from models import Model import utils import numpy as np import pandas as pd import scipy.signal from scipy.fftpack import fft import math from scipy.signal import argrelextrema from scipy.stats import gaussian_kde class JumpModel(Model): def __init__(self): super() self.segments = [] self.ijumps = [] self.model_jump = [] self.state = { 'confidence': 1.5, 'convolve_max': 230, 'convolve_min': 230, 'JUMP_HEIGHT': 1, 'JUMP_LENGTH': 1, 'WINDOW_SIZE': 240, 'conv_del_min': 54000, 'conv_del_max': 55000, } def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None: data = dataframe['value'] confidences = [] convolve_list = [] jump_height_list = [] jump_length_list = [] patterns_list = [] for segment in segments: if segment['labeled']: segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms')) segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms')) segment_data = data[segment_from_index: segment_to_index + 1] if len(segment_data) == 0: continue segment_min = min(segment_data) segment_max = max(segment_data) confidences.append(0.20 * (segment_max - segment_min)) flat_segment = segment_data.rolling(window = 5).mean() flat_segment_dropna = flat_segment.dropna() pdf = gaussian_kde(flat_segment_dropna) x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna)) y = pdf(x) ax_list = [] for i in range(len(x)): ax_list.append([x[i], y[i]]) ax_list = np.array(ax_list, np.float32) antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0] peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0] min_peak_index = peaks_kde[0] max_peak_index = peaks_kde[1] segment_median = ax_list[antipeaks_kde[0], 0] segment_min_line = ax_list[min_peak_index, 0] segment_max_line = ax_list[max_peak_index, 0] jump_height = 0.95 * (segment_max_line - segment_min_line) jump_height_list.append(jump_height) jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line) jump_length_list.append(jump_length) cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median jump_center = cen_ind[0] segment_cent_index = jump_center - 5 + segment_from_index self.ijumps.append(segment_cent_index) labeled_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1] labeled_jump = labeled_jump - min(labeled_jump) patterns_list.append(labeled_jump) self.model_jump = utils.get_av_model(patterns_list) for n in range(len(segments)): labeled_jump = data[self.ijumps[n] - self.state['WINDOW_SIZE']: self.ijumps[n] + self.state['WINDOW_SIZE'] + 1] labeled_jump = labeled_jump - min(labeled_jump) auto_convolve = scipy.signal.fftconvolve(labeled_jump, labeled_jump) convolve_jump = scipy.signal.fftconvolve(labeled_jump, self.model_jump) convolve_list.append(max(auto_convolve)) convolve_list.append(max(convolve_jump)) del_conv_list = [] for segment in segments: if segment['deleted']: segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms')) segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms')) segment_data = data[segment_from_index: segment_to_index + 1] if len(segment_data) == 0: continue flat_segment = segment_data.rolling(window = 5).mean() flat_segment_dropna = flat_segment.dropna() pdf = gaussian_kde(flat_segment_dropna) x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna)) y = pdf(x) ax_list = [] for i in range(len(x)): ax_list.append([x[i], y[i]]) ax_list = np.array(ax_list, np.float32) antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0] segment_median = ax_list[antipeaks_kde[0], 0] cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median jump_center = cen_ind[0] segment_cent_index = jump_center - 5 + segment_from_index deleted_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1] deleted_jump = deleted_jump - min(labeled_jump) del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.model_jump) del_conv_list.append(max(del_conv_jump)) if len(confidences) > 0: self.state['confidence'] = float(min(confidences)) else: self.state['confidence'] = 1.5 if len(convolve_list) > 0: self.state['convolve_max'] = float(max(convolve_list)) else: self.state['convolve_max'] = self.state['WINDOW_SIZE'] if len(convolve_list) > 0: self.state['convolve_min'] = float(min(convolve_list)) else: self.state['convolve_min'] = self.state['WINDOW_SIZE'] if len(jump_height_list) > 0: self.state['JUMP_HEIGHT'] = int(min(jump_height_list)) else: self.state['JUMP_HEIGHT'] = 1 if len(jump_length_list) > 0: self.state['JUMP_LENGTH'] = int(max(jump_length_list)) else: self.state['JUMP_LENGTH'] = 1 if len(del_conv_list) > 0: self.state['conv_del_min'] = float(min(del_conv_list)) else: self.state['conv_del_min'] = self.state['WINDOW_SIZE'] if len(del_conv_list) > 0: self.state['conv_del_max'] = float(max(del_conv_list)) else: self.state['conv_del_max'] = self.state['WINDOW_SIZE'] def do_predict(self, dataframe: pd.DataFrame) -> list: data = dataframe['value'] possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1) return self.__filter_prediction(possible_jumps, data) def __filter_prediction(self, segments, data): delete_list = [] variance_error = int(0.004 * len(data)) if variance_error > self.state['WINDOW_SIZE']: variance_error = self.state['WINDOW_SIZE'] for i in range(1, len(segments)): if segments[i] < segments[i - 1] + variance_error: delete_list.append(segments[i]) for item in delete_list: segments.remove(item) if len(segments) == 0 or len(self.ijumps) == 0 : segments = [] return segments delete_list = [] pattern_data = self.model_jump for segment in segments: if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']): convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1] conv = scipy.signal.fftconvolve(convol_data, pattern_data) if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8: delete_list.append(segment) if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: delete_list.append(segment) else: delete_list.append(segment) for item in delete_list: segments.remove(item) # TODO: implement filtering #for ijump in self.ijumps: #segments.append(ijump) return set(segments)