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-rw-r--r--resources/tools/presentation/new/generator_tables.py5
-rw-r--r--resources/tools/presentation/new/utils.py12
2 files changed, 10 insertions, 7 deletions
diff --git a/resources/tools/presentation/new/generator_tables.py b/resources/tools/presentation/new/generator_tables.py
index 6951021bb9..735fd2185f 100644
--- a/resources/tools/presentation/new/generator_tables.py
+++ b/resources/tools/presentation/new/generator_tables.py
@@ -788,8 +788,8 @@ def table_performance_trending_dashboard(table, input_data):
round(last_avg / 1000000, 2),
'-' if isnan(rel_change_last) else rel_change_last,
'-' if isnan(rel_change_long) else rel_change_long,
- classification_lst[-long_win_size:].count("regression"),
- classification_lst[-long_win_size:].count("progression")])
+ classification_lst[-win_size:].count("regression"),
+ classification_lst[-win_size:].count("progression")])
tbl_lst.sort(key=lambda rel: rel[0])
@@ -823,6 +823,7 @@ def table_performance_trending_dashboard(table, input_data):
with open(txt_file_name, "w") as txt_file:
txt_file.write(str(txt_table))
+
def table_performance_trending_dashboard_html(table, input_data):
"""Generate the table(s) with algorithm:
table_performance_trending_dashboard_html specified in the specification
diff --git a/resources/tools/presentation/new/utils.py b/resources/tools/presentation/new/utils.py
index 83f4f6249b..a688928cda 100644
--- a/resources/tools/presentation/new/utils.py
+++ b/resources/tools/presentation/new/utils.py
@@ -211,17 +211,19 @@ def archive_input_data(spec):
def classify_anomalies(data):
"""Process the data and return anomalies and trending values.
- Gathers data into groups with common trend value.
- Decorates first value in the group to be an outlier, regression,
- normal or progression.
+ Gather data into groups with average as trend value.
+ Decorate values within groups to be normal,
+ the first value of changed average as a regression, or a progression.
:param data: Full data set with unavailable samples replaced by nan.
:type data: pandas.Series
:returns: Classification and trend values
:rtype: 2-tuple, list of strings and list of floats
"""
- bare_data = [sample for _, sample in data.iteritems()
- if not np.isnan(sample)]
+ # Nan mean something went wrong.
+ # Use 0.0 to cause that being reported as a severe regression.
+ bare_data = [0.0 if np.isnan(sample) else sample
+ for _, sample in data.iteritems()]
# TODO: Put analogous iterator into jumpavg library.
groups = BitCountingClassifier.classify(bare_data)
groups.reverse() # Just to use .pop() for FIFO.