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.
124 lines
4.4 KiB
124 lines
4.4 KiB
from models import Model |
|
|
|
import scipy.signal |
|
from scipy.fftpack import fft |
|
from scipy.signal import argrelextrema |
|
|
|
import utils |
|
import numpy as np |
|
import pickle |
|
|
|
|
|
class StepModel(Model): |
|
def __init__(self): |
|
super() |
|
self.segments = [] |
|
self.idrops = [] |
|
self.state = { |
|
'confidence': 1.5, |
|
'convolve_max': 570000 |
|
} |
|
|
|
def fit(self, dataframe, segments): |
|
self.segments = segments |
|
#dataframe = dataframe.iloc[::-1] |
|
d_min = min(dataframe['value']) |
|
for i in range(0,len(dataframe['value'])): |
|
dataframe.loc[i, 'value'] = dataframe.loc[i, 'value'] - d_min |
|
data = dataframe['value'] |
|
confidences = [] |
|
convolve_list = [] |
|
for segment in segments: |
|
if segment['labeled']: |
|
segment_from_index = utils.timestamp_to_index(dataframe, segment['from']) |
|
segment_to_index = utils.timestamp_to_index(dataframe, segment['to']) |
|
|
|
segment_data = data[segment_from_index : segment_to_index + 1] |
|
segment_min = min(segment_data) |
|
segment_max = max(segment_data) |
|
confidences.append( 0.4*(segment_max - segment_min)) |
|
flat_segment = segment_data #.rolling(window=5).mean() |
|
segment_min_index = flat_segment.idxmin() - 5 |
|
self.idrops.append(segment_min_index) |
|
labeled_drop = data[segment_min_index - 240 : segment_min_index + 240] |
|
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'] = 570000 |
|
|
|
|
|
async def predict(self, dataframe): |
|
#dataframe = dataframe.iloc[::-1] |
|
d_min = min(dataframe['value']) |
|
for i in range(0,len(dataframe['value'])): |
|
dataframe.loc[i, 'value'] = dataframe.loc[i, 'value'] - d_min |
|
|
|
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() |
|
new_flat_data = [] |
|
for val in all_max_flatten_data: |
|
new_flat_data.append(val) |
|
|
|
all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0] |
|
|
|
extrema_list = [] |
|
for i in utils.exponential_smoothing(data - self.state['confidence'], 0.01): |
|
extrema_list.append(i) |
|
#extrema_list = extrema_list[::-1] |
|
|
|
segments = [] |
|
for i in all_mins: |
|
if new_flat_data[i] < extrema_list[i]: |
|
segments.append(i) #-window_size |
|
|
|
|
|
return [(x - 1, x + 1) for x in self.__filter_prediction(segments, new_flat_data)] |
|
|
|
def __filter_prediction(self, segments, new_flat_data): |
|
delete_list = [] |
|
variance_error = int(0.004 * len(new_flat_data)) |
|
if variance_error > 100: |
|
variance_error = 100 |
|
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 = [] |
|
print(self.idrops[0]) |
|
pattern_data = new_flat_data[self.idrops[0] - 240 : self.idrops[0] + 240] |
|
print(self.state['convolve_max']) |
|
for segment in segments: |
|
if segment > 240: |
|
convol_data = new_flat_data[segment - 240 : segment + 240] |
|
conv = scipy.signal.fftconvolve(pattern_data, convol_data) |
|
if conv[480] > self.state['convolve_max'] * 1.2 or conv[480] < self.state['convolve_max'] * 0.9: |
|
delete_list.append(segment) |
|
print(segment, conv[480], 0) |
|
else: |
|
print(segment, conv[480], 1) |
|
else: |
|
delete_list.append(segment) |
|
for item in delete_list: |
|
segments.remove(item) |
|
|
|
return segments
|
|
|