| 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 |
return d |
| 294 |
|
| 295 |
class SmoothScalarDistributionFromTags(ParameterSet): |
| 296 |
""" |
| 297 |
creates a smooth scalar distribution on a domain from region tags |
| 298 |
|
| 299 |
@ivar domain: domain |
| 300 |
@type domain: L{esys.escript.Domain} |
| 301 |
@ivar default: default value |
| 302 |
@ivar tag0: tag 0 |
| 303 |
@type tag0: C{int} |
| 304 |
@ivar value0: value for tag 0 |
| 305 |
@type value0: C{float} |
| 306 |
@ivar tag1: tag 1 |
| 307 |
@type tag1: C{int} |
| 308 |
@ivar value1: value for tag 1 |
| 309 |
@type value1: C{float} |
| 310 |
@ivar tag2: tag 2 |
| 311 |
@type tag2: C{int} |
| 312 |
@ivar value2: value for tag 2 |
| 313 |
@type value2: C{float} |
| 314 |
@ivar tag3: tag 3 |
| 315 |
@type tag3: C{int} |
| 316 |
@ivar value3: value for tag 3 |
| 317 |
@type value3: C{float} |
| 318 |
@ivar tag4: tag 4 |
| 319 |
@type tag4: C{int} |
| 320 |
@ivar value4: value for tag 4 |
| 321 |
@type value4: C{float} |
| 322 |
@ivar tag5: tag 5 |
| 323 |
@type tag5: C{int} |
| 324 |
@ivar value5: value for tag 5 |
| 325 |
@type value5: C{float} |
| 326 |
@ivar tag6: tag 6 |
| 327 |
@type tag6: C{int} |
| 328 |
@ivar value6: value for tag 6 |
| 329 |
@type value6: C{float} |
| 330 |
@ivar tag7: tag 7 |
| 331 |
@type tag7: C{int} |
| 332 |
@ivar value7: value for tag 7 |
| 333 |
@type value7: C{float} |
| 334 |
@ivar tag8: tag 8 |
| 335 |
@type tag8: C{int} |
| 336 |
@ivar value8: value for tag 8 |
| 337 |
@type value8: C{float} |
| 338 |
@ivar tag9: tag 9 |
| 339 |
@type tag9: C{int} |
| 340 |
@ivar value9: value for tag 9 |
| 341 |
@type value9: C{float} |
| 342 |
""" |
| 343 |
def __init__(self,**kwargs): |
| 344 |
super(SmoothScalarDistributionFromTags, self).__init__(**kwargs) |
| 345 |
self.declareParameter(domain=None, |
| 346 |
default=0., |
| 347 |
tag0=None, |
| 348 |
value0=0., |
| 349 |
tag1=None, |
| 350 |
value1=0., |
| 351 |
tag2=None, |
| 352 |
value2=0., |
| 353 |
tag3=None, |
| 354 |
value3=0., |
| 355 |
tag4=None, |
| 356 |
value4=0., |
| 357 |
tag5=None, |
| 358 |
value5=0., |
| 359 |
tag6=None, |
| 360 |
value6=0., |
| 361 |
tag7=None, |
| 362 |
value7=0., |
| 363 |
tag8=None, |
| 364 |
value8=0., |
| 365 |
tag9=None, |
| 366 |
value9=0.) |
| 367 |
|
| 368 |
|
| 369 |
def __update(self,tag,tag_value,value): |
| 370 |
if self.__pde==None: |
| 371 |
self.__pde=LinearPDE(self.domain,numSolutions=1) |
| 372 |
mask=Scalar(0.,Function(self.domain)) |
| 373 |
mask.setTaggedValue(tag,1.) |
| 374 |
self.__pde.setValue(Y=mask) |
| 375 |
mask=wherePositive(abs(self.__pde.getRightHandSide())) |
| 376 |
value*=(1.-mask) |
| 377 |
value+=tag_value*mask |
| 378 |
return value |
| 379 |
|
| 380 |
def out(self): |
| 381 |
""" |
| 382 |
returns a L{esys.escript.Data} object |
| 383 |
Link against this method to get the output of this model. |
| 384 |
""" |
| 385 |
d=Scalar(self.default,Solution(self.domain)) |
| 386 |
self.__pde=None |
| 387 |
if not self.tag0 == None: d=self.__update(self.tag0,self.value0,d) |
| 388 |
if not self.tag1 == None: d=self.__update(self.tag1,self.value1,d) |
| 389 |
if not self.tag2 == None: d=self.__update(self.tag2,self.value2,d) |
| 390 |
if not self.tag3 == None: d=self.__update(self.tag3,self.value3,d) |
| 391 |
if not self.tag4 == None: d=self.__update(self.tag4,self.value4,d) |
| 392 |
if not self.tag5 == None: d=self.__update(self.tag5,self.value5,d) |
| 393 |
if not self.tag6 == None: d=self.__update(self.tag6,self.value6,d) |
| 394 |
if not self.tag7 == None: d=self.__update(self.tag7,self.value7,d) |
| 395 |
if not self.tag8 == None: d=self.__update(self.tag8,self.value8,d) |
| 396 |
if not self.tag9 == None: d=self.__update(self.tag9,self.value9,d) |
| 397 |
return d |
| 398 |
|
| 399 |
class LinearCombination(ParameterSet): |
| 400 |
""" |
| 401 |
Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
| 402 |
|
| 403 |
@ivar f0: numerical object or None, default=None (in) |
| 404 |
@ivar v0: numerical object or None, default=None (in) |
| 405 |
@ivar f1: numerical object or None, default=None (in) |
| 406 |
@ivar v1: numerical object or None, default=None (in) |
| 407 |
@ivar f2: numerical object or None, default=None (in) |
| 408 |
@ivar v2: numerical object or None, default=None (in) |
| 409 |
@ivar f3: numerical object or None, default=None (in) |
| 410 |
@ivar v3: numerical object or None, default=None (in) |
| 411 |
@ivar f4: numerical object or None, default=None (in) |
| 412 |
@ivar v4: numerical object or None, default=None (in) |
| 413 |
""" |
| 414 |
def __init__(self,**kwargs): |
| 415 |
super(LinearCombination, self).__init__(**kwargs) |
| 416 |
self.declareParameter(f0=None, \ |
| 417 |
v0=None, \ |
| 418 |
f1=None, \ |
| 419 |
v1=None, \ |
| 420 |
f2=None, \ |
| 421 |
v2=None, \ |
| 422 |
f3=None, \ |
| 423 |
v3=None, \ |
| 424 |
f4=None, \ |
| 425 |
v4=None) |
| 426 |
|
| 427 |
def out(self): |
| 428 |
""" |
| 429 |
returns f0*v0+f1*v1+f2*v2+f3*v3+f4*v4. |
| 430 |
Link against this method to get the output of this model. |
| 431 |
""" |
| 432 |
if not self.f0 == None and not self.v0 == None: |
| 433 |
fv0 = self.f0*self.v0 |
| 434 |
else: |
| 435 |
fv0 = None |
| 436 |
|
| 437 |
if not self.f1 == None and not self.v1 == None: |
| 438 |
fv1 = self.f1*self.v1 |
| 439 |
else: |
| 440 |
fv1 = None |
| 441 |
|
| 442 |
if not self.f2 == None and not self.v2 == None: |
| 443 |
fv2 = f2*v2 |
| 444 |
else: |
| 445 |
fv2 = None |
| 446 |
|
| 447 |
if not self.f3 == None and not self.v3 == None: |
| 448 |
fv3 = self.f3*self.v3 |
| 449 |
else: |
| 450 |
fv3 = None |
| 451 |
|
| 452 |
if not self.f4 == None and not self.v4 == None: |
| 453 |
fv4 = self.f4*self.v4 |
| 454 |
else: |
| 455 |
fv4 = None |
| 456 |
|
| 457 |
if fv0 == None: |
| 458 |
out = 0. |
| 459 |
else: |
| 460 |
out = fv0 |
| 461 |
if not fv1 == None: |
| 462 |
out += fv1 |
| 463 |
if not fv2 == None: |
| 464 |
out += fv2 |
| 465 |
if not fv3 == None: |
| 466 |
out += fv3 |
| 467 |
return out |
| 468 |
|
| 469 |
class MergeConstraints(ParameterSet): |
| 470 |
""" |
| 471 |
Returns a linear combination of the f0*v0+f1*v1+f2*v2+f3*v3+f4*v4 |
| 472 |
""" |
| 473 |
def __init__(self,**kwargs): |
| 474 |
super(MergeConstraints, self).__init__(**kwargs) |
| 475 |
self.declareParameter(location_of_constraint0=None, \ |
| 476 |
value_of_constraint0=None, \ |
| 477 |
location_of_constraint1=None, \ |
| 478 |
value_of_constraint1=None, \ |
| 479 |
location_of_constraint2=None, \ |
| 480 |
value_of_constraint2=None, \ |
| 481 |
location_of_constraint3=None, \ |
| 482 |
value_of_constraint3=None, \ |
| 483 |
location_of_constraint4=None, \ |
| 484 |
value_of_constraint4=None) |
| 485 |
def location_of_constraint(self): |
| 486 |
""" |
| 487 |
return the values used to constrain a solution |
| 488 |
|
| 489 |
@return: the mask marking the locations of the constraints |
| 490 |
@rtype: L{escript.Scalar} |
| 491 |
""" |
| 492 |
out_loc=0 |
| 493 |
if not self.location_of_constraint0 == None: |
| 494 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint0)) |
| 495 |
if not self.location_of_constraint1 == None: |
| 496 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint1)) |
| 497 |
if not self.location_of_constraint2 == None: |
| 498 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint2)) |
| 499 |
if not self.location_of_constraint3 == None: |
| 500 |
out_loc=wherePositive(out_loc+wherePositive(self.location_of_constraint3)) |
| 501 |
return out_loc |
| 502 |
|
| 503 |
def value_of_constraint(self): |
| 504 |
""" |
| 505 |
return the values used to constrain a solution |
| 506 |
|
| 507 |
@return: values to be used at the locations of the constraints. If |
| 508 |
L{value} is not given C{None} is rerturned. |
| 509 |
@rtype: L{escript.Scalar} |
| 510 |
""" |
| 511 |
out_loc=0 |
| 512 |
out=0 |
| 513 |
if not self.location_of_constraint0 == None: |
| 514 |
tmp=wherePositive(self.location_of_constraint0) |
| 515 |
out=out*(1.-tmp)+self.value_of_constraint0*tmp |
| 516 |
out_loc=wherePositive(out_loc+tmp) |
| 517 |
if not self.location_of_constraint1 == None: |
| 518 |
tmp=wherePositive(self.location_of_constraint1) |
| 519 |
out=out*(1.-tmp)+self.value_of_constraint1*tmp |
| 520 |
out_loc=wherePositive(out_loc+tmp) |
| 521 |
if not self.location_of_constraint2 == None: |
| 522 |
tmp=wherePositive(self.location_of_constraint2) |
| 523 |
out=out*(1.-tmp)+self.value_of_constraint2*tmp |
| 524 |
out_loc=wherePositive(out_loc+tmp) |
| 525 |
if not self.location_of_constraint3 == None: |
| 526 |
tmp=wherePositive(self.location_of_constraint3) |
| 527 |
out=out*(1.-tmp)+self.value_of_constraint3*tmp |
| 528 |
out_loc=wherePositive(out_loc+tmp) |
| 529 |
return out |
| 530 |
# vim: expandtab shiftwidth=4: |
Parent Directory
| 
Revision Log