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