Refactor

docs/source/conf.py

@@ -24,7 +24,7 @@
 # adapt path to include the source code
 sys.path.insert(0, op.join(op.dirname(__file__), "../../", "src"))
 
-import dxtb  # noqa
+import dxtb  # pylint: disable=unused-import
 
 project = "Fully Differentiable Extended Tight-Binding"
 author = "Grimme Group"

src/dxtb/_src/calculators/types/base.py

@@ -493,7 +493,6 @@ def __init__(
         if self.opts.batch_mode == 0 and numbers.ndim > 1:
             self.opts.batch_mode = 1
 
-        # TODO: Should the IndexHelper be a singleton?
         self.ihelp = IndexHelper.from_numbers(
             numbers, par, self.opts.batch_mode
         )

src/dxtb/_src/components/interactions/coulomb/thirdorder.py

@@ -175,6 +175,8 @@ def get_cache(
 
         Parameters
         ----------
+        numbers : Tensor
+            Atomic numbers for all atoms in the system (shape: ``(..., nat)``).
         ihelp : IndexHelper
             Index mapping for the basis set.
 

src/dxtb/_src/constants/defaults.py

@@ -25,6 +25,8 @@
 
 import torch
 
+from dxtb import labels
+
 # General
 
 STRICT = False
@@ -76,7 +78,7 @@
 INTCUTOFF = 50.0
 """Real-space cutoff (in Bohr) for integral evaluation. (50.0)"""
 
-INTDRIVER = "libcint"
+INTDRIVER = labels.INTDRIVER_LIBCINT
 """Integral driver."""
 
 INTDRIVER_CHOICES = [

src/dxtb/_src/integral/container.py

@@ -189,7 +189,7 @@ def build_overlap(self, positions: Tensor, **kwargs: Any) -> Tensor:
         # `None`, the overlap will not be rebuilt if the positions change,
         # i.e., when the driver was invalidated. Hence, we would require a
         # full reset of the integrals via `reset_all`. However, the integral
-        # reset cannot be trigger by the driver manager, so we cannot add this
+        # reset cannot be triggered by the driver manager, so we cannot add this
         # check here. If we do, the hessian tests will fail as the overlap is
         # not recalculated for positions + delta.
         self.overlap.build(self.mgr.driver)

src/dxtb/_src/integral/driver/pytorch/impls/type.py

@@ -65,4 +65,4 @@ def __call__(
         Tensor
             Overlap matrix or overlap gradient.
         """
-        ...  # noqa
+        ...

test/test_a_memory_leak/test_repulsion.py

@@ -94,7 +94,7 @@ def fcn():
         del energy
         del arep
         del zeff
-        del kexp  # noqa
+        del kexp
 
     # run garbage collector to avoid leaks across other tests
     garbage_collect()

test/test_a_memory_leak/test_scf.py

@@ -72,7 +72,7 @@ def fcn():
         del charges
         del calc
         del result
-        del energy  # noqa
+        del energy
 
     # run garbage collector to avoid leaks across other tests
     garbage_collect()
@@ -156,7 +156,7 @@ def fcn():
         del charges
         del calc
         del result
-        del energy  # noqa
+        del energy
 
     # run garbage collector to avoid leaks across other tests
     garbage_collect()

test/test_a_memory_leak/util.py

@@ -107,18 +107,18 @@ def _get_tensor_memory(
     return total_mem
 
 
-def _show_memsize(fcn, ntries: int = 10, gccollect: bool = False):
-    # show the memory growth
-    size0, num0 = _get_tensor_memory(return_number_tensors=True)
-
+def _show_memsize(
+    fcn, size0: float, num0: int, ntries: int = 10, gccollect: bool = False
+):
+    # show the memory growth over n iterations
     for i in range(ntries):
         fcn()
-        if gccollect:
+        if gccollect is True:
             gc.collect()
         size, num = _get_tensor_memory(return_number_tensors=True)
 
         print(
-            f"{i + 1:2d} iteration: {size - size0:.16f} MiB of {num-num0:d} addtional tensors"
+            f"{i + 1:2d} iteration: {size - size0:.12f} MiB of {num - num0:d} addtional tensors"
         )
 
 
@@ -133,8 +133,7 @@ def has_memleak_tensor(
     fcn: Callable
         A function with no input and output to be checked.
     gccollect: bool
-        If True, then manually apply ``gc.collect()`` after the function
-        execution.
+        If True, run :func:`gc.collect` after function execution.
 
     Returns
     -------
@@ -144,12 +143,15 @@ def has_memleak_tensor(
     size0, num0 = _get_tensor_memory(return_number_tensors=True)
 
     fcn()
-    if gccollect:
+    if gccollect is True:
         gc.collect()
 
     size, num = _get_tensor_memory(return_number_tensors=True)
 
     if size0 != size or num0 != num:
-        _show_memsize(fcn, repeats, gccollect=gccollect)
+        print(
+            f"{0:2d} iteration: {size - size0:.12f} MiB of {num - num0:d} addtional tensors"
+        )
+        _show_memsize(fcn, size0, num0, repeats, gccollect=gccollect)
 
     return size0 != size

test/test_calculator/test_cache/test_properties.py

@@ -56,6 +56,11 @@ def test_energy(dtype: torch.dtype) -> None:
     assert calc._ncalcs == 1
     assert isinstance(energy, Tensor)
 
+    # different name for energy getter
+    energy = calc.get_potential_energy(positions)
+    assert calc._ncalcs == 1
+    assert isinstance(energy, Tensor)
+
     # check reset
     calc.cache.reset_all()
     assert len(calc.cache.list_cached_properties()) == 0

test/test_dispersion/test_d3.py → test/test_classical/test_dispersion/test_d3.py

@@ -35,7 +35,7 @@
 )
 from dxtb._src.typing import DD, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 

test/test_dispersion/test_d4.py → test/test_classical/test_dispersion/test_d4.py

@@ -35,7 +35,7 @@
 from dxtb._src.param.gfn2 import GFN2_XTB as par
 from dxtb._src.typing import DD, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 

test/test_dispersion/test_energy.py → test/test_classical/test_dispersion/test_energy.py

@@ -32,7 +32,7 @@
 from dxtb._src.components.classicals.dispersion import new_dispersion
 from dxtb._src.typing import DD
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 

test/test_dispersion/test_grad_general.py → test/test_classical/test_dispersion/test_grad_general.py

@@ -27,12 +27,11 @@
 from dxtb._src.components.classicals.dispersion import new_dispersion
 from dxtb._src.typing import DD
 
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["LiH", "SiH4", "MB16_43_01", "PbH4-BiH3"]
 
-from ..conftest import DEVICE
-
 
 @pytest.mark.grad
 @pytest.mark.parametrize("name", sample_list)

test/test_dispersion/test_grad_param.py → test/test_classical/test_dispersion/test_grad_param.py

@@ -28,7 +28,7 @@
 from dxtb._src.components.classicals.dispersion import DispersionD3
 from dxtb._src.typing import DD, Callable, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 slist = ["LiH", "SiH4"]

test/test_dispersion/test_grad_pos.py → test/test_classical/test_dispersion/test_grad_pos.py

@@ -29,7 +29,7 @@
 from dxtb._src.components.classicals.dispersion import new_dispersion
 from dxtb._src.typing import DD, Callable, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 slist = ["LiH", "SiH4"]

test/test_dispersion/test_hess.py → test/test_classical/test_dispersion/test_hess.py

@@ -26,13 +26,13 @@
 import torch
 from tad_mctc.autograd import jacrev
 from tad_mctc.batch import pack
+from tad_mctc.convert import reshape_fortran
 
 from dxtb import GFN1_XTB as par
 from dxtb._src.components.classicals.dispersion import new_dispersion
 from dxtb._src.typing import DD, Tensor
 
-from ..conftest import DEVICE
-from ..utils import reshape_fortran
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["LiH", "SiH4", "MB16_43_01", "PbH4-BiH3"]

test/test_halogen/test_energy.py → test/test_classical/test_halogen/test_energy.py

@@ -31,7 +31,7 @@
 from dxtb._src.components.classicals import new_halogen
 from dxtb._src.typing import DD
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 

test/test_halogen/test_grad_general.py → test/test_classical/test_halogen/test_grad_general.py

@@ -28,7 +28,7 @@
 from dxtb._src.components.classicals import new_halogen
 from dxtb._src.typing import DD
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 

test/test_halogen/test_grad_param.py → test/test_classical/test_halogen/test_grad_param.py

@@ -31,7 +31,7 @@
 from dxtb._src.param import get_elem_param
 from dxtb._src.typing import DD, Callable, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["br2nh3", "br2och2", "tmpda"]

test/test_halogen/test_grad_pos.py → test/test_classical/test_halogen/test_grad_pos.py

@@ -30,7 +30,7 @@
 from dxtb._src.components.classicals import new_halogen
 from dxtb._src.typing import DD, Callable, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 # "LYS_xao" must be the last one as we have to manually exclude it for the

test/test_halogen/test_hess.py → test/test_classical/test_halogen/test_hess.py

@@ -26,14 +26,14 @@
 import torch
 from tad_mctc.autograd import jacrev
 from tad_mctc.batch import pack
+from tad_mctc.convert import reshape_fortran
 
 from dxtb import GFN1_XTB as par
 from dxtb import IndexHelper
 from dxtb._src.components.classicals import new_halogen
 from dxtb._src.typing import DD, Tensor
 
-from ..conftest import DEVICE
-from ..utils import reshape_fortran
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["br2nh3", "br2och2", "finch", "LiH", "SiH4", "MB16_43_01"]

test/test_repulsion/test_energy.py → test/test_classical/test_repulsion/test_energy.py

@@ -34,7 +34,7 @@
 from dxtb._src.param.gfn2 import GFN2_XTB
 from dxtb._src.typing import DD, Literal
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = [

test/test_repulsion/test_grad_general.py → test/test_classical/test_repulsion/test_grad_general.py

@@ -28,7 +28,7 @@
 from dxtb._src.components.classicals import new_repulsion
 from dxtb._src.typing import DD
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["H2O", "SiH4", "MB16_43_01", "MB16_43_02", "LYS_xao"]

test/test_repulsion/test_grad_param.py → test/test_classical/test_repulsion/test_grad_param.py

@@ -31,7 +31,7 @@
 from dxtb._src.param import get_elem_param
 from dxtb._src.typing import DD, Callable, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["H2O", "SiH4", "MB16_43_01", "MB16_43_02", "LYS_xao"]

test/test_repulsion/test_grad_pos.py → test/test_classical/test_repulsion/test_grad_pos.py

@@ -37,7 +37,7 @@
 )
 from dxtb._src.typing import DD, Callable, Tensor
 
-from ..conftest import DEVICE
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["H2O", "SiH4", "MB16_43_01", "MB16_43_02", "LYS_xao"]

test/test_repulsion/test_hess.py → test/test_classical/test_repulsion/test_hess.py

@@ -26,14 +26,14 @@
 import torch
 from tad_mctc.autograd import jacrev
 from tad_mctc.batch import pack
+from tad_mctc.convert import reshape_fortran
 
 from dxtb import GFN1_XTB as par
 from dxtb import IndexHelper
 from dxtb._src.components.classicals import new_repulsion
 from dxtb._src.typing import DD, Tensor
 
-from ..conftest import DEVICE
-from ..utils import reshape_fortran
+from ...conftest import DEVICE
 from .samples import samples
 
 sample_list = ["LiH", "SiH4", "MB16_43_01"]

test/test_coulomb/test_es2_general.py

@@ -42,7 +42,25 @@ def test_none() -> None:
     assert es2.new_es2(dummy, par) is None
 
 
-def test_store_fail() -> None:
+def test_cache_input_fail() -> None:
+    numbers = torch.tensor([3, 1])
+    positions = torch.randn((2, 3))
+    ihelp = IndexHelper.from_numbers(numbers, GFN1_XTB)
+
+    es = es2.new_es2(numbers, GFN1_XTB)
+    assert es is not None
+
+    with pytest.raises(ValueError):
+        es.get_cache(numbers=None, positions=positions, ihelp=ihelp)
+
+    with pytest.raises(ValueError):
+        es.get_cache(numbers=numbers, positions=None, ihelp=ihelp)
+
+    with pytest.raises(ValueError):
+        es.get_cache(numbers=numbers, positions=positions, ihelp=None)
+
+
+def test_fail_store() -> None:
     numbers = torch.tensor([3, 1])
     positions = torch.randn((2, 3))
     ihelp = IndexHelper.from_numbers(numbers, GFN1_XTB)

test/test_coulomb/test_es3_general.py

@@ -51,7 +51,21 @@ def test_fail() -> None:
         es3.new_es3(dummy, par)
 
 
-def test_store_fail() -> None:
+def test_fail_cache_input() -> None:
+    numbers = torch.tensor([3, 1])
+    ihelp = IndexHelper.from_numbers(numbers, GFN1_XTB)
+
+    es = es3.new_es3(numbers, GFN1_XTB)
+    assert es is not None
+
+    with pytest.raises(ValueError):
+        es.get_cache(numbers=None, ihelp=ihelp)
+
+    with pytest.raises(ValueError):
+        es.get_cache(numbers=numbers, ihelp=None)
+
+
+def test_fail_store() -> None:
     numbers = torch.tensor([3, 1])
     ihelp = IndexHelper.from_numbers(numbers, GFN1_XTB)