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183 lines
8.3 KiB
183 lines
8.3 KiB
from models import Model |
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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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import utils |
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import numpy as np |
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import pandas as pd |
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class DropModel(Model): |
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def __init__(self): |
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super() |
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self.segments = [] |
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self.idrops = [] |
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self.model_drop = [] |
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self.state = { |
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'confidence': 1.5, |
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'convolve_max': 200, |
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'convolve_min': 200, |
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'DROP_HEIGHT': 1, |
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'DROP_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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drop_height_list = [] |
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drop_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 = 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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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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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 = [] |
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for i in range(len(x)): |
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ax_list.append([x[i], y[i]]) |
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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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min_peak_index = peaks_kde[0] |
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max_peak_index = peaks_kde[1] |
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segment_median = ax_list[antipeaks_kde[0], 0] |
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segment_min_line = ax_list[min_peak_index, 0] |
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segment_max_line = ax_list[max_peak_index, 0] |
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drop_height = 0.95 * (segment_max_line - segment_min_line) |
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drop_height_list.append(drop_height) |
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drop_length = utils.find_drop_length(segment_data, segment_min_line, segment_max_line) |
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drop_length_list.append(drop_length) |
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cen_ind = utils.drop_intersection(flat_segment.tolist(), segment_median) #finds all interseprions with median |
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drop_center = cen_ind[0] |
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segment_cent_index = drop_center - 5 + segment_from_index |
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self.idrops.append(segment_cent_index) |
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labeled_drop = data[segment_cent_index - self.state['WINDOW_SIZE']: segment_cent_index + self.state['WINDOW_SIZE'] + 1] |
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labeled_drop = labeled_drop - min(labeled_drop) |
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patterns_list.append(labeled_drop) |
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self.model_drop = utils.get_av_model(patterns_list) |
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for n in range(len(segments)): |
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labeled_drop = data[self.idrops[n] - self.state['WINDOW_SIZE']: self.idrops[n] + self.state['WINDOW_SIZE'] + 1] |
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labeled_drop = labeled_drop - min(labeled_drop) |
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auto_convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop) |
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convolve_drop = scipy.signal.fftconvolve(labeled_drop, self.model_drop) |
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convolve_list.append(max(auto_convolve)) |
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convolve_list.append(max(convolve_drop)) |
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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 = [] |
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for i in range(len(x)): |
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ax_list.append([x[i], y[i]]) |
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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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drop_center = cen_ind[0] # or -1? test |
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segment_cent_index = drop_center - 5 + segment_from_index |
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deleted_drop = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1] |
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deleted_drop = deleted_drop - min(labeled_drop) |
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del_conv_drop = scipy.signal.fftconvolve(deleted_drop, self.model_drop) |
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del_conv_list.append(max(del_conv_drop)) |
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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(drop_height_list) > 0: |
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self.state['DROP_HEIGHT'] = int(min(drop_height_list)) |
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else: |
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self.state['DROP_HEIGHT'] = 1 |
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if len(drop_length_list) > 0: |
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self.state['DROP_LENGTH'] = int(max(drop_length_list)) |
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else: |
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self.state['DROP_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_drops = utils.find_drop(data, self.state['DROP_HEIGHT'], self.state['DROP_LENGTH'] + 1) |
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return self.__filter_prediction(possible_drops, data) |
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def __filter_prediction(self, segments: list, data: list): |
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delete_list = [] |
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variance_error = int(0.004 * len(data)) |
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if variance_error > self.state['WINDOW_SIZE']: |
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variance_error = self.state['WINDOW_SIZE'] |
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for i in range(1, len(segments)): |
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if segments[i] < segments[i - 1] + variance_error: |
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delete_list.append(segments[i]) |
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# for item in delete_list: |
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# segments.remove(item) |
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delete_list = [] |
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if len(segments) == 0 or len(self.idrops) == 0 : |
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segments = [] |
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return segments |
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pattern_data = self.model_drop |
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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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if conv[self.state['WINDOW_SIZE']*2] > self.state['convolve_max'] * 1.2 or conv[self.state['WINDOW_SIZE']*2] < self.state['convolve_min'] * 0.8: |
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delete_list.append(segment) |
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elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98: |
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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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# TODO: implement filtering |
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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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