TeNPy Forum

is there a way to have a finite periodic chain (so a ring) for the BoseHubbard model? Kind of. We do not support finite MPS with periodic boundaries for the MPS itself i.e. writing | \Psi\rangle = \sum Tr(A^{\sigma_1} \cdots A^{\sigma_L}) | \sigma_1 \cdots \sigma_L\rangle This is a form which is us...

I've added the `flush=True` to the print statements during the loops of TEBD and DMRG in ce32fd1fc30073918371717beea110083bab8cd1 On my workstation, the flush causes the output to appear immediately. If you redirect to a file on a network-shared file system, there might be a delay of the change bein...

Welcome! There is a way to add such couplings, namly with add_multi_coupling of the tenpy.models.model.MultiCouplingModel instead of just the add_coupling of the tenpy.models.model.CouplingModel . The tenpy.models.toric_code.ToricCode is an example where this is used. Take a look at this topic for m...

There's also a option to print().

Still, there are ways around this. You're describing the problem that the file is "buffered", which Linux/Python does automatically to avoid unnecessary hard disk usage slowing down the execution of the program. However, this can be switched off. For python, try to use the -u option for th...

A such enormous correlation length \xi > 10^10 means that the second eigenvalue of the transfer matrix is 1 up to numerical errors. The reason is that the state you get is a cat state, i.e. a superposition of all |+x \rangle and all |-x \rangle states. See also the discussion in viewtopic.php?f=7&am...

Welcome Dhiman, and sorry for not answering earlier. You might want to take a look at the tenpy.models.haldane.FermionicHaldaneModel and the tenpy.models.haldane.BosonicHaldaneModel . Both are based on the Hamiltonian from the paper 1407.6985 discussing the fermionic model. As far as I see from a qu...

Thanks for the clear answer, Umberto

Just as little side node: lambda is a special keyword in python.
The usual convention is to append an underscore in such a case, i.e., use lambda_ instead as a variable name

There are different standard ways to do this (On all computing clusters I know, there is at least an option to do it automatically). Let me point out a few which should suffice your needs: If you start your program from a Linux console, it's very easy: you can use the > at the and of any command to ...

Looks like a good thing. Actually, if you update your local repository, you'll find that the code for the FermiHubbardModel has been moved to tenpy.models.hubbard.FermiHubbardModel . There it says for u1, u2, dx in self.lat.nearest_neighbors: self.add_coupling(-t, u1, 'Cdu', u2, 'Cu', dx) self.add_c...

You don't need to modify the unit_cell , if the kind of sites (Fermions with or without spin, Bosons, spins,...) doesn't change. Instead, you can give a hopping parameter t which is not just a single number, but an array, specifying at which site you want hopping with which strength. Take a look at ...

I've just added a the function from_MPOModel, which has an example how to use it.
Does that help?

Could it be that the tenpy you are importing is an old version?
The all_onsite_terms was only introduced fairly recently.

Hi Jack, welcome to TeNPy! (the sites are hardcore bosons, which I've created by simply removing the JW entries in the FermionSite class, I hope this is correct?) The more straightforward way for HardcodeBosons would be to use the tenpy.networks.site.BosonSite with the default Nmax=1 . Here is the i...

I've opened Issue #77 to keep track of this task, in case you or someone else wants to give it a try

Hi all, so far, I've kept a ToDo-list for myself. Considering, that the list got longer instead of shorter (although I spent much time working on it), I decided to try a different approach: I open up to the community and give out small tasks to be done as issues on github. If you feel like you got a...

Currently, there's nothing like that. I started thinking about it (and even writing the code for the , but then became busy implementing other stuff. First of all, the algorithms like DMRG don't care about the lattice, but only about the generated MPO and lat.mps_sites() defining the Hilbert space. ...

You can only split legs that were combined previously. In TeNPy, you can see that those legs store the charge data as tenpy.linalg.charges.LegPipe instead of tenpy.linalg.charges.LegCharge . Those pipes have the additional data needed to split the legs. The matrices generated by SVD, say A_ij = U_ik...

The conversion to_ndarray discards information (namely: which index corresponds to which charges? what are the charge blocks we have?). Thus it cannot be reverted (at least not after subsequent tensordot/svd/...) and should be avoided at all. Instead you should use the functions provided by tenpy.li...

Thanks to [mention]mpsforphysics[/mention] for fixing this!

1) No, it doesn't. The problem is that bringing the state into canonical form during the DMRG run will screw up the environments. Thus, the `norm_tol` is only checking if the final state returned by DMRG is in canonical form. It tries to ensure the canonical form by performing a few more environment...

This jumping sure looks very wrong and strange. Any chance you could provide a simple example code where this happens? Do you expect a (nearly) degenerate ground state in this case? Than it might just be jumping around between different solutions. It might help to use 'chi_list' to gradually increas...

Hi Qicheng, how exactly do you want to extract the correlation length from the B? For a finite system, this is a somewhat ill-defined question, because you have no (guaranteed) periodicity. You might want to look at the correlation functions directly and fit an exponential to that. For an infinite s...

Exactly as in the example you posted: just specify an array/list as "strength" in add_coupling. In fact, you don't even need to define your own model, if you're happy with the XXZ Chain /SpinChain with alternating strength: from tenpy.models.xxz_chain import XXZChain model_params = { 'bc_M...

Welcome! The first argument of add_coupling is the strenght of the coupling. It can be a single value (if the strength is uniform all over the chain/lattice), but also a numpy array or something convertible a numpy array (which is the case here). The different values just indicate that the strength ...