diff options
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 137 |
1 files changed, 124 insertions, 13 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index 643a368..2ed7675 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -151,6 +151,89 @@ def calc_PELT(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, print('With the current thresh-hold S=' + str(S) + ' it is not possible to select a penalty value.') +# very short benchmark yielded approx. 1/3 of speed compared to solution with sorting +def needs_refinement_no_sort(signal, mean, thresh): + # linear search for the top 10%/ bottom 10% + # should be sufficient + length_of_signal = len(signal) + percentile_size = int() + percentile_size = length_of_signal // 100 + upper_percentile = [None] * percentile_size + lower_percentile = [None] * percentile_size + fill_index_upper = percentile_size - 1 + fill_index_lower = percentile_size - 1 + index_smallest_val = fill_index_upper + index_largest_val = fill_index_lower + + for x in signal: + if x > mean: + # will be in upper percentile + if fill_index_upper >= 0: + upper_percentile[fill_index_upper] = x + if x < upper_percentile[index_smallest_val]: + index_smallest_val = fill_index_upper + fill_index_upper = fill_index_upper - 1 + continue + + if x > upper_percentile[index_smallest_val]: + # replace smallest val. Find next smallest val + upper_percentile[index_smallest_val] = x + index_smallest_val = 0 + i = 0 + for y in upper_percentile: + if upper_percentile[i] < upper_percentile[index_smallest_val]: + index_smallest_val = i + i = i + 1 + + else: + if fill_index_lower >= 0: + lower_percentile[fill_index_lower] = x + if x > lower_percentile[index_largest_val]: + index_largest_val = fill_index_upper + fill_index_lower = fill_index_lower - 1 + continue + if x < lower_percentile[index_largest_val]: + # replace smallest val. Find next smallest val + lower_percentile[index_largest_val] = x + index_largest_val = 0 + i = 0 + for y in lower_percentile: + if lower_percentile[i] > lower_percentile[index_largest_val]: + index_largest_val = i + i = i + 1 + + # should have the percentiles + lower_percentile_mean = np.mean(lower_percentile) + upper_percentile_mean = np.mean(upper_percentile) + dist = mean - lower_percentile_mean + if dist > thresh: + return True + dist = upper_percentile_mean - mean + if dist > thresh: + return True + return False + + +# Very short benchmark yielded approx. 3 times the speed of solution not using sort +def needs_refinement_sort(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 + + if __name__ == '__main__': import numpy as np import json @@ -160,6 +243,7 @@ if __name__ == '__main__': import getopt import re from dfatool.dfatool import RawData + # OPTION RECOGNITION opt = dict() optspec = ( @@ -176,6 +260,7 @@ if __name__ == '__main__': "S= " "pen_override= " "plotting= " + "refinement_thresh= " ) opt_filename = None opt_verbose = False @@ -190,6 +275,7 @@ if __name__ == '__main__': opt_S = None opt_pen_override = None opt_plotting = False + opt_refinement_thresh = None try: raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" ")) @@ -261,14 +347,38 @@ if __name__ == '__main__': except ValueError as verr: print(verr, file=sys.stderr) sys.exit(2) + if 'refinement_thresh' in opt: + try: + opt_refinement_thresh = int(opt['refinement_thresh']) + except ValueError as verr: + print(verr, file=sys.stderr) + sys.exit(2) except getopt.GetoptError as err: print(err, file=sys.stderr) sys.exit(2) + #OPENING DATA + import time if ".json" in opt_filename: # open file with trace data from json - with open(opt['filename'], 'r') as f: - tx_data = json.load(f) + print("[INFO] Will only refine the state which is present in " + opt_filename + " if necessary.") + with open(opt_filename, 'r') as f: + states = json.load(f) + # loop through all traces check if refinement is necessary + print("Checking if refinement is necessary...") + res = False + for measurements_by_state in states: + # loop through all occurrences of the looked at state + print("Looking at state '" + measurements_by_state['name'] + "'") + for measurement in measurements_by_state['offline']: + # loop through measurements of particular state + # an check if state needs refinement + signal = measurement['uW'] + # mean = measurement['uW_mean'] + # TODO: Decide if median is really the better baseline than mean + if needs_refinement_sort(signal, opt_refinement_thresh): + print("Refinement is necessary!") + break elif ".tar" in opt_filename: # open with dfatool raw_data_args = list() @@ -280,18 +390,19 @@ if __name__ == '__main__': preprocessed_data = raw_data.get_preprocessed_data() print("File fully preprocessed") + # TODO: Mal schauen, wie ich das mache. Erstmal nur mit json else: print("Unknown dataformat", file=sys.stderr) sys.exit(2) - print(tx_data[1]['parameter']) - # parse json to array for PELT - signal = np.array(tx_data[1]['offline'][0]['uW']) - - for i in range(0, len(signal)): - signal[i] = signal[i]/1000 - bkps = calc_PELT(signal, model=opt_model, range_max=opt_range_max, num_processes=opt_num_processes, jump=opt_jump, S=opt_S) - fig, ax = rpt.display(signal, bkps) - plt.xlabel('Time [us]') - plt.ylabel('Power [mW]') - plt.show() + # print(tx_data[1]['parameter']) + # # parse json to array for PELT + # signal = np.array(tx_data[1]['offline'][0]['uW']) + # + # for i in range(0, len(signal)): + # signal[i] = signal[i]/1000 + # bkps = calc_PELT(signal, model=opt_model, range_max=opt_range_max, num_processes=opt_num_processes, jump=opt_jump, S=opt_S) + # fig, ax = rpt.display(signal, bkps) + # plt.xlabel('Time [us]') + # plt.ylabel('Power [mW]') + # plt.show() |