Merge remote-tracking branch 'origin/master' into pre/v2.1

Author: dimitri-yatsenko

.pre-commit-config.yaml

@@ -47,12 +47,3 @@ repos:
         - types-tqdm
         - pandas-stubs
         - numpy
-- repo: local
-  hooks:
-    - id: unit-tests
-      name: unit tests
-      entry: pytest tests/unit/ -v --tb=short
-      language: system
-      pass_filenames: false
-      always_run: true
-      stages: [pre-commit]

src/datajoint/autopopulate.py

@@ -201,7 +201,7 @@ def _rename_attributes(table, props):
                 self._key_source *= _rename_attributes(*q)
         return self._key_source
 
-    def make(self, key: dict[str, Any]) -> None | Generator[Any, Any, None]:
+    def make(self, key: dict[str, Any], **kwargs) -> None | Generator[Any, Any, None]:
         """
         Compute and insert data for one key.
 
@@ -216,6 +216,9 @@ def make(self, key: dict[str, Any]) -> None | Generator[Any, Any, None]:
         ----------
         key : dict
             Primary key value identifying the entity to compute.
+        **kwargs
+            Keyword arguments passed from ``populate(make_kwargs=...)``.
+            These are forwarded to ``make_fetch`` for the tripartite pattern.
 
         Raises
         ------
@@ -229,7 +232,7 @@ def make(self, key: dict[str, Any]) -> None | Generator[Any, Any, None]:
 
         **Tripartite make**: For long-running computations, implement:
 
-        - ``make_fetch(key)``: Fetch data from parent tables
+        - ``make_fetch(key, **kwargs)``: Fetch data from parent tables
         - ``make_compute(key, *fetched_data)``: Compute results
         - ``make_insert(key, *computed_result)``: Insert results
 
@@ -247,7 +250,7 @@ def make(self, key: dict[str, Any]) -> None | Generator[Any, Any, None]:
         # User has implemented `_fetch`, `_compute`, and `_insert` methods instead
 
         # Step 1: Fetch data from parent tables
-        fetched_data = self.make_fetch(key)  # fetched_data is a tuple
+        fetched_data = self.make_fetch(key, **kwargs)  # fetched_data is a tuple
         computed_result = yield fetched_data  # passed as input into make_compute
 
         # Step 2: If computed result is not passed in, compute the result

tests/integration/test_autopopulate.py

@@ -147,3 +147,84 @@ def make(self, key):
             self.insert1(dict(key, crop_image=dict()))
 
     Crop.populate()
+
+
+def test_make_kwargs_regular(prefix, connection_test):
+    """Test that make_kwargs are passed to regular make method."""
+    schema = dj.Schema(f"{prefix}_make_kwargs_regular", connection=connection_test)
+
+    @schema
+    class Source(dj.Lookup):
+        definition = """
+        source_id: int
+        """
+        contents = [(1,), (2,)]
+
+    @schema
+    class Computed(dj.Computed):
+        definition = """
+        -> Source
+        ---
+        multiplier: int
+        result: int
+        """
+
+        def make(self, key, multiplier=1):
+            self.insert1(dict(key, multiplier=multiplier, result=key["source_id"] * multiplier))
+
+    # Test without make_kwargs
+    Computed.populate(Source & "source_id = 1")
+    assert (Computed & "source_id = 1").fetch1("result") == 1
+
+    # Test with make_kwargs
+    Computed.populate(Source & "source_id = 2", make_kwargs={"multiplier": 10})
+    assert (Computed & "source_id = 2").fetch1("multiplier") == 10
+    assert (Computed & "source_id = 2").fetch1("result") == 20
+
+
+def test_make_kwargs_tripartite(prefix, connection_test):
+    """Test that make_kwargs are passed to make_fetch in tripartite pattern (issue #1350)."""
+    schema = dj.Schema(f"{prefix}_make_kwargs_tripartite", connection=connection_test)
+
+    @schema
+    class Source(dj.Lookup):
+        definition = """
+        source_id: int
+        ---
+        value: int
+        """
+        contents = [(1, 100), (2, 200)]
+
+    @schema
+    class TripartiteComputed(dj.Computed):
+        definition = """
+        -> Source
+        ---
+        scale: int
+        result: int
+        """
+
+        def make_fetch(self, key, scale=1):
+            """Fetch data with optional scale parameter."""
+            value = (Source & key).fetch1("value")
+            return (value, scale)
+
+        def make_compute(self, key, value, scale):
+            """Compute result using fetched value and scale."""
+            return (value * scale, scale)
+
+        def make_insert(self, key, result, scale):
+            """Insert computed result."""
+            self.insert1(dict(key, scale=scale, result=result))
+
+    # Test without make_kwargs (scale defaults to 1)
+    TripartiteComputed.populate(Source & "source_id = 1")
+    row = (TripartiteComputed & "source_id = 1").fetch1()
+    assert row["scale"] == 1
+    assert row["result"] == 100  # 100 * 1
+
+    # Test with make_kwargs (scale = 5)
+    TripartiteComputed.populate(Source & "source_id = 2", make_kwargs={"scale": 5})
+    row = (TripartiteComputed & "source_id = 2").fetch1()
+    assert row["scale"] == 5
+    assert row["result"] == 1000  # 200 * 5