auto convert Java values(arrays/scalar) to Numpy ones and convert DH …

…nulls based on the annotations of the params of a Py UDF (#4502) * A bit of a milestone * made test suite pass * Refactor the new code * Add more tests * More refactoring and code cleanup * Fix a bug that fails vectorization * More code cleanup and clarification * More pathological test cases * Fix String/Instant array conversion issue * Fix test failures and refactor code * Trivial renaming * Respond to review comments * Apply suggestions from code review Co-authored-by: Chip Kent <5250374+chipkent@users.noreply.github.com> * Refactor the code and a minor fixes * Improve the test cases * Clearly distinguqish between params and return * Clarify some code with comments * More clarifying comments --------- Co-authored-by: Chip Kent <5250374+chipkent@users.noreply.github.com>
deephaven · Dec 1, 2023 · 19b1d7e · 19b1d7e
1 parent 6d1d1c9
commit 19b1d7e
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Showing 11 changed files with 1,061 additions and 315 deletions.
diff --git a/engine/table/src/main/java/io/deephaven/engine/util/PyCallableWrapperJpyImpl.java b/engine/table/src/main/java/io/deephaven/engine/util/PyCallableWrapperJpyImpl.java
@@ -24,17 +24,18 @@ public class PyCallableWrapperJpyImpl implements PyCallableWrapper {
     private static final PyObject NUMBA_VECTORIZED_FUNC_TYPE = getNumbaVectorizedFuncType();
     private static final PyObject NUMBA_GUVECTORIZED_FUNC_TYPE = getNumbaGUVectorizedFuncType();
 
-    private static final PyModule dh_table_module = PyModule.importModule("deephaven.table");
+    private static final PyModule dh_udf_module = PyModule.importModule("deephaven._udf");
 
     private static final Map<Character, Class<?>> numpyType2JavaClass = new HashMap<>();
 
     static {
+        numpyType2JavaClass.put('b', byte.class);
+        numpyType2JavaClass.put('h', short.class);
+        numpyType2JavaClass.put('H', char.class);
         numpyType2JavaClass.put('i', int.class);
         numpyType2JavaClass.put('l', long.class);
-        numpyType2JavaClass.put('h', short.class);
         numpyType2JavaClass.put('f', float.class);
         numpyType2JavaClass.put('d', double.class);
-        numpyType2JavaClass.put('b', byte.class);
         numpyType2JavaClass.put('?', boolean.class);
         numpyType2JavaClass.put('U', String.class);
         numpyType2JavaClass.put('M', Instant.class);
@@ -133,23 +134,21 @@ private void prepareSignature() {
                         pyCallable
                                 + " has multiple signatures; this is not currently supported for numba vectorized/guvectorized functions");
             }
-            signature = params.get(0).getStringValue();
             unwrapped = pyCallable;
             // since vectorization doesn't support array type parameters, don't flag numba guvectorized as vectorized
             numbaVectorized = isNumbaVectorized;
             vectorized = isNumbaVectorized;
         } else if (pyCallable.hasAttribute("dh_vectorized")) {
-            signature = pyCallable.getAttribute("signature").toString();
             unwrapped = pyCallable.getAttribute("callable");
             numbaVectorized = false;
             vectorized = true;
         } else {
-            signature = dh_table_module.call("_encode_signature", pyCallable).toString();
             unwrapped = pyCallable;
             numbaVectorized = false;
             vectorized = false;
         }
-        pyUdfDecoratedCallable = dh_table_module.call("_py_udf", unwrapped);
+        pyUdfDecoratedCallable = dh_udf_module.call("_py_udf", unwrapped);
+        signature = pyUdfDecoratedCallable.getAttribute("signature").toString();
     }
 
     @Override
@@ -199,7 +198,7 @@ public PyObject vectorizedCallable() {
         if (numbaVectorized || vectorized) {
             return pyCallable;
         } else {
-            return dh_table_module.call("dh_vectorize", unwrapped);
+            return dh_udf_module.call("_dh_vectorize", unwrapped);
         }
     }