From 79c7a3a965e04edee302f40080e3ca206d31f235 Mon Sep 17 00:00:00 2001
From: Kostas Vilkelis <kostasvilkelis@gmail.com>
Date: Tue, 7 May 2024 00:26:53 +0200
Subject: [PATCH] rm kham_to_tb
---
pymf/__init__.py | 3 +--
pymf/tb/transforms.py | 48 -------------------------------------------
2 files changed, 1 insertion(+), 50 deletions(-)
diff --git a/pymf/__init__.py b/pymf/__init__.py
index 613fc91..b234e9f 100644
--- a/pymf/__init__.py
+++ b/pymf/__init__.py
@@ -14,7 +14,7 @@ from .solvers import solver
from .model import Model
from .observables import expectation_value
from .tb.tb import add_tb, scale_tb
-from .tb.transforms import kham_to_tb, tb_to_khamvector
+from .tb.transforms import tb_to_khamvector
from .tb.utils import generate_guess, calculate_fermi_energy
@@ -28,7 +28,6 @@ __all__ = [
"calculate_fermi_energy",
"construct_density_matrix",
"meanfield",
- "kham_to_tb",
"tb_to_khamvector",
"__version__",
"__version_tuple__",
diff --git a/pymf/tb/transforms.py b/pymf/tb/transforms.py
index 9462ff0..3a0e990 100644
--- a/pymf/tb/transforms.py
+++ b/pymf/tb/transforms.py
@@ -62,51 +62,3 @@ def ifftn_to_tb(ifft_array: np.ndarray) -> tb_type:
keys = [np.arange(-size[0] // 2 + 1, size[0] // 2) for i in range(len(size))]
keys = itertools.product(*keys)
return {tuple(k): ifft_array[tuple(k)] for k in keys}
-
-
-def kham_to_tb(
- kham: np.ndarray,
- tb_keys: list[tuple[None] | tuple[int, ...]],
- ks: ks_type = None,
-) -> tb_type:
- """Convert a Hamiltonian evaluated on a k-grid to a tight-binding dictionary.
-
- Parameters
- ----------
- kham :
- Hamiltonian sampled on a grid of k-points with shape (nk, nk, ..., ndof, ndof),
- where ndof is number of internal degrees of freedom.
- tb_keys :
- List of keys for which to compute the tight-binding dictionary.
- ks :
- I have no clue why we need this, so this goes on the chopping board.
- Returns
- -------
- :
- Tight-binding model of Hartree-Fock solution.
- """
- if ks is not None:
- ndim = len(kham.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:]))
- kham = kham.reshape(np.prod(kham.shape[:ndim]), *kham.shape[-2:])
-
- hopps = (
- np.einsum(
- "ij,jkl->ikl",
- np.exp(1j * np.einsum("ij,jk->ik", tb_keys, k_grid)),
- kham,
- )
- * (dk / (2 * np.pi)) ** ndim
- )
-
- h = {}
- for i, vector in enumerate(tb_keys):
- h[tuple(vector)] = hopps[i]
-
- return h
- else:
- return {(): kham}
--
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