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97 lines
3.9 KiB
97 lines
3.9 KiB
from models import Model |
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import utils |
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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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import math |
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from scipy.stats import gaussian_kde |
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from scipy.stats import norm |
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class GeneralModel(Model): |
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def __init__(self): |
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super() |
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self.segments = [] |
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self.ipats = [] |
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self.model_gen = [] |
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self.state = { |
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'convolve_max': 240, |
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'convolve_min': 200, |
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'WINDOW_SIZE': 240, |
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'conv_del_min': 100, |
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'conv_del_max': 120, |
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} |
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self.all_conv = [] |
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def find_segment_center(self, dataframe: pd.DataFrame, start: int, end: int) -> int: |
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data = dataframe['value'] |
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segment = data[start: end] |
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center_ind = start + math.ceil((end - start) / 2) |
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return center_ind |
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def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list) -> None: |
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data = utils.cut_dataframe(dataframe) |
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data = data['value'] |
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convolve_list = [] |
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correlation_list = [] |
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patterns_list = [] |
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pattern_timestamp = [] |
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for segment in labeled_segments: |
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center_ind = segment.center_index |
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self.ipats.append(center_ind) |
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pattern_timestamp.append(segment.pattern_timestamp) |
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segment_data = utils.get_interval(data, center_ind, self.state['WINDOW_SIZE']) |
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segment_data = utils.subtract_min_without_nan(segment_data) |
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patterns_list.append(segment_data) |
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self.model_gen = utils.get_av_model(patterns_list) |
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convolve_list = utils.get_convolve(self.ipats, self.model_gen, data, self.state['WINDOW_SIZE']) |
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correlation_list = utils.get_correlation(self.ipats, self.model_gen, data, self.state['WINDOW_SIZE']) |
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del_conv_list = [] |
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delete_pattern_timestamp = [] |
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for segment in deleted_segments: |
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del_mid_index = segment.center_index |
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delete_pattern_timestamp.append(segment.pattern_timestamp) |
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deleted_pat = utils.get_interval(data, del_mid_index, self.state['WINDOW_SIZE']) |
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deleted_pat = utils.subtract_min_without_nan(deleted_pat) |
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del_conv_pat = scipy.signal.fftconvolve(deleted_pat, self.model_gen) |
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if len(del_conv_pat): del_conv_list.append(max(del_conv_pat)) |
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self.state['convolve_min'], self.state['convolve_max'] = utils.get_min_max(convolve_list, self.state['WINDOW_SIZE'] / 3) |
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self.state['conv_del_min'], self.state['conv_del_max'] = utils.get_min_max(del_conv_list, self.state['WINDOW_SIZE']) |
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def do_detect(self, dataframe: pd.DataFrame) -> list: |
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data = utils.cut_dataframe(dataframe) |
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data = data['value'] |
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pat_data = self.model_gen |
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y = max(pat_data) |
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for i in range(self.state['WINDOW_SIZE'] * 2, len(data)): |
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watch_data = data[i - self.state['WINDOW_SIZE'] * 2: i] |
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watch_data = utils.subtract_min_without_nan(watch_data) |
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conv = scipy.signal.fftconvolve(watch_data, pat_data) |
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self.all_conv.append(max(conv)) |
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all_conv_peaks = utils.peak_finder(self.all_conv, self.state['WINDOW_SIZE'] * 2) |
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filtered = self.__filter_detection(all_conv_peaks, data) |
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return set(item + self.state['WINDOW_SIZE'] for item in filtered) |
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def __filter_detection(self, segments: list, data: list): |
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if len(segments) == 0 or len(self.ipats) == 0: |
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return [] |
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delete_list = [] |
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for val in segments: |
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if self.all_conv[val] < self.state['convolve_min'] * 0.8: |
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delete_list.append(val) |
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elif (self.all_conv[val] < self.state['conv_del_max'] * 1.02 and self.all_conv[val] > self.state['conv_del_min'] * 0.98): |
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delete_list.append(val) |
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for item in delete_list: |
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segments.remove(item) |
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return set(segments)
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