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authorTibor Frank <tifrank@cisco.com>2018-04-19 07:21:03 +0200
committerTibor Frank <tifrank@cisco.com>2018-04-19 07:22:05 +0200
commit6942369b1102a8b9a3b705f9192f1ecb959382d1 (patch)
tree253ca6f5ad83e53c098ab0086e94ed6727cc2147 /resources/tools/presentation/generator_CPTA.py
parent22ba8a3ee489fbc67a8d1727423188be52e1ef22 (diff)
CSIT-1041: Trending dashboard
Change-Id: I2b08c3c1859302437092456da4bb8f1ebe4756bf Signed-off-by: Tibor Frank <tifrank@cisco.com>
Diffstat (limited to 'resources/tools/presentation/generator_CPTA.py')
-rw-r--r--resources/tools/presentation/generator_CPTA.py51
1 files changed, 22 insertions, 29 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py
index 9ec196c0d9..72aef537cf 100644
--- a/resources/tools/presentation/generator_CPTA.py
+++ b/resources/tools/presentation/generator_CPTA.py
@@ -164,26 +164,26 @@ def _evaluate_results(in_data, trimmed_data, window=10):
if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
- results = [0.0, ]
+ 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
- t_vals = trimmed_data.values
- for day in range(1, in_data.size):
- if np.isnan(t_vals[day]) \
- or np.isnan(m_vals[day]) \
- or np.isnan(s_vals[day]) \
- or np.isnan(d_vals[day]):
+
+ 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] - 3 * s_vals[day]):
+ elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
results.append(0.33)
- elif (m_vals[day] - 3 * s_vals[day]) <= d_vals[day] <= \
- (m_vals[day] + 3 * s_vals[day]):
- 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:
@@ -236,30 +236,23 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
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()]
+
+ 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()
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], ])
if item[0] in outliers.keys():
anomalies = anomalies.append(item_pd)