tutorials: avoid error messages in tutorial

.github/workflows/notebooks.yml

@@ -54,9 +54,11 @@ jobs:
       - name: Download the data for the tutorials
         shell: bash -l {0}
         run: |
-          wget https://zenodo.org/records/8349335/files/data_raw.zip
+          wget https://zenodo.org/records/13709906/files/data_raw.zip
           unzip data_raw.zip -d data_raw
           mv data_raw tutorials
+          echo listing files in data_raw:
+          ls tutorials/data_raw
 
       - name: Run tutorial notebooks
         run: pytest --nbmake tutorials

deeprank2/dataset.py

@@ -112,7 +112,7 @@ def _check_and_inherit_train(  # noqa: C901
                         for key in data["features_transform"].values():
                             if key["transform"] is None:
                                 continue
-                            key["transform"] = eval(key["transform"])  # noqa: S307, PGH001
+                            key["transform"] = eval(key["transform"])  # noqa: S307
             except pickle.UnpicklingError as e:
                 msg = "The path provided to `train_source` is not a valid DeepRank2 pre-trained model."
                 raise ValueError(msg) from e
@@ -277,7 +277,7 @@ def _filter_targets(self, grp: h5py.Group) -> bool:
                     for operator_string in [">", "<", "==", "<=", ">=", "!="]:
                         operation = operation.replace(operator_string, f"{target_value}" + operator_string)
 
-                    if not eval(operation):  # noqa: S307, PGH001
+                    if not eval(operation):  # noqa: S307
                         return False
 
                 elif target_condition is not None:

deeprank2/query.py

@@ -22,7 +22,7 @@
 import deeprank2.features
 from deeprank2.domain.aminoacidlist import convert_aa_nomenclature
 from deeprank2.features import components, conservation, contact
-from deeprank2.molstruct.residue import Residue, SingleResidueVariant
+from deeprank2.molstruct.residue import SingleResidueVariant
 from deeprank2.utils.buildgraph import get_contact_atoms, get_structure, get_surrounding_residues
 from deeprank2.utils.graph import Graph
 from deeprank2.utils.grid import Augmentation, GridSettings, MapMethod
@@ -265,12 +265,11 @@ def _build_helper(self) -> Graph:
         structure = self._load_structure()
 
         # find the variant residue and its surroundings
-        variant_residue: Residue = None
         for residue in structure.get_chain(self.variant_chain_id).residues:
             if residue.number == self.variant_residue_number and residue.insertion_code == self.insertion_code:
                 variant_residue = residue
                 break
-        if variant_residue is None:
+        else:  # if break is not reached
             msg = f"Residue not found in {self.pdb_path}: {self.variant_chain_id} {self.residue_id}"
             raise ValueError(msg)
         self.variant = SingleResidueVariant(variant_residue, self.variant_amino_acid)
@@ -354,19 +353,12 @@ def _build_helper(self) -> Graph:
             raise ValueError(msg)
 
         # build the graph
-        if self.resolution == "atom":
-            graph = Graph.build_graph(
-                contact_atoms,
-                self.get_query_id(),
-                self.max_edge_length,
-            )
-        elif self.resolution == "residue":
-            residues_selected = list({atom.residue for atom in contact_atoms})
-            graph = Graph.build_graph(
-                residues_selected,
-                self.get_query_id(),
-                self.max_edge_length,
-            )
+        nodes = contact_atoms if self.resolution == "atom" else list({atom.residue for atom in contact_atoms})
+        graph = Graph.build_graph(
+            nodes=nodes,
+            graph_id=self.get_query_id(),
+            max_edge_length=self.max_edge_length,
+        )
 
         graph.center = np.mean([atom.position for atom in contact_atoms], axis=0)
         structure = contact_atoms[0].residue.chain.model
@@ -453,7 +445,7 @@ def __iter__(self) -> Iterator[Query]:
     def __len__(self) -> int:
         return len(self._queries)
 
-    def _process_one_query(self, query: Query) -> None:
+    def _process_one_query(self, query: Query, log_error_traceback: bool = False) -> None:
         """Only one process may access an hdf5 file at a time."""
         try:
             output_path = f"{self._prefix}-{os.getpid()}.hdf5"
@@ -479,10 +471,12 @@ def _process_one_query(self, query: Query) -> None:
 
         except (ValueError, AttributeError, KeyError, TimeoutError) as e:
             _log.warning(
-                f"\nGraph/Query with ID {query.get_query_id()} ran into an Exception ({e.__class__.__name__}: {e}),"
-                " and it has not been written to the hdf5 file. More details below:",
+                f"Graph/Query with ID {query.get_query_id()} ran into an Exception and was not written to the hdf5 file.\n"
+                f"Exception found: {e.__class__.__name__}: {e}.\n"
+                "You may proceed with your analysis, but this query will be ignored.\n",
             )
-            _log.exception(e)
+            if log_error_traceback:
+                _log.exception(f"----Full error traceback:----\n{e}")
 
     def process(
         self,
@@ -493,6 +487,7 @@ def process(
         grid_settings: GridSettings | None = None,
         grid_map_method: MapMethod | None = None,
         grid_augmentation_count: int = 0,
+        log_error_traceback: bool = False,
     ) -> list[str]:
         """Render queries into graphs (and optionally grids).
 
@@ -510,6 +505,8 @@ def process(
             grid_settings: If valid together with `grid_map_method`, the grid data will be stored as well. Defaults to None.
             grid_map_method: If valid together with `grid_settings`, the grid data will be stored as well. Defaults to None.
             grid_augmentation_count: Number of grid data augmentations (must be >= 0). Defaults to 0.
+            log_error_traceback: if True, logs full error message in case query fails. Otherwise only the error message is logged.
+                Defaults to false.
 
         Returns:
             The list of paths of the generated HDF5 files.
@@ -536,7 +533,7 @@ def process(
         self._grid_augmentation_count = grid_augmentation_count
 
         _log.info(f"Creating pool function to process {len(self)} queries...")
-        pool_function = partial(self._process_one_query)
+        pool_function = partial(self._process_one_query, log_error_traceback=log_error_traceback)
         with Pool(self._cpu_count) as pool:
             _log.info("Starting pooling...\n")
             pool.map(pool_function, self.queries)
@@ -551,6 +548,24 @@ def process(
                 os.remove(output_path)
             return glob(f"{prefix}.hdf5")
 
+        n_processed = 0
+        for hdf5file in output_paths:
+            with h5py.File(hdf5file, "r") as hdf5:
+                # List of all graphs in hdf5, each graph representing
+                # a SRV and its sourrouding environment
+                n_processed += len(list(hdf5.keys()))
+
+        if not n_processed:
+            msg = "No queries have been processed."
+            raise ValueError(msg)
+        if n_processed != len(self.queries):
+            _log.warning(
+                f"Not all queries have been processed. You can proceed with the analysis of {n_processed}/{len(self.queries)} queries.\n"
+                "Set `log_error_traceback` to True for advanced troubleshooting.",
+            )
+        else:
+            _log.info(f"{n_processed} queries have been processed.")
+
         return output_paths
 
     def _set_feature_modules(self, feature_modules: list[ModuleType, str] | ModuleType | str) -> list[str]:

pyproject.toml

@@ -53,8 +53,8 @@ dependencies = [
     "python-louvain >= 0.16, < 1.0",
     "tqdm >= 4.66.4, < 5.0",
     "freesasa >= 2.1.1, < 3.0",
-    "biopython >= 1.83, < 2.0"
-    ]
+    "biopython >= 1.83, < 2.0",
+]
 
 [project.optional-dependencies]
 # development dependency groups
@@ -66,7 +66,7 @@ test = [
     "pytest-cov >= 4.1.0, < 5.0",
     "pytest-runner >= 6.0.0, < 7.0",
     "coveralls >= 3.3.1, < 4.0",
-    "ruff == 0.5.1"
+    "ruff == 0.6.3",
 ]
 publishing = ["build", "twine", "wheel"]
 notebooks = ["nbmake"]
@@ -88,7 +88,7 @@ include = ["deeprank2*"]
 
 [tool.pytest.ini_options]
 # pytest options: -ra: show summary info for all test outcomes
-addopts = "-ra"  
+addopts = "-ra"
 
 [tool.ruff]
 output-format = "concise"
@@ -148,3 +148,4 @@ isort.known-first-party = ["deeprank2"]
 ]
 "docs/*" = ["ALL"]
 "tests/perf/*" = ["T201"] # Use of print statements
+"*.ipynb" = ["T201", "E402", "D103"]

tests/data/hdf5/_generate_testdata.ipynb

@@ -15,11 +15,8 @@
     "PATH_TEST = ROOT / \"tests\"\n",
     "import glob\n",
     "import os\n",
-    "import re\n",
-    "import sys\n",
     "\n",
     "import h5py\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
     "from deeprank2.dataset import save_hdf5_keys\n",
@@ -79,7 +76,7 @@
     "                chain_ids=[chain_id1, chain_id2],\n",
     "                targets=targets,\n",
     "                pssm_paths={chain_id1: pssm_path1, chain_id2: pssm_path2},\n",
-    "            )\n",
+    "            ),\n",
     "        )\n",
     "\n",
     "    # Generate graphs and save them in hdf5 files\n",
@@ -128,8 +125,8 @@
     "csv_data = pd.read_csv(csv_file_path)\n",
     "csv_data.cluster = csv_data.cluster.fillna(-1)\n",
     "pdb_ids_csv = [pdb_file.split(\"/\")[-1].split(\".\")[0].replace(\"-\", \"_\") for pdb_file in pdb_files]\n",
-    "clusters = [csv_data[pdb_id == csv_data.ID].cluster.values[0] for pdb_id in pdb_ids_csv]\n",
-    "bas = [csv_data[pdb_id == csv_data.ID].measurement_value.values[0] for pdb_id in pdb_ids_csv]\n",
+    "clusters = [csv_data[pdb_id == csv_data.ID].cluster.to_numpy()[0] for pdb_id in pdb_ids_csv]\n",
+    "bas = [csv_data[pdb_id == csv_data.ID].measurement_value.to_numpy()[0] for pdb_id in pdb_ids_csv]\n",
     "\n",
     "queries = QueryCollection()\n",
     "print(f\"Adding {len(pdb_files)} queries to the query collection ...\")\n",
@@ -147,7 +144,7 @@
     "                \"cluster\": clusters[i],\n",
     "            },\n",
     "            pssm_paths={\"M\": pssm_m[i], \"P\": pssm_p[i]},\n",
-    "        )\n",
+    "        ),\n",
     "    )\n",
     "print(\"Queries created and ready to be processed.\\n\")\n",
     "\n",
@@ -183,7 +180,7 @@
     "test_ids = []\n",
     "\n",
     "with h5py.File(hdf5_path, \"r\") as hdf5:\n",
-    "    for key in hdf5.keys():\n",
+    "    for key in hdf5:\n",
     "        feature_value = float(hdf5[key][target][feature][()])\n",
     "        if feature_value in train_clusters:\n",
     "            train_ids.append(key)\n",
@@ -192,7 +189,7 @@
     "        elif feature_value in test_clusters:\n",
     "            test_ids.append(key)\n",
     "\n",
-    "        if feature_value in clusters.keys():\n",
+    "        if feature_value in clusters:\n",
     "            clusters[int(feature_value)] += 1\n",
     "        else:\n",
     "            clusters[int(feature_value)] = 1\n",
@@ -278,8 +275,12 @@
     "    targets = compute_ppi_scores(pdb_path, ref_path)\n",
     "    queries.add(\n",
     "        ProteinProteinInterfaceQuery(\n",
-    "            pdb_path=pdb_path, resolution=\"atom\", chain_ids=[chain_id1, chain_id2], targets=targets, pssm_paths={chain_id1: pssm_path1, chain_id2: pssm_path2}\n",
-    "        )\n",
+    "            pdb_path=pdb_path,\n",
+    "            resolution=\"atom\",\n",
+    "            chain_ids=[chain_id1, chain_id2],\n",
+    "            targets=targets,\n",
+    "            pssm_paths={chain_id1: pssm_path1, chain_id2: pssm_path2},\n",
+    "        ),\n",
     "    )\n",
     "\n",
     "# Generate graphs and save them in hdf5 files\n",
@@ -303,7 +304,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.14"
   },
   "orig_nbformat": 4,
   "vscode": {

tests/test_dataset.py

@@ -1201,7 +1201,7 @@ def test_inherit_info_pretrained_model_graphdataset(self) -> None:
             for key in data["features_transform"].values():
                 if key["transform"] is None:
                     continue
-                key["transform"] = eval(key["transform"])  # noqa: S307, PGH001
+                key["transform"] = eval(key["transform"])  # noqa: S307
 
         dataset_test_vars = vars(dataset_test)
         for param in dataset_test.inherited_params:

tests/utils/test_graph.py

@@ -27,7 +27,7 @@
 target_value = 1.0
 
 
-@pytest.fixture()
+@pytest.fixture
 def graph() -> Graph:
     """Build a simple graph of two nodes and one edge in between them."""
     # load the structure