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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):
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)
del_ind = del_ind[::-1]
for i in del_ind:
del cen_ind[i]
return cen_ind
def logistic_sigmoid(self, x1, x2, alpha, height):
distribution = []
for i in range(x1, x2):
F = 1 * height / (1 + math.exp(-i * alpha))
distribution.append(F)
return distribution