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186 lines
8.5 KiB
186 lines
8.5 KiB
from models import Model |
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import utils |
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from utils.segments import parse_segment |
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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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import math |
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from scipy.signal import argrelextrema |
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from scipy.stats import gaussian_kde |
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class JumpModel(Model): |
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def __init__(self): |
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super() |
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self.segments = [] |
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self.ijumps = [] |
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self.model_jump = [] |
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self.state = { |
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'confidence': 1.5, |
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'convolve_max': 230, |
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'convolve_min': 230, |
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'JUMP_HEIGHT': 1, |
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'JUMP_LENGTH': 1, |
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'WINDOW_SIZE': 240, |
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'conv_del_min': 54000, |
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'conv_del_max': 55000, |
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} |
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def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None: |
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data = dataframe['value'] |
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confidences = [] |
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convolve_list = [] |
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jump_height_list = [] |
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jump_length_list = [] |
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patterns_list = [] |
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for segment in segments: |
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if segment['labeled']: |
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segment_from_index, segment_to_index, segment_data = parse_segment(segment, dataframe) |
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if len(segment_data) == 0: |
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continue |
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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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flat_segment_dropna = flat_segment.dropna() |
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min_jump = min(flat_segment_dropna) |
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max_jump = max(flat_segment_dropna) |
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pdf = gaussian_kde(flat_segment_dropna) |
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x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna)) |
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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 |
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segment_min_line = min_jump |
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segment_median = (max_jump - min_jump) / 2 + min_jump |
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jump_height = 0.95 * (segment_max_line - segment_min_line) |
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jump_height_list.append(jump_height) |
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jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line) |
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jump_length_list.append(jump_length) |
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cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median |
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jump_center = cen_ind[0] |
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segment_cent_index = jump_center - 5 + segment_from_index |
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self.ijumps.append(segment_cent_index) |
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labeled_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1] |
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labeled_jump = labeled_jump - min(labeled_jump) |
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patterns_list.append(labeled_jump) |
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self.model_jump = utils.get_av_model(patterns_list) |
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for n in range(len(segments)): |
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labeled_jump = data[self.ijumps[n] - self.state['WINDOW_SIZE']: self.ijumps[n] + self.state['WINDOW_SIZE'] + 1] |
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labeled_jump = labeled_jump - min(labeled_jump) |
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auto_convolve = scipy.signal.fftconvolve(labeled_jump, labeled_jump) |
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convolve_jump = scipy.signal.fftconvolve(labeled_jump, self.model_jump) |
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convolve_list.append(max(auto_convolve)) |
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convolve_list.append(max(convolve_jump)) |
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del_conv_list = [] |
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for segment in segments: |
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if segment['deleted']: |
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segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms')) |
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segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms')) |
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segment_data = data[segment_from_index: segment_to_index + 1] |
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if len(segment_data) == 0: |
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continue |
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flat_segment = segment_data.rolling(window = 5).mean() |
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flat_segment_dropna = flat_segment.dropna() |
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pdf = gaussian_kde(flat_segment_dropna) |
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x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna)) |
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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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segment_median = ax_list[antipeaks_kde[0], 0] |
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cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median |
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jump_center = cen_ind[0] |
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segment_cent_index = jump_center - 5 + segment_from_index |
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deleted_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1] |
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deleted_jump = deleted_jump - min(labeled_jump) |
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del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.model_jump) |
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del_conv_list.append(max(del_conv_jump)) |
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if len(confidences) > 0: |
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self.state['confidence'] = float(min(confidences)) |
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else: |
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self.state['confidence'] = 1.5 |
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if len(convolve_list) > 0: |
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self.state['convolve_max'] = float(max(convolve_list)) |
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else: |
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self.state['convolve_max'] = self.state['WINDOW_SIZE'] |
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if len(convolve_list) > 0: |
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self.state['convolve_min'] = float(min(convolve_list)) |
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else: |
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self.state['convolve_min'] = self.state['WINDOW_SIZE'] |
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if len(jump_height_list) > 0: |
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self.state['JUMP_HEIGHT'] = float(min(jump_height_list)) |
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else: |
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self.state['JUMP_HEIGHT'] = 1 |
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if len(jump_length_list) > 0: |
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self.state['JUMP_LENGTH'] = int(max(jump_length_list)) |
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else: |
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self.state['JUMP_LENGTH'] = 1 |
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if len(del_conv_list) > 0: |
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self.state['conv_del_min'] = float(min(del_conv_list)) |
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else: |
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self.state['conv_del_min'] = self.state['WINDOW_SIZE'] |
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if len(del_conv_list) > 0: |
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self.state['conv_del_max'] = float(max(del_conv_list)) |
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else: |
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self.state['conv_del_max'] = self.state['WINDOW_SIZE'] |
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def do_predict(self, dataframe: pd.DataFrame) -> list: |
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data = dataframe['value'] |
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possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1) |
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return self.__filter_prediction(possible_jumps, data) |
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def __filter_prediction(self, segments, data): |
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delete_list = [] |
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variance_error = self.state['WINDOW_SIZE'] |
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close_patterns = utils.close_filtering(segments, variance_error) |
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segments = utils.best_pat(close_patterns, data, 'max') |
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if len(segments) == 0 or len(self.ijumps) == 0 : |
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segments = [] |
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return segments |
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pattern_data = self.model_jump |
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upper_bound = self.state['convolve_max'] * 1.2 |
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lower_bound = self.state['convolve_min'] * 0.8 |
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delete_up_bound = self.state['conv_del_max'] * 1.02 |
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delete_low_bound = self.state['conv_del_min'] * 0.98 |
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for segment in segments: |
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if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']): |
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convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1] |
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conv = scipy.signal.fftconvolve(convol_data, pattern_data) |
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try: |
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if max(conv) > upper_bound or max(conv) < lower_bound: |
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delete_list.append(segment) |
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elif max(conv) < delete_up_bound and max(conv) > delete_low_bound: |
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delete_list.append(segment) |
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except ValueError: |
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delete_list.append(segment) |
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else: |
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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 set(segments)
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