aboutsummaryrefslogtreecommitdiffstats
path: root/resources/tools
diff options
context:
space:
mode:
authorTibor Frank <tifrank@cisco.com>2018-04-24 07:28:44 +0200
committerTibor Frank <tifrank@cisco.com>2018-04-24 07:28:44 +0200
commit79e508504fcd6b5b677e567eb09092c5e0821790 (patch)
treeb02ac7500cf9c696cc0955573eb0796bf031493d /resources/tools
parent52f64f232293130904d54a62609eaffc1b145608 (diff)
CSIT-1041: Trending dashboard
Change-Id: Ida3dfcc4a7ae21424e7f6b6db597a80bc633b9da Signed-off-by: Tibor Frank <tifrank@cisco.com>
Diffstat (limited to 'resources/tools')
-rw-r--r--resources/tools/presentation/generator_CPTA.py2
-rw-r--r--resources/tools/presentation/generator_tables.py27
-rw-r--r--resources/tools/presentation/utils.py10
3 files changed, 17 insertions, 22 deletions
diff --git a/resources/tools/presentation/generator_CPTA.py b/resources/tools/presentation/generator_CPTA.py
index 3817ea9043..567b889208 100644
--- a/resources/tools/presentation/generator_CPTA.py
+++ b/resources/tools/presentation/generator_CPTA.py
@@ -298,7 +298,7 @@ def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
hoverinfo="none",
showlegend=True,
legendgroup=name,
- name="{name}: outliers".format(name=name),
+ name="{name}-anomalies".format(name=name),
marker={
"size": 15,
"symbol": "circle-open",
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 724519f2d1..0f0ed6c7a5 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -811,13 +811,17 @@ def table_performance_trending_dashboard(table, input_data):
abs(rel_change_lst[index])):
index = idx
- logging.info("{}".format(name))
- logging.info("sample_lst: {} - {}".format(len(sample_lst), sample_lst))
- logging.info("median_lst: {} - {}".format(len(median_lst), median_lst))
- logging.info("rel_change: {} - {}".format(len(rel_change_lst), rel_change_lst))
- logging.info("classn_lst: {} - {}".format(len(classification_lst), classification_lst))
- logging.info("index: {}".format(index))
- logging.info("classifica: {}".format(classification))
+ logging.debug("{}".format(name))
+ logging.debug("sample_lst: {} - {}".
+ format(len(sample_lst), sample_lst))
+ logging.debug("median_lst: {} - {}".
+ format(len(median_lst), median_lst))
+ logging.debug("rel_change: {} - {}".
+ format(len(rel_change_lst), rel_change_lst))
+ logging.debug("classn_lst: {} - {}".
+ format(len(classification_lst), classification_lst))
+ logging.debug("index: {}".format(index))
+ logging.debug("classifica: {}".format(classification))
try:
trend = round(float(median_lst[-1]) / 1000000, 2) \
@@ -828,12 +832,12 @@ def table_performance_trending_dashboard(table, input_data):
if rel_change_lst[index] is not None else '-'
if not isnan(max_median):
if not isnan(sample_lst[index]):
- long_trend_threshold = max_median * \
- (table["long-trend-threshold"] / 100)
+ long_trend_threshold = \
+ max_median * (table["long-trend-threshold"] / 100)
if sample_lst[index] < long_trend_threshold:
long_trend_classification = "failure"
else:
- long_trend_classification = '-'
+ long_trend_classification = 'normal'
else:
long_trend_classification = "failure"
else:
@@ -843,7 +847,8 @@ def table_performance_trending_dashboard(table, input_data):
long_trend_classification,
classification,
'-' if classification == "normal" else sample,
- '-' if classification == "normal" else rel_change,
+ '-' if classification == "normal" else
+ rel_change,
nr_outliers])
except IndexError as err:
logging.error("{}".format(err))
diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index a15742a21f..4277aa0334 100644
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -92,16 +92,6 @@ def remove_outliers(input_list, outlier_const=1.5, window=14):
result_lst.append(y)
return result_lst
- # input_series = pd.Series()
- # for index, value in enumerate(input_list):
- # item_pd = pd.Series([value, ], index=[index, ])
- # input_series.append(item_pd)
- # output_series, _ = split_outliers(input_series, outlier_const=outlier_const,
- # window=window)
- # output_list = [y for x, y in output_series.items() if not np.isnan(y)]
- #
- # return output_list
-
def split_outliers(input_series, outlier_const=1.5, window=14):
"""Go through the input data and generate two pandas series: