Use pytest.mark.skip for ChemRxiv tests instead of comments

tests/cases.py

@@ -17,6 +17,7 @@ class ReferenceCase:
     expected_markdown: str
     pmid: int | None = None
     arxiv: str | None = None
+    skip_reason: str | None = None
 
     @property
     def expected_text(self) -> str:
@@ -390,82 +391,83 @@ def expected_text(self) -> str:
         expected_markdown=r"Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu, Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator, *Energy Fuels*, 2021, 35, 762-769, DOI: [10.1021/acs.energyfuels.0c03211](https://doi.org/10.1021/acs.energyfuels.0c03211). [![Citations](https://citations.njzjz.win/10.1021/acs.energyfuels.0c03211)](https://badge.dimensions.ai/details/doi/10.1021/acs.energyfuels.0c03211)",
     ),
     # ChemRxiv
-    # ReferenceCase(
-    #     reference=Reference(
-    #         author=[
-    #             Author(first="Manyi", last="Yang"),
-    #             Author(first="Duo", last="Zhang"),
-    #             Author(first="Xinyan", last="Wang"),
-    #             # why not Linfeng???
-    #             Author(first="Lingfeng", last="Zhang"),
-    #             Author(first="Tong", last="Zhu"),
-    #             Author(first="Han", last="Wang"),
-    #         ],
-    #         title="Ab initio Accuracy Neural Network Potential for Drug-like Molecules",
-    #         journal="ChemRxiv",
-    #         year=2024,
-    #         annote=textwrap.dedent("""\
-    #             <jats:p>The advent of machine learning
-    #             (ML) in computational chemistry heralds a transformative approach to
-    #             one of the quintessential challenges in computer-aided drug design
-    #             (CADD): the accurate and cost-effective calculation of atomic
-    #             interactions. By leveraging a neural network (NN) potential, we
-    #             address this balance and push the boundaries of the NN potential's
-    #             representational capacity. Our work details the development of a
-    #             robust general-purpose NN potential, architected on the framework of
-    #             DPA-2, a deep learning potential with attention, which demonstrates
-    #             remarkable fidelity in replicating the interatomic potential energy
-    #             surface for drug-like molecules comprising eight critical chemical
-    #             elements: H, C, N, O, F, S, Cl, and P. We employed state-of-the-art
-    #             molecular dynamic techniques, including temperature acceleration and
-    #             enhanced sampling, to construct a comprehensive dataset to ensure
-    #             exhaustive coverage of relevant configurational spaces. Our rigorous
-    #             testing protocols, including torsion scanning, global minimum
-    #             searches, and high-temperature MD simulations across various organic
-    #             molecules, have culminated in an NN model that achieves chemical
-    #             precision commensurate with the highly regarded DFT model, while
-    #             significantly outstripping the accuracy of prevalent semi-empirical
-    #             methods. This study presents a leap forward in the predictive
-    #             modelling of molecular interactions, offering extensive applications
-    #             in drug development and beyond.</jats:p>
-    #                                """)
-    #         .strip()
-    #         .replace("\n", " "),
-    #         doi="10.26434/chemrxiv-2024-sq8nh",
-    #     ),
-    #     expected_bibtex=textwrap.dedent(r"""
-    #         @Article{Yang_ChemRxiv_2024,
-    #             author =   {Manyi Yang and Duo Zhang and Xinyan Wang and Lingfeng Zhang and Tong
-    #                      Zhu and Han Wang},
-    #             title =    {{Ab initio Accuracy Neural Network Potential for Drug-like Molecules}},
-    #             journal =  {ChemRxiv},
-    #             year =     2024,
-    #             doi =      {10.26434/chemrxiv-2024-sq8nh},
-    #             abstract = {The advent of machine learning (ML) in computational chemistry heralds
-    #                      a transformative approach to one of the quintessential challenges in
-    #                      computer-aided drug design (CADD): the accurate and cost-effective
-    #                      calculation of atomic interactions. By leveraging a neural network
-    #                      (NN) potential, we address this balance and push the boundaries of the
-    #                      NN potential's representational capacity. Our work details the
-    #                      development of a robust general-purpose NN potential, architected on
-    #                      the framework of DPA-2, a deep learning potential with attention,
-    #                      which demonstrates remarkable fidelity in replicating the interatomic
-    #                      potential energy surface for drug-like molecules comprising eight
-    #                      critical chemical elements: H, C, N, O, F, S, Cl, and P. We employed
-    #                      state-of-the-art molecular dynamic techniques, including temperature
-    #                      acceleration and enhanced sampling, to construct a comprehensive
-    #                      dataset to ensure exhaustive coverage of relevant configurational
-    #                      spaces. Our rigorous testing protocols, including torsion scanning,
-    #                      global minimum searches, and high-temperature MD simulations across
-    #                      various organic molecules, have culminated in an NN model that
-    #                      achieves chemical precision commensurate with the highly regarded DFT
-    #                      model, while significantly outstripping the accuracy of prevalent
-    #                      semi-empirical methods. This study presents a leap forward in the
-    #                      predictive modelling of molecular interactions, offering extensive
-    #                      applications in drug development and beyond.},
-    #         }""").strip(),
-    #     expected_markdown=r"Manyi Yang, Duo Zhang, Xinyan Wang, Lingfeng Zhang, Tong Zhu, Han Wang, Ab initio Accuracy Neural Network Potential for Drug-like Molecules, *ChemRxiv*, 2024, DOI: [10.26434/chemrxiv-2024-sq8nh](https://doi.org/10.26434/chemrxiv-2024-sq8nh). [![Citations](https://citations.njzjz.win/10.26434/chemrxiv-2024-sq8nh)](https://badge.dimensions.ai/details/doi/10.26434/chemrxiv-2024-sq8nh)",
-    # ),
+    ReferenceCase(
+        reference=Reference(
+            author=[
+                Author(first="Manyi", last="Yang"),
+                Author(first="Duo", last="Zhang"),
+                Author(first="Xinyan", last="Wang"),
+                # why not Linfeng???
+                Author(first="Lingfeng", last="Zhang"),
+                Author(first="Tong", last="Zhu"),
+                Author(first="Han", last="Wang"),
+            ],
+            title="Ab initio Accuracy Neural Network Potential for Drug-like Molecules",
+            journal="ChemRxiv",
+            year=2024,
+            annote=textwrap.dedent("""\
+                <jats:p>The advent of machine learning
+                (ML) in computational chemistry heralds a transformative approach to
+                one of the quintessential challenges in computer-aided drug design
+                (CADD): the accurate and cost-effective calculation of atomic
+                interactions. By leveraging a neural network (NN) potential, we
+                address this balance and push the boundaries of the NN potential's
+                representational capacity. Our work details the development of a
+                robust general-purpose NN potential, architected on the framework of
+                DPA-2, a deep learning potential with attention, which demonstrates
+                remarkable fidelity in replicating the interatomic potential energy
+                surface for drug-like molecules comprising eight critical chemical
+                elements: H, C, N, O, F, S, Cl, and P. We employed state-of-the-art
+                molecular dynamic techniques, including temperature acceleration and
+                enhanced sampling, to construct a comprehensive dataset to ensure
+                exhaustive coverage of relevant configurational spaces. Our rigorous
+                testing protocols, including torsion scanning, global minimum
+                searches, and high-temperature MD simulations across various organic
+                molecules, have culminated in an NN model that achieves chemical
+                precision commensurate with the highly regarded DFT model, while
+                significantly outstripping the accuracy of prevalent semi-empirical
+                methods. This study presents a leap forward in the predictive
+                modelling of molecular interactions, offering extensive applications
+                in drug development and beyond.</jats:p>
+                                   """)
+            .strip()
+            .replace("\n", " "),
+            doi="10.26434/chemrxiv-2024-sq8nh",
+        ),
+        expected_bibtex=textwrap.dedent(r"""
+            @Article{Yang_ChemRxiv_2024,
+                author =   {Manyi Yang and Duo Zhang and Xinyan Wang and Lingfeng Zhang and Tong
+                         Zhu and Han Wang},
+                title =    {{Ab initio Accuracy Neural Network Potential for Drug-like Molecules}},
+                journal =  {ChemRxiv},
+                year =     2024,
+                doi =      {10.26434/chemrxiv-2024-sq8nh},
+                abstract = {The advent of machine learning (ML) in computational chemistry heralds
+                         a transformative approach to one of the quintessential challenges in
+                         computer-aided drug design (CADD): the accurate and cost-effective
+                         calculation of atomic interactions. By leveraging a neural network
+                         (NN) potential, we address this balance and push the boundaries of the
+                         NN potential's representational capacity. Our work details the
+                         development of a robust general-purpose NN potential, architected on
+                         the framework of DPA-2, a deep learning potential with attention,
+                         which demonstrates remarkable fidelity in replicating the interatomic
+                         potential energy surface for drug-like molecules comprising eight
+                         critical chemical elements: H, C, N, O, F, S, Cl, and P. We employed
+                         state-of-the-art molecular dynamic techniques, including temperature
+                         acceleration and enhanced sampling, to construct a comprehensive
+                         dataset to ensure exhaustive coverage of relevant configurational
+                         spaces. Our rigorous testing protocols, including torsion scanning,
+                         global minimum searches, and high-temperature MD simulations across
+                         various organic molecules, have culminated in an NN model that
+                         achieves chemical precision commensurate with the highly regarded DFT
+                         model, while significantly outstripping the accuracy of prevalent
+                         semi-empirical methods. This study presents a leap forward in the
+                         predictive modelling of molecular interactions, offering extensive
+                         applications in drug development and beyond.},
+            }""").strip(),
+        expected_markdown=r"Manyi Yang, Duo Zhang, Xinyan Wang, Lingfeng Zhang, Tong Zhu, Han Wang, Ab initio Accuracy Neural Network Potential for Drug-like Molecules, *ChemRxiv*, 2024, DOI: [10.26434/chemrxiv-2024-sq8nh](https://doi.org/10.26434/chemrxiv-2024-sq8nh). [![Citations](https://citations.njzjz.win/10.26434/chemrxiv-2024-sq8nh)](https://badge.dimensions.ai/details/doi/10.26434/chemrxiv-2024-sq8nh)",
+        skip_reason="ChemRxiv API broken",
+    ),
     # chapter
     ReferenceCase(
         reference=Reference(

tests/test_from_identifier.py

@@ -6,21 +6,35 @@
 
 from wenxian.from_identifier import from_identifier
 
-from .cases import TEST_CASES
+from .cases import TEST_CASES, ReferenceCase
+
+
+def _create_test_param(test_case: ReferenceCase, identifier: str | None):
+    """Create a pytest.param with skip marks if needed."""
+    return pytest.param(
+        identifier,
+        test_case.reference,
+        marks=pytest.mark.skip(reason=test_case.skip_reason)
+        if test_case.skip_reason
+        else (),
+    )
 
 
 @pytest.mark.parametrize(
     "identifier, expected",
     [
-        *[(test_case.reference.doi, test_case.reference) for test_case in TEST_CASES],
+        *[
+            _create_test_param(test_case, test_case.reference.doi)
+            for test_case in TEST_CASES
+        ],
         *[
             # from_identifier accept str
-            (str(test_case.pmid), test_case.reference)
+            _create_test_param(test_case, str(test_case.pmid))
             for test_case in TEST_CASES
             if test_case.pmid is not None
         ],
         *[
-            (test_case.arxiv, test_case.reference)
+            _create_test_param(test_case, test_case.arxiv)
             for test_case in TEST_CASES
             if test_case.arxiv is not None
         ],

tests/test_output.py

@@ -6,30 +6,44 @@
 
 import pytest
 
-from .cases import TEST_CASES
+from .cases import TEST_CASES, ReferenceCase
 
 if TYPE_CHECKING:
     from wenxian.reference import Reference
 
 
+def _create_test_param(test_case: ReferenceCase, expected_value: str):
+    """Create a pytest.param with skip marks if needed."""
+    return pytest.param(
+        test_case.reference,
+        expected_value,
+        marks=pytest.mark.skip(reason=test_case.skip_reason)
+        if test_case.skip_reason
+        else (),
+    )
+
+
 @pytest.mark.parametrize(
-    "reference, expected", [(cc.reference, cc.expected_bibtex) for cc in TEST_CASES]
+    "reference, expected",
+    [_create_test_param(cc, cc.expected_bibtex) for cc in TEST_CASES],
 )
 def test_bibtex(reference: Reference, expected):
     """Test generating BibTeX entries from references."""
     assert reference.bibtex.strip() == expected
 
 
 @pytest.mark.parametrize(
-    "reference, expected", [(cc.reference, cc.expected_markdown) for cc in TEST_CASES]
+    "reference, expected",
+    [_create_test_param(cc, cc.expected_markdown) for cc in TEST_CASES],
 )
 def test_markdown(reference: Reference, expected):
     """Test generating Markdown from references."""
     assert reference.markdown.strip() == expected
 
 
 @pytest.mark.parametrize(
-    "reference, expected", [(cc.reference, cc.expected_text) for cc in TEST_CASES]
+    "reference, expected",
+    [_create_test_param(cc, cc.expected_text) for cc in TEST_CASES],
 )
 def test_text(reference: Reference, expected):
     """Test generating text from references."""