diff --git a/codes/kwant_helper/utils.py b/codes/kwant_helper/utils.py
index 9b537edcec4001da6dcabf4ad6dc54ee6a977232..88e6261136af87e1e5e2e0e076821906cf947acf 100644
--- a/codes/kwant_helper/utils.py
+++ b/codes/kwant_helper/utils.py
@@ -146,93 +146,6 @@ def builder2h_0(builder, params={}, return_data=False):
return h_0
-def model2hk(h_0):
- """
- Build Bloch Hamiltonian.
-
- Paramters:
- ----------
- nk : int
- Number of k-points along each direction.
- h_0 : dictionary
- Must have the following structure:
- - Keys are tuples for each hopping vector (in units of lattice vectors).
- - Values are hopping matrices.
- return_ks : bool
- Return k-points.
-
- Returns:
- --------
- ham : nd.array
- Hamiltonian evaluated on a k-point grid from k-points
- along each direction evaluated from zero to 2*pi.
- The indices are ordered as [k_1, ... , k_n, i, j], where
- `k_m` corresponding to the k-point element along each
- direction and `i` and `j` are the internal degrees of freedom.
- ks : 1D-array
- List of k-points over all directions. Only returned if `return_ks=True`.
-
- Returns:
- --------
- bloch_ham : function
- Evaluates the Hamiltonian at a given k-point.
- """
- assert (
- len(next(iter(h_0))) > 0
- ), "Zero-dimensional system. The Hamiltonian is simply h_0[()]."
-
- def bloch_ham(k):
- ham = 0
- for vector in h_0.keys():
- ham += h_0[vector] * np.exp(-1j * np.dot(k, np.array(vector, dtype=float)))
- return ham
-
- return bloch_ham
-
-
-def kgrid_hamiltonian(nk, hk, dim, return_ks=False, hermitian=True):
- """
- Evaluates Hamiltonian on a k-point grid.
-
- Paramters:
- ----------
- nk : int
- Number of k-points along each direction.
- hk : function
- Calculates the Hamiltonian at a given k-point.
- return_ks : bool
- If `True`, returns k-points.
-
- Returns:
- --------
- ham : nd.array
- Hamiltonian evaluated on a k-point grid from k-points
- along each direction evaluated from zero to 2*pi.
- The indices are ordered as [k_1, ... , k_n, i, j], where
- `k_m` corresponding to the k-point element along each
- direction and `i` and `j` are the internal degrees of freedom.
- ks : 1D-array
- List of k-points over all directions. Only returned if `return_ks=True`.
- """
- ks = 2 * np.pi * np.linspace(0, 1, nk, endpoint=False)
-
- k_pts = np.tile(ks, dim).reshape(dim, nk)
-
- ham = []
- for k in product(*k_pts):
- ham.append(hk(k))
- ham = np.array(ham)
- if hermitian:
- assert np.allclose(
- ham, np.transpose(ham, (0, 2, 1)).conj()
- ), "Tight-binding provided is non-Hermitian. Not supported yet"
- shape = (*[nk] * dim, ham.shape[-1], ham.shape[-1])
- if return_ks:
- return ham.reshape(*shape), ks
- else:
- return ham.reshape(*shape)
-
-
def build_interacting_syst(builder, lattice, func_onsite, func_hop, max_neighbor=1):
"""
Construct an auxiliary `kwant` system to build Hamiltonian matrix.
@@ -311,53 +224,6 @@ def generate_vectors(cutoff, dim):
return [*product(*([[*range(-cutoff, cutoff + 1)]] * dim))]
-def hk2h_0(hk, hopping_vecs, ks=None):
- """
- Extract hopping matrices from Bloch Hamiltonian.
-
- Parameters:
- -----------
- hk : nd-array
- Bloch Hamiltonian matrix hk[k_x, ..., k_n, i, j]
- h_0 : dict
- Tight-binding model of non-interacting systems.
- h_int : dict
- Tight-binding model for interacting Hamiltonian.
- ks : 1D-array
- Set of k-points. Repeated for all directions. If the system is finite, `ks=None`.
-
- Returns:
- --------
- scf_model : dict
- TIght-binding model of Hartree-Fock solution.
- """
- if ks is not None:
- ndim = len(hk.shape) - 2
- dk = np.diff(ks)[0]
- nk = len(ks)
- k_pts = np.tile(ks, ndim).reshape(ndim, nk)
- k_grid = np.array(np.meshgrid(*k_pts))
- k_grid = k_grid.reshape(k_grid.shape[0], np.prod(k_grid.shape[1:]))
- hk = hk.reshape(np.prod(hk.shape[:ndim]), *hk.shape[-2:])
-
- hopps = (
- np.einsum(
- "ij,jkl->ikl",
- np.exp(1j * np.einsum("ij,jk->ik", hopping_vecs, k_grid)),
- hk,
- )
- * (dk / (2 * np.pi)) ** ndim
- )
-
- h_0 = {}
- for i, vector in enumerate(hopping_vecs):
- h_0[tuple(vector)] = hopps[i]
-
- return h_0
- else:
- return {(): hk}
-
-
def calc_gap(vals, E_F):
"""
Compute gap.
@@ -377,35 +243,3 @@ def calc_gap(vals, E_F):
emax = np.max(vals[vals <= E_F])
emin = np.min(vals[vals > E_F])
return np.abs(emin - emax)
-
-
-def matrix_to_flat(matrix):
- """
- Flatten the upper triangle of a collection of matrices.
-
- Parameters:
- -----------
- matrix : nd-array
- Array with shape (..., n, n)
- """
- return matrix[..., *np.triu_indices(matrix.shape[-1])].flatten()
-
-
-def flat_to_matrix(flat, shape):
- """
- Undo `matrix_to_flat`.
-
- Parameters:
- -----------
- flat : 1d-array
- Output from `matrix_to_flat`.
- shape : 1d-array
- Shape of the input from `matrix_to_flat`.
- """
- matrix = np.zeros(shape, dtype=complex)
- matrix[..., *np.triu_indices(shape[-1])] = flat.reshape(*shape[:-2], -1)
- indices = np.arange(shape[-1])
- diagonal = matrix[..., indices, indices]
- matrix += np.moveaxis(matrix, -1, -2).conj()
- matrix[..., indices, indices] -= diagonal
- return matrix
\ No newline at end of file
diff --git a/codes/tb/transforms.py b/codes/tb/transforms.py
index 4bfe6b398fa6b2f6faff2d50ce11a69a8120f6bc..dbd64db699af5d78adf4804e810c01bbfc9a5594 100644
--- a/codes/tb/transforms.py
+++ b/codes/tb/transforms.py
@@ -171,3 +171,50 @@ def kfunc2tb(kfunc, nSamples, ndim=1):
raise NotImplementedError("n > 2 not implemented")
ifftnArray = ifftn(kfuncOnGrid, axes=np.arange(ndim))
return ifftn2tb(ifftnArray)
+
+
+def hk2h_0(hk, hopping_vecs, ks=None):
+ """
+ Extract hopping matrices from Bloch Hamiltonian.
+
+ Parameters:
+ -----------
+ hk : nd-array
+ Bloch Hamiltonian matrix hk[k_x, ..., k_n, i, j]
+ h_0 : dict
+ Tight-binding model of non-interacting systems.
+ h_int : dict
+ Tight-binding model for interacting Hamiltonian.
+ ks : 1D-array
+ Set of k-points. Repeated for all directions. If the system is finite, `ks=None`.
+
+ Returns:
+ --------
+ scf_model : dict
+ TIght-binding model of Hartree-Fock solution.
+ """
+ if ks is not None:
+ ndim = len(hk.shape) - 2
+ dk = np.diff(ks)[0]
+ nk = len(ks)
+ k_pts = np.tile(ks, ndim).reshape(ndim, nk)
+ k_grid = np.array(np.meshgrid(*k_pts))
+ k_grid = k_grid.reshape(k_grid.shape[0], np.prod(k_grid.shape[1:]))
+ hk = hk.reshape(np.prod(hk.shape[:ndim]), *hk.shape[-2:])
+
+ hopps = (
+ np.einsum(
+ "ij,jkl->ikl",
+ np.exp(1j * np.einsum("ij,jk->ik", hopping_vecs, k_grid)),
+ hk,
+ )
+ * (dk / (2 * np.pi)) ** ndim
+ )
+
+ h_0 = {}
+ for i, vector in enumerate(hopping_vecs):
+ h_0[tuple(vector)] = hopps[i]
+
+ return h_0
+ else:
+ return {(): hk}