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