Update tensor.where to allow for case with only condition

pytensor/tensor/basic.py

@@ -760,7 +760,31 @@ def switch(cond, ift, iff):
     """if cond then ift else iff"""
 
 
-where = switch
+def where(cond, ift=None, iff=None, **kwargs):
+    """
+    where(condition, [ift, iff])
+    Return elements chosen from `ift` or `iff` depending on `condition`.
+
+    Note: When only condition is provided, this function is a shorthand for `as_tensor(condition).nonzero()`.
+
+    Parameters
+    ----------
+    condition : tensor_like, bool
+        Where True, yield `ift`, otherwise yield `iff`.
+    x, y : tensor_like
+        Values from which to choose.
+
+    Returns
+    -------
+    out : TensorVariable
+        A tensor with elements from `ift` where `condition` is True, and elements from `iff` elsewhere.
+    """
+    if ift is not None and iff is not None:
+        return switch(cond, ift, iff, **kwargs)
+    elif ift is None and iff is None:
+        return as_tensor(cond).nonzero(**kwargs)
+    else:
+        raise ValueError("either both or neither of ift and iff should be given")
 
 
 @scalar_elemwise

tests/tensor/test_basic.py

@@ -87,6 +87,7 @@
     triu_indices,
     triu_indices_from,
     vertical_stack,
+    where,
     zeros_like,
 )
 from pytensor.tensor.blockwise import Blockwise
@@ -4608,3 +4609,20 @@ def core_np(x, y):
         vectorize_pt(x_test, y_test),
         vectorize_np(x_test, y_test),
     )
+
+
+def test_where():
+    a = np.arange(10)
+    cond = a < 5
+    ift = np.pi
+    iff = np.e
+    # Test for all 3 inputs
+    np.testing.assert_allclose(np.where(cond, ift, iff), where(cond, ift, iff).eval())
+
+    # Test for only condition input
+    for np_output, pt_output in zip(np.where(cond), where(cond)):
+        np.testing.assert_allclose(np_output, pt_output.eval())
+
+    # Test for error
+    with pytest.raises(ValueError, match="either both"):
+        where(cond, ift)