Hi Joe,
> You can also just iterate over the system's graph to get all the
> hoppings. Something like:
>
> def current(syst, psi, args=()):
> def hopping_current(i, j):
> H_ij = syst.hamiltonian(i, j, *args)
> return -2 * (psi[i].conjugate() * H_ij * psi[j]).imag
> ## returns a dictionary that maps hopping -> current
> return {(syst.sites[i], syst.sites[j]): hopping_current(i, j)
> for i, j in syst.graph}
Thanks! This is faster than other methods. I need to sharpen my Python, got
confused between list and dict.
Thanks,
Harshad
On Wed, Jan 11, 2017 at 12:44 PM, Joseph Weston <[email protected]>
wrote:
> Hi again!
>
> > > #m is the mode number
> > > def Current(m,lead_nbr=0):
> > > current=2* array([Wf(lead_nbr)[m]]).T * tsys.hamiltonian_submatrix(
> args=[phi])*
> > > (Wf(lead_nbr)[m].conj())
> > > return current.imag
> >
> > Thanks a lot for the code snippet! I now understand why my code takes so
> > long.
>
> The only thing I would say about this is that it is using dense linear
> algebra, so the complexity is O(N**2), also trying to call
> `hamiltonian_submatrix` will most likely blow up your memory, or not far
> off (10^5 sites means 10^10 matrix entries).
>
> You can also just iterate over the system's graph to get all the
> hoppings. Something like:
>
>
> def current(syst, psi, args=()):
>
> def hopping_current(i, j):
> H_ij = syst.hamiltonian(i, j, *args)
> return -2 * (psi[i].conjugate() * H_ij * psi[j]).imag
>
> ## returns a dictionary that maps hopping -> current
> return {(syst.sites[i], syst.sites[j]): hopping_current(i, j)
> for i, j in syst.graph}
>
>
> This won't work as-is if you have >1 degree of freedom per site (matrix
> onsites / hoppings), but from your example it seems that you have
> 1 degree of freedom per site anyway. There is a full example attached,
> FYI.
>
>
> > I was wondering what kind of map Python uses for storing sites? The
> lookup
> > is constant time for unordered or hash maps in C++, and the size of the
> map
> > shouldn't matter too much. I use these kinds of maps in C++ all the time
> to
> > store data mapped to points. The maps sometimes have 10-20 million
> entries
> > and the code still runs fast.
>
> I did not previously see that you were calling `sys.sites.index` in your
> loop. `sys.sites` is just a python list, so `sys.sites.index` is O(N).
> It only recently became apparent that having the inverse mapping (site
> -> index) would be useful. In bleeding edge kwant this is available
> as the `id_by_site` attribute of finalized systems. `id_by_site`
> is a python dictionary, which has the same complexity as a C++ hash
> map (i.e. O(1) to fetch an element)
>
> Thanks,
>
> Joe
>
