merge from main

pymf/tests/test_hubbard.py → meanfi/tests/test_hubbard.py

@@ -2,13 +2,15 @@
 import numpy as np
 import pytest
 
-from pymf.model import Model
-from pymf.solvers import solver
-from pymf.tb import utils
-from pymf.tb.tb import add_tb
-from pymf.tests.test_graphene import compute_gap
+from meanfi.tests.test_graphene import compute_gap
+from meanfi import (
+    Model,
+    solver,
+    guess_tb,
+    add_tb,
+)
 
-repeat_number = 10
+repeat_number = 3
 
 
 # %%
@@ -31,9 +33,9 @@ def gap_relation_hubbard(Us, nk, nk_dense, tol=1e-3):
     gaps = []
     for U in Us:
         h_int = {
-            (0,): U * np.kron(np.ones((2, 2)), np.eye(2)),
+            (0,): U * np.kron(np.eye(2), np.ones((2, 2))),
         }
-        guess = utils.generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
+        guess = guess_tb(frozenset(h_int), len(list(h_0.values())[0]))
         full_model = Model(h_0, h_int, filling=2)
         mf_sol = solver(full_model, guess, nk=nk)
         _gap = compute_gap(add_tb(h_0, mf_sol), fermi_energy=0, nk=nk_dense)
@@ -47,5 +49,5 @@ def gap_relation_hubbard(Us, nk, nk_dense, tol=1e-3):
 def test_gap_hubbard(seed):
     """Test the gap prediction for the Hubbard model."""
     np.random.seed(seed)
-    Us = np.linspace(0.5, 5, 50, endpoint=True)
-    gap_relation_hubbard(Us, nk=30, nk_dense=100, tol=1e-2)
+    Us = np.linspace(8, 10, 15, endpoint=True)
+    gap_relation_hubbard(Us, nk=20, nk_dense=100, tol=1e-1)

pymf/tests/test_params.py → meanfi/tests/test_params.py

 # %%
 import pytest
 import numpy as np
-from pymf.params.rparams import rparams_to_tb, tb_to_rparams
-from pymf.tb.tb import compare_dicts
-from pymf.tb.utils import generate_guess
+from meanfi.params.rparams import rparams_to_tb, tb_to_rparams
+from meanfi.tb.tb import compare_dicts
+from meanfi import guess_tb
 
 repeat_number = 10
 
@@ -16,7 +16,7 @@ vectors = ((0, 0), (1, 0), (-1, 0), (0, 1), (0, -1), (1, -1), (-1, 1), (1, 1), (
 def test_parametrisation(seed):
     """Test the parametrisation of the tight-binding model."""
     np.random.seed(seed)
-    mf_guess = generate_guess(vectors, ndof)
+    mf_guess = guess_tb(vectors, ndof)
     mf_params = tb_to_rparams(mf_guess)
     mf_new = rparams_to_tb(mf_params, vectors, ndof)
     compare_dicts(mf_guess, mf_new)

pymf/tests/test_tb.py → meanfi/tests/test_tb.py

@@ -5,8 +5,8 @@ import numpy as np
 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 meanfi.tb.tb import compare_dicts
+from meanfi.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)

pymf/tests/test_zero_hint.py → meanfi/tests/test_zero_hint.py

@@ -2,10 +2,9 @@
 import numpy as np
 import pytest
 
-from pymf.model import Model
-from pymf.solvers import solver
-from pymf.tb import utils
-from pymf.tb.tb import add_tb, compare_dicts
+from meanfi.tb import utils
+from meanfi.tb.tb import compare_dicts
+from meanfi import Model, solver, guess_tb, add_tb, fermi_energy
 
 # %%
 repeat_number = 10
@@ -21,16 +20,16 @@ def test_zero_hint(seed):
     dim = np.random.randint(0, 3)
     ndof = np.random.randint(2, 10)
     filling = np.random.randint(1, ndof)
-    random_hopping_vecs = utils.generate_vectors(cutoff, dim)
+    random_hopping_vecs = utils.generate_tb_keys(cutoff, dim)
 
     zero_key = tuple([0] * dim)
-    h_0_random = utils.generate_guess(random_hopping_vecs, ndof, scale=1)
-    h_int_only_phases = utils.generate_guess(random_hopping_vecs, ndof, scale=0)
-    guess = utils.generate_guess(random_hopping_vecs, ndof, scale=1)
+    h_0_random = guess_tb(random_hopping_vecs, ndof, scale=1)
+    h_int_only_phases = guess_tb(random_hopping_vecs, ndof, scale=0)
+    guess = guess_tb(random_hopping_vecs, ndof, scale=1)
     model = Model(h_0_random, h_int_only_phases, filling=filling)
 
     mf_sol = solver(model, guess, nk=40, optimizer_kwargs={"M": 0, "f_tol": 1e-10})
-    h_fermi = utils.calculate_fermi_energy(mf_sol, filling=filling, nk=20)
+    h_fermi = fermi_energy(mf_sol, filling=filling, nk=20)
     mf_sol[zero_key] -= h_fermi * np.eye(mf_sol[zero_key].shape[0])
 
     compare_dicts(add_tb(mf_sol, h_0_random), h_0_random, atol=1e-10)

noxfile.py

+import nox
+
+
+@nox.session(venv_backend="mamba")
+@nox.parametrize(
+    "python,numpy,scipy,kwant",
+    [
+        ("3.10", "=1.23", "=1.9", "=1.4"),
+        ("3.11", "=1.24", "=1.10", "=1.4"),
+        ("3.12", ">=1.26", ">=1.13", ">=1.4"),
+    ],
+    ids=["minimal", "mid", "latest"],
+)
+def tests(session, numpy, scipy, kwant):
+    session.run(
+        "mamba",
+        "install",
+        "-y",
+        f"numpy{numpy}",
+        f"scipy{scipy}",
+        f"kwant{kwant}",
+        "packaging==22.0",
+        "pytest-cov",
+        "pytest-randomly",
+        "pytest-repeat",
+        "-c",
+        "conda-forge",
+    )
+    session.install(".")
+    session.run("pip", "install", "ruff", "pytest-ruff")
+    session.run("pytest", "--ruff", "-x")

profiling/graphene.py

@@ -5,9 +5,9 @@ import memray
 import numpy as np
 from pyinstrument import Profiler
 
-from pymf.kwant_helper import kwant_examples, utils
-from pymf.model import Model
-from pymf.tb.utils import generate_guess
+from meanfi.kwant_helper import kwant_examples, utils
+from meanfi.model import Model
+from meanfi.tb.utils import guess_tb
 
 # %%
 graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()
@@ -19,7 +19,7 @@ nk = 600
 h_int = utils.builder_to_tb(int_builder, params)
 h_0 = utils.builder_to_tb(graphene_builder)
 norbs = len(list(h_0.values())[0])
-guess = generate_guess(frozenset(h_int), norbs)
+guess = guess_tb(frozenset(h_int), norbs)
 
 model = Model(h_0, h_int, filling)
 

pymf/params/rparams.py

-from pymf.params.param_transforms import (
-    complex_to_real,
-    flat_to_tb,
-    real_to_complex,
-    tb_to_flat,
-)
-
-
-def tb_to_rparams(tb):
-    """Convert a mean-field tight-binding model to a set of real parameters.
-
-    Parameters
-    ----------
-    tb : dict
-        Mean-field tight-binding model.
-
-    Returns
-    -------
-    dict
-        Real parameters.
-    """
-    return complex_to_real(tb_to_flat(tb))  # placeholder for now
-
-
-def rparams_to_tb(r_params, key_list, size):
-    """Extract mean-field tight-binding model from a set of real parameters.
-
-    Parameters
-    ----------
-    r_params : dict
-        Real parameters.
-    key_list : list
-        List of the keys of the mean-field tight-binding model, meaning all the
-        hoppings.
-    size : tuple
-        Shape of the mean-field tight-binding model.
-
-    Returns
-    -------
-    dict
-        Mean-field tight-binding model.
-    """
-    flat_matrix = real_to_complex(r_params)
-    return flat_to_tb(flat_matrix, (len(key_list), size, size), key_list)

pymf/solvers.py

-from functools import partial
-
-import numpy as np
-import scipy
-
-from pymf.params.rparams import rparams_to_tb, tb_to_rparams
-from pymf.tb.tb import add_tb
-from pymf.tb.utils import calculate_fermi_energy
-
-
-def cost(mf_param, Model, nk=100):
-    """Define the cost function for fixed point iteration.
-
-    The cost function is the difference between the input mean-field real space
-    parametrisation and a new mean-field.
-
-    Parameters
-    ----------
-    mf_param : numpy.array
-        The mean-field real space parametrisation.
-    Model : Model
-        The model object.
-    nk : int, optional
-        The number of k-points to use in the grid. The default is 100.
-    """
-    shape = Model._size
-    mf_tb = rparams_to_tb(mf_param, list(Model.h_int), shape)
-    mf_tb_new = Model.mfield(mf_tb, nk=nk)
-    mf_params_new = tb_to_rparams(mf_tb_new)
-    return mf_params_new - mf_param
-
-
-def solver(
-    Model, mf_guess, nk=100, optimizer=scipy.optimize.anderson, optimizer_kwargs={}
-):
-    """Solve the mean-field self-consistent equation.
-
-    Parameters
-    ----------
-    Model : Model
-        The model object.
-    mf_guess : numpy.array
-        The initial guess for the mean-field tight-binding model.
-    nk : int, optional
-        The number of k-points to use in the grid. The default is 100. In the
-        0-dimensional case, this parameter is ignored.
-    optimizer : scipy.optimize, optional
-        The optimizer to use to solve for fixed-points. The default is
-        scipy.optimize.anderson.
-    optimizer_kwargs : dict, optional
-        The keyword arguments to pass to the optimizer. The default is {}.
-
-    Returns
-    -------
-    result : numpy.array
-        The mean-field tight-binding model.
-    """
-    shape = Model._size
-    mf_params = tb_to_rparams(mf_guess)
-    f = partial(cost, Model=Model, nk=nk)
-    result = rparams_to_tb(
-        optimizer(f, mf_params, **optimizer_kwargs), list(Model.h_int), shape
-    )
-    fermi = calculate_fermi_energy(add_tb(Model.h_0, result), Model.filling, nk=nk)
-    return add_tb(result, {Model._local_key: -fermi * np.eye(Model._size)})

pymf/tests/test_hat.py

-# %%
-import numpy as np
-from pymf.solvers import solver
-from pymf.tb import utils
-from pymf.model import Model
-from pymf.tb.tb import add_tb, scale_tb
-from pymf import mf
-from pymf import observables
-import pytest
-
-
-# %%
-def total_energy(ham_tb, rho_tb):
-    return np.real(observables.expectation_value(rho_tb, ham_tb))
-
-
-# %%
-U0 = 1
-filling = 2
-nk = 100
-repeat_number = 10
-
-hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))
-h_0 = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}
-h_int_U0 = {
-    (0,): U0 * np.kron(np.eye(2), np.ones((2, 2))),
-}
-
-
-# %%
-@np.vectorize
-def mf_rescaled(alpha, mf0):
-    hamiltonian = add_tb(h_0, scale_tb(mf0, alpha))
-    rho, _ = mf.construct_density_matrix(hamiltonian, filling=filling, nk=nk)
-    hamiltonian = add_tb(h_0, scale_tb(mf0, np.sign(alpha)))
-    return total_energy(hamiltonian, rho)
-
-
-@pytest.mark.parametrize("seed", range(repeat_number))
-def test_mexican_hat(seed):
-    np.random.seed(seed)
-    guess = utils.generate_guess(frozenset(h_int_U0), len(h_int_U0[(0,)]))
-    _model = Model(h_0, h_int_U0, filling=filling)
-    mf_sol_groundstate = solver(
-        _model, mf_guess=guess, nk=nk, optimizer_kwargs={"M": 0}
-    )
-
-    alphas = np.random.uniform(0, 50, 100)
-    alphas = np.where(alphas == 1, 0, alphas)
-    assert np.all(
-        mf_rescaled(alphas, mf0=mf_sol_groundstate)
-        > mf_rescaled(np.array([1]), mf0=mf_sol_groundstate)
-    )

pyproject.toml

@@ -3,43 +3,44 @@ requires = ["hatchling", "hatch-vcs"]
 build-backend = "hatchling.build"
 
 [project]
-name = "pymf"
+name = "meanfi"
 dynamic = ["version"]
 authors = [
-  {name="pymf developers"},
+  {name="MeanFi developers"},
 ]
 description = "Package to perform self-consistent mean-field calculations on tight-binding systems"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 classifiers = [
     "Development Status :: 4 - Beta",
     "License :: OSI Approved :: BSD License",
     "Intended Audience :: Science/Research",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
 ]
 dependencies = [
     "numpy>=1.23",
-    "scipy>=1.8",
+    "scipy>=1.9",
+    "kwant>=1.4",
     "packaging>=22.0",  # For version parsing
 ]
 [tool.hatch.version]
 source = "vcs"
 [tool.hatch.build.hooks.vcs]
-version-file = "pymf/_version.py"
+version-file = "meanfi/_version.py"
 
 [project.urls]
-"Documentation" = "https://kwant-scf.readthedocs.io/en/latest/"
-"Repository" = "https://gitlab.kwant-project.org/qt/kwant-scf"
-"Bug Tracker" = "https://gitlab.kwant-project.org/qt/kwant-scf/-/issues"
+"Documentation" = "https://meanfi.readthedocs.io/en/latest/"
+"Repository" = "https://gitlab.kwant-project.org/qt/meanfi"
+"Bug Tracker" = "https://gitlab.kwant-project.org/qt/meanfi/-/issues"
 
 [tool.hatch.build.targets.wheel]
-packages = ["pymf"]
+packages = ["meanfi"]
 
 [tool.hatch.build.targets.sdist]
 include = [
-  "pymf",
+  "meanfi",
   "README.md",
   "LICENSE",
   "pyproject.toml",
@@ -47,5 +48,5 @@ include = [
 ]
 
 [tool.codespell]
-skip = "*.ipynb,"
-ignore-words-list = "multline,"
+skip = "*.ipynb"
+ignore-words-list = "multline, ket, bra, braket, nwo"

pytest.ini

 [pytest]
 minversion = 7.0
-addopts = --cov-config=.coveragerc --verbose --junitxml=junit.xml --cov=pymf
+addopts = --cov-config=.coveragerc --verbose --junitxml=junit.xml --cov=meanfi
     --cov-report term --cov-report html --cov-report xml --ruff
-testpaths = pymf
+testpaths = meanfi
 required_plugins = pytest-randomly pytest-cov pytest-ruff pytest-repeat