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from models import Model, AnalyticUnitCache
import scipy.signal
from scipy.fftpack import fft
from scipy.signal import argrelextrema
import utils
import numpy as np
import pandas as pd
from typing import Optional
WINDOW_SIZE = 240
class TroughModel(Model):
def __init__(self):
super()
self.segments = []
self.ipeaks = []
self.state = {
'confidence': 1.5,
'convolve_max': 570000
}
def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
if type(cache) is AnalyticUnitCache:
self.state = cache
self.segments = segments
data = dataframe['value']
confidences = []
convolve_list = []
for segment in segments:
if segment['labeled']:
segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms'))
segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms'))
segment_data = data[segment_from_index: segment_to_index + 1]
if len(segment_data) == 0:
continue
segment_min = min(segment_data)
segment_max = max(segment_data)
confidences.append(0.2 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window=5).mean()
flat_segment = flat_segment.dropna()
segment_min_index = flat_segment.idxmin() #+ segment['start']
self.ipeaks.append(segment_min_index)
labeled_drop = data[segment_min_index - WINDOW_SIZE : segment_min_index + WINDOW_SIZE]
labeled_min = min(labeled_drop)
for value in labeled_drop:
value = value - labeled_min
convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
convolve_list.append(max(convolve))
if len(confidences) > 0:
self.state['confidence'] = float(min(confidences))
else:
self.state['confidence'] = 1.5
if len(convolve_list) > 0:
self.state['convolve_max'] = float(max(convolve_list))
else:
self.state['convolve_max'] = 570000
return self.state
def do_predict(self, dataframe: pd.DataFrame):
data = dataframe['value']
window_size = 24
all_max_flatten_data = data.rolling(window=window_size).mean()
all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
extrema_list = []
for i in utils.exponential_smoothing(data - self.state['confidence'], 0.02):
extrema_list.append(i)
segments = []
for i in all_mins:
if all_max_flatten_data[i] < extrema_list[i]:
segments.append(i)
test = dataframe['timestamp'][1].value
filtered = self.__filter_prediction(segments, data)
# TODO: convert from ns to ms more proper way (not dividing by 10^6)
return [(dataframe['timestamp'][x - 1].value / 1000000, dataframe['timestamp'][x + 1].value / 1000000) for x in filtered]
def __filter_prediction(self, segments: list, all_max_flatten_data: list):
delete_list = []
variance_error = int(0.004 * len(all_max_flatten_data))
if variance_error > 100:
variance_error = 100
for i in range(1, len(segments)):
if segments[i] < segments[i - 1] + variance_error:
delete_list.append(segments[i])
for item in delete_list:
segments.remove(item)
delete_list = []
if len(segments) == 0 or len(self.ipeaks) == 0 :
segments = []
return segments
pattern_data = all_max_flatten_data[self.ipeaks[0] - WINDOW_SIZE : self.ipeaks[0] + WINDOW_SIZE]
for segment in segments:
if segment > WINDOW_SIZE:
convol_data = all_max_flatten_data[segment - WINDOW_SIZE : segment + WINDOW_SIZE]
conv = scipy.signal.fftconvolve(pattern_data, convol_data)
if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_max'] * 0.8:
delete_list.append(segment)
else:
delete_list.append(segment)
for item in delete_list:
segments.remove(item)
return segments