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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.state = {
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'pattern_center': [],
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'pattern_model': [],
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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': 0,
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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 get_model_type(self) -> (str, bool):
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model = 'drop'
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type_model = False
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return (model, type_model)
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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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segment_center_index = utils.find_pattern_center(segment, start, 'drop')
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return segment_center_index
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def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict, id: str) -> None:
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data = utils.cut_dataframe(dataframe)
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data = data['value']
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window_size = self.state['WINDOW_SIZE']
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last_pattern_center = self.state.get('pattern_center', [])
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self.state['pattern_center'] = list(set(last_pattern_center + learning_info['segment_center_list']))
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self.state['pattern_model'] = utils.get_av_model(learning_info['patterns_list'])
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convolve_list = utils.get_convolve(self.state['pattern_center'], self.state['pattern_model'], data, window_size)
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correlation_list = utils.get_correlation(self.state['pattern_center'], self.state['pattern_model'], data, window_size)
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height_list = learning_info['patterns_value']
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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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segment_cent_index = segment.center_index
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delete_pattern_timestamp.append(segment.pattern_timestamp)
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deleted_drop = utils.get_interval(data, segment_cent_index, window_size)
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deleted_drop = utils.subtract_min_without_nan(deleted_drop)
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del_conv_drop = scipy.signal.fftconvolve(deleted_drop, self.state['pattern_model'])
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if len(del_conv_drop): del_conv_list.append(max(del_conv_drop))
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self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list, height_list)
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self.state['DROP_HEIGHT'] = int(min(learning_info['pattern_height'], default = 1))
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self.state['DROP_LENGTH'] = int(max(learning_info['pattern_width'], default = 1))
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def do_detect(self, dataframe: pd.DataFrame, id: str) -> list:
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data = utils.cut_dataframe(dataframe)
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data = data['value']
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possible_drops = utils.find_drop(data, self.state['DROP_HEIGHT'], self.state['DROP_LENGTH'] + 1)
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result = self.__filter_detection(possible_drops, data)
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return [(val - 1, val + 1) for val in result]
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def __filter_detection(self, segments: list, data: list):
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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_pattern(close_patterns, data, 'min')
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if len(segments) == 0 or len(self.state.get('pattern_center', [])) == 0:
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segments = []
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return segments
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pattern_data = self.state['pattern_model']
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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 = utils.get_interval(data, segment, self.state['WINDOW_SIZE'])
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percent_of_nans = convol_data.isnull().sum() / len(convol_data)
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if len(convol_data) == 0 or percent_of_nans > 0.5:
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delete_list.append(segment)
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continue
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elif 0 < percent_of_nans <= 0.5:
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nan_list = utils.find_nan_indexes(convol_data)
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convol_data = utils.nan_to_zero(convol_data, nan_list)
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pattern_data = utils.nan_to_zero(pattern_data, nan_list)
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conv = scipy.signal.fftconvolve(convol_data, pattern_data)
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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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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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