diff options
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 77 |
1 files changed, 34 insertions, 43 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index 6912b02..452ff3f 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -1,40 +1,47 @@ -def plot_data_from_json(filename, trace_num, xaxis, yaxis): - import matplotlib.pyplot as plt - import json +import matplotlib.pyplot as plt +import json +from kneed import KneeLocator +import ruptures as rpt +import time +from multiprocessing import Pool, Manager +import numpy as np +import sys +import getopt +import re +from dfatool.dfatool import RawData + + +def plot_data_from_json(filename, trace_num, x_axis, y_axis): with open(filename, 'r') as f: tx_data = json.load(f) print(tx_data[trace_num]['parameter']) plt.plot(tx_data[trace_num]['offline'][0]['uW']) - plt.xlabel(xaxis) - plt.ylabel(yaxis) + plt.xlabel(x_axis) + plt.ylabel(y_axis) plt.show() -def plot_data_vs_mean(signal, xaxis, yaxis): - import matplotlib.pyplot as plt - from statistics import mean +def plot_data_vs_mean(signal, x_axis, y_axis): plt.plot(signal) - average = mean(signal) + average = np.mean(signal) plt.hlines(average, 0, len(signal)) - plt.xlabel(xaxis) - plt.ylabel(yaxis) + plt.xlabel(x_axis) + plt.ylabel(y_axis) plt.show() -def plot_data_vs_data_vs_means(signal1, signal2, xaxis, yaxis): - import matplotlib.pyplot as plt - from statistics import mean +def plot_data_vs_data_vs_means(signal1, signal2, x_axis, y_axis): plt.plot(signal1) lens = max(len(signal1), len(signal2)) - average = mean(signal1) + average = np.mean(signal1) plt.hlines(average, 0, lens, color='red') plt.vlines(len(signal1), 0, 100000, color='red', linestyles='dashed') plt.plot(signal2) - average = mean(signal2) + average = np.mean(signal2) plt.hlines(average, 0, lens, color='green') plt.vlines(len(signal2), 0, 100000, color='green', linestyles='dashed') - plt.xlabel(xaxis) - plt.ylabel(yaxis) + plt.xlabel(x_axis) + plt.ylabel(y_axis) plt.show() @@ -45,7 +52,6 @@ def get_bkps(algo, pen, q): def find_knee_point(data_x, data_y, S=1.0, curve='convex', direction='decreasing', plotting=False): - from kneed import KneeLocator kneedle = KneeLocator(data_x, data_y, S=S, curve=curve, direction=direction) if plotting: kneedle.plot_knee() @@ -53,13 +59,8 @@ def find_knee_point(data_x, data_y, S=1.0, curve='convex', direction='decreasing return kneepoint -def calc_PELT(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, num_processes=8, refresh_delay=1, +def calc_pelt(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, num_processes=8, refresh_delay=1, refresh_thresh=5, S=1.0, pen_override=None, plotting=False): - import ruptures as rpt - import time - import matplotlib.pylab as plt - from multiprocessing import Pool, Manager - # default params in Function if model is None: model = 'l1' @@ -104,7 +105,6 @@ def calc_PELT(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, # collect results from pool result = p.starmap_async(get_bkps, args) # monitor loop - last_percentage = -1 percentage = -100 # Force display of 0% i = 0 while True: @@ -125,22 +125,21 @@ def calc_PELT(signal, model='l1', jump=5, min_dist=2, range_min=1, range_max=50, # DECIDE WHICH PENALTY VALUE TO CHOOSE ACCORDING TO ELBOW/KNEE APPROACH # split x and y coords to pass to kneedle pen_val = [x[0] for x in res] - fittet_bkps_val = [x[1] for x in res] + fitted_bkps_val = [x[1] for x in res] # # plot to look at res - knee = find_knee_point(pen_val, fittet_bkps_val, S=S, plotting=plotting) + knee = find_knee_point(pen_val, fitted_bkps_val, S=S, plotting=plotting) plt.xlabel('Penalty') plt.ylabel('Number of Changepoints') - plt.plot(pen_val, fittet_bkps_val) - plt.vlines(knee[0], 0, max(fittet_bkps_val), linestyles='dashed') + plt.plot(pen_val, fitted_bkps_val) + plt.vlines(knee[0], 0, max(fitted_bkps_val), linestyles='dashed') print("knee: " + str(knee[0])) plt.show() else: # use forced pen value for plotting knee = (pen_override, None) - - #plt.plot(pen_val, fittet_bkps_val) + # plt.plot(pen_val, fittet_bkps_val) if knee[0] is not None: bkps = algo.predict(pen=knee[0]) if plotting: @@ -215,6 +214,7 @@ def needs_refinement_no_sort(signal, mean, thresh): # Very short benchmark yielded approx. 3 times the speed of solution not using sort +# TODO: Decide whether median is really the better baseline than mean def needs_refinement(signal, thresh): sorted_signal = sorted(signal) length_of_signal = len(signal) @@ -235,14 +235,6 @@ def needs_refinement(signal, thresh): if __name__ == '__main__': - import numpy as np - import json - import ruptures as rpt - import matplotlib.pylab as plt - import sys - import getopt - import re - from dfatool.dfatool import RawData # OPTION RECOGNITION opt = dict() @@ -357,8 +349,7 @@ if __name__ == '__main__': print(err, file=sys.stderr) sys.exit(2) - #OPENING DATA - import time + # OPENING DATA if ".json" in opt_filename: # open file with trace data from json print("[INFO] Will only refine the state which is present in " + opt_filename + " if necessary.") @@ -401,7 +392,7 @@ if __name__ == '__main__': # # 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) + # 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]') |