if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
- results = [0.0, ] * win_size
+ results = [0.0, ]
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]):
+ for day in range(1, in_data.size):
+ if np.isnan(m_vals[day]) \
+ or np.isnan(s_vals[day]) \
+ or np.isnan(d_vals[day]):
results.append(0.0)
- elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]):
+ elif d_vals[day] < (m_vals[day] - 3 * s_vals[day]):
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]):
+ elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \
+ (m_vals[day] + 3 * s_vals[day]):
results.append(0.66)
else:
results.append(1.0)
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()]
+ # 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_y = [val for val in in_data.values()]
data_pd = pd.Series(data_y, index=data_x)
- t_data, outliers = find_outliers(data_pd)
+ 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()
anomalies_res = list()
for idx, item in enumerate(in_data.items()):
- 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], ])
+ # 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"
+ text=hover_text,
+ hoverinfo="x+y+text+name"
)
traces = [trace_samples, ]
builds_lst.append(str(build["build"]))
# Get "build ID": "date" dict:
- build_info = dict()
+ build_info = OrderedDict()
for build in builds_lst:
try:
build_info[build] = (
)
except KeyError:
build_info[build] = ("", "")
+ logging.info("{}: {}, {}".format(build,
+ build_info[build][0],
+ build_info[build][1]))
# Create the header:
csv_table = list()
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: