apply naming suggestions

docs/source/documentation/pymf.md

@@ -13,7 +13,7 @@ To define the interactive problem, we use the following class:
 
 ```{eval-rst}
 .. automodule:: pymf.mf
-   :members: meanfield, construct_density_matrix, construct_density_matrix_kgrid, fermi_on_grid
+   :members: meanfield, construct_density_matrix, construct_density_matrix_kgrid, fermi_on_kgrid
    :show-inheritance:
 ```
 

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 tb_to_khamvector
+from .tb.transforms import tb_to_kgrid
 from .tb.utils import generate_guess, calculate_fermi_energy
 
 
@@ -28,7 +28,7 @@ __all__ = [
     "calculate_fermi_energy",
     "construct_density_matrix",
     "meanfield",
-    "tb_to_khamvector",
+    "tb_to_kgrid",
     "__version__",
     "__version_tuple__",
 ]

pymf/mf.py

@@ -3,7 +3,7 @@ from scipy.fftpack import ifftn
 from typing import Tuple
 
 from pymf.tb.tb import add_tb, _tb_type
-from pymf.tb.transforms import ifftn_to_tb, tb_to_khamvector
+from pymf.tb.transforms import ifftn_to_tb, tb_to_kgrid
 
 
 def construct_density_matrix_kgrid(
@@ -27,7 +27,7 @@ def construct_density_matrix_kgrid(
         Density matrix on a k-space grid with shape (nk, nk, ..., ndof, ndof) and Fermi energy.
     """
     vals, vecs = np.linalg.eigh(kham)
-    fermi = fermi_on_grid(vals, filling)
+    fermi = fermi_on_kgrid(vals, filling)
     unocc_vals = vals > fermi
     occ_vecs = vecs
     np.moveaxis(occ_vecs, -1, -2)[unocc_vals, :] = 0
@@ -58,7 +58,7 @@ def construct_density_matrix(
     """
     ndim = len(list(h)[0])
     if ndim > 0:
-        kham = tb_to_khamvector(h, nk=nk)
+        kham = tb_to_kgrid(h, nk=nk)
         density_matrix_krid, fermi = construct_density_matrix_kgrid(kham, filling)
         return (
             ifftn_to_tb(ifftn(density_matrix_krid, axes=np.arange(ndim))),
@@ -115,7 +115,7 @@ def meanfield(density_matrix: _tb_type, h_int: _tb_type) -> _tb_type:
     return add_tb(direct, exchange)
 
 
-def fermi_on_grid(vals: np.ndarray, filling: float) -> float:
+def fermi_on_kgrid(vals: np.ndarray, filling: float) -> float:
     """Compute the Fermi energy on a grid of k-points.
 
     Parameters

pymf/tb/transforms.py

@@ -4,7 +4,7 @@ import numpy as np
 from pymf.tb.tb import _tb_type
 
 
-def tb_to_khamvector(tb: _tb_type, nk: int) -> np.ndarray:
+def tb_to_kgrid(tb: _tb_type, nk: int) -> np.ndarray:
     """Evaluate a tight-binding dictionary on a k-space grid.
 
     Parameters

pymf/tb/utils.py

@@ -2,8 +2,8 @@ from itertools import product
 import numpy as np
 
 from pymf.tb.tb import _tb_type
-from pymf.mf import fermi_on_grid
-from pymf.tb.transforms import tb_to_khamvector
+from pymf.mf import fermi_on_kgrid
+from pymf.tb.transforms import tb_to_kgrid
 
 
 def generate_guess(
@@ -79,6 +79,6 @@ def calculate_fermi_energy(tb: _tb_type, filling: float, nk: int = 100):
     :
         Fermi energy.
     """
-    kham = tb_to_khamvector(tb, nk)
+    kham = tb_to_kgrid(tb, nk)
     vals = np.linalg.eigvalsh(kham)
-    return fermi_on_grid(vals, filling)
+    return fermi_on_kgrid(vals, filling)

pymf/tests/test_graphene.py

@@ -6,7 +6,7 @@ from pymf.kwant_helper import kwant_examples, utils
 from pymf import (
     Model,
     solver,
-    tb_to_khamvector,
+    tb_to_kgrid,
     generate_guess,
     add_tb,
 )
@@ -29,7 +29,7 @@ def compute_gap(tb, fermi_energy=0, nk=100):
      gap : float
      Indirect gap.
     """
-    kham = tb_to_khamvector(tb, nk)
+    kham = tb_to_kgrid(tb, nk)
     vals = np.linalg.eigvalsh(kham)
 
     emax = np.max(vals[vals <= fermi_energy])

pymf/tests/test_tb.py

@@ -6,7 +6,7 @@ import pytest
 from scipy.fftpack import ifftn
 
 from pymf.tb.tb import compare_dicts
-from pymf.tb.transforms import ifftn_to_tb, tb_to_khamvector
+from pymf.tb.transforms import ifftn_to_tb, tb_to_kgrid
 
 repeat_number = 10
 
@@ -24,6 +24,6 @@ def test_fourier(seed):
     keys = [np.arange(-max_order + 1, max_order) for i in range(ndim)]
     keys = it.product(*keys)
     h_0 = {key: (np.random.rand(matrix_size, matrix_size) - 0.5) * 2 for key in keys}
-    kham = tb_to_khamvector(h_0, nk=nk)
+    kham = tb_to_kgrid(h_0, nk=nk)
     tb_new = ifftn_to_tb(ifftn(kham, axes=np.arange(ndim)))
     compare_dicts(h_0, tb_new)