.info("{}: {}, {}".format(build,
+ build_info[build][0],
+ build_info[build][1]))
+
+ # Create the header:
+ 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"]:
logging.info(" Generating the chart '{0}' ...".
chart_data = dict()
for job in data:
for idx, build in job.items():
- for test in build:
- if chart_data.get(test["name"], None) is None:
- chart_data[test["name"]] = OrderedDict()
+ for test_name, test in build.items():
+ if chart_data.get(test_name, None) is None:
+ chart_data[test_name] = OrderedDict()
try:
- chart_data[test["name"]][int(idx)] = \
+ 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():
+ tst_lst = list()
+ for build in builds_lst:
+ item = tst_data.get(int(build), '')
+ tst_lst.append(str(item) if item else '')
+ csv_table.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
for period in chart["periods"]:
# Generate traces:
traces = list()
- win_size = 10 if period == 1 else 5 if period < 20 else 3
+ win_size = 14 if period == 1 else 5 if period < 20 else 3
idx = 0
for test_name, test_data in chart_data.items():
if not test_data:
logging.warning("No data for the test '{0}'".
format(test_name))
continue
+ 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(
logging.info(" Done.")
+ # Write the tables:
+ file_name = spec.cpta["output-file"] + "-trending"
+ with open("{0}.csv".format(file_name), 'w') as file_handler:
+ file_handler.writelines(csv_table)
+
+ 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:
+ 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))
+
+ # Evaluate result:
result = "PASS"
for item in results:
if item is None:
result = "PASS"
elif item == 0.33 or item == 0.0:
result = "FAIL"
- print(results)
- print(result)
- if result == "FAIL":
- return 1
- else:
- return 0
+
+ logging.info("Partial results: {0}".format(results))
+ logging.info("Result: {0}".format(result))
+
+ return result
Code Review / csit.git / blobdiff