merge from main

docs/source/index.md

@@ -18,6 +18,7 @@ kernelspec:
 :maxdepth: 1
 :caption: Tutorials
 
+<<<<<<< HEAD
 
 mf_notes.md
 algorithm.md
@@ -33,6 +34,17 @@ graphene_example.md
 
 mf_notes.md
 algorithm.md
+=======
+```
+
+```{toctree}
+:hidden:
+:maxdepth: 1
+:caption: Documentation
+
+mf_notes.md
+algorithm.md
+>>>>>>> main
 documentation/pymf.md
 ```
 

pymf/tests/test_graphene.py

@@ -37,7 +37,7 @@ def compute_gap(tb, fermi_energy=0, nk=100):
     return np.abs(emin - emax)
 
 
-repeat_number = 10
+repeat_number = 5
 # %%
 graphene_builder, int_builder = kwant_examples.graphene_extended_hubbard()
 h_0 = utils.builder_to_tb(graphene_builder)
@@ -77,9 +77,7 @@ def gap_prediction(U, V):
     guess = generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
     model = Model(h_0, h_int, filling)
 
-    mf_sol = solver(
-        model, guess, nk=nk, optimizer_kwargs={"verbose": True, "M": 0, "f_tol": 1e-8}
-    )
+    mf_sol = solver(model, guess, nk=nk, optimizer_kwargs={"M": 0, "f_tol": 1e-8})
     gap = compute_gap(add_tb(h_0, mf_sol), nk=200)
 
     # Check if the gap is predicted correctly

pymf/tests/test_hat.py

@@ -12,6 +12,8 @@ from pymf import (
     construct_density_matrix,
 )
 
+from pymf.tb.utils import generate_tb_keys
+
 
 # %%
 def total_energy(ham_tb, rho_tb):
@@ -21,37 +23,43 @@ def total_energy(ham_tb, rho_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))),
-}
+nk = 10
+repeat_number = 3
+ndof = 4
+cutoff = 1
 
 
 # %%
 @np.vectorize
-def mf_rescaled(alpha, mf0):
-    hamiltonian = add_tb(h_0, scale_tb(mf0, alpha))
+def mf_rescaled(alpha, mf0, h0):
+    hamiltonian = add_tb(h0, scale_tb(mf0, alpha))
     rho, _ = construct_density_matrix(hamiltonian, filling=filling, nk=nk)
-    hamiltonian = add_tb(h_0, scale_tb(mf0, np.sign(alpha)))
+    hamiltonian = add_tb(h0, 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 = 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)
-    )
+    h0s = []
+    h_ints = []
+    for ndim in np.arange(4):
+        keys = generate_tb_keys(cutoff, ndim)
+        h0s.append(generate_guess(keys, ndof))
+        h_int = generate_guess(keys, ndof)
+        h_int[keys[len(keys) // 2]] += U0
+        h_ints.append(h_int)
+
+    for h0, h_int in zip(h0s, h_ints):
+        guess = generate_guess(frozenset(h_int), ndof)
+        _model = Model(h0, h_int, 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, h0=h0)
+            > mf_rescaled(np.array([1]), mf0=mf_sol_groundstate, h0=h0)
+        )

pymf/tests/test_hubbard.py

@@ -10,7 +10,7 @@ from pymf import (
     add_tb,
 )
 
-repeat_number = 10
+repeat_number = 3
 
 
 # %%
@@ -33,7 +33,7 @@ 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 = generate_guess(frozenset(h_int), len(list(h_0.values())[0]))
         full_model = Model(h_0, h_int, filling=2)
@@ -49,5 +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)