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# $Id$ |
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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 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,debug=False): |
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""" |
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""" |
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super(Sequencer,self).__init__(debug=debug) |
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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,debug=False): |
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ParameterSet.__init__(self,debug=debug) |
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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, debug=False): |
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ParameterSet.__init__(self, debug=debug) |
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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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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,debug=False): |
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ParameterSet.__init__(self,debug=debug) |
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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 LinearCombination(Model): |
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""" |
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Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
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|
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@ivar f0: numerical object or None, default=None (in) |
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@ivar v0: numerical object or None, default=None (in) |
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@ivar f1: numerical object or None, default=None (in) |
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@ivar v1: numerical object or None, default=None (in) |
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@ivar f2: numerical object or None, default=None (in) |
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@ivar v2: numerical object or None, default=None (in) |
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@ivar f3: numerical object or None, default=None (in) |
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@ivar v3: numerical object or None, default=None (in) |
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@ivar f4: numerical object or None, default=None (in) |
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@ivar v4: numerical object or None, default=None (in) |
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""" |
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def __init__(self,debug=False): |
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Model.__init__(self,debug=debug) |
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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) |
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|
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def out(self): |
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""" |
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returns f0*v0+f1*v1+f2*v2+f3*v3+f4*v4. |
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Link against this method to get the output of this model. |
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""" |
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if not self.f0 == None and not self.v0 == None: |
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fv0 = self.f0*self.v0 |
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else: |
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fv0 = None |
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|
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if not self.f1 == None and not self.v1 == None: |
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fv1 = self.f1*self.v1 |
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else: |
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fv1 = None |
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|
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if not self.f2 == None and not self.v2 == None: |
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fv2 = f2*v2 |
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else: |
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fv2 = None |
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|
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if not self.f3 == None and not self.v3 == None: |
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fv3 = self.f3*self.v3 |
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else: |
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fv3 = None |
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|
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if not self.f4 == None and not self.v4 == None: |
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fv4 = self.f4*self.v4 |
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else: |
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fv4 = None |
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|
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if fv0 == None: |
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out = 0. |
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else: |
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out = fv0 |
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if not fv1 == None: |
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out += fv1 |
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if not fv2 == None: |
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out += fv2 |
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if not fv3 == None: |
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out += fv3 |
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return out |
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|
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# vim: expandtab shiftwidth=4: |
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