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# $Id$ |
# $Id$ |
| 2 |
from escript.escript import * |
from esys.escript import * |
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from escript.modelframe import Model |
from esys.escript.modelframe import Model,ParameterSet |
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from math import log |
from math import log |
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class Sequencer(Model): |
class Sequencer(Model): |
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"""@brief runs through time until t_end is reached. |
""" |
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Runs through time until t_end is reached. |
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@param t_end (in) - model is terminate when t_end is passed (default Model.UNDEF_DT) |
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@param dt_max (in) - maximum time step size (default Model.UNDEF_DT) |
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@param t (out) - current time |
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""" |
""" |
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def __init__(self,debug=False): |
def __init__(self,debug=False): |
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Model.__init__(self,debug=debug) |
""" |
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self.declareParameter(t=0., \ |
@param t_end: - model is terminated when t_end is passed |
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t_end=Model.UNDEF_DT, \ |
(exposed in writeXML) |
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@type t_end: float |
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@param dt_max: - maximum time step size |
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@type dt_max: float |
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@param t: - initial time |
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@type t: float |
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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) |
dt_max=Model.UNDEF_DT) |
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def doInitialization(self): |
def doInitialization(self): |
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self.__t_old=self.t |
""" |
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initialize time integration |
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""" |
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self.__t_old = self.t |
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def doStepPreprocessing(self,dt): |
def doStepPreprocessing(self, dt): |
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self.t=self.__t_old+dt |
self.t = self.__t_old+dt |
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def doStepPostprocessing(self,dt): |
def doStepPostprocessing(self, dt): |
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self.__t_old=self.t |
self.__t_old = self.t |
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def finalize(self): |
def finalize(self): |
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"""true when t has reached t_end""" |
""" |
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return self.t>=self.t_end |
true when t has reached t_end |
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""" |
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def getSafeTimeStepSize(self,dt): |
return self.t >= self.t_end |
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"""returns dt_max""" |
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return self.dt_max |
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class GausseanProfile(Model): |
def getSafeTimeStepSize(self, dt): |
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"""@brief generates a gaussean profile at center x_c, width width and height A over a domain |
""" |
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returns dt_max |
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""" |
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return self.dt_max |
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@param domain (in) - domain |
class GaussianProfile(ParameterSet): |
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@param x_c (in) - center of the Gaussean profile (default [0.,0.,0.]) |
""" |
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@param A (in) - height of the profile. A maybe a vector. (default 1.) |
Generates a Gaussian profile at center x_c, width width and height A |
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@param width (in) - width of the profile (default 0.1) |
over a domain |
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@param r (in) - radius of the circle (default = 0) |
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@param out (callable) - profile |
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@ivar domain (in): domain |
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@ivar x_c (in): 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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@ivar out (callable): profile |
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In the case that the spatial dimension is two, The third component of x_c is dropped |
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): |
def __init__(self,debug=False): |
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Model.__init__(self,debug=debug) |
ParameterSet.__init__(self,debug=debug) |
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self.declareParameter(domain=None, |
self.declareParameter(domain=None, |
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x_c=numarray.zeros([3]), |
x_c=numarray.zeros([3]), |
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A=1., |
A=1., |
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r=0) |
r=0) |
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def out(self): |
def out(self): |
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x=self.domain.getX() |
""" |
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dim=self.domain.getDim() |
Generate the Gaussian profile |
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l=length(x-self.x_c[:dim]) |
""" |
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m=(l-self.r).whereNegative() |
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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return (m+(1.-m)*exp(-log(2.)*(l/self.width)**2))*self.A |
return (m+(1.-m)*exp(-log(2.)*(l/self.width)**2))*self.A |
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class InterpolateOverBox(Model): |
class InterpolateOverBox(ParameterSet): |
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""" |
""" |
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@brief returns values at each time. The values are defined through given values at time node. |
Returns values at each time. The values are defined through given values |
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at time node. |
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@param domain (in) - domain |
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@param left_bottom_front (in) - coordinates of left,bottom,front corner of the box |
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@param right_top_back (in) - coordinates of the right, top, back corner of the box |
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@param value_left_bottom_front (in) - value at left,bottom,front corner |
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@param value_right_bottom_front (in) - value at right, bottom, front corner |
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@param value_left_top_front (in) - value at left,top,front corner |
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@param value_right_top_front (in) - value at right,top,front corner |
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@param value_left_bottom_back (in) - value at left,bottom,back corner |
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@param value_right_bottom_back (in) - value at right,bottom,back corner |
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@param value_left_top_back (in) - value at left,top,back corner |
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@param value_right_top_back (in) - value at right,top,back corner |
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@param out (callable) - values at doamin locations by bilinear interpolation. for two dimensional domains back values are ignored. |
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@ivar domain (in): domain |
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@ivar left_bottom_front (in): coordinates of left, bottom, front corner |
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of the box |
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@ivar right_top_back (in): coordinates of the right, top, back corner |
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of the box |
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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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@ivar out (callable): values at domain locations by bilinear |
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interpolation. For two dimensional domains back values are |
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ignored. |
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""" |
""" |
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def __init__(self,debug=False): |
def __init__(self, debug=False): |
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Model.__init__(self,debug=debug) |
ParameterSet.__init__(self, debug=debug) |
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self.declareParameter(domain=None, |
self.declareParameter(domain=None, |
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left_bottom_front=[0.,0.,0.], |
left_bottom_front=[0.,0.,0.], |
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right_top_back=[1.,1.,1.], |
right_top_back=[1.,1.,1.], |
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def out(self): |
def out(self): |
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x=self.domain.getX() |
x = self.domain.getX() |
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if self.domain.getDim()==2: |
if self.domain.getDim() == 2: |
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f_right=(x[0]-self.left_bottom_front[0])/(self.right_top_back[0]-self.left_bottom_front[0]) |
f_right = (x[0] - self.left_bottom_front[0])/\ |
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f_left=1.-f_right |
(self.right_top_back[0] - self.left_bottom_front[0]) |
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f_top=(x[1]-self.left_bottom_front[1])/(self.right_top_back[1]-self.left_bottom_front[1]) |
f_left = 1. - f_right |
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f_bottom=1.-f_top |
f_top = (x[1] - self.left_bottom_front[1])/\ |
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out=self.value_left_bottom_front * f_left * f_bottom \ |
(self.right_top_back[1] - self.left_bottom_front[1]) |
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+self.value_right_bottom_front* f_right * f_bottom \ |
f_bottom = 1. - f_top |
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+self.value_left_top_front * f_left * f_top \ |
out = self.value_left_bottom_front * f_left * f_bottom \ |
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+self.value_right_top_front * f_right * f_top |
+ self.value_right_bottom_front* f_right * f_bottom \ |
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+ self.value_left_top_front * f_left * f_top \ |
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else: |
+ self.value_right_top_front * f_right * f_top |
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f_right=(x[0]-self.left_bottom_front[0])/(self.right_top_back[0]-self.left_bottom_front[0]) |
else: |
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f_left=1.-f_right |
f_right = (x[0] - self.left_bottom_front[0])/\ |
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f_top=(x[1]-self.left_bottom_front[1])/(self.right_top_back[1]-self.left_bottom_front[1]) |
(self.right_top_back[0] - self.left_bottom_front[0]) |
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f_bottom=1.-f_top |
f_left = 1. - f_right |
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f_back=(x[2]-self.left_bottom_front[1])/(self.right_top_back[2]-self.left_bottom_front[2]) |
f_top = (x[1] - self.left_bottom_front[1])/\ |
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f_front=1.-f_back |
(self.right_top_back[1] - self.left_bottom_front[1]) |
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out=self.value_left_bottom_front * f_left * f_bottom * f_front \ |
f_bottom = 1. - f_top |
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+self.value_right_bottom_front* f_right * f_bottom * f_front \ |
f_back = (x[2] - self.left_bottom_front[1])/\ |
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+self.value_left_top_front * f_left * f_top * f_front \ |
(self.right_top_back[2] - self.left_bottom_front[2]) |
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+self.value_right_top_front * f_right * f_top * f_front \ |
f_front = 1. - f_back |
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+self.value_left_bottom_back * f_left * f_bottom * f_back \ |
out = self.value_left_bottom_front * f_left * f_bottom * f_front \ |
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+self.value_right_bottom_back * f_right * f_bottom * f_back \ |
+ self.value_right_bottom_front* f_right * f_bottom * f_front \ |
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+self.value_left_top_back * f_left * f_top * f_back \ |
+ self.value_left_top_front * f_left * f_top * f_front \ |
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+self.value_right_top_back * f_right * f_top * f_back |
+ self.value_right_top_front * f_right * f_top * f_front \ |
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+ self.value_left_bottom_back * f_left * f_bottom * f_back \ |
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+ self.value_right_bottom_back * f_right * f_bottom * f_back \ |
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+ self.value_left_top_back * f_left * f_top * f_back \ |
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+ self.value_right_top_back * f_right * f_top * f_back |
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return out |
return out |
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class InterpolatedTimeProfile(Model): |
class InterpolatedTimeProfile(ParameterSet): |
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"""@brief returns values at each time. The values are defined through given values at time node. |
""" |
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value[i] defines the value at time nodes[i]. Between nodes linear interpolation is used. |
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for time t<nodes[0] value[0] is used and for t>nodes[l] values[l] is used where l=len(nodes)-1. |
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@param t (in) - current time |
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@param node (in) - list of time nodes |
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@param values (in) - list of values at time nodes |
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@param out (callable) - current value |
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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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value[i] defines the value at time nodes[i]. Between nodes linear |
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interpolation is used. |
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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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@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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@ivar out (callable): current value |
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""" |
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def __init__(self,debug=False): |
def __init__(self,debug=False): |
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Model.__init__(self,debug=debug) |
ParameterSet.__init__(self,debug=debug) |
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self.declareParameter(t=0., \ |
self.declareParameter(t=0., \ |
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nodes=[0.,1.],\ |
nodes=[0.,1.],\ |
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values=[1.,1.]) |
values=[1.,1.]) |
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def out(self): |
def out(self): |
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l=len(self.nodes)-1 |
l = len(self.nodes) - 1 |
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t=self.t |
t = self.t |
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if t<=self.nodes[0]: |
if t <= self.nodes[0]: |
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return self.values[0] |
return self.values[0] |
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else: |
else: |
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for i in range(1,l): |
for i in range(1,l): |
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if t<self.nodes[i]: |
if t < self.nodes[i]: |
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m=(self.values[i-1]-self.values[i])/(self.nodes[i-1]-self.nodes[i]) |
m = (self.values[i-1] - self.values[i])/\ |
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return m*(t-self.nodes[i-1])+self.values[i-1] |
(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] |
return self.values[l] |
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class LinearCombination(Model): |
class LinearCombination(Model): |
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"""@brief returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
""" |
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Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
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@param f0 (in) numerical object or None (default: None) |
@ivar f0 (in): numerical object or None (default: None) |
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@param v0 (in) numerical object or None (default: None) |
@ivar v0 (in): numerical object or None (default: None) |
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@param f1 (in) numerical object or None (default: None) |
@ivar f1 (in): numerical object or None (default: None) |
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@param v1 (in) numerical object or None (default: None) |
@ivar v1 (in): numerical object or None (default: None) |
| 200 |
@param f2 (in) numerical object or None (default: None) |
@ivar f2 (in): numerical object or None (default: None) |
| 201 |
@param v2 (in) numerical object or None (default: None) |
@ivar v2 (in): numerical object or None (default: None) |
| 202 |
@param f3 (in) numerical object or None (default: None) |
@ivar f3 (in): numerical object or None (default: None) |
| 203 |
@param v3 (in) numerical object or None (default: None) |
@ivar v3 (in): numerical object or None (default: None) |
| 204 |
@param f4 (in) numerical object or None (default: None) |
@ivar f4 (in): numerical object or None (default: None) |
| 205 |
@param v4 (in) numerical object or None (default: None) |
@ivar v4 (in): numerical object or None (default: None) |
| 206 |
@param out (callable) - current value |
@ivar out (callable): current value |
| 207 |
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""" |
| 208 |
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def __init__(self,debug=False): |
| 209 |
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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, \ |
| 217 |
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v3=None, \ |
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f4=None, \ |
| 219 |
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v4=None) |
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def out(self): |
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if not self.f0 == None and not self.v0 == None: |
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fv0 = self.f0*self.v0 |
| 224 |
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else: |
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fv0 = None |
| 226 |
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""" |
if not self.f1 == None and not self.v1 == None: |
| 228 |
def __init__(self,debug=False): |
fv1 = self.f1*self.v1 |
| 229 |
Model.__init__(self,debug=debug) |
else: |
| 230 |
self.declareParameter(f0=None, \ |
fv1 = 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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def out(self): |
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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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| 232 |
if not self.f1==None and not self.v1==None: |
if not self.f2 == None and not self.v2 == None: |
| 233 |
fv1=self.f1*self.v1 |
fv2 = f2*v2 |
| 234 |
else: |
else: |
| 235 |
fv1=None |
fv2 = None |
| 236 |
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| 237 |
if not self.f2==None and not self.v2==None: |
if not self.f3 == None and not self.v3 == None: |
| 238 |
fv2=f2*v2 |
fv3 = self.f3*self.v3 |
| 239 |
else: |
else: |
| 240 |
fv2=None |
fv3 = None |
| 241 |
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| 242 |
if not self.f3==None and not self.v3==None: |
if not self.f4 == None and not self.v4 == None: |
| 243 |
fv3=self.f3*self.v3 |
fv4 = self.f4*self.v4 |
| 244 |
else: |
else: |
| 245 |
fv3=None |
fv4 = None |
| 246 |
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| 247 |
if not self.f4==None and not self.v4==None: |
if fv0 == None: |
| 248 |
fv4=self.f4*self.v4 |
out = 0. |
| 249 |
else: |
else: |
| 250 |
fv4=None |
out = fv0 |
| 251 |
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if not fv1 == None: |
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out += fv1 |
| 253 |
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if not fv2 == None: |
| 254 |
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out += fv2 |
| 255 |
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if not fv3 == None: |
| 256 |
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out += fv3 |
| 257 |
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return out |
| 258 |
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| 259 |
if fv0==None: |
# vim: expandtab shiftwidth=4: |
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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: out+=fv1 |
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if not fv2==None: out+=fv2 |
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if not fv3==None: out+=fv3 |
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return out |
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