plugins/data_generators/tools/weibullvariate/2017-06-20_v1.0.0.py
# The MIT License (MIT)
# Copyright (c) 2014-2017 University of Bristol
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from hyperstream import Tool, StreamInstance, ToolExecutionError
from hyperstream.utils import check_input_stream_count
import random
class Weibullvariate(Tool):
"""
Weibull distribution. alpha is the scale parameter and beta is the shape parameter.
Optionally initialize internal state of the random number generator using seed.
"""
def __init__(self, alpha, beta, seed=None):
super(Weibullvariate, self).__init__(alpha=alpha, beta=beta, seed=seed)
random.seed(self.seed)
@check_input_stream_count(0)
def _execute(self, sources, alignment_stream, interval):
if alignment_stream is None:
raise ToolExecutionError("Alignment stream expected")
for ti, _ in alignment_stream.window(interval, force_calculation=True):
yield StreamInstance(ti, random.weibullvariate(alpha=self.alpha, beta=self.beta))