Here is a simple working example of connect function that helps to
define hoppings.
def crit(site1,site2):
x,y,z =site1.pos-site2.pos
if abs(z)==1 and y==0 and x==0:
return True
else:
return False
def connect(sys,crit):
result=[]
for site1 in sys.sites():
for site2 in sys.sites():
if crit(site1,site2):
result.append((site1,site2))
return iter(result)
#example usage
lead[connect(lead,crit)] = tz
It could be improved by reducing iterating only over i<j sites, but I
do not know of simple way to code that.
Best wishes,
Sergey
On 10/07/15 16:25, Christoph Groth wrote:
Sergey wrote:
Yes, such function would be definitely useful. Sometimes, it is
useful to play with some reasonable, but a bit ad-hoc hoppings. I
propose that that the function "connect" must take a function name
"crit" as an input parameter. Where "crit" is a function taking two
sites as parameters, e.g.
crit(A,B):
if norm(A-B) < 2 and 0<np.abs(A.pos[3] - B.pos[3])<1:
return true
else:
return false
It could be nice to have an efficient realization of "connect" . Is
there a way to do it faster than N^2 ?
I believe that it can be made something like O(N log N) by using an
appropriate data structure, perhaps a k-d tree.
But it's clearly O(N^2) if crit is to be called for all pairs of
sites. How about, instead of crit, having a function that returns all
hoppings within some maximum distance. If you want to use crit, you
can do something like this:
sys[ (hop for hop in connect(A, B) if crit(hop)) ] = value
Wouldn't that be the same as your suggestion, only more generic?
Christoph
