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354af7ea0a
Nothing worrying, but good to have checked this regardless and important to have a green pipeline. Fix #175
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import datetime
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import math
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from bugsink.utils import nc_rnd
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# a way to generate some bursty streams of points-in-time.
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# I'm sure there's a 100 things wrong with this, but at least it's
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#
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# * not simply distributed at random
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# * has some form of periodic pattern in it as real data surely has
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# * has bursts (errors come in bursts!)
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#
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# this will give us at least some base to test in somewhat natural settings.
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def generate_bursty_data(nr_of_waves=1, base_amplitude=1, expected_nr_of_bursts=1, burst_amplitude=5, num_buckets=1000):
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"""returns `num_buckets` histogram-like buckets"""
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burst_prob = expected_nr_of_bursts / num_buckets
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period = num_buckets / nr_of_waves
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buckets = [0] * num_buckets
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for i in range(num_buckets):
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# We pick math.sin as an arbitrary periodic pattern. Normalize for period and >0
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periodic_pattern = (1 + math.sin(i / period * 2 * math.pi)) / 2
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# Introduce burst with probability 'burst_prob'
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if nc_rnd.random() < burst_prob:
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burst = abs(nc_rnd.gauss(0, burst_amplitude))
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buckets[i] = periodic_pattern + burst
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else:
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buckets[i] = periodic_pattern
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return buckets
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def buckets_to_points_in_time(buckets, begin, end, total_points):
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"""given:
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* histogram-like list of 'buckets', where each bucket is a float that is a relative business of that period
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* a begin and an end (both datetime)
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* a total amount of points
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generates a list of points of length `total_points` that conforms to the distribution denoted by the buckets, and
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where the points-in-time are distributed at random within the buckets.
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"""
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total_weight = sum(buckets)
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time_range_size = end - begin
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bucket_size = time_range_size.total_seconds() / len(buckets)
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points = []
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rounding_difference = 0
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for i, bucket_weight in enumerate(buckets):
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bucket_points = (bucket_weight / total_weight) * total_points + rounding_difference
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rounding_difference = bucket_points - round(bucket_points)
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bucket_points = round(bucket_points)
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for j in range(bucket_points):
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points.append(begin + datetime.timedelta(seconds=bucket_size * (i + nc_rnd.uniform(0, 1))))
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return sorted(points)
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