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119 lines
5.2 KiB
119 lines
5.2 KiB
from analytic_types import AnalyticUnitId, TimeSeries |
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from analytic_types.learning_info import LearningInfo |
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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 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 numpy as np |
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import pandas as pd |
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EXP_SMOOTHING_FACTOR = 0.01 |
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@utils.meta.JSONClass |
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class TriangleModelState(ModelState): |
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def __init__( |
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self, |
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confidence: float = 0, |
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height_max: float = 0, |
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height_min: 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.height_max = height_max |
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self.height_min = height_min |
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class TriangleModel(Model): |
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def get_state(self, cache: Optional[dict] = None) -> TriangleModelState: |
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return TriangleModelState.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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self.state.pattern_center = utils.remove_duplicates_and_sort(self.state.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, self.state.window_size) |
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correlation_list = utils.get_correlation(self.state.pattern_center, self.state.pattern_model, data, self.state.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_width = [] |
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delete_pattern_height = [] |
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delete_pattern_timestamp = [] |
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for segment in deleted_segments: |
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delete_pattern_timestamp.append(segment.pattern_timestamp) |
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deleted = utils.get_interval(data, segment.center_index, self.state.window_size) |
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deleted = utils.subtract_min_without_nan(deleted) |
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del_conv = scipy.signal.fftconvolve(deleted, self.state.pattern_model) |
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if len(del_conv): |
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del_conv_list.append(max(del_conv)) |
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delete_pattern_height.append(utils.find_confidence(deleted)[1]) |
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self._update_fitting_result(self.state, learning_info.confidence, convolve_list, del_conv_list, height_list) |
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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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all_extremum_indexes = self.get_extremum_indexes(data) |
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smoothed_data = self.get_smoothed_data(data, self.state.confidence, EXP_SMOOTHING_FACTOR) |
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segments = self.get_possible_segments(data, smoothed_data, all_extremum_indexes) |
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result = self.__filter_detection(segments, data) |
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result = utils.get_borders_of_peaks(result, data, self.state.window_size, self.state.confidence) |
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return result |
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def __filter_detection(self, segments: List[int], data: pd.Series) -> 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 = self.get_best_pattern(close_patterns, data) |
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if len(segments) == 0 or len(self.state.pattern_model) == 0: |
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return [] |
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pattern_data = self.state.pattern_model |
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up_height = self.state.height_max * (1 + self.HEIGHT_ERROR) |
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low_height = self.state.height_min * (1 - self.HEIGHT_ERROR) |
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up_conv = self.state.convolve_max * (1 + 1.5 * self.CONV_ERROR) |
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low_conv = self.state.convolve_min * (1 - self.CONV_ERROR) |
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up_del_conv = self.state.conv_del_max * (1 + self.DEL_CONV_ERROR) |
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low_del_conv = self.state.conv_del_min * (1 - self.DEL_CONV_ERROR) |
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for segment in segments: |
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if segment > self.state.window_size: |
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convol_data = utils.get_interval(data, segment, self.state.window_size) |
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convol_data = utils.subtract_min_without_nan(convol_data) |
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percent_of_nans = convol_data.isnull().sum() / len(convol_data) |
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if 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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pattern_height = convol_data.values.max() |
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if pattern_height > up_height or pattern_height < low_height: |
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delete_list.append(segment) |
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continue |
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if max(conv) > up_conv or max(conv) < low_conv: |
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delete_list.append(segment) |
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continue |
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if max(conv) < up_del_conv and max(conv) > low_del_conv: |
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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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