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115 lines
4.2 KiB
115 lines
4.2 KiB
from models import Model, AnalyticUnitCache |
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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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import utils |
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import numpy as np |
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import pandas as pd |
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from typing import Optional |
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WINDOW_SIZE = 240 |
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class PeakModel(Model): |
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def __init__(self): |
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super() |
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self.segments = [] |
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self.ipeaks = [] |
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self.state = { |
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'confidence': 1.5, |
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'convolve_max': 570000 |
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} |
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def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache: |
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if type(cache) is AnalyticUnitCache: |
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self.state = cache |
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self.segments = segments |
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data = dataframe['value'] |
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confidences = [] |
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convolve_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(0.2 * (segment_max - segment_min)) |
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flat_segment = segment_data.rolling(window=5).mean() |
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flat_segment = flat_segment.dropna() |
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segment_max_index = flat_segment.idxmax() # + segment['start'] |
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self.ipeaks.append(segment_max_index) |
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labeled_drop = data[segment_max_index - WINDOW_SIZE: segment_max_index + WINDOW_SIZE] |
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labeled_min = min(labeled_drop) |
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for value in labeled_drop: |
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value = value - labeled_min |
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convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop) |
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convolve_list.append(max(convolve)) |
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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'] = 570000 |
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return self.state |
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def do_predict(self, dataframe: pd.DataFrame): |
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data = dataframe['value'] |
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window_size = 24 |
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all_maxs = argrelextrema(np.array(data), np.greater)[0] |
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extrema_list = [] |
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for i in utils.exponential_smoothing(data + self.state['confidence'], 0.02): |
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extrema_list.append(i) |
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segments = [] |
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for i in all_maxs: |
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if data[i] > extrema_list[i]: |
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segments.append(i) |
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filtered = self.__filter_prediction(segments, data) |
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# TODO: convert from ns to ms more proper way (not dividing by 10^6) |
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return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered] |
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def __filter_prediction(self, segments: list, data: list) -> 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 > 100: |
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variance_error = 100 |
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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.ipeaks) == 0: |
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return [] |
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pattern_data = data[self.ipeaks[0] - WINDOW_SIZE: self.ipeaks[0] + WINDOW_SIZE] |
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for segment in segments: |
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if segment > WINDOW_SIZE: |
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convol_data = data[segment - WINDOW_SIZE: segment + WINDOW_SIZE] |
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conv = scipy.signal.fftconvolve(pattern_data, convol_data) |
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if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_max'] * 0.8: |
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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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