Never mind my last question. :P Having finally understood more about the stochastic variable modules, the problem is revealed to be quite trivial.
Thanks for the help! Happy 2012! On Wed, Jan 4, 2012 at 4:45 PM, Yun Tao <[email protected]> wrote: > Hi Daniel, > > What you wrote definitely guided me to the answer. Much thanks! SDE is > actually just a PDE with its variable (and initial condition) being in the > form of a probability distribution. On 1D mesh at least, fipy seems fully > capable of handling that. > > On a slightly different note, your lucid explanation of noise variable (as > second degree stochasticity) got me working through the examples in those > links. Particularly, in > < > http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.exponentialNoiseVariable > > > I may have spotted an error in the module: > > In short, I noticed that if the mean is set to Variable() -- defaulted to > be 0 -- the encapsulating noise variable ("noise" in the example) will > generate a *ValueError: scale <= 0* when called upon. Subsequently, > feeding it into the viewer will yield > * > SystemError*: error return without exception set > WARNING: Failure executing file: <enoise.py> > > A look at the exponential equation supports the condition of mean != 0. So > does this mean the default mean for ExponentialNoiseVariable is poorly set > and requires manual configuration for all future operations? > > Thanks! > > > > > > > > On Wed, Jan 4, 2012 at 8:16 AM, Daniel Wheeler > <[email protected]>wrote: > >> >> >> On Tue, Jan 3, 2012 at 2:59 PM, Yun Tao <[email protected]> wrote: >> >>> Hi all, >>> >>> Having just jumped on the Fipy bandwagon, I'm wondering how it fares in >>> handling stochastic PDEs. >>> >> >> I don't know a whole lot about stochastic PDEs. We do have some examples >> buried in doctests. See the examples in the following: >> >> < >> http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.uniformNoiseVariable >> > >> < >> http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.gammaNoiseVariable >> > >> < >> http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.exponentialNoiseVariable >> > >> < >> http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.exponentialNoiseVariable >> > >> < >> http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.gaussianNoiseVariable >> > >> < >> http://www.ctcms.nist.gov/fipy/fipy/generated/variables.html#module-fipy.variables.betaNoiseVariable >> > >> >> None of these include examples that actually solve a PDE, but the noise >> variables can be tagged onto terms in the same way as other variables to >> form PDEs that include stochastic sources. >> >> Having gone through multiple examples provided on the website so far, >>> one of the major problems I'm facing is the setting of initial condition as >>> a probability density function. This step seems to require more than just >>> setting the value of CellVariable as a function of >>> mesh.getCellCenters()[0]. Any insight? >>> >> >> Would this do for a uniform mesh (for non-uniform meshes you can use the >> the noise variables included above)? >> >> from fipy import Grid1D, CellVariable, numerix, Viewer >> m = Grid1D(nx=100) >> v = CellVariable(mesh=m) >> v[:] = numerix.random.exponential(size=len(v)) >> Viewer(v).plot() >> raw_input('wait') >> >> Hope that helps. >> >> -- >> Daniel Wheeler >> >> _______________________________________________ >> fipy mailing list >> [email protected] >> http://www.ctcms.nist.gov/fipy >> [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] >> >> > > > -- > Yun Tao > > Graduate Group of Ecology Doctoral Candidate > Department of Environmental Science and Policy > Center for Population Biology > University of California, Davis > > > -- Yun Tao Graduate Group of Ecology Doctoral Candidate Department of Environmental Science and Policy Center for Population Biology University of California, Davis
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