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import scipy.signal
from scipy.fftpack import fft
from scipy.signal import argrelextrema
import numpy as np
import pickle
def is_intersect(target_segment, segments):
for segment in segments:
start = max(segment['start'], target_segment[0])
finish = min(segment['finish'], target_segment[1])
if start <= finish:
return True
return False
def exponential_smoothing(series, alpha):
result = [series[0]]
for n in range(1, len(series)):
result.append(alpha * series[n] + (1 - alpha) * result[n-1])
return result
class StepDetector:
def __init__(self, pattern):
self.pattern = pattern
self.segments = []
self.confidence = 1.5
self.convolve_max = 570000
def fit(self, dataframe, segments):
data = dataframe['value']
confidences = []
convolve_list = []
for segment in segments:
if segment['labeled']:
segment_data = data[segment['start'] : segment['finish'] + 1]
segment_min = min(segment_data)
segment_max = max(segment_data)
confidences.append(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window=5).mean()
segment_min_index = flat_segment.idxmin() - 5
labeled_drop = data[segment_min_index - 120 : segment_min_index + 120]
convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
convolve_list.append(max(convolve))
if len(confidences) > 0:
self.confidence = min(confidences)
else:
self.confidence = 1.5
if len(convolve_list) > 0:
self.convolve_max = max(convolve_list)
else:
self.convolve_max = 570000
def predict(self, dataframe):
data = dataframe['value']
result = self.__predict(data)
result.sort()
if len(self.segments) > 0:
result = [segment for segment in result if not is_intersect(segment, self.segments)]
return result
def __predict(self, data):
window_size = 24
all_max_flatten_data = data.rolling(window=window_size).mean()
all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
extrema_list = []
for i in exponential_smoothing(data - self.confidence, 0.03):
extrema_list.append(i)
segments = []
for i in all_mins:
if all_max_flatten_data[i] < extrema_list[i]:
segments.append(i - window_size)
return [(x - 1, x + 1) for x in self.__filter_prediction(segments, all_max_flatten_data)]
def __filter_prediction(self, segments, all_max_flatten_data):
delete_list = []
variance_error = int(0.004 * len(all_max_flatten_data))
if variance_error > 200:
variance_error = 200
for i in range(1, len(segments)):
if segments[i] < segments[i - 1] + variance_error:
delete_list.append(segments[i])
for item in delete_list:
segments.remove(item)
delete_list = []
pattern_data = all_max_flatten_data[segments[0] - 120 : segments[0] + 120]
for segment in segments:
convol_data = all_max_flatten_data[segment - 120 : segment + 120]
conv = scipy.signal.fftconvolve(pattern_data, convol_data)
if max(conv) > self.convolve_max * 1.1 or max(conv) < self.convolve_max * 0.9:
delete_list.append(segment)
for item in delete_list:
segments.remove(item)
return segments
def save(self, model_filename):
with open(model_filename, 'wb') as file:
pickle.dump((self.confidence, self.convolve_max), file)
def load(self, model_filename):
try:
with open(model_filename, 'rb') as file:
(self.confidence, self.convolve_max) = pickle.load(file)
except:
pass