IRC-SPHERE/HyperStream

View on GitHub
plugins/data_generators/tools/normalvariate/2017-06-20_v1.0.0.py

Summary

Maintainability
C
1 day
Test Coverage
# The MIT License (MIT)
# Copyright (c) 2014-2017 University of Bristol
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
# OR OTHER DEALINGS IN THE SOFTWARE.

from hyperstream import Tool, StreamInstance, ToolExecutionError
from hyperstream.utils import check_input_stream_count

import random


class Normalvariate(Tool):
    """
    Normal distribution.  mu is the mean, and sigma is the standard deviation.
    
    Optionally initialize internal state of the random number generator using seed.
    """
    def __init__(self, mu=0.0, sigma=1.0, seed=None):
        super(Normalvariate, self).__init__(mu=mu, sigma=sigma, 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.normalvariate(mu=self.mu, sigma=self.sigma))