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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
from scipy.signal import argrelextrema
import math
from scipy.stats import gaussian_kde
from scipy.stats import norm
WINDOW_SIZE = 400
class JumpModel(Model):
def __init__(self):
super()
self.segments = []
self.ijumps = []
self.state = {
'confidence': 1.5,
'convolve_max': WINDOW_SIZE,
'JUMP_HEIGHT': 1,
'JUMP_LENGTH': 1,
}
def fit(self, dataframe: pd.DataFrame, segments: list, cache: dict) -> dict:
self.segments = segments
data = dataframe['value']
confidences = []
convolve_list = []
jump_height_list = []
jump_length_list = []
for segment in segments:
if segment['labeled']:
segment_data = data.loc[segment['from'] : segment['to'] + 1].reset_index(drop=True)
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()
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 = 0.9 * (segment_max_line - segment_min_line)
jump_height_list.append(jump_height)
jump_lenght = utils.find_jump_length(segment_data, segment_min_line, segment_max_line)
jump_length_list.append(jump_lenght)
cen_ind = utils.intersection_segment(flat_segment, segment_median) #finds all interseprions with median
#cen_ind = utils.find_ind_median(segment_median, flat_segment)
jump_center = cen_ind[0]
segment_cent_index = jump_center - 5 + segment['from']
self.ijumps.append(segment_cent_index)
labeled_drop = data[segment_cent_index - WINDOW_SIZE : segment_cent_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'] = min(confidences)
else:
self.state['confidence'] = 1.5
if len(convolve_list) > 0:
self.state['convolve_max'] = max(convolve_list)
else:
self.state['convolve_max'] = WINDOW_SIZE
if len(jump_height_list) > 0:
self.state['JUMP_HEIGHT'] = min(jump_height_list)
else:
self.state['JUMP_HEIGHT'] = 1
if len(jump_length_list) > 0:
self.state['JUMP_LENGTH'] = max(jump_length_list)
else:
self.state['JUMP_LENGTH'] = 1
def predict(self, dataframe: pd.DataFrame, cache: dict) -> dict:
data = dataframe['value']
result = self.__predict(data)
result.sort()
if len(self.segments) > 0:
result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
return result
def __predict(self, data):
#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]
possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1)
return [(x - 1, x + 1) for x in self.__filter_prediction(possible_jumps, data)]
def __filter_prediction(self, segments, data):
delete_list = []
variance_error = int(0.004 * len(data))
if variance_error > 50:
variance_error = 50
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.ijumps) == 0 :
segments = []
return segments
pattern_data = data[self.ijumps[0] - WINDOW_SIZE : self.ijumps[0] + WINDOW_SIZE]
for segment in segments:
if segment > WINDOW_SIZE and segment < (len(data) - WINDOW_SIZE):
convol_data = 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)
for ijump in self.ijumps:
segments.append(ijump)
return segments