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328 lines
12 KiB
328 lines
12 KiB
import numpy as np |
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import pandas as pd |
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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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from scipy.stats import gaussian_kde |
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from scipy.stats.stats import pearsonr |
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from typing import Union |
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import utils |
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SHIFT_FACTOR = 0.05 |
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CONFIDENCE_FACTOR = 0.2 |
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SMOOTHING_FACTOR = 5 |
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def exponential_smoothing(series, alpha): |
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result = [series[0]] |
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if np.isnan(result): |
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result = [0] |
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for n in range(1, len(series)): |
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if np.isnan(series[n]): |
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series[n] = 0 |
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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 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 find_pattern(data: pd.Series, height: float, length: int, pattern_type: str) -> list: |
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pattern_list = [] |
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right_bound = len(data) - length - 1 |
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for i in range(right_bound): |
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for x in range(1, length): |
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if pattern_type == 'jump': |
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if(data[i + x] > data[i] + height): |
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pattern_list.append(i) |
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elif pattern_type == 'drop': |
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if(data[i + x] < data[i] - height): |
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pattern_list.append(i) |
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return pattern_list |
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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(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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if len(patterns_list) == 0: |
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return [] |
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x = len(patterns_list[0]) |
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if len(patterns_list) > 1 and len(patterns_list[1]) != x: |
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raise NameError( |
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'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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def close_filtering(pattern_list, win_size): |
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if len(pattern_list) == 0: |
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return [] |
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s = [[pattern_list[0]]] |
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k = 0 |
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for i in range(1, len(pattern_list)): |
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if pattern_list[i] - win_size <= s[k][-1]: |
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s[k].append(pattern_list[i]) |
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else: |
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k += 1 |
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s.append([pattern_list[i]]) |
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return s |
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def best_pattern(pattern_list: list, data: pd.Series, dir: str) -> list: |
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new_pattern_list = [] |
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for val in pattern_list: |
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max_val = data[val[0]] |
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min_val = data[val[0]] |
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ind = val[0] |
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for i in val: |
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if dir == 'max': |
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if data[i] > max_val: |
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max_val = data[i] |
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ind = i |
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else: |
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if data[i] < min_val: |
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min_val = data[i] |
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ind = i |
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new_pattern_list.append(ind) |
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return new_pattern_list |
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def find_nan_indexes(segment: pd.Series) -> list: |
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nan_list = np.isnan(segment) |
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nan_indexes = [] |
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for i, val in enumerate(nan_list): |
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if val: |
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nan_indexes.append(i) |
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return nan_indexes |
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def check_nan_values(segment: Union[pd.Series, list]) -> Union[pd.Series, list]: |
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nan_list = utils.find_nan_indexes(segment) |
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if len(nan_list) > 0: |
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segment = utils.nan_to_zero(segment, nan_list) |
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return segment |
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def nan_to_zero(segment: Union[pd.Series, list], nan_list: list) -> Union[pd.Series, list]: |
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if type(segment) == pd.Series: |
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for val in nan_list: |
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segment.values[val] = 0 |
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else: |
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for val in nan_list: |
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segment[val] = 0 |
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return segment |
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def find_confidence(segment: pd.Series) -> (float, float): |
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segment = utils.check_nan_values(segment) |
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segment_min = min(segment) |
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segment_max = max(segment) |
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height = segment_max - segment_min |
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if height: |
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return (CONFIDENCE_FACTOR * height, height) |
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else: |
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return (0, 0) |
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def find_width(pattern: pd.Series, selector) -> int: |
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pattern = pattern.values |
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center = utils.find_extremum_index(pattern, selector) |
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pattern_left = pattern[:center] |
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pattern_right = pattern[center:] |
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left_extremum_index = utils.find_last_extremum(pattern_left, selector) |
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right_extremum_index = utils.find_extremum_index(pattern_right, not selector) |
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left_width = center - left_extremum_index |
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right_width = right_extremum_index + 1 |
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return right_width + left_width |
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def find_last_extremum(segment: np.ndarray, selector: bool) -> int: |
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segment = segment[::-1] |
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first_extremum_ind = find_extremum_index(segment, not selector) |
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last_extremum_ind = len(segment) - first_extremum_ind - 1 |
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return last_extremum_ind |
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def find_extremum_index(segment: np.ndarray, selector: bool) -> int: |
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if selector: |
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return segment.argmax() |
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else: |
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return segment.argmin() |
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def get_interval(data: pd.Series, center: int, window_size: int) -> pd.Series: |
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left_bound = center - window_size |
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right_bound = center + window_size + 1 |
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if left_bound < 0: |
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left_bound = 0 |
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if right_bound > len(data): |
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right_bound = len(data) |
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return data[left_bound: right_bound] |
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def subtract_min_without_nan(segment: pd.Series) -> pd.Series: |
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if len(segment) == 0: |
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return [] |
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nan_list = utils.find_nan_indexes(segment) |
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if len(nan_list) > 0: |
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return segment |
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else: |
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segment = segment - min(segment) |
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return segment |
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def get_convolve(segments: list, av_model: list, data: pd.Series, window_size: int) -> list: |
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labeled_segment = [] |
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convolve_list = [] |
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for segment in segments: |
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labeled_segment = utils.get_interval(data, segment, window_size) |
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labeled_segment = utils.subtract_min_without_nan(labeled_segment) |
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labeled_segment = utils.check_nan_values(labeled_segment) |
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auto_convolve = scipy.signal.fftconvolve(labeled_segment, labeled_segment) |
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convolve_segment = scipy.signal.fftconvolve(labeled_segment, av_model) |
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convolve_list.append(max(auto_convolve)) |
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convolve_list.append(max(convolve_segment)) |
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return convolve_list |
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def get_correlation(segments: list, av_model: list, data: pd.Series, window_size: int) -> list: |
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labeled_segment = [] |
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correlation_list = [] |
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p_value_list = [] |
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for segment in segments: |
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labeled_segment = utils.get_interval(data, segment, window_size) |
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labeled_segment = utils.subtract_min_without_nan(labeled_segment) |
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labeled_segment = utils.check_nan_values(labeled_segment) |
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correlation = pearsonr(labeled_segment, av_model) |
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correlation_list.append(correlation[0]) |
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p_value_list.append(correlation[1]) |
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return correlation_list |
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def get_distribution_density(segment: pd.Series) -> float: |
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if len(segment) < 2: |
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return (0, 0, 0) |
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min_jump = min(segment) |
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max_jump = max(segment) |
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pdf = gaussian_kde(segment) |
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x = np.linspace(segment.min() - 1, segment.max() + 1, len(segment)) |
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y = pdf(x) |
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ax_list = list(zip(x, y)) |
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ax_list = np.array(ax_list, np.float32) |
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antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0] |
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peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0] |
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try: |
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min_peak_index = peaks_kde[0] |
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segment_min_line = ax_list[min_peak_index, 0] |
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max_peak_index = peaks_kde[1] |
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segment_max_line = ax_list[max_peak_index, 0] |
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segment_median = ax_list[antipeaks_kde[0], 0] |
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except IndexError: |
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segment_max_line = max_jump * (1 - SHIFT_FACTOR) |
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segment_min_line = min_jump * (1 - SHIFT_FACTOR) |
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segment_median = (max_jump - min_jump) / 2 + min_jump |
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return segment_median, segment_max_line, segment_min_line |
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def find_parameters(segment_data: pd.Series, segment_from_index: int, pat_type: str) -> [int, float, int]: |
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segment = segment_data |
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if len(segment_data) > SMOOTHING_FACTOR * 3: |
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flat_segment = segment_data.rolling(window = SMOOTHING_FACTOR).mean() |
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segment = flat_segment.dropna() |
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segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(segment) |
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height = 0.95 * (segment_max_line - segment_min_line) |
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length = utils.find_length(segment_data, segment_min_line, segment_max_line, pat_type) |
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return height, length |
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def find_pattern_center(segment_data: pd.Series, segment_from_index: int, pattern_type: str): |
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segment_median = utils.get_distribution_density(segment_data)[0] |
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cen_ind = utils.pattern_intersection(segment_data.tolist(), segment_median, pattern_type) |
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pat_center = cen_ind[0] |
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segment_cent_index = pat_center + segment_from_index |
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return segment_cent_index |
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def find_length(segment_data: pd.Series, segment_min_line: float, segment_max_line: float, pat_type: str) -> int: |
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x_abscissa = np.arange(0, len(segment_data)) |
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segment_max = max(segment_data) |
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segment_min = min(segment_data) |
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if segment_min_line <= segment_min: |
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segment_min_line = segment_min * 1.05 |
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if segment_max_line >= segment_max: |
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segment_max_line = segment_max * 0.95 |
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min_line = [] |
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max_line = [] |
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for i in range(len(segment_data)): |
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min_line.append(segment_min_line) |
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max_line.append(segment_max_line) |
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min_line = np.array(min_line) |
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max_line = np.array(max_line) |
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segment_array = np.array(segment_data.tolist()) |
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idmin = np.argwhere(np.diff(np.sign(min_line - segment_array)) != 0).reshape(-1) |
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idmax = np.argwhere(np.diff(np.sign(max_line - segment_array)) != 0).reshape(-1) |
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if len(idmin) > 0 and len(idmax) > 0: |
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if pat_type == 'jump': |
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result_length = idmax[0] - idmin[-1] + 1 |
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elif pat_type == 'drop': |
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result_length = idmin[0] - idmax[-1] + 1 |
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return result_length if result_length > 0 else 0 |
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else: |
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return 0 |
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def pattern_intersection(segment_data: list, median: float, pattern_type: str) -> list: |
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center_index = [] |
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if pattern_type == 'jump': |
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for i in range(1, len(segment_data) - 1): |
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if segment_data[i - 1] < median and segment_data[i + 1] > median: |
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center_index.append(i) |
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elif pattern_type == 'drop': |
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for i in range(1, len(segment_data) - 1): |
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if segment_data[i - 1] > median and segment_data[i + 1] < median: |
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center_index.append(i) |
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delete_index = [] |
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for i in range(1, len(center_index)): |
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if center_index[i] == center_index[i - 1] + 1: |
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delete_index.append(i - 1) |
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return [x for (idx, x) in enumerate(center_index) if idx not in delete_index] |
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def cut_dataframe(data: pd.DataFrame) -> pd.DataFrame: |
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data_min = data['value'].min() |
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if not np.isnan(data_min) and data_min > 0: |
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data['value'] = data['value'] - data_min |
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return data |
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def get_min_max(array, default): |
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return float(min(array, default=default)), float(max(array, default=default))
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