You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 

132 lines
5.5 KiB

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.state = {
'confidence': 1.5,
'convolve_max': 230,
'JUMP_HEIGHT': 1,
'JUMP_LENGTH': 1,
}
def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
data = dataframe['value']
confidences = []
convolve_list = []
jump_height_list = []
jump_length_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']]
labeled_min = min(labeled_jump)
for value in labeled_jump:
value = value - labeled_min
convolve = scipy.signal.fftconvolve(labeled_jump, labeled_jump)
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'] = 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
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 > 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] - self.state['WINDOW_SIZE'] : self.ijumps[0] + self.state['WINDOW_SIZE']]
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']]
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)
# TODO: implement filtering
#for ijump in self.ijumps:
#segments.append(ijump)
return set(segments)