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import logging
import numpy as np
from multiprocessing import Pool
logger = logging.getLogger(__name__)
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, penalty):
changepoints = algo.predict(pen=penalty)
substates = list()
start_index = 0
end_index = 0
# calc metrics for all states
for changepoint in changepoints:
# start_index of state is end_index of previous one
# (Transitions are instantaneous)
start_index = end_index
end_index = changepoint - 1
substate = (start_index, end_index)
substates.append(substate)
return num_measurement, substates
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 # 200 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 and len(signal) > self.num_samples:
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]
changepoint_counts = list()
for i in range(0, 100):
changepoint_counts.append(len(changepoints_by_penalty[i]))
start_index = -1
end_index = -1
longest_start = -1
longest_end = -1
prev_val = -1
for i, num_changepoints in enumerate(changepoint_counts):
if num_changepoints != 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(changepoint_counts) - 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_changepoints
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, timestamps, signals, penalty, opt_model=None):
"""
Calculate substates for signals (assumed to be long to a single parameter configuration).
:returns: List of substates with duration and mean power: [(substate 1 duration, substate 1 power), ...]
"""
# 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
substate_data = list()
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, penalty))
raw_states_list = [None] * len(signals)
with Pool() as pool:
raw_states_res = pool.starmap(PELT_get_raw_states, raw_states_calc_args)
substate_counts = list(map(lambda x: len(x[1]), raw_states_res))
expected_substate_count = np.argmax(np.bincount(substate_counts))
usable_measurements = list(
filter(lambda x: len(x[1]) == expected_substate_count, raw_states_res)
)
logger.debug(
f" There are {expected_substate_count} substates (std = {np.std(substate_counts)}, {len(usable_measurements)}/{len(raw_states_res)} results are usable)"
)
for i in range(expected_substate_count):
substate_data.append(
{"duration": list(), "power": list(), "power_std": list()}
)
for num_measurement, substates in usable_measurements:
for i, substate in enumerate(substates):
power_trace = signals[num_measurement][substate[0] : substate[1]]
mean_power = np.mean(power_trace)
std_power = np.std(power_trace)
duration = (
timestamps[num_measurement][substate[1]]
- timestamps[num_measurement][substate[0]]
)
substate_data[i]["duration"].append(duration)
substate_data[i]["power"].append(mean_power)
substate_data[i]["power_std"].append(std_power)
return substate_counts, substate_data
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