/[escript]/branches/3.4.1/modellib/py_src/input.py
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Revision 814 - (show annotations)
Wed Aug 23 09:31:10 2006 UTC (12 years, 8 months ago) by gross
Original Path: trunk/modellib/py_src/input.py
File MIME type: text/x-python
File size: 10101 byte(s)
small bug in general tensor propduct fixed
1 # $Id$
2
3 __copyright__=""" Copyright (c) 2006 by ACcESS MNRF
4 http://www.access.edu.au
5 Primary Business: Queensland, Australia"""
6 __license__="""Licensed under the Open Software License version 3.0
7 http://www.opensource.org/licenses/osl-3.0.php"""
8
9 from esys.escript import *
10 from esys.escript.modelframe import Model,ParameterSet
11 from math import log
12
13 class Sequencer(Model):
14 """
15 Runs through time until t_end is reached.
16
17 @ivar t_end: model is terminated when t_end is passed, default 1 (in).
18 @type t_end: C{float}
19 @ivar dt_max: maximum time step size, default L{Model.UNDEF_DT} (in)
20 @type dt_max: C{float}
21 @ivar t: current time stamp (in/out). By default it is initialized with zero.
22 @type t: C{float}
23
24 """
25 def __init__(self,debug=False):
26 """
27 """
28 super(Sequencer,self).__init__(debug=debug)
29 self.declareParameter(t=0.,
30 t_end=1.,
31 dt_max=Model.UNDEF_DT)
32
33 def doInitialization(self):
34 """
35 initialize time integration
36 """
37 self.__t_old = self.t
38
39 def doStepPreprocessing(self, dt):
40 self.t = self.__t_old+dt
41
42 def doStepPostprocessing(self, dt):
43 self.__t_old = self.t
44
45 def finalize(self):
46 """
47 returns true when L{t} has reached L{t_end}
48 """
49 return self.t >= self.t_end
50
51 def getSafeTimeStepSize(self, dt):
52 """
53 returns L{dt_max}
54 """
55 return self.dt_max
56
57 class GaussianProfile(ParameterSet):
58 """
59 Generates a Gaussian profile at center x_c, width width and height A
60 over a domain
61
62 @ivar domain: domain
63 @ivar x_c: center of the Gaussian profile (default [0.,0.,0.])
64 @ivar A: (in) height of the profile. A maybe a vector. (default 1.)
65 @ivar width: (in) width of the profile (default 0.1)
66 @ivar r: (in) radius of the circle (default = 0)
67
68 In the case that the spatial dimension is two, The third component of
69 x_c is dropped.
70 """
71 def __init__(self,debug=False):
72 ParameterSet.__init__(self,debug=debug)
73 self.declareParameter(domain=None,
74 x_c=numarray.zeros([3]),
75 A=1.,
76 width=0.1,
77 r=0)
78
79 def out(self):
80 """
81 Generate the Gaussian profile
82
83 Link against this method to get the output of this model.
84 """
85 x = self.domain.getX()
86 dim = self.domain.getDim()
87 l = length(x-self.x_c[:dim])
88 m = whereNegative(l-self.r)
89
90 return (m+(1.-m)*exp(-log(2.)*(l/self.width)**2))*self.A
91
92 class InterpolateOverBox(ParameterSet):
93 """
94 Returns values at each time. The values are defined through given values
95 at time node. For two dimensional domains back values are ignored.
96
97 @ivar domain: domain
98 @ivar left_bottom_front: (in) coordinates of left, bottom, front corner
99 of the box
100 @ivar right_top_back: (in) coordinates of the right, top, back corner
101 of the box
102 @ivar value_left_bottom_front: (in) value at left,bottom,front corner
103 @ivar value_right_bottom_front: (in) value at right, bottom, front corner
104 @ivar value_left_top_front: (in) value at left,top,front corner
105 @ivar value_right_top_front: (in) value at right,top,front corner
106 @ivar value_left_bottom_back: (in) value at left,bottom,back corner
107 @ivar value_right_bottom_back: (in) value at right,bottom,back corner
108 @ivar value_left_top_back: (in) value at left,top,back corner
109 @ivar value_right_top_back: (in) value at right,top,back corner
110 """
111
112 def __init__(self, debug=False):
113 ParameterSet.__init__(self, debug=debug)
114 self.declareParameter(domain=None,
115 left_bottom_front=[0.,0.,0.],
116 right_top_back=[1.,1.,1.],
117 value_left_bottom_front=0.,
118 value_right_bottom_front=0.,
119 value_left_top_front=0.,
120 value_right_top_front=0.,
121 value_left_bottom_back=0.,
122 value_right_bottom_back=0.,
123 value_left_top_back=0.,
124 value_right_top_back=0.)
125
126
127 def out(self):
128 """
129 values at domain locations by bilinear interpolation of the given values.
130
131 Link against this method to get the output of this model.
132 """
133 x = self.domain.getX()
134 if self.domain.getDim() == 2:
135 f_right = (x[0] - self.left_bottom_front[0])/\
136 (self.right_top_back[0] - self.left_bottom_front[0])
137 f_left = 1. - f_right
138 f_top = (x[1] - self.left_bottom_front[1])/\
139 (self.right_top_back[1] - self.left_bottom_front[1])
140 f_bottom = 1. - f_top
141 out = self.value_left_bottom_front * f_left * f_bottom \
142 + self.value_right_bottom_front* f_right * f_bottom \
143 + self.value_left_top_front * f_left * f_top \
144 + self.value_right_top_front * f_right * f_top
145 else:
146 f_right = (x[0] - self.left_bottom_front[0])/\
147 (self.right_top_back[0] - self.left_bottom_front[0])
148 f_left = 1. - f_right
149 f_top = (x[1] - self.left_bottom_front[1])/\
150 (self.right_top_back[1] - self.left_bottom_front[1])
151 f_bottom = 1. - f_top
152 f_back = (x[2] - self.left_bottom_front[1])/\
153 (self.right_top_back[2] - self.left_bottom_front[2])
154 f_front = 1. - f_back
155 out = self.value_left_bottom_front * f_left * f_bottom * f_front \
156 + self.value_right_bottom_front* f_right * f_bottom * f_front \
157 + self.value_left_top_front * f_left * f_top * f_front \
158 + self.value_right_top_front * f_right * f_top * f_front \
159 + self.value_left_bottom_back * f_left * f_bottom * f_back \
160 + self.value_right_bottom_back * f_right * f_bottom * f_back \
161 + self.value_left_top_back * f_left * f_top * f_back \
162 + self.value_right_top_back * f_right * f_top * f_back
163 return out
164
165
166 class InterpolatedTimeProfile(ParameterSet):
167 """
168
169 Returns values at each time. The values are defined through given
170 values at time node.
171
172 value[i] defines the value at time nodes[i]. Between nodes linear
173 interpolation is used.
174
175 For time t<nodes[0], value[0] is used and for t>nodes[l], values[l]
176 is used where l=len(nodes)-1.
177
178 @ivar t: (in) current time
179 @ivar node: (in) list of time nodes
180 @ivar values: (in) list of values at time nodes
181 """
182
183 def __init__(self,debug=False):
184 ParameterSet.__init__(self,debug=debug)
185 self.declareParameter(t=0., \
186 nodes=[0.,1.],\
187 values=[1.,1.])
188 def out(self):
189 """
190 current value
191
192 Link against this method to get the output of this model.
193 """
194 l = len(self.nodes) - 1
195 t = self.t
196 if t <= self.nodes[0]:
197 return self.values[0]
198 else:
199 for i in range(1,l):
200 if t < self.nodes[i]:
201 m = (self.values[i-1] - self.values[i])/\
202 (self.nodes[i-1] - self.nodes[i])
203 return m*(t-self.nodes[i-1]) + self.values[i-1]
204 return self.values[l]
205
206 class LinearCombination(Model):
207 """
208 Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4
209
210 @ivar f0: (in) numerical object or None, default=None (in)
211 @ivar v0: (in) numerical object or None, default=None (in)
212 @ivar f1: (in) numerical object or None, default=None (in)
213 @ivar v1: (in) numerical object or None, default=None (in)
214 @ivar f2: (in) numerical object or None, default=None (in)
215 @ivar v2: (in) numerical object or None, default=None (in)
216 @ivar f3: (in) numerical object or None, default=None (in)
217 @ivar v3: (in) numerical object or None, default=None (in)
218 @ivar f4: (in) numerical object or None, default=None (in)
219 @ivar v4: (in) numerical object or None, default=None (in)
220 """
221 def __init__(self,debug=False):
222 Model.__init__(self,debug=debug)
223 self.declareParameter(f0=None, \
224 v0=None, \
225 f1=None, \
226 v1=None, \
227 f2=None, \
228 v2=None, \
229 f3=None, \
230 v3=None, \
231 f4=None, \
232 v4=None)
233
234 def out(self):
235 """
236 returns f0*v0+f1*v1+f2*v2+f3*v3+f4*v4.
237 Link against this method to get the output of this model.
238 """
239 if not self.f0 == None and not self.v0 == None:
240 fv0 = self.f0*self.v0
241 else:
242 fv0 = None
243
244 if not self.f1 == None and not self.v1 == None:
245 fv1 = self.f1*self.v1
246 else:
247 fv1 = None
248
249 if not self.f2 == None and not self.v2 == None:
250 fv2 = f2*v2
251 else:
252 fv2 = None
253
254 if not self.f3 == None and not self.v3 == None:
255 fv3 = self.f3*self.v3
256 else:
257 fv3 = None
258
259 if not self.f4 == None and not self.v4 == None:
260 fv4 = self.f4*self.v4
261 else:
262 fv4 = None
263
264 if fv0 == None:
265 out = 0.
266 else:
267 out = fv0
268 if not fv1 == None:
269 out += fv1
270 if not fv2 == None:
271 out += fv2
272 if not fv3 == None:
273 out += fv3
274 return out
275
276 # vim: expandtab shiftwidth=4:

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