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from models import Model, AnalyticUnitCache
import utils
import numpy as np
import pandas as pd
import scipy.signal
from scipy.fftpack import fft
from scipy.signal import argrelextrema
import math
from scipy.stats import gaussian_kde
from scipy.stats import norm
from typing import Optional
WINDOW_SIZE = 350
class GeneralModel(Model):
def __init__(self):
super()
self.segments = []
self.ipats = []
self.state = {
'convolve_max': WINDOW_SIZE,
}
self.all_conv = []
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']
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
self.ipats.append(segment_from_index + int((segment_to_index - segment_from_index) / 2))
segment_min = min(segment_data)
segment_data = segment_data - segment_min
segment_max = max(segment_data)
segment_data = segment_data / segment_max
convolve = scipy.signal.fftconvolve(segment_data, segment_data)
convolve_list.append(max(convolve))
if len(convolve_list) > 0:
self.state['convolve_max'] = float(max(convolve_list))
else:
self.state['convolve_max'] = WINDOW_SIZE / 3
return self.state
def do_predict(self, dataframe: pd.DataFrame):
data = dataframe['value']
pat_data = data[self.ipats[0] - WINDOW_SIZE: self.ipats[0] + WINDOW_SIZE]
x = min(pat_data)
pat_data = pat_data - x
y = max(pat_data)
pat_data = pat_data / y
for i in range(WINDOW_SIZE * 2, len(data)):
watch_data = data[i - WINDOW_SIZE * 2: i]
w = min(watch_data)
watch_data = watch_data - w
r = max(watch_data)
if r < y:
watch_data = watch_data / y
else:
watch_data = watch_data / r
conv = scipy.signal.fftconvolve(pat_data, watch_data)
self.all_conv.append(max(conv))
all_conv_peaks = utils.peak_finder(self.all_conv, WINDOW_SIZE * 2)
filtered = self.__filter_prediction(all_conv_peaks, data)
return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
def __filter_prediction(self, segments: list, data: list):
if len(segments) == 0 or len(self.ipats) == 0:
segments = []
return segments
delete_list = []
for val in segments:
if self.all_conv[val] < self.state['convolve_max'] * 0.8:
delete_list.append(val)
for item in delete_list:
segments.remove(item)
return segments