|
|
|
from models import Model
|
|
|
|
|
|
|
|
import scipy.signal
|
|
|
|
from scipy.fftpack import fft
|
|
|
|
from scipy.signal import argrelextrema
|
|
|
|
|
|
|
|
import utils
|
|
|
|
import numpy as np
|
|
|
|
import pandas as pd
|
|
|
|
|
|
|
|
SMOOTHING_COEFF = 2400
|
|
|
|
EXP_SMOOTHING_FACTOR = 0.01
|
|
|
|
|
|
|
|
class TroughModel(Model):
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
super()
|
|
|
|
self.segments = []
|
|
|
|
self.itroughs = []
|
|
|
|
self.model = []
|
|
|
|
self.state = {
|
|
|
|
'confidence': 1.5,
|
|
|
|
'convolve_max': 570000,
|
|
|
|
'convolve_min': 530000,
|
|
|
|
'WINDOW_SIZE': 240,
|
|
|
|
'conv_del_min': 54000,
|
|
|
|
'conv_del_max': 55000,
|
|
|
|
}
|
|
|
|
|
|
|
|
def get_model_type(self) -> (str, bool):
|
|
|
|
model = 'trough'
|
|
|
|
type_model = False
|
|
|
|
return (model, type_model)
|
|
|
|
|
|
|
|
def find_segment_center(self, dataframe: pd.DataFrame, start: int, end: int) -> int:
|
|
|
|
data = dataframe['value']
|
|
|
|
segment = data[start: end]
|
|
|
|
return segment.idxmin()
|
|
|
|
|
|
|
|
def do_fit(self, dataframe: pd.DataFrame, labeled_segments: list, deleted_segments: list, learning_info: dict) -> None:
|
|
|
|
data = utils.cut_dataframe(dataframe)
|
|
|
|
data = data['value']
|
|
|
|
window_size = self.state['WINDOW_SIZE']
|
|
|
|
self.itroughs = learning_info['segment_center_list']
|
|
|
|
self.model = utils.get_av_model(learning_info['patterns_list'])
|
|
|
|
convolve_list = utils.get_convolve(self.itroughs, self.model, data, window_size)
|
|
|
|
correlation_list = utils.get_correlation(self.itroughs, self.model, data, window_size)
|
|
|
|
|
|
|
|
del_conv_list = []
|
|
|
|
delete_pattern_width = []
|
|
|
|
delete_pattern_height = []
|
|
|
|
delete_pattern_timestamp = []
|
|
|
|
for segment in deleted_segments:
|
|
|
|
del_min_index = segment.center_index
|
|
|
|
delete_pattern_timestamp.append(segment.pattern_timestamp)
|
|
|
|
deleted = utils.get_interval(data, del_min_index, window_size)
|
|
|
|
deleted = utils.subtract_min_without_nan(deleted)
|
|
|
|
del_conv = scipy.signal.fftconvolve(deleted, self.model)
|
|
|
|
if len(del_conv): del_conv_list.append(max(del_conv))
|
|
|
|
delete_pattern_height.append(utils.find_confidence(deleted)[1])
|
|
|
|
delete_pattern_width.append(utils.find_width(deleted, False))
|
|
|
|
|
|
|
|
self._update_fiting_result(self.state, learning_info['confidence'], convolve_list, del_conv_list)
|
|
|
|
|
|
|
|
def do_detect(self, dataframe: pd.DataFrame):
|
|
|
|
data = utils.cut_dataframe(dataframe)
|
|
|
|
data = data['value']
|
|
|
|
window_size = int(len(data)/SMOOTHING_COEFF) #test ws on flat data
|
|
|
|
all_mins = argrelextrema(np.array(data), np.less)[0]
|
|
|
|
|
|
|
|
extrema_list = []
|
|
|
|
for i in utils.exponential_smoothing(data - self.state['confidence'], EXP_SMOOTHING_FACTOR):
|
|
|
|
extrema_list.append(i)
|
|
|
|
|
|
|
|
segments = []
|
|
|
|
for i in all_mins:
|
|
|
|
if data[i] < extrema_list[i]:
|
|
|
|
segments.append(i)
|
|
|
|
|
|
|
|
return self.__filter_detection(segments, data)
|
|
|
|
|
|
|
|
def __filter_detection(self, segments: list, data: list) -> list:
|
|
|
|
delete_list = []
|
|
|
|
variance_error = self.state['WINDOW_SIZE']
|
|
|
|
close_patterns = utils.close_filtering(segments, variance_error)
|
|
|
|
segments = utils.best_pattern(close_patterns, data, 'min')
|
|
|
|
if len(segments) == 0 or len(self.itroughs) == 0 :
|
|
|
|
segments = []
|
|
|
|
return segments
|
|
|
|
pattern_data = self.model
|
|
|
|
for segment in segments:
|
|
|
|
if segment > self.state['WINDOW_SIZE']:
|
|
|
|
convol_data = utils.get_interval(data, segment, self.state['WINDOW_SIZE'])
|
|
|
|
convol_data = utils.subtract_min_without_nan(convol_data)
|
|
|
|
percent_of_nans = convol_data.isnull().sum() / len(convol_data)
|
|
|
|
if percent_of_nans > 0.5:
|
|
|
|
delete_list.append(segment)
|
|
|
|
continue
|
|
|
|
elif 0 < percent_of_nans <= 0.5:
|
|
|
|
nan_list = utils.find_nan_indexes(convol_data)
|
|
|
|
convol_data = utils.nan_to_zero(convol_data, nan_list)
|
|
|
|
pattern_data = utils.nan_to_zero(pattern_data, nan_list)
|
|
|
|
conv = scipy.signal.fftconvolve(convol_data, pattern_data)
|
|
|
|
if max(conv) > self.state['convolve_max'] * 1.1 or max(conv) < self.state['convolve_min'] * 0.9:
|
|
|
|
delete_list.append(segment)
|
|
|
|
elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
|
|
|
|
delete_list.append(segment)
|
|
|
|
else:
|
|
|
|
delete_list.append(segment)
|
|
|
|
for item in delete_list:
|
|
|
|
segments.remove(item)
|
|
|
|
|
|
|
|
return set(segments)
|