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
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 = trimmed_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 = split_outliers(data_pd, outlier_const=1.5,
+ window=moving_win_size)
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, ]
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",
builds_lst.append(str(build["build"]))
# Get "build ID": "date" dict:
- build_dates = dict()
+ build_info = OrderedDict()
for build in builds_lst:
try:
- build_dates[build] = \
- input_data.metadata(job_name, build)["generated"][:14]
+ build_info[build] = (
+ input_data.metadata(job_name, build)["generated"][:14],
+ input_data.metadata(job_name, build)["version"]
+ )
except KeyError:
- pass
+ 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)
- header = "Build Date:," + ",".join(build_dates.values()) + '\n'
+ 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()
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,
row[idx] = str(round(float(item) / 1000000, 2))
except ValueError:
pass
- txt_table.add_row(row)
+ 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:
Code Review / csit.git / blobdiff