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import numpy as np
from multiprocessing import Pool
def PELT_get_changepoints(algo, penalty):
res = (penalty, algo.predict(pen=penalty))
return res
# calculates the raw_states for measurement measurement. num_measurement is used to identify the
# return value
# penalty, model and jump are directly passed to pelt
def PELT_get_raw_states(num_measurement, algo, signal, penalty):
bkpts = algo.predict(pen=penalty)
calced_states = list()
start_time = 0
end_time = 0
# calc metrics for all states
for bkpt in bkpts:
# start_time of state is end_time of previous one
# (Transitions are instantaneous)
start_time = end_time
end_time = bkpt
power_vals = signal[start_time:end_time]
mean_power = np.mean(power_vals)
std_dev = np.std(power_vals)
calced_state = (start_time, end_time, mean_power, std_dev)
calced_states.append(calced_state)
num = 0
new_avg_std = 0
# calc avg std for all states from this measurement
for s in calced_states:
# print_info("State " + str(num) + " starts at t=" + str(s[0])
# + " and ends at t=" + str(s[1])
# + " while using " + str(s[2])
# + "uW with sigma=" + str(s[3]))
num = num + 1
new_avg_std = new_avg_std + s[3]
# check case if no state has been found to avoid crashing
if len(calced_states) != 0:
new_avg_std = new_avg_std / len(calced_states)
else:
new_avg_std = 0
change_avg_std = None # measurement["uW_std"] - new_avg_std
# print_info("The average standard deviation for the newly found states is "
# + str(new_avg_std))
# print_info("That is a reduction of " + str(change_avg_std))
return num_measurement, calced_states, new_avg_std, change_avg_std
class PELT:
def __init__(self, **kwargs):
self.model = "l1"
self.jump = 1
self.min_dist = 10
self.num_samples = None
self.refinement_threshold = 200e-6 # µW
self.range_min = 0
self.range_max = 100
self.__dict__.update(kwargs)
# signals: a set of uW measurements belonging to a single parameter configuration (i.e., a single by_param entry)
def needs_refinement(self, signals):
count = 0
for signal in signals:
# test
p1, median, p99 = np.percentile(signal[5:-5], (1, 50, 99))
if median - p1 > self.refinement_threshold:
count += 1
elif p99 - median > self.refinement_threshold:
count += 1
refinement_ratio = count / len(signals)
return refinement_ratio > 0.3
def norm_signal(self, signal, scaler=25):
max_val = max(np.abs(signal))
normed_signal = np.zeros(shape=len(signal))
for i, signal_i in enumerate(signal):
normed_signal[i] = signal_i / max_val
normed_signal[i] = normed_signal[i] * scaler
return normed_signal
def get_penalty_and_changepoints(self, signal):
# imported here as ruptures is only used for changepoint detection.
# This way, dfatool can be used without having ruptures installed as
# long as --pelt isn't active.
import ruptures
if self.num_samples is not None:
self.jump = len(signal) // int(self.num_samples)
else:
self.jump = 1
algo = ruptures.Pelt(
model=self.model, jump=self.jump, min_size=self.min_dist
).fit(self.norm_signal(signal))
queue = list()
for i in range(0, 100):
queue.append((algo, i))
with Pool() as pool:
changepoints = pool.starmap(PELT_get_changepoints, queue)
changepoints_by_penalty = dict()
for res in changepoints:
if len(res[1]) > 0 and res[1][-1] == len(signal):
res[1].pop()
changepoints_by_penalty[res[0]] = res[1]
num_changepoints = list()
for i in range(0, 100):
num_changepoints.append(len(changepoints_by_penalty[i]))
start_index = -1
end_index = -1
longest_start = -1
longest_end = -1
prev_val = -1
for i, num_bkpts in enumerate(num_changepoints):
if num_bkpts != prev_val:
end_index = i - 1
if end_index - start_index > longest_end - longest_start:
longest_start = start_index
longest_end = end_index
start_index = i
if i == len(num_changepoints) - 1:
end_index = i
if end_index - start_index > longest_end - longest_start:
longest_start = start_index
longest_end = end_index
start_index = i
prev_val = num_bkpts
middle_of_plateau = longest_start + (longest_start - longest_start) // 2
changepoints = np.array(changepoints_by_penalty[middle_of_plateau])
return middle_of_plateau, changepoints
def get_changepoints(self, signal):
_, changepoints = self.get_penalty_and_changepoints(signal)
return changepoints
def get_penalty(self, signal):
penalty, _ = self.get_penalty_and_changepoints(signal)
return penalty
def calc_raw_states(self, signals, penalty, opt_model=None):
# imported here as ruptures is only used for changepoint detection.
# This way, dfatool can be used without having ruptures installed as
# long as --pelt isn't active.
import ruptures
raw_states_calc_args = list()
for num_measurement, measurement in enumerate(signals):
normed_signal = self.norm_signal(measurement)
algo = ruptures.Pelt(
model=self.model, jump=self.jump, min_size=self.min_dist
).fit(normed_signal)
raw_states_calc_args.append((num_measurement, algo, normed_signal, penalty))
raw_states_list = [None] * len(signals)
with Pool() as pool:
raw_states_res = pool.starmap(PELT_get_raw_states, raw_states_calc_args)
# extracting result and putting it in correct order -> index of raw_states_list
# entry still corresponds with index of measurement in measurements_by_states
# -> If measurements are discarded the used ones are easily recognized
for ret_val in raw_states_res:
num_measurement = ret_val[0]
raw_states = ret_val[1]
avg_std = ret_val[2]
change_avg_std = ret_val[3]
# FIXME: Wieso gibt mir meine IDE hier eine Warning aus? Der Index müsste doch
# int sein oder nicht? Es scheint auch vernünftig zu klappen...
raw_states_list[num_measurement] = raw_states
# print(
# "The average standard deviation for the newly found states in "
# + "measurement No. "
# + str(num_measurement)
# + " is "
# + str(avg_std)
# )
# print("That is a reduction of " + str(change_avg_std))
for i, raw_state in enumerate(raw_states):
print(
f"Measurement #{num_measurement} sub-state #{i}: {raw_state[0]} -> {raw_state[1]}, mean {raw_state[2]}"
)
# l_signal = measurements_by_config['offline'][num_measurement]['uW']
# l_bkpts = [s[1] for s in raw_states]
# fig, ax = rpt.display(np.array(l_signal), l_bkpts)
# plt.show()
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