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from models import Model, ModelState, AnalyticSegment
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import utils
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import utils.meta
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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 typing import Optional, List, Tuple
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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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from analytic_types import AnalyticUnitId, TimeSeries
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from analytic_types.learning_info import LearningInfo
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@utils.meta.JSONClass
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class JumpModelState(ModelState):
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def __init__(
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self,
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confidence: float = 0,
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jump_height: float = 0,
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jump_length: float = 0,
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**kwargs
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):
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super().__init__(**kwargs)
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self.confidence = confidence
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self.jump_height = jump_height
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self.jump_length = jump_length
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class JumpModel(Model):
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def get_model_type(self) -> (str, bool):
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model = 'jump'
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type_model = True
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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, 'jump')
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return segment_center_index
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def get_state(self, cache: Optional[dict] = None) -> JumpModelState:
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return JumpModelState.from_json(cache)
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def do_fit(
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self,
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dataframe: pd.DataFrame,
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labeled_segments: List[AnalyticSegment],
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deleted_segments: List[AnalyticSegment],
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learning_info: LearningInfo
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) -> 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.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_jump = utils.get_interval(data, segment_cent_index, window_size)
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deleted_jump = utils.subtract_min_without_nan(deleted_jump)
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del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.state.pattern_model)
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if len(del_conv_jump): del_conv_list.append(max(del_conv_jump))
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self._update_fiting_result(self.state, learning_info.confidence, convolve_list, del_conv_list)
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self.state.jump_height = float(min(learning_info.pattern_height, default = 1))
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self.state.jump_length = int(max(learning_info.pattern_width, default = 1))
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def do_detect(self, dataframe: pd.DataFrame) -> TimeSeries:
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data = utils.cut_dataframe(dataframe)
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data = data['value']
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possible_jumps = utils.find_jump(data, self.state.jump_height, self.state.jump_length + 1)
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result = self.__filter_detection(possible_jumps, data)
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return [(val - 1, val + 1) for val in result]
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def __filter_detection(self, segments: List[int], data: pd.Series):
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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, 'max')
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if len(segments) == 0 or len(self.state.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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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 = 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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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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