General spliner class

.github/workflows/coverage.yml

@@ -23,7 +23,7 @@ jobs:
       - name: setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: "3.11"
 
       - name: setup rust
         uses: dtolnay/rust-toolchain@master

docs/src/references/api/python/misc.rst

@@ -13,5 +13,3 @@ Miscelaneous
 .. autoclass:: rascaline.Profiler
     :members:
     :undoc-members:
-
-.. autofunction:: rascaline.generate_splines

docs/src/references/api/python/utils/index.rst

@@ -8,3 +8,4 @@ Utility functions and classes that extend the core usage of rascaline.
     :maxdepth: 1
 
     power-spectrum
+    splines

docs/src/references/api/python/utils/splines.rst

@@ -0,0 +1 @@
+.. automodule:: rascaline.utils.splines

python/rascaline-torch/rascaline/torch/__init__.py

@@ -19,16 +19,29 @@
 
 from . import utils  # noqa
 from .calculator_base import CalculatorModule, register_autograd  # noqa
-from .system import System, systems_to_torch  # noqa
-
 
 # don't forget to also update `rascaline/__init__.py` and
 # `rascaline/torch/calculators.py` when modifying this file
-from .calculators import AtomicComposition  # noqa  isort: skip
-from .calculators import SortedDistances  # noqa  isort: skip
-from .calculators import NeighborList  # noqa  isort: skip
-from .calculators import LodeSphericalExpansion  # noqa isort: skip
-from .calculators import SphericalExpansion  # noqa  isort: skip
-from .calculators import SphericalExpansionByPair  # noqa  isort: skip
-from .calculators import SoapRadialSpectrum  # noqa  isort: skip
-from .calculators import SoapPowerSpectrum  # noqa  isort: skip
+from .calculators import (  # noqa
+    AtomicComposition,
+    LodeSphericalExpansion,
+    NeighborList,
+    SoapPowerSpectrum,
+    SoapRadialSpectrum,
+    SortedDistances,
+    SphericalExpansion,
+    SphericalExpansionByPair,
+)
+from .system import System, systems_to_torch  # noqa
+
+
+__all__ = [
+    "AtomicComposition",
+    "LodeSphericalExpansion",
+    "NeighborList",
+    "SoapPowerSpectrum",
+    "SoapRadialSpectrum",
+    "SortedDistances",
+    "SphericalExpansion",
+    "SphericalExpansionByPair",
+]

python/rascaline-torch/rascaline/torch/calculator_base.py

@@ -100,13 +100,13 @@ def compute(
 
         :param systems: single system or list of systems on which to run the
             calculation. If any of the systems' ``positions`` or ``cell`` has
-            ``requires_grad`` set to ``True``, then the corresponding gradients are
-            computed and registered as a custom node in the computational graph, to
+            ``requires_grad`` set to :py:obj:`True`, then the corresponding gradients
+            are computed and registered as a custom node in the computational graph, to
             allow backward propagation of the gradients later.
 
         :param gradients: List of forward gradients to keep in the output. If this is
-            ``None`` or an empty list ``[]``, no gradients are kept in the output. Some
-            gradients might still be computed at runtime to allow for backward
+            :py:obj:`None` or an empty list ``[]``, no gradients are kept in the output.
+            Some gradients might still be computed at runtime to allow for backward
             propagation.
 
         :param use_native_system: This can only be :py:obj:`True`, and is here for

python/rascaline-torch/rascaline/torch/system.py

@@ -86,13 +86,14 @@ def systems_to_torch(
         this function converts them all and returns a list of converted systems.
 
     :param positions_requires_grad: The value of ``requires_grad`` on the output
-        ``positions``. If ``None`` and the positions of the input is already a
+        ``positions``. If :py:obj:`None`` and the positions of the input is already a
         :py:class:`torch.Tensor`, ``requires_grad`` is kept the same. Otherwise it is
-        initialized to ``False``.
+        initialized to :py:obj:`False`.
 
     :param cell_requires_grad: The value of ``requires_grad`` on the output ``cell``. If
-        ``None`` and the positions of the input is already a :py:class:`torch.Tensor`,
-        ``requires_grad`` is kept the same. Otherwise it is initialized to ``False``.
+        :py:obj:`None` and the positions of the input is already a
+        :py:class:`torch.Tensor`, ``requires_grad`` is kept the same. Otherwise it is
+        initialized to :py:obj:`False`.
     """
 
     try:

python/rascaline/examples/splined-radial-integral.py

@@ -1,4 +1,6 @@
 """
+.. _example-splines:
+
 Splined radial integrals
 ========================
 
@@ -20,7 +22,8 @@
 from scipy.special import jv
 from scipy.special import spherical_jn as j_l
 
-from rascaline import SphericalExpansion, generate_splines
+from rascaline import SphericalExpansion
+from rascaline.utils import RadialIntegralFromFunction
 
 
 # %%
@@ -114,16 +117,16 @@ def laplacian_eigenstate_basis_derivative(n, el, r):
 # %%
 #
 # The radial basis functions and their derivatives can be input into a spline
-# generator. This will output the positions of the spline points, the
+# generator class. This will output the positions of the spline points, the
 # values of the basis functions evaluated at the spline points, and the
 # corresponding derivatives.
-spline_points = generate_splines(
-    laplacian_eigenstate_basis,
-    laplacian_eigenstate_basis_derivative,
-    max_radial,
-    max_angular,
-    cutoff,
-    requested_accuracy=1e-5,
+spliner = RadialIntegralFromFunction(
+    radial_integral=laplacian_eigenstate_basis,
+    radial_integral_derivative=laplacian_eigenstate_basis_derivative,
+    spline_cutoff=cutoff,
+    max_radial=max_radial,
+    max_angular=max_angular,
+    accuracy=1e-5,
 )
 
 # %%
@@ -136,11 +139,7 @@ def laplacian_eigenstate_basis_derivative(n, el, r):
     "max_radial": max_radial,
     "max_angular": max_angular,
     "center_atom_weight": 0.0,
-    "radial_basis": {
-        "TabulatedRadialIntegral": {
-            "points": spline_points,
-        }
-    },
+    "radial_basis": spliner.compute(),
     "atomic_gaussian_width": 1.0,  # ignored
     "cutoff_function": {"Step": {}},
 }

python/rascaline/rascaline/__init__.py

@@ -1,20 +1,32 @@
 from . import utils  # noqa
 from .calculator_base import CalculatorBase  # noqa
+
+# don't forget to also update `rascaline/torch/__init__.py` and
+# `rascaline/torch/calculators.py` when modifying this file
+from .calculators import (
+    AtomicComposition,
+    LodeSphericalExpansion,
+    NeighborList,
+    SoapPowerSpectrum,
+    SoapRadialSpectrum,
+    SortedDistances,
+    SphericalExpansion,
+    SphericalExpansionByPair,
+)
 from .log import set_logging_callback  # noqa
 from .profiling import Profiler  # noqa
-from .splines import generate_splines  # noqa
 from .status import RascalError  # noqa
 from .systems import IntoSystem, SystemBase  # noqa
 from .version import __version__  # noqa
 
 
-# don't forget to also update `rascaline/torch/__init__.py` and
-# `rascaline/torch/calculators.py` when modifying this file
-from .calculators import AtomicComposition  # noqa  isort: skip
-from .calculators import SortedDistances  # noqa  isort: skip
-from .calculators import NeighborList  # noqa  isort: skip
-from .calculators import LodeSphericalExpansion  # noqa isort: skip
-from .calculators import SphericalExpansion  # noqa  isort: skip
-from .calculators import SphericalExpansionByPair  # noqa  isort: skip
-from .calculators import SoapRadialSpectrum  # noqa  isort: skip
-from .calculators import SoapPowerSpectrum  # noqa  isort: skip
+__all__ = [
+    "AtomicComposition",
+    "LodeSphericalExpansion",
+    "NeighborList",
+    "SoapPowerSpectrum",
+    "SoapRadialSpectrum",
+    "SortedDistances",
+    "SphericalExpansion",
+    "SphericalExpansionByPair",
+]

python/rascaline/rascaline/calculator_base.py

@@ -207,7 +207,7 @@ def compute(
             faster than having to cross the FFI boundary often when accessing
             the neighbor list. Otherwise the Python neighbor list is used.
 
-        :param gradients: List of gradients to compute. If this is ``None`` or
+        :param gradients: List of gradients to compute. If this is :py:obj:`None` or
             an empty list ``[]``, no gradients are computed. Gradients are
             stored inside the different blocks, and can be accessed with
             ``descriptor.block(...).gradient(<parameter>)``, where
@@ -251,7 +251,7 @@ def compute(
                            {\partial \mathbf{h}} \cdot \mathbf{h}
 
         :param selected_samples: Set of samples on which to run the calculation.
-            Use ``None`` to run the calculation on all samples in the
+            Use :py:obj:`None` to run the calculation on all samples in the
             ``systems`` (this is the default).
 
             If ``selected_samples`` is an :py:class:`metatensor.TensorMap`, then
@@ -269,7 +269,7 @@ def compute(
             these variables as one of the entries in ``selected_samples`` will
             be used.
 
-        :param selected_properties: Set of properties to compute. Use ``None``
+        :param selected_properties: Set of properties to compute. Use :py:obj:`None`
             to run the calculation on all properties (this is the default).
 
             If ``selected_properties`` is an :py:class:`metatensor.TensorMap`,
@@ -288,7 +288,7 @@ def compute(
             ``selected_properties`` will be used.
 
         :param selected_keys: Selection for the keys to include in the output.
-            If this is ``None``, the default set of keys (as determined by the
+            If this is :py:obj:`None`, the default set of keys (as determined by the
             calculator) will be used. Note that this default set of keys can
             depend on which systems we are running the calculation on.
         """