def is_conform(self, cdr):
# FIXME: pattern should no check conforming, it's another task
day = day_of_week(cdr.start)
freq = self.get_pattern()[day][time_of_day(cdr.start)//60//60]
current = np.roll(self.current, 1)
current[0] = cdr.start
diffs = np.array([e[0]-e[1] for e in zip(current, current[1:])])
current_freq = (60*60)/moving_average_exponential(diffs, ALPHA_FREQ)
limits = poisson_interval(freq, 1-0.997) # float
if not (current_freq <= max(1.0, limits[1]*ALARM_THRESHOLD)):
print(freq, current_freq, max(1, limits[1]*ALARM_THRESHOLD), )
return current_freq <= max(1.0, limits[1]*ALARM_THRESHOLD)
def maintain(self, cdr):
"""
Maintaining should be continuous
Calls have to be sorted by cdr.start time
"""
time = time_of_day(cdr.start)//(TIME_DISCRETIZATION//APPROX_WINDOW)
day = day_of_week(cdr.start)
if self.last_day_of_week != day and day == 0: # week switched
self.data = np.roll(self.data, 1, axis=0)
self.data[0] = self.extract_week_history()
self.week_history = np.zeros(shape=(7, (24*60*60)//(TIME_DISCRETIZATION//APPROX_WINDOW)))
self.weeks += 1
self.last_day_of_week = day
self.current = np.roll(self.current, 1) # for instantaneous frequency
self.current[0] = cdr.start
# new freq calc
current = np.roll(self.current, 1)
current[0] = cdr.start
diffs = np.array([e[0]-e[1] for e in zip(current, current[1:])])
current_freq = (60*60)/moving_average_exponential(diffs, ALPHA_FREQ)
self.week_history[day, time] = max(self.week_history[day, time], current_freq)
def get_pattern(self):
return moving_average_exponential(self.data, ALPHA_WEEKS)
