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author | Daniel Friesel <daniel.friesel@uos.de> | 2020-10-07 15:05:58 +0200 |
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committer | Daniel Friesel <daniel.friesel@uos.de> | 2020-10-07 15:05:58 +0200 |
commit | f3f571ea0d4c2f2827681b81a9b341e62d086b69 (patch) | |
tree | 000fafbda13ea44427807283033f9fe8414a3115 /bin/analyze-archive.py | |
parent | c613e83a73d467342d7798d0c10a99a28aee8ed7 (diff) |
wip
Diffstat (limited to 'bin/analyze-archive.py')
-rwxr-xr-x | bin/analyze-archive.py | 86 |
1 files changed, 85 insertions, 1 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index 66772e6..e9694b4 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -49,6 +49,32 @@ from dfatool.validation import CrossValidator from dfatool.utils import filter_aggregate_by_param from dfatool.automata import PTA +### PELT +import numpy as np + +# Very short benchmark yielded approx. 3 times the speed of solution not using sort +# checks the percentiles if refinement is necessary +def needs_refinement(signal, thresh): + sorted_signal = sorted(signal) + length_of_signal = len(signal) + percentile_size = int() + percentile_size = length_of_signal // 100 + lower_percentile = sorted_signal[0:percentile_size] + upper_percentile = sorted_signal[ + length_of_signal - percentile_size : length_of_signal + ] + lower_percentile_mean = np.mean(lower_percentile) + upper_percentile_mean = np.mean(upper_percentile) + median = np.median(sorted_signal) + dist = median - lower_percentile_mean + if dist > thresh: + return True + dist = upper_percentile_mean - median + if dist > thresh: + return True + return False + +### /PELT def print_model_quality(results): for state_or_tran in results.keys(): @@ -350,6 +376,11 @@ if __name__ == "__main__": type=str, help="Export JSON energy modle to FILE. Works out of the box for v1 and v2, requires --hwmodel for v0", ) + parser.add_argument( + "--with-substates", + action="store_true", + help="Perform substate analysis" + ) parser.add_argument("measurement", nargs="+") args = parser.parse_args() @@ -396,7 +427,7 @@ if __name__ == "__main__": raw_data = RawData( args.measurement, - with_traces=(args.export_traces is not None or args.plot_traces is not None), + with_traces=(args.export_traces is not None or args.plot_traces is not None or args.with_substates is not None), skip_cache=args.no_cache, ) @@ -445,6 +476,59 @@ if __name__ == "__main__": with open(target, "w") as f: json.dump(data, f) + if args.with_substates: + opt_refinement_thresh = 100 + uw_per_sot = dict() + for trace in preprocessed_data: + for state_or_transition in trace["trace"]: + if state_or_transition["isa"] == "state": + name = state_or_transition["name"] + if name not in uw_per_sot: + uw_per_sot[name] = list() + for elem in state_or_transition["offline"]: + elem["uW"] = list(elem["uW"]) + uw_per_sot[name].append(state_or_transition) + for name, configurations in uw_per_sot.items(): + for num_config, measurements_by_config in enumerate(configurations): + logging.debug( + "Looking at state '" + + measurements_by_config["name"] + + "' with params: " + + str(measurements_by_config["parameter"]) + + "(" + + str(num_config + 1) + + "/" + + str(len(configurations)) + + ")" + ) + num_needs_refine = 0 + logging.debug("Checking if refinement is necessary...") + for measurement in measurements_by_config["offline"]: + # loop through measurements of particular state + # and check if state needs refinement + signal = measurement["uW"] + # mean = measurement['uW_mean'] + if needs_refinement(signal, opt_refinement_thresh): + num_needs_refine = num_needs_refine + 1 + if num_needs_refine == 0: + logging.debug( + "No refinement necessary for state '" + + measurements_by_config["name"] + + "' with params: " + + str(measurements_by_config["parameter"]) + ) + elif num_needs_refine < len(measurements_by_config["offline"]) / 2: + logging.debug( + "No refinement necessary for state '" + + measurements_by_config["name"] + + "' with params: " + + str(measurements_by_config["parameter"]) + ) + logging.debug( + "However this decision was not unanimously. This could hint at poor " + "measurement quality." + ) + if args.plot_traces: traces = list() for trace in preprocessed_data: |