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Contents of /trunk/modellib/py_src/input.py

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Revision 944 - (show annotations)
Tue Jan 30 08:57:37 2007 UTC (12 years, 3 months ago) by gross
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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 esys.escript.linearPDEs import LinearPDE
12 from math import log
13
14 class Sequencer(Model):
15 """
16 Runs through time until t_end is reached.
17
18 @ivar t_end: model is terminated when t_end is passed, default 1 (in).
19 @type t_end: C{float}
20 @ivar dt_max: maximum time step size, default L{Model.UNDEF_DT} (in)
21 @type dt_max: C{float}
22 @ivar t: current time stamp (in/out). By default it is initialized with zero.
23 @type t: C{float}
24
25 """
26 def __init__(self,**kwargs):
27 """
28 """
29 super(Sequencer,self).__init__(**kwargs)
30 self.declareParameter(t=0.,
31 t_end=1.,
32 dt_max=Model.UNDEF_DT)
33
34 def doInitialization(self):
35 """
36 initialize time integration
37 """
38 self.__t_old = self.t
39
40 def doStepPreprocessing(self, dt):
41 self.t = self.__t_old+dt
42
43 def doStepPostprocessing(self, dt):
44 self.__t_old = self.t
45
46 def finalize(self):
47 """
48 returns true when L{t} has reached L{t_end}
49 """
50 return self.t >= self.t_end
51
52 def getSafeTimeStepSize(self, dt):
53 """
54 returns L{dt_max}
55 """
56 return self.dt_max
57
58 class GaussianProfile(ParameterSet):
59 """
60 Generates a Gaussian profile at center x_c, width width and height A
61 over a domain
62
63 @ivar domain: domain
64 @ivar x_c: center of the Gaussian profile (default [0.,0.,0.])
65 @ivar A: (in) height of the profile. A maybe a vector. (default 1.)
66 @ivar width: (in) width of the profile (default 0.1)
67 @ivar r: (in) radius of the circle (default = 0)
68
69 In the case that the spatial dimension is two, The third component of
70 x_c is dropped.
71 """
72 def __init__(self,**kwargs):
73 super(GaussianProfile, self).__init__(**kwargs)
74 self.declareParameter(domain=None,
75 x_c=numarray.zeros([3]),
76 A=1.,
77 width=0.1,
78 r=0)
79
80 def out(self):
81 """
82 Generate the Gaussian profile
83
84 Link against this method to get the output of this model.
85 """
86 x = self.domain.getX()
87 dim = self.domain.getDim()
88 l = length(x-self.x_c[:dim])
89 m = whereNegative(l-self.r)
90
91 return (m+(1.-m)*exp(-log(2.)*(l/self.width)**2))*self.A
92
93 class InterpolateOverBox(ParameterSet):
94 """
95 Returns values at each time. The values are defined through given values
96 at time node. For two dimensional domains back values are ignored.
97
98 @ivar domain: domain
99 @ivar value_left_bottom_front: (in) value at left,bottom,front corner
100 @ivar value_right_bottom_front: (in) value at right, bottom, front corner
101 @ivar value_left_top_front: (in) value at left,top,front corner
102 @ivar value_right_top_front: (in) value at right,top,front corner
103 @ivar value_left_bottom_back: (in) value at left,bottom,back corner
104 @ivar value_right_bottom_back: (in) value at right,bottom,back corner
105 @ivar value_left_top_back: (in) value at left,top,back corner
106 @ivar value_right_top_back: (in) value at right,top,back corner
107 """
108
109 def __init__(self, **kwargs):
110 super(InterpolateOverBox, self).__init__(self)
111 self.declareParameter(domain=None,
112 value_left_bottom_front=0.,
113 value_right_bottom_front=0.,
114 value_left_top_front=0.,
115 value_right_top_front=0.,
116 value_left_bottom_back=0.,
117 value_right_bottom_back=0.,
118 value_left_top_back=0.,
119 value_right_top_back=0.)
120
121
122 def out(self):
123 """
124 values at domain locations by bilinear interpolation of the given values.
125
126 Link against this method to get the output of this model.
127 """
128 x = self.domain.getX()
129 if self.domain.getDim() == 2:
130 x0,x1=x[0],x[1]
131 left_bottom_front0,right_top_back0=inf(x0),sup(x0)
132 left_bottom_front1,right_top_back1=inf(x1),sup(x1)
133 f_right = (x0 - left_bottom_front0)/(right_top_back0 -left_bottom_front0)
134 f_left = 1. - f_right
135 f_top = (x1 - left_bottom_front1)/(right_top_back1 - left_bottom_front1)
136 f_bottom = 1. - f_top
137 out = f_left * f_bottom * self.value_left_bottom_front \
138 + f_right * f_bottom * self.value_right_bottom_front \
139 + f_left * f_top * self.value_left_top_front \
140 + f_right * f_top * self.value_right_top_front
141 else:
142 x0,x1,x2=x[0],x[1],x[2]
143 left_bottom_front0,right_top_back0=inf(x0),sup(x0)
144 left_bottom_front1,right_top_back1=inf(x1),sup(x1)
145 left_bottom_front2,right_top_back2=inf(x2),sup(x2)
146 f_right = (x0 - left_bottom_front0)/(right_top_back0 - left_bottom_front0)
147 f_left = 1. - f_right
148 f_top = (x1 - left_bottom_front1)/(right_top_back1 - left_bottom_front1)
149 f_bottom = 1. - f_top
150 f_back = (x2 - left_bottom_front1)/(right_top_back2 - left_bottom_front2)
151 f_front = 1. - f_back
152 out = f_left * f_bottom * f_front * self.value_left_bottom_front\
153 + f_right * f_bottom * f_front * self.value_right_bottom_front\
154 + f_left * f_top * f_front * self.value_left_top_front\
155 + f_right * f_top * f_front * self.value_right_top_front\
156 + f_left * f_bottom * f_back * self.value_left_bottom_back\
157 + f_right * f_bottom * f_back * self.value_right_bottom_back\
158 + f_left * f_top * f_back * self.value_left_top_back\
159 + f_right * f_top * f_back * self.value_right_top_back
160 return out
161
162
163 class InterpolatedTimeProfile(ParameterSet):
164 """
165
166 Returns values at each time. The values are defined through given
167 values at time node.
168
169 value[i] defines the value at time nodes[i]. Between nodes linear
170 interpolation is used.
171
172 For time t<nodes[0], value[0] is used and for t>nodes[l], values[l]
173 is used where l=len(nodes)-1.
174
175 @ivar t: (in) current time
176 @ivar node: (in) list of time nodes
177 @ivar values: (in) list of values at time nodes
178 """
179
180 def __init__(self,**kwargs):
181 super( InterpolatedTimeProfile, self).__init__(**kwargs)
182 self.declareParameter(t=0., \
183 nodes=[0.,1.],\
184 values=[1.,1.])
185 def out(self):
186 """
187 current value
188
189 Link against this method to get the output of this model.
190 """
191 l = len(self.nodes) - 1
192 t = self.t
193 if t <= self.nodes[0]:
194 return self.values[0]
195 else:
196 for i in range(1,l):
197 if t < self.nodes[i]:
198 m = (self.values[i-1] - self.values[i])/\
199 (self.nodes[i-1] - self.nodes[i])
200 return m*(t-self.nodes[i-1]) + self.values[i-1]
201 return self.values[l]
202
203 class ScalarDistributionFromTags(ParameterSet):
204 """
205 creates a scalar distribution on a domain from tags
206
207 @ivar domain: domain
208 @type domain: L{esys.escript.Domain}
209 @ivar default: default value
210 @ivar tag0: tag 0
211 @type tag0: C{int}
212 @ivar value0: value for tag 0
213 @type value0: C{float}
214 @ivar tag1: tag 1
215 @type tag1: C{int}
216 @ivar value1: value for tag 1
217 @type value1: C{float}
218 @ivar tag2: tag 2
219 @type tag2: C{int}
220 @ivar value2: value for tag 2
221 @type value2: C{float}
222 @ivar tag3: tag 3
223 @type tag3: C{int}
224 @ivar value3: value for tag 3
225 @type value3: C{float}
226 @ivar tag4: tag 4
227 @type tag4: C{int}
228 @ivar value4: value for tag 4
229 @type value4: C{float}
230 @ivar tag5: tag 5
231 @type tag5: C{int}
232 @ivar value5: value for tag 5
233 @type value5: C{float}
234 @ivar tag6: tag 6
235 @type tag6: C{int}
236 @ivar value6: value for tag 6
237 @type value6: C{float}
238 @ivar tag7: tag 7
239 @type tag7: C{int}
240 @ivar value7: value for tag 7
241 @type value7: C{float}
242 @ivar tag8: tag 8
243 @type tag8: C{int}
244 @ivar value8: value for tag 8
245 @type value8: C{float}
246 @ivar tag9: tag 9
247 @type tag9: C{int}
248 @ivar value9: value for tag 9
249 @type value9: C{float}
250 """
251 def __init__(self,**kwargs):
252 super(ScalarDistributionFromTags, self).__init__(**kwargs)
253 self.declareParameter(domain=None,
254 default=0.,
255 tag0=None,
256 value0=0.,
257 tag1=None,
258 value1=0.,
259 tag2=None,
260 value2=0.,
261 tag3=None,
262 value3=0.,
263 tag4=None,
264 value4=0.,
265 tag5=None,
266 value5=0.,
267 tag6=None,
268 value6=0.,
269 tag7=None,
270 value7=0.,
271 tag8=None,
272 value8=0.,
273 tag9=None,
274 value9=0.)
275
276
277 def out(self):
278 """
279 returns a L{esys.escript.Data} object
280 Link against this method to get the output of this model.
281 """
282 d=Scalar(self.default,Function(self.domain))
283 if not self.tag0 == None: d.setTaggedValue(self.tag0,self.value0)
284 if not self.tag1 == None: d.setTaggedValue(self.tag1,self.value1)
285 if not self.tag2 == None: d.setTaggedValue(self.tag2,self.value2)
286 if not self.tag3 == None: d.setTaggedValue(self.tag3,self.value3)
287 if not self.tag4 == None: d.setTaggedValue(self.tag4,self.value4)
288 if not self.tag5 == None: d.setTaggedValue(self.tag5,self.value5)
289 if not self.tag6 == None: d.setTaggedValue(self.tag6,self.value6)
290 if not self.tag7 == None: d.setTaggedValue(self.tag7,self.value7)
291 if not self.tag8 == None: d.setTaggedValue(self.tag8,self.value8)
292 if not self.tag9 == None: d.setTaggedValue(self.tag9,self.value9)
293 print d
294 return d
295
296 class SmoothScalarDistributionFromTags(ParameterSet):
297 """
298 creates a smooth scalar distribution on a domain from region tags
299
300 @ivar domain: domain
301 @type domain: L{esys.escript.Domain}
302 @ivar default: default value
303 @ivar tag0: tag 0
304 @type tag0: C{int}
305 @ivar value0: value for tag 0
306 @type value0: C{float}
307 @ivar tag1: tag 1
308 @type tag1: C{int}
309 @ivar value1: value for tag 1
310 @type value1: C{float}
311 @ivar tag2: tag 2
312 @type tag2: C{int}
313 @ivar value2: value for tag 2
314 @type value2: C{float}
315 @ivar tag3: tag 3
316 @type tag3: C{int}
317 @ivar value3: value for tag 3
318 @type value3: C{float}
319 @ivar tag4: tag 4
320 @type tag4: C{int}
321 @ivar value4: value for tag 4
322 @type value4: C{float}
323 @ivar tag5: tag 5
324 @type tag5: C{int}
325 @ivar value5: value for tag 5
326 @type value5: C{float}
327 @ivar tag6: tag 6
328 @type tag6: C{int}
329 @ivar value6: value for tag 6
330 @type value6: C{float}
331 @ivar tag7: tag 7
332 @type tag7: C{int}
333 @ivar value7: value for tag 7
334 @type value7: C{float}
335 @ivar tag8: tag 8
336 @type tag8: C{int}
337 @ivar value8: value for tag 8
338 @type value8: C{float}
339 @ivar tag9: tag 9
340 @type tag9: C{int}
341 @ivar value9: value for tag 9
342 @type value9: C{float}
343 """
344 def __init__(self,**kwargs):
345 super(SmoothScalarDistributionFromTags, self).__init__(**kwargs)
346 self.declareParameter(domain=None,
347 default=0.,
348 tag0=None,
349 value0=0.,
350 tag1=None,
351 value1=0.,
352 tag2=None,
353 value2=0.,
354 tag3=None,
355 value3=0.,
356 tag4=None,
357 value4=0.,
358 tag5=None,
359 value5=0.,
360 tag6=None,
361 value6=0.,
362 tag7=None,
363 value7=0.,
364 tag8=None,
365 value8=0.,
366 tag9=None,
367 value9=0.)
368
369
370 def __update(self,tag,tag_value,value):
371 if self.__pde==None:
372 self.__pde=LinearPDE(self.domain,numSolutions=1)
373 mask=Scalar(0.,Function(self.domain))
374 mask.setTaggedValue(tag,1.)
375 self.__pde.setValue(Y=mask)
376 mask=wherePositive(abs(self.__pde.getRightHandSide()))
377 value*=(1.-mask)
378 value+=tag_value*mask
379 return value
380
381 def out(self):
382 """
383 returns a L{esys.escript.Data} object
384 Link against this method to get the output of this model.
385 """
386 d=Scalar(self.default,Solution(self.domain))
387 self.__pde=None
388 if not self.tag0 == None: d=self.__update(self.tag0,self.value0,d)
389 if not self.tag1 == None: d=self.__update(self.tag1,self.value1,d)
390 if not self.tag2 == None: d=self.__update(self.tag2,self.value2,d)
391 if not self.tag3 == None: d=self.__update(self.tag3,self.value3,d)
392 if not self.tag4 == None: d=self.__update(self.tag4,self.value4,d)
393 if not self.tag5 == None: d=self.__update(self.tag5,self.value5,d)
394 if not self.tag6 == None: d=self.__update(self.tag6,self.value6,d)
395 if not self.tag7 == None: d=self.__update(self.tag7,self.value7,d)
396 if not self.tag8 == None: d=self.__update(self.tag8,self.value8,d)
397 if not self.tag9 == None: d=self.__update(self.tag9,self.value9,d)
398 return d
399
400 class LinearCombination(ParameterSet):
401 """
402 Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4
403
404 @ivar f0: numerical object or None, default=None (in)
405 @ivar v0: numerical object or None, default=None (in)
406 @ivar f1: numerical object or None, default=None (in)
407 @ivar v1: numerical object or None, default=None (in)
408 @ivar f2: numerical object or None, default=None (in)
409 @ivar v2: numerical object or None, default=None (in)
410 @ivar f3: numerical object or None, default=None (in)
411 @ivar v3: numerical object or None, default=None (in)
412 @ivar f4: numerical object or None, default=None (in)
413 @ivar v4: numerical object or None, default=None (in)
414 """
415 def __init__(self,**kwargs):
416 super(LinearCombination, self).__init__(**kwargs)
417 self.declareParameter(f0=None, \
418 v0=None, \
419 f1=None, \
420 v1=None, \
421 f2=None, \
422 v2=None, \
423 f3=None, \
424 v3=None, \
425 f4=None, \
426 v4=None)
427
428 def out(self):
429 """
430 returns f0*v0+f1*v1+f2*v2+f3*v3+f4*v4.
431 Link against this method to get the output of this model.
432 """
433 if not self.f0 == None and not self.v0 == None:
434 fv0 = self.f0*self.v0
435 else:
436 fv0 = None
437
438 if not self.f1 == None and not self.v1 == None:
439 fv1 = self.f1*self.v1
440 else:
441 fv1 = None
442
443 if not self.f2 == None and not self.v2 == None:
444 fv2 = f2*v2
445 else:
446 fv2 = None
447
448 if not self.f3 == None and not self.v3 == None:
449 fv3 = self.f3*self.v3
450 else:
451 fv3 = None
452
453 if not self.f4 == None and not self.v4 == None:
454 fv4 = self.f4*self.v4
455 else:
456 fv4 = None
457
458 if fv0 == None:
459 out = 0.
460 else:
461 out = fv0
462 if not fv1 == None:
463 out += fv1
464 if not fv2 == None:
465 out += fv2
466 if not fv3 == None:
467 out += fv3
468 return out
469
470 class MergeConstraints(ParameterSet):
471 """
472 Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4
473 """
474 def __init__(self,**kwargs):
475 super(MergeConstraints, self).__init__(**kwargs)
476 self.declareParameter(location_of_constraint0=None, \
477 value_of_constraint0=None, \
478 location_of_constraint1=None, \
479 value_of_constraint1=None, \
480 location_of_constraint2=None, \
481 value_of_constraint2=None, \
482 location_of_constraint3=None, \
483 value_of_constraint3=None, \
484 location_of_constraint4=None, \
485 value_of_constraint4=None)
486 def location_of_constraint(self):
487 """
488 return the values used to constrain a solution
489
490 @return: the mask marking the locations of the constraints
491 @rtype: L{escript.Scalar}
492 """
493 out_loc=0
494 if not self.location_of_constraint0 == None:
495 out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint0))
496 if not self.location_of_constraint1 == None:
497 out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint1))
498 if not self.location_of_constraint2 == None:
499 out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint2))
500 if not self.location_of_constraint3 == None:
501 out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint3))
502 return out_loc
503
504 def value_of_constraint(self):
505 """
506 return the values used to constrain a solution
507
508 @return: values to be used at the locations of the constraints. If
509 L{value} is not given C{None} is rerturned.
510 @rtype: L{escript.Scalar}
511 """
512 out_loc=0
513 out=0
514 if not self.location_of_constraint0 == None:
515 tmp=wherePositive(self.location_of_constraint0)
516 out=out*(1.-tmp)+self.value_of_constraint0*tmp
517 out_loc=wherePositive(out_loc+tmp)
518 if not self.location_of_constraint1 == None:
519 tmp=wherePositive(self.location_of_constraint1)
520 out=out*(1.-tmp)+self.value_of_constraint1*tmp
521 out_loc=wherePositive(out_loc+tmp)
522 if not self.location_of_constraint2 == None:
523 tmp=wherePositive(self.location_of_constraint2)
524 out=out*(1.-tmp)+self.value_of_constraint2*tmp
525 out_loc=wherePositive(out_loc+tmp)
526 if not self.location_of_constraint3 == None:
527 tmp=wherePositive(self.location_of_constraint3)
528 out=out*(1.-tmp)+self.value_of_constraint3*tmp
529 out_loc=wherePositive(out_loc+tmp)
530 return out
531 # vim: expandtab shiftwidth=4:

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