|
|
|
import numpy as np
|
|
|
|
import pandas as pd
|
|
|
|
import scipy.signal
|
|
|
|
from scipy.fftpack import fft
|
|
|
|
from scipy.signal import argrelextrema
|
|
|
|
from scipy.stats import gaussian_kde
|
|
|
|
from scipy.stats.stats import pearsonr
|
|
|
|
from typing import Union
|
|
|
|
import utils
|
|
|
|
|
|
|
|
SHIFT_FACTOR = 0.05
|
|
|
|
CONFIDENCE_FACTOR = 0.2
|
|
|
|
SMOOTHING_FACTOR = 5
|
|
|
|
|
|
|
|
def exponential_smoothing(series, alpha):
|
|
|
|
result = [series[0]]
|
|
|
|
if np.isnan(result):
|
|
|
|
result = [0]
|
|
|
|
for n in range(1, len(series)):
|
|
|
|
if np.isnan(series[n]):
|
|
|
|
series[n] = 0
|
|
|
|
result.append(alpha * series[n] + (1 - alpha) * result[n - 1])
|
|
|
|
return result
|
|
|
|
|
|
|
|
def anomalies_to_timestamp(anomalies):
|
|
|
|
for anomaly in anomalies:
|
|
|
|
anomaly['from'] = int(anomaly['from'].timestamp() * 1000)
|
|
|
|
anomaly['to'] = int(anomaly['to'].timestamp() * 1000)
|
|
|
|
return anomalies
|
|
|
|
|
|
|
|
def segments_box(segments):
|
|
|
|
max_time = 0
|
|
|
|
min_time = float("inf")
|
|
|
|
for segment in segments:
|
|
|
|
min_time = min(min_time, segment['from'])
|
|
|
|
max_time = max(max_time, segment['to'])
|
|
|
|
min_time = pd.to_datetime(min_time, unit='ms')
|
|
|
|
max_time = pd.to_datetime(max_time, unit='ms')
|
|
|
|
return min_time, max_time
|
|
|
|
|
|
|
|
def find_pattern(data: pd.Series, height: float, length: int, pattern_type: str) -> list:
|
|
|
|
pattern_list = []
|
|
|
|
right_bound = len(data) - length - 1
|
|
|
|
for i in range(right_bound):
|
|
|
|
for x in range(1, length):
|
|
|
|
if pattern_type == 'jump':
|
|
|
|
if(data[i + x] > data[i] + height):
|
|
|
|
pattern_list.append(i)
|
|
|
|
elif pattern_type == 'drop':
|
|
|
|
if(data[i + x] < data[i] - height):
|
|
|
|
pattern_list.append(i)
|
|
|
|
return pattern_list
|
|
|
|
|
|
|
|
def find_jump(data, height, lenght):
|
|
|
|
j_list = []
|
|
|
|
for i in range(len(data)-lenght-1):
|
|
|
|
for x in range(1, lenght):
|
|
|
|
if(data[i + x] > data[i] + height):
|
|
|
|
j_list.append(i)
|
|
|
|
return(j_list)
|
|
|
|
|
|
|
|
def find_drop(data, height, length):
|
|
|
|
d_list = []
|
|
|
|
for i in range(len(data)-length-1):
|
|
|
|
for x in range(1, length):
|
|
|
|
if(data[i + x] < data[i] - height):
|
|
|
|
d_list.append(i)
|
|
|
|
return(d_list)
|
|
|
|
|
|
|
|
def timestamp_to_index(dataframe, timestamp):
|
|
|
|
data = dataframe['timestamp']
|
|
|
|
|
|
|
|
for i in range(len(data)):
|
|
|
|
if data[i] >= timestamp:
|
|
|
|
return i
|
|
|
|
|
|
|
|
def peak_finder(data, size):
|
|
|
|
all_max = []
|
|
|
|
for i in range(size, len(data) - size):
|
|
|
|
if data[i] == max(data[i - size: i + size]) and data[i] > data[i + 1]:
|
|
|
|
all_max.append(i)
|
|
|
|
return all_max
|
|
|
|
|
|
|
|
def ar_mean(numbers):
|
|
|
|
return float(sum(numbers)) / max(len(numbers), 1)
|
|
|
|
|
|
|
|
def get_av_model(patterns_list):
|
|
|
|
if len(patterns_list) == 0:
|
|
|
|
return []
|
|
|
|
|
|
|
|
x = len(patterns_list[0])
|
|
|
|
if len(patterns_list) > 1 and len(patterns_list[1]) != x:
|
|
|
|
raise NameError(
|
|
|
|
'All elements of patterns_list should have same length')
|
|
|
|
|
|
|
|
model_pat = []
|
|
|
|
for i in range(x):
|
|
|
|
av_val = []
|
|
|
|
for j in patterns_list:
|
|
|
|
av_val.append(j.values[i])
|
|
|
|
model_pat.append(ar_mean(av_val))
|
|
|
|
return model_pat
|
|
|
|
|
|
|
|
def close_filtering(pattern_list, win_size):
|
|
|
|
if len(pattern_list) == 0:
|
|
|
|
return []
|
|
|
|
s = [[pattern_list[0]]]
|
|
|
|
k = 0
|
|
|
|
for i in range(1, len(pattern_list)):
|
|
|
|
if pattern_list[i] - win_size <= s[k][-1]:
|
|
|
|
s[k].append(pattern_list[i])
|
|
|
|
else:
|
|
|
|
k += 1
|
|
|
|
s.append([pattern_list[i]])
|
|
|
|
return s
|
|
|
|
|
|
|
|
def best_pattern(pattern_list: list, data: pd.Series, dir: str) -> list:
|
|
|
|
new_pattern_list = []
|
|
|
|
for val in pattern_list:
|
|
|
|
max_val = data[val[0]]
|
|
|
|
min_val = data[val[0]]
|
|
|
|
ind = val[0]
|
|
|
|
for i in val:
|
|
|
|
if dir == 'max':
|
|
|
|
if data[i] > max_val:
|
|
|
|
max_val = data[i]
|
|
|
|
ind = i
|
|
|
|
else:
|
|
|
|
if data[i] < min_val:
|
|
|
|
min_val = data[i]
|
|
|
|
ind = i
|
|
|
|
new_pattern_list.append(ind)
|
|
|
|
return new_pattern_list
|
|
|
|
|
|
|
|
def find_nan_indexes(segment: pd.Series) -> list:
|
|
|
|
nan_list = np.isnan(segment)
|
|
|
|
nan_indexes = []
|
|
|
|
for i, val in enumerate(nan_list):
|
|
|
|
if val:
|
|
|
|
nan_indexes.append(i)
|
|
|
|
return nan_indexes
|
|
|
|
|
|
|
|
def check_nan_values(segment: Union[pd.Series, list]) -> Union[pd.Series, list]:
|
|
|
|
nan_list = utils.find_nan_indexes(segment)
|
|
|
|
if len(nan_list) > 0:
|
|
|
|
segment = utils.nan_to_zero(segment, nan_list)
|
|
|
|
return segment
|
|
|
|
|
|
|
|
def nan_to_zero(segment: Union[pd.Series, list], nan_list: list) -> Union[pd.Series, list]:
|
|
|
|
if type(segment) == pd.Series:
|
|
|
|
for val in nan_list:
|
|
|
|
segment.values[val] = 0
|
|
|
|
else:
|
|
|
|
for val in nan_list:
|
|
|
|
segment[val] = 0
|
|
|
|
return segment
|
|
|
|
|
|
|
|
def find_confidence(segment: pd.Series) -> (float, float):
|
|
|
|
segment = utils.check_nan_values(segment)
|
|
|
|
segment_min = min(segment)
|
|
|
|
segment_max = max(segment)
|
|
|
|
height = segment_max - segment_min
|
|
|
|
if height:
|
|
|
|
return (CONFIDENCE_FACTOR * height, height)
|
|
|
|
else:
|
|
|
|
return (0, 0)
|
|
|
|
|
|
|
|
def find_width(pattern: pd.Series, selector) -> int:
|
|
|
|
pattern = pattern.values
|
|
|
|
center = utils.find_extremum_index(pattern, selector)
|
|
|
|
pattern_left = pattern[:center]
|
|
|
|
pattern_right = pattern[center:]
|
|
|
|
left_extremum_index = utils.find_last_extremum(pattern_left, selector)
|
|
|
|
right_extremum_index = utils.find_extremum_index(pattern_right, not selector)
|
|
|
|
left_width = center - left_extremum_index
|
|
|
|
right_width = right_extremum_index + 1
|
|
|
|
return right_width + left_width
|
|
|
|
|
|
|
|
def find_last_extremum(segment: np.ndarray, selector: bool) -> int:
|
|
|
|
segment = segment[::-1]
|
|
|
|
first_extremum_ind = find_extremum_index(segment, not selector)
|
|
|
|
last_extremum_ind = len(segment) - first_extremum_ind - 1
|
|
|
|
return last_extremum_ind
|
|
|
|
|
|
|
|
def find_extremum_index(segment: np.ndarray, selector: bool) -> int:
|
|
|
|
if selector:
|
|
|
|
return segment.argmax()
|
|
|
|
else:
|
|
|
|
return segment.argmin()
|
|
|
|
|
|
|
|
def get_interval(data: pd.Series, center: int, window_size: int) -> pd.Series:
|
|
|
|
left_bound = center - window_size
|
|
|
|
right_bound = center + window_size + 1
|
|
|
|
if left_bound < 0:
|
|
|
|
left_bound = 0
|
|
|
|
if right_bound > len(data):
|
|
|
|
right_bound = len(data)
|
|
|
|
return data[left_bound: right_bound]
|
|
|
|
|
|
|
|
def subtract_min_without_nan(segment: pd.Series) -> pd.Series:
|
|
|
|
if len(segment) == 0:
|
|
|
|
return []
|
|
|
|
nan_list = utils.find_nan_indexes(segment)
|
|
|
|
if len(nan_list) > 0:
|
|
|
|
return segment
|
|
|
|
else:
|
|
|
|
segment = segment - min(segment)
|
|
|
|
return segment
|
|
|
|
|
|
|
|
def get_convolve(segments: list, av_model: list, data: pd.Series, window_size: int) -> list:
|
|
|
|
labeled_segment = []
|
|
|
|
convolve_list = []
|
|
|
|
for segment in segments:
|
|
|
|
labeled_segment = utils.get_interval(data, segment, window_size)
|
|
|
|
labeled_segment = utils.subtract_min_without_nan(labeled_segment)
|
|
|
|
labeled_segment = utils.check_nan_values(labeled_segment)
|
|
|
|
auto_convolve = scipy.signal.fftconvolve(labeled_segment, labeled_segment)
|
|
|
|
convolve_segment = scipy.signal.fftconvolve(labeled_segment, av_model)
|
|
|
|
convolve_list.append(max(auto_convolve))
|
|
|
|
convolve_list.append(max(convolve_segment))
|
|
|
|
return convolve_list
|
|
|
|
|
|
|
|
def get_correlation(segments: list, av_model: list, data: pd.Series, window_size: int) -> list:
|
|
|
|
labeled_segment = []
|
|
|
|
correlation_list = []
|
|
|
|
p_value_list = []
|
|
|
|
for segment in segments:
|
|
|
|
labeled_segment = utils.get_interval(data, segment, window_size)
|
|
|
|
labeled_segment = utils.subtract_min_without_nan(labeled_segment)
|
|
|
|
labeled_segment = utils.check_nan_values(labeled_segment)
|
|
|
|
correlation = pearsonr(labeled_segment, av_model)
|
|
|
|
correlation_list.append(correlation[0])
|
|
|
|
p_value_list.append(correlation[1])
|
|
|
|
return correlation_list
|
|
|
|
|
|
|
|
def get_distribution_density(segment: pd.Series) -> float:
|
|
|
|
if len(segment) < 2:
|
|
|
|
return (0, 0, 0)
|
|
|
|
min_jump = min(segment)
|
|
|
|
max_jump = max(segment)
|
|
|
|
pdf = gaussian_kde(segment)
|
|
|
|
x = np.linspace(segment.min() - 1, segment.max() + 1, len(segment))
|
|
|
|
y = pdf(x)
|
|
|
|
ax_list = list(zip(x, y))
|
|
|
|
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]
|
|
|
|
try:
|
|
|
|
min_peak_index = peaks_kde[0]
|
|
|
|
segment_min_line = ax_list[min_peak_index, 0]
|
|
|
|
max_peak_index = peaks_kde[1]
|
|
|
|
segment_max_line = ax_list[max_peak_index, 0]
|
|
|
|
segment_median = ax_list[antipeaks_kde[0], 0]
|
|
|
|
except IndexError:
|
|
|
|
segment_max_line = max_jump * (1 - SHIFT_FACTOR)
|
|
|
|
segment_min_line = min_jump * (1 - SHIFT_FACTOR)
|
|
|
|
segment_median = (max_jump - min_jump) / 2 + min_jump
|
|
|
|
return segment_median, segment_max_line, segment_min_line
|
|
|
|
|
|
|
|
def find_parameters(segment_data: pd.Series, segment_from_index: int, pat_type: str) -> [int, float, int]:
|
|
|
|
segment = segment_data
|
|
|
|
if len(segment_data) > SMOOTHING_FACTOR * 3:
|
|
|
|
flat_segment = segment_data.rolling(window = SMOOTHING_FACTOR).mean()
|
|
|
|
segment = flat_segment.dropna()
|
|
|
|
segment_median, segment_max_line, segment_min_line = utils.get_distribution_density(segment)
|
|
|
|
height = 0.95 * (segment_max_line - segment_min_line)
|
|
|
|
length = utils.find_length(segment_data, segment_min_line, segment_max_line, pat_type)
|
|
|
|
return height, length
|
|
|
|
|
|
|
|
def find_pattern_center(segment_data: pd.Series, segment_from_index: int, pattern_type: str):
|
|
|
|
segment_median = utils.get_distribution_density(segment_data)[0]
|
|
|
|
cen_ind = utils.pattern_intersection(segment_data.tolist(), segment_median, pattern_type)
|
|
|
|
pat_center = cen_ind[0]
|
|
|
|
segment_cent_index = pat_center + segment_from_index
|
|
|
|
return segment_cent_index
|
|
|
|
|
|
|
|
def find_length(segment_data: pd.Series, segment_min_line: float, segment_max_line: float, pat_type: str) -> int:
|
|
|
|
x_abscissa = np.arange(0, len(segment_data))
|
|
|
|
segment_max = max(segment_data)
|
|
|
|
segment_min = min(segment_data)
|
|
|
|
if segment_min_line <= segment_min:
|
|
|
|
segment_min_line = segment_min * 1.05
|
|
|
|
if segment_max_line >= segment_max:
|
|
|
|
segment_max_line = segment_max * 0.95
|
|
|
|
min_line = []
|
|
|
|
max_line = []
|
|
|
|
for i in range(len(segment_data)):
|
|
|
|
min_line.append(segment_min_line)
|
|
|
|
max_line.append(segment_max_line)
|
|
|
|
min_line = np.array(min_line)
|
|
|
|
max_line = np.array(max_line)
|
|
|
|
segment_array = np.array(segment_data.tolist())
|
|
|
|
idmin = np.argwhere(np.diff(np.sign(min_line - segment_array)) != 0).reshape(-1)
|
|
|
|
idmax = np.argwhere(np.diff(np.sign(max_line - segment_array)) != 0).reshape(-1)
|
|
|
|
if len(idmin) > 0 and len(idmax) > 0:
|
|
|
|
if pat_type == 'jump':
|
|
|
|
result_length = idmax[0] - idmin[-1] + 1
|
|
|
|
elif pat_type == 'drop':
|
|
|
|
result_length = idmin[0] - idmax[-1] + 1
|
|
|
|
return result_length if result_length > 0 else 0
|
|
|
|
else:
|
|
|
|
return 0
|
|
|
|
|
|
|
|
def pattern_intersection(segment_data: list, median: float, pattern_type: str) -> list:
|
|
|
|
center_index = []
|
|
|
|
if pattern_type == 'jump':
|
|
|
|
for i in range(1, len(segment_data) - 1):
|
|
|
|
if segment_data[i - 1] < median and segment_data[i + 1] > median:
|
|
|
|
center_index.append(i)
|
|
|
|
elif pattern_type == 'drop':
|
|
|
|
for i in range(1, len(segment_data) - 1):
|
|
|
|
if segment_data[i - 1] > median and segment_data[i + 1] < median:
|
|
|
|
center_index.append(i)
|
|
|
|
delete_index = []
|
|
|
|
for i in range(1, len(center_index)):
|
|
|
|
if center_index[i] == center_index[i - 1] + 1:
|
|
|
|
delete_index.append(i - 1)
|
|
|
|
|
|
|
|
return [x for (idx, x) in enumerate(center_index) if idx not in delete_index]
|
|
|
|
|
|
|
|
def cut_dataframe(data: pd.DataFrame) -> pd.DataFrame:
|
|
|
|
data_min = data['value'].min()
|
|
|
|
if not np.isnan(data_min) and data_min > 0:
|
|
|
|
data['value'] = data['value'] - data_min
|
|
|
|
return data
|
|
|
|
|
|
|
|
def get_min_max(array, default):
|
|
|
|
return float(min(array, default=default)), float(max(array, default=default))
|