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# |
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# $Id$ |
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# |
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####################################################### |
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# |
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# Copyright 2003-2007 by ACceSS MNRF |
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# Copyright 2007 by University of Queensland |
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# |
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# http://esscc.uq.edu.au |
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# Primary Business: Queensland, Australia |
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# Licensed under the Open Software License version 3.0 |
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# http://www.opensource.org/licenses/osl-3.0.php |
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# |
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####################################################### |
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# |
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|
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__copyright__=""" Copyright (c) 2006 by ACcESS MNRF |
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http://www.access.edu.au |
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Primary Business: Queensland, Australia""" |
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__license__="""Licensed under the Open Software License version 3.0 |
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http://www.opensource.org/licenses/osl-3.0.php""" |
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|
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from esys.escript import * |
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from esys.escript.modelframe import Model,ParameterSet |
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from esys.escript.linearPDEs import LinearPDE |
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from math import log |
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|
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class Sequencer(Model): |
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""" |
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Runs through time until t_end is reached. |
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|
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@ivar t_end: model is terminated when t_end is passed, default 1 (in). |
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@type t_end: C{float} |
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@ivar dt_max: maximum time step size, default L{Model.UNDEF_DT} (in) |
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@type dt_max: C{float} |
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@ivar t: current time stamp (in/out). By default it is initialized with zero. |
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@type t: C{float} |
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|
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""" |
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def __init__(self,**kwargs): |
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""" |
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""" |
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super(Sequencer,self).__init__(**kwargs) |
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self.declareParameter(t=0., |
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t_end=1., |
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dt_max=Model.UNDEF_DT) |
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|
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def doInitialization(self): |
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""" |
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initialize time integration |
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""" |
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self.__t_old = self.t |
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|
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def doStepPreprocessing(self, dt): |
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self.t = self.__t_old+dt |
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|
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def doStepPostprocessing(self, dt): |
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self.__t_old = self.t |
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|
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def finalize(self): |
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""" |
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returns true when L{t} has reached L{t_end} |
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""" |
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return self.t >= self.t_end |
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|
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def getSafeTimeStepSize(self, dt): |
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""" |
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returns L{dt_max} |
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""" |
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return self.dt_max |
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|
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class GaussianProfile(ParameterSet): |
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""" |
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Generates a Gaussian profile at center x_c, width width and height A |
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over a domain |
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|
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@ivar domain: domain |
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@ivar x_c: center of the Gaussian profile (default [0.,0.,0.]) |
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@ivar A: (in) height of the profile. A maybe a vector. (default 1.) |
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@ivar width: (in) width of the profile (default 0.1) |
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@ivar r: (in) radius of the circle (default = 0) |
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|
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In the case that the spatial dimension is two, The third component of |
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x_c is dropped. |
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""" |
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def __init__(self,**kwargs): |
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super(GaussianProfile, self).__init__(**kwargs) |
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self.declareParameter(domain=None, |
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x_c=numarray.zeros([3]), |
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A=1., |
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width=0.1, |
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r=0) |
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|
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def out(self): |
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""" |
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Generate the Gaussian profile |
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|
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Link against this method to get the output of this model. |
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""" |
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x = self.domain.getX() |
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dim = self.domain.getDim() |
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l = length(x-self.x_c[:dim]) |
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m = whereNegative(l-self.r) |
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|
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return (m+(1.-m)*exp(-log(2.)*(l/self.width)**2))*self.A |
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|
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class InterpolateOverBox(ParameterSet): |
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""" |
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Returns values at each time. The values are defined through given values |
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at time node. For two dimensional domains back values are ignored. |
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|
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@ivar domain: domain |
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@ivar value_left_bottom_front: (in) value at left,bottom,front corner |
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@ivar value_right_bottom_front: (in) value at right, bottom, front corner |
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@ivar value_left_top_front: (in) value at left,top,front corner |
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@ivar value_right_top_front: (in) value at right,top,front corner |
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@ivar value_left_bottom_back: (in) value at left,bottom,back corner |
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@ivar value_right_bottom_back: (in) value at right,bottom,back corner |
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@ivar value_left_top_back: (in) value at left,top,back corner |
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@ivar value_right_top_back: (in) value at right,top,back corner |
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""" |
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|
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def __init__(self, **kwargs): |
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super(InterpolateOverBox, self).__init__(self) |
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self.declareParameter(domain=None, |
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value_left_bottom_front=0., |
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value_right_bottom_front=0., |
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value_left_top_front=0., |
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value_right_top_front=0., |
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value_left_bottom_back=0., |
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value_right_bottom_back=0., |
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value_left_top_back=0., |
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value_right_top_back=0.) |
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|
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|
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def out(self): |
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""" |
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values at domain locations by bilinear interpolation of the given values. |
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|
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Link against this method to get the output of this model. |
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""" |
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x = self.domain.getX() |
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if self.domain.getDim() == 2: |
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x0,x1=x[0],x[1] |
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left_bottom_front0,right_top_back0=inf(x0),sup(x0) |
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left_bottom_front1,right_top_back1=inf(x1),sup(x1) |
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f_right = (x0 - left_bottom_front0)/(right_top_back0 -left_bottom_front0) |
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f_left = 1. - f_right |
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f_top = (x1 - left_bottom_front1)/(right_top_back1 - left_bottom_front1) |
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f_bottom = 1. - f_top |
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out = f_left * f_bottom * self.value_left_bottom_front \ |
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+ f_right * f_bottom * self.value_right_bottom_front \ |
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+ f_left * f_top * self.value_left_top_front \ |
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+ f_right * f_top * self.value_right_top_front |
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else: |
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x0,x1,x2=x[0],x[1],x[2] |
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left_bottom_front0,right_top_back0=inf(x0),sup(x0) |
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left_bottom_front1,right_top_back1=inf(x1),sup(x1) |
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left_bottom_front2,right_top_back2=inf(x2),sup(x2) |
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f_right = (x0 - left_bottom_front0)/(right_top_back0 - left_bottom_front0) |
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f_left = 1. - f_right |
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f_top = (x1 - left_bottom_front1)/(right_top_back1 - left_bottom_front1) |
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f_bottom = 1. - f_top |
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f_back = (x2 - left_bottom_front1)/(right_top_back2 - left_bottom_front2) |
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f_front = 1. - f_back |
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out = f_left * f_bottom * f_front * self.value_left_bottom_front\ |
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+ f_right * f_bottom * f_front * self.value_right_bottom_front\ |
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+ f_left * f_top * f_front * self.value_left_top_front\ |
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+ f_right * f_top * f_front * self.value_right_top_front\ |
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+ f_left * f_bottom * f_back * self.value_left_bottom_back\ |
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+ f_right * f_bottom * f_back * self.value_right_bottom_back\ |
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+ f_left * f_top * f_back * self.value_left_top_back\ |
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+ f_right * f_top * f_back * self.value_right_top_back |
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return out |
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|
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|
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class InterpolatedTimeProfile(ParameterSet): |
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""" |
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|
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Returns values at each time. The values are defined through given |
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values at time node. |
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|
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value[i] defines the value at time nodes[i]. Between nodes linear |
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interpolation is used. |
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|
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For time t<nodes[0], value[0] is used and for t>nodes[l], values[l] |
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is used where l=len(nodes)-1. |
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|
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@ivar t: (in) current time |
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@ivar node: (in) list of time nodes |
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@ivar values: (in) list of values at time nodes |
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""" |
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|
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def __init__(self,**kwargs): |
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super( InterpolatedTimeProfile, self).__init__(**kwargs) |
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self.declareParameter(t=0., \ |
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nodes=[0.,1.],\ |
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values=[1.,1.]) |
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def out(self): |
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""" |
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current value |
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|
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Link against this method to get the output of this model. |
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""" |
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l = len(self.nodes) - 1 |
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t = self.t |
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if t <= self.nodes[0]: |
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return self.values[0] |
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else: |
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for i in range(1,l): |
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if t < self.nodes[i]: |
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m = (self.values[i-1] - self.values[i])/\ |
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(self.nodes[i-1] - self.nodes[i]) |
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return m*(t-self.nodes[i-1]) + self.values[i-1] |
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return self.values[l] |
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|
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class ScalarDistributionFromTags(ParameterSet): |
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""" |
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creates a scalar distribution on a domain from tags, If tag_map is given |
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the tags can be given a names and tag_map is used to map it into domain tags. |
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|
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@ivar domain: domain |
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@type domain: L{esys.escript.Domain} |
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@ivar default: default value |
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@ivar tag0: tag 0 |
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@type tag0: C{int} |
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@ivar value0: value for tag 0 |
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@type value0: C{float} |
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@ivar tag1: tag 1 |
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@type tag1: C{int} |
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@ivar value1: value for tag 1 |
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@type value1: C{float} |
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@ivar tag2: tag 2 |
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@type tag2: C{int} |
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@ivar value2: value for tag 2 |
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@type value2: C{float} |
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@ivar tag3: tag 3 |
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@type tag3: C{int} |
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@ivar value3: value for tag 3 |
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@type value3: C{float} |
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@ivar tag4: tag 4 |
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@type tag4: C{int} |
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@ivar value4: value for tag 4 |
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@type value4: C{float} |
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@ivar tag5: tag 5 |
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@type tag5: C{int} |
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@ivar value5: value for tag 5 |
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@type value5: C{float} |
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@ivar tag6: tag 6 |
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@type tag6: C{int} |
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@ivar value6: value for tag 6 |
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@type value6: C{float} |
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@ivar tag7: tag 7 |
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@type tag7: C{int} |
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@ivar value7: value for tag 7 |
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@type value7: C{float} |
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@ivar tag8: tag 8 |
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@type tag8: C{int} |
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@ivar value8: value for tag 8 |
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@type value8: C{float} |
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@ivar tag9: tag 9 |
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@type tag9: C{int} |
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@ivar value9: value for tag 9 |
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@type value9: C{float} |
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""" |
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def __init__(self,**kwargs): |
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super(ScalarDistributionFromTags, self).__init__(**kwargs) |
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self.declareParameter(domain=None, |
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default=0., |
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tag0=None, |
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value0=0., |
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tag1=None, |
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value1=0., |
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tag2=None, |
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value2=0., |
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tag3=None, |
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value3=0., |
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tag4=None, |
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value4=0., |
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tag5=None, |
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value5=0., |
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tag6=None, |
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value6=0., |
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tag7=None, |
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value7=0., |
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tag8=None, |
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value8=0., |
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tag9=None, |
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value9=0.) |
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|
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|
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def out(self): |
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""" |
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returns a L{esys.escript.Data} object |
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Link against this method to get the output of this model. |
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""" |
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d=Scalar(self.default,Function(self.domain)) |
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if not self.tag0 == None: d.setTaggedValue(self.tag0,self.value0) |
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if not self.tag1 == None: d.setTaggedValue(self.tag1,self.value1) |
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if not self.tag2 == None: d.setTaggedValue(self.tag2,self.value2) |
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if not self.tag3 == None: d.setTaggedValue(self.tag3,self.value3) |
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if not self.tag4 == None: d.setTaggedValue(self.tag4,self.value4) |
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if not self.tag5 == None: d.setTaggedValue(self.tag5,self.value5) |
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if not self.tag6 == None: d.setTaggedValue(self.tag6,self.value6) |
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if not self.tag7 == None: d.setTaggedValue(self.tag7,self.value7) |
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if not self.tag8 == None: d.setTaggedValue(self.tag8,self.value8) |
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if not self.tag9 == None: d.setTaggedValue(self.tag9,self.value9) |
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return d |
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|
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class SmoothScalarDistributionFromTags(ParameterSet): |
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""" |
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creates a smooth scalar distribution on a domain from region tags |
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|
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@ivar domain: domain |
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@type domain: L{esys.escript.Domain} |
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@ivar default: default value |
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@ivar tag0: tag 0 |
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@type tag0: C{int} |
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@ivar value0: value for tag 0 |
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@type value0: C{float} |
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@ivar tag1: tag 1 |
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@type tag1: C{int} |
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@ivar value1: value for tag 1 |
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@type value1: C{float} |
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@ivar tag2: tag 2 |
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@type tag2: C{int} |
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@ivar value2: value for tag 2 |
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@type value2: C{float} |
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@ivar tag3: tag 3 |
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@type tag3: C{int} |
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@ivar value3: value for tag 3 |
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@type value3: C{float} |
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@ivar tag4: tag 4 |
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@type tag4: C{int} |
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@ivar value4: value for tag 4 |
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@type value4: C{float} |
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@ivar tag5: tag 5 |
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@type tag5: C{int} |
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@ivar value5: value for tag 5 |
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@type value5: C{float} |
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@ivar tag6: tag 6 |
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@type tag6: C{int} |
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@ivar value6: value for tag 6 |
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@type value6: C{float} |
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@ivar tag7: tag 7 |
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@type tag7: C{int} |
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@ivar value7: value for tag 7 |
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@type value7: C{float} |
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@ivar tag8: tag 8 |
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@type tag8: C{int} |
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@ivar value8: value for tag 8 |
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@type value8: C{float} |
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@ivar tag9: tag 9 |
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@type tag9: C{int} |
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@ivar value9: value for tag 9 |
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@type value9: C{float} |
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""" |
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def __init__(self,**kwargs): |
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super(SmoothScalarDistributionFromTags, self).__init__(**kwargs) |
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self.declareParameter(domain=None, |
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default=0., |
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tag0=None, |
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value0=0., |
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tag1=None, |
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value1=0., |
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tag2=None, |
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value2=0., |
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tag3=None, |
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value3=0., |
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tag4=None, |
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value4=0., |
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tag5=None, |
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value5=0., |
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tag6=None, |
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value6=0., |
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tag7=None, |
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value7=0., |
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tag8=None, |
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value8=0., |
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tag9=None, |
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value9=0.) |
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|
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|
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def __update(self,tag,tag_value,value): |
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if self.__pde==None: |
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self.__pde=LinearPDE(self.domain,numSolutions=1) |
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mask=Scalar(0.,Function(self.domain)) |
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mask.setTaggedValue(tag,1.) |
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self.__pde.setValue(Y=mask) |
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mask=wherePositive(abs(self.__pde.getRightHandSide())) |
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value*=(1.-mask) |
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value+=tag_value*mask |
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return value |
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|
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def out(self): |
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""" |
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returns a L{esys.escript.Data} object |
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Link against this method to get the output of this model. |
| 399 |
""" |
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d=Scalar(self.default,Solution(self.domain)) |
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self.__pde=None |
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if not self.tag0 == None: d=self.__update(self.tag0,self.value0,d) |
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if not self.tag1 == None: d=self.__update(self.tag1,self.value1,d) |
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if not self.tag2 == None: d=self.__update(self.tag2,self.value2,d) |
| 405 |
if not self.tag3 == None: d=self.__update(self.tag3,self.value3,d) |
| 406 |
if not self.tag4 == None: d=self.__update(self.tag4,self.value4,d) |
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if not self.tag5 == None: d=self.__update(self.tag5,self.value5,d) |
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if not self.tag6 == None: d=self.__update(self.tag6,self.value6,d) |
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if not self.tag7 == None: d=self.__update(self.tag7,self.value7,d) |
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if not self.tag8 == None: d=self.__update(self.tag8,self.value8,d) |
| 411 |
if not self.tag9 == None: d=self.__update(self.tag9,self.value9,d) |
| 412 |
return d |
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|
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class LinearCombination(ParameterSet): |
| 415 |
""" |
| 416 |
Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
| 417 |
|
| 418 |
@ivar f0: numerical object or None, default=None (in) |
| 419 |
@ivar v0: numerical object or None, default=None (in) |
| 420 |
@ivar f1: numerical object or None, default=None (in) |
| 421 |
@ivar v1: numerical object or None, default=None (in) |
| 422 |
@ivar f2: numerical object or None, default=None (in) |
| 423 |
@ivar v2: numerical object or None, default=None (in) |
| 424 |
@ivar f3: numerical object or None, default=None (in) |
| 425 |
@ivar v3: numerical object or None, default=None (in) |
| 426 |
@ivar f4: numerical object or None, default=None (in) |
| 427 |
@ivar v4: numerical object or None, default=None (in) |
| 428 |
""" |
| 429 |
def __init__(self,**kwargs): |
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super(LinearCombination, self).__init__(**kwargs) |
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self.declareParameter(f0=None, \ |
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v0=None, \ |
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f1=None, \ |
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v1=None, \ |
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f2=None, \ |
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v2=None, \ |
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f3=None, \ |
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v3=None, \ |
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f4=None, \ |
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v4=None) |
| 441 |
|
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def out(self): |
| 443 |
""" |
| 444 |
returns f0*v0+f1*v1+f2*v2+f3*v3+f4*v4. |
| 445 |
Link against this method to get the output of this model. |
| 446 |
""" |
| 447 |
if not self.f0 == None and not self.v0 == None: |
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fv0 = self.f0*self.v0 |
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else: |
| 450 |
fv0 = None |
| 451 |
|
| 452 |
if not self.f1 == None and not self.v1 == None: |
| 453 |
fv1 = self.f1*self.v1 |
| 454 |
else: |
| 455 |
fv1 = None |
| 456 |
|
| 457 |
if not self.f2 == None and not self.v2 == None: |
| 458 |
fv2 = f2*v2 |
| 459 |
else: |
| 460 |
fv2 = None |
| 461 |
|
| 462 |
if not self.f3 == None and not self.v3 == None: |
| 463 |
fv3 = self.f3*self.v3 |
| 464 |
else: |
| 465 |
fv3 = None |
| 466 |
|
| 467 |
if not self.f4 == None and not self.v4 == None: |
| 468 |
fv4 = self.f4*self.v4 |
| 469 |
else: |
| 470 |
fv4 = None |
| 471 |
|
| 472 |
if fv0 == None: |
| 473 |
out = 0. |
| 474 |
else: |
| 475 |
out = fv0 |
| 476 |
if not fv1 == None: |
| 477 |
out += fv1 |
| 478 |
if not fv2 == None: |
| 479 |
out += fv2 |
| 480 |
if not fv3 == None: |
| 481 |
out += fv3 |
| 482 |
return out |
| 483 |
|
| 484 |
class MergeConstraints(ParameterSet): |
| 485 |
""" |
| 486 |
Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
| 487 |
""" |
| 488 |
def __init__(self,**kwargs): |
| 489 |
super(MergeConstraints, self).__init__(**kwargs) |
| 490 |
self.declareParameter(location_of_constraint0=None, \ |
| 491 |
value_of_constraint0=None, \ |
| 492 |
location_of_constraint1=None, \ |
| 493 |
value_of_constraint1=None, \ |
| 494 |
location_of_constraint2=None, \ |
| 495 |
value_of_constraint2=None, \ |
| 496 |
location_of_constraint3=None, \ |
| 497 |
value_of_constraint3=None, \ |
| 498 |
location_of_constraint4=None, \ |
| 499 |
value_of_constraint4=None) |
| 500 |
def location_of_constraint(self): |
| 501 |
""" |
| 502 |
return the values used to constrain a solution |
| 503 |
|
| 504 |
@return: the mask marking the locations of the constraints |
| 505 |
@rtype: L{escript.Scalar} |
| 506 |
""" |
| 507 |
out_loc=0 |
| 508 |
if not self.location_of_constraint0 == None: |
| 509 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint0)) |
| 510 |
if not self.location_of_constraint1 == None: |
| 511 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint1)) |
| 512 |
if not self.location_of_constraint2 == None: |
| 513 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint2)) |
| 514 |
if not self.location_of_constraint3 == None: |
| 515 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint3)) |
| 516 |
return out_loc |
| 517 |
|
| 518 |
def value_of_constraint(self): |
| 519 |
""" |
| 520 |
return the values used to constrain a solution |
| 521 |
|
| 522 |
@return: values to be used at the locations of the constraints. If |
| 523 |
L{value} is not given C{None} is rerturned. |
| 524 |
@rtype: L{escript.Scalar} |
| 525 |
""" |
| 526 |
out_loc=0 |
| 527 |
out=0 |
| 528 |
if not self.location_of_constraint0 == None: |
| 529 |
tmp=wherePositive(self.location_of_constraint0) |
| 530 |
out=out*(1.-tmp)+self.value_of_constraint0*tmp |
| 531 |
out_loc=wherePositive(out_loc+tmp) |
| 532 |
if not self.location_of_constraint1 == None: |
| 533 |
tmp=wherePositive(self.location_of_constraint1) |
| 534 |
out=out*(1.-tmp)+self.value_of_constraint1*tmp |
| 535 |
out_loc=wherePositive(out_loc+tmp) |
| 536 |
if not self.location_of_constraint2 == None: |
| 537 |
tmp=wherePositive(self.location_of_constraint2) |
| 538 |
out=out*(1.-tmp)+self.value_of_constraint2*tmp |
| 539 |
out_loc=wherePositive(out_loc+tmp) |
| 540 |
if not self.location_of_constraint3 == None: |
| 541 |
tmp=wherePositive(self.location_of_constraint3) |
| 542 |
out=out*(1.-tmp)+self.value_of_constraint3*tmp |
| 543 |
out_loc=wherePositive(out_loc+tmp) |
| 544 |
return out |
| 545 |
# vim: expandtab shiftwidth=4: |
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