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| author | Daniel Friesel <daniel.friesel@uos.de> | 2021-01-14 15:35:33 +0100 |
|---|---|---|
| committer | Daniel Friesel <daniel.friesel@uos.de> | 2021-01-14 15:35:33 +0100 |
| commit | e7f181ed3eafbff9c38a993a263f771fe7377694 (patch) | |
| tree | 0c1c3300202e89afa661c90f4efecd00d570dc33 /lib/lennart/DataProcessor.py | |
| parent | 3b3d166b266a6467591e1617a8856b211d9006ca (diff) | |
energytrace drift compensation: handle arbitrarily long detection failures
Diffstat (limited to 'lib/lennart/DataProcessor.py')
| -rw-r--r-- | lib/lennart/DataProcessor.py | 25 |
1 files changed, 6 insertions, 19 deletions
diff --git a/lib/lennart/DataProcessor.py b/lib/lennart/DataProcessor.py index 7c161ab..40f1a26 100644 --- a/lib/lennart/DataProcessor.py +++ b/lib/lennart/DataProcessor.py @@ -289,6 +289,8 @@ class DataProcessor: edge_dsts.append(new_node) delta_drift = np.abs(prev_drift - new_drift) + # TODO evaluate "delta_drift ** 2" or similar nonlinear + # weights -> further penalize large drift deltas csr_weights.append(delta_drift) # a transition's candidate list may be empty @@ -362,26 +364,11 @@ class DataProcessor: transition = transition_by_node[node] drift = node_drifts[node] - if transition - prev_transition >= 2: - # previous transition was skipped due to lack of detected changepoints + while transition - prev_transition > 1: prev_drift = node_drifts[nodes[i - 1]] - mean_drift = np.mean([prev_drift, drift]) - expected_start_ts = ( - sync_timestamps[(prev_transition + 1) * 2] + mean_drift - ) - expected_end_ts = ( - sync_timestamps[(prev_transition + 1) * 2 + 1] + mean_drift - ) - compensated_timestamps.append(expected_start_ts) - compensated_timestamps.append(expected_end_ts) - if transition - prev_transition >= 3: - # previous transition was skipped due to lack of detected changepoints - expected_start_ts = ( - sync_timestamps[(prev_transition + 2) * 2] + mean_drift - ) - expected_end_ts = ( - sync_timestamps[(prev_transition + 2) * 2 + 1] + mean_drift - ) + prev_transition += 1 + expected_start_ts = sync_timestamps[prev_transition * 2] + prev_drift + expected_end_ts = sync_timestamps[prev_transition * 2 + 1] + prev_drift compensated_timestamps.append(expected_start_ts) compensated_timestamps.append(expected_end_ts) |