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from models import Model
import utils
import numpy as np
import pandas as pd
import scipy.signal
from scipy.fftpack import fft
import math
from scipy.signal import argrelextrema
from scipy.stats import gaussian_kde
class JumpModel(Model):
def __init__(self):
super()
self.segments = []
self.ijumps = []
self.model_jump = []
self.state = {
'confidence': 1.5,
'convolve_max': 230,
'convolve_min': 230,
'JUMP_HEIGHT': 1,
'JUMP_LENGTH': 1,
'WINDOW_SIZE': 240,
'conv_del_min': 54000,
'conv_del_max': 55000,
}
def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
data = dataframe['value']
confidences = []
convolve_list = []
jump_height_list = []
jump_length_list = []
patterns_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(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window = 5).mean()
flat_segment_dropna = flat_segment.dropna()
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
peaks_kde = argrelextrema(np.array(ax_list), np.greater)[0]
min_peak_index = peaks_kde[0]
max_peak_index = peaks_kde[1]
segment_median = ax_list[antipeaks_kde[0], 0]
segment_min_line = ax_list[min_peak_index, 0]
segment_max_line = ax_list[max_peak_index, 0]
jump_height = 0.95 * (segment_max_line - segment_min_line)
jump_height_list.append(jump_height)
jump_length = utils.find_jump_length(segment_data, segment_min_line, segment_max_line)
jump_length_list.append(jump_length)
cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median
jump_center = cen_ind[0]
segment_cent_index = jump_center - 5 + segment_from_index
self.ijumps.append(segment_cent_index)
labeled_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1]
labeled_jump = labeled_jump - min(labeled_jump)
patterns_list.append(labeled_jump)
self.model_jump = utils.get_av_model(patterns_list)
for n in range(len(segments)):
labeled_jump = data[self.ijumps[n] - self.state['WINDOW_SIZE']: self.ijumps[n] + self.state['WINDOW_SIZE'] + 1]
labeled_jump = labeled_jump - min(labeled_jump)
auto_convolve = scipy.signal.fftconvolve(labeled_jump, labeled_jump)
convolve_jump = scipy.signal.fftconvolve(labeled_jump, self.model_jump)
convolve_list.append(max(auto_convolve))
convolve_list.append(max(convolve_jump))
del_conv_list = []
for segment in segments:
if segment['deleted']:
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
flat_segment = segment_data.rolling(window = 5).mean()
flat_segment_dropna = flat_segment.dropna()
pdf = gaussian_kde(flat_segment_dropna)
x = np.linspace(flat_segment_dropna.min() - 1, flat_segment_dropna.max() + 1, len(flat_segment_dropna))
y = pdf(x)
ax_list = []
for i in range(len(x)):
ax_list.append([x[i], y[i]])
ax_list = np.array(ax_list, np.float32)
antipeaks_kde = argrelextrema(np.array(ax_list), np.less)[0]
segment_median = ax_list[antipeaks_kde[0], 0]
cen_ind = utils.intersection_segment(flat_segment.tolist(), segment_median) #finds all interseprions with median
jump_center = cen_ind[0]
segment_cent_index = jump_center - 5 + segment_from_index
deleted_jump = data[segment_cent_index - self.state['WINDOW_SIZE'] : segment_cent_index + self.state['WINDOW_SIZE'] + 1]
deleted_jump = deleted_jump - min(labeled_jump)
del_conv_jump = scipy.signal.fftconvolve(deleted_jump, self.model_jump)
del_conv_list.append(max(del_conv_jump))
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'] = self.state['WINDOW_SIZE']
if len(convolve_list) > 0:
self.state['convolve_min'] = float(min(convolve_list))
else:
self.state['convolve_min'] = self.state['WINDOW_SIZE']
if len(jump_height_list) > 0:
self.state['JUMP_HEIGHT'] = int(min(jump_height_list))
else:
self.state['JUMP_HEIGHT'] = 1
if len(jump_length_list) > 0:
self.state['JUMP_LENGTH'] = int(max(jump_length_list))
else:
self.state['JUMP_LENGTH'] = 1
if len(del_conv_list) > 0:
self.state['conv_del_min'] = float(min(del_conv_list))
else:
self.state['conv_del_min'] = self.state['WINDOW_SIZE']
if len(del_conv_list) > 0:
self.state['conv_del_max'] = float(max(del_conv_list))
else:
self.state['conv_del_max'] = self.state['WINDOW_SIZE']
def do_predict(self, dataframe: pd.DataFrame) -> list:
data = dataframe['value']
possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1)
return self.__filter_prediction(possible_jumps, data)
def __filter_prediction(self, segments, data):
delete_list = []
variance_error = int(0.004 * len(data))
if variance_error > self.state['WINDOW_SIZE']:
variance_error = self.state['WINDOW_SIZE']
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)
if len(segments) == 0 or len(self.ijumps) == 0 :
segments = []
return segments
delete_list = []
pattern_data = self.model_jump
for segment in segments:
if segment > self.state['WINDOW_SIZE'] and segment < (len(data) - self.state['WINDOW_SIZE']):
convol_data = data[segment - self.state['WINDOW_SIZE'] : segment + self.state['WINDOW_SIZE'] + 1]
conv = scipy.signal.fftconvolve(convol_data, pattern_data)
if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8:
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)
# TODO: implement filtering
#for ijump in self.ijumps:
#segments.append(ijump)
return set(segments)