hastic-server/analytics/utils/__init__.py

import numpy as np


def is_intersect(target_segment, segments):
    for segment in segments:
        start = max(segment['start'], target_segment[0])
        finish = min(segment['finish'], target_segment[1])
        if start <= finish:
            return True
    return False

def exponential_smoothing(series, alpha):
    result = [series[0]]
    for n in range(1, len(series)):
        result.append(alpha * series[n] + (1 - alpha) * result[n - 1])
    return result

def find_steps(array, threshold):
    """
    Finds local maxima by segmenting array based on positions at which
    the threshold value is crossed. Note that this thresholding is
    applied after the absolute value of the array is taken. Thus,
    the distinction between upward and downward steps is lost. However,
    get_step_sizes can be used to determine directionality after the
    fact.
    Parameters
    ----------
    array : numpy array
        1 dimensional array that represents time series of data points
    threshold : int / float
        Threshold value that defines a step
    Returns
    -------
    steps : list
        List of indices of the detected steps
    """
    steps        = []
    array        = np.abs(array)
    above_points = np.where(array > threshold, 1, 0)
    ap_dif       = np.diff(above_points)
    cross_ups    = np.where(ap_dif == 1)[0]
    cross_dns    = np.where(ap_dif == -1)[0]
    for upi, dni in zip(cross_ups,cross_dns):
        steps.append(np.argmax(array[upi:dni]) + upi)
    return steps

def anomalies_to_timestamp(anomalies):
    for anomaly in anomalies:
        anomaly['start'] = int(anomaly['start'].timestamp() * 1000)
        anomaly['finish'] = int(anomaly['finish'].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['start'])
        max_time = max(max_time, segment['finish'])
    min_time = pd.to_datetime(min_time, unit='ms')
    max_time = pd.to_datetime(max_time, unit='ms')
    return min_time, max_time

def intersection_segment(data, median):
    """
    Finds all intersections between flatten data and median
    """
    cen_ind = []
    for i in range(1, len(data)-1):
        if data[i - 1] < median and data[i + 1] > median:
            cen_ind.append(i)
    del_ind = []
    for i in range(1, len(cen_ind)):
        if cen_ind[i] == cen_ind[i - 1] + 1:
            del_ind.append(i - 1)
 
    return [x for (idx, x) in enumerate(cen_ind) if idx not in del_ind]

def logistic_sigmoid_distribution(self, x1, x2, alpha, height):
    return map(lambda x: logistic_sigmoid(x, alpha, height), range(x1, x2))

def logistic_sigmoid(x, alpha, height):
    return height / (1 + math.exp(-x * alpha))

def find_one_jump(data, x, size, height, err):
    l = []
    for i in range(x + 1, x + size):
        if (data[i] > data[x] and data[x + size] > data[x] + height):
            l.append(data[i])
    if len(l) > size * err:
        return x
    else:
        return 0

def find_all_jumps(data, size, height):
    possible_jump_list = []
    for i in range(len(data - size)):
        x = find_one_jump(data, i, size, height, 0.9)
        if x > 0:
            possible_jump_list.append(x)
    return possible_jump_list

def find_jump_center(cen_ind):
    jump_center = cen_ind[0]
    for i in range(len(cen_ind)):
        x = cen_ind[i]
        cx = scipy.signal.fftconvolve(pat_sigm, flat_data[x - WINDOW_SIZE : x + WINDOW_SIZE])
        c.append(cx[2 * WINDOW_SIZE])
        if i > 0 and cx > c[i - 1]:
            jump_center = x
    return jump_center