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223 lines
6.6 KiB
223 lines
6.6 KiB
import numpy as np |
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import pandas as pd |
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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['from'] = int(anomaly['from'].timestamp() * 1000) |
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anomaly['to'] = int(anomaly['to'].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['from']) |
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max_time = max(max_time, segment['to']) |
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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 MyLogisticSigmoid(interval, alpha, heigh): |
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distribution = [] |
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for i in range(-interval, interval): |
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F = height / (1 + math.exp(-i * alpha)) |
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distribution.append(F) |
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return distribution |
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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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def find_ind_median(median, segment_data): |
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x = np.arange(0, len(segment_data)) |
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f = [] |
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for i in range(len(segment_data)): |
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f.append(median) |
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f = np.array(f) |
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g = [] |
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for i in segment_data: |
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g.append(i) |
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g = np.array(g) |
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idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0 |
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return idx |
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def find_jump_length(segment_data, min_line, max_line): |
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x = np.arange(0, len(segment_data)) |
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f = [] |
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l = [] |
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for i in range(len(segment_data)): |
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f.append(min_line) |
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l.append(max_line) |
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f = np.array(f) |
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l = np.array(l) |
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g = [] |
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for i in segment_data: |
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g.append(i) |
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g = np.array(g) |
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idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0 |
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idl = np.argwhere(np.diff(np.sign(l - g)) != 0).reshape(-1) + 0 |
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if (idl[0] - idx[-1] + 1) > 0: |
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return idl[0] - idx[-1] + 1 |
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else: |
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print("retard alert!") |
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return 0 |
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def find_jump(data, height, lenght): |
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j_list = [] |
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for i in range(len(data)-lenght-1): |
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for x in range(1, lenght): |
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if(data[i+x] > data[i] + height): |
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j_list.append(i) |
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return(j_list) |
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def find_drop_length(segment_data, min_line, max_line): |
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x = np.arange(0, len(segment_data)) |
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f = [] |
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l = [] |
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for i in range(len(segment_data)): |
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f.append(min_line) |
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l.append(max_line) |
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f = np.array(f) |
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l = np.array(l) |
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g = [] |
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for i in segment_data: |
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g.append(i) |
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g = np.array(g) |
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idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0 #min_line |
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idl = np.argwhere(np.diff(np.sign(l - g)) != 0).reshape(-1) + 0 #max_line |
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if (idx[0] - idl[-1] + 1) > 0: |
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return idx[0] - idl[-1] + 1 |
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else: |
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print("retard alert!") |
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return 0 |
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def drop_intersection(segment_data, median_line): |
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x = np.arange(0, len(segment_data)) |
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f = [] |
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for i in range(len(segment_data)): |
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f.append(median_line) |
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f = np.array(f) |
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g = [] |
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for i in segment_data: |
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g.append(i) |
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g = np.array(g) |
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idx = np.argwhere(np.diff(np.sign(f - g)) != 0).reshape(-1) + 0 |
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return idx |
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def find_drop(data, height, length): |
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d_list = [] |
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for i in range(len(data)-length-1): |
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for x in range(1, length): |
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if(data[i+x] < data[i] - height): |
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d_list.append(i) |
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return(d_list) |
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def timestamp_to_index(dataframe, timestamp): |
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data = dataframe['timestamp'] |
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for i in range(len(data)): |
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if data[i] >= timestamp: |
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return i |
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def peak_finder(data, size): |
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all_max = [] |
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for i in range(size, len(data) - size): |
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if data[i] == max(data[i - size: i + size]) and data[i] > data[i + 1]: |
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all_max.append(i) |
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return all_max |
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def ar_mean(numbers): |
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return float(sum(numbers)) / max(len(numbers), 1) |
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def get_av_model(patterns_list): |
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x = len(patterns_list[0]) |
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if len(patterns_list[1]) != x: |
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raise NameError('All elements of patterns_list should have same length') |
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model_pat = [] |
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for i in range(x): |
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av_val = [] |
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for j in patterns_list: |
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av_val.append(j.values[i]) |
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model_pat.append(ar_mean(av_val)) |
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return model_pat
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