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.

142 lines
5.7 KiB

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 = 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: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
if type(cache) is AnalyticUnitCache:
self.state = cache
self.segments = segments
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']))
segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to']))
segment_data = data.loc[segment_from_index : segment_to_index + 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()
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 = 0.9 * (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, 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_index
self.ijumps.append(segment_cent_index)
6 years ago
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'] = 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'] = 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
return self.state
def do_predict(self, dataframe: pd.DataFrame):
data = dataframe['value']
possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1)
filtered = self.__filter_prediction(possible_jumps, data)
return [(dataframe['timestamp'][x - 1].value, dataframe['timestamp'][x + 1].value) for x in filtered]
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