diff options
Diffstat (limited to 'resources/tools/dash/app/pal/trending')
| -rw-r--r-- | resources/tools/dash/app/pal/trending/graphs.py | 57 |
1 files changed, 2 insertions, 55 deletions
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py index 8950558166..a63bebb818 100644 --- a/resources/tools/dash/app/pal/trending/graphs.py +++ b/resources/tools/dash/app/pal/trending/graphs.py @@ -14,7 +14,6 @@ """ """ -import logging import plotly.graph_objects as go import pandas as pd @@ -22,10 +21,8 @@ import hdrh.histogram import hdrh.codec from datetime import datetime -from numpy import isnan - -from ..jumpavg import classify +from ..data.utils import classify_anomalies _NORM_FREQUENCY = 2.0 # [GHz] _FREQURENCY = { # [GHz] @@ -131,56 +128,6 @@ def _get_hdrh_latencies(row: pd.Series, name: str) -> dict: return latencies -def _classify_anomalies(data): - """Process the data and return anomalies and trending values. - - 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: OrderedDict - :returns: Classification and trend values - :rtype: 3-tuple, list of strings, list of floats and list of floats - """ - # NaN means something went wrong. - # Use 0.0 to cause that being reported as a severe regression. - bare_data = [0.0 if isnan(sample) else sample for sample in data.values()] - # TODO: Make BitCountingGroupList a subclass of list again? - group_list = classify(bare_data).group_list - group_list.reverse() # Just to use .pop() for FIFO. - classification = list() - avgs = list() - stdevs = list() - active_group = None - values_left = 0 - avg = 0.0 - stdv = 0.0 - for sample in data.values(): - if isnan(sample): - classification.append("outlier") - avgs.append(sample) - stdevs.append(sample) - continue - if values_left < 1 or active_group is None: - values_left = 0 - while values_left < 1: # Ignore empty groups (should not happen). - active_group = group_list.pop() - values_left = len(active_group.run_list) - avg = active_group.stats.avg - stdv = active_group.stats.stdev - classification.append(active_group.comment) - avgs.append(avg) - stdevs.append(stdv) - values_left -= 1 - continue - classification.append("normal") - avgs.append(avg) - stdevs.append(stdv) - values_left -= 1 - return classification, avgs, stdevs - - def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: """ """ @@ -242,7 +189,7 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, else: y_data = [(itm * norm_factor) for itm in df[_VALUE[ttype]].tolist()] - anomalies, trend_avg, trend_stdev = _classify_anomalies( + anomalies, trend_avg, trend_stdev = classify_anomalies( {k: v for k, v in zip(x_axis, y_data)} ) |