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110 lines
3.5 KiB
110 lines
3.5 KiB
import numpy as np |
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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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def find_steps(array, threshold): |
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""" |
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Finds local maxima by segmenting array based on positions at which |
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the threshold value is crossed. Note that this thresholding is |
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applied after the absolute value of the array is taken. Thus, |
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the distinction between upward and downward steps is lost. However, |
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get_step_sizes can be used to determine directionality after the |
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fact. |
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Parameters |
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---------- |
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array : numpy array |
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1 dimensional array that represents time series of data points |
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threshold : int / float |
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Threshold value that defines a step |
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Returns |
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------- |
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steps : list |
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List of indices of the detected steps |
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""" |
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steps = [] |
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array = np.abs(array) |
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above_points = np.where(array > threshold, 1, 0) |
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ap_dif = np.diff(above_points) |
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cross_ups = np.where(ap_dif == 1)[0] |
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cross_dns = np.where(ap_dif == -1)[0] |
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for upi, dni in zip(cross_ups,cross_dns): |
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steps.append(np.argmax(array[upi:dni]) + upi) |
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return steps |
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def anomalies_to_timestamp(anomalies): |
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for anomaly in anomalies: |
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anomaly['start'] = int(anomaly['start'].timestamp() * 1000) |
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anomaly['finish'] = int(anomaly['finish'].timestamp() * 1000) |
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return anomalies |
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def segments_box(segments): |
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max_time = 0 |
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min_time = float("inf") |
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for segment in segments: |
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min_time = min(min_time, segment['start']) |
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max_time = max(max_time, segment['finish']) |
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min_time = pd.to_datetime(min_time, unit='ms') |
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max_time = pd.to_datetime(max_time, unit='ms') |
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return min_time, max_time |
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def intersection_segment(data, median): |
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""" |
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Finds all intersections between flatten data and median |
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""" |
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cen_ind = [] |
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for i in range(1, len(data)-1): |
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if data[i - 1] < median and data[i + 1] > median: |
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cen_ind.append(i) |
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del_ind = [] |
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for i in range(1, len(cen_ind)): |
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if cen_ind[i] == cen_ind[i - 1] + 1: |
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del_ind.append(i - 1) |
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return [x for (idx, x) in enumerate(cen_ind) if idx not in del_ind] |
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def logistic_sigmoid_distribution(self, x1, x2, alpha, height): |
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return map(lambda x: logistic_sigmoid(x, alpha, height), range(x1, x2)) |
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def logistic_sigmoid(x, alpha, height): |
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return height / (1 + math.exp(-x * alpha)) |
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def find_one_jump(data, x, size, height, err): |
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l = [] |
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for i in range(x + 1, x + size): |
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if (data[i] > data[x] and data[x + size] > data[x] + height): |
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l.append(data[i]) |
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if len(l) > size * err: |
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return x |
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else: |
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return 0 |
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def find_all_jumps(data, size, height): |
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possible_jump_list = [] |
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for i in range(len(data - size)): |
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x = find_one_jump(data, i, size, height, 0.9) |
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if x > 0: |
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possible_jump_list.append(x) |
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return possible_jump_list |
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def find_jump_center(cen_ind): |
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jump_center = cen_ind[0] |
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for i in range(len(cen_ind)): |
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x = cen_ind[i] |
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cx = scipy.signal.fftconvolve(pat_sigm, flat_data[x - WINDOW_SIZE : x + WINDOW_SIZE]) |
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c.append(cx[2 * WINDOW_SIZE]) |
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if i > 0 and cx > c[i - 1]: |
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jump_center = x |
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return jump_center
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