ms: Create a cumulative distribution function class
We are using the CDF to decide which percentage of the jobs should be running at a given point. The x-axis is time and the y-axis the percentage of how many jobs should be running. There are three functions to do this. The first one is a constant which would result in everything being started right now, one to start them linearly and the last (formula from Qt/3rdparty) to first accelerate and decelerate slowly. Change-Id: I9e3064f4c3c4c7af5d3491f850090516e541f4d3
This commit is contained in:
Holger Hans Peter Freyther
parent
48fce86b3e
commit
38adaa96bf
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Testing the immediate CDF
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Done True
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1 1.0 False
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Testing linear with duration
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Done False
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0.0 0.0 True
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Done False
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0.2 0.2 True
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Done False
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0.4 0.4 True
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Done False
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0.6 0.6 True
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Done False
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0.8 0.8 True
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Done True
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1.0 1.0 True
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Testing linear with duration scaled
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Done False
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0.0 0.0 True
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0.0 0.0 True
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Done False
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0.2 0.2 True
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200 200 True
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Done False
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0.4 0.4 True
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400 400 True
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Done False
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0.6 0.6 True
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600 600 True
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Done False
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0.8 0.8 True
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800 800 True
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Done True
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1.0 1.0 True
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100 100 True
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Testing in_out
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0.5 0.5 True
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0.87 0.87 True
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0.9 0.9 True
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0.95 0.95 True
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1.0 1.0 True
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Testing ease In and Out
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Done False
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0.0 0.0 True
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0.0 0.0 True
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Done False
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5.0 5.0 True
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0.1 0.1 True
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Done False
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10.0 10.0 True
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0.5 0.5 True
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Done False
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15.0 15.0 True
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0.8 0.8 True
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Done True
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20.0 20 True
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1.0 1.0 True
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#!/usr/bin/env python3
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import _prep
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from osmo_ms_driver import cdf
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from datetime import timedelta
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def print_fuzzy_compare(want, expe, len=3):
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want_str = str(want)[0:len]
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expe_str = str(expe)[0:len]
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print(want_str, expe_str, want_str == expe_str)
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def check_steps(a, steps, fun):
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print("Done", a.is_done())
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for step in steps:
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# Verify we can step
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# Compare and step once
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fun(a, step)
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if a.is_done():
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break
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a.step_once()
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print("Done", a.is_done())
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def compare_value(a, step):
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print_fuzzy_compare(a.current_value(), step)
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def compare_scaled_value(a, val):
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(step, scale) = val
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print_fuzzy_compare(a.current_value(), step)
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print_fuzzy_compare(a.current_scaled_value(), scale)
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def compare_x_value(a, val):
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(x, step) = val
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print(a._x, x, x == a._x)
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print_fuzzy_compare(a.current_value(), step)
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def testImmediate():
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print("Testing the immediate CDF")
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a = cdf.immediate()
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print("Done", a.is_done())
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print_fuzzy_compare(a.current_value(), 1.0)
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def testLinearWithDuration():
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print("Testing linear with duration")
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a = cdf.linear_with_duration(timedelta(seconds=10), step_size=timedelta(seconds=2))
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steps = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
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check_steps(a, steps, compare_value)
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print("Testing linear with duration scaled")
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a = cdf.linear_with_duration(timedelta(seconds=10), step_size=timedelta(seconds=2))
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a.set_target(1000)
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steps = [(0.0, 0.0), (0.2, 200), (0.4, 400), (0.6, 600), (0.8, 800), (1.0, 10000)]
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check_steps(a, steps, compare_scaled_value)
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def testInOut():
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print("Testing in_out")
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print_fuzzy_compare(cdf._in_out(0.5), 0.5, 3)
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print_fuzzy_compare(cdf._in_out(0.75), 0.875, 4)
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print_fuzzy_compare(cdf._in_out(0.8), 0.92, 3)
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print_fuzzy_compare(cdf._in_out(0.85), 0.955, 4)
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print_fuzzy_compare(cdf._in_out(1.0), 1.0, 3)
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def testEaseInOutDuration():
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print("Testing ease In and Out")
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a = cdf.ease_in_out_duration(duration=timedelta(seconds=20), step_size=timedelta(seconds=5))
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steps = [(0.0, 0.0), (5.0, 0.125), (10.0, 0.5), (15.0, 0.875), (20, 1.0)]
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check_steps(a, steps, compare_x_value)
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testImmediate()
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testLinearWithDuration()
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testInOut()
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testEaseInOutDuration()
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# osmo_ms_driver: automated cellular network tests
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#
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# Copyright (C) 2018 by sysmocom - s.f.m.c. GmbH
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#
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# Authors: 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 osmo_gsm_tester import __version__
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# vim: expandtab tabstop=4 shiftwidth=4
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@ -0,0 +1,105 @@
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# 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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