import pandas as pd
from collections import OrderedDict
-from utils import find_outliers, archive_input_data, execute_command
+from utils import split_outliers, archive_input_data, execute_command
# Command to build the html format of the report
return OrderedDict(sorted(data_dict.items(), key=lambda t: t[0]))
-def _evaluate_results(in_data, trimmed_data, window=10):
+def _evaluate_results(trimmed_data, window=10):
"""Evaluates if the sample value is regress, normal or progress compared to
previous data within the window.
We use the intervals defined as:
- - regress: less than median - 3 * stdev
- - normal: between median - 3 * stdev and median + 3 * stdev
- - progress: more than median + 3 * stdev
+ - regress: less than trimmed moving median - 3 * stdev
+ - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev
+ - progress: more than trimmed moving median + 3 * stdev
+ where stdev is trimmed moving standard deviation.
- :param in_data: Full data set.
- :param trimmed_data: Full data set without the outliers.
- :param window: Window size used to calculate moving median and moving stdev.
- :type in_data: pandas.Series
+ :param trimmed_data: Full data set with the outliers replaced by nan.
+ :param window: Window size used to calculate moving average and moving stdev.
:type trimmed_data: pandas.Series
:type window: int
:returns: Evaluated results.
:rtype: list
"""
- 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()
- 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]):
+ if len(trimmed_data) > 2:
+ win_size = trimmed_data.size if trimmed_data.size < window else window
+ results = [0.66, ]
+ tmm = trimmed_data.rolling(window=win_size, min_periods=2).median()
+ tmstd = trimmed_data.rolling(window=win_size, min_periods=2).std()
+
+ first = True
+ for build_nr, value in trimmed_data.iteritems():
+ if first:
+ first = False
+ continue
+ if (np.isnan(value)
+ or np.isnan(tmm[build_nr])
+ or np.isnan(tmstd[build_nr])):
results.append(0.0)
- elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]):
+ elif value < (tmm[build_nr] - 2 * tmstd[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 > (tmm[build_nr] + 2 * tmstd[build_nr]):
results.append(1.0)
+ else:
+ results.append(0.66)
else:
results = [0.0, ]
try:
- median = np.median(in_data)
- stdev = np.std(in_data)
- if in_data.values[-1] < (median - 3 * stdev):
+ tmm = np.median(trimmed_data)
+ tmstd = np.std(trimmed_data)
+ if trimmed_data.values[-1] < (tmm - 2 * tmstd):
results.append(0.33)
- elif (median - 3 * stdev) <= in_data.values[-1] <= (
- median + 3 * stdev):
+ elif (tmm - 2 * tmstd) <= trimmed_data.values[-1] <= (
+ tmm + 2 * tmstd):
results.append(0.66)
else:
results.append(1.0)
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=""):
+ show_trend_line=True, name="", color=""):
"""Generate the trending traces:
- samples,
- - moving median (trending plot)
+ - trimmed moving median (trending line)
- 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
- nearest neighbour is used.
+ nearest neighbour is used.
:param use_first: Use the first sample even though it is not chosen.
- :param show_moving_median: Show moving median (trending plot).
+ :param show_trend_line: Show moving median (trending plot).
: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
:type use_first: bool
- :type show_moving_median: bool
+ :type show_trend_line: bool
:type name: str
:type color: str
:returns: Generated traces (list) and the evaluated result (float).
fill_missing=fill_missing,
use_first=use_first)
- 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)
+ data_x = list(in_data.keys())
+ data_y = list(in_data.values())
+
+ hover_text = list()
+ for idx in data_x:
+ hover_text.append("vpp-build: {0}".
+ format(build_info[str(idx)][1].split("~")[-1]))
- t_data, outliers = find_outliers(data_pd)
+ data_pd = pd.Series(data_y, index=data_x)
- results = _evaluate_results(data_pd, t_data, window=moving_win_size)
+ t_data, outliers = split_outliers(data_pd, outlier_const=1.5,
+ window=moving_win_size)
+ results = _evaluate_results(t_data, window=moving_win_size)
anomalies = pd.Series()
anomalies_res = list()
"color": color,
"symbol": "circle",
},
+ text=hover_text,
+ hoverinfo="x+y+text+name"
)
traces = [trace_samples, ]
y=anomalies.values,
mode='markers',
hoverinfo="none",
- showlegend=False,
+ showlegend=True,
legendgroup=name,
- name="{name}: outliers".format(name=name),
+ name="{name}-anomalies".format(name=name),
marker={
"size": 15,
"symbol": "circle-open",
"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
)
traces.append(trace_anomalies)
- if show_moving_median:
- data_mean_y = pd.Series(data_y).rolling(
- window=moving_win_size).median()
- trace_median = plgo.Scatter(
- x=data_x,
- y=data_mean_y,
+ if show_trend_line:
+ data_trend = t_data.rolling(window=moving_win_size,
+ min_periods=2).median()
+ trace_trend = plgo.Scatter(
+ x=data_trend.keys(),
+ y=data_trend.tolist(),
mode='lines',
line={
"shape": "spline",
"width": 1,
"color": color,
},
- name='{name}-trend'.format(name=name, size=moving_win_size)
+ name='{name}-trend'.format(name=name)
)
- traces.append(trace_median)
+ traces.append(trace_trend)
return traces, results[-1]
: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 = 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:
- 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():
tst_lst = list()
for build in builds_lst:
item = tst_data.get(int(build), '')
- tst_lst.append(str(item) if item else '')
+ tst_lst.append(str(item))
+ # 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:
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))
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