remove redundant functions

codes/hf.py

@@ -126,35 +126,4 @@ def total_energy(h, rho):
     total_energy : float
         System total energy computed as tr[h@rho].
     """
-    return np.sum(np.trace(h @ rho, axis1=-1, axis2=-2)).real / np.prod(rho.shape[:-2])
-
-def updated_matrices(mf_k, model):
-    """
-    Self-consistent loop.
-
-    Parameters:
-    -----------
-    mf : nd-array
-        Mean-field correction. Same format as the initial guess.
-    H_int : nd-array
-        Interaction matrix.
-    filling: int
-        Number of electrons per cell.
-    hamiltonians_0 : nd-array
-        Non-interacting Hamiltonian. Same format as `H_int`.
-
-    Returns:
-    --------
-    mf_new : nd-array
-        Updated mean-field solution.
-    """
-    # Generate the Hamiltonian
-    hamiltonians = model.hamiltonians_0 + mf_k
-    vals, vecs = np.linalg.eigh(hamiltonians)
-    vecs = np.linalg.qr(vecs)[0]
-    E_F = utils.get_fermi_energy(vals, model.filling)
-    rho = density_matrix(vals=vals, vecs=vecs, E_F=E_F)
-    return rho, compute_mf(
-        rho=rho,
-        H_int=model.H_int) - E_F * np.eye(model.hamiltonians_0.shape[-1])
-
+    return np.sum(np.trace(h @ rho, axis1=-1, axis2=-2)).real / np.prod(rho.shape[:-2])
\ No newline at end of file

codes/interface.py

-from scipy import optimize
-from . import utils, solvers
-import numpy as np
-
-def find_groundstate_ham(
-    model,
-    filling,
-    nk=10,
-    cutoff_Vk=0,
-    solver=solvers.kspace_solver,
-    cost_function=solvers.kspace_cost,
-    optimizer=optimize.anderson,
-    optimizer_kwargs={},
-    return_mf=False,
-    return_kspace=False
-):
-    """
-    Self-consistent loop to find groundstate Hamiltonian.
-
-    Parameters:
-    -----------
-    tb_model : dict
-        Tight-binding model. Must have the following structure:
-            - Keys are tuples for each hopping vector (in units of lattice vectors).
-            - Values are hopping matrices.
-    filling: int
-        Number of electrons per cell.
-    guess : nd-array
-        Initial guess. Same format as `H_int`.
-    return_mf : bool
-        Returns mean-field result. Useful if wanted to reuse as guess in upcoming run.
-
-    Returns:
-    --------
-    scf_model : dict
-        Tight-binding model of Hartree-Fock solution.
-    """
-    model.nk=nk
-    model.filling=filling
-    if model.int_model is not None:
-        model.vectors=[*model.int_model.keys()]
-    else:
-        model.vectors = utils.generate_vectors(cutoff_Vk, model.dim)
-    if model.guess is None:
-        model.random_guess(model.vectors)
-    solver(model, optimizer, cost_function, optimizer_kwargs)
-    model.vectors=[*model.vectors, *model.tb_model.keys()]
-    assert np.allclose(model.mf_k - np.moveaxis(model.mf_k, -1, -2).conj(), 0, atol=1e-15)
-    vals, _ = np.linalg.eigh(model.hamiltonians_0 + model.mf_k)
-    EF = utils.get_fermi_energy(vals, filling)
-    model.mf_k -=  EF * np.eye(model.hamiltonians_0.shape[-1])
-    if return_kspace:
-        return model.hamiltonians_0 + model.mf_k
-    else:
-        if model.dim > 0:
-            scf_tb = utils.hk2tb_model(model.hamiltonians_0 + model.mf_k, model.vectors, model.ks)
-            if return_mf:
-                mf_tb = utils.hk2tb_model(model.mf_k, model.vectors, model.ks)
-                return scf_tb, mf_tb
-            else:
-                return scf_tb
-        else:
-            if return_mf:
-                return {() : model.hamiltonians_0 + model.mf_k}, {() : model.mf_k}
-            else:
-                return {() : model.hamiltonians_0 + model.mf_k}