removing objects doesn't reset grid permittivity

Very cool project! I look forward to seeing where it goes.

Small observation here, after playing around a little bit:
When you add an object, and then later remove it, you end up with a section of the grid with altered permittivity, but without an object present in the visualization. Lacking a grid.remove_object() method, I naively just popped the object from grid.objects… and used grid.reset(). but obviously, that doesn’t undo the changes to the grid that occurred when the object was registered.

I’d definitely understand if support for removing items (rather than just re-instantiating a new grid) was low priority for now, but just figured I’d mention it. thanks again!

[0xDBFB7]

@Kiranalu There’s an even easier method; use the pyEVTK library and Paraview!

    def dump_to_vtk(self, filename, iteration, Ex_dump=False, Ey_dump=False, Ez_dump=False, Emag_dump=True, objects_dump=True, ports_dump=True):
        '''
        Extension is automatically chosen, you don't need to supply it

        thanks
        https://pyscience.wordpress.com/2014/09/06/numpy-to-vtk-converting-your-numpy-arrays-to-vtk-arrays-and-files/
        https://bitbucket.org/pauloh/pyevtk/src/default/src/hl.py

        Paraview needs a threshold operation to view the objects correctly.

        '''


        x = np.linspace(0, self.cell_size*self.grid.Nx, self.grid.Nx+1) #there might be an off-by-one error here.
        y = np.linspace(0, self.cell_size*self.grid.Ny, self.grid.Ny+1)
        z = np.linspace(0, self.cell_size*self.grid.Nz, self.grid.Nz+1)

        cellData = {}

        if(not isinstance(fdtd.backend, NumpyBackend)):
            E_copy = self.grid.E.cpu()
        else:
            E_copy = self.grid.E

        if(objects_dump):
            objects = np.zeros_like(E_copy[:,:,:,X])
            for obj in self.grid.objects:
                objects[obj.x.start:obj.x.stop, obj.y.start:obj.y.stop, obj.z.start:obj.z.stop] = 1
            cellData['objects'] = objects

        if(Ex_dump):
            cellData['Ex'] = np.ascontiguousarray(E_copy[:,:,:,X])
        if(Ey_dump):
            cellData['Ey'] = np.ascontiguousarray(E_copy[:,:,:,Y])
        if(Ez_dump):
            cellData['Ez'] = np.ascontiguousarray(E_copy[:,:,:,Z])
        if(Emag_dump):
            cellData['Emag'] = np.ascontiguousarray(self.E_magnitude(E_copy)) # gridToVTK expects a contiguous array.


        gridToVTK(filename + str(iteration), x, y, z, cellData = cellData)

Paraview makes very pretty videos.

[0xDBFB7]

@Kiranalu I don’t know how the OP did it, but something like this might work (untested):

for i in range(0, N_timesteps):
    grid.step()
    grid.visualize(show=False)
    plt.savefig(str(i) + ".png")

and then use something like ffmpeg to make the .png images into a movie.

Matplotlib also has an animation module that might be easier, but I haven’t tried it.

Good luck!