113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
# osmo_ms_driver: A cumululative distribution function class.
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# Help to start processes over time.
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#
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# Copyright (C) 2018 by Holger Hans Peter Freyther
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as
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# published by the Free Software Foundation, either version 3 of the
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# License, or (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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from datetime import timedelta
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class DistributionFunctionHandler(object):
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"""
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The goal is to start n "mobile" processes. We like to see some
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conflicts (RACH bursts being ignored) but starting n processes
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at the same time is not a realistic model.
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We use the concept of cumulative distribution function here. On
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the x-axis we have time (maybe in steps of 10ms) and on the
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y-axis we have the percentage (from 0.0 to 1.0) of how many
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processes should run at the given time.
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"""
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def __init__(self, step, duration, fun):
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self._step = step
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self._fun = fun
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self._x = 0.0
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self._y = self._fun(self._x)
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self._target = 1.0
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self._duration = duration
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def step_size(self):
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return self._step
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def set_target(self, scale):
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"""
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Scale the percentage to the target value..
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"""
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self._target = scale
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def is_done(self):
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return self._y >= 1.0
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def current_value(self):
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return self._y
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def current_scaled_value(self):
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return self._y * self._target
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def step_once(self):
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self._x = self._x + self._step.total_seconds()
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self._y = self._fun(self._x)
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def duration(self):
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return self._duration
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def immediate(step_size=timedelta(milliseconds=20)):
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"""
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Reaches 100% at the first step.
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"""
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duration = timedelta(seconds=0)
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return DistributionFunctionHandler(step_size, duration, lambda x: 1)
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def linear_with_slope(slope, duration, step_size=timedelta(milliseconds=20)):
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"""
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Use the slope and step size you want
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"""
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return DistributionFunctionHandler(step_size, duration, lambda x: slope*x)
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def linear_with_duration(duration, step_size=timedelta(milliseconds=20)):
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"""
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Linear progression that reaches 100% after duration.total_seconds()
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"""
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slope = 1.0/duration.total_seconds()
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return linear_with_slope(slope, duration, step_size)
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def _in_out(x):
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"""
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Internal in/out function inspired by Qt
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"""
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assert x <= 1.0
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# Needs to be between 0..1 and increase first
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if x < 0.5:
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return (x*x) * 2
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# deaccelerate now. in_out(0.5) == 0.5, in_out(1.0) == 1.0
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x = x * 2 - 1
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return -0.5 * (x*(x-2)- 1)
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def ease_in_out_duration(duration, step_size=timedelta(milliseconds=20)):
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"""
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Example invocation
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"""
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scale = 1.0/duration.total_seconds()
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return DistributionFunctionHandler(step_size, duration,
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lambda x: _in_out(x*scale))
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cdfs = {
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'immediate': lambda x,y: immediate(y),
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'linear': linear_with_duration,
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'ease_in_out': ease_in_out_duration,
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}
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