Merge pull request #1 from tad-mctc/fixes

Minor fixes

.readthedocs.yaml

@@ -1,3 +1,21 @@
+# This file is part of tad-multicharge.
+#
+# SPDX-Identifier: LGPL-3.0
+# Copyright (C) 2023 Marvin Friede
+#
+# tad-multicharge is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# tad-multicharge is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with tad-multicharge. If not, see <https://www.gnu.org/licenses/>.
+
 # .readthedocs.yaml
 # Read the Docs configuration file
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
@@ -9,7 +27,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.8"
+    python: "3.10"
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

.vscode/settings.json

@@ -7,11 +7,10 @@
     "editor.insertSpaces": true,
     "editor.tabSize": 4
   },
-  "pylint.args": ["--indent-string='    '"],
   "python.analysis.diagnosticSeverityOverrides": {
     "reportPrivateImportUsage": "information"
   },
-  "python.defaultInterpreterPath": "${env:CONDA_PREFIX}/envs/dxtb/bin/python",
+  "python.defaultInterpreterPath": "${env:CONDA_PREFIX}/envs/torch/bin/python",
   "python.testing.pytestArgs": [],
   "python.testing.unittestEnabled": false,
   "python.testing.pytestEnabled": true

README.rst

@@ -5,8 +5,8 @@ Torch Autodiff Multicharge
     :target: https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10%20|%203.11-blue.svg
     :alt: Python Versions
 
-.. image:: https://img.shields.io/github/v/release/tad-mctc/tad-mctc
-    :target: https://github.com/tad-mctc/tad-mctc/releases/latest
+.. image:: https://img.shields.io/github/v/release/tad-mctc/tad-multicharge
+    :target: https://github.com/tad-mctc/tad-multicharge/releases/latest
     :alt: Release
 
 .. image:: https://img.shields.io/pypi/v/tad-mctc
@@ -17,20 +17,20 @@ Torch Autodiff Multicharge
     :target: https://www.gnu.org/licenses/lgpl-3.0
     :alt: LGPL-3.0
 
-.. image:: https://github.com/tad-mctc/tad-mctc/actions/workflows/python.yaml/badge.svg
-    :target: https://github.com/tad-mctc/tad-mctc/actions/workflows/python.yaml
+.. image:: https://github.com/tad-mctc/tad-multicharge/actions/workflows/python.yaml/badge.svg
+    :target: https://github.com/tad-mctc/tad-multicharge/actions/workflows/python.yaml
     :alt: CI
 
 .. image:: https://readthedocs.org/projects/tad-mctc/badge/?version=latest
     :target: https://tad-mctc.readthedocs.io
     :alt: Documentation Status
 
-.. image:: https://codecov.io/gh/tad-mctc/tad-mctc/branch/main/graph/badge.svg?token=OGJJnZ6t4G
-    :target: https://codecov.io/gh/tad-mctc/tad-mctc
+.. image:: https://codecov.io/gh/tad-mctc/tad-multicharge/branch/main/graph/badge.svg?token=OGJJnZ6t4G
+    :target: https://codecov.io/gh/tad-mctc/tad-multicharge
     :alt: Coverage
 
-.. image:: https://results.pre-commit.ci/badge/github/tad-mctc/tad-mctc/main.svg
-    :target: https://results.pre-commit.ci/latest/github/tad-mctc/tad-mctc/main
+.. image:: https://results.pre-commit.ci/badge/github/tad-mctc/tad-multicharge/main.svg
+    :target: https://results.pre-commit.ci/latest/github/tad-mctc/tad-multicharge/main
     :alt: pre-commit.ci status
 
 PyTorch implementation of the electronegativity equilibration (EEQ) model for atomic partial charges.

docs/index.rst

@@ -5,8 +5,8 @@ Torch Autodiff Multicharge
     :target: https://img.shields.io/badge/python-3.8%20|%203.9%20|%203.10%20|%203.11-blue.svg
     :alt: Python Versions
 
-.. image:: https://img.shields.io/github/v/release/tad-mctc/tad-mctc
-    :target: https://github.com/tad-mctc/tad-mctc/releases/latest
+.. image:: https://img.shields.io/github/v/release/tad-mctc/tad-multicharge
+    :target: https://github.com/tad-mctc/tad-multicharge/releases/latest
     :alt: Release
 
 .. image:: https://img.shields.io/pypi/v/tad-mctc
@@ -17,22 +17,23 @@ Torch Autodiff Multicharge
     :target: https://www.gnu.org/licenses/lgpl-3.0
     :alt: LGPL-3.0
 
-.. image:: https://github.com/tad-mctc/tad-mctc/actions/workflows/python.yaml/badge.svg
-    :target: https://github.com/tad-mctc/tad-mctc/actions/workflows/python.yaml
+.. image:: https://github.com/tad-mctc/tad-multicharge/actions/workflows/python.yaml/badge.svg
+    :target: https://github.com/tad-mctc/tad-multicharge/actions/workflows/python.yaml
     :alt: CI
 
 .. image:: https://readthedocs.org/projects/tad-mctc/badge/?version=latest
     :target: https://tad-mctc.readthedocs.io
     :alt: Documentation Status
 
-.. image:: https://codecov.io/gh/tad-mctc/tad-mctc/branch/main/graph/badge.svg?token=OGJJnZ6t4G
-    :target: https://codecov.io/gh/tad-mctc/tad-mctc
+.. image:: https://codecov.io/gh/tad-mctc/tad-multicharge/branch/main/graph/badge.svg?token=OGJJnZ6t4G
+    :target: https://codecov.io/gh/tad-mctc/tad-multicharge
     :alt: Coverage
 
-.. image:: https://results.pre-commit.ci/badge/github/tad-mctc/tad-mctc/main.svg
-    :target: https://results.pre-commit.ci/latest/github/tad-mctc/tad-mctc/main
+.. image:: https://results.pre-commit.ci/badge/github/tad-mctc/tad-multicharge/main.svg
+    :target: https://results.pre-commit.ci/latest/github/tad-mctc/tad-multicharge/main
     :alt: pre-commit.ci status
 
+
 PyTorch implementation of the electronegativity equilibration (EEQ) model for atomic partial charges.
 This module allows to process a single structure or a batch of structures for the calculation of atom-resolved dispersion energies.
 

environment.yaml

@@ -21,7 +21,7 @@ channels:
   - conda-forge
   - pytorch
 dependencies:
-  - numpy
-  - pytorch
+  - python>=3.8
+  - pytorch>=1.11.0
   - pip:
-      - tad-mctc
+      - tad-mctc>=0.0.2

src/tad_multicharge/__init__.py

@@ -72,6 +72,7 @@
 >>>
 >>> # calculate dispersion energy in Hartree
 >>> energy = torch.sum(d4.dftd4(numbers, positions, charge, param), -1)
+>>>
 >>> torch.set_printoptions(precision=10)
 >>> print(energy)
 tensor([-0.0088341432, -0.0027013607])
@@ -83,3 +84,4 @@
 from . import eeq, model
 from .__version__ import __version__
 from .eeq import get_charges as get_eeq_charges
+from .eeq import get_eeq

test/test_grad/test_dedr.py

@@ -24,6 +24,7 @@
 import torch
 from tad_mctc.autograd import dgradcheck, dgradgradcheck, jac
 from tad_mctc.batch import pack
+from tad_mctc.convert import tensor_to_numpy
 from tad_mctc.typing import DD, Callable, Tensor
 
 from tad_multicharge import eeq
@@ -174,6 +175,7 @@ def run_autograd(dtype: torch.dtype, name: str) -> None:
     (grad,) = torch.autograd.grad(energy.sum(), positions)
 
     positions.detach_()
+    grad.detach_()
 
     assert pytest.approx(numgrad.cpu(), abs=tol * 10) == grad.cpu()
 
@@ -271,8 +273,9 @@ def run_jacobian(dtype: torch.dtype, name: str, atol: float) -> None:
     jacobian: Tensor = fjac(numbers, positions, charge)  # type: ignore
 
     positions.detach_()
+    jac_np = tensor_to_numpy(jacobian)
 
-    assert pytest.approx(numgrad.cpu(), abs=atol) == jacobian.cpu()
+    assert pytest.approx(numgrad.cpu(), abs=atol) == jac_np
 
 
 @pytest.mark.grad

test/test_grad/test_dqdr.py

@@ -24,7 +24,7 @@
 import torch
 from tad_mctc.autograd import dgradcheck, dgradgradcheck, jac
 from tad_mctc.batch import pack
-from tad_mctc.convert import reshape_fortran
+from tad_mctc.convert import reshape_fortran, tensor_to_numpy
 from tad_mctc.typing import DD, Callable, Tensor
 
 from tad_multicharge import eeq
@@ -150,12 +150,9 @@ def test_gradgradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None
     assert dgradgradcheck(func, diffvars, atol=tol, fast_mode=FAST_MODE)
 
 
-sample_list = sample_list[:-1]
-
-
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name", sample_list[:-1])
 def test_jacobian(dtype: torch.dtype, name: str) -> None:
     """Compare with reference values from tblite."""
     dd: DD = {"device": DEVICE, "dtype": dtype}
@@ -185,12 +182,13 @@ def test_jacobian(dtype: torch.dtype, name: str) -> None:
     jacobian: Tensor = fjac(numbers, positions, charge)  # type: ignore
 
     positions.detach_()
+    jac_np = tensor_to_numpy(jacobian)
 
     # 1 / 768 element in MB16_43_01 is slightly off
-    assert pytest.approx(ref.cpu(), abs=tol * 10.5) == jacobian.cpu()
+    assert pytest.approx(ref.cpu(), abs=tol * 10.5) == jac_np
 
     assert pytest.approx(ref.cpu(), abs=tol * 10) == numgrad.cpu()
-    assert pytest.approx(numgrad.cpu(), abs=tol) == jacobian.cpu()
+    assert pytest.approx(numgrad.cpu(), abs=tol) == jac_np
 
 
 def calc_numgrad(numbers: Tensor, positions: Tensor, charge: Tensor) -> Tensor: