Dear all,
we are currently using the fipy.Viewer class to visualise a scalar field as a
function of time (as was demonstrated in the diffusion example provided by
fipy); example code attached below.
In addition, I would like to mark a particular x,y position on that plot which
changes from iteration to iteration.
If I was not using fipy, I could use matplotlib to update for every iteration a
plot of using
>>> plot( [x], [y], 'o')
to plot a circle at position (x,y), say.
I couldn't make this work together with fipy -- presumably because fipy wraps
up some matplotlib functionality to make things more convenient.
Can anybody give some general advice on what approach is likely to deliver what
I need here? (I hope I have described the problem clearly enough). If there was
an example related to this that I haven't found, please point me to that and
this may answer my question.
Thanks in advance, and thank you for providing fipy and the user support.
Best wishes,
Hans
import fipy
Ly = 20
Lx = Ly
nx = 20
ny = nx
dx = 1.
dy = dx
mesh = fipy.Grid2D(dx=dx, dy=dy, nx=nx, ny=ny)
phi = fipy.CellVariable(name = "solution variable",
mesh = mesh,
value = 0.0)
#Basic equation
D = 1
eq = fipy.TransientTerm() == fipy.DiffusionTerm(coeff=D)
#boundary conditions
phi_outside = 0.1
x, y = mesh.getFaceCenters()
facesTopLeft = ((mesh.getFacesLeft() & (y > Ly / 2))
| (mesh.getFacesTop() & (x < Lx / 2)))
facesBottomRight = ((mesh.getFacesRight() & (y < Ly / 2))
| (mesh.getFacesBottom() & (x > Lx / 2)))
BCs = (fipy.FixedValue(faces=facesTopLeft, value=phi_outside),
fipy.FixedValue(faces=facesBottomRight, value=-phi_outside))
viewer = fipy.Viewer(vars=phi, datamin=-phi_outside*1., datamax=phi_outside*1)
viewer.plot()
#and solve the equation by repeatedly looping in time:
explicit_max_timeStepDuration = 0.9 * dx*dy / (2 * D)
timeStepDuration = 10 * explicit_max_timeStepDuration
steps = 100
for step in range(steps):
eq.solve(var=phi,
boundaryConditions=BCs,
dt=timeStepDuration)
viewer.plot()
